diff --git a/.flake8 b/.flake8 index e086460b9..3b113fe12 100644 --- a/.flake8 +++ b/.flake8 @@ -3,4 +3,4 @@ max-line-length = 120 # Ignore rules from the "black" formatter. This is added # to avoid "Black would make changes." in houndci-bot. -ignore = BLK, E121, E123, E126, E133, E226, E241, E242, E704, W503, W504, W505 \ No newline at end of file +ignore = BLK, E121, E123, E126, E133, E226, E241, E242, E704, W503, W504, W505 diff --git a/.github/workflows/cartoframes-ci.yml b/.github/workflows/cartoframes-ci.yml new file mode 100644 index 000000000..03527e729 --- /dev/null +++ b/.github/workflows/cartoframes-ci.yml @@ -0,0 +1,36 @@ +name: Run CARTOFrames tests + +on: + push: + pull_request: + branches: + - master + - develop + +jobs: + test: + strategy: + matrix: + python-version: [3.5, 3.6, 3.7, 3.8] + + name: Run tests on Python ${{ matrix.python-version }} + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 + with: + python-version: ${{ matrix.python-version }} + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install tox + pip install tox-gh-actions + + - name: Test with tox + run: | + tox diff --git a/.gitignore b/.gitignore index 29c508f24..e332fc6b7 100644 --- a/.gitignore +++ b/.gitignore @@ -6,10 +6,12 @@ sandbox/ # Misc creds.json credentials.json +docs/guides/*.csv __pycache__ .*.sw[nop] .vscode +_debug # OS .DS_Store @@ -50,6 +52,7 @@ CARTOCREDS.json SITEKEY.txt secret.json examples/scratch/* +_debug # JavaScript node_modules @@ -61,6 +64,7 @@ htmlcov test_*.json .pytest_cache tmp_file.csv +my_dataset.csv fake_path # Sphinx documentation diff --git a/.hound.yml b/.hound.yml index 43800ce38..a4134d78d 100644 --- a/.hound.yml +++ b/.hound.yml @@ -4,4 +4,4 @@ flake8: jshint: enabled: true - config_file: .jshintrc \ No newline at end of file + config_file: .jshintrc diff --git a/.jshintignore b/.jshintignore index 2a78000d2..dca4bfd44 100644 --- a/.jshintignore +++ b/.jshintignore @@ -1 +1 @@ -cartoframes/assets/src/bundle.js \ No newline at end of file +cartoframes/assets/src/bundle.js diff --git a/.jshintrc b/.jshintrc index a82e6632a..fa0c9c89b 100644 --- a/.jshintrc +++ b/.jshintrc @@ -1,4 +1,4 @@ { "esversion": 6, "laxbreak" : true -} \ No newline at end of file +} diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 8cf6af66f..e1d27950e 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -2,4 +2,4 @@ repos: - repo: https://github.com/pre-commit/pre-commit-hooks rev: v2.3.0 hooks: - - id: flake8 \ No newline at end of file + - id: flake8 diff --git a/.pylintrc b/.pylintrc index 7930b096c..8b42939a8 100644 --- a/.pylintrc +++ b/.pylintrc @@ -42,4 +42,4 @@ disable= len-as-condition, old-style-class, superfluous-parens, - wrong-import-order, \ No newline at end of file + wrong-import-order, diff --git a/.template/GUIDE_PULL_REQUEST_TEMPLATE.md b/.template/GUIDE_PULL_REQUEST_TEMPLATE.md deleted file mode 100644 index f0a128f7b..000000000 --- a/.template/GUIDE_PULL_REQUEST_TEMPLATE.md +++ /dev/null @@ -1,46 +0,0 @@ -# [Guide Title] - -## Introduction - -The introduction to the guide goes here. It should say what the purpose is and the main topics it covers. -In some cases it's possible to highlight here the **final result** you'll have learnt when finishing the guide. - -## [Data Used] (*optional*) - -- [Dataset 1 Name](dataset_link): Dataset description -- [Dataset 2 Name](dataset_link): Dataset description - -## [Section 1 Title] - -Explain what the Section 1 covers. - -To embed maps, use this iframe inside an `"example-map"` div container. Set a unique `id` as an attribute and assign your published map link in the `src` attribute. - -```html -
- -
-``` - -To add a link to the **reference** section, use the same namespace you'll use for any class or method in CARTOframes, but using dashes instead of dots. For example, `cartoframes.data.observatory.Category` is `cartoframes-data-observatory-Category`: - -To add a link to an **example**: - -``` - [Data Observatory - Category](/developers/cartoframes/reference/#cartoframes-data-observatory-Category) -``` - -## [Section n Title] - -Explain what the Section n covers. - -## Conclusion - -Explain the guide results, conclusions or main topics to enhance them. \ No newline at end of file diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index ad4fcf438..000000000 --- a/.travis.yml +++ /dev/null @@ -1,12 +0,0 @@ -language: python -python: -- '3.5' -- '3.6' -- '3.7' -- '3.8' -install: -- pip install tox-travis -script: -- tox -after_success: -- coveralls diff --git a/CHANGELOG.md b/CHANGELOG.md index cd2ce692c..8aaf8d140 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,19 +5,53 @@ All notable changes to this project will be documented in this file. The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html). +## [1.1.0] - 2020-12-04 + +### Added +- Add new API to delete map publications (#1497) +- Provide Google (Data Observatory) credentials (#1688) + +### Changed +- Allow to set a value for null geometries (#1667) +- Add documentation for executing a single test (#1668) +- Add global datasets message in catalog functions (#1670) +- Remove sort by data_range when retrieving isolines (#1673) +- Switch from Travis to Github Actions (#1672) +- Upload table using to_carto in chunks (#1676) +- Check account disk quotas before writing using to_carto (#1674) +- Return GeoDataFrame in the to_dataframe function (#1681) +- Improve metrics for on-prem and staging (#1685) +- Optimize datasets/geographies download (#1693) +- Check geom_coverage and print a message if None (#1695) +- Remove unused BigQueryClient code (#1602) +- Repo clean-up. Refactor docs (#1682) +- Add tests for notebook execution (#1696) +- Use regenerate table in replace strategy (#1707) + +### Fixed +- Remove the batch_size parameter in the call to bulk_geocode (#1666) +- Fix credentials link for set_default_credentials (#1665) +- Fix identifier quoting for columns (#1675, #1678) +- Fix uploading extra the_geom column (#1677) +- Fix wrong username error (#1687) +- Fix empty popups (#1689, #1690) +- Remove None from geometry_types to check validity (#1691) +- Remove nan raw geometries for visualization (#1694) +- Generate carto_geocode_hash with NULL values (#1702) + ## [1.0.4] - 2020-07-06 -## Added +### Added - Add list_tables function (#1649) - Add catalog public filter to providers, countries and categories (#1658) - Add set_default_do_credentials function for DO authentication (#1655) -## Changed +### Changed - Open publication link in another window (#1647) - Show a warning when uploading a GeoDataFrame without geometry (#1650) - Improve GeoDataFrame CRS check, docs and examples (#1656) -## Fixed +### Fixed - Fix empty geometries issue (#1652) - Fix Layout publication API key issue (#1654) - Fix ColumnInfo comparison when replacing a table (#1660) diff --git a/CONTRIBUTORS b/CONTRIBUTORS index 9ecb4f85f..2fe9538fd 100644 --- a/CONTRIBUTORS +++ b/CONTRIBUTORS @@ -15,3 +15,5 @@ Víctor Velarde Mario de Frutos Dieguez Jesús Arroyo Mamata Akella +Josema Camacho +Antonio Carlón diff --git a/Makefile b/Makefile index b1f0ae1d3..5456d0a20 100644 --- a/Makefile +++ b/Makefile @@ -1,22 +1,17 @@ init: - pip install -r requirements.txt - -docs: - cd docs && make html + pip install -e . test: - nosetests -v test/ + pytest tests/unit/ + +clean: + rm -fr build/* dist/* .egg cartoframes.egg-info dist: python setup.py sdist bdist_wheel --universal -publish: clean dist send - send: twine upload dist/* -clean: - find . -name '*DS_Store' | xargs rm - rm -fr build/* dist/* .egg cartoframes.egg-info - -.PHONY: init docs test dist release clean send +publish: + clean dist send diff --git a/README.rst b/README.rst index 239990384..642a255ea 100644 --- a/README.rst +++ b/README.rst @@ -4,8 +4,8 @@ CARTOframes .. image:: https://travis-ci.org/CartoDB/cartoframes.svg?branch=develop :target: https://travis-ci.org/CartoDB/cartoframes -.. image:: https://img.shields.io/badge/pypi-v1.0.4-orange - :target: https://pypi.org/project/cartoframes/1.0.4 +.. image:: https://img.shields.io/badge/pypi-v1.1.0-orange + :target: https://pypi.org/project/cartoframes/1.1.0 A Python package for integrating `CARTO `__ maps, analysis, and data services into data science workflows. @@ -14,11 +14,11 @@ Python data analysis workflows often rely on the de facto standards `pandas =1.11.1') +check_package('carto', '>=1.11.2') check_package('pandas', '>=0.25.0') check_package('geopandas', '>=0.6.0') diff --git a/cartoframes/_version.py b/cartoframes/_version.py index 8a81504c5..1a72d32e5 100644 --- a/cartoframes/_version.py +++ b/cartoframes/_version.py @@ -1 +1 @@ -__version__ = '1.0.4' +__version__ = '1.1.0' diff --git a/cartoframes/assets/src/bundle.js b/cartoframes/assets/src/bundle.js index 9c4cf6dcb..dd823068e 100644 --- a/cartoframes/assets/src/bundle.js +++ b/cartoframes/assets/src/bundle.js @@ -485,14 +485,14 @@ var init = (function () { const lat = center.lat.toFixed(6); const lng = center.lng.toFixed(6); const zoom = map.getZoom().toFixed(2); - + mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`; } updateMapInfo(); map.on('zoom', updateMapInfo); - map.on('move', updateMapInfo); + map.on('move', updateMapInfo); } function getBasecolorSettings(basecolor) { @@ -566,7 +566,7 @@ var init = (function () { } // Track layers to add only one feature per layer layerIDs.push(feature.layerId); - + for (const item of attrs) { const variable = feature.variables[item.name]; if (variable) { @@ -581,12 +581,16 @@ var init = (function () { } } - popup - .setLngLat([event.coordinates.lng, event.coordinates.lat]) - .setHTML(`
${popupHTML}
`); + if (popupHTML) { + popup + .setLngLat([event.coordinates.lng, event.coordinates.lat]) + .setHTML(`
${popupHTML}
`); - if (!popup.isOpen()) { - popup.addTo(map); + if (!popup.isOpen()) { + popup.addTo(map); + } + } else { + popup.remove(); } } else { popup.remove(); @@ -945,7 +949,7 @@ var init = (function () { }); const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers); - + layerSelector.build(); } diff --git a/cartoframes/assets/src/map/popups.js b/cartoframes/assets/src/map/popups.js index ee7528954..1cbf31159 100644 --- a/cartoframes/assets/src/map/popups.js +++ b/cartoframes/assets/src/map/popups.js @@ -47,12 +47,16 @@ export function updatePopup(map, popup, event, attrs) { } } - popup - .setLngLat([event.coordinates.lng, event.coordinates.lat]) - .setHTML(`
${popupHTML}
`); + if (popupHTML) { + popup + .setLngLat([event.coordinates.lng, event.coordinates.lat]) + .setHTML(`
${popupHTML}
`); - if (!popup.isOpen()) { - popup.addTo(map); + if (!popup.isOpen()) { + popup.addTo(map); + } + } else { + popup.remove(); } } else { popup.remove(); diff --git a/cartoframes/auth/credentials.py b/cartoframes/auth/credentials.py index 9cd61ba68..ebaf8fc96 100644 --- a/cartoframes/auth/credentials.py +++ b/cartoframes/auth/credentials.py @@ -3,13 +3,13 @@ import os from urllib.parse import urlparse + from carto.auth import APIKeyAuthClient from carto.do_token import DoTokenManager from .. import __version__ from ..utils.logger import log -from ..utils.utils import is_valid_str, check_do_enabled, save_in_config, \ - read_from_config, default_config_path +from ..utils.utils import is_valid_str, check_do_enabled, save_in_config, read_from_config, default_config_path from warnings import filterwarnings filterwarnings('ignore', category=FutureWarning, module='carto') @@ -22,7 +22,7 @@ class Credentials: """Credentials class is used for managing and storing user CARTO credentials. The arguments are listed in order of precedence: :obj:`Credentials` instances are first, `key` and `base_url`/`username` are taken next, and - `config_file` (if given) is taken last. The config file is `cartocreds.json` + `config_file` (if given) is taken last. The config file is `creds.json` by default. If no arguments are passed, then there will be an attempt to retrieve credentials from a previously saved session. One of the above scenarios needs to be met to successfully @@ -64,6 +64,7 @@ def __init__(self, username=None, api_key='default_public', base_url=None, sessi self._user_id = None self._api_key_auth_client = None self._allow_non_secure = allow_non_secure + self._do_credentials = None self._norm_credentials() @@ -109,15 +110,25 @@ def session(self, session): """Set session""" self._session = session + @property + def me_data(self): + me_data = {} + + try: + me_data = self.get_api_key_auth_client().send(ME_SERVICE, 'get').json() + except Exception: + pass + + return me_data + @property def user_id(self): """Credentials user ID""" if not self._user_id: log.debug('Getting `user_id` for {}'.format(self._username)) - api_key_auth_client = self.get_api_key_auth_client() try: - user_me = api_key_auth_client.send(ME_SERVICE, 'get').json() + user_me = self.me_data() user_data = user_me.get('user_data') if user_data: self._user_id = user_data.get('id') @@ -194,7 +205,7 @@ def save(self, config_file=None): Args: config_file (str, optional): Location where credentials are to be stored. If no argument is provided, it will be send to the - default location. + default location (`creds.json`). Example: >>> credentials = Credentials(username='johnsmith', api_key='abcdefg') @@ -222,7 +233,7 @@ def save(self, config_file=None): def delete(cls, config_file=None): """Deletes the credentials file specified in `config_file`. If no file is specified, it deletes the default user credential file - (`cartocreds.json`) + (`creds.json`) Args: config_file (str): Path to configuration file. Defaults to delete @@ -246,11 +257,24 @@ def delete(cls, config_file=None): except OSError: log.warning('No credential file found at {}.'.format(path_to_remove)) - @check_do_enabled - def get_do_credentials(self): - """Returns the Data Observatory v2 credentials""" - do_token_manager = DoTokenManager(self.get_api_key_auth_client()) - return do_token_manager.get() + def is_instant_licensing_active(self): + """Returns if the user has instant licensing activated for the Data Observatory v2.""" + do_credentials = self._get_do_credentials() + return do_credentials.instant_licensing + + def get_gcloud_credentials(self): + """Returns the Data Observatory v2 Google Cloud Platform project and token. + + Example: + >>> from cartoframes.auth import Credentials + >>> from google.oauth2.credentials import Credentials as GoogleCredentials + >>> creds = Credentials(username='johnsmith', api_key='abcdefg') + >>> gcloud_project, gcloud_token = creds.get_gcloud_credentials() + >>> gcloud_credentials = GoogleCredentials(gcloud_token) + + """ + do_credentials = self._get_do_credentials() + return do_credentials.bq_project, do_credentials.access_token def get_api_key_auth_client(self): if not self._api_key_auth_client: @@ -264,6 +288,16 @@ def get_api_key_auth_client(self): return self._api_key_auth_client + @check_do_enabled + def _get_do_credentials(self): + """Returns the Data Observatory v2 credentials""" + if self._do_credentials: + return self._do_credentials + + do_token_manager = DoTokenManager(self.get_api_key_auth_client()) + self._do_credentials = do_token_manager.get() + return self._do_credentials + def _norm_credentials(self): """Standardize credentials""" if self._base_url: diff --git a/cartoframes/auth/defaults.py b/cartoframes/auth/defaults.py index 9f71930b3..4b8ede4bc 100644 --- a/cartoframes/auth/defaults.py +++ b/cartoframes/auth/defaults.py @@ -13,8 +13,10 @@ def set_default_credentials( Args: credentials (:py:class:`Credentials `, optional): - A :py:class:`Credentials ` + A :py:class:`Credentials ` instance can be used in place of a `username | base_url`/`api_key` combination. + filepath (str, optional): Location where credentials are stored as a JSON file. + username (str, optional): CARTO user name of the account. base_url (str, optional): Base URL of CARTO user account. Cloud-based accounts should use the form ``https://{username}.carto.com`` (e.g., https://johnsmith.carto.com for user ``johnsmith``) whether on @@ -22,12 +24,12 @@ def set_default_credentials( should ask their admin. api_key (str, optional): CARTO API key. Depending on the application, this can be a project API key or the account master API key. - username (str, optional): CARTO user name of the account. - filepath (str, optional): Location where credentials are stored as a JSON file. session (requests.Session, optional): requests session. See `requests documentation `__ for more information. + allow_non_secure (bool, optional): Allow non secure http connections. + By default is not allowed. .. note:: diff --git a/cartoframes/data/clients/bigquery_client.py b/cartoframes/data/clients/bigquery_client.py deleted file mode 100644 index f9710432b..000000000 --- a/cartoframes/data/clients/bigquery_client.py +++ /dev/null @@ -1,207 +0,0 @@ -import os -import csv -import tqdm -import pandas as pd - -from google.auth.exceptions import RefreshError -from google.cloud import bigquery, storage, bigquery_storage_v1beta1 as bigquery_storage -from google.oauth2.credentials import Credentials as GoogleCredentials -from google.api_core.exceptions import DeadlineExceeded - -from ...auth import get_default_credentials -from ...utils.logger import log -from ...utils.utils import timelogger, is_ipython_notebook -from ...exceptions import DOError - -_GCS_CHUNK_SIZE = 25 * 1024 * 1024 # 25MB. This must be a multiple of 256 KB per the API specification. -_BQS_TIMEOUT = 2 * 3600 # 2 hours in seconds - - -def refresh_clients(func): - def wrapper(self, *args, **kwargs): - try: - return func(self, *args, **kwargs) - except RefreshError: - self._init_clients() - try: - return func(self, *args, **kwargs) - except RefreshError: - raise DOError('Something went wrong accessing data. ' - 'Please, try again in a few seconds or contact support for help.') - return wrapper - - -class BigQueryClient: - - def __init__(self, credentials): - self._credentials = credentials or get_default_credentials() - self.bq_client = None - self.gcs_client = None - self.bq_storage_client = None - - self._gcp_execution_project = None - self.bq_public_project = None - self.bq_project = None - self.bq_dataset = None - self.instant_licensing = None - self._gcs_bucket = None - - self._init_clients() - - def _init_clients(self): - do_credentials = self._credentials.get_do_credentials() - google_credentials = GoogleCredentials(do_credentials.access_token) - - self.bq_client = bigquery.Client( - project=do_credentials.gcp_execution_project, - credentials=google_credentials - ) - - self.gcs_client = storage.Client( - project=do_credentials.bq_project, - credentials=google_credentials - ) - - self.bq_storage_client = bigquery_storage.BigQueryStorageClient( - credentials=google_credentials - ) - - self._gcp_execution_project = do_credentials.gcp_execution_project - self.bq_public_project = do_credentials.bq_public_project - self.bq_project = do_credentials.bq_project - self.bq_dataset = do_credentials.bq_dataset - self.instant_licensing = do_credentials.instant_licensing - self._gcs_bucket = do_credentials.gcs_bucket - - @refresh_clients - def query(self, query, **kwargs): - return self.bq_client.query(query, **kwargs) - - def upload_dataframe(self, dataframe, schema, tablename): - self._upload_dataframe_to_GCS(dataframe, tablename) - self._import_from_GCS_to_BQ(schema, tablename) - - @timelogger - def download_to_file(self, job, file_path, fail_if_exists=False, column_names=None, progress_bar=True): - if fail_if_exists and os.path.isfile(file_path): - raise OSError('The file `{}` already exists.'.format(file_path)) - - try: - rows = self._download_by_bq_storage_api(job) - except Exception: - log.debug('Cannot download using BigQuery Storage API, fallback to standard') - rows = _get_job_result(job, 'Error downloading data') - - try: - _rows_to_file(rows, file_path, column_names, progress_bar) - except DeadlineExceeded: - log.debug('Cannot download using BigQuery Storage API, fallback to standard') - rows = _get_job_result(job, 'Error downloading data') - _rows_to_file(rows, file_path, column_names, progress_bar) - - @timelogger - def download_to_dataframe(self, job): - try: - rows = self._download_by_bq_storage_api(job) - data = list(rows) - return pd.DataFrame(data) - except Exception: - log.debug('Cannot download using BigQuery Storage API, fallback to standard') - - try: - return job.to_dataframe() - except Exception: - if job.errors: - log.error([error['message'] for error in job.errors if 'message' in error]) - - raise DOError('Error downloading data') - - def _download_by_bq_storage_api(self, job, timeout=_BQS_TIMEOUT): - table_ref = job.destination.to_bqstorage() - - parent = 'projects/{}'.format(self._gcp_execution_project) - session = self.bq_storage_client.create_read_session( - table_ref, - parent, - requested_streams=1, - format_=bigquery_storage.enums.DataFormat.AVRO, - # We use a LIQUID strategy because we only read from a - # single stream. Consider BALANCED if requested_streams > 1 - sharding_strategy=(bigquery_storage.enums.ShardingStrategy.LIQUID) - ) - - reader = self.bq_storage_client.read_rows( - bigquery_storage.types.StreamPosition(stream=session.streams[0]), - timeout=timeout - ) - - return reader.rows(session) - - @refresh_clients - @timelogger - def _upload_dataframe_to_GCS(self, dataframe, tablename): - log.debug('Uploading to GCS') - bucket = self.gcs_client.get_bucket(self._gcs_bucket) - blob = bucket.blob(tablename, chunk_size=_GCS_CHUNK_SIZE) - dataframe.to_csv(tablename, index=False, header=False) - try: - blob.upload_from_filename(tablename) - finally: - os.remove(tablename) - - @refresh_clients - @timelogger - def _import_from_GCS_to_BQ(self, schema, tablename): - log.debug('Importing to BQ from GCS') - - dataset_ref = self.bq_client.dataset(self.bq_dataset, project=self.bq_project) - table_ref = dataset_ref.table(tablename) - schema_wrapped = [bigquery.SchemaField(column, dtype) for column, dtype in schema.items()] - - job_config = bigquery.LoadJobConfig() - job_config.schema = schema_wrapped - job_config.source_format = bigquery.SourceFormat.CSV - uri = 'gs://{bucket}/{tablename}'.format(bucket=self._gcs_bucket, tablename=tablename) - - job = self.bq_client.load_table_from_uri( - uri, table_ref, job_config=job_config - ) - - _get_job_result(job, 'Error uploading data') - - def get_table_column_names(self, project, dataset, table): - table_info = self._get_table(project, dataset, table) - return [field.name for field in table_info.schema] - - @refresh_clients - def _get_table(self, project, dataset, table): - full_table_name = '{}.{}.{}'.format(project, dataset, table) - return self.bq_client.get_table(full_table_name) - - -def _rows_to_file(rows, file_path, column_names=None, progress_bar=True): - show_progress_bar = progress_bar and is_ipython_notebook() - - if show_progress_bar: - pb = tqdm.tqdm_notebook(total=rows.total_rows) - - with open(file_path, 'w') as csvfile: - csvwriter = csv.writer(csvfile) - - if column_names: - csvwriter.writerow(column_names) - - for row in rows: - csvwriter.writerow(row.values()) - if show_progress_bar: - pb.update(1) - - -def _get_job_result(job, error_message): - try: - return job.result() - except Exception: - if job.errors: - log.error([error['message'] for error in job.errors if 'message' in error]) - - raise DOError(error_message) diff --git a/cartoframes/data/clients/data_obs_client.py b/cartoframes/data/clients/data_obs_client.py index c9097de03..bf6a5bbfa 100644 --- a/cartoframes/data/clients/data_obs_client.py +++ b/cartoframes/data/clients/data_obs_client.py @@ -9,14 +9,16 @@ from ...io.carto import read_carto, to_carto from ...utils import utils +from ...utils.utils import deprecated from ...io.managers.context_manager import ContextManager +@deprecated(message='The Data Observatory v1 is being deprecated. Use the `data.observatory` package instead') class DataObsClient: - """Data Observatory v1 class. `Data Observatory documentation + """Data Observatory v1 class (deprecated). `Data Observatory v1 documentation (deprecated) `__. - This class provides the following methods to interact with Data Observatory: + This class provides the following methods to interact with Data Observatory v1 (deprecated): - boundaries: returns a geopandas.GeoDataFrame with the geographic boundaries (geometries) or their metadata. - discovery: returns a pandas.DataFrame with the measures found. @@ -33,6 +35,7 @@ def __init__(self, credentials=None): self._credentials = credentials self._manager = ContextManager(credentials) + @deprecated(message='The Data Observatory v1 is being deprecated. Use the `data.observatory` package instead') def boundaries(self, boundary=None, region=None, decode_geom=False, timespan=None, include_nonclipped=False): """ @@ -47,45 +50,6 @@ def boundaries(self, boundary=None, region=None, decode_geom=False, to analyze how median income has changed in a region (see examples section for more). - Examples: - Find all boundaries available for Australia. The columns - `geom_name` gives us the name of the boundary and `geom_id` - is what we need for the `boundary` argument. - - >>> do = DataObsClient(credentials) - >>> au_boundaries = do.boundaries(region='Australia') - >>> au_boundaries[['geom_name', 'geom_id']] - - Get the boundaries for Australian Postal Areas and map them. - - >>> au_postal_areas = do.boundaries(boundary='au.geo.POA') - >>> Map(Layer(au_postal_areas)) - - Get census tracts around Idaho Falls, Idaho, USA, and add median - income from the US census. Without limiting the metadata, we get - median income measures for each census in the Data Observatory. - - >>> # Note: default credentials will be supported in a future release - >>> do = DataObsClient(credentials) - >>> # will return GeoDataFrame with columns `the_geom` and `geom_ref` - >>> tracts = do.boundaries( - ... boundary='us.census.tiger.census_tract', - ... region=[-112.096642,43.429932,-111.974213,43.553539]) - >>> # write geometries to a CARTO table - >>> tracts.upload('idaho_falls_tracts') - >>> # gather metadata needed to look up median income - >>> median_income_meta = do.discovery( - ... 'idaho_falls_tracts', - ... keywords='median income', - ... boundaries='us.census.tiger.census_tract') - >>> # get median income data and original table as new GeoDataFrame - >>> idaho_falls_income = do.augment( - ... 'idaho_falls_tracts', - ... median_income_meta, - ... how='geom_refs') - >>> # overwrite existing table with newly-enriched GeoDataFrame - >>> idaho_falls_income.upload('idaho_falls_tracts', if_exists='replace') - Args: boundary (str, optional): Boundary identifier for the boundaries @@ -94,8 +58,8 @@ def boundaries(self, boundary=None, region=None, decode_geom=False, Municipios have an ID of ``br.geo.municipios``. Find IDs by running :py:meth:`DataObsClient.boundaries ` - without any arguments, or by looking in the `Data Observatory - catalog `__. + without any arguments, or by looking in the `Data Observatory v1 catalog (deprecated) + `__. region (str, optional): Region where boundary information or, if `boundary` is specified, boundary polygons are of interest. @@ -111,7 +75,7 @@ def boundaries(self, boundary=None, region=None, decode_geom=False, ``[5.9559111595,45.8179931641,10.4920501709,47.808380127]`` timespan (str, optional): Specific timespan to get geometries from. - Defaults to use the most recent. See the Data Observatory catalog + Defaults to use the most recent. See the Data Observatory v1 catalog (deprecated) for more information. decode_geom (bool, optional): Whether to return the geometries as @@ -129,6 +93,47 @@ def boundaries(self, boundary=None, region=None, decode_geom=False, if `region` is ``None`` or not specified) are returned. If `boundary` is not specified, then a GeoDataFrame of all available boundaries in `region` (or the world if `region` is ``None``). + + Examples: + Find all boundaries available for Australia. The columns + `geom_name` gives us the name of the boundary and `geom_id` + is what we need for the `boundary` argument. + + >>> do = DataObsClient(credentials) + >>> au_boundaries = do.boundaries(region='Australia') + >>> au_boundaries[['geom_name', 'geom_id']] + + Get the boundaries for Australian Postal Areas and map them. + + >>> au_postal_areas = do.boundaries(boundary='au.geo.POA') + >>> Map(Layer(au_postal_areas)) + + Get census tracts around Idaho Falls, Idaho, USA, and add median + income from the US census. Without limiting the metadata, we get + median income measures for each census in the + Data Observatory v1 (deprecated). + + >>> # Note: default credentials will be supported in a future release + >>> do = DataObsClient(credentials) + >>> # will return GeoDataFrame with columns `the_geom` and `geom_ref` + >>> tracts = do.boundaries( + ... boundary='us.census.tiger.census_tract', + ... region=[-112.096642,43.429932,-111.974213,43.553539]) + >>> # write geometries to a CARTO table + >>> tracts.upload('idaho_falls_tracts') + >>> # gather metadata needed to look up median income + >>> median_income_meta = do.discovery( + ... 'idaho_falls_tracts', + ... keywords='median income', + ... boundaries='us.census.tiger.census_tract') + >>> # get median income data and original table as new GeoDataFrame + >>> idaho_falls_income = do.augment( + ... 'idaho_falls_tracts', + ... median_income_meta, + ... how='geom_refs') + >>> # overwrite existing table with newly-enriched GeoDataFrame + >>> idaho_falls_income.upload('idaho_falls_tracts', if_exists='replace') + """ # TODO: create a function out of this? if isinstance(region, str): @@ -193,14 +198,15 @@ def boundaries(self, boundary=None, region=None, decode_geom=False, time=utils.pgquote(timespan)) return self._fetch(query, decode_geom=decode_geom) + @deprecated(message='The Data Observatory v1 is being deprecated. Use the `data.observatory` package instead') def discovery(self, region, keywords=None, regex=None, time=None, boundaries=None, include_quantiles=False): - """Discover Data Observatory measures. This method returns the full - Data Observatory metadata model for each measure or measures that + """Discover Data Observatory v1 measures (deprecated). This method returns the full + Data Observatory v1 metadata model (deprecated) for each measure or measures that match the conditions from the inputs. The full metadata in each row uniquely defines a measure based on the timespan, geographic resolution, and normalization (if any). Read more about the metadata - response in `Data Observatory + response in `Data Observatory v1 (deprecated) `__ documentation. @@ -223,13 +229,13 @@ def discovery(self, region, keywords=None, regex=None, time=None, To remove the guesswork in how geographical resolutions are selected, specify one or more boundaries in `boundaries`. See - the boundaries section for each region in the `Data Observatory + the boundaries section for each region in the `Data Observatory v1 (deprecated) catalog `__. The metadata returned from this method can then be used to create raw tables or for augmenting an existing table from these measures using :py:meth:`DataObsClient.augment `. - For the full Data Observatory catalog, visit + For the full Data Observatory v1 catalog (deprecated), visit https://cartodb.github.io/bigmetadata/. When working with the metadata DataFrame returned from this method, be careful to only remove rows not columns as `DataObsClient.augment ` @@ -246,14 +252,6 @@ def discovery(self, region, keywords=None, regex=None, time=None, For example, setting the region to be United States counties with no filter values set will result in many thousands of measures. - Examples: - Get all European Union measures that mention ``freight``. - - >>> freight_meta = do.discovery('European Union', - ... keywords='freight', - ... time='2010') - >>> freight_meta['numer_name'].head() - Args: region (str or list of float): Information about the region of interest. @@ -269,8 +267,8 @@ def discovery(self, region, keywords=None, regex=None, time=None, with geometries. The region will be the bounding box of the table. - .. Note:: If a table name is also a valid Data Observatory - region name, the Data Observatory name will be chosen + .. Note:: If a table name is also a valid Data Observatory v1 (deprecated) + region name, the Data Observatory v1 (deprecated) name will be chosen over the table. - bounding box (list of float): @@ -296,10 +294,12 @@ def discovery(self, region, keywords=None, regex=None, time=None, case insensitive operator ``~*``. See `PostgreSQL docs `__ for more information. + time (str or list of str, optional): + Timespan or list of timespans to filter thje result. boundaries (str or list of str, optional): Boundary or list of boundaries that specify the measure resolution. See the - boundaries section for each region in the `Data Observatory - catalog `__. + boundaries section for each region in the `Data Observatory v1 + catalog (deprecated) `__. include_quantiles (bool, optional): Include quantiles calculations which are a calculation of how a measure compares to all measures in the full GeoDataFrame. @@ -315,6 +315,15 @@ def discovery(self, region, keywords=None, regex=None, time=None, ValueError: If `region` is a :obj:`list` and does not consist of four elements, or if `region` is not an acceptable region CartoException: If `region` is not a table in user account + + Examples: + Get all European Union measures that mention ``freight``. + + >>> freight_meta = do.discovery('European Union', + ... keywords='freight', + ... time='2010') + >>> freight_meta['numer_name'].head() + """ if isinstance(region, str): try: @@ -459,39 +468,19 @@ def discovery(self, region, keywords=None, regex=None, time=None, utils.debug_print(self._verbose, query=query) return DataFrame(self._fetch(query, decode_geom=True)) + @deprecated(message='The Data Observatory v1 is being deprecated. Use the `data.observatory` package instead') def augment(self, table_name, metadata, persist_as=None, how='the_geom'): - """Get an augmented CARTO dataset with `Data Observatory + """Get an augmented CARTO dataset with `Data Observatory v1 (deprecated) `__ measures. Use `DataObsClient.discovery <#DataObsClient.discovery>`__ to search for available - measures, or see the full `Data Observatory catalog + measures, or see the full `Data Observatory v1 catalog (deprecated) `__. Optionally persist the data as a new table. - Example: - Get a DataFrame with Data Observatory measures based on the - geometries in a CARTO table. - - >>> do = DataObsClient(credentials) - >>> median_income = do.discovery( - ... 'transaction_events', - ... regex='.*median income.*', - ... time='2011 - 2015') - >>> ds = do.augment('transaction_events', median_income) - - Pass in cherry-picked measures from the Data Observatory catalog. - The rest of the metadata will be filled in, but it's important to - specify the geographic level as this will not show up in the column - name. - - >>> median_income = [{'numer_id': 'us.census.acs.B19013001', - ... 'geom_id': 'us.census.tiger.block_group', - ... 'numer_timespan': '2011 - 2015'}] - >>> ds = do.augment('transaction_events', median_income) - Args: table_name (str): - Name of table on CARTO account that Data Observatory measures + Name of table on CARTO account that Data Observatory v1 measures (deprecated) are to be added to. metadata (pandas.DataFrame): List of all measures to add to @@ -525,7 +514,29 @@ def augment(self, table_name, metadata, persist_as=None, how='the_geom'): If metadata object is invalid or empty, or if the number of requested measures exceeds 50. CartoException: - If user account consumes all of Data Observatory quota + If user account consumes all of Data Observatory v1 (deprecated) quota + + Example: + Get a DataFrame with Data Observatory v1 measures (deprecated) based + on the geometries in a CARTO table. + + >>> do = DataObsClient(credentials) + >>> median_income = do.discovery( + ... 'transaction_events', + ... regex='.*median income.*', + ... time='2011 - 2015') + >>> ds = do.augment('transaction_events', median_income) + + Pass in cherry-picked measures from the Data Observatory v1 catalog (deprecated). + The rest of the metadata will be filled in, but it's important to + specify the geographic level as this will not show up in the column + name. + + >>> median_income = [{'numer_id': 'us.census.acs.B19013001', + ... 'geom_id': 'us.census.tiger.block_group', + ... 'numer_timespan': '2011 - 2015'}] + >>> ds = do.augment('transaction_events', median_income) + """ if isinstance(metadata, DataFrame): diff --git a/cartoframes/data/observatory/catalog/catalog.py b/cartoframes/data/observatory/catalog/catalog.py index 918dd1382..050b868f5 100644 --- a/cartoframes/data/observatory/catalog/catalog.py +++ b/cartoframes/data/observatory/catalog/catalog.py @@ -5,8 +5,10 @@ from .dataset import Dataset from .geography import Geography from .subscriptions import Subscriptions -from .repository.constants import COUNTRY_FILTER, CATEGORY_FILTER, GEOGRAPHY_FILTER, PROVIDER_FILTER, PUBLIC_FILTER +from .repository.constants import (COUNTRY_FILTER, CATEGORY_FILTER, GEOGRAPHY_FILTER, GLOBAL_COUNTRY_FILTER, + PROVIDER_FILTER, PUBLIC_FILTER) +from ....utils.logger import log from ....utils.utils import get_credentials @@ -126,7 +128,7 @@ def countries(self): :py:class:`CatalogList ` Raises: - CatalogError: if there's a problem when connecting to the catalog or no datasets are found. + CatalogError: if there's a problem when connecting to the catalog or no countries are found. """ return Country.get_all(self.filters) @@ -139,9 +141,10 @@ def categories(self): :py:class:`CatalogList ` Raises: - CatalogError: if there's a problem when connecting to the catalog or no datasets are found. + CatalogError: if there's a problem when connecting to the catalog or no categories are found. """ + self._global_message() return Category.get_all(self.filters) @property @@ -152,9 +155,10 @@ def providers(self): :py:class:`CatalogList ` Raises: - CatalogError: if there's a problem when connecting to the catalog or no datasets are found. + CatalogError: if there's a problem when connecting to the catalog or no providers are found. """ + self._global_message() return Provider.get_all(self.filters) @property @@ -168,6 +172,7 @@ def datasets(self): CatalogError: if there's a problem when connecting to the catalog or no datasets are found. """ + self._global_message() return Dataset.get_all(self.filters) @property @@ -178,9 +183,10 @@ def geographies(self): :py:class:`CatalogList ` Raises: - CatalogError: if there's a problem when connecting to the catalog or no datasets are found. + CatalogError: if there's a problem when connecting to the catalog or no geographies are found. """ + self._global_message() return Geography.get_all(self.filters) def country(self, country_id): @@ -188,7 +194,7 @@ def country(self, country_id): Args: country_id (str): - Id value of the country to be used for filtering the Catalog. + ID of the country to be used for filtering the Catalog. Returns: :py:class:`Catalog ` @@ -202,7 +208,7 @@ def category(self, category_id): Args: category_id (str): - Id value of the category to be used for filtering the Catalog. + ID of the category to be used for filtering the Catalog. Returns: :py:class:`Catalog ` @@ -216,7 +222,7 @@ def geography(self, geography_id): Args: geography_id (str): - Id or slug value of the geography to be used for filtering the Catalog + ID or slug of the geography to be used for filtering the Catalog Returns: :py:class:`Catalog ` @@ -236,7 +242,7 @@ def provider(self, provider_id): Args: provider_id (str): - Id value of the provider to be used for filtering the Catalog. + ID of the provider to be used for filtering the Catalog. Returns: :py:class:`CatalogList ` @@ -295,3 +301,8 @@ def datasets_filter(self, filter_dataset): """ return Dataset.get_datasets_spatial_filtered(filter_dataset) + + def _global_message(self): + if self.filters and self.filters.get(COUNTRY_FILTER) != GLOBAL_COUNTRY_FILTER: + log.info('You can find more entities with the Global country filter. To apply that filter run:' + "\n\tCatalog().country('glo')") diff --git a/cartoframes/data/observatory/catalog/dataset.py b/cartoframes/data/observatory/catalog/dataset.py index 915229235..ecbf714b7 100644 --- a/cartoframes/data/observatory/catalog/dataset.py +++ b/cartoframes/data/observatory/catalog/dataset.py @@ -17,9 +17,14 @@ from ....utils.utils import get_credentials, check_credentials, check_do_enabled from ....exceptions import DOError +DATASET_SUBSCRIPTION_ERROR = ( + 'You are not subscribed to this Dataset yet. ' + 'Please, use the subscribe method first.' +) + class Dataset(CatalogEntity): - """A Dataset represents the metadata of a particular dataset in the catalog. + """A Dataset represents the metadata of a particular dataset in the catalog. If you have Data Observatory enabled in your CARTO account you can: @@ -103,7 +108,7 @@ def description(self): @property def provider(self): - """Id of the :py:class:`Provider` of this dataset.""" + """ID of the :py:class:`Provider` of this dataset.""" return self.data['provider_id'] @property @@ -123,7 +128,7 @@ def category_name(self): @property def data_source(self): - """Id of the data source of this dataset.""" + """ID of the data source of this dataset.""" return self.data['data_source_id'] @property @@ -324,7 +329,6 @@ def get_all(cls, filters=None, credentials=None): credentials (:py:class:`Credentials `, optional): credentials of CARTO user account. If provided, only datasets granted for those credentials are returned. - filters (dict, optional): Dict containing pairs of dataset properties and its value to be used as filters to query the available datasets. If none is provided, no filters will be applied to the query. @@ -386,6 +390,9 @@ def to_csv(self, file_path, credentials=None, limit=None, order_by=None, sql_que credentials of CARTO user account. If not provided, a default credentials (if set with :py:meth:`set_default_credentials `) will be used. + limit (int, optional): + The number of rows to download. Default is to download all rows. + order_by (str, optional): Field(s) used to order the rows to download. Default is unordered. sql_query (str, optional): a query to select, filter or aggregate the content of the dataset. For instance, to download just one row: `select * from $dataset$ limit 1`. The placeholder `$dataset$` is mandatory and it will be replaced by the actual dataset before running the query. @@ -400,15 +407,14 @@ def to_csv(self, file_path, credentials=None, limit=None, order_by=None, sql_que """ _credentials = get_credentials(credentials) - if not self._is_subscribed(_credentials): - raise DOError('You are not subscribed to this Dataset yet. ' - 'Please, use the subscribe method first.') + if not self.is_subscribed(_credentials, DATASET_TYPE): + raise DOError(DATASET_SUBSCRIPTION_ERROR) self._download(_credentials, file_path, limit=limit, order_by=order_by, sql_query=sql_query, add_geom=add_geom) @check_do_enabled def to_dataframe(self, credentials=None, limit=None, order_by=None, sql_query=None, add_geom=None): - """Download dataset data as a pandas.DataFrame. You need Data Observatory enabled in your CARTO + """Download dataset data as a geopandas.GeoDataFrame. You need Data Observatory enabled in your CARTO account, please contact us at support@carto.com for more information. For premium datasets (those with `is_public_data` set to False), you need a subscription to the dataset. @@ -419,6 +425,9 @@ def to_dataframe(self, credentials=None, limit=None, order_by=None, sql_query=No credentials of CARTO user account. If not provided, a default credentials (if set with :py:meth:`set_default_credentials `) will be used. + limit (int, optional): + The number of rows to download. Default is to download all rows. + order_by (str, optional): Field(s) used to order the rows to download. Default is unordered. sql_query (str, optional): a query to select, filter or aggregate the content of the dataset. For instance, to download just one row: `select * from $dataset$ limit 1`. The placeholder `$dataset$` is mandatory and it will be replaced by the actual dataset before running the query. @@ -427,7 +436,7 @@ def to_dataframe(self, credentials=None, limit=None, order_by=None, sql_query=No Returns: - pandas.DataFrame + geopandas.GeoDataFrame Raises: DOError: if you have not a valid license for the dataset being downloaded, @@ -437,9 +446,8 @@ def to_dataframe(self, credentials=None, limit=None, order_by=None, sql_query=No """ _credentials = get_credentials(credentials) - if not self._is_subscribed(_credentials): - raise DOError('You are not subscribed to this Dataset yet. ' - 'Please, use the subscribe method first.') + if not self.is_subscribed(_credentials, DATASET_TYPE): + raise DOError(DATASET_SUBSCRIPTION_ERROR) return self._download(_credentials, limit=limit, order_by=order_by, sql_query=sql_query, add_geom=add_geom) @@ -506,14 +514,6 @@ def subscription_info(self, credentials=None): return subscription_info.SubscriptionInfo( subscription_info.fetch_subscription_info(self.id, DATASET_TYPE, _credentials)) - def _is_subscribed(self, credentials): - if self.is_public_data: - return True - - datasets = Dataset.get_all({}, credentials) - - return datasets is not None and self in datasets - def _get_summary_data(self): data = self.data.get('summary_json') diff --git a/cartoframes/data/observatory/catalog/entity.py b/cartoframes/data/observatory/catalog/entity.py index d75505393..9044fa769 100644 --- a/cartoframes/data/observatory/catalog/entity.py +++ b/cartoframes/data/observatory/catalog/entity.py @@ -1,8 +1,11 @@ import pandas as pd from abc import ABC +from geopandas import GeoDataFrame from carto.do_dataset import DODataset +from . import subscriptions +from ....utils.geom_utils import set_geometry from ....utils.logger import log _DATASET_READ_MSG = '''To load it as a DataFrame you can do: @@ -18,6 +21,8 @@ gdf = GeoDataFrame(df, geometry=decode_geometry(df['geom'])) ''' +GEOM_COL = 'geom' + class CatalogEntity(ABC): """This is an internal class the rest of the classes related to the catalog discovery extend. @@ -83,7 +88,7 @@ def get_list(cls, id_list): Args: id_list (list): - List of sD or slugs of entities in the catalog to retrieve instances. + List of ID or slugs of entities in the catalog to retrieve instances. Raises: CatalogError: if there's a problem when connecting to the catalog or no entities are found. @@ -99,6 +104,10 @@ def to_dict(self): """Converts the entity instance to a Python dict.""" return {key: value for key, value in self.data.items() if key not in self.export_excluded_fields} + def is_subscribed(self, credentials, entity_type): + """Check if the entity is subscribed""" + return self.is_public_data or self.id in subscriptions.get_subscription_ids(credentials, entity_type) + def __eq__(self, other): return self.data == other.data @@ -142,7 +151,12 @@ def _download(self, credentials, file_path=None, limit=None, order_by=None, sql_ log.info(_GEOGRAPHY_READ_MSG.format(file_path)) else: dataframe = pd.read_csv(rows) - return dataframe + gdf = GeoDataFrame(dataframe) + + if GEOM_COL in gdf: + set_geometry(gdf, GEOM_COL, inplace=True) + + return gdf def _get_remote_full_table_name(self, user_project, user_dataset, public_project): project, dataset, table = self.id.split('.') diff --git a/cartoframes/data/observatory/catalog/geography.py b/cartoframes/data/observatory/catalog/geography.py index 621a26bac..890989bde 100644 --- a/cartoframes/data/observatory/catalog/geography.py +++ b/cartoframes/data/observatory/catalog/geography.py @@ -8,6 +8,11 @@ from ....utils.utils import get_credentials, check_credentials, check_do_enabled from ....exceptions import DOError +GEOGRAPHY_SUBSCRIPTION_ERROR = ( + 'You are not subscribed to this Geography yet. ' + 'Please, use the subscribe method first.' +) + class Geography(CatalogEntity): """A Geography represents the metadata of a particular geography dataset in the catalog. @@ -95,7 +100,7 @@ def language(self): @property def provider(self): - """Id of the Provider of this geography.""" + """ID of the Provider of this geography.""" return self.data['provider_id'] @property @@ -189,6 +194,9 @@ def to_csv(self, file_path, credentials=None, limit=None, order_by=None, sql_que credentials of CARTO user account. If not provided, a default credentials (if set with :py:meth:`set_default_credentials `) will be used. + limit (int, optional): + The number of rows to download. Default is to download all rows. + order_by (str, optional): Field(s) used to order the rows to download. Default is unordered. sql_query (str, optional): a query to select, filter or aggregate the content of the geography dataset. For instance, to download just one row: `select * from $geography$ limit 1`. The placeholder `$geography$` is mandatory and it will be replaced by the actual geography dataset before running @@ -202,9 +210,8 @@ def to_csv(self, file_path, credentials=None, limit=None, order_by=None, sql_que """ _credentials = get_credentials(credentials) - if not self._is_subscribed(_credentials): - raise DOError('You are not subscribed to this Geography yet. ' - 'Please, use the subscribe method first.') + if not self.is_subscribed(_credentials, GEOGRAPHY_TYPE): + raise DOError(GEOGRAPHY_SUBSCRIPTION_ERROR) self._download(_credentials, file_path, limit=limit, order_by=order_by, sql_query=sql_query) @@ -221,6 +228,9 @@ def to_dataframe(self, credentials=None, limit=None, order_by=None, sql_query=No credentials of CARTO user account. If not provided, a default credentials (if set with :py:meth:`set_default_credentials `) will be used. + limit (int, optional): + The number of rows to download. Default is to download all rows. + order_by (str, optional): Field(s) used to order the rows to download. Default is unordered. sql_query (str, optional): a query to select, filter or aggregate the content of the geography dataset. For instance, to download just one row: `select * from $geography$ limit 1`. The placeholder `$geography$` is mandatory and it will be replaced by the actual geography dataset before running @@ -237,9 +247,8 @@ def to_dataframe(self, credentials=None, limit=None, order_by=None, sql_query=No """ _credentials = get_credentials(credentials) - if not self._is_subscribed(_credentials): - raise DOError('You are not subscribed to this Geography yet. ' - 'Please, use the subscribe method first.') + if not self.is_subscribed(_credentials, GEOGRAPHY_TYPE): + raise DOError(GEOGRAPHY_SUBSCRIPTION_ERROR) return self._download(_credentials, limit=limit, order_by=order_by, sql_query=sql_query) @@ -305,13 +314,5 @@ def subscription_info(self, credentials=None): return subscription_info.SubscriptionInfo( subscription_info.fetch_subscription_info(self.id, GEOGRAPHY_TYPE, _credentials)) - def _is_subscribed(self, credentials): - if self.is_public_data: - return True - - geographies = Geography.get_all({}, credentials) - - return geographies is not None and self in geographies - def __str__(self): return "".format(self._get_print_id()) diff --git a/cartoframes/data/observatory/catalog/repository/constants.py b/cartoframes/data/observatory/catalog/repository/constants.py index d44986a5e..9d9f24353 100644 --- a/cartoframes/data/observatory/catalog/repository/constants.py +++ b/cartoframes/data/observatory/catalog/repository/constants.py @@ -6,3 +6,4 @@ PUBLIC_FILTER = 'public' VARIABLE_FILTER = 'variable' VARIABLE_GROUP_FILTER = 'variable_group' +GLOBAL_COUNTRY_FILTER = 'glo' diff --git a/cartoframes/data/observatory/catalog/repository/geography_repo.py b/cartoframes/data/observatory/catalog/repository/geography_repo.py index 353ac0e17..d23fc9227 100644 --- a/cartoframes/data/observatory/catalog/repository/geography_repo.py +++ b/cartoframes/data/observatory/catalog/repository/geography_repo.py @@ -61,7 +61,7 @@ def _map_row(self, row): def get_geographies_gdf(self): data = self.client.get_geographies({'get_geoms_coverage': True}) gdf = GeoDataFrame(data, crs='epsg:4326') - set_geometry(gdf, col='geom_coverage', inplace=True) + set_geometry(gdf, 'geom_coverage', inplace=True) return gdf diff --git a/cartoframes/data/observatory/catalog/subscriptions.py b/cartoframes/data/observatory/catalog/subscriptions.py index 21e017191..ebf098304 100644 --- a/cartoframes/data/observatory/catalog/subscriptions.py +++ b/cartoframes/data/observatory/catalog/subscriptions.py @@ -43,7 +43,7 @@ def geographies(self): def get_subscription_ids(credentials, stype=None): subs = fetch_subscriptions(credentials) - return [s.id for s in subs if stype is None or stype == s.type] + return [s.id for s in subs if (stype is None or stype == s.type) and s.status == 'active'] def fetch_subscriptions(credentials): diff --git a/cartoframes/data/observatory/catalog/summary.py b/cartoframes/data/observatory/catalog/summary.py index 45b8447de..7df739c64 100644 --- a/cartoframes/data/observatory/catalog/summary.py +++ b/cartoframes/data/observatory/catalog/summary.py @@ -4,6 +4,7 @@ from shapely import wkb from ....utils.utils import check_package +from ....utils.logger import log def variable_describe(data): @@ -101,10 +102,13 @@ def geom_coverage(geography_id): from ....viz import Map, Layer geography = Geography.get(geography_id) - geom_coverage = wkb.loads(geography.geom_coverage, hex=True) - geom_coverage_gdf = gpd.GeoDataFrame({'geometry': [geom_coverage]}, geometry='geometry') - return Map(Layer(geom_coverage_gdf)) + if geography.geom_coverage: + geom_coverage = wkb.loads(geography.geom_coverage, hex=True) + geom_coverage_gdf = gpd.GeoDataFrame({'geometry': [geom_coverage]}, geometry='geometry') + return Map(Layer(geom_coverage_gdf)) + else: + log.info('Geometry coverage not available') def histogram(data): diff --git a/cartoframes/data/observatory/catalog/utils.py b/cartoframes/data/observatory/catalog/utils.py index d0a2edfb7..a0f3a589a 100644 --- a/cartoframes/data/observatory/catalog/utils.py +++ b/cartoframes/data/observatory/catalog/utils.py @@ -15,7 +15,7 @@ def display_existing_subscription_message(entity_id, entity_type): def display_subscription_form(entity_id, entity_type, credentials): info = fetch_subscription_info(entity_id, entity_type, credentials) - instant_licensing = credentials.get_do_credentials().instant_licensing + instant_licensing = credentials.is_instant_licensing_active() if is_ipython_notebook(): _display_subscription_form_notebook(entity_id, entity_type, info, instant_licensing, credentials) diff --git a/cartoframes/data/services/geocoding.py b/cartoframes/data/services/geocoding.py index 68bc301a7..4ef8b7e65 100644 --- a/cartoframes/data/services/geocoding.py +++ b/cartoframes/data/services/geocoding.py @@ -57,7 +57,8 @@ def geocode(self, source, street, city=None, state=None, country=None, status=geocoding_constants.DEFAULT_STATUS, table_name=None, if_exists='fail', - dry_run=False, cached=None): + dry_run=False, cached=None, + null_geom_value=None): """Geocode method. Args: @@ -92,6 +93,8 @@ def geocode(self, source, street, table. This parameter should be used along with ``table_name``. dry_run (bool, optional): no actual geocoding will be performed (useful to check the needed quota) + null_geom_value (Object, optional): value for the `the_geom` column when it's null. + Defaults to None Returns: A named-tuple ``(data, metadata)`` containing either a ``data`` geopandas.GeoDataFrame @@ -184,7 +187,7 @@ def geocode(self, source, street, if dry_run: return self.result(data=None, metadata=metadata) - gdf = read_carto(input_table_name, self._credentials) + gdf = read_carto(input_table_name, self._credentials, null_geom_value=null_geom_value) if self._source_manager.is_dataframe() and CARTO_INDEX_KEY in gdf: del gdf[CARTO_INDEX_KEY] diff --git a/cartoframes/data/services/isolines.py b/cartoframes/data/services/isolines.py index 4da8f7c26..08a197636 100644 --- a/cartoframes/data/services/isolines.py +++ b/cartoframes/data/services/isolines.py @@ -202,8 +202,7 @@ def _iso_areas(self, # Execute and download the query to generate the isolines gdf = read_carto(sql, self._credentials) - # Sorting by `data_range` column and recalculating `cartodb_id` - gdf.sort_values(by=[DATA_RANGE_KEY], ascending=ascending, inplace=True) + # Recalculating `cartodb_id` gdf.reset_index(drop=True, inplace=True) if CARTO_INDEX_KEY in gdf.columns: gdf[CARTO_INDEX_KEY] = gdf.index + 1 diff --git a/cartoframes/data/services/utils/geocoding_constants.py b/cartoframes/data/services/utils/geocoding_constants.py index fcd6895b0..51c480611 100644 --- a/cartoframes/data/services/utils/geocoding_constants.py +++ b/cartoframes/data/services/utils/geocoding_constants.py @@ -1,6 +1,5 @@ __all__ = [ 'HASH_COLUMN', - 'BATCH_SIZE', 'DEFAULT_STATUS', 'QUOTA_SERVICE', 'STATUS_FIELDS', @@ -12,8 +11,6 @@ HASH_COLUMN = 'carto_geocode_hash' -BATCH_SIZE = 200 - DEFAULT_STATUS = {'gc_status_rel': 'relevance'} QUOTA_SERVICE = 'hires_geocoder' diff --git a/cartoframes/data/services/utils/geocoding_utils.py b/cartoframes/data/services/utils/geocoding_utils.py index d73ef1578..ac11cc7ef 100644 --- a/cartoframes/data/services/utils/geocoding_utils.py +++ b/cartoframes/data/services/utils/geocoding_utils.py @@ -55,8 +55,8 @@ def prefixed_column_or_value(attr, prefix): def hash_expr(street, city, state, country, table_prefix=None): street, city, state, country = (prefixed_column_or_value(v, table_prefix) for v in (street, city, state, country)) - hashed_expr = " || '<>' || ".join([street, city or "''", state or "''", country or "''"]) - return "md5({hashed_expr})".format(hashed_expr=hashed_expr) + hashed_cols = ", '<>' , ".join([street, city or "''", state or "''", country or "''"]) + return "md5(concat({hashed_cols}))".format(hashed_cols=hashed_cols) def needs_geocoding_expr(hash_expr): @@ -144,16 +144,14 @@ def geocode_query(table, schema, street, city, state, country, status): {street}, {city}, {state}, - {country}, - {batch_size} + {country} ) """.format( query=query, street=column_name(street), city=column_name(city), state=column_name(state), - country=column_name(country), - batch_size=geocoding_constants.BATCH_SIZE + country=column_name(country) ) status_assignment, status_columns = status_assignment_columns(status) diff --git a/cartoframes/io/carto.py b/cartoframes/io/carto.py index d8e5bee14..51e510921 100644 --- a/cartoframes/io/carto.py +++ b/cartoframes/io/carto.py @@ -1,12 +1,13 @@ """Functions to interact with the CARTO platform""" +import math from pandas import DataFrame from geopandas import GeoDataFrame from carto.exceptions import CartoException -from .managers.context_manager import ContextManager -from ..utils.geom_utils import check_crs, has_geometry, set_geometry +from .managers.context_manager import ContextManager, _compute_copy_data, get_dataframe_columns_info +from ..utils.geom_utils import is_reprojection_needed, reproject, has_geometry, set_geometry from ..utils.logger import log from ..utils.utils import is_valid_str, is_sql_query from ..utils.metrics import send_metrics @@ -15,9 +16,14 @@ GEOM_COLUMN_NAME = 'the_geom' IF_EXISTS_OPTIONS = ['fail', 'replace', 'append'] +MAX_UPLOAD_SIZE_BYTES = 2000000000 # 2GB +SAMPLE_ROWS_NUMBER = 100 +CSV_TO_CARTO_RATIO = 1.4 + @send_metrics('data_downloaded') -def read_carto(source, credentials=None, limit=None, retry_times=3, schema=None, index_col=None, decode_geom=True): +def read_carto(source, credentials=None, limit=None, retry_times=3, schema=None, index_col=None, decode_geom=True, + null_geom_value=None): """Read a table or a SQL query from the CARTO account. Args: @@ -32,6 +38,8 @@ def read_carto(source, credentials=None, limit=None, retry_times=3, schema=None, `current_schema()` using the credentials. index_col (str, optional): name of the column to be loaded as index. It can be used also to set the index name. decode_geom (bool, optional): convert the "the_geom" column into a valid geometry column. + null_geom_value (Object, optional): value for the `the_geom` column when it's null. + Defaults to None Returns: geopandas.GeoDataFrame @@ -59,12 +67,16 @@ def read_carto(source, credentials=None, limit=None, retry_times=3, schema=None, # Decode geometry column set_geometry(gdf, GEOM_COLUMN_NAME, inplace=True) + if null_geom_value is not None: + gdf[GEOM_COLUMN_NAME].fillna(null_geom_value, inplace=True) + return gdf @send_metrics('data_uploaded') def to_carto(dataframe, table_name, credentials=None, if_exists='fail', geom_col=None, index=False, index_label=None, - cartodbfy=True, log_enabled=True): + cartodbfy=True, log_enabled=True, retry_times=3, max_upload_size=MAX_UPLOAD_SIZE_BYTES, + skip_quota_warning=False): """Upload a DataFrame to CARTO. The geometry's CRS must be WGS 84 (EPSG:4326) so you can use it on CARTO. Args: @@ -79,6 +91,14 @@ def to_carto(dataframe, table_name, credentials=None, if_exists='fail', geom_col uses the name of the index from the dataframe. cartodbfy (bool, optional): convert the table to CARTO format. Default True. More info `here `. + log_enabled (bool, optional): enable the logging mechanism. Default is True. + retry_times (int, optional): + Number of time to retry the upload in case it fails. Default is 3. + max_upload_size (int, optional): defines the maximum size of the dataframe to be uploaded. + Default is 2GB. + skip_quota_warning (bool, optional): skip the quota exceeded check and force the upload. + (The upload will still fail if the size of the dataset exceeds the remaining DB quota). + Default is False. Returns: string: the table name normalized. @@ -91,7 +111,8 @@ def to_carto(dataframe, table_name, credentials=None, if_exists='fail', geom_col raise ValueError('Wrong dataframe. You should provide a valid DataFrame instance.') if isinstance(dataframe, GeoDataFrame): - check_crs(dataframe) + if is_reprojection_needed(dataframe): + dataframe = reproject(dataframe) if not is_valid_str(table_name): raise ValueError('Wrong table name. You should provide a valid table name.') @@ -102,6 +123,19 @@ def to_carto(dataframe, table_name, credentials=None, if_exists='fail', geom_col context_manager = ContextManager(credentials) + if not skip_quota_warning: + me_data = context_manager.credentials.me_data + if me_data is not None and me_data.get('user_data'): + n = min(SAMPLE_ROWS_NUMBER, len(dataframe)) + estimated_byte_size = len(dataframe.sample(n=n).to_csv(header=False)) * len(dataframe) \ + / n / CSV_TO_CARTO_RATIO + remaining_byte_quota = me_data.get('user_data').get('remaining_byte_quota') + + if remaining_byte_quota is not None and estimated_byte_size > remaining_byte_quota: + raise CartoException('DB Quota will be exceeded. ' + 'The remaining quota is {} bytes and the dataset size is {} bytes.'.format( + remaining_byte_quota, estimated_byte_size)) + gdf = GeoDataFrame(dataframe, copy=True) if index: @@ -118,13 +152,23 @@ def to_carto(dataframe, table_name, credentials=None, if_exists='fail', geom_col gdf.set_geometry(dataframe.geometry.name, inplace=True) if has_geometry(gdf): + if GEOM_COLUMN_NAME in gdf and dataframe.geometry.name != GEOM_COLUMN_NAME: + gdf.drop(columns=[GEOM_COLUMN_NAME], inplace=True) + # Prepare geometry column for the upload gdf.rename_geometry(GEOM_COLUMN_NAME, inplace=True) elif isinstance(dataframe, GeoDataFrame): log.warning('Geometry column not found in the GeoDataFrame.') - table_name = context_manager.copy_from(gdf, table_name, if_exists, cartodbfy) + chunk_count = math.ceil(estimate_csv_size(gdf) / max_upload_size) + chunk_row_size = int(math.ceil(len(gdf) / chunk_count)) + chunked_gdf = [gdf[i:i + chunk_row_size] for i in range(0, gdf.shape[0], chunk_row_size)] + + for i, chunk in enumerate(chunked_gdf): + if i > 0: + if_exists = 'append' + table_name = context_manager.copy_from(chunk, table_name, if_exists, cartodbfy, retry_times) if log_enabled: log.info('Success! Data uploaded to table "{}" correctly'.format(table_name)) @@ -138,11 +182,9 @@ def list_tables(credentials=None): Args: credentials (:py:class:`Credentials `, optional): instance of Credentials (username, api_key, etc). - schema (str, optional): prefix of the table. By default, it gets the - `current_schema()` using the credentials. Returns: - DataFrame: A DataFrame with all the table names for the given credentials and schema. + DataFrame: A DataFrame with all the table names for the given credentials. """ context_manager = ContextManager(credentials) @@ -180,6 +222,7 @@ def delete_table(table_name, credentials=None, log_enabled=True): table_name (str): name of the table. credentials (:py:class:`Credentials `, optional): instance of Credentials (username, api_key, etc). + log_enabled (bool, optional): enable the logging mechanism. Default is True. Raises: ValueError: if the table name is not a valid table name. @@ -207,6 +250,7 @@ def rename_table(table_name, new_table_name, credentials=None, if_exists='fail', credentials (:py:class:`Credentials `, optional): instance of Credentials (username, api_key, etc). if_exists (str, optional): 'fail', 'replace'. Default is 'fail'. + log_enabled (bool, optional): enable the logging mechanism. Default is True. Raises: ValueError: if the table names provided are wrong or the if_exists param is not valid. @@ -239,6 +283,7 @@ def copy_table(table_name, new_table_name, credentials=None, if_exists='fail', l credentials (:py:class:`Credentials `, optional): instance of Credentials (username, api_key, etc). if_exists (str, optional): 'fail', 'replace', 'append'. Default is 'fail'. + log_enabled (bool, optional): enable the logging mechanism. Default is True. Raises: ValueError: if the table names provided are wrong or the if_exists param is not valid. @@ -272,6 +317,7 @@ def create_table_from_query(query, new_table_name, credentials=None, if_exists=' credentials (:py:class:`Credentials `, optional): instance of Credentials (username, api_key, etc). if_exists (str, optional): 'fail', 'replace', 'append'. Default is 'fail'. + log_enabled (bool, optional): enable the logging mechanism. Default is True. Raises: ValueError: if the query or table name provided is wrong or the if_exists param is not valid. @@ -340,6 +386,7 @@ def update_privacy_table(table_name, privacy, credentials=None, log_enabled=True privacy (str): privacy of the table: 'private', 'public', 'link'. credentials (:py:class:`Credentials `, optional): instance of Credentials (username, api_key, etc). + log_enabled (bool, optional): enable the logging mechanism. Default is True. Raises: ValueError: if the table name is wrong or the privacy name @@ -358,3 +405,10 @@ def update_privacy_table(table_name, privacy, credentials=None, log_enabled=True if log_enabled: log.info('Success! Table "{}" privacy updated correctly'.format(table_name)) + + +def estimate_csv_size(gdf): + n = min(SAMPLE_ROWS_NUMBER, len(gdf)) + columns = get_dataframe_columns_info(gdf) + return sum([len(x) for x in + _compute_copy_data(gdf.sample(n=n), columns)]) * len(gdf) / n diff --git a/cartoframes/io/managers/context_manager.py b/cartoframes/io/managers/context_manager.py index df6fcee43..7343f03ed 100644 --- a/cartoframes/io/managers/context_manager.py +++ b/cartoframes/io/managers/context_manager.py @@ -8,19 +8,56 @@ from carto.datasets import DatasetManager from carto.exceptions import CartoException, CartoRateLimitException from carto.sql import SQLClient, BatchSQLClient, CopySQLClient +from pyrestcli.exceptions import NotFoundException from ..dataset_info import DatasetInfo from ... import __version__ from ...auth.defaults import get_default_credentials from ...utils.logger import log from ...utils.geom_utils import encode_geometry_ewkb -from ...utils.utils import is_sql_query, check_credentials, encode_row, map_geom_type, PG_NULL -from ...utils.columns import get_dataframe_columns_info, get_query_columns_info, obtain_converters, \ - date_columns_names, normalize_name +from ...utils.utils import is_sql_query, check_credentials, encode_row, map_geom_type, PG_NULL, double_quote +from ...utils.columns import (get_dataframe_columns_info, get_query_columns_info, obtain_converters, date_columns_names, + normalize_name) DEFAULT_RETRY_TIMES = 3 +def retry_copy(func): + def wrapper(*args, **kwargs): + m_retry_times = kwargs.get('retry_times', DEFAULT_RETRY_TIMES) + while m_retry_times >= 1: + try: + return func(*args, **kwargs) + except CartoRateLimitException as err: + m_retry_times -= 1 + + if m_retry_times <= 0: + warn(('Read call was rate-limited. ' + 'This usually happens when there are multiple queries being read at the same time.')) + raise err + + warn('Read call rate limited. Waiting {s} seconds'.format(s=err.retry_after)) + time.sleep(err.retry_after) + warn('Retrying...') + return func(*args, **kwargs) + return wrapper + + +def not_found(func): + def decorator_func(*args, **kwargs): + try: + return func(*args, **kwargs) + + except CartoException as e: + if hasattr(e, 'args') and isinstance(e.args, (list, tuple)) and type(e.args[0]) == NotFoundException: + raise Exception('Resource not found') from None + + else: + raise e + + return decorator_func + + class ContextManager: def __init__(self, credentials): @@ -32,19 +69,22 @@ def __init__(self, credentials): self.copy_client = CopySQLClient(self.auth_client) self.batch_sql_client = BatchSQLClient(self.auth_client) + @not_found def execute_query(self, query, parse_json=True, do_post=True, format=None, **request_args): return self.sql_client.send(query.strip(), parse_json, do_post, format, **request_args) + @not_found def execute_long_running_query(self, query): return self.batch_sql_client.create_and_wait_for_completion(query.strip()) - def copy_to(self, source, schema, limit=None, retry_times=DEFAULT_RETRY_TIMES): + def copy_to(self, source, schema=None, limit=None, retry_times=DEFAULT_RETRY_TIMES): query = self.compute_query(source, schema) columns = self._get_query_columns_info(query) copy_query = self._get_copy_query(query, columns, limit) return self._copy_to(copy_query, columns, retry_times) - def copy_from(self, gdf, table_name, if_exists='fail', cartodbfy=True): + def copy_from(self, gdf, table_name, if_exists='fail', cartodbfy=True, + retry_times=DEFAULT_RETRY_TIMES): schema = self.get_schema() table_name = self.normalize_table_name(table_name) df_columns = get_dataframe_columns_info(gdf) @@ -59,7 +99,8 @@ def copy_from(self, gdf, table_name, if_exists='fail', cartodbfy=True): self._truncate_table(table_name, schema, cartodbfy) else: # Diff columns: truncate table and drop + add columns - self._truncate_and_drop_add_columns(table_name, schema, df_columns, table_columns, cartodbfy) + self._truncate_and_drop_add_columns( + table_name, schema, df_columns, table_columns, cartodbfy) elif if_exists == 'fail': raise Exception('Table "{schema}.{table_name}" already exists in your CARTO account. ' @@ -71,7 +112,7 @@ def copy_from(self, gdf, table_name, if_exists='fail', cartodbfy=True): else: self._create_table_from_columns(table_name, schema, df_columns, cartodbfy) - self._copy_from(gdf, table_name, df_columns) + self._copy_from(gdf, table_name, df_columns, retry_times) return table_name def create_table_from_query(self, query, table_name, if_exists, cartodbfy=True): @@ -151,7 +192,9 @@ def get_schema(self): """Get user schema from current credentials""" query = 'SELECT current_schema()' result = self.execute_query(query, do_post=False) - return result['rows'][0]['current_schema'] + schema = result['rows'][0]['current_schema'] + log.debug('schema: {}'.format(schema)) + return schema def get_geom_type(self, query): """Fetch geom type of a remote table or query""" @@ -169,7 +212,7 @@ def get_geom_type(self, query): def get_num_rows(self, query): """Get the number of rows in the query""" - result = self.execute_query("SELECT COUNT(*) FROM ({query}) _query".format(query=query)) + result = self.execute_query('SELECT COUNT(*) FROM ({query}) _query'.format(query=query)) return result.get('rows')[0].get('count') def get_bounds(self, query): @@ -251,7 +294,8 @@ def _truncate_table(self, table_name, schema, cartodbfy): def _truncate_and_drop_add_columns(self, table_name, schema, df_columns, table_columns, cartodbfy): log.debug('TRUNCATE AND DROP + ADD columns table "{}"'.format(table_name)) - query = 'BEGIN; {truncate}; {drop_columns}; {add_columns}; {cartodbfy}; COMMIT;'.format( + query = '{regenerate}; BEGIN; {truncate}; {drop_columns}; {add_columns}; {cartodbfy}; COMMIT;'.format( + regenerate=_regenerate_table_query(table_name, schema) if self._check_regenerate_table_exists() else '', truncate=_truncate_table_query(table_name), drop_columns=_drop_columns_query(table_name, table_columns), add_columns=_add_columns_query(table_name, df_columns), @@ -278,6 +322,16 @@ def _check_exists(self, query): except CartoException: return False + def _check_regenerate_table_exists(self): + query = ''' + SELECT 1 + FROM pg_catalog.pg_proc p + LEFT JOIN pg_catalog.pg_namespace n ON n.oid = p.pronamespace + WHERE p.proname = 'cdb_regeneratetable' AND n.nspname = 'cartodb'; + ''' + result = self.execute_query(query) + return len(result['rows']) > 0 + def _get_query_columns_info(self, query): query = 'SELECT * FROM ({}) _q LIMIT 0'.format(query) table_info = self.execute_query(query) @@ -285,7 +339,9 @@ def _get_query_columns_info(self, query): def _get_copy_query(self, query, columns, limit): query_columns = [ - column.name for column in columns if (column.name != 'the_geom_webmercator')] + double_quote(column.name) for column in columns + if (column.name != 'the_geom_webmercator') + ] query = 'SELECT {columns} FROM ({query}) _q'.format( query=query, @@ -299,23 +355,12 @@ def _get_copy_query(self, query, columns, limit): return query - def _copy_to(self, query, columns, retry_times): + @retry_copy + def _copy_to(self, query, columns, retry_times=DEFAULT_RETRY_TIMES): log.debug('COPY TO') - copy_query = 'COPY ({0}) TO stdout WITH (FORMAT csv, HEADER true, NULL \'{1}\')'.format(query, PG_NULL) + copy_query = "COPY ({0}) TO stdout WITH (FORMAT csv, HEADER true, NULL '{1}')".format(query, PG_NULL) - try: - raw_result = self.copy_client.copyto_stream(copy_query) - except CartoRateLimitException as err: - if retry_times > 0: - retry_times -= 1 - warn('Read call rate limited. Waiting {s} seconds'.format(s=err.retry_after)) - time.sleep(err.retry_after) - warn('Retrying...') - return self._copy_to(query, columns, retry_times) - else: - warn(('Read call was rate-limited. ' - 'This usually happens when there are multiple queries being read at the same time.')) - raise err + raw_result = self.copy_client.copyto_stream(copy_query) converters = obtain_converters(columns) parse_dates = date_columns_names(columns) @@ -327,14 +372,16 @@ def _copy_to(self, query, columns, retry_times): return df - def _copy_from(self, dataframe, table_name, columns): + @retry_copy + def _copy_from(self, dataframe, table_name, columns, retry_times=DEFAULT_RETRY_TIMES): log.debug('COPY FROM') query = """ COPY {table_name}({columns}) FROM stdin WITH (FORMAT csv, DELIMITER '|', NULL '{null}'); """.format( table_name=table_name, null=PG_NULL, - columns=','.join(column.dbname for column in columns)).strip() + columns=','.join(double_quote(column.dbname) for column in columns)).strip() data = _compute_copy_data(dataframe, columns) + self.copy_client.copyfrom(query, data) def _rename_table(self, table_name, new_table_name): @@ -360,17 +407,19 @@ def _truncate_table_query(table_name): def _drop_columns_query(table_name, columns): - columns = ['DROP COLUMN {0}'.format(c.dbname) for c in columns if _not_reserved(c.dbname)] + columns = ['DROP COLUMN {name}'.format(name=double_quote(c.dbname)) + for c in columns if _not_reserved(c.dbname)] return 'ALTER TABLE {table_name} {drop_columns}'.format( table_name=table_name, - drop_columns=', '.join(columns)) + drop_columns=','.join(columns)) def _add_columns_query(table_name, columns): - columns = ['ADD COLUMN {0} {1}'.format(c.dbname, c.dbtype) for c in columns if _not_reserved(c.dbname)] + columns = ['ADD COLUMN {name} {type}'.format(name=double_quote(c.dbname), type=c.dbtype) + for c in columns if _not_reserved(c.dbname)] return 'ALTER TABLE {table_name} {add_columns}'.format( table_name=table_name, - add_columns=', '.join(columns)) + add_columns=','.join(columns)) def _not_reserved(column): @@ -379,10 +428,10 @@ def _not_reserved(column): def _create_table_from_columns_query(table_name, columns): - columns = ['{name} {type}'.format(name=c.dbname, type=c.dbtype) for c in columns] + columns = ['{name} {type}'.format(name=double_quote(c.dbname), type=c.dbtype) for c in columns] return 'CREATE TABLE {table_name} ({columns})'.format( table_name=table_name, - columns=', '.join(columns)) + columns=','.join(columns)) def _create_table_from_query_query(table_name, query): @@ -394,6 +443,11 @@ def _cartodbfy_query(table_name, schema): schema=schema, table_name=table_name) +def _regenerate_table_query(table_name, schema): + return "SELECT CDB_RegenerateTable('{schema}.{table_name}'::regclass)".format( + schema=schema, table_name=table_name) + + def _rename_table_query(table_name, new_table_name): return 'ALTER TABLE {table_name} RENAME TO {new_table_name};'.format( table_name=table_name, new_table_name=new_table_name) @@ -409,7 +463,7 @@ def _create_auth_client(credentials, public=False): def _compute_copy_data(df, columns): - for index, _ in df.iterrows(): + for index in df.index: row_data = [] for column in columns: val = df.at[index, column.name] diff --git a/cartoframes/utils/geom_utils.py b/cartoframes/utils/geom_utils.py index 1f8221c9f..dbd8d4cee 100644 --- a/cartoframes/utils/geom_utils.py +++ b/cartoframes/utils/geom_utils.py @@ -49,9 +49,9 @@ def set_geometry(gdf, col, drop=False, inplace=False, crs=None): if isinstance(col, str): if col not in frame: raise Exception('Column "{0}" does not exist.'.format(col)) - frame[col] = decode_geometry(frame[col]) + frame[col] = decode_geometry(frame[col].dropna()) else: - col = decode_geometry(col) + col = decode_geometry(col.dropna()) # Call set_geometry with decoded column frame.set_geometry(col, drop=drop, inplace=True, crs=crs) @@ -255,11 +255,13 @@ def to_geojson(geom, buffer_simplify=True): return json.dumps(shapely.geometry.mapping(geom), sort_keys=True) -def check_crs(gdf): - current_crs = get_crs(gdf) - expected_crs = 'epsg:4326' - if current_crs is not None and current_crs != expected_crs: - raise ValueError('No valid geometry CRS "{}", it must be "{}".'.format(current_crs, expected_crs)) +def is_reprojection_needed(gdf): + crs = get_crs(gdf) + return crs is not None and crs != 'epsg:4326' + + +def reproject(gdf, epsg=4326): + return gdf.to_crs(epsg=epsg) def get_crs(gdf): diff --git a/cartoframes/utils/logger.py b/cartoframes/utils/logger.py index fdaf4dbbd..02b7ad3ac 100644 --- a/cartoframes/utils/logger.py +++ b/cartoframes/utils/logger.py @@ -31,6 +31,7 @@ def set_log_level(level): 'notset': logging.NOTSET } + level = level.lower() if level not in levels: return ValueError('Wrong log level. Valid log levels are: critical, error, warning, info, debug, notset.') diff --git a/cartoframes/utils/metrics.py b/cartoframes/utils/metrics.py index a1f0a1c92..118644665 100644 --- a/cartoframes/utils/metrics.py +++ b/cartoframes/utils/metrics.py @@ -3,10 +3,11 @@ import requests import functools +from urllib.parse import urlparse + from .logger import log -from .utils import default_config_path, read_from_config, save_in_config, \ - is_uuid, get_local_time, silent_fail, get_runtime_env, \ - get_credentials, get_parameter_from_decorator +from .utils import (default_config_path, read_from_config, save_in_config, is_uuid, get_local_time, silent_fail, + get_runtime_env, get_credentials, get_parameter_from_decorator) from .. import __version__ EVENT_VERSION = '1' @@ -15,6 +16,13 @@ UUID_KEY = 'uuid' ENABLED_KEY = 'enabled' METRICS_FILENAME = 'metrics.json' +CLOUD_API = 'cloud' +CUSTOM_API = 'custom' + +PROD_DOMAIN_TLD = 'carto.com' +STAG_DOMAIN_TLD = 'carto-staging.com' +PROD_METRICS_SERVER = 'https://bmetrics.cartodb.net' +STAG_METRICS_SERVER = 'https://bmetrics-staging.cartodb.net' _metrics_config = None @@ -69,7 +77,7 @@ def check_valid_metrics_uuid(metrics_config): return metrics_config is not None and is_uuid(metrics_config.get(UUID_KEY)) -def build_metrics_data(event_name, extra_metrics_data): +def build_metrics_data(event_name, extra_metrics_data, server_domain_tld): metrics_data = { 'event_version': EVENT_VERSION, 'event_time': get_local_time(), @@ -77,7 +85,8 @@ def build_metrics_data(event_name, extra_metrics_data): 'event_name': event_name, 'source_version': __version__, 'installation_id': get_metrics_uuid(), - 'runtime_env': get_runtime_env() + 'runtime_env': get_runtime_env(), + 'api_used': get_api_used(server_domain_tld) } if isinstance(extra_metrics_data, dict): @@ -87,9 +96,10 @@ def build_metrics_data(event_name, extra_metrics_data): @silent_fail -def post_metrics(event_name, extra_metrics_data): - json_data = build_metrics_data(event_name, extra_metrics_data) - result = requests.post('https://bmetrics.cartodb.net', json=json_data, timeout=2) +def post_metrics(event_name, extra_metrics_data, server_domain_tld): + metrics_server = STAG_METRICS_SERVER if server_domain_tld == STAG_DOMAIN_TLD else PROD_METRICS_SERVER + json_data = build_metrics_data(event_name, extra_metrics_data, server_domain_tld) + result = requests.post(metrics_server, json=json_data, timeout=2) log.debug('Metrics sent! {0} {1}'.format(result.status_code, json_data)) @@ -100,8 +110,8 @@ def wrapper_func(*args, **kwargs): result = func(*args, **kwargs) if get_metrics_enabled(): - extra_metrics_data = build_extra_metrics_data(func, *args, **kwargs) - post_metrics(event_name, extra_metrics_data) + extra_metrics_data, server_domain_tld = build_extra_metrics_data(func, *args, **kwargs) + post_metrics(event_name, extra_metrics_data, server_domain_tld) return result return wrapper_func @@ -109,13 +119,30 @@ def wrapper_func(*args, **kwargs): def build_extra_metrics_data(decorated_function, *args, **kwargs): + extra_metrics = {} + server_domain_tld = PROD_DOMAIN_TLD + try: - credentials = get_parameter_from_decorator( - 'credentials', decorated_function, *args, **kwargs) + credentials = get_parameter_from_decorator('credentials', decorated_function, *args, **kwargs) credentials = get_credentials(credentials) - return {'user_id': credentials.user_id} if credentials and credentials.user_id else {} + server_domain_tld = get_server_domain_tld(credentials.base_url) + + if credentials and credentials.user_id: + extra_metrics['user_id'] = credentials.user_id + + return extra_metrics, server_domain_tld + except Exception: - return {} + return extra_metrics, server_domain_tld + + +def get_server_domain_tld(server_url): + hostname = urlparse(server_url).hostname + return '.'.join(hostname.split('.')[-2:]) + + +def get_api_used(server_domain_tld): + return CLOUD_API if server_domain_tld in [PROD_DOMAIN_TLD, STAG_DOMAIN_TLD] else CUSTOM_API # Run this once diff --git a/cartoframes/utils/utils.py b/cartoframes/utils/utils.py index 772675f35..492d37a3d 100644 --- a/cartoframes/utils/utils.py +++ b/cartoframes/utils/utils.py @@ -11,11 +11,14 @@ import hashlib import inspect import requests +import warnings +import functools import geopandas import numpy as np import pkg_resources import semantic_version + from functools import wraps from datetime import datetime, timezone from warnings import catch_warnings, filterwarnings @@ -96,6 +99,11 @@ def pgquote(string): return '\'{}\''.format(string) if string else 'null' +def double_quote(text): + """double-quotes a text""" + return '"{}"'.format(text) + + def temp_ignore_warnings(func): """Temporarily ignores warnings like those emitted by the carto python sdk """ @@ -543,3 +551,23 @@ def get_parameter_from_decorator(parameter_name, decorated_function, *args, **kw pass return parameter + + +def deprecated(message=''): + def decorator(func): + + @functools.wraps(func) + def decorated(*args, **kwargs): + warnings.simplefilter('always', DeprecationWarning) + warnings.warn( + 'The {type_} `{name}` is deprecated. {message}'.format( + type_='class' if inspect.isclass(func) else 'function', + name=func.__name__, message=message), + category=DeprecationWarning, stacklevel=2) + warnings.simplefilter('default', DeprecationWarning) + + return func(*args, **kwargs) + + return decorated + + return decorator diff --git a/cartoframes/viz/__init__.py b/cartoframes/viz/__init__.py index 83bad4b73..89d60e84d 100644 --- a/cartoframes/viz/__init__.py +++ b/cartoframes/viz/__init__.py @@ -38,6 +38,8 @@ from .popups import popup_element from .popups import default_popup_element +from .kuviz import all_publications +from .kuviz import delete_publication __all__ = [ 'Map', @@ -77,5 +79,8 @@ 'default_widget', 'popup_element', - 'default_popup_element' + 'default_popup_element', + + 'all_publications', + 'delete_publication' ] diff --git a/cartoframes/viz/kuviz.py b/cartoframes/viz/kuviz.py index 23db067aa..f23d2d8c9 100644 --- a/cartoframes/viz/kuviz.py +++ b/cartoframes/viz/kuviz.py @@ -6,6 +6,7 @@ from ..data.clients.auth_api_client import AuthAPIClient from ..exceptions import PublishError from ..utils.logger import log +from ..utils.utils import get_credentials filterwarnings('ignore', category=FutureWarning, module='carto') @@ -19,13 +20,6 @@ def __init__(self, credentials=None): self._auth_client = _create_auth_client(credentials) self._auth_api_client = _create_auth_api_client(credentials) - @staticmethod - def all(credentials=None): - auth_client = _create_auth_client(credentials) - kmanager = _get_kuviz_manager(auth_client) - kuvizs = kmanager.all() - return [kuviz_to_dict(kuviz) for kuviz in kuvizs] - def get_layers(self): return self._layers @@ -135,3 +129,45 @@ def manage_kuviz_exception(error, name): "Upgrade your account or delete some of your previous maps to be able to create new ones.") raise error + + +def all_publications(credentials=None): + """Get all map visualizations published by the current user. + + Args: + credentials (:py:class:`Credentials `, optional): + A Credentials instance. If not provided, the credentials will be automatically + obtained from the default credentials if available. + + """ + _credentials = get_credentials(credentials) + auth_client = _create_auth_client(_credentials) + kmanager = _get_kuviz_manager(auth_client) + kuvizs = kmanager.all() + return [kuviz_to_dict(kuviz) for kuviz in kuvizs] + + +def delete_publication(name, credentials=None): + """Delete a map visualization published by id. + + Args: + name (str): name of the publication to be deleted. + credentials (:py:class:`Credentials `, optional): + A Credentials instance. If not provided, the credentials will be automatically + obtained from the default credentials if available. + + """ + _credentials = get_credentials(credentials) + auth_client = _create_auth_client(_credentials) + kmanager = _get_kuviz_manager(auth_client) + kuvizs = kmanager.all() + kuviz = next((kuviz for kuviz in kuvizs if kuviz.name == name), None) + + if kuviz is None: + raise PublishError('Publication "{}" not found.'.format(name)) + + try: + kuviz.delete() + log.info('Success! Publication "{0}" deleted'.format(name)) + except Exception as e: + manage_kuviz_exception(e, name) diff --git a/cartoframes/viz/layout.py b/cartoframes/viz/layout.py index 2e8700623..524754b15 100644 --- a/cartoframes/viz/layout.py +++ b/cartoframes/viz/layout.py @@ -12,8 +12,8 @@ class Layout: Args: maps (list of :py:class:`Map `): List of maps. Zero or more of :py:class:`Map `. - N_SIZE (number, optional): Number of columns of the layout - M_SIZE (number, optional): Number of rows of the layout + n_size (number, optional): Number of columns of the layout + m_size (number, optional): Number of rows of the layout viewport (dict, optional): Properties for display of the maps viewport. Keys can be `bearing` or `pitch`. is_static (boolean, optional): By default is False. All the maps in each visualization @@ -155,10 +155,6 @@ def publish(self, name, password, credentials=None, if_exists='fail', maps_api_k html = self._get_publication_html() return self._publisher.publish(html, name, password, if_exists) - def delete_publication(self): - """Delete the published layout visualization.""" - return self._publisher.delete() - def update_publication(self, name, password, if_exists='fail'): """Update the published layout visualization. @@ -176,20 +172,6 @@ def update_publication(self, name, password, if_exists='fail'): html = self._get_publication_html() return self._publisher.update(html, name, password, if_exists) - @staticmethod - def all_publications(credentials=None): - """Get all map visualization published by the current user. - - Args: - credentials (:py:class:`Credentials `, optional): - A Credentials instance. If not provided, the credentials will be automatically - obtained from the default credentials if available. - - """ - _credentials = get_credentials(credentials) - - return KuvizPublisher.all(_credentials) - def _get_publication_html(self): if not self._publisher: _credentials = get_credentials(None) diff --git a/cartoframes/viz/legend_list.py b/cartoframes/viz/legend_list.py index 1c3da7801..b91f5961a 100644 --- a/cartoframes/viz/legend_list.py +++ b/cartoframes/viz/legend_list.py @@ -6,6 +6,8 @@ class LegendList: """LegendList Args: legends (list, Legend): List of legends for a layer. + default_legend (Legend, optional): Default legend for a layer. + geom_type (str, optional): The type of the geometry. """ diff --git a/cartoframes/viz/map.py b/cartoframes/viz/map.py index e79c76a47..eba469d75 100644 --- a/cartoframes/viz/map.py +++ b/cartoframes/viz/map.py @@ -219,10 +219,6 @@ def publish(self, name, password, credentials=None, if_exists='fail', maps_api_k html = self._get_publication_html(name) return self._publisher.publish(html, name, password, if_exists) - def delete_publication(self): - """Delete the published map visualization.""" - return self._publisher.delete() - def update_publication(self, name, password, if_exists='fail'): """Update the published map visualization. @@ -240,20 +236,6 @@ def update_publication(self, name, password, if_exists='fail'): html = self._get_publication_html(name) return self._publisher.update(html, name, password, if_exists) - @staticmethod - def all_publications(credentials=None): - """Get all map visualization published by the current user. - - Args: - credentials (:py:class:`Credentials `, optional): - A Credentials instance. If not provided, the credentials will be automatically - obtained from the default credentials if available. - - """ - _credentials = get_credentials(credentials) - - return KuvizPublisher.all(_credentials) - def _get_publication_html(self, name): html_map = HTMLMap('templates/viz/main.html.j2') html_map.set_content( diff --git a/cartoframes/viz/popup_list.py b/cartoframes/viz/popup_list.py index 5210123a4..0a4b6691c 100644 --- a/cartoframes/viz/popup_list.py +++ b/cartoframes/viz/popup_list.py @@ -7,8 +7,10 @@ class PopupList: """PopupList - Args: + Args: popups (dict, PopupElement): List of popups for a layer classified by interactivity event + default_popup_hover (PopupElement, optional): The default popup for the hover. + default_click_hover (PopupElement, optional): The default popup for the click. Example: >>> popupList = PopupList({ diff --git a/cartoframes/viz/source.py b/cartoframes/viz/source.py index af5b8c05a..7a526b761 100644 --- a/cartoframes/viz/source.py +++ b/cartoframes/viz/source.py @@ -2,7 +2,7 @@ from geopandas import GeoDataFrame from ..io.managers.context_manager import ContextManager -from ..utils.geom_utils import check_crs, has_geometry, set_geometry +from ..utils.geom_utils import is_reprojection_needed, reproject, has_geometry, set_geometry from ..utils.utils import get_geodataframe_data, get_geodataframe_bounds, \ get_geodataframe_geom_type, get_datetime_column_names @@ -29,16 +29,14 @@ class Source: """Source Args: - data (str, pandas.DataFrame, geopandas.GeoDataFrame): a table name, + source (str, pandas.DataFrame, geopandas.GeoDataFrame): a table name, SQL query, DataFrame, GeoDataFrame instance. credentials (:py:class:`Credentials `, optional): A Credentials instance. If not provided, the credentials will be automatically obtained from the default credentials if available. - bounds (dict or list, optional): a dict with `west`, `south`, `east`, `north` - keys, or an array of floats in the following structure: [[west, - south], [east, north]]. If not provided the bounds will be automatically - calculated to fit all features. geom_col (str, optional): string indicating the geometry column name in the source `DataFrame`. + encode_data (bool, optional): Indicates whether the data needs to be encoded. + Default is True. Example: @@ -76,7 +74,8 @@ def __init__(self, source, credentials=None, geom_col=None, encode_data=True): self.credentials = self.manager.credentials elif isinstance(source, DataFrame): if isinstance(source, GeoDataFrame): - check_crs(source) + if is_reprojection_needed(source): + source = reproject(source) # DataFrame, GeoDataFrame self.type = SourceType.GEOJSON @@ -91,11 +90,14 @@ def __init__(self, source, credentials=None, geom_col=None, encode_data=True): raise ValueError('No valid geometry found. Please provide an input source with ' + 'a valid geometry or specify the "geom_col" param with a geometry column.') + # Remove nan geometries + self.gdf.dropna(subset=[self.gdf.geometry.name], inplace=True) + # Remove empty geometries self.gdf = self.gdf[~self.gdf.geometry.is_empty] # Checking the uniqueness of the geometry type - geometry_types = set(self.gdf.geom_type.unique()) + geometry_types = set(self.gdf.geom_type.unique()).difference({None}) if geometry_types not in VALID_GEOMETRY_TYPES: raise ValueError('No valid geometry column types ({}), it has '.format(geometry_types) + 'to be one of the next type sets: {}.'.format(VALID_GEOMETRY_TYPES)) diff --git a/cartoframes/viz/styles/color_continuous_style.py b/cartoframes/viz/styles/color_continuous_style.py index 4201c6c94..5305671c5 100644 --- a/cartoframes/viz/styles/color_continuous_style.py +++ b/cartoframes/viz/styles/color_continuous_style.py @@ -18,7 +18,6 @@ def color_continuous_style(value, size=None, range_min=None, range_max=None, pal palette (str, optional): Palette that can be a named cartocolor palette or other valid color palette. Use `help(cartoframes.viz.palettes)` to get more information. Default is "bluyl". - size (int, optional): Size of point or line features. opacity (float, optional): Opacity value. Default is 1 for points and lines and 0.9 for polygons. stroke_color (str, optional): Color of the stroke on point features. diff --git a/cartoframes/viz/styles/size_continuous_style.py b/cartoframes/viz/styles/size_continuous_style.py index e5d5a6184..639c3aca6 100644 --- a/cartoframes/viz/styles/size_continuous_style.py +++ b/cartoframes/viz/styles/size_continuous_style.py @@ -6,7 +6,7 @@ def size_continuous_style(value, size_range=None, range_min=None, range_max=None, color=None, opacity=None, - stroke_color=None, stroke_width=None, animate=None, credentials=None): + stroke_color=None, stroke_width=None, animate=None): """Helper function for quickly creating a size continuous style. Args: diff --git a/cartoframes/viz/widget_list.py b/cartoframes/viz/widget_list.py index f28f8ca3c..056d31cfc 100644 --- a/cartoframes/viz/widget_list.py +++ b/cartoframes/viz/widget_list.py @@ -7,6 +7,7 @@ class WidgetList: Args: widgets (list, Widget): The list of widgets for a layer. + default_widget (Widget, optional): The widget to be used by default. """ def __init__(self, widgets=None, default_widget=None): diff --git a/cartoframes/viz/widgets/category_widget.py b/cartoframes/viz/widgets/category_widget.py index 3c28bea7e..a445089b1 100644 --- a/cartoframes/viz/widgets/category_widget.py +++ b/cartoframes/viz/widgets/category_widget.py @@ -11,6 +11,7 @@ def category_widget(value, title=None, description=None, footer=None, read_only= footer (str, optional): Footer text placed on the widget bottom. read_only (boolean, optional): Interactively filter a category by selecting it in the widget. Set to "False" by default. + weight (int, optional): Weight of the category widget. Default value is 1. Returns: cartoframes.viz.widget.Widget diff --git a/cartoframes/viz/widgets/formula_widget.py b/cartoframes/viz/widgets/formula_widget.py index 13eec4372..c3ae720e2 100644 --- a/cartoframes/viz/widgets/formula_widget.py +++ b/cartoframes/viz/widgets/formula_widget.py @@ -50,7 +50,7 @@ def formula_widget(value, operation=None, title=None, description=None, footer=N def _get_value_expression(operation, value, is_global): - if value == 'count' or operation == 'count': + if operation == 'count': formula_operation = _get_formula_operation('count', is_global) return formula_operation + '()' elif operation in ['avg', 'max', 'min', 'sum']: diff --git a/cartoframes/viz/widgets/histogram_widget.py b/cartoframes/viz/widgets/histogram_widget.py index 353b695bf..56146ad29 100644 --- a/cartoframes/viz/widgets/histogram_widget.py +++ b/cartoframes/viz/widgets/histogram_widget.py @@ -19,6 +19,7 @@ def histogram_widget(value, title=None, description=None, footer=None, read_only read_only (boolean, optional): Interactively filter a range of numeric values by selecting them in the widget. Set to "False" by default. buckets (number, optional): Number of histogram buckets. Set to 20 by default. + weight (int, optional): Weight of the category widget. Default value is 1. Returns: cartoframes.viz.widget.Widget diff --git a/cartoframes/viz/widgets/time_series_widget.py b/cartoframes/viz/widgets/time_series_widget.py index 16da40a71..327e1d05d 100644 --- a/cartoframes/viz/widgets/time_series_widget.py +++ b/cartoframes/viz/widgets/time_series_widget.py @@ -17,6 +17,7 @@ def time_series_widget(value, title=None, description=None, footer=None, read_on read_only (boolean, optional): Interactively filter a range of numeric values by selecting them in the widget. Set to "False" by default. buckets (number, optional): Number of histogram buckets. Set to 20 by default. + weight (int, optional): Weight of the category widget. Default value is 1. Returns: cartoframes.viz.widget.Widget diff --git a/docs/RELEASING.md b/docs/RELEASING.md index d507d2264..a75621d4c 100644 --- a/docs/RELEASING.md +++ b/docs/RELEASING.md @@ -84,8 +84,6 @@ The final step is to deploy the Developer Center to production with the new rele - Point to the release branch in `config.js`, in the `'cartoframes'` section: - Remove the release branch: `branch: '',` - Add the tag attribute: `tag: 'vM.m.u',` - - Update the guides_releases attribute: `guides_releases: ['vM.m.u'],` - - Update the autodoc_releases attribute: `autodoc_releases: ['vM.m.u', ...` - Update the releases attribute: `releases: ['vM.m.u', ...` - Merge the PR to master (this will deploys the Developer Center in a "production" environment) diff --git a/docs/build.sh b/docs/build.sh index caaf8ce5d..24c6b5ba1 100755 --- a/docs/build.sh +++ b/docs/build.sh @@ -2,7 +2,7 @@ cd .. pip uninstall cartoframes -y -pip install . +pip install -e . cd docs make clean && make html && make json diff --git a/docs/cartoframes.rst b/docs/cartoframes.rst index 41c947298..a1e37fb10 100644 --- a/docs/cartoframes.rst +++ b/docs/cartoframes.rst @@ -1,9 +1,9 @@ -.. include:: includes/introduction.rst -.. include:: includes/auth.rst -.. include:: includes/io_functions.rst -.. include:: includes/data_observatory.rst -.. include:: includes/data_services.rst -.. include:: includes/data_clients.rst -.. include:: includes/viz.rst -.. include:: includes/utils.rst -.. include:: includes/exceptions.rst +.. include:: reference/introduction.rst +.. include:: reference/auth.rst +.. include:: reference/io_functions.rst +.. include:: reference/data_services.rst +.. include:: reference/data_observatory.rst +.. include:: reference/data_clients.rst +.. include:: reference/viz.rst +.. include:: reference/utils.rst +.. include:: reference/exceptions.rst diff --git a/docs/conf.py b/docs/conf.py index 54a0dfeba..8cedba10f 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -19,7 +19,7 @@ # -- Project information ----------------------------------------------------- project = u'cartoframes' -copyright = u'2019, CARTO' +copyright = u'2020, CARTO' author = u'CARTO' # The short X.Y version diff --git a/docs/developer-center/examples/data_enrichment.md b/docs/developer-center/examples/data_enrichment.md deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/developer-center/examples/data_management.md b/docs/developer-center/examples/data_management.md deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/developer-center/examples/data_observatory.md b/docs/developer-center/examples/data_observatory.md deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/developer-center/examples/data_services.md b/docs/developer-center/examples/data_services.md deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/developer-center/examples/data_visualization.md b/docs/developer-center/examples/data_visualization.md deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/developer-center/examples/examples.json b/docs/developer-center/examples/examples.json deleted file mode 100644 index 1c8bbe334..000000000 --- a/docs/developer-center/examples/examples.json +++ /dev/null @@ -1,437 +0,0 @@ -{ - "main": { - "title": "Introduction", - "file": "introduction.md" - }, - "categories": [{ - "title": "Data management", - "desc": "Explore working with data both locally and with a CARTO account", - "file": "data_management.md", - "hide_sidebar_samples": true, - "samples": [{ - "title": "Load a CSV file", - "desc": "Load data from a CSV file", - "file": "load_data_from_csv", - "path": "data_management" - }, { - "title": "Load a JSON file", - "desc": "Load data from a JSON file", - "file": "load_data_from_json", - "path": "data_management" - }, { - "title": "Load a GeoJSON file", - "desc": "Load data from a GeoJSON file", - "file": "load_data_from_geojson", - "path": "data_management" - }, { - "title": "Load a shapefile", - "desc": "Load data from a shapefile", - "file": "load_data_from_shapefile", - "path": "data_management" - }, { - "title": "Load a CARTO table", - "desc": "Load data from a CARTO table", - "file": "load_data_from_carto_table", - "path": "data_management" - }, { - "title": "Load a CARTO SQL query", - "desc": "Load data from a CARTO table using a SQL Query", - "file": "load_data_from_carto_query", - "path": "data_management" - }, { - "title": "Load data with a different CRS", - "desc": "Load data with a different CRS than EPGS:4326", - "file": "load_data_different_crs", - "path": "data_management" - }, { - "title": "Upload to CARTO", - "desc": "Upload data to CARTO", - "file": "upload_data_to_carto", - "path": "data_management" - }, { - "title": "Change CARTO table privacy", - "desc": "Change the privacy of a CARTO table", - "file": "change_carto_table_privacy", - "path": "data_management" - }] - }, { - "title": "Data visualization", - "desc": "Explore the building blocks for creating web-based, dynamic, and interactive map visualizations", - "file": "data_visualization.md", - "hide_sidebar_samples": true, - "samples": [{ - "title": "Single layer", - "desc": "Add a Layer", - "file": "add_layer", - "path": "layers", - "group": "Layers" - }, { - "title": "Multiple layers", - "desc": "Add multiple Layers", - "file": "add_multiple_layers", - "path": "layers", - "group": "Layers" - }, { - "title": "Basemaps", - "desc": "Change the default CARTO basemap", - "file": "change_default_carto_basemap", - "path": "map_configuration", - "group": "Configuration" - }, { - "title": "Viewport", - "desc": "Set a custom viewport", - "file": "custom_viewport", - "path": "map_configuration", - "group": "Configuration" - }, { - "title": " Background", - "desc": "Set a background color", - "file": "solid_color_background", - "path": "map_configuration", - "group": "Configuration" - }, { - "title": "Dark theme", - "desc": "Switch to the dark theme", - "file": "dark_theme", - "path": "map_configuration", - "group": "Configuration" - }, { - "title": "Basic style", - "desc": "Use the basic_style method to change the basic style properties", - "file": "basic_style", - "path": "styles", - "group": "Style" - }, { - "title": "Color category style", - "desc": "Use the color_category_style method to color features by categorical values", - "file": "color_category_style", - "path": "styles", - "group": "Style" - }, { - "title": "Color continuous style", - "desc": "Use the color_continuous_style method to color features by continuous numeric values", - "file": "color_continuous_style", - "path": "styles", - "group": "Style" - }, { - "title": "Color bins style", - "desc": "Use the color_bins_style method to color features by discrete numeric values", - "file": "color_bins_style", - "path": "styles", - "group": "Style" - }, { - "title": "Size category style", - "desc": "Use the size_category_style method to resize each feature by categorical values", - "file": "size_category_style", - "path": "styles", - "group": "Style" - }, { - "title": "Size continuous style", - "desc": "Use the size_continuous_style method to resize each feature by continuous numeric values", - "file": "size_continuous_style", - "path": "styles", - "group": "Style" - }, { - "title": "Size bins style", - "desc": "Use the size_bins_style method to resize each feature by discrete numeric values", - "file": "size_bins_style", - "path": "styles", - "group": "Style" - }, { - "title": "Cluster size style", - "desc": "Use the cluster_size_style method to aggregate features by continuous numeric values", - "file": "cluster_size_style", - "path": "styles", - "group": "Style" - }, { - "title": "Animation style", - "desc": "Use the animation_style helper method to simply animate a visualization", - "file": "animation_style", - "path": "styles", - "group": "Style" - }, { - "title": "Add multiple style helpers", - "desc": "Combine different visualization styles", - "file": "combine_visualization_styles", - "path": "styles", - "group": "Style" - }, { - "title": "Default legend", - "desc": "Set the default legend needed for the style helper", - "file": "default_legend", - "path": "legends", - "group": "Legends" - }, { - "title": "Basic legend", - "desc": "Use the basic_legend method to display a simple default legend", - "file": "basic_legend", - "path": "legends", - "group": "Legends" - }, { - "title": "Color category cegend", - "desc": "Use the color_category_legend method to represent categorical values by color", - "file": "color_category_legend", - "path": "legends", - "group": "Legends" - }, { - "title": "Color bins legend", - "desc": "Use the color_bins_legend method to represent discrete numeric values by color", - "file": "color_bins_legend", - "path": "legends", - "group": "Legends" - }, { - "title": "Color continuous legend", - "desc": "Use the color_continuous_legend method to represent continuous numeric values by color", - "file": "color_continuous_legend", - "path": "legends", - "group": "Legends" - }, { - "title": "Size category legend", - "desc": "Use the size_category_legend method to represent categorical values by size", - "file": "size_category_legend", - "path": "legends", - "group": "Legends" - }, { - "title": "Size bins legend", - "desc": "Use the size_bins_legend method to represent discrete numeric values by size", - "file": "size_bins_legend", - "path": "legends", - "group": "Legends" - }, { - "title": "Size continuous legend", - "desc": "Use the size_bins_legend method to represent continuous numeric values by size", - "file": "size_continuous_legend", - "path": "legends", - "group": "Legends" - }, { - "title": "Add multiple legends", - "desc": "Combine different legends in the same visualization", - "file": "multiple_legends", - "path": "legends", - "group": "Legends" - }, { - "title": "Legend by geometry", - "desc": "Specify the geometry type in a legend helper", - "file": "legend_line_geometry", - "path": "legends", - "group": "Legends" - }, { - "title": "Default widget", - "desc": "Set the default widget needed for the style helper", - "file": "default_widget", - "path": "widgets", - "group": "Widgets" - }, { - "title": "Basic widget", - "desc": "Use the basic_widget method to display a simple default widget", - "file": "basic_widget", - "path": "widgets", - "group": "Widgets" - }, { - "title": "Category widget", - "desc": "Use the category_widget to create a widget to represent categorical values", - "file": "category_widget", - "path": "widgets", - "group": "Widgets" - }, { - "title": "Histogram widget", - "desc": "Use the histogram_widget to create a widget to represent categorical, numeric and date values in a histogram", - "file": "histogram_widget", - "path": "widgets", - "group": "Widgets" - }, { - "title": "Formula widget", - "desc": "Use the formula_widget to display the result of a count, avg, max, min or sum operation in a numeric", - "file": "formula_widget", - "path": "widgets", - "group": "Widgets" - }, { - "title": "Animation widget", - "desc": "Use the animation_widget to be able to play, pause and change an animated visualization through animation controls", - "file": "animation_widget", - "path": "widgets", - "group": "Widgets" - }, { - "title": "Time series widget", - "desc": "Use the time_series_widget to represent a sequence of categorical, numeric and date values in a histogram indexed by a numeric or date value", - "file": "time_series_widget", - "path": "widgets", - "group": "Widgets" - }, { - "title": "Add multiple widgets", - "desc": "Combine multiple widgets in the same visualization", - "file": "multiple_widgets", - "path": "widgets", - "group": "Widgets" - }, { - "title": "Default popup", - "desc": "Set the default popup needed for the style helper", - "file": "default_popup", - "path": "popups", - "group": "Popups" - }, { - "title": "Popup on click", - "desc": "Display a popup triggered by a click event", - "file": "popup_on_click", - "path": "popups", - "group": "Popups" - }, { - "title": "Popup on hover", - "desc": "Display a popup triggered by a hover event", - "file": "popup_on_hover", - "path": "popups", - "group": "Popups" - }, { - "title": "Customize popup title and format", - "desc": "Change the title and format for the popup values", - "file": "popup_titles_and_format", - "path": "popups", - "group": "Popups" - }, { - "title": "Add multiple popup elements", - "desc": "Display popups triggered by both click and hover events", - "file": "multiple_popup_events", - "path": "popups", - "group": "Popups" - }, { - "title": "Default layout", - "desc": "Create a default visualization layout", - "file": "default_layout", - "path": "map_layout", - "group": "Layout" - }, { - "title": "Custom layout", - "desc": "Use different settings to create a custom visualization layout", - "file": "custom_layout", - "path": "map_layout", - "group": "Layout" - }, { - "title": "Add layout titles", - "desc": "Use different custom titles to create a visualization layout", - "file": "layout_titles", - "path": "map_layout", - "group": "Layout" - }, { - "title": "Customize layout viewport", - "desc": "Use different viewport settings to create a visualization layout", - "file": "layout_viewport", - "path": "map_layout", - "group": "Layout" - }, { - "title": "Make layout static", - "desc": "Create a static visualization layout", - "file": "static_layout", - "path": "map_layout", - "group": "Layout" - }] - }, { - "title": "Data services", - "desc": "Learn how to combine your data and CARTO's Location Data Services for geospatial analyses", - "file": "data_services.md", - "hide_sidebar_samples": true, - "samples": [{ - "title": "Geocoding", - "desc": "Using the Geocoding Services to geocode a DataFrame", - "file": "geocoding_dataframe", - "path": "data_services" - }, { - "title": "Isochrones", - "desc": "Using the Isolines Services to calculate isochrones", - "file": "isochrones", - "path": "data_services" - }, { - "title": "Isodistances", - "desc": "Using the Isolines Services to calculate isodistances", - "file": "isodistances", - "path": "data_services" - }, { - "title": "Combining geocoding and isolines", - "desc": "Use both Geocoding and Isolines services", - "file": "geocoding_and_isolines", - "path": "data_services" - }] - }, { - "title": "Data Observatory", - "desc": "Learn how to discover third party datasets and enrich your data through CARTO's Data Observatory", - "file": "data_observatory.md", - "hide_sidebar_samples": true, - "samples": [{ - "title": "Discover a dataset", - "desc": "Basic steps to discover a Dataset in the Data Observatory", - "file": "discover_dataset", - "path": "data_observatory" - }, { - "title": "Download a dataset", - "desc": "Download a dataset applying filters", - "file": "download_dataset", - "path": "data_observatory" - }, { - "title": "Enrich points", - "desc": "Use the enrichment to enrich points for a dataset", - "file": "points_enrichment_dataset", - "path": "data_observatory" - }, { - "title": "Enrich polygons", - "desc": "Use the enrichment to enrich polygons for a dataset", - "file": "polygons_enrichment_dataset", - "path": "data_observatory" - }, { - "title": "Subscribe to a premium dataset", - "desc": "Basic steps to subscribe to a premium dataset in the Data Observatory to start enriching your data", - "file": "enrichment_subscription_workflow", - "path": "data_observatory" - }] - }, { - "title": "Publish and share", - "desc": "Learn how to publish and share interactive visualizations for others to explore", - "file": "publish_share.md", - "hide_sidebar_samples": true, - "samples": [{ - "title": "Publish Public Table Visualization", - "desc": "Publish a visualization from a public table", - "file": "publish_visualization_public_table", - "path": "publish_and_share" - }, { - "title": "Publish Private Table Visualization", - "desc": "Publish a visualization from a private table", - "file": "publish_visualization_private_table", - "path": "publish_and_share" - }, { - "title": "Publish GeoDataFrame Visualization", - "desc": "Publish a visualization from a GeoDataFrame", - "file": "publish_visualization_gdf", - "path": "publish_and_share" - }, { - "title": "Publish Layout Visualization", - "desc": "Publish a visualization of a layout", - "file": "publish_visualization_layout", - "path": "publish_and_share" - }] - }, { - "title": "Use cases", - "desc": "Explore a variety of ways to use CARTOframes in your workflows", - "file": "use_cases.md", - "hide_sidebar_samples": true, - "samples": [{ - "title": "Visualize temperatures", - "desc": "Visualize warm temperatures", - "file": "visualize_temperatures", - "path": "real_use_cases" - }, { - "title": "Paris WiFi services", - "desc": "Download and visualize WiFi Services data in json format", - "file": "paris_wifi_services", - "path": "real_use_cases" - }, { - "title": "Paris remarkable trees", - "desc": "Transform and visualize data from a .json file", - "file": "paris_remarkable_trees", - "path": "real_use_cases" - }, { - "title": "Geocode London stations", - "desc": "Geocode a dataset in xls format", - "file": "geocoding_london_stations", - "path": "real_use_cases" - }] - }] -} diff --git a/docs/developer-center/examples/publish_share.md b/docs/developer-center/examples/publish_share.md deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/developer-center/examples/use_cases.md b/docs/developer-center/examples/use_cases.md deleted file mode 100644 index e69de29bb..000000000 diff --git a/docs/developer-center/guides/guides.json b/docs/developer-center/guides/guides.json deleted file mode 100644 index d92549ed7..000000000 --- a/docs/developer-center/guides/guides.json +++ /dev/null @@ -1,52 +0,0 @@ -{ - "main": { - "file": "introduction.md" - }, - "categories": [{ - "title": "Quickstart", - "samples": [{ - "title": "Quickstart Guide", - "desc": "Complete a real project that walks through the library step by step", - "file": "quickstart", - "path": "" - }] - }, { - "title": "Guides", - "samples": [{ - "title": "Installation", - "desc": "Install the library and configure your environment", - "file": "installation", - "path": "" - }, { - "title": "Authentication", - "desc": "Login into CARTO to unlock the power of spatial analysis (data management, data enrichment, location data services, etc)", - "file": "authentication", - "path": "" - }, { - "title": "Data management", - "desc": "Work with data both locally and with a CARTO account", - "file": "data_management", - "path": "" - }, { - "title": "Visualization", - "desc": "Explore and visualize your spatial data with interactive maps", - "file": "visualization", - "path": "" - }, { - "title": "Data Discovery", - "desc": "Discover the spatial data you need by searching the data catalog", - "file": "data_discovery", - "path": "" - }, { - "title": "Data Enrichment", - "desc": "Enrich your data with data from the Data Observatory", - "file": "data_enrichment", - "path": "" - }, { - "title": "Location Data Services", - "desc": "Geocode and discover areas of influence with Isolines", - "file": "data_services", - "path": "" - }] - }] -} diff --git a/docs/developer-center/img/guides/explore_data_observatory_catalog_files/explore_data_observatory_catalog_37_0.png b/docs/developer-center/img/guides/explore_data_observatory_catalog_files/explore_data_observatory_catalog_37_0.png deleted file mode 100644 index 2848e513e..000000000 Binary files a/docs/developer-center/img/guides/explore_data_observatory_catalog_files/explore_data_observatory_catalog_37_0.png and /dev/null differ diff --git a/docs/developer-center/img/guides/quickstart/combine_layers.png b/docs/developer-center/img/guides/quickstart/combine_layers.png deleted file mode 100644 index d5132c521..000000000 Binary files a/docs/developer-center/img/guides/quickstart/combine_layers.png and /dev/null differ diff --git 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similarity index 50% rename from docs/developers/documentation.rst rename to docs/developers/dev_guide.rst index 69e44b3a4..dde57988e 100644 --- a/docs/developers/documentation.rst +++ b/docs/developers/dev_guide.rst @@ -22,17 +22,18 @@ The docs directory The docs directory contains: -- developer-center: Guides and examples extructure to be published in the developer center -- developers: Internal documentation -- guides: Old guides. These guides will be moved to the new ones and deleted eventually -- includes: Contains the different doc sections, which are included in `cartoframes.rst` file +- developers: Internal documentation. +- examples: Contains the examples (notebooks) for the Developer Center. +- guides: Contains the guides (notebooks) for the Developer Center. +- reference: Contains the rst index documents to generate the reference for the Developer Center. +- support: Contains the support files for the Developer Center. Structure ~~~~~~~~~ `cartoframes.rst` file is the main entry point for the rest of the documentation. We generate a single document file from this one to build the result documentation. -In the `includes` directory we place the different sections, that are included from `cartoframes.rst`. +In the `reference` directory we place the different sections, that are included from `cartoframes.rst`. Generating docs locally ~~~~~~~~~~~~~~~~~~~~~~~ @@ -63,7 +64,7 @@ Adding documentation Add documentation to the API reference: -- If you want to add a new section, create a file with the name of this section in `docs/includes`, and include it in `docs/cartoframes.rst`. +- If you want to add a new section, create a file with the name of this section in `docs/reference`, and include it in `docs/cartoframes.rst`. - Use `automodule` syntax to include module content: .. code:: @@ -88,7 +89,7 @@ we have separated branches in order to be able to change the docs after the rele Writing Documentation ~~~~~~~~~~~~~~~~~~~~~ -Documentation is placed in `/docs/developer-center` directory. +Documentation is placed in `/docs` directory. - In order to test it in the developer center, it's necessary to change the branch to be tested in the `config.js` (instructions are explained in the developer center repository) - In order to modify the documentation of a specific version, just: @@ -118,14 +119,6 @@ And, finally, open a Pull Request against the docs branch. After that, in order to see the changes applied in the developer center, deploy the production version through Jenkins. -Examples --------- - -Examples are placed in the `/examples` directory. These examples are Jupyter Notebooks, that are converted to `html` to be rendered in the developer center. -The `examples.json` file placed in `/docs/developer-center/examples` is used to select and organize the examples to expose in the developer center. -When adding a new notebook, if it needs to be published in the developer center, it must be included in this config file. - - Errors and Exceptions ~~~~~~~~~~~~~~~~~~~~~ @@ -156,10 +149,164 @@ Development with Staging Before releasing to production we need to test everything in staging. In order to do that, we need to configure CARTOframes to point to staging. There is a set of internal functions to configure the default DO credentials used by the DO Catalog. +.. code:: + from cartoframes.auth import set_default_credentials + + set_default_credentials('https://USER.carto-staging.com', 'API_KEY') + .. code:: from cartoframes.auth.defaults import set_default_do_credentials set_default_do_credentials(username='USER', base_url='https://ORG.carto-staging.com') # After that, every request to the DO Catalog will be done with the provided credentials - # instead of the default ones for production (user 'do-metadata'). \ No newline at end of file + # instead of the default ones for production (user 'do-metadata'). + + +Local Development Setup +======================= + +Let's run a local Jupyter Notebook with CARTOFrames, CARTO VL and Airship for local development. + +Jupyter Notebook +^^^^^^^^^^^^^^^^ + +1. Install Python 3 + +https://www.python.org/downloads/ + +2. Create virtualenv + +.. code:: + + python3 -m virtualenv venv + +3. Activate virtualenv + +.. code:: + + source venv/bin/activate + +In order to deactivate the virtualenv, run: + +.. code:: + + deactivate + +4. Install jupyter + +.. code:: + + pip install jupyter + +5. Install cartoframes package in dev mode + +.. code:: + + cd cartoframes + pip install -e . + +6. Launch Jupyter notebook + +.. code:: + + jupyter notebook + + +CARTO VL +^^^^^^^^ + +We're going to clone the repository. We can do it in the jupyter-cartoframes directory we've created to set up our local workspace. + +.. code:: + + git clone git@github.com:CartoDB/carto-vl.git + cd carto-vl + + +The local installation of CARTO VL is explained `in this guide `__ + +Airship +^^^^^^^ + +Let's clone the Airship repository as well. + +.. code:: + + git clone git@github.com:CartoDB/airship.git + cd airship + + +The local installation of Airship is explained `in this guide `__ + +CARTOframes JavaScript code +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +The JavaScript code is generated using Rollup, a module bundler. In order to save changes in the all the .js files, we need to bundle the code: + +Watch changes: + +.. code:: + + cd cartoframes + npm run dev + +Build: + +.. code:: + + cd cartoframes + npm run build + + +Run all the projects +^^^^^^^^^^^^^^^^^^^^ + +We've to serve now all the projects as follows: + +.. code:: + + +---------------------------+---------------------------+ + |/carto-vl |/carto-vl | + |$ npm run build:watch | $ npm run serve | + | | | + | | | + | | | + +---------------------------+---------------------------+ + |/airship |/airship | + |$ npm run dev | $ npm run serve | + | | | + | | | + | | | + +---------------------------+---------------------------+ + |/cartoframes |/cartoframes | + |(env)$ jupyter notebook |$ npm run dev | + | | | + | | | + | | | + +---------------------------+---------------------------+ + +Load JavaScript libraries locally +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +In order to get Airship and CARTO VL libraries we're serving locally in CARTOFrames, we need to instantiate the map indicating the paths: + +.. code:: python + + from cartoframes.auth import set_default_credentials + from cartoframes.viz import Map, Layer + + set_default_credentials('cartoframes') + + Map( + Layer('dataset_name'), + _carto_vl_path='http://localhost:8080', + _airship_path='http://localhost:5000' + ) + +We've a lot of public datasets in both `cartoframes` and `cartovl` accounts that we use for the examples, but I've you've a personal CARTO account you can use your credentials. + +Reload changes +^^^^^^^^^^^^^^ + +When making changes in CARTOFrames library, in the notebook, click on kernel > Restart and Run all. When making changes in CARTO VL or Airship, click on run (the page doesn't need to be reloaded) diff --git a/docs/developers/local_development.rst b/docs/developers/local_development.rst deleted file mode 100644 index 5f6181f18..000000000 --- a/docs/developers/local_development.rst +++ /dev/null @@ -1,147 +0,0 @@ -Local Development Setup -======================= - -Let's run a local Jupyter Notebook with CARTOFrames, CARTO VL and Airship for local development. - -Jupyter Notebook -^^^^^^^^^^^^^^^^ - -1. Install Python 3 - -https://www.python.org/downloads/ - -2. Create virtualenv - -.. code:: - - python3 -m virtualenv venv - -3. Activate virtualenv - -.. code:: - - source venv/bin/activate - -In order to deactivate the virtualenv, run: - -.. code:: - - deactivate - -4. Install jupyter - -.. code:: - - pip install jupyter - -5. Install cartoframes package in dev mode - -.. code:: - - cd cartoframes - pip install -e . - -6. Launch Jupyter notebook - -.. code:: - - jupyter notebook - - -CARTO VL -^^^^^^^^ - -We're going to clone the repository. We can do it in the jupyter-cartoframes directory we've created to set up our local workspace. - -.. code:: - - git clone git@github.com:CartoDB/carto-vl.git - cd carto-vl - - -The local installation of CARTO VL is explained `in this guide `__ - -Airship -^^^^^^^ - -Let's clone the Airship repository as well. - -.. code:: - - git clone git@github.com:CartoDB/airship.git - cd airship - - -The local installation of Airship is explained `in this guide `__ - -CARTOframes JavaScript code -^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -The JavaScript code is generated using Rollup, a module bundler. In order to save changes in the all the .js files, we need to bundle the code: - -Watch changes: - -.. code:: - - cd cartoframes - npm run dev - -Build: - -.. code:: - - cd cartoframes - npm run build - - -Run all the projects -^^^^^^^^^^^^^^^^^^^^ - -We've to serve now all the projects as follows: - -.. code:: - - +---------------------------+---------------------------+ - |/carto-vl |/carto-vl | - |$ npm run build:watch | $ npm run serve | - | | | - | | | - | | | - +---------------------------+---------------------------+ - |/airship |/airship | - |$ npm run dev | $ npm run serve | - | | | - | | | - | | | - +---------------------------+---------------------------+ - |/cartoframes |/cartoframes | - |(env)$ jupyter notebook |$ npm run dev | - | | | - | | | - | | | - +---------------------------+---------------------------+ - -Load JavaScript libraries locally -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -In order to get Airship and CARTO VL libraries we're serving locally in CARTOFrames, we need to instantiate the map indicating the paths: - -.. code:: python - - from cartoframes.auth import set_default_credentials - from cartoframes.viz import Map, Layer - - set_default_credentials('cartoframes') - - Map( - Layer('dataset_name'), - _carto_vl_path='http://localhost:8080', - _airship_path='http://localhost:5000' - ) - -We've a lot of public datasets in both `cartoframes` and `cartovl` accounts that we use for the examples, but I've you've a personal CARTO account you can use your credentials. - -Reload changes -^^^^^^^^^^^^^^ - -When making changes in CARTOFrames library, in the notebook, click on kernel > Restart and Run all. When making changes in CARTO VL or Airship, click on run (the page doesn't need to be reloaded) diff --git a/docs/examples/README.md b/docs/examples/README.md new file mode 100644 index 000000000..34d0dfdbc --- /dev/null +++ b/docs/examples/README.md @@ -0,0 +1,15 @@ +## List of available samples + +- https://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn.csv +- https://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn_geocoded.csv +- https://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn_geocoded.zip +- https://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn_geocoded.geojson +- https://libs.cartocdn.com/cartoframes/samples/chicago_cbg.csv +- https://libs.cartocdn.com/cartoframes/samples/la_cbg.csv +- https://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn_iso_enriched.geojson +- https://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn_iso_enriched.csv +- https://libs.cartocdn.com/cartoframes/samples/paris_wifi_services.json +- https://libs.cartocdn.com/cartoframes/samples/arbres_remarquables_paris.json +- https://libs.cartocdn.com/cartoframes/samples/sf_incidents.csv +- https://libs.cartocdn.com/cartoframes/samples/london_stations.xls + diff --git a/examples/data_management/change_carto_table_privacy.ipynb b/docs/examples/data_management/change_carto_table_privacy.ipynb similarity index 97% rename from examples/data_management/change_carto_table_privacy.ipynb rename to docs/examples/data_management/change_carto_table_privacy.ipynb index 7a38732c1..a86bd5f0c 100644 --- a/examples/data_management/change_carto_table_privacy.ipynb +++ b/docs/examples/data_management/change_carto_table_privacy.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Change CARTO Table Privacy\n", + "## Change CARTO Table Privacy\n", "\n", "This example illustrates how to change the privacy of a CARTO table\n", "\n", @@ -99,7 +99,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.4" + "version": "3.6.7" } }, "nbformat": 4, diff --git a/docs/examples/data_management/read_carto_query.ipynb b/docs/examples/data_management/read_carto_query.ipynb new file mode 100644 index 000000000..696426ef0 --- /dev/null +++ b/docs/examples/data_management/read_carto_query.ipynb @@ -0,0 +1,1405 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Read data from a CARTO SQL Query\n", + "\n", + "This example illustrates how to read data from a CARTO table using a SQL Query.\n", + "\n", + "> Use this when you need to see or modify the data locally. If you only need to visualize the data, just use the query directly in the Layer: `Layer('SELECT * FROM dataset_name')`\n", + "\n", + "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to reproduce this example._" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('cartoframes')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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cartodb_idthe_geomfield_1nameaddressrevenue
01POINT (-73.95901 40.67109)0Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381321040.772
12POINT (-73.96122 40.57796)1607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351268080.418
23POINT (-73.98976 40.61912)265th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041248133.699
\n", + "
" + ], + "text/plain": [ + " cartodb_id the_geom field_1 \\\n", + "0 1 POINT (-73.95901 40.67109) 0 \n", + "1 2 POINT (-73.96122 40.57796) 1 \n", + "2 3 POINT (-73.98976 40.61912) 2 \n", + "\n", + " name address \\\n", + "0 Franklin Ave & Eastern Pkwy 341 Eastern Pkwy,Brooklyn, NY 11238 \n", + "1 607 Brighton Beach Ave 607 Brighton Beach Avenue,Brooklyn, NY 11235 \n", + "2 65th St & 18th Ave 6423 18th Avenue,Brooklyn, NY 11204 \n", + "\n", + " revenue \n", + "0 1321040.772 \n", + "1 1268080.418 \n", + "2 1248133.699 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes import read_carto\n", + "\n", + "gdf = read_carto(\"SELECT * FROM starbucks_brooklyn WHERE revenue > 1200000\")\n", + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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    5 rows × 24 columns

    \n", "" ], "text/plain": [ - " cartodb_id the_geom fuel4 country_abbrev country \\\n", - "0 8465 POINT (10.84410 48.58820) DEU Germany \n", - "1 8410 POINT (11.20140 48.32320) DEU Germany \n", - "2 11314 POINT (14.50530 -22.95750) NAM Namibia \n", - "3 25385 POINT (-70.09890 8.83630) VEN Venezuela \n", - "4 13042 POINT (126.51120 34.40910) KOR South Korea \n", - "\n", - " name gppd_idnr capacity_mw latitude \\\n", - "0 Nordendorf Solar Power Plant WRI1030340 7.4 48.5882 \n", - "1 Miegersbach Solar Power Plant WRI1030364 5.2 48.3232 \n", - "2 Anixas WRI1023132 24.3 -22.9575 \n", - "3 Masparro WRI1018659 25.0 8.8363 \n", - "4 Gochang WRI1029915 15.0 34.4091 \n", - "\n", - " longitude ... owner source \\\n", - "0 10.8441 ... Sinosol AG \n", - "1 11.2014 ... Phoenix Solar AG \n", - "2 14.5053 ... African Development Bank Group \n", - "3 -70.0989 ... Venezuela Electricity Sector \n", - "4 126.5112 ... CDM \n", - "\n", - " url geolocation_source \\\n", - "0 http://www.sinosol.de Industry About \n", - "1 http://www.phoenixsolar-group.com Industry About \n", - "2 http://powerafrica.opendataforafrica.org Power Africa \n", - "3 http://www.mppee.gob.ve/download/anuario_estad... GEODB \n", - "4 https://cdm.unfccc.int/Projects/DB/KEMCO125427... WRI \n", - "\n", - " year_of_capacity_data generation_gwh_2013 generation_gwh_2014 \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN 165.0 \n", - "4 NaN NaN \n", - "\n", - " generation_gwh_2015 generation_gwh_2016 estimated_generation_gwh \n", - "0 NaN NaN 68.537617 \n", - "1 NaN NaN 48.161569 \n", - "2 NaN NaN 5.273790 \n", - "3 NaN NaN 123.392392 \n", - "4 NaN NaN 405.443975 \n", - "\n", - "[5 rows x 24 columns]" + " cartodb_id the_geom field_1 \\\n", + "0 1 POINT (-73.95901 40.67109) 0 \n", + "1 2 POINT (-73.96122 40.57796) 1 \n", + "2 3 POINT (-73.98976 40.61912) 2 \n", + "3 4 POINT (-74.02744 40.63152) 3 \n", + "4 5 POINT (-74.00098 40.59321) 4 \n", + "\n", + " name address \\\n", + "0 Franklin Ave & Eastern Pkwy 341 Eastern Pkwy,Brooklyn, NY 11238 \n", + "1 607 Brighton Beach Ave 607 Brighton Beach Avenue,Brooklyn, NY 11235 \n", + "2 65th St & 18th Ave 6423 18th Avenue,Brooklyn, NY 11204 \n", + "3 Bay Ridge Pkwy & 3rd Ave 7419 3rd Avenue,Brooklyn, NY 11209 \n", + "4 Caesar's Bay Shopping Center 8973 Bay Parkway,Brooklyn, NY 11214 \n", + "\n", + " revenue \n", + "0 1321040.772 \n", + "1 1268080.418 \n", + "2 1248133.699 \n", + "3 1185702.676 \n", + "4 1148427.411 " ] }, "execution_count": 2, @@ -253,7 +139,7 @@ "source": [ "from cartoframes import read_carto\n", "\n", - "gdf = read_carto('global_power_plants', limit=100)\n", + "gdf = read_carto('starbucks_brooklyn')\n", "gdf.head()" ] }, @@ -963,21 +849,21 @@ " return value.toLocaleString();\n", " }\n", "\n", - " function updateViewport(map) {\n", + " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", - " const mapInfo$ = document.getElementById('map-info');\n", + " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -1051,7 +937,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -1066,12 +952,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
    ${popupHTML}
    `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
    ${popupHTML}
    `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1355,7 +1245,8 @@ " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", - " updateViewport(map);\n", + " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", + " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", @@ -1429,7 +1320,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1470,12 +1361,12 @@ " .addEventListener('ready', () => {\n", " const basecolor = '';\n", " const basemap = 'Positron';\n", - " const bounds = [[-76.759, -43.0914], [152.8145, 54.1255]];\n", + " const bounds = [[-74.03313, 40.57796], [-73.87015, 40.6915]];\n", " const camera = null;\n", " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": "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", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": "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", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\n", "widgets": []}];\n", + " const layers = [{"credentials": null, "data": "H4sIAO8Nyl8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAO8Nyl8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -1499,7 +1390,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 3, diff --git a/examples/data_management/load_data_from_csv.ipynb b/docs/examples/data_management/read_csv.ipynb similarity index 70% rename from examples/data_management/load_data_from_csv.ipynb rename to docs/examples/data_management/read_csv.ipynb index cc0a47eee..e7509007f 100644 --- a/examples/data_management/load_data_from_csv.ipynb +++ b/docs/examples/data_management/read_csv.ipynb @@ -4,16 +4,16 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Load data from a CSV file\n", + "## Read data from a CSV file\n", "\n", - "These examples illustrate how to load data from a CSV file using the Pandas and GeoPandas libraries." + "These examples illustrate how to read data from a CSV file using the Pandas and GeoPandas libraries." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "## From latitude and longitude columns" + "### From latitude and longitude columns" ] }, { @@ -68,123 +68,123 @@ " \n", " \n", " 0\n", - " 2019-05-01T01:00:00.000\n", - " 2019-05-01T00:00:00.000\n", - " 01:00\n", - " 2019\n", - " Wednesday\n", - " 2019-06-12T20:27:00.000\n", - " 81097515200\n", - " 810975\n", - " 190424067\n", - " 191634131.0\n", + " 2020-08-16T03:13:00.000\n", + " 2020-08-16T00:00:00.000\n", + " 03:13\n", + " 2020\n", + " Sunday\n", + " 2020-08-16T03:14:00.000\n", + " 95319604083\n", + " 953196\n", + " 200491669\n", + " 202290313.0\n", " ...\n", - " 10.0\n", - " 7.0\n", - " 35.0\n", + " 2.0\n", + " 9\n", + " 26.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 1.0\n", - " POINT (-122.49963 37.76257)\n", + " 2.0\n", + " POINT (-122.39773 37.75483)\n", " \n", " \n", " 1\n", - " 2019-06-22T07:45:00.000\n", - " 2019-06-22T00:00:00.000\n", - " 07:45\n", - " 2019\n", - " Saturday\n", - " 2019-06-22T08:05:00.000\n", - " 81465564020\n", - " 814655\n", - " 190450880\n", - " 191730737.0\n", + " 2020-08-16T03:38:00.000\n", + " 2020-08-16T00:00:00.000\n", + " 03:38\n", + " 2020\n", + " Sunday\n", + " 2020-08-16T04:56:00.000\n", + " 95326228100\n", + " 953262\n", + " 200491738\n", + " 202290404.0\n", " ...\n", - " 1.0\n", - " 10.0\n", - " 34.0\n", - " 1.0\n", + " 3.0\n", + " 2\n", + " 20.0\n", + " 3.0\n", + " NaN\n", " NaN\n", - " 1.0\n", " NaN\n", " NaN\n", " 2.0\n", - " POINT (-122.40816 37.78054)\n", + " POINT (-122.42204 37.76654)\n", " \n", " \n", " 2\n", - " 2019-06-03T16:16:00.000\n", - " 2019-06-03T00:00:00.000\n", - " 16:16\n", - " 2019\n", - " Monday\n", - " 2019-06-03T16:16:00.000\n", - " 80769875000\n", - " 807698\n", - " 190397016\n", - " 191533509.0\n", + " 2020-08-16T13:40:00.000\n", + " 2020-08-16T00:00:00.000\n", + " 13:40\n", + " 2020\n", + " Sunday\n", + " 2020-08-16T13:56:00.000\n", + " 95336264020\n", + " 953362\n", + " 200492463\n", + " 202291631.0\n", " ...\n", - " 2.0\n", - " 9.0\n", " 1.0\n", + " 10\n", + " 8.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 2.0\n", - " POINT (-122.39075 37.72160)\n", + " 1.0\n", + " POINT (-122.40371 37.78404)\n", " \n", " \n", " 3\n", - " 2018-11-16T16:34:00.000\n", - " 2018-11-16T00:00:00.000\n", - " 16:34\n", - " 2018\n", - " Friday\n", - " 2018-11-16T16:34:00.000\n", - " 73857915041\n", - " 738579\n", - " 180870806\n", - " 183202539.0\n", + " 2020-08-16T16:18:00.000\n", + " 2020-08-16T00:00:00.000\n", + " 16:18\n", + " 2020\n", + " Sunday\n", + " 2020-08-16T16:18:00.000\n", + " 95335012010\n", + " 953350\n", + " 200492792\n", + " 202292091.0\n", " ...\n", - " 6.0\n", - " 3.0\n", - " 6.0\n", + " 10.0\n", + " 7\n", + " 35.0\n", " NaN\n", - " 18.0\n", " NaN\n", " NaN\n", " NaN\n", - " 2.0\n", - " POINT (-122.40488 37.79486)\n", + " NaN\n", + " 1.0\n", + " POINT (-122.50742 37.75100)\n", " \n", " \n", " 4\n", - " 2019-05-27T02:25:00.000\n", - " 2019-05-27T00:00:00.000\n", - " 02:25\n", - " 2019\n", - " Monday\n", - " 2019-05-27T02:55:00.000\n", - " 80509204134\n", - " 805092\n", - " 190378555\n", - " 191470256.0\n", + " 2020-08-12T22:00:00.000\n", + " 2020-08-12T00:00:00.000\n", + " 22:00\n", + " 2020\n", + " Wednesday\n", + " 2020-08-15T08:30:00.000\n", + " 95300674000\n", + " 953006\n", + " 200489880\n", + " 202280827.0\n", " ...\n", " 4.0\n", - " 6.0\n", - " 13.0\n", + " 11\n", + " 39.0\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", " NaN\n", - " 1.0\n", - " POINT (-122.43056 37.79772)\n", + " 2.0\n", + " POINT (-122.43214 37.78050)\n", " \n", " \n", "\n", @@ -193,60 +193,60 @@ ], "text/plain": [ " incident_datetime incident_date incident_time \\\n", - "0 2019-05-01T01:00:00.000 2019-05-01T00:00:00.000 01:00 \n", - "1 2019-06-22T07:45:00.000 2019-06-22T00:00:00.000 07:45 \n", - "2 2019-06-03T16:16:00.000 2019-06-03T00:00:00.000 16:16 \n", - "3 2018-11-16T16:34:00.000 2018-11-16T00:00:00.000 16:34 \n", - "4 2019-05-27T02:25:00.000 2019-05-27T00:00:00.000 02:25 \n", + "0 2020-08-16T03:13:00.000 2020-08-16T00:00:00.000 03:13 \n", + "1 2020-08-16T03:38:00.000 2020-08-16T00:00:00.000 03:38 \n", + "2 2020-08-16T13:40:00.000 2020-08-16T00:00:00.000 13:40 \n", + "3 2020-08-16T16:18:00.000 2020-08-16T00:00:00.000 16:18 \n", + "4 2020-08-12T22:00:00.000 2020-08-12T00:00:00.000 22:00 \n", "\n", " incident_year incident_day_of_week report_datetime row_id \\\n", - "0 2019 Wednesday 2019-06-12T20:27:00.000 81097515200 \n", - "1 2019 Saturday 2019-06-22T08:05:00.000 81465564020 \n", - "2 2019 Monday 2019-06-03T16:16:00.000 80769875000 \n", - "3 2018 Friday 2018-11-16T16:34:00.000 73857915041 \n", - "4 2019 Monday 2019-05-27T02:55:00.000 80509204134 \n", + "0 2020 Sunday 2020-08-16T03:14:00.000 95319604083 \n", + "1 2020 Sunday 2020-08-16T04:56:00.000 95326228100 \n", + "2 2020 Sunday 2020-08-16T13:56:00.000 95336264020 \n", + "3 2020 Sunday 2020-08-16T16:18:00.000 95335012010 \n", + "4 2020 Wednesday 2020-08-15T08:30:00.000 95300674000 \n", "\n", " incident_id incident_number cad_number ... :@computed_region_qgnn_b9vv \\\n", - "0 810975 190424067 191634131.0 ... 10.0 \n", - "1 814655 190450880 191730737.0 ... 1.0 \n", - "2 807698 190397016 191533509.0 ... 2.0 \n", - "3 738579 180870806 183202539.0 ... 6.0 \n", - "4 805092 190378555 191470256.0 ... 4.0 \n", + "0 953196 200491669 202290313.0 ... 2.0 \n", + "1 953262 200491738 202290404.0 ... 3.0 \n", + "2 953362 200492463 202291631.0 ... 1.0 \n", + "3 953350 200492792 202292091.0 ... 10.0 \n", + "4 953006 200489880 202280827.0 ... 4.0 \n", "\n", " :@computed_region_26cr_cadq :@computed_region_ajp5_b2md \\\n", - "0 7.0 35.0 \n", - "1 10.0 34.0 \n", - "2 9.0 1.0 \n", - "3 3.0 6.0 \n", - "4 6.0 13.0 \n", + "0 9 26.0 \n", + "1 2 20.0 \n", + "2 10 8.0 \n", + "3 7 35.0 \n", + "4 11 39.0 \n", "\n", " :@computed_region_nqbw_i6c3 :@computed_region_2dwj_jsy4 \\\n", "0 NaN NaN \n", - "1 1.0 NaN \n", + "1 3.0 NaN \n", "2 NaN NaN \n", - "3 NaN 18.0 \n", + "3 NaN NaN \n", "4 NaN NaN \n", "\n", " :@computed_region_h4ep_8xdi :@computed_region_y6ts_4iup \\\n", "0 NaN NaN \n", - "1 1.0 NaN \n", + "1 NaN NaN \n", "2 NaN NaN \n", "3 NaN NaN \n", "4 NaN NaN \n", "\n", " :@computed_region_jg9y_a9du :@computed_region_6pnf_4xz7 \\\n", - "0 NaN 1.0 \n", + "0 NaN 2.0 \n", "1 NaN 2.0 \n", - "2 NaN 2.0 \n", - "3 NaN 2.0 \n", - "4 NaN 1.0 \n", + "2 NaN 1.0 \n", + "3 NaN 1.0 \n", + "4 NaN 2.0 \n", "\n", " geometry \n", - "0 POINT (-122.49963 37.76257) \n", - "1 POINT (-122.40816 37.78054) \n", - "2 POINT (-122.39075 37.72160) \n", - "3 POINT (-122.40488 37.79486) \n", - "4 POINT (-122.43056 37.79772) \n", + "0 POINT (-122.39773 37.75483) \n", + "1 POINT (-122.42204 37.76654) \n", + "2 POINT (-122.40371 37.78404) \n", + "3 POINT (-122.50742 37.75100) \n", + "4 POINT (-122.43214 37.78050) \n", "\n", "[5 rows x 37 columns]" ] @@ -260,12 +260,7 @@ "from pandas import read_csv\n", "from geopandas import GeoDataFrame, points_from_xy\n", "\n", - "remote_file_path = 'http://data.sfgov.org/resource/wg3w-h783.csv'\n", - "\n", - "df = read_csv(remote_file_path)\n", - "\n", - "# Clean rows where the `longitude` column is NULL\n", - "df = df[df['longitude'].notna()]\n", + "df = read_csv('https://libs.cartocdn.com/cartoframes/samples/sf_incidents.csv')\n", "\n", "gdf = GeoDataFrame(df, geometry=points_from_xy(df['longitude'], df['latitude']))\n", "gdf.head()" @@ -977,21 +972,21 @@ " return value.toLocaleString();\n", " }\n", "\n", - " function updateViewport(map) {\n", + " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", - " const mapInfo$ = document.getElementById('map-info');\n", + " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -1065,7 +1060,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -1080,12 +1075,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
    ${popupHTML}
    `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
    ${popupHTML}
    `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1369,7 +1368,8 @@ " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", - " updateViewport(map);\n", + " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", + " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", @@ -1443,7 +1443,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1484,12 +1484,12 @@ " .addEventListener('ready', () => {\n", " const basecolor = '';\n", " const basemap = 'Positron';\n", - " const bounds = [[-122.51129492624534, 37.70899533715967], [-122.3703523516931, 37.82411913273524]];\n", + " const bounds = [[-122.51129492620001, 37.708311265], [-122.3712458991, 37.817823897800004]];\n", " const camera = null;\n", " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": "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", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\n", "widgets": []}];\n", + " const layers = [{"credentials": null, "data": 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", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -1513,7 +1513,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -1531,7 +1531,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## From a WKT column" + "### From a WKT/WKB column" ] }, { @@ -1659,11 +1659,11 @@ } ], "source": [ + "from pandas import read_csv\n", + "from geopandas import GeoDataFrame\n", "from cartoframes.utils import decode_geometry\n", "\n", - "remote_file_path='http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_geocoded.csv'\n", - "\n", - "df = read_csv(remote_file_path)\n", + "df = read_csv('https://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn_geocoded.csv')\n", "\n", "gdf = GeoDataFrame(df, geometry=decode_geometry(df['the_geom']))\n", "gdf.head()" @@ -2375,21 +2375,21 @@ " return value.toLocaleString();\n", " }\n", "\n", - " function updateViewport(map) {\n", + " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", - " const mapInfo$ = document.getElementById('map-info');\n", + " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -2463,7 +2463,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -2478,12 +2478,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
    ${popupHTML}
    `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
    ${popupHTML}
    `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -2767,7 +2771,8 @@ " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", - " updateViewport(map);\n", + " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", + " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", @@ -2841,7 +2846,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -2887,7 +2892,7 @@ " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": "H4sIAPQnvF4C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAPQnvF4C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\n", "widgets": []}];\n", + " const layers = [{"credentials": null, "data": "H4sIAPINyl8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAPINyl8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -2911,7 +2916,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 4, @@ -2940,7 +2945,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.6.7" } }, "nbformat": 4, diff --git a/examples/data_management/load_data_from_geojson.ipynb b/docs/examples/data_management/read_geojson.ipynb similarity index 70% rename from examples/data_management/load_data_from_geojson.ipynb rename to docs/examples/data_management/read_geojson.ipynb index 77a2938f2..30501499c 100644 --- a/examples/data_management/load_data_from_geojson.ipynb +++ b/docs/examples/data_management/read_geojson.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Load data from a GeoJSON file\n", + "## Read data from a GeoJSON file\n", "\n", - "This example illustrates how to load data from a GeoJSON file using GeoPandas." + "This example illustrates how to read data from a GeoJSON file using GeoPandas." ] }, { @@ -37,26 +37,11 @@ " \n", " \n", " \n", - " objectid\n", " cartodb_id\n", - " entity_id\n", - " latitude\n", - " longitude\n", - " audit_stat\n", - " legal_radi\n", - " illegal_ra\n", - " radius_umd\n", - " radius_for\n", - " ...\n", - " carbon_r_3\n", - " peat_for_2\n", - " peat_for_3\n", - " primary_10\n", - " primary_11\n", - " mill_name\n", - " parent_com\n", - " rspo_certi\n", - " date_updat\n", + " field_1\n", + " name\n", + " address\n", + " revenue\n", " geometry\n", " \n", " \n", @@ -64,165 +49,73 @@ " \n", " 0\n", " 1\n", - " 59\n", - " ID1822\n", - " -1.585833\n", - " 103.205556\n", - " ASA 1\n", " 0\n", - " 0.321764\n", - " 0.225759\n", - " 1099\n", - " ...\n", - " 1224\n", - " 0\n", - " 29\n", - " 0.004508\n", - " 0.135391\n", - " Muara Bulian Mill\n", - " PT Inti Indosawit Subur\n", - " yes\n", - " 14-Aug\n", - " POINT (103.20556 -1.58583)\n", + " Franklin Ave & Eastern Pkwy\n", + " 341 Eastern Pkwy,Brooklyn, NY 11238\n", + " 1321040.772\n", + " POINT (-73.95901 40.67109)\n", " \n", " \n", " 1\n", " 2\n", - " 153\n", - " ID1847\n", - " 0.077043\n", - " 102.030838\n", - " Renewal Certification\n", - " 0\n", - " 0.445960\n", - " 0.258855\n", - " 1979\n", - " ...\n", - " 2038\n", - " 7\n", - " 523\n", - " 0.017304\n", - " 0.321418\n", - " Pabrik Kelapa Sawit Batang Kulim POM\n", - " PT Musim Mas\n", - " yes\n", - " 14-Aug\n", - " POINT (102.03084 0.07704)\n", + " 1\n", + " 607 Brighton Beach Ave\n", + " 607 Brighton Beach Avenue,Brooklyn, NY 11235\n", + " 1268080.418\n", + " POINT (-73.96122 40.57796)\n", " \n", " \n", " 2\n", " 3\n", - " 103\n", - " ID1720\n", - " 1.660222\n", - " 100.590611\n", - " Initial Certification\n", - " 0\n", - " 0.498531\n", - " 0.248520\n", - " 1432\n", - " ...\n", - " 1518\n", - " 0\n", - " 476\n", - " 0.000811\n", - " 0.193365\n", - " Kayangan and Kencana POM\n", - " PT Salim Ivomas Pratama Tbk\n", - " yes\n", - " 14-Aug\n", - " POINT (100.59061 1.66022)\n", + " 2\n", + " 65th St & 18th Ave\n", + " 6423 18th Avenue,Brooklyn, NY 11204\n", + " 1248133.699\n", + " POINT (-73.98976 40.61912)\n", " \n", " \n", " 3\n", " 4\n", - " 216\n", - " ID1945\n", - " -2.894444\n", - " 112.543611\n", - " ASA 1\n", - " 0\n", - " 0.662863\n", - " 0.186332\n", - " 226\n", - " ...\n", - " 269\n", - " 59\n", - " 66\n", - " 0.124882\n", - " 0.189169\n", - " PT Sarana Titian Permata POM\n", - " Wilmar International Ltd\n", - " yes\n", - " 14-Aug\n", - " POINT (112.54361 -2.89444)\n", + " 3\n", + " Bay Ridge Pkwy & 3rd Ave\n", + " 7419 3rd Avenue,Brooklyn, NY 11209\n", + " 1185702.676\n", + " POINT (-74.02744 40.63152)\n", " \n", " \n", " 4\n", " 5\n", - " 156\n", - " ID1553\n", - " 3.593333\n", - " 98.947222\n", - " Initial Certification\n", - " 0\n", - " 0.533668\n", - " 0.028972\n", - " 382\n", - " ...\n", - " 412\n", - " 0\n", - " 0\n", - " 0.000216\n", - " 0.009296\n", - " Adolina POM\n", - " PT Perkebunan Nusantara IV (PERSERO)\n", - " yes\n", - " 14-Aug\n", - " POINT (98.94722 3.59333)\n", + " 4\n", + " Caesar's Bay Shopping Center\n", + " 8973 Bay Parkway,Brooklyn, NY 11214\n", + " 1148427.411\n", + " POINT (-74.00098 40.59321)\n", " \n", " \n", "\n", - "

    5 rows × 73 columns

    \n", "" ], "text/plain": [ - " objectid cartodb_id entity_id latitude longitude \\\n", - "0 1 59 ID1822 -1.585833 103.205556 \n", - "1 2 153 ID1847 0.077043 102.030838 \n", - "2 3 103 ID1720 1.660222 100.590611 \n", - "3 4 216 ID1945 -2.894444 112.543611 \n", - "4 5 156 ID1553 3.593333 98.947222 \n", - "\n", - " audit_stat legal_radi illegal_ra radius_umd radius_for ... \\\n", - "0 ASA 1 0 0.321764 0.225759 1099 ... \n", - "1 Renewal Certification 0 0.445960 0.258855 1979 ... \n", - "2 Initial Certification 0 0.498531 0.248520 1432 ... \n", - "3 ASA 1 0 0.662863 0.186332 226 ... \n", - "4 Initial Certification 0 0.533668 0.028972 382 ... \n", - "\n", - " carbon_r_3 peat_for_2 peat_for_3 primary_10 primary_11 \\\n", - "0 1224 0 29 0.004508 0.135391 \n", - "1 2038 7 523 0.017304 0.321418 \n", - "2 1518 0 476 0.000811 0.193365 \n", - "3 269 59 66 0.124882 0.189169 \n", - "4 412 0 0 0.000216 0.009296 \n", - "\n", - " mill_name parent_com \\\n", - "0 Muara Bulian Mill PT Inti Indosawit Subur \n", - "1 Pabrik Kelapa Sawit Batang Kulim POM PT Musim Mas \n", - "2 Kayangan and Kencana POM PT Salim Ivomas Pratama Tbk \n", - "3 PT Sarana Titian Permata POM Wilmar International Ltd \n", - "4 Adolina POM PT Perkebunan Nusantara IV (PERSERO) \n", - "\n", - " rspo_certi date_updat geometry \n", - "0 yes 14-Aug POINT (103.20556 -1.58583) \n", - "1 yes 14-Aug POINT (102.03084 0.07704) \n", - "2 yes 14-Aug POINT (100.59061 1.66022) \n", - "3 yes 14-Aug POINT (112.54361 -2.89444) \n", - "4 yes 14-Aug POINT (98.94722 3.59333) \n", - "\n", - "[5 rows x 73 columns]" + " cartodb_id field_1 name \\\n", + "0 1 0 Franklin Ave & Eastern Pkwy \n", + "1 2 1 607 Brighton Beach Ave \n", + "2 3 2 65th St & 18th Ave \n", + "3 4 3 Bay Ridge Pkwy & 3rd Ave \n", + "4 5 4 Caesar's Bay Shopping Center \n", + "\n", + " address revenue \\\n", + "0 341 Eastern Pkwy,Brooklyn, NY 11238 1321040.772 \n", + "1 607 Brighton Beach Avenue,Brooklyn, NY 11235 1268080.418 \n", + "2 6423 18th Avenue,Brooklyn, NY 11204 1248133.699 \n", + "3 7419 3rd Avenue,Brooklyn, NY 11209 1185702.676 \n", + "4 8973 Bay Parkway,Brooklyn, NY 11214 1148427.411 \n", + "\n", + " geometry \n", + "0 POINT (-73.95901 40.67109) \n", + "1 POINT (-73.96122 40.57796) \n", + "2 POINT (-73.98976 40.61912) \n", + "3 POINT (-74.02744 40.63152) \n", + "4 POINT (-74.00098 40.59321) " ] }, "execution_count": 1, @@ -233,7 +126,7 @@ "source": [ "from geopandas import read_file\n", "\n", - "gdf = read_file('http://libs.cartocdn.com/cartoframes/files/sustainable_palm_oil_production_mills.geojson')\n", + "gdf = read_file('https://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn_geocoded.geojson')\n", "gdf.head()" ] }, @@ -943,21 +836,21 @@ " return value.toLocaleString();\n", " }\n", "\n", - " function updateViewport(map) {\n", + " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", - " const mapInfo$ = document.getElementById('map-info');\n", + " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -1031,7 +924,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -1046,12 +939,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
    ${popupHTML}
    `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
    ${popupHTML}
    `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1335,7 +1232,8 @@ " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", - " updateViewport(map);\n", + " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", + " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", @@ -1409,7 +1307,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1450,12 +1348,12 @@ " .addEventListener('ready', () => {\n", " const basecolor = '';\n", " const basemap = 'Positron';\n", - " const bounds = [[-88.55981399975727, -10.316332999911578], [160.21845000011172, 15.582959000387486]];\n", + " const bounds = [[-74.03313, 40.57796], [-73.87015, 40.6915]];\n", " const camera = null;\n", " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": "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", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": 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", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\n", "widgets": []}];\n", + " const layers = [{"credentials": null, "data": "H4sIAPQNyl8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAPQNyl8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -1479,7 +1377,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, diff --git a/examples/data_management/load_data_from_json.ipynb b/docs/examples/data_management/read_json.ipynb similarity index 69% rename from examples/data_management/load_data_from_json.ipynb rename to docs/examples/data_management/read_json.ipynb index 6645aa10f..fe2b7960d 100644 --- a/examples/data_management/load_data_from_json.ipynb +++ b/docs/examples/data_management/read_json.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Load data from a JSON file\n", + "## Read data from a JSON file\n", "\n", - "This example illustrates how to load data from a remote JSON file using pandas and the process of preparing the data for spatial operations." + "This example illustrates how to read data from a remote JSON file using pandas and the process of preparing the data for spatial operations." ] }, { @@ -15,25 +15,13 @@ "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "dict_keys(['datasetid', 'recordid', 'fields', 'geometry', 'record_timestamp'])" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "import requests\n", "\n", "# Download the JSON file\n", - "remote_file_path = 'http://opendata.paris.fr/api/records/1.0/search/?dataset=arbresremarquablesparis&rows=200'\n", - "data_json = requests.get(remote_file_path).json()['records']\n", - "data_json[0].keys()" + "remote_file_path = 'https://libs.cartocdn.com/cartoframes/samples/arbres_remarquables_paris.json'\n", + "data_json = requests.get(remote_file_path).json()" ] }, { @@ -77,134 +65,134 @@ " fields.remarquable\n", " fields.idbase\n", " fields.genre\n", + " fields.complementadresse\n", " fields.typeemplacement\n", " fields.dateplantation\n", " geometry.type\n", " geometry.coordinates\n", " fields.varieteoucultivar\n", - " fields.complementadresse\n", " \n", " \n", " \n", " \n", " 0\n", " arbresremarquablesparis\n", - " 29a0ea77b5379a1e15b5c34d0ed8bbadb9f5789c\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.8542732882, 2.33573525468]\n", - " Paulownia\n", - " 7493\n", - " 000101001\n", - " PARIS 6E ARRDT\n", - " 295.0\n", - " 20.0\n", + " a236960f295288698ca57fdccfd9825547ffb4d0\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8630096006, 2.2417737512]\n", + " Pin\n", + " 49884\n", + " 000901001\n", + " BOIS DE BOULOGNE\n", + " 241.0\n", + " 30.0\n", " ...\n", " M\n", " 1\n", - " 216766.0\n", - " Paulownia\n", + " 2002349.0\n", + " Pinus\n", + " 16-08\n", " Arbre\n", - " 1999-01-25T01:00:00+00:00\n", + " 1882-01-01T00:09:21+00:00\n", " Point\n", - " [2.33573525468, 48.8542732882]\n", - " NaN\n", + " [2.2417737512, 48.8630096006]\n", " NaN\n", " \n", " \n", " 1\n", " arbresremarquablesparis\n", - " 24ab994e9b2b51c4a27394ec52f038c4faa5fc1c\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.8217882346, 2.3228497157]\n", + " d7635937c3636051c99963676d3a97dd4d9dd8c9\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8731110898, 2.24886478886]\n", " Hêtre\n", - " 57013\n", - " 00000174\n", - " PARIS 14E ARRDT\n", - " 310.0\n", - " 15.0\n", + " 52761\n", + " 000701004\n", + " BOIS DE BOULOGNE\n", + " 231.0\n", + " 16.0\n", " ...\n", " M\n", " 1\n", - " 121632.0\n", + " 2002354.0\n", " Fagus\n", + " 16-17\n", " Arbre\n", - " 1700-01-01T00:09:21+00:00\n", + " 1868-01-01T00:09:21+00:00\n", " Point\n", - " [2.3228497157, 48.8217882346]\n", - " ''Atropunicea''\n", - " 14-09\n", + " [2.24886478886, 48.8731110898]\n", + " ''Pendula''\n", " \n", " \n", " 2\n", " arbresremarquablesparis\n", - " 2138bdaedd6c46f681ff30904f8017a292fc2b3b\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.8648326277, 2.25217460792]\n", - " Plaqueminier\n", - " 58739\n", - " 000501002\n", - " BOIS DE BOULOGNE\n", - " 146.0\n", - " 15.0\n", + " 460e8eccd3ebc816f16317702223bf406e7d75e6\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8448875264, 2.35366916319]\n", + " Hêtre\n", + " 62456\n", + " 00000075\n", + " PARIS 5E ARRDT\n", + " 85.0\n", + " 3.0\n", " ...\n", - " A\n", + " M\n", " 1\n", - " 2002388.0\n", - " Diospyros\n", + " 132669.0\n", + " Fagus\n", + " NaN\n", " Arbre\n", - " 1897-01-01T00:09:21+00:00\n", + " 1905-01-01T00:09:21+00:00\n", " Point\n", - " [2.25217460792, 48.8648326277]\n", - " NaN\n", - " 16-13\n", + " [2.35366916319, 48.8448875264]\n", + " ''Tortuosa''\n", " \n", " \n", " 3\n", " arbresremarquablesparis\n", - " 63bc7232fe659c4585923d9e65e5275418c359b9\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.8564741998, 2.39461847904]\n", - " Platane\n", - " 70026\n", - " 001802036\n", - " PARIS 20E ARRDT\n", - " 407.0\n", - " 23.0\n", + " b60b80e0f8327afba6d0c513b0b6b85639e516b6\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8787029872, 2.30647417371]\n", + " Erable\n", + " 68801\n", + " 00NO0144\n", + " PARIS 8E ARRDT\n", + " 450.0\n", + " 28.0\n", " ...\n", " M\n", " 1\n", - " 223748.0\n", - " Platanus\n", + " 305507.0\n", + " Acer\n", + " 08-01\n", " Arbre\n", " 1700-01-01T00:09:21+00:00\n", " Point\n", - " [2.39461847904, 48.8564741998]\n", + " [2.30647417371, 48.8787029872]\n", " NaN\n", - " 148\n", " \n", " \n", " 4\n", " arbresremarquablesparis\n", - " 602c7b2fd878c56e5efc532739abb28d86d9f365\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.831216573, 2.41167739693]\n", - " Cryptomeria\n", - " 85651\n", - " 12-13\n", - " BOIS DE VINCENNES\n", - " 122.0\n", - " 13.0\n", + " 47e163d6f0536bc9e26ba2993447c6f50b14a6f6\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8671184424, 2.25294211329]\n", + " Platane\n", + " 82341\n", + " 001201001\n", + " BOIS DE BOULOGNE\n", + " 534.0\n", + " 45.0\n", " ...\n", " M\n", " 1\n", - " 2002359.0\n", - " Cryptomeria\n", + " 2002389.0\n", + " Platanus\n", + " 16-10\n", " Arbre\n", - " 1893-01-01T00:09:21+00:00\n", + " 1872-01-01T00:09:21+00:00\n", " Point\n", - " [2.41167739693, 48.831216573]\n", + " [2.25294211329, 48.8671184424]\n", " NaN\n", - " 12-13\n", " \n", " \n", "\n", @@ -213,60 +201,53 @@ ], "text/plain": [ " datasetid recordid \\\n", - "0 arbresremarquablesparis 29a0ea77b5379a1e15b5c34d0ed8bbadb9f5789c \n", - "1 arbresremarquablesparis 24ab994e9b2b51c4a27394ec52f038c4faa5fc1c \n", - "2 arbresremarquablesparis 2138bdaedd6c46f681ff30904f8017a292fc2b3b \n", - "3 arbresremarquablesparis 63bc7232fe659c4585923d9e65e5275418c359b9 \n", - "4 arbresremarquablesparis 602c7b2fd878c56e5efc532739abb28d86d9f365 \n", + "0 arbresremarquablesparis a236960f295288698ca57fdccfd9825547ffb4d0 \n", + "1 arbresremarquablesparis d7635937c3636051c99963676d3a97dd4d9dd8c9 \n", + "2 arbresremarquablesparis 460e8eccd3ebc816f16317702223bf406e7d75e6 \n", + "3 arbresremarquablesparis b60b80e0f8327afba6d0c513b0b6b85639e516b6 \n", + "4 arbresremarquablesparis 47e163d6f0536bc9e26ba2993447c6f50b14a6f6 \n", "\n", " record_timestamp fields.geom_x_y \\\n", - "0 2020-05-08T10:45:25.056000+00:00 [48.8542732882, 2.33573525468] \n", - "1 2020-05-08T10:45:25.056000+00:00 [48.8217882346, 2.3228497157] \n", - "2 2020-05-08T10:45:25.056000+00:00 [48.8648326277, 2.25217460792] \n", - "3 2020-05-08T10:45:25.056000+00:00 [48.8564741998, 2.39461847904] \n", - "4 2020-05-08T10:45:25.056000+00:00 [48.831216573, 2.41167739693] \n", + "0 2019-10-04T10:00:41.063000+00:00 [48.8630096006, 2.2417737512] \n", + "1 2019-10-04T10:00:41.063000+00:00 [48.8731110898, 2.24886478886] \n", + "2 2019-10-04T10:00:41.063000+00:00 [48.8448875264, 2.35366916319] \n", + "3 2019-10-04T10:00:41.063000+00:00 [48.8787029872, 2.30647417371] \n", + "4 2019-10-04T10:00:41.063000+00:00 [48.8671184424, 2.25294211329] \n", "\n", " fields.libellefrancais fields.objectid fields.idemplacement \\\n", - "0 Paulownia 7493 000101001 \n", - "1 Hêtre 57013 00000174 \n", - "2 Plaqueminier 58739 000501002 \n", - "3 Platane 70026 001802036 \n", - "4 Cryptomeria 85651 12-13 \n", + "0 Pin 49884 000901001 \n", + "1 Hêtre 52761 000701004 \n", + "2 Hêtre 62456 00000075 \n", + "3 Erable 68801 00NO0144 \n", + "4 Platane 82341 001201001 \n", "\n", " fields.arrondissement fields.circonferenceencm fields.hauteurenm ... \\\n", - "0 PARIS 6E ARRDT 295.0 20.0 ... \n", - "1 PARIS 14E ARRDT 310.0 15.0 ... \n", - "2 BOIS DE BOULOGNE 146.0 15.0 ... \n", - "3 PARIS 20E ARRDT 407.0 23.0 ... \n", - "4 BOIS DE VINCENNES 122.0 13.0 ... \n", + "0 BOIS DE BOULOGNE 241.0 30.0 ... \n", + "1 BOIS DE BOULOGNE 231.0 16.0 ... \n", + "2 PARIS 5E ARRDT 85.0 3.0 ... \n", + "3 PARIS 8E ARRDT 450.0 28.0 ... \n", + "4 BOIS DE BOULOGNE 534.0 45.0 ... \n", "\n", " fields.stadedeveloppement fields.remarquable fields.idbase fields.genre \\\n", - "0 M 1 216766.0 Paulownia \n", - "1 M 1 121632.0 Fagus \n", - "2 A 1 2002388.0 Diospyros \n", - "3 M 1 223748.0 Platanus \n", - "4 M 1 2002359.0 Cryptomeria \n", - "\n", - " fields.typeemplacement fields.dateplantation geometry.type \\\n", - "0 Arbre 1999-01-25T01:00:00+00:00 Point \n", - "1 Arbre 1700-01-01T00:09:21+00:00 Point \n", - "2 Arbre 1897-01-01T00:09:21+00:00 Point \n", - "3 Arbre 1700-01-01T00:09:21+00:00 Point \n", - "4 Arbre 1893-01-01T00:09:21+00:00 Point \n", - "\n", - " geometry.coordinates fields.varieteoucultivar \\\n", - "0 [2.33573525468, 48.8542732882] NaN \n", - "1 [2.3228497157, 48.8217882346] ''Atropunicea'' \n", - "2 [2.25217460792, 48.8648326277] NaN \n", - "3 [2.39461847904, 48.8564741998] NaN \n", - "4 [2.41167739693, 48.831216573] NaN \n", - "\n", - " fields.complementadresse \n", - "0 NaN \n", - "1 14-09 \n", - "2 16-13 \n", - "3 148 \n", - "4 12-13 \n", + "0 M 1 2002349.0 Pinus \n", + "1 M 1 2002354.0 Fagus \n", + "2 M 1 132669.0 Fagus \n", + "3 M 1 305507.0 Acer \n", + "4 M 1 2002389.0 Platanus \n", + "\n", + " fields.complementadresse fields.typeemplacement fields.dateplantation \\\n", + "0 16-08 Arbre 1882-01-01T00:09:21+00:00 \n", + "1 16-17 Arbre 1868-01-01T00:09:21+00:00 \n", + "2 NaN Arbre 1905-01-01T00:09:21+00:00 \n", + "3 08-01 Arbre 1700-01-01T00:09:21+00:00 \n", + "4 16-10 Arbre 1872-01-01T00:09:21+00:00 \n", + "\n", + " geometry.type geometry.coordinates fields.varieteoucultivar \n", + "0 Point [2.2417737512, 48.8630096006] NaN \n", + "1 Point [2.24886478886, 48.8731110898] ''Pendula'' \n", + "2 Point [2.35366916319, 48.8448875264] ''Tortuosa'' \n", + "3 Point [2.30647417371, 48.8787029872] NaN \n", + "4 Point [2.25294211329, 48.8671184424] NaN \n", "\n", "[5 rows x 24 columns]" ] @@ -280,7 +261,7 @@ "from pandas import json_normalize\n", "\n", "# Normalize the data\n", - "df = json_normalize(data_json)\n", + "df = json_normalize(data_json['records'])\n", "df.head()" ] }, @@ -323,13 +304,13 @@ " ...\n", " fields.idbase\n", " fields.genre\n", + " fields.complementadresse\n", " fields.typeemplacement\n", " fields.dateplantation\n", " geometry.type\n", " geometry.coordinates\n", " fields.varieteoucultivar\n", - " fields.complementadresse\n", - " lng\n", + " lon\n", " lat\n", " \n", " \n", @@ -337,122 +318,122 @@ " \n", " 0\n", " arbresremarquablesparis\n", - " 29a0ea77b5379a1e15b5c34d0ed8bbadb9f5789c\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.8542732882, 2.33573525468]\n", - " Paulownia\n", - " 7493\n", - " 000101001\n", - " PARIS 6E ARRDT\n", - " 295.0\n", - " 20.0\n", + " a236960f295288698ca57fdccfd9825547ffb4d0\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8630096006, 2.2417737512]\n", + " Pin\n", + " 49884\n", + " 000901001\n", + " BOIS DE BOULOGNE\n", + " 241.0\n", + " 30.0\n", " ...\n", - " 216766.0\n", - " Paulownia\n", + " 2002349.0\n", + " Pinus\n", + " 16-08\n", " Arbre\n", - " 1999-01-25T01:00:00+00:00\n", + " 1882-01-01T00:09:21+00:00\n", " Point\n", - " [2.33573525468, 48.8542732882]\n", - " NaN\n", + " [2.2417737512, 48.8630096006]\n", " NaN\n", - " 2.335735\n", - " 48.854273\n", + " 2.241774\n", + " 48.863010\n", " \n", " \n", " 1\n", " arbresremarquablesparis\n", - " 24ab994e9b2b51c4a27394ec52f038c4faa5fc1c\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.8217882346, 2.3228497157]\n", + " d7635937c3636051c99963676d3a97dd4d9dd8c9\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8731110898, 2.24886478886]\n", " Hêtre\n", - " 57013\n", - " 00000174\n", - " PARIS 14E ARRDT\n", - " 310.0\n", - " 15.0\n", + " 52761\n", + " 000701004\n", + " BOIS DE BOULOGNE\n", + " 231.0\n", + " 16.0\n", " ...\n", - " 121632.0\n", + " 2002354.0\n", " Fagus\n", + " 16-17\n", " Arbre\n", - " 1700-01-01T00:09:21+00:00\n", + " 1868-01-01T00:09:21+00:00\n", " Point\n", - " [2.3228497157, 48.8217882346]\n", - " ''Atropunicea''\n", - " 14-09\n", - " 2.322850\n", - " 48.821788\n", + " [2.24886478886, 48.8731110898]\n", + " ''Pendula''\n", + " 2.248865\n", + " 48.873111\n", " \n", " \n", " 2\n", " arbresremarquablesparis\n", - " 2138bdaedd6c46f681ff30904f8017a292fc2b3b\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.8648326277, 2.25217460792]\n", - " Plaqueminier\n", - " 58739\n", - " 000501002\n", - " BOIS DE BOULOGNE\n", - " 146.0\n", - " 15.0\n", + " 460e8eccd3ebc816f16317702223bf406e7d75e6\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8448875264, 2.35366916319]\n", + " Hêtre\n", + " 62456\n", + " 00000075\n", + " PARIS 5E ARRDT\n", + " 85.0\n", + " 3.0\n", " ...\n", - " 2002388.0\n", - " Diospyros\n", + " 132669.0\n", + " Fagus\n", + " NaN\n", " Arbre\n", - " 1897-01-01T00:09:21+00:00\n", + " 1905-01-01T00:09:21+00:00\n", " Point\n", - " [2.25217460792, 48.8648326277]\n", - " NaN\n", - " 16-13\n", - " 2.252175\n", - " 48.864833\n", + " [2.35366916319, 48.8448875264]\n", + " ''Tortuosa''\n", + " 2.353669\n", + " 48.844888\n", " \n", " \n", " 3\n", " arbresremarquablesparis\n", - " 63bc7232fe659c4585923d9e65e5275418c359b9\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.8564741998, 2.39461847904]\n", - " Platane\n", - " 70026\n", - " 001802036\n", - " PARIS 20E ARRDT\n", - " 407.0\n", - " 23.0\n", + " b60b80e0f8327afba6d0c513b0b6b85639e516b6\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8787029872, 2.30647417371]\n", + " Erable\n", + " 68801\n", + " 00NO0144\n", + " PARIS 8E ARRDT\n", + " 450.0\n", + " 28.0\n", " ...\n", - " 223748.0\n", - " Platanus\n", + " 305507.0\n", + " Acer\n", + " 08-01\n", " Arbre\n", " 1700-01-01T00:09:21+00:00\n", " Point\n", - " [2.39461847904, 48.8564741998]\n", + " [2.30647417371, 48.8787029872]\n", " NaN\n", - " 148\n", - " 2.394618\n", - " 48.856474\n", + " 2.306474\n", + " 48.878703\n", " \n", " \n", " 4\n", " arbresremarquablesparis\n", - " 602c7b2fd878c56e5efc532739abb28d86d9f365\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.831216573, 2.41167739693]\n", - " Cryptomeria\n", - " 85651\n", - " 12-13\n", - " BOIS DE VINCENNES\n", - " 122.0\n", - " 13.0\n", + " 47e163d6f0536bc9e26ba2993447c6f50b14a6f6\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8671184424, 2.25294211329]\n", + " Platane\n", + " 82341\n", + " 001201001\n", + " BOIS DE BOULOGNE\n", + " 534.0\n", + " 45.0\n", " ...\n", - " 2002359.0\n", - " Cryptomeria\n", + " 2002389.0\n", + " Platanus\n", + " 16-10\n", " Arbre\n", - " 1893-01-01T00:09:21+00:00\n", + " 1872-01-01T00:09:21+00:00\n", " Point\n", - " [2.41167739693, 48.831216573]\n", + " [2.25294211329, 48.8671184424]\n", " NaN\n", - " 12-13\n", - " 2.411677\n", - " 48.831217\n", + " 2.252942\n", + " 48.867118\n", " \n", " \n", "\n", @@ -461,53 +442,53 @@ ], "text/plain": [ " datasetid recordid \\\n", - "0 arbresremarquablesparis 29a0ea77b5379a1e15b5c34d0ed8bbadb9f5789c \n", - "1 arbresremarquablesparis 24ab994e9b2b51c4a27394ec52f038c4faa5fc1c \n", - "2 arbresremarquablesparis 2138bdaedd6c46f681ff30904f8017a292fc2b3b \n", - "3 arbresremarquablesparis 63bc7232fe659c4585923d9e65e5275418c359b9 \n", - "4 arbresremarquablesparis 602c7b2fd878c56e5efc532739abb28d86d9f365 \n", + "0 arbresremarquablesparis a236960f295288698ca57fdccfd9825547ffb4d0 \n", + "1 arbresremarquablesparis d7635937c3636051c99963676d3a97dd4d9dd8c9 \n", + "2 arbresremarquablesparis 460e8eccd3ebc816f16317702223bf406e7d75e6 \n", + "3 arbresremarquablesparis b60b80e0f8327afba6d0c513b0b6b85639e516b6 \n", + "4 arbresremarquablesparis 47e163d6f0536bc9e26ba2993447c6f50b14a6f6 \n", "\n", " record_timestamp fields.geom_x_y \\\n", - "0 2020-05-08T10:45:25.056000+00:00 [48.8542732882, 2.33573525468] \n", - "1 2020-05-08T10:45:25.056000+00:00 [48.8217882346, 2.3228497157] \n", - "2 2020-05-08T10:45:25.056000+00:00 [48.8648326277, 2.25217460792] \n", - "3 2020-05-08T10:45:25.056000+00:00 [48.8564741998, 2.39461847904] \n", - "4 2020-05-08T10:45:25.056000+00:00 [48.831216573, 2.41167739693] \n", + "0 2019-10-04T10:00:41.063000+00:00 [48.8630096006, 2.2417737512] \n", + "1 2019-10-04T10:00:41.063000+00:00 [48.8731110898, 2.24886478886] \n", + "2 2019-10-04T10:00:41.063000+00:00 [48.8448875264, 2.35366916319] \n", + "3 2019-10-04T10:00:41.063000+00:00 [48.8787029872, 2.30647417371] \n", + "4 2019-10-04T10:00:41.063000+00:00 [48.8671184424, 2.25294211329] \n", "\n", " fields.libellefrancais fields.objectid fields.idemplacement \\\n", - "0 Paulownia 7493 000101001 \n", - "1 Hêtre 57013 00000174 \n", - "2 Plaqueminier 58739 000501002 \n", - "3 Platane 70026 001802036 \n", - "4 Cryptomeria 85651 12-13 \n", + "0 Pin 49884 000901001 \n", + "1 Hêtre 52761 000701004 \n", + "2 Hêtre 62456 00000075 \n", + "3 Erable 68801 00NO0144 \n", + "4 Platane 82341 001201001 \n", "\n", " fields.arrondissement fields.circonferenceencm fields.hauteurenm ... \\\n", - "0 PARIS 6E ARRDT 295.0 20.0 ... \n", - "1 PARIS 14E ARRDT 310.0 15.0 ... \n", - "2 BOIS DE BOULOGNE 146.0 15.0 ... \n", - "3 PARIS 20E ARRDT 407.0 23.0 ... \n", - "4 BOIS DE VINCENNES 122.0 13.0 ... \n", - "\n", - " fields.idbase fields.genre fields.typeemplacement \\\n", - "0 216766.0 Paulownia Arbre \n", - "1 121632.0 Fagus Arbre \n", - "2 2002388.0 Diospyros Arbre \n", - "3 223748.0 Platanus Arbre \n", - "4 2002359.0 Cryptomeria Arbre \n", + "0 BOIS DE BOULOGNE 241.0 30.0 ... \n", + "1 BOIS DE BOULOGNE 231.0 16.0 ... \n", + "2 PARIS 5E ARRDT 85.0 3.0 ... \n", + "3 PARIS 8E ARRDT 450.0 28.0 ... \n", + "4 BOIS DE BOULOGNE 534.0 45.0 ... \n", + "\n", + " fields.idbase fields.genre fields.complementadresse fields.typeemplacement \\\n", + "0 2002349.0 Pinus 16-08 Arbre \n", + "1 2002354.0 Fagus 16-17 Arbre \n", + "2 132669.0 Fagus NaN Arbre \n", + "3 305507.0 Acer 08-01 Arbre \n", + "4 2002389.0 Platanus 16-10 Arbre \n", "\n", " fields.dateplantation geometry.type geometry.coordinates \\\n", - "0 1999-01-25T01:00:00+00:00 Point [2.33573525468, 48.8542732882] \n", - "1 1700-01-01T00:09:21+00:00 Point [2.3228497157, 48.8217882346] \n", - "2 1897-01-01T00:09:21+00:00 Point [2.25217460792, 48.8648326277] \n", - "3 1700-01-01T00:09:21+00:00 Point [2.39461847904, 48.8564741998] \n", - "4 1893-01-01T00:09:21+00:00 Point [2.41167739693, 48.831216573] \n", - "\n", - " fields.varieteoucultivar fields.complementadresse lng lat \n", - "0 NaN NaN 2.335735 48.854273 \n", - "1 ''Atropunicea'' 14-09 2.322850 48.821788 \n", - "2 NaN 16-13 2.252175 48.864833 \n", - "3 NaN 148 2.394618 48.856474 \n", - "4 NaN 12-13 2.411677 48.831217 \n", + "0 1882-01-01T00:09:21+00:00 Point [2.2417737512, 48.8630096006] \n", + "1 1868-01-01T00:09:21+00:00 Point [2.24886478886, 48.8731110898] \n", + "2 1905-01-01T00:09:21+00:00 Point [2.35366916319, 48.8448875264] \n", + "3 1700-01-01T00:09:21+00:00 Point [2.30647417371, 48.8787029872] \n", + "4 1872-01-01T00:09:21+00:00 Point [2.25294211329, 48.8671184424] \n", + "\n", + " fields.varieteoucultivar lon lat \n", + "0 NaN 2.241774 48.863010 \n", + "1 ''Pendula'' 2.248865 48.873111 \n", + "2 ''Tortuosa'' 2.353669 48.844888 \n", + "3 NaN 2.306474 48.878703 \n", + "4 NaN 2.252942 48.867118 \n", "\n", "[5 rows x 26 columns]" ] @@ -518,8 +499,8 @@ } ], "source": [ - "# Add Latitude and Longitude columns\n", - "df['lng'] = df.apply(lambda row: row['geometry.coordinates'][0], axis=1)\n", + "# Add longitude and latitude columns\n", + "df['lon'] = df.apply(lambda row: row['geometry.coordinates'][0], axis=1)\n", "df['lat'] = df.apply(lambda row: row['geometry.coordinates'][1], axis=1)\n", "df.head()" ] @@ -562,13 +543,13 @@ " fields.hauteurenm\n", " ...\n", " fields.genre\n", + " fields.complementadresse\n", " fields.typeemplacement\n", " fields.dateplantation\n", " geometry.type\n", " geometry.coordinates\n", " fields.varieteoucultivar\n", - " fields.complementadresse\n", - " lng\n", + " lon\n", " lat\n", " geometry\n", " \n", @@ -577,122 +558,122 @@ " \n", " 0\n", " arbresremarquablesparis\n", - " 29a0ea77b5379a1e15b5c34d0ed8bbadb9f5789c\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.8542732882, 2.33573525468]\n", - " Paulownia\n", - " 7493\n", - " 000101001\n", - " PARIS 6E ARRDT\n", - " 295.0\n", - " 20.0\n", + " a236960f295288698ca57fdccfd9825547ffb4d0\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8630096006, 2.2417737512]\n", + " Pin\n", + " 49884\n", + " 000901001\n", + " BOIS DE BOULOGNE\n", + " 241.0\n", + " 30.0\n", " ...\n", - " Paulownia\n", + " Pinus\n", + " 16-08\n", " Arbre\n", - " 1999-01-25T01:00:00+00:00\n", + " 1882-01-01T00:09:21+00:00\n", " Point\n", - " [2.33573525468, 48.8542732882]\n", + " [2.2417737512, 48.8630096006]\n", " NaN\n", - " NaN\n", - " 2.335735\n", - " 48.854273\n", - " POINT (2.33574 48.85427)\n", + " 2.241774\n", + " 48.863010\n", + " POINT (2.24177 48.86301)\n", " \n", " \n", " 1\n", " arbresremarquablesparis\n", - " 24ab994e9b2b51c4a27394ec52f038c4faa5fc1c\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.8217882346, 2.3228497157]\n", + " d7635937c3636051c99963676d3a97dd4d9dd8c9\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8731110898, 2.24886478886]\n", " Hêtre\n", - " 57013\n", - " 00000174\n", - " PARIS 14E ARRDT\n", - " 310.0\n", - " 15.0\n", + " 52761\n", + " 000701004\n", + " BOIS DE BOULOGNE\n", + " 231.0\n", + " 16.0\n", " ...\n", " Fagus\n", + " 16-17\n", " Arbre\n", - " 1700-01-01T00:09:21+00:00\n", + " 1868-01-01T00:09:21+00:00\n", " Point\n", - " [2.3228497157, 48.8217882346]\n", - " ''Atropunicea''\n", - " 14-09\n", - " 2.322850\n", - " 48.821788\n", - " POINT (2.32285 48.82179)\n", + " [2.24886478886, 48.8731110898]\n", + " ''Pendula''\n", + " 2.248865\n", + " 48.873111\n", + " POINT (2.24886 48.87311)\n", " \n", " \n", " 2\n", " arbresremarquablesparis\n", - " 2138bdaedd6c46f681ff30904f8017a292fc2b3b\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.8648326277, 2.25217460792]\n", - " Plaqueminier\n", - " 58739\n", - " 000501002\n", - " BOIS DE BOULOGNE\n", - " 146.0\n", - " 15.0\n", + " 460e8eccd3ebc816f16317702223bf406e7d75e6\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8448875264, 2.35366916319]\n", + " Hêtre\n", + " 62456\n", + " 00000075\n", + " PARIS 5E ARRDT\n", + " 85.0\n", + " 3.0\n", " ...\n", - " Diospyros\n", + " Fagus\n", + " NaN\n", " Arbre\n", - " 1897-01-01T00:09:21+00:00\n", + " 1905-01-01T00:09:21+00:00\n", " Point\n", - " [2.25217460792, 48.8648326277]\n", - " NaN\n", - " 16-13\n", - " 2.252175\n", - " 48.864833\n", - " POINT (2.25217 48.86483)\n", + " [2.35366916319, 48.8448875264]\n", + " ''Tortuosa''\n", + " 2.353669\n", + " 48.844888\n", + " POINT (2.35367 48.84489)\n", " \n", " \n", " 3\n", " arbresremarquablesparis\n", - " 63bc7232fe659c4585923d9e65e5275418c359b9\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.8564741998, 2.39461847904]\n", - " Platane\n", - " 70026\n", - " 001802036\n", - " PARIS 20E ARRDT\n", - " 407.0\n", - " 23.0\n", + " b60b80e0f8327afba6d0c513b0b6b85639e516b6\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8787029872, 2.30647417371]\n", + " Erable\n", + " 68801\n", + " 00NO0144\n", + " PARIS 8E ARRDT\n", + " 450.0\n", + " 28.0\n", " ...\n", - " Platanus\n", + " Acer\n", + " 08-01\n", " Arbre\n", " 1700-01-01T00:09:21+00:00\n", " Point\n", - " [2.39461847904, 48.8564741998]\n", + " [2.30647417371, 48.8787029872]\n", " NaN\n", - " 148\n", - " 2.394618\n", - " 48.856474\n", - " POINT (2.39462 48.85647)\n", + " 2.306474\n", + " 48.878703\n", + " POINT (2.30647 48.87870)\n", " \n", " \n", " 4\n", " arbresremarquablesparis\n", - " 602c7b2fd878c56e5efc532739abb28d86d9f365\n", - " 2020-05-08T10:45:25.056000+00:00\n", - " [48.831216573, 2.41167739693]\n", - " Cryptomeria\n", - " 85651\n", - " 12-13\n", - " BOIS DE VINCENNES\n", - " 122.0\n", - " 13.0\n", + " 47e163d6f0536bc9e26ba2993447c6f50b14a6f6\n", + " 2019-10-04T10:00:41.063000+00:00\n", + " [48.8671184424, 2.25294211329]\n", + " Platane\n", + " 82341\n", + " 001201001\n", + " BOIS DE BOULOGNE\n", + " 534.0\n", + " 45.0\n", " ...\n", - " Cryptomeria\n", + " Platanus\n", + " 16-10\n", " Arbre\n", - " 1893-01-01T00:09:21+00:00\n", + " 1872-01-01T00:09:21+00:00\n", " Point\n", - " [2.41167739693, 48.831216573]\n", + " [2.25294211329, 48.8671184424]\n", " NaN\n", - " 12-13\n", - " 2.411677\n", - " 48.831217\n", - " POINT (2.41168 48.83122)\n", + " 2.252942\n", + " 48.867118\n", + " POINT (2.25294 48.86712)\n", " \n", " \n", "\n", @@ -701,53 +682,53 @@ ], "text/plain": [ " datasetid recordid \\\n", - "0 arbresremarquablesparis 29a0ea77b5379a1e15b5c34d0ed8bbadb9f5789c \n", - "1 arbresremarquablesparis 24ab994e9b2b51c4a27394ec52f038c4faa5fc1c \n", - "2 arbresremarquablesparis 2138bdaedd6c46f681ff30904f8017a292fc2b3b \n", - "3 arbresremarquablesparis 63bc7232fe659c4585923d9e65e5275418c359b9 \n", - "4 arbresremarquablesparis 602c7b2fd878c56e5efc532739abb28d86d9f365 \n", + "0 arbresremarquablesparis a236960f295288698ca57fdccfd9825547ffb4d0 \n", + "1 arbresremarquablesparis d7635937c3636051c99963676d3a97dd4d9dd8c9 \n", + "2 arbresremarquablesparis 460e8eccd3ebc816f16317702223bf406e7d75e6 \n", + "3 arbresremarquablesparis b60b80e0f8327afba6d0c513b0b6b85639e516b6 \n", + "4 arbresremarquablesparis 47e163d6f0536bc9e26ba2993447c6f50b14a6f6 \n", "\n", " record_timestamp fields.geom_x_y \\\n", - "0 2020-05-08T10:45:25.056000+00:00 [48.8542732882, 2.33573525468] \n", - "1 2020-05-08T10:45:25.056000+00:00 [48.8217882346, 2.3228497157] \n", - "2 2020-05-08T10:45:25.056000+00:00 [48.8648326277, 2.25217460792] \n", - "3 2020-05-08T10:45:25.056000+00:00 [48.8564741998, 2.39461847904] \n", - "4 2020-05-08T10:45:25.056000+00:00 [48.831216573, 2.41167739693] \n", + "0 2019-10-04T10:00:41.063000+00:00 [48.8630096006, 2.2417737512] \n", + "1 2019-10-04T10:00:41.063000+00:00 [48.8731110898, 2.24886478886] \n", + "2 2019-10-04T10:00:41.063000+00:00 [48.8448875264, 2.35366916319] \n", + "3 2019-10-04T10:00:41.063000+00:00 [48.8787029872, 2.30647417371] \n", + "4 2019-10-04T10:00:41.063000+00:00 [48.8671184424, 2.25294211329] \n", "\n", " fields.libellefrancais fields.objectid fields.idemplacement \\\n", - "0 Paulownia 7493 000101001 \n", - "1 Hêtre 57013 00000174 \n", - "2 Plaqueminier 58739 000501002 \n", - "3 Platane 70026 001802036 \n", - "4 Cryptomeria 85651 12-13 \n", + "0 Pin 49884 000901001 \n", + "1 Hêtre 52761 000701004 \n", + "2 Hêtre 62456 00000075 \n", + "3 Erable 68801 00NO0144 \n", + "4 Platane 82341 001201001 \n", "\n", " fields.arrondissement fields.circonferenceencm fields.hauteurenm ... \\\n", - "0 PARIS 6E ARRDT 295.0 20.0 ... \n", - "1 PARIS 14E ARRDT 310.0 15.0 ... \n", - "2 BOIS DE BOULOGNE 146.0 15.0 ... \n", - "3 PARIS 20E ARRDT 407.0 23.0 ... \n", - "4 BOIS DE VINCENNES 122.0 13.0 ... \n", - "\n", - " fields.genre fields.typeemplacement fields.dateplantation \\\n", - "0 Paulownia Arbre 1999-01-25T01:00:00+00:00 \n", - "1 Fagus Arbre 1700-01-01T00:09:21+00:00 \n", - "2 Diospyros Arbre 1897-01-01T00:09:21+00:00 \n", - "3 Platanus Arbre 1700-01-01T00:09:21+00:00 \n", - "4 Cryptomeria Arbre 1893-01-01T00:09:21+00:00 \n", - "\n", - " geometry.type geometry.coordinates fields.varieteoucultivar \\\n", - "0 Point [2.33573525468, 48.8542732882] NaN \n", - "1 Point [2.3228497157, 48.8217882346] ''Atropunicea'' \n", - "2 Point [2.25217460792, 48.8648326277] NaN \n", - "3 Point [2.39461847904, 48.8564741998] NaN \n", - "4 Point [2.41167739693, 48.831216573] NaN \n", - "\n", - " fields.complementadresse lng lat geometry \n", - "0 NaN 2.335735 48.854273 POINT (2.33574 48.85427) \n", - "1 14-09 2.322850 48.821788 POINT (2.32285 48.82179) \n", - "2 16-13 2.252175 48.864833 POINT (2.25217 48.86483) \n", - "3 148 2.394618 48.856474 POINT (2.39462 48.85647) \n", - "4 12-13 2.411677 48.831217 POINT (2.41168 48.83122) \n", + "0 BOIS DE BOULOGNE 241.0 30.0 ... \n", + "1 BOIS DE BOULOGNE 231.0 16.0 ... \n", + "2 PARIS 5E ARRDT 85.0 3.0 ... \n", + "3 PARIS 8E ARRDT 450.0 28.0 ... \n", + "4 BOIS DE BOULOGNE 534.0 45.0 ... \n", + "\n", + " fields.genre fields.complementadresse fields.typeemplacement \\\n", + "0 Pinus 16-08 Arbre \n", + "1 Fagus 16-17 Arbre \n", + "2 Fagus NaN Arbre \n", + "3 Acer 08-01 Arbre \n", + "4 Platanus 16-10 Arbre \n", + "\n", + " fields.dateplantation geometry.type geometry.coordinates \\\n", + "0 1882-01-01T00:09:21+00:00 Point [2.2417737512, 48.8630096006] \n", + "1 1868-01-01T00:09:21+00:00 Point [2.24886478886, 48.8731110898] \n", + "2 1905-01-01T00:09:21+00:00 Point [2.35366916319, 48.8448875264] \n", + "3 1700-01-01T00:09:21+00:00 Point [2.30647417371, 48.8787029872] \n", + "4 1872-01-01T00:09:21+00:00 Point [2.25294211329, 48.8671184424] \n", + "\n", + " fields.varieteoucultivar lon lat geometry \n", + "0 NaN 2.241774 48.863010 POINT (2.24177 48.86301) \n", + "1 ''Pendula'' 2.248865 48.873111 POINT (2.24886 48.87311) \n", + "2 ''Tortuosa'' 2.353669 48.844888 POINT (2.35367 48.84489) \n", + "3 NaN 2.306474 48.878703 POINT (2.30647 48.87870) \n", + "4 NaN 2.252942 48.867118 POINT (2.25294 48.86712) \n", "\n", "[5 rows x 27 columns]" ] @@ -760,7 +741,7 @@ "source": [ "from geopandas import GeoDataFrame, points_from_xy\n", "\n", - "gdf = GeoDataFrame(df, geometry=points_from_xy(df['lng'], df['lat']))\n", + "gdf = GeoDataFrame(df, geometry=points_from_xy(df['lon'], df['lat']))\n", "gdf.head()" ] }, @@ -1470,21 +1451,21 @@ " return value.toLocaleString();\n", " }\n", "\n", - " function updateViewport(map) {\n", + " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", - " const mapInfo$ = document.getElementById('map-info');\n", + " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -1558,7 +1539,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -1573,12 +1554,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
    ${popupHTML}
    `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
    ${popupHTML}
    `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1862,7 +1847,8 @@ " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", - " updateViewport(map);\n", + " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", + " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", @@ -1936,7 +1922,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1982,7 +1968,7 @@ " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": "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", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": 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"title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\n", "widgets": []}];\n", + " const layers = [{"credentials": null, "data": 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"encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": 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"title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -2006,7 +1992,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 5, diff --git a/examples/data_management/load_data_from_shapefile.ipynb b/docs/examples/data_management/read_shapefile.ipynb similarity index 70% rename from examples/data_management/load_data_from_shapefile.ipynb rename to docs/examples/data_management/read_shapefile.ipynb index a71268a80..d532ce3ee 100644 --- a/examples/data_management/load_data_from_shapefile.ipynb +++ b/docs/examples/data_management/read_shapefile.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Load data from a Shapefile\n", + "## Read data from a Shapefile\n", "\n", - "This example illustrates how to load data from a shapefile using GeoPandas.\n", + "This example illustrates how to read data from a Shapefile using GeoPandas.\n", "A shapefile is a complex format, compared to CSV or GeoJSON, so [GeoPandas offers more options](https://geopandas.org/io.html#reading-spatial-data) for reading it." ] }, @@ -36,192 +36,85 @@ " \n", " \n", " \n", - " carbon_con\n", - " objectid\n", - " entity_id\n", - " latitude\n", - " longitude\n", - " audit_stat\n", - " legal_radi\n", - " illegal_ra\n", - " radius_umd\n", - " radius_for\n", - " ...\n", - " carbon_r_3\n", - " peat_for_2\n", - " peat_for_3\n", - " primary_10\n", - " primary_11\n", - " mill_name\n", - " parent_com\n", - " rspo_certi\n", - " date_updat\n", + " cartodb_id\n", + " field_1\n", + " name\n", + " address\n", + " revenue\n", " geometry\n", " \n", " \n", " \n", " \n", " 0\n", - " 460.463240\n", " 1\n", - " ID1822\n", - " -1.585833\n", - " 103.205556\n", - " ASA 1\n", " 0\n", - " 0.321764\n", - " 0.225759\n", - " 1099\n", - " ...\n", - " 1224\n", - " 0\n", - " 29\n", - " 0.004508\n", - " 0.135391\n", - " Muara Bulian Mill\n", - " PT Inti Indosawit Subur\n", - " yes\n", - " 14-Aug\n", - " POINT (103.20556 -1.58583)\n", + " Franklin Ave & Eastern Pkwy\n", + " 341 Eastern Pkwy,Brooklyn, NY 11238\n", + " 1321040.772\n", + " POINT (-73.95901 40.67109)\n", " \n", " \n", " 1\n", - " 2408.120739\n", " 2\n", - " ID1847\n", - " 0.077043\n", - " 102.030838\n", - " Renewal Certification\n", - " 0\n", - " 0.445960\n", - " 0.258855\n", - " 1979\n", - " ...\n", - " 2038\n", - " 7\n", - " 523\n", - " 0.017304\n", - " 0.321418\n", - " Pabrik Kelapa Sawit Batang Kulim POM\n", - " PT Musim Mas\n", - " yes\n", - " 14-Aug\n", - " POINT (102.03084 0.07704)\n", + " 1\n", + " 607 Brighton Beach Ave\n", + " 607 Brighton Beach Avenue,Brooklyn, NY 11235\n", + " 1268080.418\n", + " POINT (-73.96122 40.57796)\n", " \n", " \n", " 2\n", - " 273.474468\n", " 3\n", - " ID1720\n", - " 1.660222\n", - " 100.590611\n", - " Initial Certification\n", - " 0\n", - " 0.498531\n", - " 0.248520\n", - " 1432\n", - " ...\n", - " 1518\n", - " 0\n", - " 476\n", - " 0.000811\n", - " 0.193365\n", - " Kayangan and Kencana POM\n", - " PT Salim Ivomas Pratama Tbk\n", - " yes\n", - " 14-Aug\n", - " POINT (100.59061 1.66022)\n", + " 2\n", + " 65th St & 18th Ave\n", + " 6423 18th Avenue,Brooklyn, NY 11204\n", + " 1248133.699\n", + " POINT (-73.98976 40.61912)\n", " \n", " \n", " 3\n", - " 37544.098740\n", " 4\n", - " ID1945\n", - " -2.894444\n", - " 112.543611\n", - " ASA 1\n", - " 0\n", - " 0.662863\n", - " 0.186332\n", - " 226\n", - " ...\n", - " 269\n", - " 59\n", - " 66\n", - " 0.124882\n", - " 0.189169\n", - " PT Sarana Titian Permata POM\n", - " Wilmar International Ltd\n", - " yes\n", - " 14-Aug\n", - " POINT (112.54361 -2.89444)\n", + " 3\n", + " Bay Ridge Pkwy & 3rd Ave\n", + " 7419 3rd Avenue,Brooklyn, NY 11209\n", + " 1185702.676\n", + " POINT (-74.02744 40.63152)\n", " \n", " \n", " 4\n", - " 2475.128290\n", " 5\n", - " ID1553\n", - " 3.593333\n", - " 98.947222\n", - " Initial Certification\n", - " 0\n", - " 0.533668\n", - " 0.028972\n", - " 382\n", - " ...\n", - " 412\n", - " 0\n", - " 0\n", - " 0.000216\n", - " 0.009296\n", - " Adolina POM\n", - " PT Perkebunan Nusantara IV (PERSERO)\n", - " yes\n", - " 14-Aug\n", - " POINT (98.94722 3.59333)\n", + " 4\n", + " Caesar's Bay Shopping Center\n", + " 8973 Bay Parkway,Brooklyn, NY 11214\n", + " 1148427.411\n", + " POINT (-74.00098 40.59321)\n", " \n", " \n", "\n", - "

    5 rows × 73 columns

    \n", "" ], "text/plain": [ - " carbon_con objectid entity_id latitude longitude \\\n", - "0 460.463240 1 ID1822 -1.585833 103.205556 \n", - "1 2408.120739 2 ID1847 0.077043 102.030838 \n", - "2 273.474468 3 ID1720 1.660222 100.590611 \n", - "3 37544.098740 4 ID1945 -2.894444 112.543611 \n", - "4 2475.128290 5 ID1553 3.593333 98.947222 \n", - "\n", - " audit_stat legal_radi illegal_ra radius_umd radius_for ... \\\n", - "0 ASA 1 0 0.321764 0.225759 1099 ... \n", - "1 Renewal Certification 0 0.445960 0.258855 1979 ... \n", - "2 Initial Certification 0 0.498531 0.248520 1432 ... \n", - "3 ASA 1 0 0.662863 0.186332 226 ... \n", - "4 Initial Certification 0 0.533668 0.028972 382 ... \n", - "\n", - " carbon_r_3 peat_for_2 peat_for_3 primary_10 primary_11 \\\n", - "0 1224 0 29 0.004508 0.135391 \n", - "1 2038 7 523 0.017304 0.321418 \n", - "2 1518 0 476 0.000811 0.193365 \n", - "3 269 59 66 0.124882 0.189169 \n", - "4 412 0 0 0.000216 0.009296 \n", - "\n", - " mill_name parent_com \\\n", - "0 Muara Bulian Mill PT Inti Indosawit Subur \n", - "1 Pabrik Kelapa Sawit Batang Kulim POM PT Musim Mas \n", - "2 Kayangan and Kencana POM PT Salim Ivomas Pratama Tbk \n", - "3 PT Sarana Titian Permata POM Wilmar International Ltd \n", - "4 Adolina POM PT Perkebunan Nusantara IV (PERSERO) \n", - "\n", - " rspo_certi date_updat geometry \n", - "0 yes 14-Aug POINT (103.20556 -1.58583) \n", - "1 yes 14-Aug POINT (102.03084 0.07704) \n", - "2 yes 14-Aug POINT (100.59061 1.66022) \n", - "3 yes 14-Aug POINT (112.54361 -2.89444) \n", - "4 yes 14-Aug POINT (98.94722 3.59333) \n", - "\n", - "[5 rows x 73 columns]" + " cartodb_id field_1 name \\\n", + "0 1 0 Franklin Ave & Eastern Pkwy \n", + "1 2 1 607 Brighton Beach Ave \n", + "2 3 2 65th St & 18th Ave \n", + "3 4 3 Bay Ridge Pkwy & 3rd Ave \n", + "4 5 4 Caesar's Bay Shopping Center \n", + "\n", + " address revenue \\\n", + "0 341 Eastern Pkwy,Brooklyn, NY 11238 1321040.772 \n", + "1 607 Brighton Beach Avenue,Brooklyn, NY 11235 1268080.418 \n", + "2 6423 18th Avenue,Brooklyn, NY 11204 1248133.699 \n", + "3 7419 3rd Avenue,Brooklyn, NY 11209 1185702.676 \n", + "4 8973 Bay Parkway,Brooklyn, NY 11214 1148427.411 \n", + "\n", + " geometry \n", + "0 POINT (-73.95901 40.67109) \n", + "1 POINT (-73.96122 40.57796) \n", + "2 POINT (-73.98976 40.61912) \n", + "3 POINT (-74.02744 40.63152) \n", + "4 POINT (-74.00098 40.59321) " ] }, "execution_count": 1, @@ -232,7 +125,7 @@ "source": [ "from geopandas import read_file\n", "\n", - "gdf = read_file('https://libs.cartocdn.com/cartoframes/files/sustainable_palm_oil_production_mills.zip')\n", + "gdf = read_file('https://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn_geocoded.zip')\n", "gdf.head()" ] }, @@ -942,21 +835,21 @@ " return value.toLocaleString();\n", " }\n", "\n", - " function updateViewport(map) {\n", + " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", - " const mapInfo$ = document.getElementById('map-info');\n", + " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -1030,7 +923,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -1045,12 +938,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
    ${popupHTML}
    `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
    ${popupHTML}
    `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1334,7 +1231,8 @@ " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", - " updateViewport(map);\n", + " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", + " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", @@ -1408,7 +1306,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1449,12 +1347,12 @@ " .addEventListener('ready', () => {\n", " const basecolor = '';\n", " const basemap = 'Positron';\n", - " const bounds = [[-88.55981399975727, -10.316332999911578], [160.21845000011172, 15.582959000387486]];\n", + " const bounds = [[-74.03313, 40.57796], [-73.87015, 40.6915]];\n", " const camera = null;\n", " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": "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", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": 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"title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\n", "widgets": []}];\n", + " const layers = [{"credentials": null, "data": "H4sIAPkNyl8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAPkNyl8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -1478,7 +1376,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, diff --git a/docs/examples/data_management/upload_to_carto.ipynb b/docs/examples/data_management/upload_to_carto.ipynb new file mode 100644 index 000000000..2fd3e2e95 --- /dev/null +++ b/docs/examples/data_management/upload_to_carto.ipynb @@ -0,0 +1,171 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Upload data to CARTO\n", + "\n", + "This example illustrates how to upload local data to a CARTO account.\n", + "\n", + "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to reproduce this example._" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
    cartodb_idthe_geomfield_1nameaddressrevenue
    01POINT (-73.95901 40.67109)0Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381321040.772
    12POINT (-73.96122 40.57796)1607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351268080.418
    23POINT (-73.98976 40.61912)265th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041248133.699
    \n", + "
    " + ], + "text/plain": [ + " cartodb_id the_geom field_1 \\\n", + "0 1 POINT (-73.95901 40.67109) 0 \n", + "1 2 POINT (-73.96122 40.57796) 1 \n", + "2 3 POINT (-73.98976 40.61912) 2 \n", + "\n", + " name address \\\n", + "0 Franklin Ave & Eastern Pkwy 341 Eastern Pkwy,Brooklyn, NY 11238 \n", + "1 607 Brighton Beach Ave 607 Brighton Beach Avenue,Brooklyn, NY 11235 \n", + "2 65th St & 18th Ave 6423 18th Avenue,Brooklyn, NY 11204 \n", + "\n", + " revenue \n", + "0 1321040.772 \n", + "1 1268080.418 \n", + "2 1248133.699 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes import read_carto\n", + "\n", + "gdf = read_carto(\"SELECT * FROM starbucks_brooklyn WHERE revenue > 1200000\")\n", + "gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success! Data uploaded to table \"starbucks_brooklyn_filtered\" correctly\n" + ] + }, + { + "data": { + "text/plain": [ + "'starbucks_brooklyn_filtered'" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes import to_carto\n", + "\n", + "to_carto(gdf, 'starbucks_brooklyn_filtered', if_exists='replace')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/data_observatory/access_dataset.ipynb b/docs/examples/data_observatory/access_dataset.ipynb new file mode 100644 index 000000000..7e3ff1100 --- /dev/null +++ b/docs/examples/data_observatory/access_dataset.ipynb @@ -0,0 +1,8263 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Access a Dataset\n", + "\n", + "This example illustrates how to download a dataset from CARTO's Data Observatory. As datasets can be really big, we will show you how to apply [standard SQL filters](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax) and [spatial SQL filters](https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions) to download them. \n", + "\n", + "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to reproduce this example._" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As datasets may have a lot of columns, let's force pandas to display all the columns" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas\n", + "\n", + "pandas.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Choose a dataset\n", + "Learn how to discover a dataset through the catalog by checking the Data Discovery example.\n", + "\n", + "You can also choose a dataset from your subscriptions. Call `Catalog().subscriptions()` to list your active subscriptions." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'slug': 'acs_sociodemogr_8c2655e0',\n", + " 'name': 'Sociodemographics - United States of America (County, 2017, 5yrs)',\n", + " 'description': 'The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about the USA and its people. This dataset contains only a subset of the variables that have been deemed most relevant. More info: https://www.census.gov/programs-surveys/acs/about.html',\n", + " 'category_id': 'demographics',\n", + " 'country_id': 'usa',\n", + " 'data_source_id': 'sociodemographics',\n", + " 'provider_id': 'usa_acs',\n", + " 'geography_name': 'County - United States of America (2015)',\n", + " 'geography_description': 'Shoreline clipped TIGER/Line boundaries. More info: https://carto.com/blog/tiger-shoreline-clip/',\n", + " 'temporal_aggregation': '5yrs',\n", + " 'time_coverage': '[2013-01-01, 2018-01-01)',\n", + " 'update_frequency': 'yearly',\n", + " 'is_public_data': True,\n", + " 'lang': 'eng',\n", + " 'version': '20132017',\n", + " 'category_name': 'Demographics',\n", + " 'provider_name': 'American Community Survey',\n", + " 'geography_id': 'carto-do-public-data.carto.geography_usa_county_2015',\n", + " 'id': 'carto-do-public-data.usa_acs.demographics_sociodemographics_usa_county_2015_5yrs_20132017'}" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.data.observatory import Dataset\n", + "\n", + "dataset = Dataset.get('acs_sociodemogr_8c2655e0')\n", + "dataset.to_dict()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call `head()` to check a sample of the dataset and see the available columns." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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    9410214.02013201719.016.025.0176.0188.0426.0962.0330.00.00.00.014.0396.0893.01910.01643.0948.00.4153805.028.061.047.0191.079.0537.01529.0None0.0751.0154.051.0146.0None51.01070.051.044.022.013.018.049.041.032.051.0283.086.057.047.029.077.048.029.039831.0None99.054.083.015.064.00.061.064.055.064.032.01.026.044.058.049.049.069.031.065.061.044.022.032.062.044.022.0NoneNone0.00.0200.00.00.0191.00.0102.0101.014.01756.0None114.0130.077.0684.015.0512.0154.096.052.024178.01373.0None200.0101.081.024.018.034.00.020.020.041.05.054.050.020.073.056.077.077.020.052.050.077.090.0420.0717.030.0812.0426.011.00.0113.0739.0812.024.019.0341.037.014.026.080.020.02.00.0293.026.0265.0575.074.057.09.090.014.0297.032.046.08.034.026.015.013.022.0156.0None0.06.06.022.0111.0805.085.0257.0156.01870.00.00.00.021.020.00.068.01783.0493.0612.07.0863.06.012.015.0233.01903.0None15.040.01957.0109.0128.064.0169.05.013.00.0515.025.411965.099.073.0290.01906.022.038.062.0152.00.0194.0121.0None0.0108.0464.099.0233.011.0114.089.049.079.017.0110900.096.0126.021.083300.0162900.096.0123.010.072.0814.00.021.067.02.0
    \n", + "
    " + ], + "text/plain": [ + " geoid no_car do_date male_20 male_21 no_cars one_car poverty \\\n", + "0 30007 43.0 20132017 32.0 20.0 65.0 463.0 448.0 \n", + "1 19137 106.0 20132017 17.0 42.0 291.0 1298.0 1653.0 \n", + "2 20101 10.0 20132017 8.0 0.0 36.0 181.0 181.0 \n", + "3 48307 133.0 20132017 1.0 93.0 220.0 883.0 1296.0 \n", + "4 46123 66.0 20132017 8.0 21.0 118.0 544.0 1045.0 \n", + "5 29155 308.0 20132017 85.0 189.0 934.0 2639.0 4859.0 \n", + "6 21159 28.0 20132017 69.0 89.0 286.0 1464.0 3190.0 \n", + "7 31005 0.0 20132017 0.0 8.0 0.0 35.0 46.0 \n", + "8 01067 205.0 20132017 124.0 74.0 427.0 1953.0 2310.0 \n", + "9 41021 4.0 20132017 19.0 16.0 25.0 176.0 188.0 \n", + "\n", + " children male_pop two_cars asian_pop black_pop female_20 female_21 \\\n", + "0 1151.0 2896.0 887.0 0.0 4.0 0.0 0.0 \n", + "1 2349.0 4931.0 1826.0 0.0 0.0 42.0 103.0 \n", + "2 407.0 864.0 350.0 0.0 1.0 1.0 8.0 \n", + "3 1935.0 4171.0 1232.0 0.0 161.0 39.0 14.0 \n", + "4 1237.0 2781.0 649.0 0.0 0.0 9.0 59.0 \n", + "5 4597.0 8210.0 2043.0 0.0 4639.0 197.0 153.0 \n", + "6 2498.0 6744.0 1695.0 0.0 575.0 83.0 64.0 \n", + "7 132.0 204.0 46.0 0.0 0.0 1.0 1.0 \n", + "8 3581.0 8208.0 2346.0 0.0 4762.0 33.0 120.0 \n", + "9 426.0 962.0 330.0 0.0 0.0 0.0 14.0 \n", + "\n", + " in_school pop_25_64 total_pop white_pop female_pop gini_index \\\n", + "0 1017.0 2936.0 5755.0 5432.0 2859.0 0.4452 \n", + "1 2094.0 5037.0 10239.0 9640.0 5308.0 0.4270 \n", + "2 332.0 822.0 1702.0 1539.0 838.0 0.4473 \n", + "3 1623.0 4096.0 8145.0 5067.0 3974.0 0.4495 \n", + "4 1121.0 2688.0 5480.0 4500.0 2699.0 0.4633 \n", + "5 4301.0 8474.0 17344.0 11948.0 9134.0 0.4862 \n", + "6 2290.0 6830.0 12175.0 11250.0 5431.0 0.4422 \n", + "7 149.0 172.0 421.0 411.0 217.0 0.4498 \n", + "8 3517.0 8535.0 17110.0 11757.0 8902.0 0.4529 \n", + "9 396.0 893.0 1910.0 1643.0 948.0 0.4153 \n", + "\n", + " households male_60_61 male_62_64 median_age three_cars male_5_to_9 \\\n", + "0 2405.0 105.0 196.0 46.3 622.0 132.0 \n", + "1 4614.0 149.0 256.0 44.5 774.0 385.0 \n", + "2 828.0 10.0 27.0 41.5 133.0 26.0 \n", + "3 3143.0 240.0 134.0 43.5 510.0 370.0 \n", + "4 2424.0 133.0 141.0 46.5 639.0 135.0 \n", + "5 6875.0 225.0 238.0 37.7 871.0 566.0 \n", + "6 4316.0 213.0 261.0 39.0 645.0 326.0 \n", + "7 177.0 9.0 3.0 43.3 54.0 27.0 \n", + "8 6727.0 176.0 317.0 43.5 1373.0 459.0 \n", + "9 805.0 28.0 61.0 47.0 191.0 79.0 \n", + "\n", + " median_rent pop_16_over pop_widowed armed_forces employed_pop \\\n", + "0 550.0 4762.0 None 0.0 2621.0 \n", + "1 482.0 8243.0 None 11.0 4838.0 \n", + "2 345.0 1379.0 None 0.0 885.0 \n", + "3 471.0 6496.0 None 0.0 3658.0 \n", + "4 341.0 4362.0 None 0.0 2859.0 \n", + "5 344.0 13251.0 None 0.0 6326.0 \n", + "6 307.0 9909.0 None 0.0 3032.0 \n", + "7 388.0 298.0 None 0.0 177.0 \n", + "8 398.0 13972.0 None 10.0 6949.0 \n", + "9 537.0 1529.0 None 0.0 751.0 \n", + "\n", + " hispanic_pop male_under_5 mobile_homes pop_divorced female_5_to_9 \\\n", + "0 152.0 112.0 377.0 None 207.0 \n", + "1 371.0 259.0 178.0 None 310.0 \n", + "2 113.0 74.0 46.0 None 74.0 \n", + "3 2571.0 327.0 393.0 None 263.0 \n", + "4 18.0 140.0 496.0 None 125.0 \n", + "5 421.0 614.0 742.0 None 496.0 \n", + "6 226.0 323.0 1911.0 None 342.0 \n", + "7 0.0 3.0 22.0 None 21.0 \n", + "8 435.0 468.0 2311.0 None 438.0 \n", + "9 154.0 51.0 146.0 None 51.0 \n", + "\n", + " housing_units male_10_to_14 male_15_to_17 male_18_to_19 male_22_to_24 \\\n", + "0 2743.0 196.0 117.0 75.0 54.0 \n", + "1 5231.0 313.0 238.0 115.0 182.0 \n", + "2 991.0 46.0 54.0 22.0 45.0 \n", + "3 4302.0 212.0 220.0 33.0 111.0 \n", + "4 3085.0 222.0 141.0 50.0 127.0 \n", + "5 8178.0 747.0 421.0 226.0 282.0 \n", + "6 5240.0 432.0 217.0 125.0 481.0 \n", + "7 261.0 20.0 4.0 2.0 13.0 \n", + "8 9055.0 594.0 307.0 171.0 396.0 \n", + "9 1070.0 51.0 44.0 22.0 13.0 \n", + "\n", + " male_25_to_29 male_30_to_34 male_35_to_39 male_40_to_44 male_45_to_49 \\\n", + "0 135.0 131.0 160.0 163.0 184.0 \n", + "1 217.0 239.0 252.0 323.0 301.0 \n", + "2 59.0 26.0 76.0 49.0 56.0 \n", + "3 284.0 236.0 272.0 152.0 206.0 \n", + "4 129.0 143.0 155.0 116.0 164.0 \n", + "5 479.0 431.0 449.0 422.0 526.0 \n", + "6 485.0 720.0 476.0 592.0 430.0 \n", + "7 8.0 1.0 6.0 18.0 17.0 \n", + "8 369.0 350.0 658.0 418.0 528.0 \n", + "9 18.0 49.0 41.0 32.0 51.0 \n", + "\n", + " male_45_to_64 male_50_to_54 male_55_to_59 male_65_to_66 male_67_to_69 \\\n", + "0 908.0 210.0 213.0 63.0 171.0 \n", + "1 1449.0 366.0 377.0 148.0 162.0 \n", + "2 226.0 72.0 61.0 19.0 22.0 \n", + "3 1054.0 310.0 164.0 106.0 111.0 \n", + "4 881.0 238.0 205.0 34.0 116.0 \n", + "5 2156.0 567.0 600.0 227.0 234.0 \n", + "6 1631.0 433.0 294.0 123.0 142.0 \n", + "7 50.0 8.0 13.0 2.0 5.0 \n", + "8 2232.0 536.0 675.0 252.0 324.0 \n", + "9 283.0 86.0 57.0 47.0 29.0 \n", + "\n", + " male_70_to_74 male_75_to_79 male_80_to_84 median_income pop_separated \\\n", + "0 191.0 129.0 66.0 55295.0 None \n", + "1 193.0 146.0 137.0 43674.0 None \n", + "2 25.0 40.0 39.0 51765.0 None \n", + "3 296.0 94.0 139.0 42367.0 None \n", + "4 112.0 143.0 42.0 48409.0 None \n", + "5 239.0 164.0 153.0 32468.0 None \n", + "6 247.0 166.0 61.0 29239.0 None \n", + "7 8.0 10.0 17.0 41250.0 None \n", + "8 452.0 293.0 121.0 45569.0 None \n", + "9 77.0 48.0 29.0 39831.0 None \n", + "\n", + " amerindian_pop female_under_5 four_more_cars group_quarters \\\n", + "0 84.0 146.0 368.0 44.0 \n", + "1 40.0 335.0 425.0 210.0 \n", + "2 39.0 48.0 128.0 8.0 \n", + "3 1.0 154.0 298.0 154.0 \n", + "4 729.0 172.0 474.0 159.0 \n", + "5 18.0 671.0 388.0 202.0 \n", + "6 9.0 295.0 226.0 1441.0 \n", + "7 0.0 8.0 42.0 0.0 \n", + "8 5.0 452.0 628.0 289.0 \n", + "9 99.0 54.0 83.0 15.0 \n", + "\n", + " masters_degree other_race_pop unemployed_pop walked_to_work \\\n", + "0 293.0 0.0 112.0 135.0 \n", + "1 227.0 0.0 349.0 194.0 \n", + "2 69.0 0.0 32.0 32.0 \n", + "3 213.0 202.0 122.0 104.0 \n", + "4 112.0 0.0 52.0 132.0 \n", + "5 483.0 0.0 756.0 153.0 \n", + "6 225.0 0.0 552.0 34.0 \n", + "7 15.0 1.0 1.0 13.0 \n", + "8 835.0 0.0 496.0 48.0 \n", + "9 64.0 0.0 61.0 64.0 \n", + "\n", + " worked_at_home female_10_to_14 female_15_to_17 female_18_to_19 \\\n", + "0 205.0 124.0 117.0 123.0 \n", + "1 233.0 302.0 207.0 127.0 \n", + "2 27.0 47.0 38.0 35.0 \n", + "3 61.0 258.0 131.0 66.0 \n", + "4 223.0 203.0 99.0 46.0 \n", + "5 109.0 689.0 393.0 194.0 \n", + "6 8.0 334.0 229.0 101.0 \n", + "7 23.0 35.0 14.0 0.0 \n", + "8 176.0 511.0 352.0 162.0 \n", + "9 55.0 64.0 32.0 1.0 \n", + "\n", + " female_22_to_24 female_25_to_29 female_30_to_34 female_35_to_39 \\\n", + "0 90.0 91.0 113.0 165.0 \n", + "1 119.0 220.0 233.0 262.0 \n", + "2 13.0 40.0 29.0 48.0 \n", + "3 21.0 340.0 82.0 271.0 \n", + "4 65.0 122.0 123.0 133.0 \n", + "5 209.0 573.0 521.0 629.0 \n", + "6 118.0 303.0 308.0 384.0 \n", + "7 0.0 6.0 7.0 5.0 \n", + "8 326.0 415.0 465.0 527.0 \n", + "9 26.0 44.0 58.0 49.0 \n", + "\n", + " female_40_to_44 female_45_to_49 female_50_to_54 female_55_to_59 \\\n", + "0 153.0 218.0 250.0 196.0 \n", + "1 345.0 320.0 373.0 452.0 \n", + "2 53.0 44.0 45.0 63.0 \n", + "3 321.0 285.0 188.0 273.0 \n", + "4 97.0 173.0 242.0 190.0 \n", + "5 452.0 564.0 593.0 715.0 \n", + "6 316.0 401.0 419.0 466.0 \n", + "7 13.0 14.0 11.0 25.0 \n", + "8 598.0 547.0 617.0 586.0 \n", + "9 49.0 69.0 31.0 65.0 \n", + "\n", + " female_60_to_61 female_62_to_64 female_65_to_66 female_67_to_69 \\\n", + "0 116.0 137.0 74.0 114.0 \n", + "1 152.0 200.0 126.0 168.0 \n", + "2 19.0 45.0 22.0 14.0 \n", + "3 121.0 217.0 147.0 117.0 \n", + "4 108.0 76.0 87.0 84.0 \n", + "5 199.0 291.0 159.0 271.0 \n", + "6 74.0 255.0 158.0 176.0 \n", + "7 3.0 5.0 9.0 3.0 \n", + "8 222.0 531.0 166.0 404.0 \n", + "9 61.0 44.0 22.0 32.0 \n", + "\n", + " female_70_to_74 female_75_to_79 female_80_to_84 pop_15_and_over \\\n", + "0 173.0 158.0 52.0 None \n", + "1 273.0 233.0 177.0 None \n", + "2 40.0 42.0 38.0 None \n", + "3 228.0 156.0 134.0 None \n", + "4 114.0 174.0 55.0 None \n", + "5 397.0 291.0 254.0 None \n", + "6 203.0 158.0 150.0 None \n", + "7 10.0 15.0 10.0 None \n", + "8 567.0 353.0 222.0 None \n", + "9 62.0 44.0 22.0 None \n", + "\n", + " pop_now_married asian_male_45_54 asian_male_55_64 bachelors_degree \\\n", + "0 None 0.0 0.0 866.0 \n", + "1 None 0.0 0.0 867.0 \n", + "2 None 0.0 0.0 206.0 \n", + "3 None 0.0 0.0 671.0 \n", + "4 None 0.0 0.0 643.0 \n", + "5 None 0.0 0.0 815.0 \n", + "6 None 0.0 0.0 373.0 \n", + "7 None 0.0 0.0 60.0 \n", + "8 None 0.0 0.0 1087.0 \n", + "9 None 0.0 0.0 200.0 \n", + "\n", + " black_male_45_54 black_male_55_64 commute_5_9_mins commuters_by_bus \\\n", + "0 0.0 0.0 359.0 0.0 \n", + "1 0.0 0.0 1023.0 21.0 \n", + "2 0.0 0.0 178.0 0.0 \n", + "3 18.0 0.0 867.0 2.0 \n", + "4 0.0 0.0 878.0 37.0 \n", + "5 239.0 230.0 1273.0 23.0 \n", + "6 159.0 17.0 195.0 0.0 \n", + "7 0.0 0.0 16.0 0.0 \n", + "8 291.0 314.0 605.0 0.0 \n", + "9 0.0 0.0 191.0 0.0 \n", + "\n", + " in_grades_1_to_4 in_grades_5_to_8 male_85_and_over not_hispanic_pop \\\n", + "0 253.0 252.0 41.0 5603.0 \n", + "1 569.0 512.0 114.0 9868.0 \n", + "2 64.0 79.0 8.0 1589.0 \n", + "3 483.0 334.0 60.0 5574.0 \n", + "4 272.0 269.0 66.0 5462.0 \n", + "5 877.0 1260.0 126.0 16923.0 \n", + "6 554.0 575.0 39.0 11949.0 \n", + "7 32.0 48.0 2.0 421.0 \n", + "8 750.0 892.0 146.0 16675.0 \n", + "9 102.0 101.0 14.0 1756.0 \n", + "\n", + " pop_5_years_over white_male_45_54 white_male_55_64 associates_degree \\\n", + "0 None 369.0 463.0 268.0 \n", + "1 None 628.0 782.0 926.0 \n", + "2 None 121.0 96.0 135.0 \n", + "3 None 327.0 391.0 217.0 \n", + "4 None 310.0 392.0 319.0 \n", + "5 None 824.0 828.0 660.0 \n", + "6 None 657.0 726.0 728.0 \n", + "7 None 24.0 25.0 33.0 \n", + "8 None 757.0 839.0 925.0 \n", + "9 None 114.0 130.0 77.0 \n", + "\n", + " commuters_16_over dwellings_2_units family_households hispanic_any_race \\\n", + "0 2287.0 15.0 1552.0 152.0 \n", + "1 4560.0 177.0 3080.0 371.0 \n", + "2 817.0 0.0 484.0 113.0 \n", + "3 3389.0 229.0 2164.0 2571.0 \n", + "4 2592.0 24.0 1473.0 18.0 \n", + "5 6067.0 517.0 4342.0 421.0 \n", + "6 2791.0 36.0 2940.0 226.0 \n", + "7 146.0 0.0 96.0 0.0 \n", + "8 6689.0 291.0 4545.0 435.0 \n", + "9 684.0 15.0 512.0 154.0 \n", + "\n", + " in_grades_9_to_12 income_less_10000 income_per_capita pop_25_years_over \\\n", + "0 336.0 96.0 32268.0 4210.0 \n", + "1 530.0 282.0 25005.0 7143.0 \n", + "2 67.0 62.0 29768.0 1163.0 \n", + "3 453.0 228.0 23398.0 5832.0 \n", + "4 273.0 239.0 27613.0 3858.0 \n", + "5 1082.0 956.0 18883.0 11212.0 \n", + "6 481.0 782.0 14914.0 8547.0 \n", + "7 18.0 6.0 21799.0 264.0 \n", + "8 844.0 552.0 23983.0 12123.0 \n", + "9 96.0 52.0 24178.0 1373.0 \n", + "\n", + " pop_never_married bachelors_degree_2 commute_10_14_mins \\\n", + "0 None 866.0 337.0 \n", + "1 None 867.0 568.0 \n", + "2 None 206.0 98.0 \n", + "3 None 671.0 535.0 \n", + "4 None 643.0 347.0 \n", + "5 None 815.0 792.0 \n", + "6 None 373.0 117.0 \n", + "7 None 60.0 9.0 \n", + "8 None 1087.0 694.0 \n", + "9 None 200.0 101.0 \n", + "\n", + " commute_15_19_mins commute_20_24_mins commute_25_29_mins \\\n", + "0 132.0 225.0 159.0 \n", + "1 487.0 485.0 205.0 \n", + "2 93.0 64.0 38.0 \n", + "3 412.0 410.0 23.0 \n", + "4 158.0 167.0 120.0 \n", + "5 998.0 913.0 366.0 \n", + "6 575.0 297.0 111.0 \n", + "7 3.0 16.0 4.0 \n", + "8 858.0 1025.0 749.0 \n", + "9 81.0 24.0 18.0 \n", + "\n", + " commute_30_34_mins commute_35_39_mins commute_35_44_mins \\\n", + "0 162.0 214.0 247.0 \n", + "1 236.0 39.0 82.0 \n", + "2 47.0 15.0 15.0 \n", + "3 202.0 71.0 117.0 \n", + "4 92.0 17.0 56.0 \n", + "5 587.0 78.0 167.0 \n", + "6 235.0 220.0 384.0 \n", + "7 12.0 3.0 17.0 \n", + "8 1067.0 154.0 395.0 \n", + "9 34.0 0.0 20.0 \n", + "\n", + " commute_40_44_mins commute_45_59_mins commute_60_89_mins \\\n", + "0 33.0 271.0 150.0 \n", + "1 43.0 292.0 205.0 \n", + "2 0.0 9.0 35.0 \n", + "3 46.0 75.0 52.0 \n", + "4 39.0 55.0 143.0 \n", + "5 89.0 181.0 87.0 \n", + "6 164.0 358.0 325.0 \n", + "7 14.0 15.0 3.0 \n", + "8 241.0 445.0 301.0 \n", + "9 20.0 41.0 5.0 \n", + "\n", + " female_85_and_over income_10000_14999 income_15000_19999 \\\n", + "0 42.0 135.0 96.0 \n", + "1 229.0 419.0 298.0 \n", + "2 32.0 42.0 50.0 \n", + "3 148.0 265.0 288.0 \n", + "4 143.0 174.0 110.0 \n", + "5 223.0 689.0 583.0 \n", + "6 94.0 288.0 476.0 \n", + "7 1.0 21.0 25.0 \n", + "8 288.0 504.0 566.0 \n", + "9 54.0 50.0 20.0 \n", + "\n", + " income_20000_24999 income_25000_29999 income_30000_34999 \\\n", + "0 101.0 88.0 84.0 \n", + "1 345.0 218.0 343.0 \n", + "2 67.0 67.0 35.0 \n", + "3 127.0 156.0 203.0 \n", + "4 132.0 111.0 119.0 \n", + "5 512.0 504.0 377.0 \n", + "6 430.0 203.0 138.0 \n", + "7 13.0 4.0 9.0 \n", + "8 332.0 278.0 457.0 \n", + "9 73.0 56.0 77.0 \n", + "\n", + " income_35000_39999 income_40000_44999 income_45000_49999 \\\n", + "0 210.0 166.0 76.0 \n", + "1 211.0 226.0 222.0 \n", + "2 32.0 35.0 12.0 \n", + "3 207.0 137.0 124.0 \n", + "4 168.0 91.0 124.0 \n", + "5 385.0 382.0 336.0 \n", + "6 224.0 365.0 182.0 \n", + "7 8.0 12.0 10.0 \n", + "8 308.0 323.0 412.0 \n", + "9 77.0 20.0 52.0 \n", + "\n", + " income_50000_59999 income_60000_74999 income_75000_99999 \\\n", + "0 305.0 241.0 267.0 \n", + "1 433.0 515.0 485.0 \n", + "2 79.0 109.0 139.0 \n", + "3 259.0 329.0 385.0 \n", + "4 227.0 270.0 279.0 \n", + "5 544.0 392.0 555.0 \n", + "6 271.0 373.0 356.0 \n", + "7 14.0 22.0 20.0 \n", + "8 616.0 694.0 614.0 \n", + "9 50.0 77.0 90.0 \n", + "\n", + " married_households not_in_labor_force not_us_citizen_pop \\\n", + "0 1348.0 2029.0 20.0 \n", + "1 2221.0 3045.0 136.0 \n", + "2 401.0 462.0 12.0 \n", + "3 1592.0 2716.0 207.0 \n", + "4 1217.0 1451.0 27.0 \n", + "5 2758.0 6169.0 97.0 \n", + "6 2311.0 6325.0 34.0 \n", + "7 87.0 120.0 2.0 \n", + "8 3625.0 6517.0 169.0 \n", + "9 420.0 717.0 30.0 \n", + "\n", + " pop_in_labor_force high_school_diploma hispanic_male_45_54 \\\n", + "0 2733.0 1366.0 0.0 \n", + "1 5198.0 2447.0 39.0 \n", + "2 917.0 302.0 6.0 \n", + "3 3780.0 1597.0 164.0 \n", + "4 2911.0 1367.0 0.0 \n", + "5 7082.0 3602.0 19.0 \n", + "6 3584.0 2353.0 33.0 \n", + "7 178.0 73.0 0.0 \n", + "8 7455.0 3398.0 13.0 \n", + "9 812.0 426.0 11.0 \n", + "\n", + " hispanic_male_55_64 occupation_services workers_16_and_over \\\n", + "0 5.0 461.0 2492.0 \n", + "1 0.0 824.0 4793.0 \n", + "2 2.0 165.0 844.0 \n", + "3 147.0 770.0 3450.0 \n", + "4 0.0 393.0 2815.0 \n", + "5 19.0 1441.0 6176.0 \n", + "6 17.0 428.0 2799.0 \n", + "7 0.0 17.0 169.0 \n", + "8 0.0 1319.0 6865.0 \n", + "9 0.0 113.0 739.0 \n", + "\n", + " civilian_labor_force commute_60_more_mins commute_90_more_mins \\\n", + "0 2733.0 203.0 53.0 \n", + "1 5187.0 319.0 114.0 \n", + "2 917.0 43.0 8.0 \n", + "3 3780.0 133.0 81.0 \n", + "4 2911.0 175.0 32.0 \n", + "5 7082.0 159.0 72.0 \n", + "6 3584.0 447.0 122.0 \n", + "7 178.0 6.0 3.0 \n", + "8 7445.0 431.0 130.0 \n", + "9 812.0 24.0 19.0 \n", + "\n", + " commute_less_10_mins commuters_by_carpool employed_information \\\n", + "0 551.0 267.0 31.0 \n", + "1 1886.0 453.0 153.0 \n", + "2 410.0 42.0 10.0 \n", + "3 1482.0 424.0 17.0 \n", + "4 1422.0 247.0 30.0 \n", + "5 1904.0 594.0 26.0 \n", + "6 267.0 460.0 30.0 \n", + "7 64.0 24.0 1.0 \n", + "8 1025.0 513.0 74.0 \n", + "9 341.0 37.0 14.0 \n", + "\n", + " in_undergrad_college income_100000_124999 income_125000_149999 \\\n", + "0 79.0 170.0 82.0 \n", + "1 176.0 317.0 121.0 \n", + "2 48.0 45.0 11.0 \n", + "3 70.0 176.0 114.0 \n", + "4 102.0 226.0 37.0 \n", + "5 492.0 347.0 96.0 \n", + "6 292.0 120.0 50.0 \n", + "7 17.0 7.0 1.0 \n", + "8 595.0 379.0 287.0 \n", + "9 26.0 80.0 20.0 \n", + "\n", + " income_150000_199999 male_male_households nonfamily_households \\\n", + "0 186.0 0.0 853.0 \n", + "1 93.0 0.0 1534.0 \n", + "2 18.0 0.0 344.0 \n", + "3 69.0 0.0 979.0 \n", + "4 32.0 0.0 951.0 \n", + "5 143.0 6.0 2533.0 \n", + "6 58.0 0.0 1376.0 \n", + "7 2.0 0.0 81.0 \n", + "8 257.0 10.0 2182.0 \n", + "9 2.0 0.0 293.0 \n", + "\n", + " rent_over_50_percent vacant_housing_units commuters_drove_alone \\\n", + "0 47.0 338.0 1852.0 \n", + "1 246.0 617.0 3813.0 \n", + "2 16.0 163.0 732.0 \n", + "3 227.0 1159.0 2857.0 \n", + "4 128.0 661.0 2163.0 \n", + "5 641.0 1303.0 5259.0 \n", + "6 267.0 924.0 2292.0 \n", + "7 8.0 84.0 109.0 \n", + "8 185.0 2328.0 5900.0 \n", + "9 26.0 265.0 575.0 \n", + "\n", + " employed_construction employed_retail_trade income_200000_or_more \\\n", + "0 370.0 180.0 102.0 \n", + "1 309.0 641.0 86.0 \n", + "2 112.0 73.0 25.0 \n", + "3 171.0 383.0 76.0 \n", + "4 271.0 363.0 85.0 \n", + "5 194.0 741.0 74.0 \n", + "6 197.0 520.0 0.0 \n", + "7 15.0 13.0 3.0 \n", + "8 386.0 769.0 148.0 \n", + "9 74.0 57.0 9.0 \n", + "\n", + " less_one_year_college male_45_64_grade_9_12 one_year_more_college \\\n", + "0 261.0 24.0 683.0 \n", + "1 716.0 111.0 935.0 \n", + "2 106.0 7.0 207.0 \n", + "3 500.0 201.0 1092.0 \n", + "4 196.0 31.0 571.0 \n", + "5 705.0 408.0 1354.0 \n", + "6 360.0 347.0 1107.0 \n", + "7 26.0 0.0 34.0 \n", + "8 828.0 237.0 1843.0 \n", + "9 90.0 14.0 297.0 \n", + "\n", + " rent_10_to_15_percent rent_15_to_20_percent rent_20_to_25_percent \\\n", + "0 94.0 22.0 52.0 \n", + "1 180.0 193.0 164.0 \n", + "2 27.0 16.0 18.0 \n", + "3 64.0 100.0 59.0 \n", + "4 87.0 63.0 58.0 \n", + "5 263.0 285.0 371.0 \n", + "6 109.0 22.0 83.0 \n", + "7 13.0 3.0 1.0 \n", + "8 149.0 96.0 162.0 \n", + "9 32.0 46.0 8.0 \n", + "\n", + " rent_25_to_30_percent rent_30_to_35_percent rent_35_to_40_percent \\\n", + "0 15.0 45.0 5.0 \n", + "1 144.0 51.0 77.0 \n", + "2 13.0 19.0 8.0 \n", + "3 44.0 46.0 25.0 \n", + "4 79.0 2.0 37.0 \n", + "5 367.0 188.0 167.0 \n", + "6 98.0 107.0 49.0 \n", + "7 3.0 2.0 5.0 \n", + "8 91.0 35.0 109.0 \n", + "9 34.0 26.0 15.0 \n", + "\n", + " rent_40_to_50_percent rent_under_10_percent sales_office_employed \\\n", + "0 32.0 6.0 404.0 \n", + "1 168.0 75.0 1081.0 \n", + "2 0.0 10.0 185.0 \n", + "3 24.0 19.0 809.0 \n", + "4 104.0 64.0 484.0 \n", + "5 267.0 155.0 1164.0 \n", + "6 35.0 71.0 894.0 \n", + "7 0.0 0.0 20.0 \n", + "8 75.0 51.0 1347.0 \n", + "9 13.0 22.0 156.0 \n", + "\n", + " speak_spanish_at_home two_or_more_races_pop dwellings_3_to_4_units \\\n", + "0 None 83.0 41.0 \n", + "1 None 188.0 291.0 \n", + "2 None 3.0 33.0 \n", + "3 None 143.0 110.0 \n", + "4 None 233.0 108.0 \n", + "5 None 316.0 513.0 \n", + "6 None 107.0 53.0 \n", + "7 None 9.0 0.0 \n", + "8 None 151.0 163.0 \n", + "9 None 0.0 6.0 \n", + "\n", + " dwellings_5_to_9_units employed_manufacturing male_45_64_high_school \\\n", + "0 49.0 215.0 287.0 \n", + "1 101.0 870.0 680.0 \n", + "2 0.0 6.0 82.0 \n", + "3 16.0 214.0 458.0 \n", + "4 70.0 41.0 387.0 \n", + "5 49.0 1328.0 866.0 \n", + "6 105.0 45.0 736.0 \n", + "7 0.0 13.0 21.0 \n", + "8 0.0 787.0 918.0 \n", + "9 6.0 22.0 111.0 \n", + "\n", + " occupied_housing_units male_45_64_some_college mortgaged_housing_units \\\n", + "0 2405.0 247.0 1122.0 \n", + "1 4614.0 264.0 1552.0 \n", + "2 828.0 61.0 306.0 \n", + "3 3143.0 203.0 889.0 \n", + "4 2424.0 140.0 663.0 \n", + "5 6875.0 337.0 1652.0 \n", + "6 4316.0 218.0 958.0 \n", + "7 177.0 7.0 25.0 \n", + "8 6727.0 540.0 2932.0 \n", + "9 805.0 85.0 257.0 \n", + "\n", + " occupation_sales_office population_3_years_over asian_including_hispanic \\\n", + "0 404.0 5553.0 0.0 \n", + "1 1081.0 9869.0 0.0 \n", + "2 185.0 1623.0 0.0 \n", + "3 809.0 7863.0 0.0 \n", + "4 484.0 5334.0 0.0 \n", + "5 1164.0 16514.0 0.0 \n", + "6 894.0 11860.0 0.0 \n", + "7 20.0 415.0 0.0 \n", + "8 1347.0 16615.0 0.0 \n", + "9 156.0 1870.0 0.0 \n", + "\n", + " black_including_hispanic dwellings_10_to_19_units \\\n", + "0 4.0 41.0 \n", + "1 0.0 49.0 \n", + "2 1.0 0.0 \n", + "3 161.0 6.0 \n", + "4 0.0 38.0 \n", + "5 4717.0 90.0 \n", + "6 606.0 28.0 \n", + "7 0.0 0.0 \n", + "8 4762.0 52.0 \n", + "9 0.0 0.0 \n", + "\n", + " dwellings_20_to_49_units employed_wholesale_trade \\\n", + "0 0.0 42.0 \n", + "1 40.0 120.0 \n", + "2 0.0 13.0 \n", + "3 18.0 39.0 \n", + "4 52.0 75.0 \n", + "5 16.0 110.0 \n", + "6 18.0 88.0 \n", + "7 0.0 3.0 \n", + "8 12.0 221.0 \n", + "9 21.0 20.0 \n", + "\n", + " female_female_households rent_burden_not_computed \\\n", + "0 0.0 67.0 \n", + "1 3.0 185.0 \n", + "2 0.0 52.0 \n", + "3 0.0 137.0 \n", + "4 0.0 125.0 \n", + "5 5.0 540.0 \n", + "6 0.0 361.0 \n", + "7 0.0 30.0 \n", + "8 0.0 243.0 \n", + "9 0.0 68.0 \n", + "\n", + " white_including_hispanic high_school_including_ged \\\n", + "0 5533.0 1564.0 \n", + "1 9756.0 2785.0 \n", + "2 1649.0 361.0 \n", + "3 6969.0 1862.0 \n", + "4 4500.0 1511.0 \n", + "5 12113.0 4279.0 \n", + "6 11397.0 3313.0 \n", + "7 411.0 79.0 \n", + "8 12144.0 4105.0 \n", + "9 1783.0 493.0 \n", + "\n", + " commuters_by_car_truck_van dwellings_1_units_attached \\\n", + "0 2119.0 0.0 \n", + "1 4266.0 77.0 \n", + "2 774.0 22.0 \n", + "3 3281.0 23.0 \n", + "4 2410.0 16.0 \n", + "5 5853.0 54.0 \n", + "6 2752.0 37.0 \n", + "7 133.0 10.0 \n", + "8 6413.0 41.0 \n", + "9 612.0 7.0 \n", + "\n", + " dwellings_1_units_detached dwellings_50_or_more_units \\\n", + "0 2208.0 0.0 \n", + "1 4318.0 0.0 \n", + "2 890.0 0.0 \n", + "3 3498.0 0.0 \n", + "4 2281.0 0.0 \n", + "5 6098.0 91.0 \n", + "6 2976.0 54.0 \n", + "7 229.0 0.0 \n", + "8 6183.0 2.0 \n", + "9 863.0 6.0 \n", + "\n", + " housing_built_2000_to_2004 male_45_64_graduate_degree \\\n", + "0 61.0 87.0 \n", + "1 27.0 32.0 \n", + "2 15.0 3.0 \n", + "3 78.0 17.0 \n", + "4 43.0 32.0 \n", + "5 206.0 25.0 \n", + "6 143.0 41.0 \n", + "7 3.0 1.0 \n", + "8 218.0 113.0 \n", + "9 12.0 15.0 \n", + "\n", + " occupation_management_arts population_1_year_and_over \\\n", + "0 955.0 5634.0 \n", + "1 1470.0 10138.0 \n", + "2 264.0 1659.0 \n", + "3 850.0 8140.0 \n", + "4 1217.0 5425.0 \n", + "5 1560.0 17033.0 \n", + "6 875.0 12072.0 \n", + "7 83.0 421.0 \n", + "8 2279.0 16964.0 \n", + "9 233.0 1903.0 \n", + "\n", + " speak_only_english_at_home housing_built_2005_or_later \\\n", + "0 None 16.0 \n", + "1 None 5.0 \n", + "2 None 0.0 \n", + "3 None 39.0 \n", + "4 None 19.0 \n", + "5 None 20.0 \n", + "6 None 55.0 \n", + "7 None 0.0 \n", + "8 None 102.0 \n", + "9 None 15.0 \n", + "\n", + " male_45_64_bachelors_degree median_year_structure_built \\\n", + "0 158.0 1987.0 \n", + "1 178.0 1952.0 \n", + "2 48.0 1957.0 \n", + "3 93.0 1969.0 \n", + "4 152.0 1966.0 \n", + "5 211.0 1968.0 \n", + "6 19.0 1985.0 \n", + "7 6.0 1959.0 \n", + "8 153.0 1983.0 \n", + "9 40.0 1957.0 \n", + "\n", + " children_in_single_female_hh families_with_young_children \\\n", + "0 147.0 335.0 \n", + "1 756.0 662.0 \n", + "2 43.0 137.0 \n", + "3 396.0 589.0 \n", + "4 220.0 338.0 \n", + "5 2120.0 1330.0 \n", + "6 473.0 787.0 \n", + "7 3.0 27.0 \n", + "8 761.0 1033.0 \n", + "9 109.0 128.0 \n", + "\n", + " graduate_professional_degree households_retirement_income \\\n", + "0 337.0 625.0 \n", + "1 281.0 807.0 \n", + "2 72.0 107.0 \n", + "3 261.0 679.0 \n", + "4 210.0 261.0 \n", + "5 532.0 1138.0 \n", + "6 327.0 987.0 \n", + "7 19.0 23.0 \n", + "8 987.0 1480.0 \n", + "9 64.0 169.0 \n", + "\n", + " male_45_64_associates_degree male_45_64_less_than_9_grade \\\n", + "0 92.0 13.0 \n", + "1 151.0 33.0 \n", + "2 22.0 3.0 \n", + "3 7.0 75.0 \n", + "4 83.0 56.0 \n", + "5 58.0 251.0 \n", + "6 117.0 153.0 \n", + "7 15.0 0.0 \n", + "8 175.0 96.0 \n", + "9 5.0 13.0 \n", + "\n", + " million_dollar_housing_units owner_occupied_housing_units \\\n", + "0 26.0 2020.0 \n", + "1 0.0 3131.0 \n", + "2 0.0 649.0 \n", + "3 35.0 2398.0 \n", + "4 16.0 1677.0 \n", + "5 0.0 3631.0 \n", + "6 0.0 3114.0 \n", + "7 0.0 112.0 \n", + "8 0.0 5531.0 \n", + "9 0.0 515.0 \n", + "\n", + " percent_income_spent_on_rent aggregate_travel_time_to_work \\\n", + "0 23.6 60655.0 \n", + "1 26.3 86680.0 \n", + "2 22.9 NaN \n", + "3 32.0 53750.0 \n", + "4 27.5 40040.0 \n", + "5 28.8 105750.0 \n", + "6 31.8 98955.0 \n", + "7 25.8 3040.0 \n", + "8 26.0 171195.0 \n", + "9 25.4 11965.0 \n", + "\n", + " amerindian_including_hispanic housing_built_1939_or_earlier \\\n", + "0 84.0 97.0 \n", + "1 64.0 181.0 \n", + "2 39.0 95.0 \n", + "3 1.0 551.0 \n", + "4 747.0 255.0 \n", + "5 18.0 853.0 \n", + "6 9.0 141.0 \n", + "7 0.0 31.0 \n", + "8 5.0 343.0 \n", + "9 99.0 73.0 \n", + "\n", + " housing_units_renter_occupied pop_determined_poverty_status \\\n", + "0 385.0 5711.0 \n", + "1 1483.0 10014.0 \n", + "2 179.0 1690.0 \n", + "3 745.0 7955.0 \n", + "4 747.0 5294.0 \n", + "5 3244.0 17042.0 \n", + "6 1202.0 10690.0 \n", + "7 65.0 421.0 \n", + "8 1196.0 16806.0 \n", + "9 290.0 1906.0 \n", + "\n", + " vacant_housing_units_for_rent vacant_housing_units_for_sale \\\n", + "0 11.0 34.0 \n", + "1 142.0 67.0 \n", + "2 28.0 0.0 \n", + "3 31.0 57.0 \n", + "4 95.0 39.0 \n", + "5 313.0 89.0 \n", + "6 63.0 38.0 \n", + "7 2.0 0.0 \n", + "8 222.0 164.0 \n", + "9 22.0 38.0 \n", + "\n", + " employed_public_administration less_than_high_school_graduate \\\n", + "0 291.0 231.0 \n", + "1 122.0 633.0 \n", + "2 28.0 76.0 \n", + "3 192.0 1229.0 \n", + "4 118.0 408.0 \n", + "5 313.0 2867.0 \n", + "6 164.0 2339.0 \n", + "7 3.0 13.0 \n", + "8 196.0 2348.0 \n", + "9 62.0 152.0 \n", + "\n", + " commuters_by_subway_or_elevated bachelors_degree_or_higher_25_64 \\\n", + "0 0.0 873.0 \n", + "1 0.0 856.0 \n", + "2 0.0 211.0 \n", + "3 0.0 556.0 \n", + "4 0.0 580.0 \n", + "5 0.0 1066.0 \n", + "6 0.0 553.0 \n", + "7 0.0 59.0 \n", + "8 0.0 1485.0 \n", + "9 0.0 194.0 \n", + "\n", + " employed_education_health_social speak_spanish_at_home_low_english \\\n", + "0 380.0 None \n", + "1 1172.0 None \n", + "2 196.0 None \n", + "3 647.0 None \n", + "4 674.0 None \n", + "5 1780.0 None \n", + "6 775.0 None \n", + "7 37.0 None \n", + "8 1673.0 None \n", + "9 121.0 None \n", + "\n", + " commuters_by_public_transportation different_house_year_ago_same_city \\\n", + "0 0.0 32.0 \n", + "1 21.0 550.0 \n", + "2 0.0 61.0 \n", + "3 2.0 634.0 \n", + "4 37.0 182.0 \n", + "5 23.0 1577.0 \n", + "6 0.0 74.0 \n", + "7 0.0 1.0 \n", + "8 0.0 378.0 \n", + "9 0.0 108.0 \n", + "\n", + " some_college_and_associates_degree households_public_asst_or_food_stamps \\\n", + "0 1212.0 179.0 \n", + "1 2577.0 831.0 \n", + "2 448.0 61.0 \n", + "3 1809.0 600.0 \n", + "4 1086.0 340.0 \n", + "5 2719.0 2237.0 \n", + "6 2195.0 1218.0 \n", + "7 93.0 6.0 \n", + "8 3596.0 1129.0 \n", + "9 464.0 99.0 \n", + "\n", + " management_business_sci_arts_employed \\\n", + "0 955.0 \n", + "1 1470.0 \n", + "2 264.0 \n", + "3 850.0 \n", + "4 1217.0 \n", + "5 1560.0 \n", + "6 875.0 \n", + "7 83.0 \n", + "8 2279.0 \n", + "9 233.0 \n", + "\n", + " employed_finance_insurance_real_estate \\\n", + "0 141.0 \n", + "1 170.0 \n", + "2 30.0 \n", + "3 133.0 \n", + "4 184.0 \n", + "5 282.0 \n", + "6 162.0 \n", + "7 1.0 \n", + "8 279.0 \n", + "9 11.0 \n", + "\n", + " different_house_year_ago_different_city \\\n", + "0 471.0 \n", + "1 839.0 \n", + "2 84.0 \n", + "3 829.0 \n", + "4 444.0 \n", + "5 1594.0 \n", + "6 1449.0 \n", + "7 54.0 \n", + "8 983.0 \n", + "9 114.0 \n", + "\n", + " employed_science_management_admin_waste \\\n", + "0 141.0 \n", + "1 173.0 \n", + "2 34.0 \n", + "3 231.0 \n", + "4 92.0 \n", + "5 164.0 \n", + "6 85.0 \n", + "7 3.0 \n", + "8 495.0 \n", + "9 89.0 \n", + "\n", + " one_parent_families_with_young_children \\\n", + "0 64.0 \n", + "1 327.0 \n", + "2 20.0 \n", + "3 206.0 \n", + "4 85.0 \n", + "5 813.0 \n", + "6 184.0 \n", + "7 2.0 \n", + "8 394.0 \n", + "9 49.0 \n", + "\n", + " two_parent_families_with_young_children \\\n", + "0 271.0 \n", + "1 335.0 \n", + "2 117.0 \n", + "3 383.0 \n", + "4 253.0 \n", + "5 517.0 \n", + "6 603.0 \n", + "7 25.0 \n", + "8 639.0 \n", + "9 79.0 \n", + "\n", + " employed_other_services_not_public_admin \\\n", + "0 94.0 \n", + "1 301.0 \n", + "2 51.0 \n", + "3 363.0 \n", + "4 139.0 \n", + "5 304.0 \n", + "6 183.0 \n", + "7 6.0 \n", + "8 231.0 \n", + "9 17.0 \n", + "\n", + " owner_occupied_housing_units_median_value \\\n", + "0 195000.0 \n", + "1 81800.0 \n", + "2 76400.0 \n", + "3 82000.0 \n", + "4 85800.0 \n", + "5 73300.0 \n", + "6 65500.0 \n", + "7 101500.0 \n", + "8 112600.0 \n", + "9 110900.0 \n", + "\n", + " employed_transportation_warehousing_utilities \\\n", + "0 90.0 \n", + "1 245.0 \n", + "2 55.0 \n", + "3 401.0 \n", + "4 52.0 \n", + "5 299.0 \n", + "6 165.0 \n", + "7 9.0 \n", + "8 788.0 \n", + "9 96.0 \n", + "\n", + " occupation_production_transportation_material \\\n", + "0 310.0 \n", + "1 949.0 \n", + "2 73.0 \n", + "3 510.0 \n", + "4 306.0 \n", + "5 1565.0 \n", + "6 319.0 \n", + "7 21.0 \n", + "8 1238.0 \n", + "9 126.0 \n", + "\n", + " father_one_parent_families_with_young_children \\\n", + "0 0.0 \n", + "1 29.0 \n", + "2 13.0 \n", + "3 165.0 \n", + "4 26.0 \n", + "5 89.0 \n", + "6 24.0 \n", + "7 2.0 \n", + "8 89.0 \n", + "9 21.0 \n", + "\n", + " owner_occupied_housing_units_lower_value_quartile \\\n", + "0 119800.0 \n", + "1 49500.0 \n", + "2 44300.0 \n", + "3 41600.0 \n", + "4 51600.0 \n", + "5 37000.0 \n", + "6 30000.0 \n", + "7 57000.0 \n", + "8 62700.0 \n", + "9 83300.0 \n", + "\n", + " owner_occupied_housing_units_upper_value_quartile \\\n", + "0 301300.0 \n", + "1 126300.0 \n", + "2 124400.0 \n", + "3 165300.0 \n", + "4 156600.0 \n", + "5 120900.0 \n", + "6 117600.0 \n", + "7 162500.0 \n", + "8 189100.0 \n", + "9 162900.0 \n", + "\n", + " employed_agriculture_forestry_fishing_hunting_mining \\\n", + "0 460.0 \n", + "1 349.0 \n", + "2 253.0 \n", + "3 602.0 \n", + "4 706.0 \n", + "5 405.0 \n", + "6 392.0 \n", + "7 67.0 \n", + "8 353.0 \n", + "9 96.0 \n", + "\n", + " occupation_natural_resources_construction_maintenance \\\n", + "0 491.0 \n", + "1 514.0 \n", + "2 198.0 \n", + "3 719.0 \n", + "4 459.0 \n", + "5 596.0 \n", + "6 516.0 \n", + "7 36.0 \n", + "8 766.0 \n", + "9 123.0 \n", + "\n", + " two_parents_in_labor_force_families_with_young_children \\\n", + "0 90.0 \n", + "1 248.0 \n", + "2 65.0 \n", + "3 214.0 \n", + "4 215.0 \n", + "5 216.0 \n", + "6 275.0 \n", + "7 13.0 \n", + "8 386.0 \n", + "9 10.0 \n", + "\n", + " employed_arts_entertainment_recreation_accommodation_food \\\n", + "0 186.0 \n", + "1 213.0 \n", + "2 24.0 \n", + "3 265.0 \n", + "4 114.0 \n", + "5 380.0 \n", + "6 226.0 \n", + "7 6.0 \n", + "8 697.0 \n", + "9 72.0 \n", + "\n", + " renter_occupied_housing_units_paying_cash_median_gross_rent \\\n", + "0 640.0 \n", + "1 664.0 \n", + "2 551.0 \n", + "3 721.0 \n", + "4 562.0 \n", + "5 568.0 \n", + "6 483.0 \n", + "7 658.0 \n", + "8 603.0 \n", + "9 814.0 \n", + "\n", + " two_parents_not_in_labor_force_families_with_young_children \\\n", + "0 18.0 \n", + "1 12.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "5 2.0 \n", + "6 78.0 \n", + "7 0.0 \n", + "8 8.0 \n", + "9 0.0 \n", + "\n", + " father_in_labor_force_one_parent_families_with_young_children \\\n", + "0 0.0 \n", + "1 25.0 \n", + "2 13.0 \n", + "3 165.0 \n", + "4 14.0 \n", + "5 69.0 \n", + "6 24.0 \n", + "7 2.0 \n", + "8 80.0 \n", + "9 21.0 \n", + "\n", + " two_parents_father_in_labor_force_families_with_young_children \\\n", + "0 163.0 \n", + "1 64.0 \n", + "2 48.0 \n", + "3 169.0 \n", + "4 32.0 \n", + "5 299.0 \n", + "6 211.0 \n", + "7 12.0 \n", + "8 245.0 \n", + "9 67.0 \n", + "\n", + " two_parents_mother_in_labor_force_families_with_young_children \n", + "0 0.0 \n", + "1 11.0 \n", + "2 4.0 \n", + "3 0.0 \n", + "4 6.0 \n", + "5 0.0 \n", + "6 39.0 \n", + "7 0.0 \n", + "8 0.0 \n", + "9 2.0 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply a standard filter when downloading the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's say we are interested in the `children` variable to know the number of children per county, we can download just that variable and the geometry and visualise the result in a map." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_df = dataset.to_dataframe(sql_query=\"select children, geom from $dataset$\")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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    \n", + " StackTrace\n", + "
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      \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.viz import Map, Layer, color_bins_style\n", + "\n", + "Map(\n", + " Layer(dataset_df, color_bins_style('children'), geom_col='geom'),\n", + " show_info=True,\n", + " viewport={'zoom': 3.17, 'lat': 38.170209, 'lng': -102.736148}\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Apply a spatial filter when downloading the dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, instead of downloading just one field, let's download all of them but for a smaller region using a spatial filter. We will use a bounding box for this and you can calculate yours from here: http://bboxfinder.com." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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      250032013-01-0120771.08182.010126.010645.041.8536.0865.0713.0478.0269.080.079.0577.0429.0560.0599.0513.0622.0692.0807.0306.0260.0438.0289.0350.0346.0164.0154.0578.0834.0482.0333.0209.082.051.0439.0611.0511.0767.0634.0759.0813.0755.0235.0415.0284.0303.0626.0447.0223.0254.014126.020622.020198.0NaNNaN16422.014166.010288.0NaNNaNNaNNaNNaN416.05309.00.01083.049.031.0173.014939.05309.00.049.01083.019688.00.00.0341.0352.023.028.0950.0993.036407.020703.0857.0784.0637.0498.0503.0637.0560.0369.0283.0527.0646.0872.0542.0178.0164.0125.01722.020552.04368.00.465810152.0608.038900.068200.0115200.08182.02128.01970.0376.0136.07052.056.0229.012.0190.034.082.041.02446.066.0208.0480.01976.04163.02622.05560.01651.00.00.04819.01436.0393.034.2357.0534.0216.0118.0109.0247.0169.0235.057.086.06054.06.02373.01188.0494.01186.0823.0351.0450.022.00.0363.022.022.01807.02364.01463.01102.0722.0341.0524.018.0144.0162.0400.0578.0348.0230.07656.0178.0126.0248.0542.02795.02854.01490.0501.0156480.019.019.07226.0607.00.06619.0193.0704.01333.05003.0786.0462.02485.02409.05883.01333.01483.0566.03975.0135.0219.052.077.0391.01158.0655.02687.07905.0870.08775.07647.07782.00.08775.0640.0287.0722.02045.0176.047.01407.0244.0418.0951.0329.0539.0100.02176.01086.01820.01494.01329.02176.01494.01344.01124.01022.05037.0801.0NaNNaNNaNAshley212505.7482.432888e+0937POLYGON ((-91.56167 33.39156, -91.60694 33.391...
      \n", + "
      " + ], + "text/plain": [ + " geoid do_date total_pop households male_pop female_pop median_age \\\n", + "0 18025 2013-01-01 10598.0 4007.0 5355.0 5243.0 43.7 \n", + "1 1091 2013-01-01 19743.0 7975.0 9235.0 10508.0 42.1 \n", + "2 5003 2013-01-01 20771.0 8182.0 10126.0 10645.0 41.8 \n", + "\n", + " male_under_5 male_5_to_9 male_10_to_14 male_15_to_17 male_18_to_19 \\\n", + "0 283.0 369.0 309.0 235.0 142.0 \n", + "1 514.0 601.0 660.0 421.0 236.0 \n", + "2 536.0 865.0 713.0 478.0 269.0 \n", + "\n", + " male_20 male_21 male_22_to_24 male_25_to_29 male_30_to_34 \\\n", + "0 11.0 41.0 205.0 282.0 301.0 \n", + "1 133.0 117.0 307.0 635.0 487.0 \n", + "2 80.0 79.0 577.0 429.0 560.0 \n", + "\n", + " male_35_to_39 male_40_to_44 male_45_to_49 male_50_to_54 male_55_to_59 \\\n", + "0 284.0 341.0 355.0 417.0 438.0 \n", + "1 529.0 506.0 569.0 628.0 728.0 \n", + "2 599.0 513.0 622.0 692.0 807.0 \n", + "\n", + " male_60_to_61 male_62_to_64 male_65_to_66 male_67_to_69 male_70_to_74 \\\n", + "0 163.0 259.0 243.0 171.0 194.0 \n", + "1 212.0 358.0 242.0 234.0 475.0 \n", + "2 306.0 260.0 438.0 289.0 350.0 \n", + "\n", + " male_75_to_79 male_80_to_84 male_85_and_over female_under_5 \\\n", + "0 145.0 100.0 67.0 278.0 \n", + "1 372.0 215.0 56.0 681.0 \n", + "2 346.0 164.0 154.0 578.0 \n", + "\n", + " female_5_to_9 female_10_to_14 female_15_to_17 female_18_to_19 \\\n", + "0 353.0 326.0 192.0 94.0 \n", + "1 503.0 755.0 399.0 366.0 \n", + "2 834.0 482.0 333.0 209.0 \n", + "\n", + " female_20 female_21 female_22_to_24 female_25_to_29 female_30_to_34 \\\n", + "0 54.0 42.0 191.0 256.0 276.0 \n", + "1 177.0 82.0 234.0 600.0 561.0 \n", + "2 82.0 51.0 439.0 611.0 511.0 \n", + "\n", + " female_35_to_39 female_40_to_44 female_45_to_49 female_50_to_54 \\\n", + "0 307.0 328.0 318.0 415.0 \n", + "1 566.0 661.0 667.0 717.0 \n", + "2 767.0 634.0 759.0 813.0 \n", + "\n", + " female_55_to_59 female_60_to_61 female_62_to_64 female_65_to_66 \\\n", + "0 462.0 160.0 176.0 131.0 \n", + "1 579.0 396.0 470.0 266.0 \n", + "2 755.0 235.0 415.0 284.0 \n", + "\n", + " female_67_to_69 female_70_to_74 female_75_to_79 female_80_to_84 \\\n", + "0 192.0 264.0 134.0 182.0 \n", + "1 361.0 459.0 439.0 347.0 \n", + "2 303.0 626.0 447.0 223.0 \n", + "\n", + " female_85_and_over white_pop population_1_year_and_over \\\n", + "0 112.0 10214.0 10469.0 \n", + "1 222.0 8909.0 19541.0 \n", + "2 254.0 14126.0 20622.0 \n", + "\n", + " population_3_years_over pop_5_years_over pop_15_and_over pop_16_over \\\n", + "0 10216.0 NaN NaN 8559.0 \n", + "1 19169.0 NaN NaN 15822.0 \n", + "2 20198.0 NaN NaN 16422.0 \n", + "\n", + " pop_25_years_over pop_25_64 pop_never_married pop_now_married \\\n", + "0 7473.0 5538.0 NaN NaN \n", + "1 13557.0 9869.0 NaN NaN \n", + "2 14166.0 10288.0 NaN NaN \n", + "\n", + " pop_separated pop_widowed pop_divorced not_us_citizen_pop black_pop \\\n", + "0 NaN NaN NaN 6.0 21.0 \n", + "1 NaN NaN NaN 78.0 10689.0 \n", + "2 NaN NaN NaN 416.0 5309.0 \n", + "\n", + " asian_pop hispanic_pop amerindian_pop other_race_pop \\\n", + "0 0.0 154.0 63.0 0.0 \n", + "1 0.0 88.0 11.0 9.0 \n", + "2 0.0 1083.0 49.0 31.0 \n", + "\n", + " two_or_more_races_pop white_including_hispanic black_including_hispanic \\\n", + "0 146.0 10309.0 21.0 \n", + "1 37.0 8954.0 10689.0 \n", + "2 173.0 14939.0 5309.0 \n", + "\n", + " asian_including_hispanic amerindian_including_hispanic hispanic_any_race \\\n", + "0 14.0 87.0 154.0 \n", + "1 0.0 11.0 88.0 \n", + "2 0.0 49.0 1083.0 \n", + "\n", + " not_hispanic_pop asian_male_45_54 asian_male_55_64 black_male_45_54 \\\n", + "0 10444.0 0.0 7.0 2.0 \n", + "1 19655.0 0.0 0.0 583.0 \n", + "2 19688.0 0.0 0.0 341.0 \n", + "\n", + " black_male_55_64 hispanic_male_45_54 hispanic_male_55_64 \\\n", + "0 0.0 9.0 7.0 \n", + "1 650.0 0.0 0.0 \n", + "2 352.0 23.0 28.0 \n", + "\n", + " white_male_45_54 white_male_55_64 median_income income_per_capita \\\n", + "0 757.0 834.0 40067.0 19424.0 \n", + "1 605.0 648.0 32255.0 22996.0 \n", + "2 950.0 993.0 36407.0 20703.0 \n", + "\n", + " income_less_10000 income_10000_14999 income_15000_19999 \\\n", + "0 360.0 286.0 314.0 \n", + "1 1429.0 656.0 701.0 \n", + "2 857.0 784.0 637.0 \n", + "\n", + " income_20000_24999 income_25000_29999 income_30000_34999 \\\n", + "0 362.0 179.0 316.0 \n", + "1 580.0 451.0 468.0 \n", + "2 498.0 503.0 637.0 \n", + "\n", + " income_35000_39999 income_40000_44999 income_45000_49999 \\\n", + "0 183.0 189.0 189.0 \n", + "1 258.0 335.0 310.0 \n", + "2 560.0 369.0 283.0 \n", + "\n", + " income_50000_59999 income_60000_74999 income_75000_99999 \\\n", + "0 404.0 524.0 395.0 \n", + "1 412.0 713.0 663.0 \n", + "2 527.0 646.0 872.0 \n", + "\n", + " income_100000_124999 income_125000_149999 income_150000_199999 \\\n", + "0 160.0 76.0 60.0 \n", + "1 321.0 272.0 301.0 \n", + "2 542.0 178.0 164.0 \n", + "\n", + " income_200000_or_more households_retirement_income \\\n", + "0 10.0 819.0 \n", + "1 105.0 1763.0 \n", + "2 125.0 1722.0 \n", + "\n", + " pop_determined_poverty_status poverty gini_index housing_units \\\n", + "0 10412.0 1851.0 0.3975 5543.0 \n", + "1 19502.0 5000.0 0.5303 10307.0 \n", + "2 20552.0 4368.0 0.4658 10152.0 \n", + "\n", + " renter_occupied_housing_units_paying_cash_median_gross_rent \\\n", + "0 562.0 \n", + "1 552.0 \n", + "2 608.0 \n", + "\n", + " owner_occupied_housing_units_lower_value_quartile \\\n", + "0 51400.0 \n", + "1 40700.0 \n", + "2 38900.0 \n", + "\n", + " owner_occupied_housing_units_median_value \\\n", + "0 86700.0 \n", + "1 88100.0 \n", + "2 68200.0 \n", + "\n", + " owner_occupied_housing_units_upper_value_quartile occupied_housing_units \\\n", + "0 146100.0 4007.0 \n", + "1 172700.0 7975.0 \n", + "2 115200.0 8182.0 \n", + "\n", + " housing_units_renter_occupied vacant_housing_units \\\n", + "0 673.0 1536.0 \n", + "1 2286.0 2332.0 \n", + "2 2128.0 1970.0 \n", + "\n", + " vacant_housing_units_for_rent vacant_housing_units_for_sale \\\n", + "0 67.0 0.0 \n", + "1 98.0 86.0 \n", + "2 376.0 136.0 \n", + "\n", + " dwellings_1_units_detached dwellings_1_units_attached dwellings_2_units \\\n", + "0 4190.0 19.0 30.0 \n", + "1 6806.0 64.0 109.0 \n", + "2 7052.0 56.0 229.0 \n", + "\n", + " dwellings_3_to_4_units dwellings_5_to_9_units dwellings_10_to_19_units \\\n", + "0 30.0 60.0 14.0 \n", + "1 183.0 494.0 105.0 \n", + "2 12.0 190.0 34.0 \n", + "\n", + " dwellings_20_to_49_units dwellings_50_or_more_units mobile_homes \\\n", + "0 66.0 0.0 1094.0 \n", + "1 43.0 9.0 2472.0 \n", + "2 82.0 41.0 2446.0 \n", + "\n", + " housing_built_2005_or_later housing_built_2000_to_2004 \\\n", + "0 2.0 68.0 \n", + "1 83.0 128.0 \n", + "2 66.0 208.0 \n", + "\n", + " housing_built_1939_or_earlier median_year_structure_built \\\n", + "0 210.0 1985.0 \n", + "1 715.0 1979.0 \n", + "2 480.0 1976.0 \n", + "\n", + " married_households nonfamily_households family_households \\\n", + "0 2069.0 1297.0 2710.0 \n", + "1 3102.0 3358.0 4617.0 \n", + "2 4163.0 2622.0 5560.0 \n", + "\n", + " households_public_asst_or_food_stamps male_male_households \\\n", + "0 625.0 0.0 \n", + "1 1971.0 0.0 \n", + "2 1651.0 0.0 \n", + "\n", + " female_female_households children children_in_single_female_hh \\\n", + "0 2.0 2345.0 484.0 \n", + "1 1.0 4534.0 1909.0 \n", + "2 0.0 4819.0 1436.0 \n", + "\n", + " median_rent percent_income_spent_on_rent rent_burden_not_computed \\\n", + "0 348.0 30.1 164.0 \n", + "1 349.0 29.9 706.0 \n", + "2 393.0 34.2 357.0 \n", + "\n", + " rent_over_50_percent rent_40_to_50_percent rent_35_to_40_percent \\\n", + "0 116.0 72.0 26.0 \n", + "1 349.0 195.0 67.0 \n", + "2 534.0 216.0 118.0 \n", + "\n", + " rent_30_to_35_percent rent_25_to_30_percent rent_20_to_25_percent \\\n", + "0 41.0 75.0 77.0 \n", + "1 176.0 189.0 99.0 \n", + "2 109.0 247.0 169.0 \n", + "\n", + " rent_15_to_20_percent rent_10_to_15_percent rent_under_10_percent \\\n", + "0 50.0 38.0 14.0 \n", + "1 212.0 133.0 160.0 \n", + "2 235.0 57.0 86.0 \n", + "\n", + " owner_occupied_housing_units million_dollar_housing_units \\\n", + "0 3334.0 7.0 \n", + "1 5689.0 5.0 \n", + "2 6054.0 6.0 \n", + "\n", + " mortgaged_housing_units different_house_year_ago_different_city \\\n", + "0 1764.0 704.0 \n", + "1 2383.0 985.0 \n", + "2 2373.0 1188.0 \n", + "\n", + " different_house_year_ago_same_city families_with_young_children \\\n", + "0 98.0 615.0 \n", + "1 734.0 1398.0 \n", + "2 494.0 1186.0 \n", + "\n", + " two_parent_families_with_young_children \\\n", + "0 492.0 \n", + "1 572.0 \n", + "2 823.0 \n", + "\n", + " two_parents_in_labor_force_families_with_young_children \\\n", + "0 287.0 \n", + "1 268.0 \n", + "2 351.0 \n", + "\n", + " two_parents_father_in_labor_force_families_with_young_children \\\n", + "0 196.0 \n", + "1 168.0 \n", + "2 450.0 \n", + "\n", + " two_parents_mother_in_labor_force_families_with_young_children \\\n", + "0 0.0 \n", + "1 56.0 \n", + "2 22.0 \n", + "\n", + " two_parents_not_in_labor_force_families_with_young_children \\\n", + "0 9.0 \n", + "1 80.0 \n", + "2 0.0 \n", + "\n", + " one_parent_families_with_young_children \\\n", + "0 123.0 \n", + "1 826.0 \n", + "2 363.0 \n", + "\n", + " father_one_parent_families_with_young_children \\\n", + "0 54.0 \n", + "1 122.0 \n", + "2 22.0 \n", + "\n", + " father_in_labor_force_one_parent_families_with_young_children \\\n", + "0 48.0 \n", + "1 122.0 \n", + "2 22.0 \n", + "\n", + " commute_5_9_mins commute_less_10_mins commute_10_14_mins \\\n", + "0 309.0 475.0 362.0 \n", + "1 1043.0 1643.0 698.0 \n", + "2 1807.0 2364.0 1463.0 \n", + "\n", + " commute_15_19_mins commute_20_24_mins commute_25_29_mins \\\n", + "0 374.0 401.0 216.0 \n", + "1 695.0 822.0 397.0 \n", + "2 1102.0 722.0 341.0 \n", + "\n", + " commute_30_34_mins commute_35_39_mins commute_40_44_mins \\\n", + "0 660.0 143.0 140.0 \n", + "1 749.0 143.0 144.0 \n", + "2 524.0 18.0 144.0 \n", + "\n", + " commute_35_44_mins commute_45_59_mins commute_60_more_mins \\\n", + "0 283.0 670.0 647.0 \n", + "1 287.0 377.0 690.0 \n", + "2 162.0 400.0 578.0 \n", + "\n", + " commute_60_89_mins commute_90_more_mins commuters_16_over \\\n", + "0 530.0 117.0 4088.0 \n", + "1 433.0 257.0 6358.0 \n", + "2 348.0 230.0 7656.0 \n", + "\n", + " walked_to_work worked_at_home no_car no_cars one_car two_cars \\\n", + "0 44.0 107.0 47.0 179.0 1004.0 1405.0 \n", + "1 81.0 184.0 297.0 922.0 3037.0 2720.0 \n", + "2 178.0 126.0 248.0 542.0 2795.0 2854.0 \n", + "\n", + " three_cars four_more_cars aggregate_travel_time_to_work \\\n", + "0 957.0 462.0 133665.0 \n", + "1 783.0 513.0 161900.0 \n", + "2 1490.0 501.0 156480.0 \n", + "\n", + " commuters_by_public_transportation commuters_by_bus \\\n", + "0 13.0 13.0 \n", + "1 0.0 0.0 \n", + "2 19.0 19.0 \n", + "\n", + " commuters_by_car_truck_van commuters_by_carpool \\\n", + "0 3987.0 310.0 \n", + "1 6126.0 371.0 \n", + "2 7226.0 607.0 \n", + "\n", + " commuters_by_subway_or_elevated commuters_drove_alone group_quarters \\\n", + "0 0.0 3677.0 102.0 \n", + "1 0.0 5755.0 233.0 \n", + "2 0.0 6619.0 193.0 \n", + "\n", + " associates_degree bachelors_degree high_school_diploma \\\n", + "0 502.0 486.0 2782.0 \n", + "1 1084.0 1315.0 4881.0 \n", + "2 704.0 1333.0 5003.0 \n", + "\n", + " less_one_year_college masters_degree one_year_more_college \\\n", + "0 434.0 226.0 861.0 \n", + "1 800.0 611.0 1930.0 \n", + "2 786.0 462.0 2485.0 \n", + "\n", + " less_than_high_school_graduate high_school_including_ged \\\n", + "0 1354.0 3543.0 \n", + "1 2279.0 5375.0 \n", + "2 2409.0 5883.0 \n", + "\n", + " bachelors_degree_2 bachelors_degree_or_higher_25_64 \\\n", + "0 486.0 532.0 \n", + "1 1315.0 1665.0 \n", + "2 1333.0 1483.0 \n", + "\n", + " graduate_professional_degree some_college_and_associates_degree \\\n", + "0 293.0 1797.0 \n", + "1 774.0 3814.0 \n", + "2 566.0 3975.0 \n", + "\n", + " male_45_64_associates_degree male_45_64_bachelors_degree \\\n", + "0 114.0 102.0 \n", + "1 277.0 366.0 \n", + "2 135.0 219.0 \n", + "\n", + " male_45_64_graduate_degree male_45_64_less_than_9_grade \\\n", + "0 57.0 110.0 \n", + "1 126.0 86.0 \n", + "2 52.0 77.0 \n", + "\n", + " male_45_64_grade_9_12 male_45_64_high_school male_45_64_some_college \\\n", + "0 241.0 832.0 176.0 \n", + "1 351.0 993.0 296.0 \n", + "2 391.0 1158.0 655.0 \n", + "\n", + " male_45_to_64 employed_pop unemployed_pop pop_in_labor_force \\\n", + "0 1632.0 4241.0 191.0 4439.0 \n", + "1 2495.0 6645.0 975.0 7620.0 \n", + "2 2687.0 7905.0 870.0 8775.0 \n", + "\n", + " not_in_labor_force workers_16_and_over armed_forces \\\n", + "0 4120.0 4195.0 7.0 \n", + "1 8202.0 6542.0 0.0 \n", + "2 7647.0 7782.0 0.0 \n", + "\n", + " civilian_labor_force employed_agriculture_forestry_fishing_hunting_mining \\\n", + "0 4432.0 127.0 \n", + "1 7620.0 249.0 \n", + "2 8775.0 640.0 \n", + "\n", + " employed_arts_entertainment_recreation_accommodation_food \\\n", + "0 391.0 \n", + "1 371.0 \n", + "2 287.0 \n", + "\n", + " employed_construction employed_education_health_social \\\n", + "0 275.0 873.0 \n", + "1 266.0 1606.0 \n", + "2 722.0 2045.0 \n", + "\n", + " employed_finance_insurance_real_estate employed_information \\\n", + "0 139.0 79.0 \n", + "1 278.0 107.0 \n", + "2 176.0 47.0 \n", + "\n", + " employed_manufacturing employed_other_services_not_public_admin \\\n", + "0 1071.0 116.0 \n", + "1 1144.0 376.0 \n", + "2 1407.0 244.0 \n", + "\n", + " employed_public_administration employed_retail_trade \\\n", + "0 204.0 448.0 \n", + "1 295.0 916.0 \n", + "2 418.0 951.0 \n", + "\n", + " employed_science_management_admin_waste \\\n", + "0 226.0 \n", + "1 340.0 \n", + "2 329.0 \n", + "\n", + " employed_transportation_warehousing_utilities employed_wholesale_trade \\\n", + "0 264.0 28.0 \n", + "1 607.0 90.0 \n", + "2 539.0 100.0 \n", + "\n", + " occupation_management_arts \\\n", + "0 912.0 \n", + "1 1970.0 \n", + "2 2176.0 \n", + "\n", + " occupation_natural_resources_construction_maintenance \\\n", + "0 493.0 \n", + "1 760.0 \n", + "2 1086.0 \n", + "\n", + " occupation_production_transportation_material occupation_sales_office \\\n", + "0 1246.0 712.0 \n", + "1 1386.0 1361.0 \n", + "2 1820.0 1494.0 \n", + "\n", + " occupation_services management_business_sci_arts_employed \\\n", + "0 878.0 912.0 \n", + "1 1168.0 1970.0 \n", + "2 1329.0 2176.0 \n", + "\n", + " sales_office_employed in_grades_1_to_4 in_grades_5_to_8 \\\n", + "0 712.0 626.0 492.0 \n", + "1 1361.0 918.0 1229.0 \n", + "2 1494.0 1344.0 1124.0 \n", + "\n", + " in_grades_9_to_12 in_school in_undergrad_college \\\n", + "0 617.0 2321.0 306.0 \n", + "1 1185.0 4829.0 811.0 \n", + "2 1022.0 5037.0 801.0 \n", + "\n", + " speak_only_english_at_home speak_spanish_at_home \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "\n", + " speak_spanish_at_home_low_english do_label do_perimeter do_area \\\n", + "0 NaN Crawford 150379.205 7.998498e+08 \n", + "1 NaN Marengo 252631.606 2.550527e+09 \n", + "2 NaN Ashley 212505.748 2.432888e+09 \n", + "\n", + " do_num_vertices geom \n", + "0 38 POLYGON ((-86.67951 38.26309, -86.57014 38.266... \n", + "1 68 POLYGON ((-87.96789 32.29825, -87.97567 32.307... \n", + "2 37 POLYGON ((-91.56167 33.39156, -91.60694 33.391... " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sql_query = \"SELECT * FROM $dataset$ WHERE ST_IntersectsBox(geom, -95.031738, 32.082575, -84.616699, 39.537940)\"\n", + "dataset_df = dataset.to_dataframe(sql_query=sql_query)\n", + "dataset_df.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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To apply that filter run:\n", + "\tCatalog().country('glo')\n" + ] + }, { "data": { "text/plain": [ - "[,\n", + "[,\n", + " ,\n", " ,\n", + " ,\n", " ,\n", " ,\n", - " ,\n", " ,\n", " ,\n", " ,\n", @@ -44,6 +53,14 @@ "execution_count": 2, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can find more entities with the Global country filter. To apply that filter run:\n", + "\tCatalog().country('glo')\n" + ] + }, { "data": { "text/plain": [ @@ -51,6 +68,8 @@ " ,\n", " ,\n", " ,\n", + " ,\n", + " ,\n", " ]" ] }, @@ -67,7 +86,16 @@ "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can find more entities with the Global country filter. To apply that filter run:\n", + "\tCatalog().country('glo')\n" + ] + } + ], "source": [ "datasets_acs_df = Catalog().country('usa').category('demographics').provider('usa_acs').datasets.to_dataframe()" ] @@ -122,21 +150,21 @@ " \n", " \n", " 0\n", - " acs_sociodemogr_ecbce31e\n", + " acs_sociodemogr_a0c48b07\n", " Sociodemographics - United States of America (...\n", " The American Community Survey (ACS) is an ongo...\n", " demographics\n", " usa\n", " sociodemographics\n", " usa_acs\n", - " County - United States of America\n", + " County - United States of America (2015)\n", " Shoreline clipped TIGER/Line boundaries. More ...\n", - " 3yrs\n", - " [2007-01-01, 2010-01-01)\n", + " yearly\n", + " [2007-01-01, 2008-01-01)\n", " yearly\n", " True\n", " eng\n", - " 20072009\n", + " 2007\n", " Demographics\n", " American Community Survey\n", " carto-do-public-data.carto.geography_usa_count...\n", @@ -144,51 +172,51 @@ " \n", " \n", " 1\n", - " acs_sociodemogr_516e1d44\n", + " acs_sociodemogr_a03fb95f\n", " Sociodemographics - United States of America (...\n", " The American Community Survey (ACS) is an ongo...\n", " demographics\n", " usa\n", " sociodemographics\n", " usa_acs\n", - " County - United States of America\n", + " Congressional District - United States of Amer...\n", " Shoreline clipped TIGER/Line boundaries. More ...\n", - " 3yrs\n", - " [2011-01-01, 2014-01-01)\n", + " yearly\n", + " [2017-01-01, 2018-01-01)\n", " yearly\n", " True\n", " eng\n", - " 20112013\n", + " 2017\n", " Demographics\n", " American Community Survey\n", - " carto-do-public-data.carto.geography_usa_count...\n", + " carto-do-public-data.carto.geography_usa_congr...\n", " carto-do-public-data.usa_acs.demographics_soci...\n", " \n", " \n", " 2\n", - " acs_sociodemogr_477ca600\n", + " acs_sociodemogr_e7b702b0\n", " Sociodemographics - United States of America (...\n", " The American Community Survey (ACS) is an ongo...\n", " demographics\n", " usa\n", " sociodemographics\n", " usa_acs\n", - " County - United States of America\n", + " Core-based Statistical Area - United States of...\n", " Shoreline clipped TIGER/Line boundaries. More ...\n", - " yearly\n", - " [2009-01-01, 2010-01-01)\n", + " 3yrs\n", + " [2006-01-01, 2009-01-01)\n", " yearly\n", " True\n", " eng\n", - " 2009\n", + " 20062008\n", " Demographics\n", " American Community Survey\n", - " carto-do-public-data.carto.geography_usa_count...\n", + " carto-do-public-data.carto.geography_usa_cbsa_...\n", " carto-do-public-data.usa_acs.demographics_soci...\n", " \n", " \n", " 3\n", - " acs_sociodemogr_5f00d4dc\n", + " acs_sociodemogr_e1e92d8d\n", " Sociodemographics - United States of America (...\n", " The American Community Survey (ACS) is an ongo...\n", " demographics\n", @@ -197,12 +225,12 @@ " usa_acs\n", " Core-based Statistical Area - United States of...\n", " Shoreline clipped TIGER/Line boundaries. More ...\n", - " 5yrs\n", - " [2007-01-01, 2012-01-01)\n", + " yearly\n", + " [2013-01-01, 2014-01-01)\n", " yearly\n", " True\n", " eng\n", - " 20072011\n", + " 2013\n", " Demographics\n", " American Community Survey\n", " carto-do-public-data.carto.geography_usa_cbsa_...\n", @@ -210,24 +238,24 @@ " \n", " \n", " 4\n", - " acs_sociodemogr_18e867ac\n", + " acs_sociodemogr_30a865f1\n", " Sociodemographics - United States of America (...\n", " The American Community Survey (ACS) is an ongo...\n", " demographics\n", " usa\n", " sociodemographics\n", " usa_acs\n", - " County - United States of America\n", + " Core-based Statistical Area - United States of...\n", " Shoreline clipped TIGER/Line boundaries. More ...\n", - " 5yrs\n", - " [2011-01-01, 2016-01-01)\n", + " 3yrs\n", + " [2005-01-01, 2008-01-01)\n", " yearly\n", " True\n", " eng\n", - " 20112015\n", + " 20052007\n", " Demographics\n", " American Community Survey\n", - " carto-do-public-data.carto.geography_usa_count...\n", + " carto-do-public-data.carto.geography_usa_cbsa_...\n", " carto-do-public-data.usa_acs.demographics_soci...\n", " \n", " \n", @@ -236,11 +264,11 @@ ], "text/plain": [ " slug \\\n", - "0 acs_sociodemogr_ecbce31e \n", - "1 acs_sociodemogr_516e1d44 \n", - "2 acs_sociodemogr_477ca600 \n", - "3 acs_sociodemogr_5f00d4dc \n", - "4 acs_sociodemogr_18e867ac \n", + "0 acs_sociodemogr_a0c48b07 \n", + "1 acs_sociodemogr_a03fb95f \n", + "2 acs_sociodemogr_e7b702b0 \n", + "3 acs_sociodemogr_e1e92d8d \n", + "4 acs_sociodemogr_30a865f1 \n", "\n", " name \\\n", "0 Sociodemographics - United States of America (... \n", @@ -264,25 +292,25 @@ "4 sociodemographics usa_acs \n", "\n", " geography_name \\\n", - "0 County - United States of America \n", - "1 County - United States of America \n", - "2 County - United States of America \n", + "0 County - United States of America (2015) \n", + "1 Congressional District - United States of Amer... \n", + "2 Core-based Statistical Area - United States of... \n", "3 Core-based Statistical Area - United States of... \n", - "4 County - United States of America \n", + "4 Core-based Statistical Area - United States of... \n", "\n", " geography_description temporal_aggregation \\\n", - "0 Shoreline clipped TIGER/Line boundaries. More ... 3yrs \n", - "1 Shoreline clipped TIGER/Line boundaries. More ... 3yrs \n", - "2 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", - "3 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", - "4 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", + "0 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", + "1 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", + "2 Shoreline clipped TIGER/Line boundaries. More ... 3yrs \n", + "3 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", + "4 Shoreline clipped TIGER/Line boundaries. More ... 3yrs \n", "\n", " time_coverage update_frequency is_public_data lang version \\\n", - "0 [2007-01-01, 2010-01-01) yearly True eng 20072009 \n", - "1 [2011-01-01, 2014-01-01) yearly True eng 20112013 \n", - "2 [2009-01-01, 2010-01-01) yearly True eng 2009 \n", - "3 [2007-01-01, 2012-01-01) yearly True eng 20072011 \n", - "4 [2011-01-01, 2016-01-01) yearly True eng 20112015 \n", + "0 [2007-01-01, 2008-01-01) yearly True eng 2007 \n", + "1 [2017-01-01, 2018-01-01) yearly True eng 2017 \n", + "2 [2006-01-01, 2009-01-01) yearly True eng 20062008 \n", + "3 [2013-01-01, 2014-01-01) yearly True eng 2013 \n", + "4 [2005-01-01, 2008-01-01) yearly True eng 20052007 \n", "\n", " category_name provider_name \\\n", "0 Demographics American Community Survey \n", @@ -293,10 +321,10 @@ "\n", " geography_id \\\n", "0 carto-do-public-data.carto.geography_usa_count... \n", - "1 carto-do-public-data.carto.geography_usa_count... \n", - "2 carto-do-public-data.carto.geography_usa_count... \n", + "1 carto-do-public-data.carto.geography_usa_congr... \n", + "2 carto-do-public-data.carto.geography_usa_cbsa_... \n", "3 carto-do-public-data.carto.geography_usa_cbsa_... \n", - "4 carto-do-public-data.carto.geography_usa_count... \n", + "4 carto-do-public-data.carto.geography_usa_cbsa_... \n", "\n", " id \n", "0 carto-do-public-data.usa_acs.demographics_soci... \n", @@ -1106,14 +1134,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -1187,7 +1215,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -1202,12 +1230,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
        ${popupHTML}
        `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
        ${popupHTML}
        `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1566,7 +1598,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1612,7 +1644,7 @@ " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": 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", 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", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#826DBA\\")\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": []}];\n", + " const layers = [{"credentials": null, "data": 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", 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", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#826DBA\\")\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -1636,7 +1668,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 7, diff --git a/docs/examples/data_observatory/enrichment_subscription_workflow.ipynb b/docs/examples/data_observatory/enrichment_subscription_workflow.ipynb new file mode 100644 index 000000000..887559884 --- /dev/null +++ b/docs/examples/data_observatory/enrichment_subscription_workflow.ipynb @@ -0,0 +1,1861 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Enrichment Subscription Workflow\n", + "\n", + "This example illustrates how to subscribe and enrich with CARTO's Data Observatory.\n", + "\n", + "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to reproduce this example._" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "dataset_name = 'carto-do.ags.demographics_retailpotential_usa_blockgroup_2015_yearly_2019'" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.data.observatory import Catalog, Dataset, Geography\n", + "\n", + "dataset = Dataset.get(dataset_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "01229efd49bb493db0108acc19d7dc92", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "HTML(value='\\n

        Subscription already purchased

        \\n The dataset carto-do.ags.demographic…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dataset.subscribe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "NOTE: After the subscription request of the premium dataset, we will contact you to **perform and complete the premium subscription**. When the process is completed, you will be able to access your premium dataset directly from CARTOframes or from the Dashboard in your CARTO account." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Datasets: [, ]\n", + "Geographies: []" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Catalog().subscriptions()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[ #'Geographic Identifier',\n", + " #'Households (2019A)',\n", + " #'New Car Dealers (Total Exp)',\n", + " #'User Car Dealers (Total Exp)',\n", + " #'Recreational Vehicle Dealers (Total Exp)',\n", + " #'Motorcycle and Boat Dealers (Total Exp)',\n", + " #'Auto Parts and Accessories (Total Exp)',\n", + " #'Tire Dealers (Total Exp)',\n", + " #'Furniture Stores (Total Exp)',\n", + " #'Floor Covering Stores (Total Exp)',\n", + " #'Other Home Furnishing Stores (Total Exp)',\n", + " #'Appliances and Electronics Stores (Total Exp)',\n", + " #'Home Centers (Total Exp)',\n", + " #'Paint and Wallpaper Stores (Total Exp)',\n", + " #'Hardware Stores (Total Exp)',\n", + " #'Other Building Materials Stores (Total Exp)',\n", + " #'Outdoor Power Equipment Stores (Total Exp)',\n", + " #'Nursery and Garden Stores (Total Exp)',\n", + " #'Grocery Stores (Total Exp)',\n", + " #'Convenience Stores (Total Exp)',\n", + " #'Meat Markets (Total Exp)',\n", + " #'Fish and Seafood Markets (Total Exp)',\n", + " #'Fruit and Vegetable Markets (Total Exp)',\n", + " #'Other Specialty Food Markets (Total Exp)',\n", + " #'Liquor Stores (Total Exp)',\n", + " #'Pharmacy and Drug Stores (Total Exp)',\n", + " #'Costmetics and Beauty Stores (Total Exp)',\n", + " #'Optical Goods Stores (Total Exp)',\n", + " #'Other Health and Personal Care Stores (Total Exp)',\n", + " #'Gasoline Stations (Total Exp)',\n", + " #'Mens Clothing Stores (Total Exp)',\n", + " #'Womens Clothing Stores (Total Exp)',\n", + " #'Childrens and Infants Clothing Stores (Total Exp)',\n", + " #'Family Clothing Stores (Total Exp)',\n", + " #'Clothing Accessory Stores (Total Exp)',\n", + " #'Other Apparel Stores (Total Exp)',\n", + " #'Shoe Stores (Total Exp)',\n", + " #'Jewelry Stores (Total Exp)',\n", + " #'Luggage Stores (Total Exp)',\n", + " #'Sporting Goods Stores (Total Exp)',\n", + " #'Hobby, Toy, and Game Stores (Total Exp)',\n", + " #'Sewing and Needlecraft Stores (Total Exp)',\n", + " #'Musical Instrument Stores (Total Exp)',\n", + " #'Book Stores (Total Exp)',\n", + " #'Department Stores (Total Exp)',\n", + " #'Warehouse Superstores (Total Exp)',\n", + " #'Other General Merchandise Stores (Total Exp)',\n", + " #'Florists (Total Exp)',\n", + " #'Office and Stationary Stores (Total Exp)',\n", + " #'Gift and Souvenir Stores (Total Exp)',\n", + " #'Used Merchandise Stores (Total Exp)',\n", + " #'Pet and Pet Supply Stores (Total Exp)',\n", + " #'Art Dealers (Total Exp)',\n", + " #'Mobile Home Dealers (Total Exp)',\n", + " #'Other Miscellaneous Retail Stores (Total Exp)',\n", + " #'Mail Order and Catalog Stores (Total Exp)',\n", + " #'Vending Machines (Total Exp)',\n", + " #'Fuel Dealers (Total Exp)',\n", + " #'Other Direct Selling Establishments (Total Exp)',\n", + " #'Retail at Hotels and Other Travel Accomodations (T...',\n", + " #'Retail at RV Parks (Total Exp)',\n", + " #'Retail at Rooming and Boarding Houses (Total Exp)',\n", + " #'Full Service Restaurants (Total Exp)',\n", + " #'Limited Service Restaurants (Total Exp)',\n", + " #'Special Food Services and Catering (Total Exp)',\n", + " #'Drinking Places (Total Exp)',\n", + " #'Retail Gap: New Car Dealers (44111)',\n", + " #'Retail Gap: User Car Dealers (44112)',\n", + " #'Retail Gap: Recreational Vehicle Dealers (44121)',\n", + " #'Retail Gap: Motorcycle and Boat Dealers (44122)',\n", + " #'Retail Gap: Auto Parts and Accessories (44131)',\n", + " #'Retail Gap: Tire Dealers (44132)',\n", + " #'Retail Gap: Furniture Stores (44211)',\n", + " #'Retail Gap: Floor Covering Stores (44221)',\n", + " #'Retail Gap: Other Home Furnishing Stores (44229)',\n", + " #'Retail Gap: Appliances and Electronics Stores (443...',\n", + " #'Retail Gap: Home Centers (44411)',\n", + " #'Retail Gap: Paint and Wallpaper Stores (44412)',\n", + " #'Retail Gap: Hardware Stores (44413)',\n", + " #'Retail Gap: Other Building Materials Stores (44419...',\n", + " #'Retail Gap: Outdoor Power Equipment Stores (44421)',\n", + " #'Retail Gap: Nursery and Garden Stores (44422)',\n", + " #'Retail Gap: Grocery Stores (44511)',\n", + " #'Retail Gap: Convenience Stores (44512)',\n", + " #'Retail Gap: Meat Markets (44521)',\n", + " #'Retail Gap: Fish and Seafood Markets (44522)',\n", + " #'Retail Gap: Fruit and Vegetable Markets (44523)',\n", + " #'Retail Gap: Other Specialty Food Markets (44529)',\n", + " #'Retail Gap: Liquor Stores (44531)',\n", + " #'Retail Gap: Pharmacy and Drug Stores (44611)',\n", + " #'Retail Gap: Costmetics and Beauty Stores (44612)',\n", + " #'Retail Gap: Optical Goods Stores (44613)',\n", + " #'Retail Gap: Other Health and Personal Care Stores ...',\n", + " #'Retail Gap: Gasoline Stations (44719)',\n", + " #'Retail Gap: Men's Clothing Stores (44811)',\n", + " #'Retail Gap: Women's Clothing Stores (44812)',\n", + " #'Retail Gap: Childrens' and Infant's Clothing Store...',\n", + " #'Retail Gap: Family Clothing Stores (44814)',\n", + " #'Retail Gap: Clothing Accessory Stores (44815)',\n", + " #'Retail Gap: Other Apparel Stores (44819)',\n", + " #'Retail Gap: Shoe Stores (44821)',\n", + " #'Retail Gap: Jewelry Stores (44831)',\n", + " #'Retail Gap: Luggage Stores (44832)',\n", + " #'Retail Gap: Sporting Goods Stores (45111)',\n", + " #'Retail Gap: Hobby, Toy, and Game Stores (45112)',\n", + " #'Retail Gap: Sewing and Needlecraft Stores (45113)',\n", + " #'Retail Gap: Musical Instrument Stores (45114)',\n", + " #'Retail Gap: Book Stores (45121)',\n", + " #'Retail Gap: Department Stores (45211)',\n", + " #'Retail Gap: Warehouse Superstores (45291)',\n", + " #'Retail Gap: Other General Merchandise Stores (4529...',\n", + " #'Retail Gap: Florists (45311)',\n", + " #'Retail Gap: Office and Stationary Stores (45321)',\n", + " #'Retail Gap: Gift and Souvenir Stores (45322)',\n", + " #'Retail Gap: Used Merchandise Stores (45331)',\n", + " #'Retail Gap: Pet and Pet Supply Stores (45391)',\n", + " #'Retail Gap: Art Dealers (45392)',\n", + " #'Retail Gap: Mobile Home Dealers (45393)',\n", + " #'Retail Gap: Other Miscellaneous Retail Stores (453...',\n", + " #'Retail Gap: Mail Order and Catalog Stores (45411)',\n", + " #'Retail Gap: Vending Machines (45421)',\n", + " #'Retail Gap: Fuel Dealers (45431)',\n", + " #'Retail Gap: Other Direct Selling Establishments (4...',\n", + " #'Retail Gap: Retail at Hotels and Other Travel Acco...',\n", + " #'Retail Gap: Retail at RV Parks (7212)',\n", + " #'Retail Gap: Retail at Rooming and Boarding Houses ...',\n", + " #'Retail Gap: Full Service Restaurants (7221)',\n", + " #'Retail Gap: Limited Service Restaurants (7222)',\n", + " #'Retail Gap: Special Food Services and Catering (72...',\n", + " #'Retail Gap: Drinking Places (7224)',\n", + " #'Geographical Identifier',\n", + " #'First day of time period']" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.variables" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " #'Home Centers (Total Exp)'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.data.observatory import Variable\n", + "\n", + "variable = Variable.get('MLTCY44411_acc8382c')\n", + "variable" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
        \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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        \n", + " :\n", + "
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        \n", + " StackTrace\n", + "
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          \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.viz import Layer, color_continuous_style\n", + "\n", + "Layer(enriched_dataset_gdf, color_continuous_style('MLTCY44411'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "01229efd49bb493db0108acc19d7dc92": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_11013d16054c40439066ef01958a7325", + "placeholder": "​", + "style": "IPY_MODEL_6023b440db1d4a4ca46ed696c182fe1f", + "value": "\n

          Subscription already purchased

          \n The dataset carto-do.ags.demographics_retailpotential_usa_blockgroup_2015_yearly_2019 has already been purchased.\n " + } + }, + "11013d16054c40439066ef01958a7325": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "6023b440db1d4a4ca46ed696c182fe1f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + }, + "version_major": 2, + "version_minor": 0 + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/data_observatory/google_cloud_credentials.ipynb b/docs/examples/data_observatory/google_cloud_credentials.ipynb new file mode 100644 index 000000000..d153533c3 --- /dev/null +++ b/docs/examples/data_observatory/google_cloud_credentials.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Google Cloud credentials\n", + "\n", + "This example illustrates how to create a Google cloud credentials object from your Data Observatory enabled CARTO account." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import Credentials\n", + "\n", + "creds = Credentials.from_file('creds.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from google.oauth2.credentials import Credentials as GoogleCredentials\n", + "\n", + "gcloud_project, gcloud_token = creds.get_gcloud_credentials()\n", + "gcloud_credentials = GoogleCredentials(gcloud_token)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from google.cloud import bigquery, storage, bigquery_storage_v1beta1 as bigquery_storage\n", + "\n", + "bq_client = bigquery.Client(project=gcloud_project, credentials=gcloud_credentials)\n", + "gcs_client = storage.Client(project=gcloud_project, credentials=gcloud_credentials)\n", + "bqs_client = bigquery_storage.BigQueryStorageClient(credentials=gcloud_credentials)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As an example, the following code shows how to use the Google BigQuery client to access to a public dataset from Data Observatory:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.data.observatory import Dataset\n", + "\n", + "dataset = Dataset.get('acs_sociodemogr_8c2655e0')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'carto-do-public-data.usa_acs.demographics_sociodemographics_usa_county_2015_5yrs_20132017'" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.id" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "query_job = bq_client.query('SELECT * FROM `{}` LIMIT 1'.format(dataset.id))\n", + "results = query_job.result()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[SchemaField('geoid', 'STRING', 'NULLABLE', None, ()),\n", + " SchemaField('do_date', 'DATE', 'NULLABLE', None, ()),\n", + " SchemaField('total_pop', 'FLOAT', 'NULLABLE', None, ())]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "results.schema[0:3]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "18025 10598.0\n" + ] + } + ], + "source": [ + "for row in results:\n", + " print(row['geoid'], row['total_pop'])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/data_observatory/points_enrichment_dataset.ipynb b/docs/examples/data_observatory/points_enrichment_dataset.ipynb similarity index 99% rename from examples/data_observatory/points_enrichment_dataset.ipynb rename to docs/examples/data_observatory/points_enrichment_dataset.ipynb index 43be6f0d2..45d96c1fd 100644 --- a/examples/data_observatory/points_enrichment_dataset.ipynb +++ b/docs/examples/data_observatory/points_enrichment_dataset.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Enrich Points from a Dataset\n", + "## Enrich Points from a Dataset\n", "\n", "This example illustrates how to enrich points that are in a dataset with variables from CARTO's Data Observatory.\n", "\n", @@ -1453,14 +1453,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -1534,7 +1534,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -1549,12 +1549,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
          ${popupHTML}
          `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
          ${popupHTML}
          `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1913,7 +1917,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1959,7 +1963,7 @@ " const has_legends = 'true' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": "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", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "v20b5e3", "title": "no_cars"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "width", "title": "no_cars", "type": "size-continuous", "variable": "size_value"}], "map_index": 0, "options": {"dateColumns": []}, "source": "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", "title": null, "type": "GeoJSON", "viz": "@v20b5e3: prop(\\u0027no_cars\\u0027)\\n@size_value: ramp(linear(prop(\\u0027no_cars\\u0027), globalMIN(prop(\\u0027no_cars\\u0027)), globalMAX(prop(\\u0027no_cars\\u0027))), [2, 40])\\ncolor: opacity(#FFB927, 0.8)\\nfilter: 1\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(sqrt(prop(\\u0027no_cars\\u0027)), sqrt(globalMIN(prop(\\u0027no_cars\\u0027))), sqrt(globalMAX(prop(\\u0027no_cars\\u0027)))), [2, 40])\\n", "widgets": []}];\n", + " const layers = [{"credentials": null, "data": "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", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "v20b5e3", "title": "no_cars"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "width", "title": "no_cars", "type": "size-continuous", "variable": "size_value"}], "map_index": 0, "options": {"dateColumns": []}, "source": "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", "title": null, "type": "GeoJSON", "viz": "@v20b5e3: prop(\\u0027no_cars\\u0027)\\n@size_value: ramp(linear(prop(\\u0027no_cars\\u0027), globalMIN(prop(\\u0027no_cars\\u0027)), globalMAX(prop(\\u0027no_cars\\u0027))), [2, 40])\\ncolor: opacity(#FFB927, 0.8)\\nfilter: 1\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(sqrt(prop(\\u0027no_cars\\u0027)), sqrt(globalMIN(prop(\\u0027no_cars\\u0027))), sqrt(globalMAX(prop(\\u0027no_cars\\u0027)))), [2, 40])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -1983,7 +1987,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 7, diff --git a/docs/examples/data_observatory/polygons_enrichment_dataset.ipynb b/docs/examples/data_observatory/polygons_enrichment_dataset.ipynb new file mode 100644 index 000000000..35bf02923 --- /dev/null +++ b/docs/examples/data_observatory/polygons_enrichment_dataset.ipynb @@ -0,0 +1,2281 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Enrich Polygons from a Dataset\n", + "\n", + "This example illustrates how to enrich polygons that are in a dataset with variables from CARTO's Data Observatory.\n", + "\n", + "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to reproduce this example._" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
          \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          OBJECTIDFULLTRACTIDTRACTIDgeometry
          0151013102901102901POLYGON ((-77.09099 38.84516, -77.08957 38.844...
          1251013103000103000POLYGON ((-77.08558 38.82992, -77.08625 38.828...
          2351013102902102902POLYGON ((-77.09520 38.84499, -77.09442 38.844...
          \n", + "
          " + ], + "text/plain": [ + " OBJECTID FULLTRACTID TRACTID \\\n", + "0 1 51013102901 102901 \n", + "1 2 51013103000 103000 \n", + "2 3 51013102902 102902 \n", + "\n", + " geometry \n", + "0 POLYGON ((-77.09099 38.84516, -77.08957 38.844... \n", + "1 POLYGON ((-77.08558 38.82992, -77.08625 38.828... \n", + "2 POLYGON ((-77.09520 38.84499, -77.09442 38.844... " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from geopandas import read_file\n", + "\n", + "census_track = 'http://libs.cartocdn.com/cartoframes/files/census_track.geojson'\n", + "census_track_gdf = read_file(census_track)\n", + "census_track_gdf.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can find more entities with the Global country filter. To apply that filter run:\n", + "\tCatalog().country('glo')\n" + ] + }, + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.data.observatory import Catalog\n", + "\n", + "Catalog().country('usa').category('demographics').geographies" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can find more entities with the Global country filter. To apply that filter run:\n", + "\tCatalog().country('glo')\n" + ] + }, + { + "data": { + "text/html": [ + "
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          0acs_sociodemogr_858c104eSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2008-01-01, 2013-01-01)yearlyTrueeng20082012DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
          1acs_sociodemogr_6bf5c7f4Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrsNoneyearlyTrueeng20142018DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
          2acs_sociodemogr_dda43439Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2011-01-01, 2016-01-01)yearlyTrueeng20112015DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
          3acs_sociodemogr_97c32d1fSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2010-01-01, 2015-01-01)yearlyTrueeng20102014DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
          4acs_sociodemogr_d4b2cf03Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2006-01-01, 2011-01-01)yearlyTrueeng20062010DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
          5acs_sociodemogr_496a0675Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2013-01-01, 2018-01-01)yearlyTrueeng20132017DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
          6acs_sociodemogr_9ed5d625Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2007-01-01, 2012-01-01)yearlyTrueeng20072011DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
          7acs_sociodemogr_cfeb0968Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2009-01-01, 2014-01-01)yearlyTrueeng20092013DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
          8acs_sociodemogr_30d1f53Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2012-01-01, 2017-01-01)yearlyTrueeng20122016DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
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" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "datasets = Catalog().country('usa').category('demographics').geography('cdb_censustract_af861cba').datasets\n", + "datasets.to_dataframe()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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          slugnamedescriptiondb_typeagg_methodcolumn_namevariable_group_iddataset_idid
          79median_income_57be5af4Median IncomeMedian Household Income in the past 12 Months....FLOATAVGmedian_incomeNonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
          165no_car_2207f034no_carWorkers age 16 and over with no vehicle. All p...FLOATSUMno_carcarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
          166no_cars_3a983c4eno_carsCar-free households. The number of households ...FLOATSUMno_carscarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
          167one_car_13b3a60bone_carOne car households. The number of households w...FLOATSUMone_carcarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
          168two_cars_fec37223two_carsTwo car households. The number of households w...FLOATSUMtwo_carscarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
          169three_cars_32e541e7three_carsThree car households. The number of households...FLOATSUMthree_carscarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
          170four_more_cars_297e8a8afour_more_carsFour car households. The number of households ...FLOATSUMfour_more_carscarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
          174commuters_by_ca_120481e3commuters_by_car_truck_vanCommuters by Car, Truck, or Van. The number of...FLOATSUMcommuters_by_car_truck_vancarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
          175commuters_by_ca_59febe6fcommuters_by_carpoolCommuters by Carpool. The number of workers ag...FLOATSUMcommuters_by_carpoolcarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
          177commuters_drove_acf77a1commuters_drove_aloneCommuters who drove alone. The number of worke...FLOATSUMcommuters_drove_alonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
          197employed_educat_958fdcfeemployed_education_health_socialWorkers employed in firms in educational servi...FLOATSUMemployed_education_health_socialcarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
          201employed_other__a5acf594employed_other_services_not_public_adminWorkers employed in firms in other services ex...FLOATSUMemployed_other_services_not_public_admincarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
          205employed_transp_66aedbfdemployed_transportation_warehousing_utilitiesWorkers employed in firms in transportation, w...FLOATSUMemployed_transportation_warehousing_utilitiescarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
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          " + ], + "text/plain": [ + " slug name \\\n", + "79 median_income_57be5af4 Median Income \n", + "165 no_car_2207f034 no_car \n", + "166 no_cars_3a983c4e no_cars \n", + "167 one_car_13b3a60b one_car \n", + "168 two_cars_fec37223 two_cars \n", + "169 three_cars_32e541e7 three_cars \n", + "170 four_more_cars_297e8a8a four_more_cars \n", + "174 commuters_by_ca_120481e3 commuters_by_car_truck_van \n", + "175 commuters_by_ca_59febe6f commuters_by_carpool \n", + "177 commuters_drove_acf77a1 commuters_drove_alone \n", + "197 employed_educat_958fdcfe employed_education_health_social \n", + "201 employed_other__a5acf594 employed_other_services_not_public_admin \n", + "205 employed_transp_66aedbfd employed_transportation_warehousing_utilities \n", + "\n", + " description db_type agg_method \\\n", + "79 Median Household Income in the past 12 Months.... FLOAT AVG \n", + "165 Workers age 16 and over with no vehicle. All p... FLOAT SUM \n", + "166 Car-free households. The number of households ... FLOAT SUM \n", + "167 One car households. The number of households w... FLOAT SUM \n", + "168 Two car households. The number of households w... FLOAT SUM \n", + "169 Three car households. The number of households... FLOAT SUM \n", + "170 Four car households. The number of households ... FLOAT SUM \n", + "174 Commuters by Car, Truck, or Van. The number of... FLOAT SUM \n", + "175 Commuters by Carpool. The number of workers ag... FLOAT SUM \n", + "177 Commuters who drove alone. The number of worke... FLOAT SUM \n", + "197 Workers employed in firms in educational servi... FLOAT SUM \n", + "201 Workers employed in firms in other services ex... FLOAT SUM \n", + "205 Workers employed in firms in transportation, w... FLOAT SUM \n", + "\n", + " column_name \\\n", + "79 median_income \n", + "165 no_car \n", + "166 no_cars \n", + "167 one_car \n", + "168 two_cars \n", + "169 three_cars \n", + "170 four_more_cars \n", + "174 commuters_by_car_truck_van \n", + "175 commuters_by_carpool \n", + "177 commuters_drove_alone \n", + "197 employed_education_health_social \n", + "201 employed_other_services_not_public_admin \n", + "205 employed_transportation_warehousing_utilities \n", + "\n", + " variable_group_id \\\n", + "79 None \n", + "165 carto-do-public-data.usa_acs.demographics_soci... \n", + "166 carto-do-public-data.usa_acs.demographics_soci... \n", + "167 carto-do-public-data.usa_acs.demographics_soci... \n", + "168 carto-do-public-data.usa_acs.demographics_soci... \n", + "169 carto-do-public-data.usa_acs.demographics_soci... \n", + "170 carto-do-public-data.usa_acs.demographics_soci... \n", + "174 carto-do-public-data.usa_acs.demographics_soci... \n", + "175 carto-do-public-data.usa_acs.demographics_soci... \n", + "177 carto-do-public-data.usa_acs.demographics_soci... \n", + "197 carto-do-public-data.usa_acs.demographics_soci... \n", + "201 carto-do-public-data.usa_acs.demographics_soci... \n", + "205 carto-do-public-data.usa_acs.demographics_soci... \n", + "\n", + " dataset_id \\\n", + "79 carto-do-public-data.usa_acs.demographics_soci... \n", + "165 carto-do-public-data.usa_acs.demographics_soci... \n", + "166 carto-do-public-data.usa_acs.demographics_soci... \n", + "167 carto-do-public-data.usa_acs.demographics_soci... \n", + "168 carto-do-public-data.usa_acs.demographics_soci... \n", + "169 carto-do-public-data.usa_acs.demographics_soci... \n", + "170 carto-do-public-data.usa_acs.demographics_soci... \n", + "174 carto-do-public-data.usa_acs.demographics_soci... \n", + "175 carto-do-public-data.usa_acs.demographics_soci... \n", + "177 carto-do-public-data.usa_acs.demographics_soci... \n", + "197 carto-do-public-data.usa_acs.demographics_soci... \n", + "201 carto-do-public-data.usa_acs.demographics_soci... \n", + "205 carto-do-public-data.usa_acs.demographics_soci... \n", + "\n", + " id \n", + "79 carto-do-public-data.usa_acs.demographics_soci... \n", + "165 carto-do-public-data.usa_acs.demographics_soci... \n", + "166 carto-do-public-data.usa_acs.demographics_soci... \n", + "167 carto-do-public-data.usa_acs.demographics_soci... \n", + "168 carto-do-public-data.usa_acs.demographics_soci... \n", + "169 carto-do-public-data.usa_acs.demographics_soci... \n", + "170 carto-do-public-data.usa_acs.demographics_soci... \n", + "174 carto-do-public-data.usa_acs.demographics_soci... \n", + "175 carto-do-public-data.usa_acs.demographics_soci... \n", + "177 carto-do-public-data.usa_acs.demographics_soci... \n", + "197 carto-do-public-data.usa_acs.demographics_soci... \n", + "201 carto-do-public-data.usa_acs.demographics_soci... \n", + "205 carto-do-public-data.usa_acs.demographics_soci... " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.data.observatory import Dataset\n", + "\n", + "dataset = Dataset.get('acs_sociodemogr_d4b2cf03')\n", + "variables_df = dataset.variables.to_dataframe()\n", + "variables_df[variables_df['description'].str.contains('car', case=False, na=False)]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'slug': 'no_car_2207f034',\n", + " 'name': 'no_car',\n", + " 'description': 'Workers age 16 and over with no vehicle. All people in a geographic area over the age of 16 who do not own a car.',\n", + " 'db_type': 'FLOAT',\n", + " 'agg_method': 'SUM',\n", + " 'column_name': 'no_car',\n", + " 'variable_group_id': 'carto-do-public-data.usa_acs.demographics_sociodemographics_usa_censustract_2015_5yrs_20062010.car_ownership',\n", + " 'dataset_id': 'carto-do-public-data.usa_acs.demographics_sociodemographics_usa_censustract_2015_5yrs_20062010',\n", + " 'id': 'carto-do-public-data.usa_acs.demographics_sociodemographics_usa_censustract_2015_5yrs_20062010.no_car'}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.data.observatory import Variable\n", + "\n", + "variable = Variable.get('no_car_2207f034')\n", + "variable.to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.data.observatory import Enrichment\n", + "\n", + "enrichment = Enrichment()\n", + "\n", + "enriched_dataset_gdf = enrichment.enrich_polygons(\n", + " census_track_gdf,\n", + " variables=[variable],\n", + " aggregation='SUM'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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            \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.viz import Layer, color_continuous_style\n", + "\n", + "Layer(enriched_dataset_gdf, color_continuous_style('no_car'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/data_services/geocoding.ipynb b/docs/examples/data_services/geocoding.ipynb new file mode 100644 index 000000000..53c52c353 --- /dev/null +++ b/docs/examples/data_services/geocoding.ipynb @@ -0,0 +1,1492 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Geocode a DataFrame\n", + "\n", + "This example illustrates how simply use the Geocoding Data Service.\n", + "\n", + "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to reproduce this example._" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
            \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
            addresscity
            0Gran Vía 46Madrid
            1Ebro 1Sevilla
            \n", + "
            " + ], + "text/plain": [ + " address city\n", + "0 Gran Vía 46 Madrid\n", + "1 Ebro 1 Sevilla" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pandas import DataFrame\n", + "\n", + "df = DataFrame([['Gran Vía 46', 'Madrid'], ['Ebro 1', 'Sevilla']], columns=['address','city'])\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success! Data geocoded correctly\n" + ] + } + ], + "source": [ + "from cartoframes.data.services import Geocoding\n", + "\n", + "gc = Geocoding()\n", + "gdf, metadata = gc.geocode(df, street='address', city='city', country={'value': 'Spain'})" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
            \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
            the_geomaddresscitygc_status_relcarto_geocode_hash
            0POINT (-3.70588 40.42049)Gran Vía 46Madrid0.8495e4f39284efeab8e759aaa547d84567
            1POINT (-5.98312 37.35547)Ebro 1Sevilla0.7066940c4beeb395e1b628587ac772763a
            \n", + "
            " + ], + "text/plain": [ + " the_geom address city gc_status_rel \\\n", + "0 POINT (-3.70588 40.42049) Gran Vía 46 Madrid 0.84 \n", + "1 POINT (-5.98312 37.35547) Ebro 1 Sevilla 0.70 \n", + "\n", + " carto_geocode_hash \n", + "0 95e4f39284efeab8e759aaa547d84567 \n", + "1 66940c4beeb395e1b628587ac772763a " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "gdf" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'total_rows': 2,\n", + " 'required_quota': 2,\n", + " 'previously_geocoded': 0,\n", + " 'previously_failed': 0,\n", + " 'records_with_geometry': 0,\n", + " 'final_records_with_geometry': 2,\n", + " 'geocoded_increment': 2,\n", + " 'successfully_geocoded': 2,\n", + " 'failed_geocodings': 0}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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              \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.viz import Layer\n", + "\n", + "Layer(gdf)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/data_services/geocoding_and_isolines.ipynb b/docs/examples/data_services/geocoding_and_isolines.ipynb similarity index 63% rename from examples/data_services/geocoding_and_isolines.ipynb rename to docs/examples/data_services/geocoding_and_isolines.ipynb index b7df6f555..d2a48c758 100644 --- a/examples/data_services/geocoding_and_isolines.ipynb +++ b/docs/examples/data_services/geocoding_and_isolines.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Geocoding and Isolines Services\n", + "## Geocoding and Isolines Services\n", "\n", - "This example illustrates how to combine both the Geocoding and Isolines Location Data Services.\n", + "This example illustrates how to combine both the Geocoding and Isolines Data Services.\n", "\n", "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to reproduce this example._" ] @@ -101,9 +101,9 @@ } ], "source": [ - "import pandas\n", + "from pandas import DataFrame\n", "\n", - "df = pandas.DataFrame([\n", + "df = DataFrame([\n", " ['Calle Serrano 15'],\n", " ['Calle de San Pedro 21'],\n", " ['Calle Gran Vía 46'],\n", @@ -343,6 +343,11 @@ " max-width: 100%;\n", " height: auto;\n", " }\n", + "\n", + " as-layer-selector-slot .as-layer-selector-slot--wrapper .as-caption { // FIXME\n", + " font-size: 14px;\n", + " line-height: 14px;\n", + " }\n", "\n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.viz import Layer\n", + "\n", + "Layer(gdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success! Isolines created correctly\n" + ] + } + ], + "source": [ + "from cartoframes.data.services import Isolines\n", + "\n", + "iso_service = Isolines()\n", + "isodistances_gdf, isodistances_metadata = iso_service.isodistances(gdf, [600, 1200, 2400], exclusive=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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                " + ], + "text/plain": [ + " source_id data_range lower_data_range \\\n", + "0 0 600 0 \n", + "1 0 1200 600 \n", + "2 0 2400 1200 \n", + "3 1 600 0 \n", + "4 1 1200 600 \n", + "\n", + " the_geom range_label \n", + "0 MULTIPOLYGON (((-3.69149 40.42368, -3.69123 40... 10 min. \n", + "1 POLYGON ((-3.69750 40.42694, -3.69690 40.42694... 20 min. \n", + "2 POLYGON ((-3.70917 40.43037, -3.70651 40.43037... 40 min. \n", + "3 MULTIPOLYGON (((-3.69973 40.41338, -3.69947 40... 10 min. \n", + "4 MULTIPOLYGON (((-3.70660 40.41578, -3.70634 40... 20 min. " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "isodistances_gdf.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'required_quota': 21}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "isodistances_metadata" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
                \n", + "
                \n", + "
                \n", + " \n", + " \n", + "
                \n", + "
                \n", + "                 \n", + "\n", + " \n", + "\n", + "
                \n", + "
                \n", + " :\n", + "
                \n", + " \n", + " \n", + "
                \n", + "
                \n", + "\n", + "
                \n", + " StackTrace\n", + "
                  \n", + "
                  \n", + "
                  \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.viz import Map, Layer, basic_style\n", + "\n", + "Map([\n", + " Layer(isodistances_gdf, basic_style(color='green', opacity='0.3', stroke_width=0)),\n", + " Layer(gdf)\n", + "])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/map_layout/custom_layout.ipynb b/docs/examples/data_visualization/grid_layout/custom_layout.ipynb similarity index 99% rename from examples/map_layout/custom_layout.ipynb rename to docs/examples/data_visualization/grid_layout/custom_layout.ipynb index be29ba53d..095e3e7b6 100644 --- a/examples/map_layout/custom_layout.ipynb +++ b/docs/examples/data_visualization/grid_layout/custom_layout.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Custom Layout" + "## Custom Layout" ] }, { @@ -25,7 +25,6 @@ "outputs": [], "source": [ "from cartoframes.auth import set_default_credentials\n", - "from cartoframes.viz import Map, Layer, Layout\n", "\n", "set_default_credentials('cartoframes')" ] @@ -877,14 +876,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -958,7 +957,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -973,12 +972,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                  ${popupHTML}
                  `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                  ${popupHTML}
                  `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1337,7 +1340,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1387,7 +1390,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -1396,6 +1399,8 @@ } ], "source": [ + "from cartoframes.viz import Map, Layer, Layout\n", + "\n", "Layout([\n", " Map(Layer('drought_wk_1')),\n", " Map(Layer('drought_wk_2')),\n", @@ -2259,14 +2264,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -2340,7 +2345,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -2355,12 +2360,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                  ${popupHTML}
                  `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                  ${popupHTML}
                  `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -2719,7 +2728,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -2769,7 +2778,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 3, diff --git a/examples/map_layout/default_layout.ipynb b/docs/examples/data_visualization/grid_layout/default_layout.ipynb similarity index 99% rename from examples/map_layout/default_layout.ipynb rename to docs/examples/data_visualization/grid_layout/default_layout.ipynb index 1f37b14ae..9d9dc4c16 100644 --- a/examples/map_layout/default_layout.ipynb +++ b/docs/examples/data_visualization/grid_layout/default_layout.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Default Layout" + "## Default Layout" ] }, { @@ -25,7 +25,6 @@ "outputs": [], "source": [ "from cartoframes.auth import set_default_credentials\n", - "from cartoframes.viz import Map, Layer, Layout\n", "\n", "set_default_credentials('cartoframes')" ] @@ -852,14 +851,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -933,7 +932,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -948,12 +947,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                  ${popupHTML}
                  `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                  ${popupHTML}
                  `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1312,7 +1315,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1362,7 +1365,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -1371,6 +1374,8 @@ } ], "source": [ + "from cartoframes.viz import Map, Layer, Layout\n", + "\n", "Layout([\n", " Map(Layer('drought_wk_1')),\n", " Map(Layer('drought_wk_2')),\n", diff --git a/examples/map_layout/layout_titles.ipynb b/docs/examples/data_visualization/grid_layout/layout_titles.ipynb similarity index 99% rename from examples/map_layout/layout_titles.ipynb rename to docs/examples/data_visualization/grid_layout/layout_titles.ipynb index b614968e0..3f37b472f 100644 --- a/examples/map_layout/layout_titles.ipynb +++ b/docs/examples/data_visualization/grid_layout/layout_titles.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Layout titles" + "## Layout titles" ] }, { @@ -25,7 +25,6 @@ "outputs": [], "source": [ "from cartoframes.auth import set_default_credentials\n", - "from cartoframes.viz import Map, Layer, Layout\n", "\n", "set_default_credentials('cartoframes')" ] @@ -839,14 +838,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -920,7 +919,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -935,12 +934,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                  ${popupHTML}
                  `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                  ${popupHTML}
                  `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1299,7 +1302,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1349,7 +1352,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -1358,6 +1361,8 @@ } ], "source": [ + "from cartoframes.viz import Map, Layer, Layout\n", + "\n", "Layout([\n", " Map(Layer('drought_wk_1'), title=\"US Drought: Week 1\"),\n", " Map(Layer('drought_wk_2'), title=\"US Drought: Week 2\"),\n", diff --git a/examples/map_layout/layout_viewport.ipynb b/docs/examples/data_visualization/grid_layout/layout_viewport.ipynb similarity index 99% rename from examples/map_layout/layout_viewport.ipynb rename to docs/examples/data_visualization/grid_layout/layout_viewport.ipynb index e9285a637..cd67cce22 100644 --- a/examples/map_layout/layout_viewport.ipynb +++ b/docs/examples/data_visualization/grid_layout/layout_viewport.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Layout viewport" + "## Layout viewport" ] }, { @@ -25,7 +25,6 @@ "outputs": [], "source": [ "from cartoframes.auth import set_default_credentials\n", - "from cartoframes.viz import Map, Layer, Layout, basic_style\n", "\n", "set_default_credentials('cartoframes')" ] @@ -880,14 +879,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -961,7 +960,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -976,12 +975,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                  ${popupHTML}
                  `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                  ${popupHTML}
                  `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1340,7 +1343,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1390,7 +1393,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -1399,6 +1402,8 @@ } ], "source": [ + "from cartoframes.viz import Map, Layer, Layout, basic_style\n", + "\n", "Layout([\n", " Map(Layer('select * from drought_wk_1 where dm = 3', basic_style(color='#e15383'))),\n", " Map(Layer('select * from drought_wk_2 where dm = 3', basic_style(color='#e15383'))),\n", @@ -2257,14 +2262,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -2338,7 +2343,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -2353,12 +2358,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                  ${popupHTML}
                  `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                  ${popupHTML}
                  `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -2717,7 +2726,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -2767,7 +2776,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -2776,6 +2785,8 @@ } ], "source": [ + "from cartoframes.viz import Map, Layer, Layout, basic_style\n", + "\n", "Layout([\n", " Map(Layer('drought_wk_1'), viewport={ 'zoom': 0.5 }),\n", " Map(Layer('select * from drought_wk_1 where dm = 1', basic_style(color='#ffc285'))),\n", diff --git a/examples/map_layout/static_layout.ipynb b/docs/examples/data_visualization/grid_layout/static_layout.ipynb similarity index 99% rename from examples/map_layout/static_layout.ipynb rename to docs/examples/data_visualization/grid_layout/static_layout.ipynb index bb0ac0602..c3d140e60 100644 --- a/examples/map_layout/static_layout.ipynb +++ b/docs/examples/data_visualization/grid_layout/static_layout.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Static Layout" + "## Static Layout" ] }, { @@ -25,7 +25,6 @@ "outputs": [], "source": [ "from cartoframes.auth import set_default_credentials\n", - "from cartoframes.viz import Map, Layer, Layout, color_bins_style\n", "\n", "set_default_credentials('cartoframes')" ] @@ -831,14 +830,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -912,7 +911,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -927,12 +926,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                  ${popupHTML}
                  `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                  ${popupHTML}
                  `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1291,7 +1294,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1341,7 +1344,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -1350,6 +1353,8 @@ } ], "source": [ + "from cartoframes.viz import Map, Layer, Layout, color_bins_style\n", + "\n", "Layout([\n", " Map(Layer('drought_wk_1', color_bins_style('dm', method='equal', palette='pinkyl'))),\n", " Map(Layer('drought_wk_4', color_bins_style('dm', method='equal', palette='pinkyl'))),\n", diff --git a/examples/layers/add_layer.ipynb b/docs/examples/data_visualization/layers/add_layer.ipynb similarity index 64% rename from examples/layers/add_layer.ipynb rename to docs/examples/data_visualization/layers/add_layer.ipynb index 45fe73750..e5d1dc33e 100644 --- a/examples/layers/add_layer.ipynb +++ b/docs/examples/data_visualization/layers/add_layer.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Add a Layer\n", + "## Add a Layer\n", "\n", "This example illustrates how to add one Layer to a Map.\n", "\n", @@ -86,6 +86,11 @@ " max-width: 100%;\n", " height: auto;\n", " }\n", + "\n", + " as-layer-selector-slot .as-layer-selector-slot--wrapper .as-caption { // FIXME\n", + " font-size: 14px;\n", + " line-height: 14px;\n", + " }\n", "\n", " \n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
                  \n", + "
                  \n", + "
                  \n", + " \n", + " \n", + "
                  \n", + "
                  \n", + "                   \n", + "\n", + " \n", + "\n", + "
                  \n", + "
                  \n", + " :\n", + "
                  \n", + " \n", + " \n", + "
                  \n", + "
                  \n", + "\n", + "
                  \n", + " StackTrace\n", + "
                    \n", + "
                    \n", + "
                    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Map(\n", + " Layer('pittsburgh_311'),\n", + " basemap=basemaps.voyager\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/data_visualization/map_configuration/custom_viewport.ipynb b/docs/examples/data_visualization/map_configuration/custom_viewport.ipynb new file mode 100644 index 000000000..295aeb36f --- /dev/null +++ b/docs/examples/data_visualization/map_configuration/custom_viewport.ipynb @@ -0,0 +1,1311 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set a custom viewport\n", + "\n", + "By default, CARTOframes sets the center and zoom of your map to encompass all features in a dataset. In the cases where you want to set a custom extent, setting `show_info=True` places zoom and latitude/longitude coordinates at the bottom left corner of the map. Copying and pasting the values to the `viewport` parameter sets the opening zoom and center of the map." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "from cartoframes.viz import Map, Layer\n", + "\n", + "set_default_credentials('cartoframes')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                      \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Map(\n", + " Layer('pittsburgh_311'),\n", + " show_info=True,\n", + " viewport={'zoom': 13.5, 'lat': 40.4353, 'lng': -79.9916}\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/data_visualization/map_configuration/dark_theme.ipynb b/docs/examples/data_visualization/map_configuration/dark_theme.ipynb new file mode 100644 index 000000000..7ec811c97 --- /dev/null +++ b/docs/examples/data_visualization/map_configuration/dark_theme.ipynb @@ -0,0 +1,1409 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Switch to a dark theme\n", + "\n", + "By default, CARTOframes uses Positron, a light basemap with light themed widget and legend panels. When the basemap is swithed to Dark Matter and legends and widgets are present, the theme updates accordingly.\n", + "\n", + "In the example below, by setting the `basemap` parameter to `darkmatter`, all map components switch to a dark theme that is better suited for the dark basemap." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "from cartoframes.viz import Map, Layer, basemaps, color_category_style\n", + "\n", + "set_default_credentials('cartoframes')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                        \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Map(\n", + " Layer(\n", + " 'pittsburgh_311',\n", + " color_category_style('request_type', top=3, palette='[#4ABD9A,#4A5798,#F9CA34]'),\n", + " title=\"Requests\"\n", + " ),\n", + " basemap=basemaps.darkmatter\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/data_visualization/map_configuration/solid_color_background.ipynb b/docs/examples/data_visualization/map_configuration/solid_color_background.ipynb new file mode 100644 index 000000000..0ac26a880 --- /dev/null +++ b/docs/examples/data_visualization/map_configuration/solid_color_background.ipynb @@ -0,0 +1,1310 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Use a solid color background\n", + "\n", + "By default, CARTOframes uses CARTO's Positron basemap with labels under. The basemap can be customized to use another CARTO style (Voyager, Dark Matter), a Mapbox basemap, or a custom background color; all through the `basemap` parameter.\n", + "\n", + "In the example below, the default basemap is changed to a solid color background." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "from cartoframes.viz import Map, Layer\n", + "\n", + "set_default_credentials('cartovl')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
                        \n", + "
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                        \n", + " :\n", + "
                        \n", + " \n", + " \n", + "
                        \n", + "
                        \n", + "\n", + "
                        \n", + " StackTrace\n", + "
                          \n", + "
                          \n", + "
                          \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Map(\n", + " Layer('pittsburgh_311'),\n", + " basemap='#dadcf4'\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/popups/default_popup.ipynb b/docs/examples/data_visualization/popups/default_popup.ipynb similarity index 97% rename from examples/popups/default_popup.ipynb rename to docs/examples/data_visualization/popups/default_popup.ipynb index 1c630e20b..3f3691c6a 100644 --- a/examples/popups/default_popup.ipynb +++ b/docs/examples/data_visualization/popups/default_popup.ipynb @@ -14,7 +14,6 @@ "outputs": [], "source": [ "from cartoframes.auth import set_default_credentials\n", - "from cartoframes.viz import Layer, color_bins_style, default_popup_element\n", "\n", "set_default_credentials('cartoframes')" ] @@ -50,11 +49,11 @@ " \n", "\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", "\n", " \n", "\n", @@ -721,9 +720,8 @@ " return locale;\n", " }\n", "\n", - " function format$1(value, formatString) {\n", - " // TODO: Check what to do with legend's format call with parameters (legends.html.j2 e.g.)\n", - " const formatFunc = formatString ? format(formatString) : formatValue;\n", + " function formatter(value, specifier) {\n", + " const formatFunc = specifier ? format(specifier) : formatValue;\n", "\n", " if (Array.isArray(value)) {\n", " const [first, second] = value;\n", @@ -755,21 +753,21 @@ " return value.toLocaleString();\n", " }\n", "\n", - " function updateViewport(map) {\n", + " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", - " const mapInfo$ = document.getElementById('map-info');\n", + " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -843,12 +841,12 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", " let value = variable.value;\n", - " value = format$1(value, item.format);\n", + " value = formatter(value, item.format);\n", "\n", " popupHTML = `\n", " ${item.title}\n", @@ -858,12 +856,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                          ${popupHTML}
                          `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                          ${popupHTML}
                          `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -918,7 +920,7 @@ " widget.element = widget.element || document.querySelector(`#${widget.id}-value`);\n", "\n", " if (value && widget.element) {\n", - " widget.element.innerText = typeof value === 'number' ? format$1(value, widget.options.format) : value;\n", + " widget.element.innerText = typeof value === 'number' ? formatter(value, widget.options.format) : value;\n", " }\n", " }\n", "\n", @@ -930,7 +932,6 @@ " const type = _getWidgetType(mapLayer, widget.value, widget.prop);\n", " const histogram = type === 'category' ? 'categoricalHistogram' : 'numericalHistogram';\n", " bridge[histogram](widget.element, widget.value, widget.options);\n", - "\n", " break;\n", " case 'category':\n", " bridge.category(widget.element, widget.value, widget.options);\n", @@ -991,10 +992,7 @@ " const order = legend.ascending ? 'ASC' : 'DESC';\n", " const variable = legend.variable;\n", " const config = { othersLabel, variable, order };\n", - " const formatString = legend.format;\n", - " const formatFunc = formatString\n", - " ? (value) => format$1(value, formatString)\n", - " : format$1;\n", + " const formatFunc = (value) => formatter(value, legend.format);\n", " const options = { format: formatFunc, config, dynamic };\n", "\n", " if (legend.type.startsWith('size-continuous')) {\n", @@ -1151,7 +1149,8 @@ " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", - " updateViewport(map);\n", + " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", + " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", @@ -1225,7 +1224,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1271,7 +1270,7 @@ " const has_legends = 'true' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"eng_wales_pop\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": ",.3r", "name": "v18f1e3", "title": "Population per square km"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "color", "title": "pop_sq_km", "type": "color-bins", "variable": null}], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"eng_wales_pop\\"", "title": null, "type": "Query", "viz": "@v18f1e3: prop(\\u0027pop_sq_km\\u0027)\\ncolor: opacity(ramp(globalQuantiles(prop(\\u0027pop_sq_km\\u0027), 5), purpor), 0.9)\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nfilter: 1\\n", "widgets": []}];\n", + " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"eng_wales_pop\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": ",.3r", "name": "v18f1e3", "title": "Population per square km"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "color", "title": "pop_sq_km", "type": "color-bins", "variable": null}], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"eng_wales_pop\\"", "title": null, "type": "Query", "viz": "@v18f1e3: prop(\\u0027pop_sq_km\\u0027)\\ncolor: opacity(ramp(globalQuantiles(prop(\\u0027pop_sq_km\\u0027), 5), purpor), 0.9)\\nfilter: 1\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -1295,7 +1294,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -1304,6 +1303,8 @@ } ], "source": [ + "from cartoframes.viz import Layer, color_bins_style, default_popup_element\n", + "\n", "Layer(\n", " 'eng_wales_pop',\n", " color_bins_style('pop_sq_km'),\n", @@ -1330,7 +1331,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.6.7" } }, "nbformat": 4, diff --git a/docs/examples/data_visualization/popups/multiple_popup_events.ipynb b/docs/examples/data_visualization/popups/multiple_popup_events.ipynb new file mode 100644 index 000000000..7ec021527 --- /dev/null +++ b/docs/examples/data_visualization/popups/multiple_popup_events.ipynb @@ -0,0 +1,1321 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Multiple popup events\n", + "\n", + "Popups provide information about features through either `click` or `hover` interactivity events. Features can listen to both events or multiple properties from the same event.\n", + "\n", + "For more information on popups, run `help(Popup)`.\n", + "\n", + "In this example, there are two different definitions for popup information depending on whether it is a `hover` or `click` event." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('cartoframes')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
                          \n", + "
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                          \n", + "
                          \n", + " :\n", + "
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                          \n", + " StackTrace\n", + "
                            \n", + "
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                            \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.viz import Layer, popup_element\n", + "\n", + "Layer(\n", + " 'countries_africa',\n", + " popup_hover=[\n", + " popup_element('name', title='Country'),\n", + " ],\n", + " popup_click=[\n", + " popup_element('pop_est', title='Population'),\n", + " popup_element('gdp_md_est', title='GDP')\n", + " ]\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/data_visualization/popups/popup_on_click.ipynb b/docs/examples/data_visualization/popups/popup_on_click.ipynb new file mode 100644 index 000000000..3a2baed23 --- /dev/null +++ b/docs/examples/data_visualization/popups/popup_on_click.ipynb @@ -0,0 +1,1317 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Popup on click\n", + "\n", + "Popups provide information about features through either `click` or `hover` interactivity events. Features can listen to both events or multiple properties from the same event.\n", + "For more information on popups, run `help(Popup)`.\n", + "\n", + "In this example, the `click` event shows two attributes from the Layer with default column name titles." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('cartoframes')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                            \n", + " :\n", + "
                            \n", + " \n", + " \n", + "
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                            \n", + " StackTrace\n", + "
                              \n", + "
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                              \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.viz import Layer, popup_element\n", + "\n", + "Layer(\n", + " 'countries_africa',\n", + " popup_click=[\n", + " popup_element('pop_est'),\n", + " popup_element('name_long')\n", + " ]\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/data_visualization/popups/popup_on_hover.ipynb b/docs/examples/data_visualization/popups/popup_on_hover.ipynb new file mode 100644 index 000000000..4b1504c21 --- /dev/null +++ b/docs/examples/data_visualization/popups/popup_on_hover.ipynb @@ -0,0 +1,1318 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Popup on hover\n", + "\n", + "Popups provide information about features through either `click` or `hover` interactivity events. Features can listen to both events or multiple properties from the same event.\n", + "\n", + "For more information on popups, run `help(Popup)`.\n", + "\n", + "In this example, the `hover` event shows two attributes from the Layer with default column name titles." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('cartoframes')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                              \n", + " :\n", + "
                              \n", + " \n", + " \n", + "
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                              \n", + " StackTrace\n", + "
                                \n", + "
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                                \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.viz import Layer, popup_element\n", + "\n", + "Layer(\n", + " 'countries_africa',\n", + " popup_hover=[\n", + " popup_element('pop_est'),\n", + " popup_element('name_long')\n", + " ]\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/popups/popup_titles_and_format.ipynb b/docs/examples/data_visualization/popups/popup_titles_and_format.ipynb similarity index 97% rename from examples/popups/popup_titles_and_format.ipynb rename to docs/examples/data_visualization/popups/popup_titles_and_format.ipynb index ff52ab033..fdac65b1c 100644 --- a/examples/popups/popup_titles_and_format.ipynb +++ b/docs/examples/data_visualization/popups/popup_titles_and_format.ipynb @@ -4,7 +4,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Popup titles and format\n", + "## Popup titles and format\n", "\n", "Popups provide information about features through either `click` or `hover` interactivity events. Features can listen to both events or multiple properties from the same event.\n", "\n", @@ -20,7 +20,6 @@ "outputs": [], "source": [ "from cartoframes.auth import set_default_credentials\n", - "from cartoframes.viz import Layer, basic_style, popup_element\n", "\n", "set_default_credentials('cartoframes')" ] @@ -733,21 +732,21 @@ " return value.toLocaleString();\n", " }\n", "\n", - " function updateViewport(map) {\n", + " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", - " const mapInfo$ = document.getElementById('map-info');\n", + " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -821,7 +820,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -836,12 +835,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                ${popupHTML}
                                `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                ${popupHTML}
                                `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1125,7 +1128,8 @@ " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", - " updateViewport(map);\n", + " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", + " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", @@ -1199,7 +1203,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1245,7 +1249,7 @@ " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"spain_populated_places\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": ",.2r", "name": "vf721bc", "title": "Rounded population"}, "event": "hover"}, {"attrs": {"format": null, "name": "v6ae999", "title": "City"}, "event": "hover"}], "legends": [], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"spain_populated_places\\"", "title": null, "type": "Query", "viz": "@vf721bc: prop(\\u0027pop_max\\u0027)\\n@v6ae999: prop(\\u0027name\\u0027)\\ncolor: opacity(hex(\\"#EE4D5A\\"), 1)\\nwidth: 10\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\n", "widgets": []}];\n", + " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"spain_populated_places\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": ",.2r", "name": "vf721bc", "title": "Rounded population"}, "event": "hover"}, {"attrs": {"format": null, "name": "v6ae999", "title": "City"}, "event": "hover"}], "legends": [], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"spain_populated_places\\"", "title": null, "type": "Query", "viz": "@v6ae999: prop(\\u0027name\\u0027)\\n@vf721bc: prop(\\u0027pop_max\\u0027)\\ncolor: opacity(hex(\\"#EE4D5A\\"), 1)\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: 10\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -1269,7 +1273,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -1278,7 +1282,7 @@ } ], "source": [ - "from cartoframes.viz import Layer, popup_element\n", + "from cartoframes.viz import Layer, basic_style, popup_element\n", "\n", "Layer(\n", " 'spain_populated_places',\n", diff --git a/examples/publish_and_share/publish_visualization_gdf.ipynb b/docs/examples/data_visualization/publish_and_share/publish_visualization_gdf.ipynb similarity index 95% rename from examples/publish_and_share/publish_visualization_gdf.ipynb rename to docs/examples/data_visualization/publish_and_share/publish_visualization_gdf.ipynb index f6d66cea8..438597ad3 100644 --- a/examples/publish_and_share/publish_visualization_gdf.ipynb +++ b/docs/examples/data_visualization/publish_and_share/publish_visualization_gdf.ipynb @@ -4,13 +4,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Publish a visualization from a GeoDataFrame\n", + "## Publish a visualization from a GeoDataFrame\n", "\n", "This example illustrate how to publish a visualization using a GeoDataFrame.\n", "\n", "Read more about Maps API Keys: https://carto.com/developers/auth-api/guides/types-of-API-Keys/\n", "\n", - "> Note: CARTO Account credentials are needed to reproduce this example. https://carto.com/signup" + ">_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to reproduce this example._" ] }, { @@ -76,7 +76,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Recomendation\n", + "### Recomendation\n", "\n", "The performance of visualizations can be affected by using large GeoDataFrames. There is also a size limit of 10MB per visualization published. Our recomentation in these cases is to upload the data to a table in CARTO, and use the table as the source in the Layer." ] diff --git a/examples/publish_and_share/publish_visualization_layout.ipynb b/docs/examples/data_visualization/publish_and_share/publish_visualization_layout.ipynb similarity index 93% rename from examples/publish_and_share/publish_visualization_layout.ipynb rename to docs/examples/data_visualization/publish_and_share/publish_visualization_layout.ipynb index a4a96c8e3..1dd084904 100644 --- a/examples/publish_and_share/publish_visualization_layout.ipynb +++ b/docs/examples/data_visualization/publish_and_share/publish_visualization_layout.ipynb @@ -4,11 +4,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Publish a visualization of a layout\n", + "## Publish a visualization of a layout\n", "\n", "This example illustrate how to publish a layout of map visualizations.\n", "\n", - "> Note: CARTO Account credentials are needed to reproduce this example. https://carto.com/signup" + ">_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to reproduce this example._" ] }, { diff --git a/examples/publish_and_share/publish_visualization_private_table.ipynb b/docs/examples/data_visualization/publish_and_share/publish_visualization_private_table.ipynb similarity index 95% rename from examples/publish_and_share/publish_visualization_private_table.ipynb rename to docs/examples/data_visualization/publish_and_share/publish_visualization_private_table.ipynb index 73f28ebf4..97342fc2d 100644 --- a/examples/publish_and_share/publish_visualization_private_table.ipynb +++ b/docs/examples/data_visualization/publish_and_share/publish_visualization_private_table.ipynb @@ -4,13 +4,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Publish a visualization from a private table\n", + "## Publish a visualization from a private table\n", "\n", "This example illustrate how to publish a visualization using a private table. Using private tables requires to create Maps API keys for the visualization. By default it creates a custom Maps API key for all the private tables used in the visualization if not provided.\n", "\n", "Read more about Maps API Keys: https://carto.com/developers/auth-api/guides/types-of-API-Keys/\n", "\n", - "> Note: CARTO Account credentials are needed to reproduce this example. https://carto.com/signup" + ">_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to reproduce this example._" ] }, { diff --git a/examples/publish_and_share/publish_visualization_public_table.ipynb b/docs/examples/data_visualization/publish_and_share/publish_visualization_public_table.ipynb similarity index 92% rename from examples/publish_and_share/publish_visualization_public_table.ipynb rename to docs/examples/data_visualization/publish_and_share/publish_visualization_public_table.ipynb index aae06ca6b..f6de6bfc0 100644 --- a/examples/publish_and_share/publish_visualization_public_table.ipynb +++ b/docs/examples/data_visualization/publish_and_share/publish_visualization_public_table.ipynb @@ -4,13 +4,13 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Publish a visualization from a public table\n", + "## Publish a visualization from a public table\n", "\n", "This example illustrates how to publish a visualization using a public table. You do not need a Maps API key to visualize a public table within a notebook. However you do need an API Key to publish any visualization, whether its data is public or private.\n", "\n", "Read more about API Keys here: https://carto.com/developers/auth-api/guides/types-of-API-Keys/\n", "\n", - "> Note: CARTO Account credentials are needed to reproduce this example. https://carto.com/signup" + ">_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to reproduce this example._" ] }, { diff --git a/examples/styles/animation_style.ipynb b/docs/examples/data_visualization/styles/animation_style.ipynb similarity index 65% rename from examples/styles/animation_style.ipynb rename to docs/examples/data_visualization/styles/animation_style.ipynb index 14f1f3180..c44771241 100644 --- a/examples/styles/animation_style.ipynb +++ b/docs/examples/data_visualization/styles/animation_style.ipynb @@ -383,18 +383,330 @@ " stacktrace$.innerHTML = list.join('\\n');\n", " }\n", "\n", - " function format(value) {\n", + " // Computes the decimal coefficient and exponent of the specified number x with\n", + " // significant digits p, where x is positive and p is in [1, 21] or undefined.\n", + " // For example, formatDecimal(1.23) returns ["123", 0].\n", + " function formatDecimal(x, p) {\n", + " if ((i = (x = p ? x.toExponential(p - 1) : x.toExponential()).indexOf("e")) < 0) return null; // NaN, ±Infinity\n", + " var i, coefficient = x.slice(0, i);\n", + "\n", + " // The string returned by toExponential either has the form \\d\\.\\d+e[-+]\\d+\n", + " // (e.g., 1.2e+3) or the form \\de[-+]\\d+ (e.g., 1e+3).\n", + " return [\n", + " coefficient.length > 1 ? coefficient[0] + coefficient.slice(2) : coefficient,\n", + " +x.slice(i + 1)\n", + " ];\n", + " }\n", + "\n", + " function exponent(x) {\n", + " return x = formatDecimal(Math.abs(x)), x ? x[1] : NaN;\n", + " }\n", + "\n", + " function formatGroup(grouping, thousands) {\n", + " return function(value, width) {\n", + " var i = value.length,\n", + " t = [],\n", + " j = 0,\n", + " g = grouping[0],\n", + " length = 0;\n", + "\n", + " while (i > 0 && g > 0) {\n", + " if (length + g + 1 > width) g = Math.max(1, width - length);\n", + " t.push(value.substring(i -= g, i + g));\n", + " if ((length += g + 1) > width) break;\n", + " g = grouping[j = (j + 1) % grouping.length];\n", + " }\n", + "\n", + " return t.reverse().join(thousands);\n", + " };\n", + " }\n", + "\n", + " function formatNumerals(numerals) {\n", + " return function(value) {\n", + " return value.replace(/[0-9]/g, function(i) {\n", + " return numerals[+i];\n", + " });\n", + " };\n", + " }\n", + "\n", + " // [[fill]align][sign][symbol][0][width][,][.precision][~][type]\n", + " var re = /^(?:(.)?([<>=^]))?([+\\-( ])?([$#])?(0)?(\\d+)?(,)?(\\.\\d+)?(~)?([a-z%])?$/i;\n", + "\n", + " function formatSpecifier(specifier) {\n", + " if (!(match = re.exec(specifier))) throw new Error("invalid format: " + specifier);\n", + " var match;\n", + " return new FormatSpecifier({\n", + " fill: match[1],\n", + " align: match[2],\n", + " sign: match[3],\n", + " symbol: match[4],\n", + " zero: match[5],\n", + " width: match[6],\n", + " comma: match[7],\n", + " precision: match[8] && match[8].slice(1),\n", + " trim: match[9],\n", + " type: match[10]\n", + " });\n", + " }\n", + "\n", + " formatSpecifier.prototype = FormatSpecifier.prototype; // instanceof\n", + "\n", + " function FormatSpecifier(specifier) {\n", + " this.fill = specifier.fill === undefined ? " " : specifier.fill + "";\n", + " this.align = specifier.align === undefined ? ">" : specifier.align + "";\n", + " this.sign = specifier.sign === undefined ? "-" : specifier.sign + "";\n", + " this.symbol = specifier.symbol === undefined ? "" : specifier.symbol + "";\n", + " this.zero = !!specifier.zero;\n", + " this.width = specifier.width === undefined ? undefined : +specifier.width;\n", + " this.comma = !!specifier.comma;\n", + " this.precision = specifier.precision === undefined ? undefined : +specifier.precision;\n", + " this.trim = !!specifier.trim;\n", + " this.type = specifier.type === undefined ? "" : specifier.type + "";\n", + " }\n", + "\n", + " FormatSpecifier.prototype.toString = function() {\n", + " return this.fill\n", + " + this.align\n", + " + this.sign\n", + " + this.symbol\n", + " + (this.zero ? "0" : "")\n", + " + (this.width === undefined ? "" : Math.max(1, this.width | 0))\n", + " + (this.comma ? "," : "")\n", + " + (this.precision === undefined ? "" : "." + Math.max(0, this.precision | 0))\n", + " + (this.trim ? "~" : "")\n", + " + this.type;\n", + " };\n", + "\n", + " // Trims insignificant zeros, e.g., replaces 1.2000k with 1.2k.\n", + " function formatTrim(s) {\n", + " out: for (var n = s.length, i = 1, i0 = -1, i1; i < n; ++i) {\n", + " switch (s[i]) {\n", + " case ".": i0 = i1 = i; break;\n", + " case "0": if (i0 === 0) i0 = i; i1 = i; break;\n", + " default: if (!+s[i]) break out; if (i0 > 0) i0 = 0; break;\n", + " }\n", + " }\n", + " return i0 > 0 ? s.slice(0, i0) + s.slice(i1 + 1) : s;\n", + " }\n", + "\n", + " var prefixExponent;\n", + "\n", + " function formatPrefixAuto(x, p) {\n", + " var d = formatDecimal(x, p);\n", + " if (!d) return x + "";\n", + " var coefficient = d[0],\n", + " exponent = d[1],\n", + " i = exponent - (prefixExponent = Math.max(-8, Math.min(8, Math.floor(exponent / 3))) * 3) + 1,\n", + " n = coefficient.length;\n", + " return i === n ? coefficient\n", + " : i > n ? coefficient + new Array(i - n + 1).join("0")\n", + " : i > 0 ? coefficient.slice(0, i) + "." + coefficient.slice(i)\n", + " : "0." + new Array(1 - i).join("0") + formatDecimal(x, Math.max(0, p + i - 1))[0]; // less than 1y!\n", + " }\n", + "\n", + " function formatRounded(x, p) {\n", + " var d = formatDecimal(x, p);\n", + " if (!d) return x + "";\n", + " var coefficient = d[0],\n", + " exponent = d[1];\n", + " return exponent < 0 ? "0." + new Array(-exponent).join("0") + coefficient\n", + " : coefficient.length > exponent + 1 ? coefficient.slice(0, exponent + 1) + "." + coefficient.slice(exponent + 1)\n", + " : coefficient + new Array(exponent - coefficient.length + 2).join("0");\n", + " }\n", + "\n", + " var formatTypes = {\n", + " "%": function(x, p) { return (x * 100).toFixed(p); },\n", + " "b": function(x) { return Math.round(x).toString(2); },\n", + " "c": function(x) { return x + ""; },\n", + " "d": function(x) { return Math.round(x).toString(10); },\n", + " "e": function(x, p) { return x.toExponential(p); },\n", + " "f": function(x, p) { return x.toFixed(p); },\n", + " "g": function(x, p) { return x.toPrecision(p); },\n", + " "o": function(x) { return Math.round(x).toString(8); },\n", + " "p": function(x, p) { return formatRounded(x * 100, p); },\n", + " "r": formatRounded,\n", + " "s": formatPrefixAuto,\n", + " "X": function(x) { return Math.round(x).toString(16).toUpperCase(); },\n", + " "x": function(x) { return Math.round(x).toString(16); }\n", + " };\n", + "\n", + " function identity(x) {\n", + " return x;\n", + " }\n", + "\n", + " var map = Array.prototype.map,\n", + " prefixes = ["y","z","a","f","p","n","µ","m","","k","M","G","T","P","E","Z","Y"];\n", + "\n", + " function formatLocale(locale) {\n", + " var group = locale.grouping === undefined || locale.thousands === undefined ? identity : formatGroup(map.call(locale.grouping, Number), locale.thousands + ""),\n", + " currencyPrefix = locale.currency === undefined ? "" : locale.currency[0] + "",\n", + " currencySuffix = locale.currency === undefined ? "" : locale.currency[1] + "",\n", + " decimal = locale.decimal === undefined ? "." : locale.decimal + "",\n", + " numerals = locale.numerals === undefined ? identity : formatNumerals(map.call(locale.numerals, String)),\n", + " percent = locale.percent === undefined ? "%" : locale.percent + "",\n", + " minus = locale.minus === undefined ? "-" : locale.minus + "",\n", + " nan = locale.nan === undefined ? "NaN" : locale.nan + "";\n", + "\n", + " function newFormat(specifier) {\n", + " specifier = formatSpecifier(specifier);\n", + "\n", + " var fill = specifier.fill,\n", + " align = specifier.align,\n", + " sign = specifier.sign,\n", + " symbol = specifier.symbol,\n", + " zero = specifier.zero,\n", + " width = specifier.width,\n", + " comma = specifier.comma,\n", + " precision = specifier.precision,\n", + " trim = specifier.trim,\n", + " type = specifier.type;\n", + "\n", + " // The "n" type is an alias for ",g".\n", + " if (type === "n") comma = true, type = "g";\n", + "\n", + " // The "" type, and any invalid type, is an alias for ".12~g".\n", + " else if (!formatTypes[type]) precision === undefined && (precision = 12), trim = true, type = "g";\n", + "\n", + " // If zero fill is specified, padding goes after sign and before digits.\n", + " if (zero || (fill === "0" && align === "=")) zero = true, fill = "0", align = "=";\n", + "\n", + " // Compute the prefix and suffix.\n", + " // For SI-prefix, the suffix is lazily computed.\n", + " var prefix = symbol === "$" ? currencyPrefix : symbol === "#" && /[boxX]/.test(type) ? "0" + type.toLowerCase() : "",\n", + " suffix = symbol === "$" ? currencySuffix : /[%p]/.test(type) ? percent : "";\n", + "\n", + " // What format function should we use?\n", + " // Is this an integer type?\n", + " // Can this type generate exponential notation?\n", + " var formatType = formatTypes[type],\n", + " maybeSuffix = /[defgprs%]/.test(type);\n", + "\n", + " // Set the default precision if not specified,\n", + " // or clamp the specified precision to the supported range.\n", + " // For significant precision, it must be in [1, 21].\n", + " // For fixed precision, it must be in [0, 20].\n", + " precision = precision === undefined ? 6\n", + " : /[gprs]/.test(type) ? Math.max(1, Math.min(21, precision))\n", + " : Math.max(0, Math.min(20, precision));\n", + "\n", + " function format(value) {\n", + " var valuePrefix = prefix,\n", + " valueSuffix = suffix,\n", + " i, n, c;\n", + "\n", + " if (type === "c") {\n", + " valueSuffix = formatType(value) + valueSuffix;\n", + " value = "";\n", + " } else {\n", + " value = +value;\n", + "\n", + " // Determine the sign. -0 is not less than 0, but 1 / -0 is!\n", + " var valueNegative = value < 0 || 1 / value < 0;\n", + "\n", + " // Perform the initial formatting.\n", + " value = isNaN(value) ? nan : formatType(Math.abs(value), precision);\n", + "\n", + " // Trim insignificant zeros.\n", + " if (trim) value = formatTrim(value);\n", + "\n", + " // If a negative value rounds to zero after formatting, and no explicit positive sign is requested, hide the sign.\n", + " if (valueNegative && +value === 0 && sign !== "+") valueNegative = false;\n", + "\n", + " // Compute the prefix and suffix.\n", + " valuePrefix = (valueNegative ? (sign === "(" ? sign : minus) : sign === "-" || sign === "(" ? "" : sign) + valuePrefix;\n", + " valueSuffix = (type === "s" ? prefixes[8 + prefixExponent / 3] : "") + valueSuffix + (valueNegative && sign === "(" ? ")" : "");\n", + "\n", + " // Break the formatted value into the integer “value” part that can be\n", + " // grouped, and fractional or exponential “suffix” part that is not.\n", + " if (maybeSuffix) {\n", + " i = -1, n = value.length;\n", + " while (++i < n) {\n", + " if (c = value.charCodeAt(i), 48 > c || c > 57) {\n", + " valueSuffix = (c === 46 ? decimal + value.slice(i + 1) : value.slice(i)) + valueSuffix;\n", + " value = value.slice(0, i);\n", + " break;\n", + " }\n", + " }\n", + " }\n", + " }\n", + "\n", + " // If the fill character is not "0", grouping is applied before padding.\n", + " if (comma && !zero) value = group(value, Infinity);\n", + "\n", + " // Compute the padding.\n", + " var length = valuePrefix.length + value.length + valueSuffix.length,\n", + " padding = length < width ? new Array(width - length + 1).join(fill) : "";\n", + "\n", + " // If the fill character is "0", grouping is applied after padding.\n", + " if (comma && zero) value = group(padding + value, padding.length ? width - valueSuffix.length : Infinity), padding = "";\n", + "\n", + " // Reconstruct the final output based on the desired alignment.\n", + " switch (align) {\n", + " case "<": value = valuePrefix + value + valueSuffix + padding; break;\n", + " case "=": value = valuePrefix + padding + value + valueSuffix; break;\n", + " case "^": value = padding.slice(0, length = padding.length >> 1) + valuePrefix + value + valueSuffix + padding.slice(length); break;\n", + " default: value = padding + valuePrefix + value + valueSuffix; break;\n", + " }\n", + "\n", + " return numerals(value);\n", + " }\n", + "\n", + " format.toString = function() {\n", + " return specifier + "";\n", + " };\n", + "\n", + " return format;\n", + " }\n", + "\n", + " function formatPrefix(specifier, value) {\n", + " var f = newFormat((specifier = formatSpecifier(specifier), specifier.type = "f", specifier)),\n", + " e = Math.max(-8, Math.min(8, Math.floor(exponent(value) / 3))) * 3,\n", + " k = Math.pow(10, -e),\n", + " prefix = prefixes[8 + e / 3];\n", + " return function(value) {\n", + " return f(k * value) + prefix;\n", + " };\n", + " }\n", + "\n", + " return {\n", + " format: newFormat,\n", + " formatPrefix: formatPrefix\n", + " };\n", + " }\n", + "\n", + " var locale;\n", + " var format;\n", + " var formatPrefix;\n", + "\n", + " defaultLocale({\n", + " decimal: ".",\n", + " thousands: ",",\n", + " grouping: [3],\n", + " currency: ["$", ""],\n", + " minus: "-"\n", + " });\n", + "\n", + " function defaultLocale(definition) {\n", + " locale = formatLocale(definition);\n", + " format = locale.format;\n", + " formatPrefix = locale.formatPrefix;\n", + " return locale;\n", + " }\n", + "\n", + " function formatter(value, specifier) {\n", + " const formatFunc = specifier ? format(specifier) : formatValue;\n", + "\n", " if (Array.isArray(value)) {\n", " const [first, second] = value;\n", " if (first === -Infinity) {\n", - " return `< ${formatValue(second)}`;\n", + " return `< ${formatFunc(second)}`;\n", " }\n", " if (second === Infinity) {\n", - " return `> ${formatValue(first)}`;\n", + " return `> ${formatFunc(first)}`;\n", " }\n", - " return `${formatValue(first)} - ${formatValue(second)}`;\n", + " return `${formatFunc(first)} - ${formatFunc(second)}`;\n", " }\n", - " return formatValue(value);\n", + " return formatFunc(value);\n", " }\n", "\n", " function formatValue(value) {\n", @@ -414,21 +726,21 @@ " return value.toLocaleString();\n", " }\n", "\n", - " function updateViewport(map) {\n", + " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", - " const mapInfo$ = document.getElementById('map-info');\n", + " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -502,12 +814,12 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", " let value = variable.value;\n", - " value = formatValue(value);\n", + " value = formatter(value, item.format);\n", "\n", " popupHTML = `\n", " ${item.title}\n", @@ -517,12 +829,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                ${popupHTML}
                                `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                ${popupHTML}
                                `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -577,7 +893,7 @@ " widget.element = widget.element || document.querySelector(`#${widget.id}-value`);\n", "\n", " if (value && widget.element) {\n", - " widget.element.innerText = typeof value === 'number' ? format(value) : value;\n", + " widget.element.innerText = typeof value === 'number' ? formatter(value, widget.options.format) : value;\n", " }\n", " }\n", "\n", @@ -589,7 +905,6 @@ " const type = _getWidgetType(mapLayer, widget.value, widget.prop);\n", " const histogram = type === 'category' ? 'categoricalHistogram' : 'numericalHistogram';\n", " bridge[histogram](widget.element, widget.value, widget.options);\n", - "\n", " break;\n", " case 'category':\n", " bridge.category(widget.element, widget.value, widget.options);\n", @@ -650,7 +965,8 @@ " const order = legend.ascending ? 'ASC' : 'DESC';\n", " const variable = legend.variable;\n", " const config = { othersLabel, variable, order };\n", - " const options = { format, config, dynamic };\n", + " const formatFunc = (value) => formatter(value, legend.format);\n", + " const options = { format: formatFunc, config, dynamic };\n", "\n", " if (legend.type.startsWith('size-continuous')) {\n", " config.samples = 4;\n", @@ -806,7 +1122,8 @@ " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", - " updateViewport(map);\n", + " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", + " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", @@ -880,7 +1197,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -921,12 +1238,12 @@ " .addEventListener('ready', () => {\n", " const basecolor = '';\n", " const basemap = 'Positron';\n", - " const bounds = [[-105.007825536403, 39.7402045274956], [-104.987547762685, 39.7594219853596]];\n", + " const bounds = [[-105.00782553640346, 39.74020452749562], [-104.98754776268481, 39.75942198535955]];\n", " const camera = null;\n", " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"parcels\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"name": "v52277c", "title": "ccyrblt"}, "event": "hover"}], "legends": [], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"parcels\\"", "title": null, "type": "Query", "viz": "@v52277c: prop(\\u0027ccyrblt\\u0027)\\ncolor: opacity(hex(\\"#826DBA\\"), 0.9)\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nfilter: animation(linear(prop(\\u0027ccyrblt\\u0027)), 20, fade(1, 1))\\n", "widgets": []}];\n", + " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"parcels\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "v52277c", "title": "ccyrblt"}, "event": "hover"}], "legends": [], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"parcels\\"", "title": null, "type": "Query", "viz": "@v52277c: prop(\\u0027ccyrblt\\u0027)\\ncolor: opacity(hex(\\"#826DBA\\"), 0.9)\\nfilter: animation(linear(prop(\\u0027ccyrblt\\u0027)), 20, fade(1, 1))\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -950,7 +1267,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -1355,18 +1672,330 @@ " stacktrace$.innerHTML = list.join('\\n');\n", " }\n", "\n", - " function format(value) {\n", + " // Computes the decimal coefficient and exponent of the specified number x with\n", + " // significant digits p, where x is positive and p is in [1, 21] or undefined.\n", + " // For example, formatDecimal(1.23) returns ["123", 0].\n", + " function formatDecimal(x, p) {\n", + " if ((i = (x = p ? x.toExponential(p - 1) : x.toExponential()).indexOf("e")) < 0) return null; // NaN, ±Infinity\n", + " var i, coefficient = x.slice(0, i);\n", + "\n", + " // The string returned by toExponential either has the form \\d\\.\\d+e[-+]\\d+\n", + " // (e.g., 1.2e+3) or the form \\de[-+]\\d+ (e.g., 1e+3).\n", + " return [\n", + " coefficient.length > 1 ? coefficient[0] + coefficient.slice(2) : coefficient,\n", + " +x.slice(i + 1)\n", + " ];\n", + " }\n", + "\n", + " function exponent(x) {\n", + " return x = formatDecimal(Math.abs(x)), x ? x[1] : NaN;\n", + " }\n", + "\n", + " function formatGroup(grouping, thousands) {\n", + " return function(value, width) {\n", + " var i = value.length,\n", + " t = [],\n", + " j = 0,\n", + " g = grouping[0],\n", + " length = 0;\n", + "\n", + " while (i > 0 && g > 0) {\n", + " if (length + g + 1 > width) g = Math.max(1, width - length);\n", + " t.push(value.substring(i -= g, i + g));\n", + " if ((length += g + 1) > width) break;\n", + " g = grouping[j = (j + 1) % grouping.length];\n", + " }\n", + "\n", + " return t.reverse().join(thousands);\n", + " };\n", + " }\n", + "\n", + " function formatNumerals(numerals) {\n", + " return function(value) {\n", + " return value.replace(/[0-9]/g, function(i) {\n", + " return numerals[+i];\n", + " });\n", + " };\n", + " }\n", + "\n", + " // [[fill]align][sign][symbol][0][width][,][.precision][~][type]\n", + " var re = /^(?:(.)?([<>=^]))?([+\\-( ])?([$#])?(0)?(\\d+)?(,)?(\\.\\d+)?(~)?([a-z%])?$/i;\n", + "\n", + " function formatSpecifier(specifier) {\n", + " if (!(match = re.exec(specifier))) throw new Error("invalid format: " + specifier);\n", + " var match;\n", + " return new FormatSpecifier({\n", + " fill: match[1],\n", + " align: match[2],\n", + " sign: match[3],\n", + " symbol: match[4],\n", + " zero: match[5],\n", + " width: match[6],\n", + " comma: match[7],\n", + " precision: match[8] && match[8].slice(1),\n", + " trim: match[9],\n", + " type: match[10]\n", + " });\n", + " }\n", + "\n", + " formatSpecifier.prototype = FormatSpecifier.prototype; // instanceof\n", + "\n", + " function FormatSpecifier(specifier) {\n", + " this.fill = specifier.fill === undefined ? " " : specifier.fill + "";\n", + " this.align = specifier.align === undefined ? ">" : specifier.align + "";\n", + " this.sign = specifier.sign === undefined ? "-" : specifier.sign + "";\n", + " this.symbol = specifier.symbol === undefined ? "" : specifier.symbol + "";\n", + " this.zero = !!specifier.zero;\n", + " this.width = specifier.width === undefined ? undefined : +specifier.width;\n", + " this.comma = !!specifier.comma;\n", + " this.precision = specifier.precision === undefined ? undefined : +specifier.precision;\n", + " this.trim = !!specifier.trim;\n", + " this.type = specifier.type === undefined ? "" : specifier.type + "";\n", + " }\n", + "\n", + " FormatSpecifier.prototype.toString = function() {\n", + " return this.fill\n", + " + this.align\n", + " + this.sign\n", + " + this.symbol\n", + " + (this.zero ? "0" : "")\n", + " + (this.width === undefined ? "" : Math.max(1, this.width | 0))\n", + " + (this.comma ? "," : "")\n", + " + (this.precision === undefined ? "" : "." + Math.max(0, this.precision | 0))\n", + " + (this.trim ? "~" : "")\n", + " + this.type;\n", + " };\n", + "\n", + " // Trims insignificant zeros, e.g., replaces 1.2000k with 1.2k.\n", + " function formatTrim(s) {\n", + " out: for (var n = s.length, i = 1, i0 = -1, i1; i < n; ++i) {\n", + " switch (s[i]) {\n", + " case ".": i0 = i1 = i; break;\n", + " case "0": if (i0 === 0) i0 = i; i1 = i; break;\n", + " default: if (!+s[i]) break out; if (i0 > 0) i0 = 0; break;\n", + " }\n", + " }\n", + " return i0 > 0 ? s.slice(0, i0) + s.slice(i1 + 1) : s;\n", + " }\n", + "\n", + " var prefixExponent;\n", + "\n", + " function formatPrefixAuto(x, p) {\n", + " var d = formatDecimal(x, p);\n", + " if (!d) return x + "";\n", + " var coefficient = d[0],\n", + " exponent = d[1],\n", + " i = exponent - (prefixExponent = Math.max(-8, Math.min(8, Math.floor(exponent / 3))) * 3) + 1,\n", + " n = coefficient.length;\n", + " return i === n ? coefficient\n", + " : i > n ? coefficient + new Array(i - n + 1).join("0")\n", + " : i > 0 ? coefficient.slice(0, i) + "." + coefficient.slice(i)\n", + " : "0." + new Array(1 - i).join("0") + formatDecimal(x, Math.max(0, p + i - 1))[0]; // less than 1y!\n", + " }\n", + "\n", + " function formatRounded(x, p) {\n", + " var d = formatDecimal(x, p);\n", + " if (!d) return x + "";\n", + " var coefficient = d[0],\n", + " exponent = d[1];\n", + " return exponent < 0 ? "0." + new Array(-exponent).join("0") + coefficient\n", + " : coefficient.length > exponent + 1 ? coefficient.slice(0, exponent + 1) + "." + coefficient.slice(exponent + 1)\n", + " : coefficient + new Array(exponent - coefficient.length + 2).join("0");\n", + " }\n", + "\n", + " var formatTypes = {\n", + " "%": function(x, p) { return (x * 100).toFixed(p); },\n", + " "b": function(x) { return Math.round(x).toString(2); },\n", + " "c": function(x) { return x + ""; },\n", + " "d": function(x) { return Math.round(x).toString(10); },\n", + " "e": function(x, p) { return x.toExponential(p); },\n", + " "f": function(x, p) { return x.toFixed(p); },\n", + " "g": function(x, p) { return x.toPrecision(p); },\n", + " "o": function(x) { return Math.round(x).toString(8); },\n", + " "p": function(x, p) { return formatRounded(x * 100, p); },\n", + " "r": formatRounded,\n", + " "s": formatPrefixAuto,\n", + " "X": function(x) { return Math.round(x).toString(16).toUpperCase(); },\n", + " "x": function(x) { return Math.round(x).toString(16); }\n", + " };\n", + "\n", + " function identity(x) {\n", + " return x;\n", + " }\n", + "\n", + " var map = Array.prototype.map,\n", + " prefixes = ["y","z","a","f","p","n","µ","m","","k","M","G","T","P","E","Z","Y"];\n", + "\n", + " function formatLocale(locale) {\n", + " var group = locale.grouping === undefined || locale.thousands === undefined ? identity : formatGroup(map.call(locale.grouping, Number), locale.thousands + ""),\n", + " currencyPrefix = locale.currency === undefined ? "" : locale.currency[0] + "",\n", + " currencySuffix = locale.currency === undefined ? "" : locale.currency[1] + "",\n", + " decimal = locale.decimal === undefined ? "." : locale.decimal + "",\n", + " numerals = locale.numerals === undefined ? identity : formatNumerals(map.call(locale.numerals, String)),\n", + " percent = locale.percent === undefined ? "%" : locale.percent + "",\n", + " minus = locale.minus === undefined ? "-" : locale.minus + "",\n", + " nan = locale.nan === undefined ? "NaN" : locale.nan + "";\n", + "\n", + " function newFormat(specifier) {\n", + " specifier = formatSpecifier(specifier);\n", + "\n", + " var fill = specifier.fill,\n", + " align = specifier.align,\n", + " sign = specifier.sign,\n", + " symbol = specifier.symbol,\n", + " zero = specifier.zero,\n", + " width = specifier.width,\n", + " comma = specifier.comma,\n", + " precision = specifier.precision,\n", + " trim = specifier.trim,\n", + " type = specifier.type;\n", + "\n", + " // The "n" type is an alias for ",g".\n", + " if (type === "n") comma = true, type = "g";\n", + "\n", + " // The "" type, and any invalid type, is an alias for ".12~g".\n", + " else if (!formatTypes[type]) precision === undefined && (precision = 12), trim = true, type = "g";\n", + "\n", + " // If zero fill is specified, padding goes after sign and before digits.\n", + " if (zero || (fill === "0" && align === "=")) zero = true, fill = "0", align = "=";\n", + "\n", + " // Compute the prefix and suffix.\n", + " // For SI-prefix, the suffix is lazily computed.\n", + " var prefix = symbol === "$" ? currencyPrefix : symbol === "#" && /[boxX]/.test(type) ? "0" + type.toLowerCase() : "",\n", + " suffix = symbol === "$" ? currencySuffix : /[%p]/.test(type) ? percent : "";\n", + "\n", + " // What format function should we use?\n", + " // Is this an integer type?\n", + " // Can this type generate exponential notation?\n", + " var formatType = formatTypes[type],\n", + " maybeSuffix = /[defgprs%]/.test(type);\n", + "\n", + " // Set the default precision if not specified,\n", + " // or clamp the specified precision to the supported range.\n", + " // For significant precision, it must be in [1, 21].\n", + " // For fixed precision, it must be in [0, 20].\n", + " precision = precision === undefined ? 6\n", + " : /[gprs]/.test(type) ? Math.max(1, Math.min(21, precision))\n", + " : Math.max(0, Math.min(20, precision));\n", + "\n", + " function format(value) {\n", + " var valuePrefix = prefix,\n", + " valueSuffix = suffix,\n", + " i, n, c;\n", + "\n", + " if (type === "c") {\n", + " valueSuffix = formatType(value) + valueSuffix;\n", + " value = "";\n", + " } else {\n", + " value = +value;\n", + "\n", + " // Determine the sign. -0 is not less than 0, but 1 / -0 is!\n", + " var valueNegative = value < 0 || 1 / value < 0;\n", + "\n", + " // Perform the initial formatting.\n", + " value = isNaN(value) ? nan : formatType(Math.abs(value), precision);\n", + "\n", + " // Trim insignificant zeros.\n", + " if (trim) value = formatTrim(value);\n", + "\n", + " // If a negative value rounds to zero after formatting, and no explicit positive sign is requested, hide the sign.\n", + " if (valueNegative && +value === 0 && sign !== "+") valueNegative = false;\n", + "\n", + " // Compute the prefix and suffix.\n", + " valuePrefix = (valueNegative ? (sign === "(" ? sign : minus) : sign === "-" || sign === "(" ? "" : sign) + valuePrefix;\n", + " valueSuffix = (type === "s" ? prefixes[8 + prefixExponent / 3] : "") + valueSuffix + (valueNegative && sign === "(" ? ")" : "");\n", + "\n", + " // Break the formatted value into the integer “value” part that can be\n", + " // grouped, and fractional or exponential “suffix” part that is not.\n", + " if (maybeSuffix) {\n", + " i = -1, n = value.length;\n", + " while (++i < n) {\n", + " if (c = value.charCodeAt(i), 48 > c || c > 57) {\n", + " valueSuffix = (c === 46 ? decimal + value.slice(i + 1) : value.slice(i)) + valueSuffix;\n", + " value = value.slice(0, i);\n", + " break;\n", + " }\n", + " }\n", + " }\n", + " }\n", + "\n", + " // If the fill character is not "0", grouping is applied before padding.\n", + " if (comma && !zero) value = group(value, Infinity);\n", + "\n", + " // Compute the padding.\n", + " var length = valuePrefix.length + value.length + valueSuffix.length,\n", + " padding = length < width ? new Array(width - length + 1).join(fill) : "";\n", + "\n", + " // If the fill character is "0", grouping is applied after padding.\n", + " if (comma && zero) value = group(padding + value, padding.length ? width - valueSuffix.length : Infinity), padding = "";\n", + "\n", + " // Reconstruct the final output based on the desired alignment.\n", + " switch (align) {\n", + " case "<": value = valuePrefix + value + valueSuffix + padding; break;\n", + " case "=": value = valuePrefix + padding + value + valueSuffix; break;\n", + " case "^": value = padding.slice(0, length = padding.length >> 1) + valuePrefix + value + valueSuffix + padding.slice(length); break;\n", + " default: value = padding + valuePrefix + value + valueSuffix; break;\n", + " }\n", + "\n", + " return numerals(value);\n", + " }\n", + "\n", + " format.toString = function() {\n", + " return specifier + "";\n", + " };\n", + "\n", + " return format;\n", + " }\n", + "\n", + " function formatPrefix(specifier, value) {\n", + " var f = newFormat((specifier = formatSpecifier(specifier), specifier.type = "f", specifier)),\n", + " e = Math.max(-8, Math.min(8, Math.floor(exponent(value) / 3))) * 3,\n", + " k = Math.pow(10, -e),\n", + " prefix = prefixes[8 + e / 3];\n", + " return function(value) {\n", + " return f(k * value) + prefix;\n", + " };\n", + " }\n", + "\n", + " return {\n", + " format: newFormat,\n", + " formatPrefix: formatPrefix\n", + " };\n", + " }\n", + "\n", + " var locale;\n", + " var format;\n", + " var formatPrefix;\n", + "\n", + " defaultLocale({\n", + " decimal: ".",\n", + " thousands: ",",\n", + " grouping: [3],\n", + " currency: ["$", ""],\n", + " minus: "-"\n", + " });\n", + "\n", + " function defaultLocale(definition) {\n", + " locale = formatLocale(definition);\n", + " format = locale.format;\n", + " formatPrefix = locale.formatPrefix;\n", + " return locale;\n", + " }\n", + "\n", + " function formatter(value, specifier) {\n", + " const formatFunc = specifier ? format(specifier) : formatValue;\n", + "\n", " if (Array.isArray(value)) {\n", " const [first, second] = value;\n", " if (first === -Infinity) {\n", - " return `< ${formatValue(second)}`;\n", + " return `< ${formatFunc(second)}`;\n", " }\n", " if (second === Infinity) {\n", - " return `> ${formatValue(first)}`;\n", + " return `> ${formatFunc(first)}`;\n", " }\n", - " return `${formatValue(first)} - ${formatValue(second)}`;\n", + " return `${formatFunc(first)} - ${formatFunc(second)}`;\n", " }\n", - " return formatValue(value);\n", + " return formatFunc(value);\n", " }\n", "\n", " function formatValue(value) {\n", @@ -1386,21 +2015,21 @@ " return value.toLocaleString();\n", " }\n", "\n", - " function updateViewport(map) {\n", + " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", - " const mapInfo$ = document.getElementById('map-info');\n", + " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -1474,12 +2103,12 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", " let value = variable.value;\n", - " value = formatValue(value);\n", + " value = formatter(value, item.format);\n", "\n", " popupHTML = `\n", " ${item.title}\n", @@ -1489,12 +2118,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                ${popupHTML}
                                `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                ${popupHTML}
                                `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1549,7 +2182,7 @@ " widget.element = widget.element || document.querySelector(`#${widget.id}-value`);\n", "\n", " if (value && widget.element) {\n", - " widget.element.innerText = typeof value === 'number' ? format(value) : value;\n", + " widget.element.innerText = typeof value === 'number' ? formatter(value, widget.options.format) : value;\n", " }\n", " }\n", "\n", @@ -1561,7 +2194,6 @@ " const type = _getWidgetType(mapLayer, widget.value, widget.prop);\n", " const histogram = type === 'category' ? 'categoricalHistogram' : 'numericalHistogram';\n", " bridge[histogram](widget.element, widget.value, widget.options);\n", - "\n", " break;\n", " case 'category':\n", " bridge.category(widget.element, widget.value, widget.options);\n", @@ -1622,7 +2254,8 @@ " const order = legend.ascending ? 'ASC' : 'DESC';\n", " const variable = legend.variable;\n", " const config = { othersLabel, variable, order };\n", - " const options = { format, config, dynamic };\n", + " const formatFunc = (value) => formatter(value, legend.format);\n", + " const options = { format: formatFunc, config, dynamic };\n", "\n", " if (legend.type.startsWith('size-continuous')) {\n", " config.samples = 4;\n", @@ -1778,7 +2411,8 @@ " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", - " updateViewport(map);\n", + " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", + " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", @@ -1852,7 +2486,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1893,12 +2527,12 @@ " .addEventListener('ready', () => {\n", " const basecolor = '';\n", " const basemap = 'Positron';\n", - " const bounds = [[-105.007825536403, 39.7402045274956], [-104.987547762685, 39.7594219853596]];\n", + " const bounds = [[-105.00782553640346, 39.74020452749562], [-104.98754776268481, 39.75942198535955]];\n", " const camera = null;\n", " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"parcels\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"name": "v52277c", "title": "ccyrblt"}, "event": "hover"}], "legends": [], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"parcels\\"", "title": null, "type": "Query", "viz": "@v52277c: prop(\\u0027ccyrblt\\u0027)\\ncolor: opacity(hex(\\"#826DBA\\"), 0.9)\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nfilter: animation(linear(prop(\\u0027ccyrblt\\u0027)), 20, fade(1, 1))\\n", "widgets": [{"description": "", "footer": "", "has_bridge": true, "options": {"autoplay": true, "buckets": 20, "readOnly": false, "weight": 1}, "prop": "filter", "title": "ccyrblt", "type": "time-series", "value": "ccyrblt", "variable_name": "v52277c"}]}];\n", + " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"parcels\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "v52277c", "title": "ccyrblt"}, "event": "hover"}], "legends": [], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"parcels\\"", "title": null, "type": "Query", "viz": "@v52277c: prop(\\u0027ccyrblt\\u0027)\\ncolor: opacity(hex(\\"#826DBA\\"), 0.9)\\nfilter: animation(linear(prop(\\u0027ccyrblt\\u0027)), 20, fade(1, 1))\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": [{"description": "", "footer": "", "has_bridge": true, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "filter", "title": "ccyrblt", "type": "time-series", "value": "ccyrblt", "variable_name": "v52277c"}]}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -1922,7 +2556,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 3, diff --git a/docs/examples/data_visualization/styles/basic_style.ipynb b/docs/examples/data_visualization/styles/basic_style.ipynb new file mode 100644 index 000000000..cb70e16f5 --- /dev/null +++ b/docs/examples/data_visualization/styles/basic_style.ipynb @@ -0,0 +1,1314 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Basic Style\n", + "\n", + "The `basic_style` helper can be used to simply modify the style properties. Use `help(cartoframes.viz.palettes)` to get more information." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('cartoframes')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
                                \n", + "
                                \n", + "
                                \n", + " \n", + " \n", + "
                                \n", + "
                                \n", + "                                 \n", + "\n", + " \n", + "\n", + "
                                \n", + "
                                \n", + " :\n", + "
                                \n", + " \n", + " \n", + "
                                \n", + "
                                \n", + "\n", + "
                                \n", + " StackTrace\n", + "
                                  \n", + "
                                  \n", + "
                                  \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.viz import Layer, basic_style\n", + "\n", + "Layer('sf_nbhd_crime',\n", + " basic_style(\n", + " color='blue',\n", + " size=20,\n", + " stroke_color='rgba(255, 255, 255, 0.8)',\n", + " stroke_width=10\n", + " ),\n", + " title='San Francisco Crime'\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/styles/cluster_size_style.ipynb b/docs/examples/data_visualization/styles/cluster_size_style.ipynb similarity index 66% rename from examples/styles/cluster_size_style.ipynb rename to docs/examples/data_visualization/styles/cluster_size_style.ipynb index d4c095839..0fc237fa4 100644 --- a/examples/styles/cluster_size_style.ipynb +++ b/docs/examples/data_visualization/styles/cluster_size_style.ipynb @@ -414,18 +414,330 @@ " stacktrace$.innerHTML = list.join('\\n');\n", " }\n", "\n", - " function format(value) {\n", + " // Computes the decimal coefficient and exponent of the specified number x with\n", + " // significant digits p, where x is positive and p is in [1, 21] or undefined.\n", + " // For example, formatDecimal(1.23) returns ["123", 0].\n", + " function formatDecimal(x, p) {\n", + " if ((i = (x = p ? x.toExponential(p - 1) : x.toExponential()).indexOf("e")) < 0) return null; // NaN, ±Infinity\n", + " var i, coefficient = x.slice(0, i);\n", + "\n", + " // The string returned by toExponential either has the form \\d\\.\\d+e[-+]\\d+\n", + " // (e.g., 1.2e+3) or the form \\de[-+]\\d+ (e.g., 1e+3).\n", + " return [\n", + " coefficient.length > 1 ? coefficient[0] + coefficient.slice(2) : coefficient,\n", + " +x.slice(i + 1)\n", + " ];\n", + " }\n", + "\n", + " function exponent(x) {\n", + " return x = formatDecimal(Math.abs(x)), x ? x[1] : NaN;\n", + " }\n", + "\n", + " function formatGroup(grouping, thousands) {\n", + " return function(value, width) {\n", + " var i = value.length,\n", + " t = [],\n", + " j = 0,\n", + " g = grouping[0],\n", + " length = 0;\n", + "\n", + " while (i > 0 && g > 0) {\n", + " if (length + g + 1 > width) g = Math.max(1, width - length);\n", + " t.push(value.substring(i -= g, i + g));\n", + " if ((length += g + 1) > width) break;\n", + " g = grouping[j = (j + 1) % grouping.length];\n", + " }\n", + "\n", + " return t.reverse().join(thousands);\n", + " };\n", + " }\n", + "\n", + " function formatNumerals(numerals) {\n", + " return function(value) {\n", + " return value.replace(/[0-9]/g, function(i) {\n", + " return numerals[+i];\n", + " });\n", + " };\n", + " }\n", + "\n", + " // [[fill]align][sign][symbol][0][width][,][.precision][~][type]\n", + " var re = /^(?:(.)?([<>=^]))?([+\\-( ])?([$#])?(0)?(\\d+)?(,)?(\\.\\d+)?(~)?([a-z%])?$/i;\n", + "\n", + " function formatSpecifier(specifier) {\n", + " if (!(match = re.exec(specifier))) throw new Error("invalid format: " + specifier);\n", + " var match;\n", + " return new FormatSpecifier({\n", + " fill: match[1],\n", + " align: match[2],\n", + " sign: match[3],\n", + " symbol: match[4],\n", + " zero: match[5],\n", + " width: match[6],\n", + " comma: match[7],\n", + " precision: match[8] && match[8].slice(1),\n", + " trim: match[9],\n", + " type: match[10]\n", + " });\n", + " }\n", + "\n", + " formatSpecifier.prototype = FormatSpecifier.prototype; // instanceof\n", + "\n", + " function FormatSpecifier(specifier) {\n", + " this.fill = specifier.fill === undefined ? " " : specifier.fill + "";\n", + " this.align = specifier.align === undefined ? ">" : specifier.align + "";\n", + " this.sign = specifier.sign === undefined ? "-" : specifier.sign + "";\n", + " this.symbol = specifier.symbol === undefined ? "" : specifier.symbol + "";\n", + " this.zero = !!specifier.zero;\n", + " this.width = specifier.width === undefined ? undefined : +specifier.width;\n", + " this.comma = !!specifier.comma;\n", + " this.precision = specifier.precision === undefined ? undefined : +specifier.precision;\n", + " this.trim = !!specifier.trim;\n", + " this.type = specifier.type === undefined ? "" : specifier.type + "";\n", + " }\n", + "\n", + " FormatSpecifier.prototype.toString = function() {\n", + " return this.fill\n", + " + this.align\n", + " + this.sign\n", + " + this.symbol\n", + " + (this.zero ? "0" : "")\n", + " + (this.width === undefined ? "" : Math.max(1, this.width | 0))\n", + " + (this.comma ? "," : "")\n", + " + (this.precision === undefined ? "" : "." + Math.max(0, this.precision | 0))\n", + " + (this.trim ? "~" : "")\n", + " + this.type;\n", + " };\n", + "\n", + " // Trims insignificant zeros, e.g., replaces 1.2000k with 1.2k.\n", + " function formatTrim(s) {\n", + " out: for (var n = s.length, i = 1, i0 = -1, i1; i < n; ++i) {\n", + " switch (s[i]) {\n", + " case ".": i0 = i1 = i; break;\n", + " case "0": if (i0 === 0) i0 = i; i1 = i; break;\n", + " default: if (!+s[i]) break out; if (i0 > 0) i0 = 0; break;\n", + " }\n", + " }\n", + " return i0 > 0 ? s.slice(0, i0) + s.slice(i1 + 1) : s;\n", + " }\n", + "\n", + " var prefixExponent;\n", + "\n", + " function formatPrefixAuto(x, p) {\n", + " var d = formatDecimal(x, p);\n", + " if (!d) return x + "";\n", + " var coefficient = d[0],\n", + " exponent = d[1],\n", + " i = exponent - (prefixExponent = Math.max(-8, Math.min(8, Math.floor(exponent / 3))) * 3) + 1,\n", + " n = coefficient.length;\n", + " return i === n ? coefficient\n", + " : i > n ? coefficient + new Array(i - n + 1).join("0")\n", + " : i > 0 ? coefficient.slice(0, i) + "." + coefficient.slice(i)\n", + " : "0." + new Array(1 - i).join("0") + formatDecimal(x, Math.max(0, p + i - 1))[0]; // less than 1y!\n", + " }\n", + "\n", + " function formatRounded(x, p) {\n", + " var d = formatDecimal(x, p);\n", + " if (!d) return x + "";\n", + " var coefficient = d[0],\n", + " exponent = d[1];\n", + " return exponent < 0 ? "0." + new Array(-exponent).join("0") + coefficient\n", + " : coefficient.length > exponent + 1 ? coefficient.slice(0, exponent + 1) + "." + coefficient.slice(exponent + 1)\n", + " : coefficient + new Array(exponent - coefficient.length + 2).join("0");\n", + " }\n", + "\n", + " var formatTypes = {\n", + " "%": function(x, p) { return (x * 100).toFixed(p); },\n", + " "b": function(x) { return Math.round(x).toString(2); },\n", + " "c": function(x) { return x + ""; },\n", + " "d": function(x) { return Math.round(x).toString(10); },\n", + " "e": function(x, p) { return x.toExponential(p); },\n", + " "f": function(x, p) { return x.toFixed(p); },\n", + " "g": function(x, p) { return x.toPrecision(p); },\n", + " "o": function(x) { return Math.round(x).toString(8); },\n", + " "p": function(x, p) { return formatRounded(x * 100, p); },\n", + " "r": formatRounded,\n", + " "s": formatPrefixAuto,\n", + " "X": function(x) { return Math.round(x).toString(16).toUpperCase(); },\n", + " "x": function(x) { return Math.round(x).toString(16); }\n", + " };\n", + "\n", + " function identity(x) {\n", + " return x;\n", + " }\n", + "\n", + " var map = Array.prototype.map,\n", + " prefixes = ["y","z","a","f","p","n","µ","m","","k","M","G","T","P","E","Z","Y"];\n", + "\n", + " function formatLocale(locale) {\n", + " var group = locale.grouping === undefined || locale.thousands === undefined ? identity : formatGroup(map.call(locale.grouping, Number), locale.thousands + ""),\n", + " currencyPrefix = locale.currency === undefined ? "" : locale.currency[0] + "",\n", + " currencySuffix = locale.currency === undefined ? "" : locale.currency[1] + "",\n", + " decimal = locale.decimal === undefined ? "." : locale.decimal + "",\n", + " numerals = locale.numerals === undefined ? identity : formatNumerals(map.call(locale.numerals, String)),\n", + " percent = locale.percent === undefined ? "%" : locale.percent + "",\n", + " minus = locale.minus === undefined ? "-" : locale.minus + "",\n", + " nan = locale.nan === undefined ? "NaN" : locale.nan + "";\n", + "\n", + " function newFormat(specifier) {\n", + " specifier = formatSpecifier(specifier);\n", + "\n", + " var fill = specifier.fill,\n", + " align = specifier.align,\n", + " sign = specifier.sign,\n", + " symbol = specifier.symbol,\n", + " zero = specifier.zero,\n", + " width = specifier.width,\n", + " comma = specifier.comma,\n", + " precision = specifier.precision,\n", + " trim = specifier.trim,\n", + " type = specifier.type;\n", + "\n", + " // The "n" type is an alias for ",g".\n", + " if (type === "n") comma = true, type = "g";\n", + "\n", + " // The "" type, and any invalid type, is an alias for ".12~g".\n", + " else if (!formatTypes[type]) precision === undefined && (precision = 12), trim = true, type = "g";\n", + "\n", + " // If zero fill is specified, padding goes after sign and before digits.\n", + " if (zero || (fill === "0" && align === "=")) zero = true, fill = "0", align = "=";\n", + "\n", + " // Compute the prefix and suffix.\n", + " // For SI-prefix, the suffix is lazily computed.\n", + " var prefix = symbol === "$" ? currencyPrefix : symbol === "#" && /[boxX]/.test(type) ? "0" + type.toLowerCase() : "",\n", + " suffix = symbol === "$" ? currencySuffix : /[%p]/.test(type) ? percent : "";\n", + "\n", + " // What format function should we use?\n", + " // Is this an integer type?\n", + " // Can this type generate exponential notation?\n", + " var formatType = formatTypes[type],\n", + " maybeSuffix = /[defgprs%]/.test(type);\n", + "\n", + " // Set the default precision if not specified,\n", + " // or clamp the specified precision to the supported range.\n", + " // For significant precision, it must be in [1, 21].\n", + " // For fixed precision, it must be in [0, 20].\n", + " precision = precision === undefined ? 6\n", + " : /[gprs]/.test(type) ? Math.max(1, Math.min(21, precision))\n", + " : Math.max(0, Math.min(20, precision));\n", + "\n", + " function format(value) {\n", + " var valuePrefix = prefix,\n", + " valueSuffix = suffix,\n", + " i, n, c;\n", + "\n", + " if (type === "c") {\n", + " valueSuffix = formatType(value) + valueSuffix;\n", + " value = "";\n", + " } else {\n", + " value = +value;\n", + "\n", + " // Determine the sign. -0 is not less than 0, but 1 / -0 is!\n", + " var valueNegative = value < 0 || 1 / value < 0;\n", + "\n", + " // Perform the initial formatting.\n", + " value = isNaN(value) ? nan : formatType(Math.abs(value), precision);\n", + "\n", + " // Trim insignificant zeros.\n", + " if (trim) value = formatTrim(value);\n", + "\n", + " // If a negative value rounds to zero after formatting, and no explicit positive sign is requested, hide the sign.\n", + " if (valueNegative && +value === 0 && sign !== "+") valueNegative = false;\n", + "\n", + " // Compute the prefix and suffix.\n", + " valuePrefix = (valueNegative ? (sign === "(" ? sign : minus) : sign === "-" || sign === "(" ? "" : sign) + valuePrefix;\n", + " valueSuffix = (type === "s" ? prefixes[8 + prefixExponent / 3] : "") + valueSuffix + (valueNegative && sign === "(" ? ")" : "");\n", + "\n", + " // Break the formatted value into the integer “value” part that can be\n", + " // grouped, and fractional or exponential “suffix” part that is not.\n", + " if (maybeSuffix) {\n", + " i = -1, n = value.length;\n", + " while (++i < n) {\n", + " if (c = value.charCodeAt(i), 48 > c || c > 57) {\n", + " valueSuffix = (c === 46 ? decimal + value.slice(i + 1) : value.slice(i)) + valueSuffix;\n", + " value = value.slice(0, i);\n", + " break;\n", + " }\n", + " }\n", + " }\n", + " }\n", + "\n", + " // If the fill character is not "0", grouping is applied before padding.\n", + " if (comma && !zero) value = group(value, Infinity);\n", + "\n", + " // Compute the padding.\n", + " var length = valuePrefix.length + value.length + valueSuffix.length,\n", + " padding = length < width ? new Array(width - length + 1).join(fill) : "";\n", + "\n", + " // If the fill character is "0", grouping is applied after padding.\n", + " if (comma && zero) value = group(padding + value, padding.length ? width - valueSuffix.length : Infinity), padding = "";\n", + "\n", + " // Reconstruct the final output based on the desired alignment.\n", + " switch (align) {\n", + " case "<": value = valuePrefix + value + valueSuffix + padding; break;\n", + " case "=": value = valuePrefix + padding + value + valueSuffix; break;\n", + " case "^": value = padding.slice(0, length = padding.length >> 1) + valuePrefix + value + valueSuffix + padding.slice(length); break;\n", + " default: value = padding + valuePrefix + value + valueSuffix; break;\n", + " }\n", + "\n", + " return numerals(value);\n", + " }\n", + "\n", + " format.toString = function() {\n", + " return specifier + "";\n", + " };\n", + "\n", + " return format;\n", + " }\n", + "\n", + " function formatPrefix(specifier, value) {\n", + " var f = newFormat((specifier = formatSpecifier(specifier), specifier.type = "f", specifier)),\n", + " e = Math.max(-8, Math.min(8, Math.floor(exponent(value) / 3))) * 3,\n", + " k = Math.pow(10, -e),\n", + " prefix = prefixes[8 + e / 3];\n", + " return function(value) {\n", + " return f(k * value) + prefix;\n", + " };\n", + " }\n", + "\n", + " return {\n", + " format: newFormat,\n", + " formatPrefix: formatPrefix\n", + " };\n", + " }\n", + "\n", + " var locale;\n", + " var format;\n", + " var formatPrefix;\n", + "\n", + " defaultLocale({\n", + " decimal: ".",\n", + " thousands: ",",\n", + " grouping: [3],\n", + " currency: ["$", ""],\n", + " minus: "-"\n", + " });\n", + "\n", + " function defaultLocale(definition) {\n", + " locale = formatLocale(definition);\n", + " format = locale.format;\n", + " formatPrefix = locale.formatPrefix;\n", + " return locale;\n", + " }\n", + "\n", + " function formatter(value, specifier) {\n", + " const formatFunc = specifier ? format(specifier) : formatValue;\n", + "\n", " if (Array.isArray(value)) {\n", " const [first, second] = value;\n", " if (first === -Infinity) {\n", - " return `< ${formatValue(second)}`;\n", + " return `< ${formatFunc(second)}`;\n", " }\n", " if (second === Infinity) {\n", - " return `> ${formatValue(first)}`;\n", + " return `> ${formatFunc(first)}`;\n", " }\n", - " return `${formatValue(first)} - ${formatValue(second)}`;\n", + " return `${formatFunc(first)} - ${formatFunc(second)}`;\n", " }\n", - " return formatValue(value);\n", + " return formatFunc(value);\n", " }\n", "\n", " function formatValue(value) {\n", @@ -445,21 +757,21 @@ " return value.toLocaleString();\n", " }\n", "\n", - " function updateViewport(map) {\n", + " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", - " const mapInfo$ = document.getElementById('map-info');\n", + " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -533,12 +845,12 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", " let value = variable.value;\n", - " value = formatValue(value);\n", + " value = formatter(value, item.format);\n", "\n", " popupHTML = `\n", " ${item.title}\n", @@ -548,12 +860,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                  ${popupHTML}
                                  `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                  ${popupHTML}
                                  `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -608,7 +924,7 @@ " widget.element = widget.element || document.querySelector(`#${widget.id}-value`);\n", "\n", " if (value && widget.element) {\n", - " widget.element.innerText = typeof value === 'number' ? format(value) : value;\n", + " widget.element.innerText = typeof value === 'number' ? formatter(value, widget.options.format) : value;\n", " }\n", " }\n", "\n", @@ -620,7 +936,6 @@ " const type = _getWidgetType(mapLayer, widget.value, widget.prop);\n", " const histogram = type === 'category' ? 'categoricalHistogram' : 'numericalHistogram';\n", " bridge[histogram](widget.element, widget.value, widget.options);\n", - "\n", " break;\n", " case 'category':\n", " bridge.category(widget.element, widget.value, widget.options);\n", @@ -681,7 +996,8 @@ " const order = legend.ascending ? 'ASC' : 'DESC';\n", " const variable = legend.variable;\n", " const config = { othersLabel, variable, order };\n", - " const options = { format, config, dynamic };\n", + " const formatFunc = (value) => formatter(value, legend.format);\n", + " const options = { format: formatFunc, config, dynamic };\n", "\n", " if (legend.type.startsWith('size-continuous')) {\n", " config.samples = 4;\n", @@ -837,7 +1153,8 @@ " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", - " updateViewport(map);\n", + " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", + " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", @@ -911,7 +1228,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -957,7 +1274,7 @@ " const has_legends = 'true' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"vancouver_trees\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"name": "v40e601", "title": "clusterCount()"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "prop": "width", "title": "diameter", "type": "size-continuous", "variable": "size_value"}], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"vancouver_trees\\"", "title": null, "type": "Query", "viz": "@v40e601: clusterCount()\\ncolor: opacity(#FFB927, 0.8)\\nwidth: ramp(linear(clusterCount(), viewportMIN(clusterCount()), viewportMAX(clusterCount())), [4.0, 16.0, 32])\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\n@size_value: ramp(linear(clusterCount(), viewportMIN(clusterCount()), viewportMAX(clusterCount())), [4.0, 16.0, 32])\\nfilter: 1\\nresolution: 32\\n", "widgets": []}];\n", + " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"vancouver_trees\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "v40e601", "title": "clusterCount()"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "width", "title": "diameter", "type": "size-continuous", "variable": "size_value"}], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"vancouver_trees\\"", "title": null, "type": "Query", "viz": "@v40e601: clusterCount()\\n@size_value: ramp(linear(clusterCount(), viewportMIN(clusterCount()), viewportMAX(clusterCount())), [4.0, 16.0, 32])\\ncolor: opacity(#FFB927, 0.8)\\nfilter: 1\\nresolution: 32\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(clusterCount(), viewportMIN(clusterCount()), viewportMAX(clusterCount())), [4.0, 16.0, 32])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -981,7 +1298,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -1387,18 +1704,330 @@ " stacktrace$.innerHTML = list.join('\\n');\n", " }\n", "\n", - " function format(value) {\n", + " // Computes the decimal coefficient and exponent of the specified number x with\n", + " // significant digits p, where x is positive and p is in [1, 21] or undefined.\n", + " // For example, formatDecimal(1.23) returns ["123", 0].\n", + " function formatDecimal(x, p) {\n", + " if ((i = (x = p ? x.toExponential(p - 1) : x.toExponential()).indexOf("e")) < 0) return null; // NaN, ±Infinity\n", + " var i, coefficient = x.slice(0, i);\n", + "\n", + " // The string returned by toExponential either has the form \\d\\.\\d+e[-+]\\d+\n", + " // (e.g., 1.2e+3) or the form \\de[-+]\\d+ (e.g., 1e+3).\n", + " return [\n", + " coefficient.length > 1 ? coefficient[0] + coefficient.slice(2) : coefficient,\n", + " +x.slice(i + 1)\n", + " ];\n", + " }\n", + "\n", + " function exponent(x) {\n", + " return x = formatDecimal(Math.abs(x)), x ? x[1] : NaN;\n", + " }\n", + "\n", + " function formatGroup(grouping, thousands) {\n", + " return function(value, width) {\n", + " var i = value.length,\n", + " t = [],\n", + " j = 0,\n", + " g = grouping[0],\n", + " length = 0;\n", + "\n", + " while (i > 0 && g > 0) {\n", + " if (length + g + 1 > width) g = Math.max(1, width - length);\n", + " t.push(value.substring(i -= g, i + g));\n", + " if ((length += g + 1) > width) break;\n", + " g = grouping[j = (j + 1) % grouping.length];\n", + " }\n", + "\n", + " return t.reverse().join(thousands);\n", + " };\n", + " }\n", + "\n", + " function formatNumerals(numerals) {\n", + " return function(value) {\n", + " return value.replace(/[0-9]/g, function(i) {\n", + " return numerals[+i];\n", + " });\n", + " };\n", + " }\n", + "\n", + " // [[fill]align][sign][symbol][0][width][,][.precision][~][type]\n", + " var re = /^(?:(.)?([<>=^]))?([+\\-( ])?([$#])?(0)?(\\d+)?(,)?(\\.\\d+)?(~)?([a-z%])?$/i;\n", + "\n", + " function formatSpecifier(specifier) {\n", + " if (!(match = re.exec(specifier))) throw new Error("invalid format: " + specifier);\n", + " var match;\n", + " return new FormatSpecifier({\n", + " fill: match[1],\n", + " align: match[2],\n", + " sign: match[3],\n", + " symbol: match[4],\n", + " zero: match[5],\n", + " width: match[6],\n", + " comma: match[7],\n", + " precision: match[8] && match[8].slice(1),\n", + " trim: match[9],\n", + " type: match[10]\n", + " });\n", + " }\n", + "\n", + " formatSpecifier.prototype = FormatSpecifier.prototype; // instanceof\n", + "\n", + " function FormatSpecifier(specifier) {\n", + " this.fill = specifier.fill === undefined ? " " : specifier.fill + "";\n", + " this.align = specifier.align === undefined ? ">" : specifier.align + "";\n", + " this.sign = specifier.sign === undefined ? "-" : specifier.sign + "";\n", + " this.symbol = specifier.symbol === undefined ? "" : specifier.symbol + "";\n", + " this.zero = !!specifier.zero;\n", + " this.width = specifier.width === undefined ? undefined : +specifier.width;\n", + " this.comma = !!specifier.comma;\n", + " this.precision = specifier.precision === undefined ? undefined : +specifier.precision;\n", + " this.trim = !!specifier.trim;\n", + " this.type = specifier.type === undefined ? "" : specifier.type + "";\n", + " }\n", + "\n", + " FormatSpecifier.prototype.toString = function() {\n", + " return this.fill\n", + " + this.align\n", + " + this.sign\n", + " + this.symbol\n", + " + (this.zero ? "0" : "")\n", + " + (this.width === undefined ? "" : Math.max(1, this.width | 0))\n", + " + (this.comma ? "," : "")\n", + " + (this.precision === undefined ? "" : "." + Math.max(0, this.precision | 0))\n", + " + (this.trim ? "~" : "")\n", + " + this.type;\n", + " };\n", + "\n", + " // Trims insignificant zeros, e.g., replaces 1.2000k with 1.2k.\n", + " function formatTrim(s) {\n", + " out: for (var n = s.length, i = 1, i0 = -1, i1; i < n; ++i) {\n", + " switch (s[i]) {\n", + " case ".": i0 = i1 = i; break;\n", + " case "0": if (i0 === 0) i0 = i; i1 = i; break;\n", + " default: if (!+s[i]) break out; if (i0 > 0) i0 = 0; break;\n", + " }\n", + " }\n", + " return i0 > 0 ? s.slice(0, i0) + s.slice(i1 + 1) : s;\n", + " }\n", + "\n", + " var prefixExponent;\n", + "\n", + " function formatPrefixAuto(x, p) {\n", + " var d = formatDecimal(x, p);\n", + " if (!d) return x + "";\n", + " var coefficient = d[0],\n", + " exponent = d[1],\n", + " i = exponent - (prefixExponent = Math.max(-8, Math.min(8, Math.floor(exponent / 3))) * 3) + 1,\n", + " n = coefficient.length;\n", + " return i === n ? coefficient\n", + " : i > n ? coefficient + new Array(i - n + 1).join("0")\n", + " : i > 0 ? coefficient.slice(0, i) + "." + coefficient.slice(i)\n", + " : "0." + new Array(1 - i).join("0") + formatDecimal(x, Math.max(0, p + i - 1))[0]; // less than 1y!\n", + " }\n", + "\n", + " function formatRounded(x, p) {\n", + " var d = formatDecimal(x, p);\n", + " if (!d) return x + "";\n", + " var coefficient = d[0],\n", + " exponent = d[1];\n", + " return exponent < 0 ? "0." + new Array(-exponent).join("0") + coefficient\n", + " : coefficient.length > exponent + 1 ? coefficient.slice(0, exponent + 1) + "." + coefficient.slice(exponent + 1)\n", + " : coefficient + new Array(exponent - coefficient.length + 2).join("0");\n", + " }\n", + "\n", + " var formatTypes = {\n", + " "%": function(x, p) { return (x * 100).toFixed(p); },\n", + " "b": function(x) { return Math.round(x).toString(2); },\n", + " "c": function(x) { return x + ""; },\n", + " "d": function(x) { return Math.round(x).toString(10); },\n", + " "e": function(x, p) { return x.toExponential(p); },\n", + " "f": function(x, p) { return x.toFixed(p); },\n", + " "g": function(x, p) { return x.toPrecision(p); },\n", + " "o": function(x) { return Math.round(x).toString(8); },\n", + " "p": function(x, p) { return formatRounded(x * 100, p); },\n", + " "r": formatRounded,\n", + " "s": formatPrefixAuto,\n", + " "X": function(x) { return Math.round(x).toString(16).toUpperCase(); },\n", + " "x": function(x) { return Math.round(x).toString(16); }\n", + " };\n", + "\n", + " function identity(x) {\n", + " return x;\n", + " }\n", + "\n", + " var map = Array.prototype.map,\n", + " prefixes = ["y","z","a","f","p","n","µ","m","","k","M","G","T","P","E","Z","Y"];\n", + "\n", + " function formatLocale(locale) {\n", + " var group = locale.grouping === undefined || locale.thousands === undefined ? identity : formatGroup(map.call(locale.grouping, Number), locale.thousands + ""),\n", + " currencyPrefix = locale.currency === undefined ? "" : locale.currency[0] + "",\n", + " currencySuffix = locale.currency === undefined ? "" : locale.currency[1] + "",\n", + " decimal = locale.decimal === undefined ? "." : locale.decimal + "",\n", + " numerals = locale.numerals === undefined ? identity : formatNumerals(map.call(locale.numerals, String)),\n", + " percent = locale.percent === undefined ? "%" : locale.percent + "",\n", + " minus = locale.minus === undefined ? "-" : locale.minus + "",\n", + " nan = locale.nan === undefined ? "NaN" : locale.nan + "";\n", + "\n", + " function newFormat(specifier) {\n", + " specifier = formatSpecifier(specifier);\n", + "\n", + " var fill = specifier.fill,\n", + " align = specifier.align,\n", + " sign = specifier.sign,\n", + " symbol = specifier.symbol,\n", + " zero = specifier.zero,\n", + " width = specifier.width,\n", + " comma = specifier.comma,\n", + " precision = specifier.precision,\n", + " trim = specifier.trim,\n", + " type = specifier.type;\n", + "\n", + " // The "n" type is an alias for ",g".\n", + " if (type === "n") comma = true, type = "g";\n", + "\n", + " // The "" type, and any invalid type, is an alias for ".12~g".\n", + " else if (!formatTypes[type]) precision === undefined && (precision = 12), trim = true, type = "g";\n", + "\n", + " // If zero fill is specified, padding goes after sign and before digits.\n", + " if (zero || (fill === "0" && align === "=")) zero = true, fill = "0", align = "=";\n", + "\n", + " // Compute the prefix and suffix.\n", + " // For SI-prefix, the suffix is lazily computed.\n", + " var prefix = symbol === "$" ? currencyPrefix : symbol === "#" && /[boxX]/.test(type) ? "0" + type.toLowerCase() : "",\n", + " suffix = symbol === "$" ? currencySuffix : /[%p]/.test(type) ? percent : "";\n", + "\n", + " // What format function should we use?\n", + " // Is this an integer type?\n", + " // Can this type generate exponential notation?\n", + " var formatType = formatTypes[type],\n", + " maybeSuffix = /[defgprs%]/.test(type);\n", + "\n", + " // Set the default precision if not specified,\n", + " // or clamp the specified precision to the supported range.\n", + " // For significant precision, it must be in [1, 21].\n", + " // For fixed precision, it must be in [0, 20].\n", + " precision = precision === undefined ? 6\n", + " : /[gprs]/.test(type) ? Math.max(1, Math.min(21, precision))\n", + " : Math.max(0, Math.min(20, precision));\n", + "\n", + " function format(value) {\n", + " var valuePrefix = prefix,\n", + " valueSuffix = suffix,\n", + " i, n, c;\n", + "\n", + " if (type === "c") {\n", + " valueSuffix = formatType(value) + valueSuffix;\n", + " value = "";\n", + " } else {\n", + " value = +value;\n", + "\n", + " // Determine the sign. -0 is not less than 0, but 1 / -0 is!\n", + " var valueNegative = value < 0 || 1 / value < 0;\n", + "\n", + " // Perform the initial formatting.\n", + " value = isNaN(value) ? nan : formatType(Math.abs(value), precision);\n", + "\n", + " // Trim insignificant zeros.\n", + " if (trim) value = formatTrim(value);\n", + "\n", + " // If a negative value rounds to zero after formatting, and no explicit positive sign is requested, hide the sign.\n", + " if (valueNegative && +value === 0 && sign !== "+") valueNegative = false;\n", + "\n", + " // Compute the prefix and suffix.\n", + " valuePrefix = (valueNegative ? (sign === "(" ? sign : minus) : sign === "-" || sign === "(" ? "" : sign) + valuePrefix;\n", + " valueSuffix = (type === "s" ? prefixes[8 + prefixExponent / 3] : "") + valueSuffix + (valueNegative && sign === "(" ? ")" : "");\n", + "\n", + " // Break the formatted value into the integer “value” part that can be\n", + " // grouped, and fractional or exponential “suffix” part that is not.\n", + " if (maybeSuffix) {\n", + " i = -1, n = value.length;\n", + " while (++i < n) {\n", + " if (c = value.charCodeAt(i), 48 > c || c > 57) {\n", + " valueSuffix = (c === 46 ? decimal + value.slice(i + 1) : value.slice(i)) + valueSuffix;\n", + " value = value.slice(0, i);\n", + " break;\n", + " }\n", + " }\n", + " }\n", + " }\n", + "\n", + " // If the fill character is not "0", grouping is applied before padding.\n", + " if (comma && !zero) value = group(value, Infinity);\n", + "\n", + " // Compute the padding.\n", + " var length = valuePrefix.length + value.length + valueSuffix.length,\n", + " padding = length < width ? new Array(width - length + 1).join(fill) : "";\n", + "\n", + " // If the fill character is "0", grouping is applied after padding.\n", + " if (comma && zero) value = group(padding + value, padding.length ? width - valueSuffix.length : Infinity), padding = "";\n", + "\n", + " // Reconstruct the final output based on the desired alignment.\n", + " switch (align) {\n", + " case "<": value = valuePrefix + value + valueSuffix + padding; break;\n", + " case "=": value = valuePrefix + padding + value + valueSuffix; break;\n", + " case "^": value = padding.slice(0, length = padding.length >> 1) + valuePrefix + value + valueSuffix + padding.slice(length); break;\n", + " default: value = padding + valuePrefix + value + valueSuffix; break;\n", + " }\n", + "\n", + " return numerals(value);\n", + " }\n", + "\n", + " format.toString = function() {\n", + " return specifier + "";\n", + " };\n", + "\n", + " return format;\n", + " }\n", + "\n", + " function formatPrefix(specifier, value) {\n", + " var f = newFormat((specifier = formatSpecifier(specifier), specifier.type = "f", specifier)),\n", + " e = Math.max(-8, Math.min(8, Math.floor(exponent(value) / 3))) * 3,\n", + " k = Math.pow(10, -e),\n", + " prefix = prefixes[8 + e / 3];\n", + " return function(value) {\n", + " return f(k * value) + prefix;\n", + " };\n", + " }\n", + "\n", + " return {\n", + " format: newFormat,\n", + " formatPrefix: formatPrefix\n", + " };\n", + " }\n", + "\n", + " var locale;\n", + " var format;\n", + " var formatPrefix;\n", + "\n", + " defaultLocale({\n", + " decimal: ".",\n", + " thousands: ",",\n", + " grouping: [3],\n", + " currency: ["$", ""],\n", + " minus: "-"\n", + " });\n", + "\n", + " function defaultLocale(definition) {\n", + " locale = formatLocale(definition);\n", + " format = locale.format;\n", + " formatPrefix = locale.formatPrefix;\n", + " return locale;\n", + " }\n", + "\n", + " function formatter(value, specifier) {\n", + " const formatFunc = specifier ? format(specifier) : formatValue;\n", + "\n", " if (Array.isArray(value)) {\n", " const [first, second] = value;\n", " if (first === -Infinity) {\n", - " return `< ${formatValue(second)}`;\n", + " return `< ${formatFunc(second)}`;\n", " }\n", " if (second === Infinity) {\n", - " return `> ${formatValue(first)}`;\n", + " return `> ${formatFunc(first)}`;\n", " }\n", - " return `${formatValue(first)} - ${formatValue(second)}`;\n", + " return `${formatFunc(first)} - ${formatFunc(second)}`;\n", " }\n", - " return formatValue(value);\n", + " return formatFunc(value);\n", " }\n", "\n", " function formatValue(value) {\n", @@ -1418,21 +2047,21 @@ " return value.toLocaleString();\n", " }\n", "\n", - " function updateViewport(map) {\n", + " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", - " const mapInfo$ = document.getElementById('map-info');\n", + " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -1506,12 +2135,12 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", " let value = variable.value;\n", - " value = formatValue(value);\n", + " value = formatter(value, item.format);\n", "\n", " popupHTML = `\n", " ${item.title}\n", @@ -1521,12 +2150,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                  ${popupHTML}
                                  `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                  ${popupHTML}
                                  `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1581,7 +2214,7 @@ " widget.element = widget.element || document.querySelector(`#${widget.id}-value`);\n", "\n", " if (value && widget.element) {\n", - " widget.element.innerText = typeof value === 'number' ? format(value) : value;\n", + " widget.element.innerText = typeof value === 'number' ? formatter(value, widget.options.format) : value;\n", " }\n", " }\n", "\n", @@ -1593,7 +2226,6 @@ " const type = _getWidgetType(mapLayer, widget.value, widget.prop);\n", " const histogram = type === 'category' ? 'categoricalHistogram' : 'numericalHistogram';\n", " bridge[histogram](widget.element, widget.value, widget.options);\n", - "\n", " break;\n", " case 'category':\n", " bridge.category(widget.element, widget.value, widget.options);\n", @@ -1654,7 +2286,8 @@ " const order = legend.ascending ? 'ASC' : 'DESC';\n", " const variable = legend.variable;\n", " const config = { othersLabel, variable, order };\n", - " const options = { format, config, dynamic };\n", + " const formatFunc = (value) => formatter(value, legend.format);\n", + " const options = { format: formatFunc, config, dynamic };\n", "\n", " if (legend.type.startsWith('size-continuous')) {\n", " config.samples = 4;\n", @@ -1810,7 +2443,8 @@ " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", - " updateViewport(map);\n", + " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", + " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", @@ -1884,7 +2518,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1930,7 +2564,7 @@ " const has_legends = 'true' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"vancouver_trees\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"name": "vc16a1f", "title": "clusterSum(prop(\\u0027diameter\\u0027))"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "prop": "width", "title": "diameter", "type": "size-continuous", "variable": "size_value"}], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"vancouver_trees\\"", "title": null, "type": "Query", "viz": "@vc16a1f: clusterSum(prop(\\u0027diameter\\u0027))\\ncolor: opacity(#FFB927, 0.8)\\nwidth: ramp(linear(clusterSum(prop(\\u0027diameter\\u0027)), viewportMIN(clusterSum(prop(\\u0027diameter\\u0027))), viewportMAX(clusterSum(prop(\\u0027diameter\\u0027)))), [8.0, 32.0, 64])\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\n@size_value: ramp(linear(clusterSum(prop(\\u0027diameter\\u0027)), viewportMIN(clusterSum(prop(\\u0027diameter\\u0027))), viewportMAX(clusterSum(prop(\\u0027diameter\\u0027)))), [8.0, 32.0, 64])\\nfilter: 1\\nresolution: 64\\n", "widgets": []}];\n", + " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"vancouver_trees\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "vc16a1f", "title": "clusterSum(prop(\\u0027diameter\\u0027))"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "width", "title": "diameter", "type": "size-continuous", "variable": "size_value"}], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"vancouver_trees\\"", "title": null, "type": "Query", "viz": "@vc16a1f: clusterSum(prop(\\u0027diameter\\u0027))\\n@size_value: ramp(linear(clusterSum(prop(\\u0027diameter\\u0027)), viewportMIN(clusterSum(prop(\\u0027diameter\\u0027))), viewportMAX(clusterSum(prop(\\u0027diameter\\u0027)))), [8.0, 32.0, 64])\\ncolor: opacity(#FFB927, 0.8)\\nfilter: 1\\nresolution: 64\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(clusterSum(prop(\\u0027diameter\\u0027)), viewportMIN(clusterSum(prop(\\u0027diameter\\u0027))), viewportMAX(clusterSum(prop(\\u0027diameter\\u0027)))), [8.0, 32.0, 64])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -1954,7 +2588,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 3, diff --git a/examples/legends/opacity_in_legend.ipynb b/docs/examples/data_visualization/styles/color_bins_style.ipynb similarity index 95% rename from examples/legends/opacity_in_legend.ipynb rename to docs/examples/data_visualization/styles/color_bins_style.ipynb index 5dced7f11..97a996edb 100644 --- a/examples/legends/opacity_in_legend.ipynb +++ b/docs/examples/data_visualization/styles/color_bins_style.ipynb @@ -4,9 +4,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Opacity in legend\n", + "## Color Bins Style\n", "\n", - "In this example, the color bins legend reads from the `color` property which has `opacity` set to `0.6`. This is reflected in both the map and the legend." + "Helper function for quickly creating a color bins style. Use `help(color_bins_style)` to get more information." ] }, { @@ -16,7 +16,6 @@ "outputs": [], "source": [ "from cartoframes.auth import set_default_credentials\n", - "from cartoframes.viz import Layer, color_bins_style, color_bins_legend\n", "\n", "set_default_credentials('cartoframes')" ] @@ -238,12 +237,10 @@ " \n", " \n", " \n", + " heading="pop_sq_km"\n", + " description="">\n", " \n", " \n", - " Data: US Census\n", - " \n", " \n", " \n", " \n", @@ -758,21 +755,21 @@ " return value.toLocaleString();\n", " }\n", "\n", - " function updateViewport(map) {\n", + " function updateViewport(id, map) {\n", " function updateMapInfo() {\n", - " const mapInfo$ = document.getElementById('map-info');\n", + " const mapInfo$ = document.getElementById(id);\n", " const center = map.getCenter();\n", " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -846,7 +843,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -861,12 +858,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                  ${popupHTML}
                                  `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                  ${popupHTML}
                                  `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1150,7 +1151,8 @@ " const map = createMap(container, basemapStyle, settings.bounds, settings.mapboxtoken);\n", "\n", " if (settings.show_info) {\n", - " updateViewport(map);\n", + " const id = mapIndex !== undefined ? `map-info-${mapIndex}` : 'map-info';\n", + " updateViewport(id, map);\n", " }\n", "\n", " if (settings.camera) {\n", @@ -1224,7 +1226,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1265,12 +1267,12 @@ " .addEventListener('ready', () => {\n", " const basecolor = '';\n", " const basemap = 'Positron';\n", - " const bounds = [[-122.5301, 37.708131], [-122.356967, 37.833365]];\n", + " const bounds = [[-6.365712, 49.883409], [1.771169, 55.805486]];\n", " const camera = null;\n", " const has_legends = 'true' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"demographics_sf\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "v77f7c2", "title": "masters_percent"}, "event": "hover"}], "legends": [{"ascending": false, "description": "by block group", "dynamic": true, "footer": "Data: US Census", "format": null, "prop": "color", "title": "Percent Master\\u0027s Degree", "type": "color-bins", "variable": null}], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"demographics_sf\\"", "title": null, "type": "Query", "viz": "@v77f7c2: prop(\\u0027masters_percent\\u0027)\\ncolor: opacity(ramp(globalQuantiles(prop(\\u0027masters_percent\\u0027), 5), purpor), 0.5)\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nfilter: 1\\n", "widgets": []}];\n", + " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"eng_wales_pop\\"", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "v18f1e3", "title": "pop_sq_km"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "color", "title": "pop_sq_km", "type": "color-bins", "variable": null}], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"eng_wales_pop\\"", "title": null, "type": "Query", "viz": "@v18f1e3: prop(\\u0027pop_sq_km\\u0027)\\ncolor: opacity(ramp(globalQuantiles(prop(\\u0027pop_sq_km\\u0027), 5), purpor), 0.9)\\nfilter: 1\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -1294,7 +1296,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -1303,15 +1305,9 @@ } ], "source": [ - "Layer(\n", - " 'demographics_sf',\n", - " color_bins_style('masters_percent', opacity=0.5),\n", - " legends=color_bins_legend(\n", - " title=\"Percent Master's Degree\",\n", - " description='by block group',\n", - " footer='Data: US Census'\n", - " )\n", - ")" + "from cartoframes.viz import Layer, color_bins_style\n", + "\n", + "Layer('eng_wales_pop', color_bins_style('pop_sq_km'))" ] } ], diff --git a/docs/examples/data_visualization/styles/color_category_style.ipynb b/docs/examples/data_visualization/styles/color_category_style.ipynb new file mode 100644 index 000000000..bfb9f06ad --- /dev/null +++ b/docs/examples/data_visualization/styles/color_category_style.ipynb @@ -0,0 +1,1335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Color Category Style\n", + "\n", + "Helper function for quickly creating a color category style. Use `help(color_category_style)` to get more information." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('cartoframes')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                                      \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.viz import Map, Layer, size_continuous_style, color_category_style, color_bins_legend\n", + "\n", + "Map([\n", + " Layer(\n", + " 'sf_nbhd',\n", + " style=color_category_style('name', 'SF Neighborhoods',\n", + " cat=[\n", + " 'Downtown/Civic Center',\n", + " 'South of Market',\n", + " 'Mission',\n", + " 'Western Addition',\n", + " 'Financial District','Bayview'\n", + " ],\n", + " palette='[#E58606,#5D69B1,#52BCA3,#99C945,#CC61B0,#ED645A],#ccc',\n", + " stroke_color='#c4c4c4'\n", + " ),\n", + " legends=color_bins_legend(\n", + " description='Hover each to see total crime reports',\n", + " footer='Source: City of SF',\n", + " ascending=True\n", + " )\n", + " ),\n", + " Layer(\n", + " 'sf_nbhd_crime',\n", + " style=size_continuous_style(\n", + " 'value',\n", + " size_range=[8,60],\n", + " opacity=0.05,\n", + " color='black',\n", + " stroke_color='#ffea00',\n", + " stroke_width=2\n", + " ),\n", + " default_legend=False\n", + " )\n", + "])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/data_visualization/styles/size_bins_style.ipynb b/docs/examples/data_visualization/styles/size_bins_style.ipynb new file mode 100644 index 000000000..52bdeb7b8 --- /dev/null +++ b/docs/examples/data_visualization/styles/size_bins_style.ipynb @@ -0,0 +1,1335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Size Bins Style\n", + "\n", + "Helper function for quickly creating a size bins style. Use `help(size_bins_style)` to get more information." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('cartoframes')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                                      \n", + " :\n", + "
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                                      \n", + " StackTrace\n", + "
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                                        \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.viz import Layer, size_bins_style\n", + "\n", + "Layer('clev_sales', size_bins_style('sale_price'))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/styles/size_category_style.ipynb b/docs/examples/data_visualization/styles/size_category_style.ipynb similarity index 64% rename from examples/styles/size_category_style.ipynb rename to docs/examples/data_visualization/styles/size_category_style.ipynb index cfbfaeb95..f801475d9 100644 --- a/examples/styles/size_category_style.ipynb +++ b/docs/examples/data_visualization/styles/size_category_style.ipynb @@ -83,6 +83,11 @@ " max-width: 100%;\n", " height: auto;\n", " }\n", + "\n", + " as-layer-selector-slot .as-layer-selector-slot--wrapper .as-caption { // FIXME\n", + " font-size: 14px;\n", + " line-height: 14px;\n", + " }\n", "\n", " \n", " \n", " \n", " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + "
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                                        \n", + " :\n", + "
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                                        \n", + " StackTrace\n", + "
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                                          \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.viz import Layer, animation_style, time_series_widget\n", + "\n", + "Layer(\n", + " 'seattle_collisions',\n", + " animation_style('incdate', duration=20, fade_in=0.5, fade_out=0.5),\n", + " widgets=[\n", + " time_series_widget(\n", + " value='incdate',\n", + " title='Number of Collisions by Date',\n", + " description= 'Play, pause, or select a range for the animation'\n", + " )]\n", + ")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/examples.json b/docs/examples/examples.json new file mode 100644 index 000000000..35a70a2e6 --- /dev/null +++ b/docs/examples/examples.json @@ -0,0 +1,554 @@ +{ + "main": { + "title": "Introduction", + "file": "introduction.md" + }, + "categories": [ + { + "title": "Use Cases", + "desc": "Explore how to make the most of your Spatial Analysis with CARTOframes", + "hide_sidebar_samples": true, + "samples": [ + { + "title": "Building a dashboard", + "desc": "Building a dashboard to plan a marketing campaign leveraging CARTO Data Observatory", + "file": "building_a_dashboard", + "path": "use_cases" + }, + { + "title": "Combining two datasets", + "desc": "Using geosocial media data to understand retail performance", + "file": "combining_two_datasets", + "path": "use_cases" + }, + { + "title": "Visualize temperatures", + "desc": "Visualize warm temperatures", + "file": "visualize_temperatures", + "path": "use_cases" + }, + { + "title": "Paris WiFi services", + "desc": "Download and visualize WiFi Services data in JSON format", + "file": "paris_wifi_services", + "path": "use_cases" + }, + { + "title": "Paris remarkable trees", + "desc": "Transform and visualize data from a JSON file", + "file": "paris_remarkable_trees", + "path": "use_cases" + }, + { + "title": "Geocode London stations", + "desc": "Geocode a dataset in XLS format", + "file": "geocoding_london_stations", + "path": "use_cases" + } + ] + }, + { + "title": "Templates", + "desc": "Incorporate CARTOframes in your workflows easily with functionality templates", + "hide_sidebar_samples": true, + "samples": [ + { + "title": "Data Discovery", + "desc": "How to explore the data catalog", + "file": "do_data_discovery", + "path": "templates" + }, + { + "title": "Access public data", + "desc": "Manage DO public data", + "file": "do_access_public_data", + "path": "templates" + }, + { + "title": "Access premium data", + "desc": "Manage DO premium data", + "file": "do_access_premium_data", + "path": "templates" + }, + { + "title": "Data Enrichment", + "desc": "Enrich your data", + "file": "do_data_enrichment", + "path": "templates" + } + ] + }, + { + "title": "Data Management", + "desc": "Explore working with data both locally and with a CARTO account", + "hide_sidebar_samples": true, + "samples": [ + { + "title": "Read a GeoJSON file", + "desc": "Read data from a GeoJSON file", + "file": "read_geojson", + "path": "data_management" + }, + { + "title": "Read a Shapefile", + "desc": "Read data from a Shapefile", + "file": "read_shapefile", + "path": "data_management" + }, + { + "title": "Read a CSV file", + "desc": "Read data from a CSV file", + "file": "read_csv", + "path": "data_management" + }, + { + "title": "Read a JSON file", + "desc": "Read data from a JSON file", + "file": "read_json", + "path": "data_management" + }, + { + "title": "Read a CARTO table", + "desc": "Read data from a CARTO table", + "file": "read_carto_table", + "path": "data_management" + }, + { + "title": "Read a CARTO SQL query", + "desc": "Read data from a CARTO table using a SQL Query", + "file": "read_carto_query", + "path": "data_management" + }, + { + "title": "Upload to CARTO", + "desc": "Upload data to CARTO", + "file": "upload_to_carto", + "path": "data_management" + }, + { + "title": "Change CARTO table privacy", + "desc": "Change the privacy of a CARTO table", + "file": "change_carto_table_privacy", + "path": "data_management" + } + ] + }, + { + "title": "Data Visualization", + "desc": "Explore the building blocks for creating web-based, dynamic, and interactive map visualizations", + "hide_sidebar_samples": true, + "samples": [ + { + "title": "Single layer", + "desc": "Add a Layer", + "file": "add_layer", + "path": "data_visualization/layers", + "group": "Layers" + }, + { + "title": "Multiple layers", + "desc": "Add multiple Layers", + "file": "add_multiple_layers", + "path": "data_visualization/layers", + "group": "Layers" + }, + { + "title": "Basemaps", + "desc": "Change the default CARTO basemap", + "file": "change_default_carto_basemap", + "path": "data_visualization/map_configuration", + "group": "Map configuration" + }, + { + "title": "Viewport", + "desc": "Set a custom viewport", + "file": "custom_viewport", + "path": "data_visualization/map_configuration", + "group": "Map configuration" + }, + { + "title": " Background", + "desc": "Set a background color", + "file": "solid_color_background", + "path": "data_visualization/map_configuration", + "group": "Map configuration" + }, + { + "title": "Dark theme", + "desc": "Switch to the dark theme", + "file": "dark_theme", + "path": "data_visualization/map_configuration", + "group": "Map configuration" + }, + { + "title": "Basic style", + "desc": "Use the basic_style method to change the basic style properties", + "file": "basic_style", + "path": "data_visualization/styles", + "group": "Data-driven styling" + }, + { + "title": "Color category style", + "desc": "Use the color_category_style method to color features by categorical values", + "file": "color_category_style", + "path": "data_visualization/styles", + "group": "Data-driven styling" + }, + { + "title": "Color continuous style", + "desc": "Use the color_continuous_style method to color features by continuous numeric values", + "file": "color_continuous_style", + "path": "data_visualization/styles", + "group": "Data-driven styling" + }, + { + "title": "Color bins style", + "desc": "Use the color_bins_style method to color features by discrete numeric values", + "file": "color_bins_style", + "path": "data_visualization/styles", + "group": "Data-driven styling" + }, + { + "title": "Size category style", + "desc": "Use the size_category_style method to resize each feature by categorical values", + "file": "size_category_style", + "path": "data_visualization/styles", + "group": "Data-driven styling" + }, + { + "title": "Size continuous style", + "desc": "Use the size_continuous_style method to resize each feature by continuous numeric values", + "file": "size_continuous_style", + "path": "data_visualization/styles", + "group": "Data-driven styling" + }, + { + "title": "Size bins style", + "desc": "Use the size_bins_style method to resize each feature by discrete numeric values", + "file": "size_bins_style", + "path": "data_visualization/styles", + "group": "Data-driven styling" + }, + { + "title": "Cluster size style", + "desc": "Use the cluster_size_style method to aggregate features by continuous numeric values", + "file": "cluster_size_style", + "path": "data_visualization/styles", + "group": "Data-driven styling" + }, + { + "title": "Animation style", + "desc": "Use the animation_style helper method to simply animate a visualization", + "file": "animation_style", + "path": "data_visualization/styles", + "group": "Data-driven styling" + }, + { + "title": "Add multiple style helpers", + "desc": "Combine different visualization styles", + "file": "combine_visualization_styles", + "path": "data_visualization/styles", + "group": "Data-driven styling" + }, + { + "title": "Default legend", + "desc": "Set the default legend needed for the style helper", + "file": "default_legend", + "path": "data_visualization/legends", + "group": "Legends" + }, + { + "title": "Basic legend", + "desc": "Use the basic_legend method to display a simple default legend", + "file": "basic_legend", + "path": "data_visualization/legends", + "group": "Legends" + }, + { + "title": "Color category cegend", + "desc": "Use the color_category_legend method to represent categorical values by color", + "file": "color_category_legend", + "path": "data_visualization/legends", + "group": "Legends" + }, + { + "title": "Color bins legend", + "desc": "Use the color_bins_legend method to represent discrete numeric values by color", + "file": "color_bins_legend", + "path": "data_visualization/legends", + "group": "Legends" + }, + { + "title": "Color continuous legend", + "desc": "Use the color_continuous_legend method to represent continuous numeric values by color", + "file": "color_continuous_legend", + "path": "data_visualization/legends", + "group": "Legends" + }, + { + "title": "Size category legend", + "desc": "Use the size_category_legend method to represent categorical values by size", + "file": "size_category_legend", + "path": "data_visualization/legends", + "group": "Legends" + }, + { + "title": "Size bins legend", + "desc": "Use the size_bins_legend method to represent discrete numeric values by size", + "file": "size_bins_legend", + "path": "data_visualization/legends", + "group": "Legends" + }, + { + "title": "Size continuous legend", + "desc": "Use the size_bins_legend method to represent continuous numeric values by size", + "file": "size_continuous_legend", + "path": "data_visualization/legends", + "group": "Legends" + }, + { + "title": "Add multiple legends", + "desc": "Combine different legends in the same visualization", + "file": "multiple_legends", + "path": "data_visualization/legends", + "group": "Legends" + }, + { + "title": "Legend by geometry", + "desc": "Specify the geometry type in a legend helper", + "file": "legend_line_geometry", + "path": "data_visualization/legends", + "group": "Legends" + }, + { + "title": "Default widget", + "desc": "Set the default widget needed for the style helper", + "file": "default_widget", + "path": "data_visualization/widgets", + "group": "Interactive widgets" + }, + { + "title": "Basic widget", + "desc": "Use the basic_widget method to display a simple default widget", + "file": "basic_widget", + "path": "data_visualization/widgets", + "group": "Interactive widgets" + }, + { + "title": "Category widget", + "desc": "Use the category_widget to create a widget to represent categorical values", + "file": "category_widget", + "path": "data_visualization/widgets", + "group": "Interactive widgets" + }, + { + "title": "Histogram widget", + "desc": "Use the histogram_widget to create a widget to represent categorical, numeric and date values in a histogram", + "file": "histogram_widget", + "path": "data_visualization/widgets", + "group": "Interactive widgets" + }, + { + "title": "Formula widget", + "desc": "Use the formula_widget to display the result of a count, avg, max, min or sum operation in a numeric", + "file": "formula_widget", + "path": "data_visualization/widgets", + "group": "Interactive widgets" + }, + { + "title": "Animation widget", + "desc": "Use the animation_widget to be able to play, pause and change an animated visualization through animation controls", + "file": "animation_widget", + "path": "data_visualization/widgets", + "group": "Interactive widgets" + }, + { + "title": "Time series widget", + "desc": "Use the time_series_widget to represent a sequence of categorical, numeric and date values in a histogram indexed by a numeric or date value", + "file": "time_series_widget", + "path": "data_visualization/widgets", + "group": "Interactive widgets" + }, + { + "title": "Add multiple widgets", + "desc": "Combine multiple widgets in the same visualization", + "file": "multiple_widgets", + "path": "data_visualization/widgets", + "group": "Interactive widgets" + }, + { + "title": "Default popup", + "desc": "Set the default popup needed for the style helper", + "file": "default_popup", + "path": "data_visualization/popups", + "group": "Click and hover popups" + }, + { + "title": "Popup on click", + "desc": "Display a popup triggered by a click event", + "file": "popup_on_click", + "path": "data_visualization/popups", + "group": "Click and hover popups" + }, + { + "title": "Popup on hover", + "desc": "Display a popup triggered by a hover event", + "file": "popup_on_hover", + "path": "data_visualization/popups", + "group": "Click and hover popups" + }, + { + "title": "Customize popup title and format", + "desc": "Change the title and format for the popup values", + "file": "popup_titles_and_format", + "path": "data_visualization/popups", + "group": "Click and hover popups" + }, + { + "title": "Add multiple popup elements", + "desc": "Display popups triggered by both click and hover events", + "file": "multiple_popup_events", + "path": "data_visualization/popups", + "group": "Click and hover popups" + }, + { + "title": "Default layout", + "desc": "Create a default visualization layout", + "file": "default_layout", + "path": "data_visualization/grid_layout", + "group": "Grid layout" + }, + { + "title": "Custom layout", + "desc": "Use different settings to create a custom visualization layout", + "file": "custom_layout", + "path": "data_visualization/grid_layout", + "group": "Grid layout" + }, + { + "title": "Add layout titles", + "desc": "Use different custom titles to create a visualization layout", + "file": "layout_titles", + "path": "data_visualization/grid_layout", + "group": "Grid layout" + }, + { + "title": "Customize layout viewport", + "desc": "Use different viewport settings to create a visualization layout", + "file": "layout_viewport", + "path": "data_visualization/grid_layout", + "group": "Grid layout" + }, + { + "title": "Make layout static", + "desc": "Create a static visualization layout", + "file": "static_layout", + "path": "data_visualization/grid_layout", + "group": "Grid layout" + }, + { + "title": "Publish Public Table Visualization", + "desc": "Publish a visualization from a public table", + "file": "publish_visualization_public_table", + "path": "data_visualization/publish_and_share", + "group": "Publish and share" + }, + { + "title": "Publish Private Table Visualization", + "desc": "Publish a visualization from a private table", + "file": "publish_visualization_private_table", + "path": "data_visualization/publish_and_share", + "group": "Publish and share" + }, + { + "title": "Publish GeoDataFrame Visualization", + "desc": "Publish a visualization from a GeoDataFrame", + "file": "publish_visualization_gdf", + "path": "data_visualization/publish_and_share", + "group": "Publish and share" + }, + { + "title": "Publish Layout Visualization", + "desc": "Publish a visualization of a layout", + "file": "publish_visualization_layout", + "path": "data_visualization/publish_and_share", + "group": "Publish and share" + } + ] + }, + { + "title": "Data Services", + "desc": "Learn how to combine your data and CARTO's Location Data Services for geospatial analyses", + "hide_sidebar_samples": true, + "samples": [ + { + "title": "Geocoding", + "desc": "Using the Geocoding Services to geocode a DataFrame", + "file": "geocoding", + "path": "data_services" + }, + { + "title": "Isochrones", + "desc": "Using the Isolines Services to calculate isochrones", + "file": "isochrones", + "path": "data_services" + }, + { + "title": "Isodistances", + "desc": "Using the Isolines Services to calculate isodistances", + "file": "isodistances", + "path": "data_services" + }, + { + "title": "Combining geocoding and isolines", + "desc": "Use both Geocoding and Isolines services", + "file": "geocoding_and_isolines", + "path": "data_services" + } + ] + }, + { + "title": "Data Observatory", + "desc": "Learn how to discover third party datasets and enrich your data through CARTO's Data Observatory", + "hide_sidebar_samples": true, + "samples": [ + { + "title": "Discover a dataset", + "desc": "Basic steps to discover a Dataset in the Data Observatory", + "file": "discover_dataset", + "path": "data_observatory" + }, + { + "title": "Access a dataset", + "desc": "Access a dataset applying filters", + "file": "access_dataset", + "path": "data_observatory" + }, + { + "title": "Enrich points", + "desc": "Use the enrichment to enrich points for a dataset", + "file": "points_enrichment_dataset", + "path": "data_observatory" + }, + { + "title": "Enrich polygons", + "desc": "Use the enrichment to enrich polygons for a dataset", + "file": "polygons_enrichment_dataset", + "path": "data_observatory" + }, + { + "title": "Request premium dataset subscription", + "desc": "Basic steps to request a premium dataset subscription in the Data Observatory", + "file": "enrichment_subscription_workflow", + "path": "data_observatory" + }, + { + "title": "Google Cloud credentials", + "desc": "Get and use your Data Observatory's Google Cloud credentials", + "file": "google_cloud_credentials", + "path": "data_observatory" + } + ] + } + ] +} \ No newline at end of file diff --git a/docs/developer-center/examples/introduction.md b/docs/examples/introduction.md similarity index 100% rename from docs/developer-center/examples/introduction.md rename to docs/examples/introduction.md diff --git a/docs/examples/templates/do_access_premium_data.ipynb b/docs/examples/templates/do_access_premium_data.ipynb new file mode 100644 index 000000000..1fcdfa22c --- /dev/null +++ b/docs/examples/templates/do_access_premium_data.ipynb @@ -0,0 +1,7094 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "7LKLhQBzJMOz" + }, + "source": [ + "## CARTO Data Observatory. Access premium data.\n", + "\n", + "This notebook shows how to use CARTOframes for discovering and downloading **premium** datasets from CARTO's [Data Observatory](https://carto.com/spatial-data-catalog/).\n", + "\n", + "In particular, we will download touristic [POI's from Pitney Bowes](https://carto.com/spatial-data-catalog/browser/?category=points_of_interest&provider=pitney_bowes) within a specific bounding box.\n", + "\n", + "The notebook is organized in the following sections:\n", + " - [Check your subscriptions to premium datasets](#section1)\n", + " - [Download a small sample of a dataset applying spatial filtering to explore it further](#section2)\n", + " - [Download dataset filtering by column value and bounding box](#section3)\n", + " - [Upload filtered dataset to your CARTO account](#section4)\n", + " \n", + " \n", + "**Note** for this notebook we are using the premium [dataset of Pitney Bowes POI's in Spain](https://carto.com/spatial-data-catalog/browser/dataset/pb_points_of_i_94bda91b/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V4-0hJjiVsen" + }, + "source": [ + "### Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "6vLRH4fEEkms" + }, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import pandas as pd\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "from cartoframes import to_carto\n", + "from cartoframes.auth import set_default_credentials\n", + "from cartoframes.data.observatory import *\n", + "from cartoframes.viz import *\n", + "from shapely.geometry import box" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set CARTO default credentials" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to be able to use the Data Observatory via CARTOframes, you need to set your CARTO account credentials first.\n", + "\n", + "Please, visit the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/) for further detail." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note about credentials**\n", + "\n", + "For security reasons, we recommend storing your credentials in an external file to prevent publishing them by accident when sharing your notebooks. You can get more information in the section _Setting your credentials_ of the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zo5IKlj36qFF" + }, + "source": [ + "### Download a premium dataset\n", + "\n", + "When working with very large datasets, you might need to explore the dataset in detail to decide if you need the whole data or just part of it. In order to speed up your time to identifying the exact data, it might be very helpful to download just a small sample of your data, to later decide what you need. In this section, we will show how to identify toutistic POI's from a dataset we are already subscribed to." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "opOc5utoCGpx" + }, + "source": [ + "\n", + "#### Check your subscriptions to premium datasets\n", + "\n", + "First, we check we're already subscribed to the dataset we want to use for our analysis. In this case, we would like to use [Pitney Bowes POI's in Spain](https://carto.com/spatial-data-catalog/browser/dataset/pb_points_of_i_94bda91b/). The dataset is `pb_points_of_i_94bda91b`.\n", + "\n", + "You can subscribe to this premium [dataset](https://carto.com/spatial-data-catalog/browser/dataset/pb_points_of_i_94bda91b/) on your [CARTO dashboard](https://carto.com/help/working-with-data/subscribe_datasets_do/) or contacting CARTO." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "id": "XeLjV_0cCFIz", + "outputId": "5fd18729-5b09-4626-c548-7b65a0ee6e8f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                          0ags_sociodemogr_a7e14220Sociodemographics - United States of America (...Census and ACS sociodemographic data estimated...demographicsusasociodemographicsagsCensus Block Group - United States of AmericaNoneyearlyNoneyearlyFalseeng2020DemographicsApplied Geographic Solutionscarto-do.ags.geography_usa_blockgroup_2015carto-do.ags.demographics_sociodemographics_us...
                                          1ags_retailpoten_aaf25a8cRetail Potential - United States of America (C...The retail potential database consists of aver...demographicsusaretailpotentialagsCensus Block Group - United States of America ...Shoreline clipped TIGER/Line boundaries. More ...yearly[2018-01-01, 2019-01-01)yearlyFalseeng2019DemographicsApplied Geographic Solutionscarto-do-public-data.carto.geography_usa_block...carto-do.ags.demographics_retailpotential_usa_...
                                          2pb_consumer_po_62cddc04Points Of Interest - Consumer - United States ...Consumer Point of interest database per catego...points_of_interestusaconsumer_points_of_interestpitney_bowesLatitude/Longitude - United States of AmericaLocation of Points of InterestmonthlyNonemonthlyFalseengv1Points of InterestPitney Bowescarto-do.pitney_bowes.geography_usa_latlon_v1carto-do.pitney_bowes.pointsofinterest_consume...
                                          3ags_sociodemogr_f510a947Sociodemographics - United States of America (...Census and ACS sociodemographic data estimated...demographicsusasociodemographicsagsCensus Block Group - United States of America ...Shoreline clipped TIGER/Line boundaries. More ...yearly[2019-01-01, 2020-01-01)yearlyFalseeng2019DemographicsApplied Geographic Solutionscarto-do-public-data.carto.geography_usa_block...carto-do.ags.demographics_sociodemographics_us...
                                          4ags_consumer_sp_dbabddfbConsumer Spending - United States of America (...The Consumer Expenditure database consists of ...demographicsusaconsumer_spendingagsCensus Block Group - United States of AmericaNoneyearlyNoneyearlyFalseeng2020DemographicsApplied Geographic Solutionscarto-do.ags.geography_usa_blockgroup_2015carto-do.ags.demographics_consumerspending_usa...
                                          5spa_geosocial_s_d5dc42aeGeosocial Segments - United States of America ...By analysing feeds from Twitter, Instagram, Me...behavioralusageosocial_segmentsspatial_aiCensus Block Group - United States of America ...Shoreline clipped TIGER/Line boundaries. More ...quarterly[2020-01-01, 2020-04-01)quarterlyFalseengv1BehavioralSpatial.aicarto-do-public-data.carto.geography_usa_block...carto-do.spatial_ai.behavioral_geosocialsegmen...
                                          6mc_geographic__7980c5c3Geographic Insights - United States of America...Geographic Insights validate, evaluate and ben...financialusageographic_insightsmastercardCensus Block Group - United States of America ...Shoreline clipped TIGER/Line boundaries. More ...monthly[2019-01-01, 2020-01-01)monthlyFalseengv1FinancialMastercardcarto-do-public-data.carto.geography_usa_block...carto-do.mastercard.financial_geographicinsigh...
                                          7pb_points_of_i_94bda91bPoints Of Interest - Spain (Latitude/Longitude)Point of interest database per categoriespoints_of_interestesppoints_of_interestpitney_bowesLatitude/Longitude - SpainLocation of Points of InterestmonthlyNonemonthlyFalseengv1Points of InterestPitney Bowescarto-do.pitney_bowes.geography_esp_latlon_v1carto-do.pitney_bowes.pointsofinterest_pointso...
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SERVICES \n", + "1 00 None None DIVISION I. - SERVICES \n", + "2 00 None None DIVISION I. - SERVICES \n", + "3 00 None None DIVISION I. - SERVICES \n", + "4 00 None None DIVISION I. - SERVICES \n", + "5 00 None None DIVISION I. - SERVICES \n", + "6 00 None None DIVISION I. - SERVICES \n", + "7 00 None None DIVISION I. - SERVICES \n", + "8 00 None None DIVISION I. - SERVICES \n", + "9 00 None None DIVISION I. - SERVICES \n", + "\n", + " ADDRESSLASTLINE CONFIDENCE_CODE MAINADDRESSLINE \\\n", + "0 10869, TORRECILLA DE LOS ANGELES, CÁCERES LOW None \n", + "1 28911, LEGANES, MADRID LOW None \n", + "2 08301, MATARO, BARCELONA MEDIUM None \n", + "3 30009, MURCIA, MURCIA HIGH None \n", + "4 50180, UTEBO, ZARAGOZA LOW None \n", + "5 21006, HUELVA, HUELVA HIGH None \n", + "6 08026, BARCELONA, BARCELONA MEDIUM None \n", + "7 08030, BARCELONA, BARCELONA LOW None \n", + "8 29014, MALAGA, MÁLAGA HIGH None \n", + "9 41020, SEVILLA, SEVILLA LOW None \n", + "\n", + " PARENT_POSTCODE FORMATTEDADDRESS PARENT_AREANAME1 PARENT_AREANAME3 \\\n", + "0 None None MALAGA None \n", + "1 None None GUIPUZCOA None \n", + "2 None None MADRID None \n", + "3 None None MALAGA None \n", + "4 None None LERIDA None \n", + "5 None None SEVILLA None \n", + "6 None None BARCELONA None \n", + "7 None None None None \n", + "8 None None BARCELONA None \n", + "9 None None ALBACETE None \n", + "\n", + " SIC8_DESCRIPTION ALT_INDUSTRY_CODE LEGAL_STATUS_CODE \\\n", + "0 FRUIT AND VEGETABLE MARKETS None None \n", + "1 FRUIT AND VEGETABLE MARKETS None None \n", + "2 FRUIT AND VEGETABLE MARKETS None None \n", + "3 FRUIT AND VEGETABLE MARKETS None None \n", + "4 FRUIT AND VEGETABLE MARKETS None None \n", + "5 FRUIT AND VEGETABLE MARKETS None None \n", + "6 FRUIT AND VEGETABLE MARKETS None None \n", + "7 FRUIT AND VEGETABLE MARKETS None None \n", + "8 FRUIT AND VEGETABLE MARKETS None None \n", + "9 FRUIT AND VEGETABLE MARKETS None None \n", + "\n", + " SALES_VOLUME_LOCAL COUNTRY_ACCESS_CODE PARENT_BUSINESS_NAME \\\n", + "0 NaN None None \n", + "1 NaN None None \n", + "2 NaN None None \n", + "3 NaN None None \n", + "4 NaN None None \n", + "5 59118.0 None None \n", + "6 60384.0 None None \n", + "7 61505.0 None None \n", + "8 67464.0 None None \n", + "9 60587.0 None None \n", + "\n", + " SUBSIDIARY_INDICATOR PARENT_STREET_ADDRESS GLOBAL_ULTIMATE_ADDRESS \\\n", + "0 None None None \n", + "1 None None None \n", + "2 None None None \n", + "3 None None None \n", + "4 None None None \n", + "5 None None None \n", + "6 None None None \n", + "7 None None None \n", + "8 None None None \n", + "9 None None None \n", + "\n", + " GLOBAL_ULTIMATE_COUNTRY SALES_VOLUME_US_DOLLARS GLOBAL_ULTIMATE_POSTCODE \\\n", + "0 None None None \n", + "1 None None None \n", + "2 None None None \n", + "3 None None None \n", + "4 None None None \n", + "5 None None None \n", + "6 None None None \n", + "7 None None None \n", + "8 None None None \n", + "9 None None None \n", + "\n", + " DOMESTIC_ULTIMATE_ADDRESS GLOBAL_ULTIMATE_AREANAME1 \\\n", + "0 None None \n", + "1 None None \n", + "2 None None \n", + "3 None None \n", + "4 None None \n", + "5 None None \n", + "6 None None \n", + "7 None None \n", + "8 None None \n", + "9 None None \n", + "\n", + " GLOBAL_ULTIMATE_AREANAME3 GLOBAL_ULTIMATE_INDICATOR \\\n", + "0 None None \n", + "1 None None \n", + "2 None None \n", + "3 None None \n", + "4 None None \n", + "5 None None \n", + "6 None None \n", + "7 None None \n", + "8 None None \n", + "9 None None \n", + "\n", + " DOMESTIC_ULTIMATE_POSTCODE DOMESTIC_ULTIMATE_AREANAME1 \\\n", + "0 None None \n", + "1 None None \n", + "2 None None \n", + "3 None None \n", + "4 None None \n", + "5 None None \n", + "6 None None \n", + "7 None None \n", + "8 None None \n", + "9 None None \n", + "\n", + " DOMESTIC_ULTIMATE_AREANAME3 GLOBAL_ULTIMATE_BUSINESS_NAME \\\n", + "0 None None \n", + "1 None None \n", + "2 None None \n", + "3 None None \n", + "4 None None \n", + "5 None None \n", + "6 None None \n", + "7 None None \n", + "8 None None \n", + "9 None None \n", + "\n", + " GLOBAL_ULTIMATE_STREET_ADDRESS DOMESTIC_ULTIMATE_BUSINESS_NAME \\\n", + "0 None None \n", + "1 None None \n", + "2 None None \n", + "3 None None \n", + "4 None None \n", + "5 None None \n", + "6 None None \n", + "7 None None \n", + "8 None None \n", + "9 None None \n", + "\n", + " DOMESTIC_ULTIMATE_STREET_ADDRESS \n", + "0 CARRETERA DE CADIZ 206 \n", + "1 PASEO DE MIRAMON 185 \n", + "2 PASEO CASTELLANA, 120 - IZ BJ \n", + "3 AVENIDA DEL CARMEN (ED EL FARO), BL 3 LOC \n", + "4 CARRETERA PALAU (KM 1) \n", + "5 CALLE MIGUEL VAZQUEZ DELGADO 71 \n", + "6 CALLE ANTIC CAMI DE XIMELIS 19 \n", + "7 AVENIDA GENERAL PERON (ED MASTER'S I), 38 - PI... \n", + "8 CALLE BRUC DEL MIG 8 \n", + "9 CALLE MAYOR, 32 - 1 A " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pois_ds.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's make sure the dataset covers our area of interest." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "LtfCUHlIsJFu", + "outputId": "72c3c8f8-72cf-40f2-aee3-c2d8cfa51b9d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                                            NAMEgeoiddo_dateBRANDNAMEPB_IDTRADE_NAMEFRANCHISE_NAMEISO3AREANAME4AREANAME3AREANAME2AREANAME1STABBPOSTCODEFORMATTEDADDRESSMAINADDRESSLINEADDRESSLASTLINELONGITUDELATITUDEGEORESULTCONFIDENCE_CODECOUNTRY_ACCESS_CODETEL_NUMFAXNUMEMAILHTTPOPEN_24HBUSINESS_LINESIC1SIC2SIC8SIC8_DESCRIPTIONALT_INDUSTRY_CODEMICODETRADE_DIVISIONGROUPCLASSSUB_CLASSEMPLOYEE_HEREEMPLOYEE_COUNTYEAR_STARTSALES_VOLUME_LOCALSALES_VOLUME_US_DOLLARSCURRENCY_CODEAGENT_CODELEGAL_STATUS_CODESTATUS_CODESUBSIDIARY_INDICATORPARENT_BUSINESS_NAMEPARENT_ADDRESSPARENT_STREET_ADDRESSPARENT_AREANAME3PARENT_AREANAME1PARENT_COUNTRYPARENT_POSTCODEDOMESTIC_ULTIMATE_BUSINESS_NAMEDOMESTIC_ULTIMATE_ADDRESSDOMESTIC_ULTIMATE_STREET_ADDRESSDOMESTIC_ULTIMATE_AREANAME3DOMESTIC_ULTIMATE_AREANAME1DOMESTIC_ULTIMATE_POSTCODEGLOBAL_ULTIMATE_INDICATORGLOBAL_ULTIMATE_BUSINESS_NAMEGLOBAL_ULTIMATE_ADDRESSGLOBAL_ULTIMATE_STREET_ADDRESSGLOBAL_ULTIMATE_AREANAME3GLOBAL_ULTIMATE_AREANAME1GLOBAL_ULTIMATE_COUNTRYGLOBAL_ULTIMATE_POSTCODEFAMILY_MEMBERSHIERARCHY_CODETICKER_SYMBOLEXCHANGE_NAMEgeom
                                            0100 MONTADITOS2173220473#-3.70582#40.4162019-12-01NaN2173220473100 MONTADITOSNaNESPNaNMADRIDMADRIDCOMUNIDAD DE MADRIDMD28012.0CALLE POSTAS 12, 28012, MADRID, MADRIDCALLE POSTAS 1228012, MADRID, MADRID-3.70582040.416000S8HPNTSCZAHIGHNaN915 23 11 40913 51 90 03ATTCLIENTE@GRUPORESTALIA.COMSPAIN.100MONTADITOS.COMNaNNaNNaNNaNNaNTAPAS RESTAURANTSNaN10021076DIVISION G. - RETAIL TRADEEATING AND DRINKING PLACESEATING PLACES/RESTAURANTSEATING PLACES/RESTAURANTSNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPOINT (-3.70582 40.41600)
                                            11005 DISTRIBUCION SL1277167953#-3.7055463461631111#40.4202951923090132019-12-01NaN1277167953NaNNaNESPNaNMADRIDMADRIDMADRIDMD28013.0CALLE GRAN VIA, 28013, MADRID, MADRIDCALLE GRAN VIA28013, MADRID, MADRID-3.70554640.420295S4-PNTSCZAMEDIUM34.0915 22 16 12NaNNaNNaNNaNMETALS SERVICE CENTERS AND OFFICES5051.0NaN50510000.0METALS SERVICE CENTERS AND OFFICES350.010035051DIVISION F. - WHOLESALE TRADEWHOLESALE TRADE - DURABLE GOODSMETALS AND MINERALS, EXCEPT PETROLEUMMETALS SERVICE CENTERS AND OFFICES1.01.02004.0149127.0170724.05080.0G3.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNPOINT (-3.70555 40.42030)
                                            215K ANGELS AND INVESTORS SL.1369422585#-3.70587#40.420482019-12-01NaN1369422585NaNNaNESPMADRIDMADRIDMADRIDMADRIDMD28013.0CALLE GRAN VIA 46, 28013, MADRID, MADRIDCALLE GRAN VIA 4628013, MADRID, MADRID-3.70587040.420480S8HPNTSCZAHIGH34.0NaNNaNNaNWWW.15KANGELS.COMNaNSECURITY AND COMMODITY SERVICES, NEC, NSK6289.0NaN62890000.0SECURITY AND COMMODITY SERVICE350.010010324DIVISION H. - FINANCE, INSURANCE, AND REAL ESTATESECURITY AND COMMODITY BROKERS, DEALERS, EXCHA...SERVICES ALLIED WITH THE EXCHANGE OF SECURITIE...SECURITY AND COMMODITY SERVICE2.02.02017.054756.061000.05080.0G3.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNPOINT (-3.70587 40.42048)
                                            31610 PLAZA DEL CARMEN 5 SL.1289731188#-3.7028#40.418922019-12-01NaN1289731188NaNNaNESPMADRIDMADRIDMADRIDMADRIDMD28013.0PLAZA CARMEN 5, 28013, MADRID, MADRIDPLAZA CARMEN 528013, MADRID, MADRID-3.70280040.418920S8HPNTSCZAHIGH34.0NaNNaNNaNNaNNaNEATING PLACES5812.0NaN58120000.0EATING PLACES350.010020100DIVISION G. - RETAIL TRADEEATING AND DRINKING PLACESEATING PLACES/RESTAURANTSEATING PLACES/RESTAURANTS - UNSPECIFIED35.035.02016.02766588.03167262.05080.0G3.00.00.0PUZZLE DE RESTAURANTES SL.CALLE BALLESTA, 32 - LOC DR, 28004, MADRID, MA...CALLE BALLESTA, 32 - LOC DRMADRIDMADRIDSPAIN28004PUZZLE DE RESTAURANTES SL.CALLE BALLESTA, 32 - LOC DR, 28004, MADRID, MA...CALLE BALLESTA, 32 - LOC DRMADRIDMADRID28004.0NPUZZLE DE RESTAURANTES SL.CALLE BALLESTA, 32 - LOC DR, 28004, MADRID, MA...CALLE BALLESTA, 32 - LOC DRMADRIDMADRIDSPAIN280040.00.0NaNNaNPOINT (-3.70280 40.41892)
                                            41ST WANDA SERVICE SERVICIOS INTEGRADOS DE CONT...1277282874#-3.70641#40.419562019-12-01NaN1277282874NaNNaNESPMADRIDMADRIDMADRIDMADRIDMD28013.0CALLE PRECIADOS, 29 - 5 A, 28013, MADRID, MADRIDCALLE PRECIADOS, 29 - 5 A28013, MADRID, MADRID-3.70641040.419560S8HPNTSCZAHIGH34.0NaNNaNNaNNaNNaNBUSINESS SERVICES, NEC, NSK7389.0NaN73890900.0FINANCIAL SERVICES350.010905900DIVISION I. - SERVICESBUSINESS SERVICESMISCELLANEOUS BUSINESS SERVICESBUSINESS SERVICES, NEC0.01.02003.058097.068508.05080.0G3.01.00.01ST WANDA SERVICE SERVICIOS INTEGRADOS DE CONT...CALLE PRECIADOS, 29 - 5 A, 28013, MADRID, MADR...CALLE PRECIADOS, 29 - 5 AMADRIDMADRIDSPAIN280131ST WANDA SERVICE SERVICIOS INTEGRADOS DE CONT...CALLE PRECIADOS, 29 - 5 A, 28013, MADRID, MADRIDCALLE PRECIADOS, 29 - 5 AMADRIDMADRID28013.0Y1ST WANDA SERVICE SERVICIOS INTEGRADOS DE CONT...CALLE PRECIADOS, 29 - 5 A, 28013, MADRID, MADR...CALLE PRECIADOS, 29 - 5 AMADRIDMADRIDSPAIN280132.01.0NaNNaNPOINT (-3.70641 40.41956)
                                            \n", + "
                                            " + ], + "text/plain": [ + " NAME \\\n", + "0 100 MONTADITOS \n", + "1 1005 DISTRIBUCION SL \n", + "2 15K ANGELS AND INVESTORS SL. \n", + "3 1610 PLAZA DEL CARMEN 5 SL. \n", + "4 1ST WANDA SERVICE SERVICIOS INTEGRADOS DE CONT... \n", + "\n", + " geoid do_date BRANDNAME \\\n", + "0 2173220473#-3.70582#40.416 2019-12-01 NaN \n", + "1 1277167953#-3.7055463461631111#40.420295192309013 2019-12-01 NaN \n", + "2 1369422585#-3.70587#40.42048 2019-12-01 NaN \n", + "3 1289731188#-3.7028#40.41892 2019-12-01 NaN \n", + "4 1277282874#-3.70641#40.41956 2019-12-01 NaN \n", + "\n", + " PB_ID TRADE_NAME FRANCHISE_NAME ISO3 AREANAME4 AREANAME3 \\\n", + "0 2173220473 100 MONTADITOS NaN ESP NaN MADRID \n", + "1 1277167953 NaN NaN ESP NaN MADRID \n", + "2 1369422585 NaN NaN ESP MADRID MADRID \n", + "3 1289731188 NaN NaN ESP MADRID MADRID \n", + "4 1277282874 NaN NaN ESP MADRID MADRID \n", + "\n", + " AREANAME2 AREANAME1 STABB POSTCODE \\\n", + "0 MADRID COMUNIDAD DE MADRID MD 28012.0 \n", + "1 MADRID MADRID MD 28013.0 \n", + "2 MADRID MADRID MD 28013.0 \n", + "3 MADRID MADRID MD 28013.0 \n", + "4 MADRID MADRID MD 28013.0 \n", + "\n", + " FORMATTEDADDRESS \\\n", + "0 CALLE POSTAS 12, 28012, MADRID, MADRID \n", + "1 CALLE GRAN VIA, 28013, MADRID, MADRID \n", + "2 CALLE GRAN VIA 46, 28013, MADRID, MADRID \n", + "3 PLAZA CARMEN 5, 28013, MADRID, MADRID \n", + "4 CALLE PRECIADOS, 29 - 5 A, 28013, MADRID, MADRID \n", + "\n", + " MAINADDRESSLINE ADDRESSLASTLINE LONGITUDE LATITUDE \\\n", + "0 CALLE POSTAS 12 28012, MADRID, MADRID -3.705820 40.416000 \n", + "1 CALLE GRAN VIA 28013, MADRID, MADRID -3.705546 40.420295 \n", + "2 CALLE GRAN VIA 46 28013, MADRID, MADRID -3.705870 40.420480 \n", + "3 PLAZA CARMEN 5 28013, MADRID, MADRID -3.702800 40.418920 \n", + "4 CALLE PRECIADOS, 29 - 5 A 28013, MADRID, MADRID -3.706410 40.419560 \n", + "\n", + " GEORESULT CONFIDENCE_CODE COUNTRY_ACCESS_CODE TEL_NUM \\\n", + "0 S8HPNTSCZA HIGH NaN 915 23 11 40 \n", + "1 S4-PNTSCZA MEDIUM 34.0 915 22 16 12 \n", + "2 S8HPNTSCZA HIGH 34.0 NaN \n", + "3 S8HPNTSCZA HIGH 34.0 NaN \n", + "4 S8HPNTSCZA HIGH 34.0 NaN \n", + "\n", + " FAXNUM EMAIL HTTP \\\n", + "0 913 51 90 03 ATTCLIENTE@GRUPORESTALIA.COM SPAIN.100MONTADITOS.COM \n", + "1 NaN NaN NaN \n", + "2 NaN NaN WWW.15KANGELS.COM \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " OPEN_24H BUSINESS_LINE SIC1 SIC2 \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN METALS SERVICE CENTERS AND OFFICES 5051.0 NaN \n", + "2 NaN SECURITY AND COMMODITY SERVICES, NEC, NSK 6289.0 NaN \n", + "3 NaN EATING PLACES 5812.0 NaN \n", + "4 NaN BUSINESS SERVICES, NEC, NSK 7389.0 NaN \n", + "\n", + " SIC8 SIC8_DESCRIPTION ALT_INDUSTRY_CODE \\\n", + "0 NaN TAPAS RESTAURANTS NaN \n", + "1 50510000.0 METALS SERVICE CENTERS AND OFFICES 350.0 \n", + "2 62890000.0 SECURITY AND COMMODITY SERVICE 350.0 \n", + "3 58120000.0 EATING PLACES 350.0 \n", + "4 73890900.0 FINANCIAL SERVICES 350.0 \n", + "\n", + " MICODE TRADE_DIVISION \\\n", + "0 10021076 DIVISION G. - RETAIL TRADE \n", + "1 10035051 DIVISION F. - WHOLESALE TRADE \n", + "2 10010324 DIVISION H. - FINANCE, INSURANCE, AND REAL ESTATE \n", + "3 10020100 DIVISION G. - RETAIL TRADE \n", + "4 10905900 DIVISION I. - SERVICES \n", + "\n", + " GROUP \\\n", + "0 EATING AND DRINKING PLACES \n", + "1 WHOLESALE TRADE - DURABLE GOODS \n", + "2 SECURITY AND COMMODITY BROKERS, DEALERS, EXCHA... \n", + "3 EATING AND DRINKING PLACES \n", + "4 BUSINESS SERVICES \n", + "\n", + " CLASS \\\n", + "0 EATING PLACES/RESTAURANTS \n", + "1 METALS AND MINERALS, EXCEPT PETROLEUM \n", + "2 SERVICES ALLIED WITH THE EXCHANGE OF SECURITIE... \n", + "3 EATING PLACES/RESTAURANTS \n", + "4 MISCELLANEOUS BUSINESS SERVICES \n", + "\n", + " SUB_CLASS EMPLOYEE_HERE EMPLOYEE_COUNT \\\n", + "0 EATING PLACES/RESTAURANTS NaN NaN \n", + "1 METALS SERVICE CENTERS AND OFFICES 1.0 1.0 \n", + "2 SECURITY AND COMMODITY SERVICE 2.0 2.0 \n", + "3 EATING PLACES/RESTAURANTS - UNSPECIFIED 35.0 35.0 \n", + "4 BUSINESS SERVICES, NEC 0.0 1.0 \n", + "\n", + " YEAR_START SALES_VOLUME_LOCAL SALES_VOLUME_US_DOLLARS CURRENCY_CODE \\\n", + "0 NaN NaN NaN NaN \n", + "1 2004.0 149127.0 170724.0 5080.0 \n", + "2 2017.0 54756.0 61000.0 5080.0 \n", + "3 2016.0 2766588.0 3167262.0 5080.0 \n", + "4 2003.0 58097.0 68508.0 5080.0 \n", + "\n", + " AGENT_CODE LEGAL_STATUS_CODE STATUS_CODE SUBSIDIARY_INDICATOR \\\n", + "0 NaN NaN NaN NaN \n", + "1 G 3.0 0.0 0.0 \n", + "2 G 3.0 0.0 0.0 \n", + "3 G 3.0 0.0 0.0 \n", + "4 G 3.0 1.0 0.0 \n", + "\n", + " PARENT_BUSINESS_NAME \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 PUZZLE DE RESTAURANTES SL. \n", + "4 1ST WANDA SERVICE SERVICIOS INTEGRADOS DE CONT... \n", + "\n", + " PARENT_ADDRESS \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 CALLE BALLESTA, 32 - LOC DR, 28004, MADRID, MA... \n", + "4 CALLE PRECIADOS, 29 - 5 A, 28013, MADRID, MADR... \n", + "\n", + " PARENT_STREET_ADDRESS PARENT_AREANAME3 PARENT_AREANAME1 \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 CALLE BALLESTA, 32 - LOC DR MADRID MADRID \n", + "4 CALLE PRECIADOS, 29 - 5 A MADRID MADRID \n", + "\n", + " PARENT_COUNTRY PARENT_POSTCODE \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 SPAIN 28004 \n", + "4 SPAIN 28013 \n", + "\n", + " DOMESTIC_ULTIMATE_BUSINESS_NAME \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 PUZZLE DE RESTAURANTES SL. \n", + "4 1ST WANDA SERVICE SERVICIOS INTEGRADOS DE CONT... \n", + "\n", + " DOMESTIC_ULTIMATE_ADDRESS \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 CALLE BALLESTA, 32 - LOC DR, 28004, MADRID, MA... \n", + "4 CALLE PRECIADOS, 29 - 5 A, 28013, MADRID, MADRID \n", + "\n", + " DOMESTIC_ULTIMATE_STREET_ADDRESS DOMESTIC_ULTIMATE_AREANAME3 \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 CALLE BALLESTA, 32 - LOC DR MADRID \n", + "4 CALLE PRECIADOS, 29 - 5 A MADRID \n", + "\n", + " DOMESTIC_ULTIMATE_AREANAME1 DOMESTIC_ULTIMATE_POSTCODE \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 MADRID 28004.0 \n", + "4 MADRID 28013.0 \n", + "\n", + " GLOBAL_ULTIMATE_INDICATOR \\\n", + "0 NaN \n", + "1 N \n", + "2 N \n", + "3 N \n", + "4 Y \n", + "\n", + " GLOBAL_ULTIMATE_BUSINESS_NAME \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 PUZZLE DE RESTAURANTES SL. \n", + "4 1ST WANDA SERVICE SERVICIOS INTEGRADOS DE CONT... \n", + "\n", + " GLOBAL_ULTIMATE_ADDRESS \\\n", + "0 NaN \n", + "1 NaN \n", + "2 NaN \n", + "3 CALLE BALLESTA, 32 - LOC DR, 28004, MADRID, MA... \n", + "4 CALLE PRECIADOS, 29 - 5 A, 28013, MADRID, MADR... \n", + "\n", + " GLOBAL_ULTIMATE_STREET_ADDRESS GLOBAL_ULTIMATE_AREANAME3 \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 CALLE BALLESTA, 32 - LOC DR MADRID \n", + "4 CALLE PRECIADOS, 29 - 5 A MADRID \n", + "\n", + " GLOBAL_ULTIMATE_AREANAME1 GLOBAL_ULTIMATE_COUNTRY GLOBAL_ULTIMATE_POSTCODE \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 MADRID SPAIN 28004 \n", + "4 MADRID SPAIN 28013 \n", + "\n", + " FAMILY_MEMBERS HIERARCHY_CODE TICKER_SYMBOL EXCHANGE_NAME \\\n", + "0 NaN NaN NaN NaN \n", + "1 0.0 0.0 NaN NaN \n", + "2 0.0 0.0 NaN NaN \n", + "3 0.0 0.0 NaN NaN \n", + "4 2.0 1.0 NaN NaN \n", + "\n", + " geom \n", + "0 POINT (-3.70582 40.41600) \n", + "1 POINT (-3.70555 40.42030) \n", + "2 POINT (-3.70587 40.42048) \n", + "3 POINT (-3.70280 40.41892) \n", + "4 POINT (-3.70641 40.41956) " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sql_query = \"SELECT * except(do_label) FROM $dataset$ WHERE ST_IntersectsBox(geom, -3.707628,40.415947,-3.700891,40.421403)\"\n", + "sample_df = pois_ds.to_dataframe(sql_query=sql_query)\n", + "\n", + "#To keep only most updated POI's (based on the do_date)\n", + "sample_df = sample_df.sort_values(['NAME', 'do_date']).groupby('NAME').first().reset_index()\n", + "\n", + "sample_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After some exploration of the DataFrame, we find out `TRADE_DIVISION` is our variable. There is a category called `DIVISION L. - TOURISM`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 265 + }, + "id": "ByHc8RilgDrk", + "outputId": "0c448b77-a030-47f6-ca2f-8919228aa7ec" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "DIVISION I. - SERVICES 1769\n", + "DIVISION G. - RETAIL TRADE 1370\n", + "DIVISION E. - TRANSPORTATION AND PUBLIC UTILITIES 724\n", + "DIVISION H. - FINANCE, INSURANCE, AND REAL ESTATE 613\n", + "DIVISION F. - WHOLESALE TRADE 205\n", + "DIVISION D. - MANUFACTURING 198\n", + "DIVISION C. - CONSTRUCTION 67\n", + "DIVISION J. - PUBLIC ADMINISTRATION 25\n", + "DIVISION L. - TOURISM 14\n", + "DIVISION A. - AGRICULTURE, FORESTRY, AND FISHING 10\n", + "DIVISION M. - SPORTS 8\n", + "Name: TRADE_DIVISION, dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_df['TRADE_DIVISION'].value_counts()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Visualize the data sample" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "D1osAfHTFhJY", + "outputId": "debd318b-2994-4901-c9b0-b436f5be72a2" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                                              NAMEgeoiddo_dateBRANDNAMEPB_IDTRADE_NAMEFRANCHISE_NAMEISO3AREANAME4AREANAME3AREANAME2AREANAME1STABBPOSTCODEFORMATTEDADDRESSMAINADDRESSLINEADDRESSLASTLINELONGITUDELATITUDEGEORESULTCONFIDENCE_CODECOUNTRY_ACCESS_CODETEL_NUMFAXNUMEMAILHTTPOPEN_24HBUSINESS_LINESIC1SIC2SIC8SIC8_DESCRIPTIONALT_INDUSTRY_CODEMICODETRADE_DIVISIONGROUPCLASSSUB_CLASSEMPLOYEE_HEREEMPLOYEE_COUNTYEAR_STARTSALES_VOLUME_LOCALSALES_VOLUME_US_DOLLARSCURRENCY_CODEAGENT_CODELEGAL_STATUS_CODESTATUS_CODESUBSIDIARY_INDICATORPARENT_BUSINESS_NAMEPARENT_ADDRESSPARENT_STREET_ADDRESSPARENT_AREANAME3PARENT_AREANAME1PARENT_COUNTRYPARENT_POSTCODEDOMESTIC_ULTIMATE_BUSINESS_NAMEDOMESTIC_ULTIMATE_ADDRESSDOMESTIC_ULTIMATE_STREET_ADDRESSDOMESTIC_ULTIMATE_AREANAME3DOMESTIC_ULTIMATE_AREANAME1DOMESTIC_ULTIMATE_POSTCODEGLOBAL_ULTIMATE_INDICATORGLOBAL_ULTIMATE_BUSINESS_NAMEGLOBAL_ULTIMATE_ADDRESSGLOBAL_ULTIMATE_STREET_ADDRESSGLOBAL_ULTIMATE_AREANAME3GLOBAL_ULTIMATE_AREANAME1GLOBAL_ULTIMATE_COUNTRYGLOBAL_ULTIMATE_POSTCODEFAMILY_MEMBERSHIERARCHY_CODETICKER_SYMBOLEXCHANGE_NAMEgeom
                                              0ARCO DE CUCHILLEROS2033946578#-3.708101#40.41479832019-12-01NaN2033946578NaNNaNESPNaNMADRIDMADRIDCOMUNIDAD DE MADRIDMD28012.0CALLE DE LOS CUCHILLEROS, 28012, MADRID, MADRIDCALLE DE LOS CUCHILLEROS28012, MADRID, MADRID-3.70810140.414798T20LOWNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNARCHNaN10110112DIVISION L. - TOURISMTOURISMIMPORTANT TOURIST ATTRACTIONARCHNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPOINT (-3.70810 40.41480)
                                              1ATENEO DE MADRID2033838561#-3.6982188#40.41503082019-12-01NaN2033838561NaNNaNESPNaNMADRIDMADRIDCOMUNIDAD DE MADRIDMD28014.0CALLE DEL PRADO, 28014, MADRID, MADRIDCALLE DEL PRADO28014, MADRID, MADRID-3.69821940.415031T20LOWNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTOURIST BUILDINGNaN10110200DIVISION L. - TOURISMTOURISMIMPORTANT TOURIST ATTRACTIONTOURIST BUILDINGNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPOINT (-3.69822 40.41503)
                                              2BANCO DE ESPAÑA2022507776#-3.6939777#40.41897372019-12-01NaN2022507776NaNNaNESPNaNMADRIDMADRIDCOMUNIDAD DE MADRIDMDNaNMADRID, MADRIDNaNMADRID, MADRID-3.69397840.418974T1HIGHNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTOURIST BUILDINGNaN10110200DIVISION L. - TOURISMTOURISMIMPORTANT TOURIST ATTRACTIONTOURIST BUILDINGNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPOINT (-3.69398 40.41897)
                                              3BASÍLICA DE NUESTRO PADRE JESÚS DE MEDINACELI2033893910#-3.6957089#40.41419562019-12-01NaN2033893910NaNNaNESPNaNMADRIDMADRIDCOMUNIDAD DE MADRIDMD28014.0PLAZA DE JESÚS, 28014, MADRID, MADRIDPLAZA DE JESÚS28014, MADRID, MADRID-3.69570940.414196T20LOWNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTOURIST BUILDINGNaN10110200DIVISION L. - TOURISMTOURISMIMPORTANT TOURIST ATTRACTIONTOURIST BUILDINGNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPOINT (-3.69571 40.41420)
                                              4BIBLIOTECA NACIONAL DE ESPAÑA2033804926#-3.6906236#40.42385262019-12-01NaN2033804926NaNNaNESPNaNMADRIDMADRIDCOMUNIDAD DE MADRIDMD28001.0PASEO DE RECOLETOS, 28001, MADRID, MADRIDPASEO DE RECOLETOS28001, MADRID, MADRID-3.69062440.423853T20LOWNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNTOURIST BUILDINGNaN10110200DIVISION L. - TOURISMTOURISMIMPORTANT TOURIST ATTRACTIONTOURIST BUILDINGNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPOINT (-3.69062 40.42385)
                                              \n", + "
                                              " + ], + "text/plain": [ + " NAME \\\n", + "0 ARCO DE CUCHILLEROS \n", + "1 ATENEO DE MADRID \n", + "2 BANCO DE ESPAÑA \n", + "3 BASÍLICA DE NUESTRO PADRE JESÚS DE MEDINACELI \n", + "4 BIBLIOTECA NACIONAL DE ESPAÑA \n", + "\n", + " geoid do_date BRANDNAME PB_ID \\\n", + "0 2033946578#-3.708101#40.4147983 2019-12-01 NaN 2033946578 \n", + "1 2033838561#-3.6982188#40.4150308 2019-12-01 NaN 2033838561 \n", + "2 2022507776#-3.6939777#40.4189737 2019-12-01 NaN 2022507776 \n", + "3 2033893910#-3.6957089#40.4141956 2019-12-01 NaN 2033893910 \n", + "4 2033804926#-3.6906236#40.4238526 2019-12-01 NaN 2033804926 \n", + "\n", + " TRADE_NAME FRANCHISE_NAME ISO3 AREANAME4 AREANAME3 AREANAME2 \\\n", + "0 NaN NaN ESP NaN MADRID MADRID \n", + "1 NaN NaN ESP NaN MADRID MADRID \n", + "2 NaN NaN ESP NaN MADRID MADRID \n", + "3 NaN NaN ESP NaN MADRID MADRID \n", + "4 NaN NaN ESP NaN MADRID MADRID \n", + "\n", + " AREANAME1 STABB POSTCODE \\\n", + "0 COMUNIDAD DE MADRID MD 28012.0 \n", + "1 COMUNIDAD DE MADRID MD 28014.0 \n", + "2 COMUNIDAD DE MADRID MD NaN \n", + "3 COMUNIDAD DE MADRID MD 28014.0 \n", + "4 COMUNIDAD DE MADRID MD 28001.0 \n", + "\n", + " FORMATTEDADDRESS MAINADDRESSLINE \\\n", + "0 CALLE DE LOS CUCHILLEROS, 28012, MADRID, MADRID CALLE DE LOS CUCHILLEROS \n", + "1 CALLE DEL PRADO, 28014, MADRID, MADRID CALLE DEL PRADO \n", + "2 MADRID, MADRID NaN \n", + "3 PLAZA DE JESÚS, 28014, MADRID, MADRID PLAZA DE JESÚS \n", + "4 PASEO DE RECOLETOS, 28001, MADRID, MADRID PASEO DE RECOLETOS \n", + "\n", + " ADDRESSLASTLINE LONGITUDE LATITUDE GEORESULT CONFIDENCE_CODE \\\n", + "0 28012, MADRID, MADRID -3.708101 40.414798 T20 LOW \n", + "1 28014, MADRID, MADRID -3.698219 40.415031 T20 LOW \n", + "2 MADRID, MADRID -3.693978 40.418974 T1 HIGH \n", + "3 28014, MADRID, MADRID -3.695709 40.414196 T20 LOW \n", + "4 28001, MADRID, MADRID -3.690624 40.423853 T20 LOW \n", + "\n", + " COUNTRY_ACCESS_CODE TEL_NUM FAXNUM EMAIL HTTP OPEN_24H BUSINESS_LINE \\\n", + "0 NaN NaN NaN NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN NaN NaN NaN \n", + "\n", + " SIC1 SIC2 SIC8 SIC8_DESCRIPTION ALT_INDUSTRY_CODE MICODE \\\n", + "0 NaN NaN NaN ARCH NaN 10110112 \n", + "1 NaN NaN NaN TOURIST BUILDING NaN 10110200 \n", + "2 NaN NaN NaN TOURIST BUILDING NaN 10110200 \n", + "3 NaN NaN NaN TOURIST BUILDING NaN 10110200 \n", + "4 NaN NaN NaN TOURIST BUILDING NaN 10110200 \n", + "\n", + " TRADE_DIVISION GROUP CLASS \\\n", + "0 DIVISION L. - TOURISM TOURISM IMPORTANT TOURIST ATTRACTION \n", + "1 DIVISION L. - TOURISM TOURISM IMPORTANT TOURIST ATTRACTION \n", + "2 DIVISION L. - TOURISM TOURISM IMPORTANT TOURIST ATTRACTION \n", + "3 DIVISION L. - TOURISM TOURISM IMPORTANT TOURIST ATTRACTION \n", + "4 DIVISION L. - TOURISM TOURISM IMPORTANT TOURIST ATTRACTION \n", + "\n", + " SUB_CLASS EMPLOYEE_HERE EMPLOYEE_COUNT YEAR_START \\\n", + "0 ARCH NaN NaN NaN \n", + "1 TOURIST BUILDING NaN NaN NaN \n", + "2 TOURIST BUILDING NaN NaN NaN \n", + "3 TOURIST BUILDING NaN NaN NaN \n", + "4 TOURIST BUILDING NaN NaN NaN \n", + "\n", + " SALES_VOLUME_LOCAL SALES_VOLUME_US_DOLLARS CURRENCY_CODE AGENT_CODE \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " LEGAL_STATUS_CODE STATUS_CODE SUBSIDIARY_INDICATOR PARENT_BUSINESS_NAME \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " PARENT_ADDRESS PARENT_STREET_ADDRESS PARENT_AREANAME3 PARENT_AREANAME1 \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " PARENT_COUNTRY PARENT_POSTCODE DOMESTIC_ULTIMATE_BUSINESS_NAME \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " DOMESTIC_ULTIMATE_ADDRESS DOMESTIC_ULTIMATE_STREET_ADDRESS \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " DOMESTIC_ULTIMATE_AREANAME3 DOMESTIC_ULTIMATE_AREANAME1 \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " DOMESTIC_ULTIMATE_POSTCODE GLOBAL_ULTIMATE_INDICATOR \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " GLOBAL_ULTIMATE_BUSINESS_NAME GLOBAL_ULTIMATE_ADDRESS \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " GLOBAL_ULTIMATE_STREET_ADDRESS GLOBAL_ULTIMATE_AREANAME3 \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " GLOBAL_ULTIMATE_AREANAME1 GLOBAL_ULTIMATE_COUNTRY \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " GLOBAL_ULTIMATE_POSTCODE FAMILY_MEMBERS HIERARCHY_CODE TICKER_SYMBOL \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " EXCHANGE_NAME geom \n", + "0 NaN POINT (-3.70810 40.41480) \n", + "1 NaN POINT (-3.69822 40.41503) \n", + "2 NaN POINT (-3.69398 40.41897) \n", + "3 NaN POINT (-3.69571 40.41420) \n", + "4 NaN POINT (-3.69062 40.42385) " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sql_query = \"\"\"\n", + " SELECT * except(do_label) FROM $dataset$ \n", + " WHERE TRADE_DIVISION = 'DIVISION L. - TOURISM' \n", + " AND ST_IntersectsBox(geom, -3.716398,40.407437,-3.690477,40.425277)\n", + "\"\"\"\n", + "tourism_pois = pois_ds.to_dataframe(sql_query=sql_query)\n", + "\n", + "#To keep only most updated POIs (based on the do_date)\n", + "tourism_pois = tourism_pois.sort_values(['NAME', 'do_date']).groupby('NAME').first().reset_index()\n", + "\n", + "tourism_pois.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can classify the tourism POI's using the variable `SUB_CLASS`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 176 + }, + "id": "tzJj21dQo9Kv", + "outputId": "b67bbcb9-4f85-411b-c5ab-efdd3811f3a6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "TOURIST BUILDING 66\n", + "MONUMENT 20\n", + "IMPORTANT TOURIST ATTRACTION -UNSPECIFIED 16\n", + "SCENIC, PANORAMIC VIEW 4\n", + "STATUE 2\n", + "TOWER 2\n", + "IMPORTANT TOURIST ATTRACTION 2\n", + "MEMORIAL 1\n", + "ARCH 1\n", + "Name: SUB_CLASS, dtype: int64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tourism_pois['SUB_CLASS'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "2IEOMMsZkUzY", + "outputId": "3f7b6f27-03e8-46a5-cc51-25613b0ddbbe" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                                                \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Map(Layer(tourism_pois, \n", + " geom_col='geom',\n", + " style=color_category_style('SUB_CLASS', stroke_width=0.2)))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gnpItWtBFPke" + }, + "source": [ + "\n", + "#### Upload filtered dataset to your CARTO account\n", + "\n", + "Finally, we will save this filtered dataset on our CARTO account so that we can use it in the future. We can do this using the CARTOframes function `to_carto`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "TDHvu80KpcK7" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success! Data uploaded to table \"demo_tourism_pois_madrid\" correctly\n" + ] + }, + { + "data": { + "text/plain": [ + "'demo_tourism_pois_madrid'" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "to_carto(tourism_pois, 'demo_tourism_pois_madrid', geom_col='geom')" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "CARTO | Data Observatory v2.0 - Access to Data.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/templates/do_access_public_data.ipynb b/docs/examples/templates/do_access_public_data.ipynb new file mode 100644 index 000000000..94b03c1b8 --- /dev/null +++ b/docs/examples/templates/do_access_public_data.ipynb @@ -0,0 +1,13696 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "7LKLhQBzJMOz" + }, + "source": [ + "## CARTO Data Observatory. Access public data.\n", + "\n", + "This notebook shows how to use CARTOframes for discovering and downloading **public** datasets from CARTO's [Data Observatory](https://carto.com/spatial-data-catalog/).\n", + "\n", + "For more details please visit the [Guides](https://carto.com/developers/cartoframes/guides). If you'd like further detail on how to discover and explore datasets from the catalog, take a look at Explore CARTO Data Observatory notebook.\n", + "\n", + "The notebook is organized in the following sections:\n", + " - [Discover datasets applying the public filter](#section1)\n", + " - [Download a selection of variables of a dataset](#section2)\n", + " - [Download a dataset applying spatial filtering. Filter by bounding box or polygon](#section3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V4-0hJjiVsen" + }, + "source": [ + "### Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "6vLRH4fEEkms" + }, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import pandas as pd\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "from cartoframes.auth import set_default_credentials\n", + "from cartoframes.data.observatory import *\n", + "from cartoframes.viz import *\n", + "from shapely.geometry import box" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set CARTO default credentials" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to be able to use the Data Observatory via CARTOframes, you need to set your CARTO account credentials first.\n", + "\n", + "Please, visit the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/) for further detail." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note about credentials**\n", + "\n", + "For security reasons, we recommend storing your credentials in an external file to prevent publishing them by accident when sharing your notebooks. You can get more information in the section _Setting your credentials_ of the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gTkF0eWzjlOw" + }, + "source": [ + "### Discover and download a Public Dataset\n", + "\n", + "First, we'll discover the two public datasets applying the public filter:\n", + "1. Public dataset providing total population per county in US.\n", + "2. Public dataset providing sociodemographics data at the census tract level for the US." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0Vhi0kdatNfG" + }, + "source": [ + "\n", + "#### Dicover datasets applying the public filter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition to the country, category, provider, and geography filters, we can filter public datasets using the `public()` filter.\n", + "\n", + "We start by looking for the providers in the US providing public demographics data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 + }, + "id": "iNDxjNRgr_FC", + "outputId": "bb1e4690-7918-485f-9924-ccd7e777d272" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can find more entities with the Global country filter. To apply that filter run:\n", + "\tCatalog().country('glo')\n" + ] + }, + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Catalog().country('usa').category('demographics').public().providers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also discover public demographics datasets in the US.\n", + "\n", + "Note all datasets have the `is_public_data` flag that allows to identify public datasets." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "2bImQIlTqBzf", + "outputId": "b8b0b4f3-064d-4f60-bdd9-c3181360d00a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can find more entities with the Global country filter. To apply that filter run:\n", + "\tCatalog().country('glo')\n" + ] + }, + { + "data": { + "text/html": [ + "
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                                                slugnamedescriptioncategory_idcountry_iddata_source_idprovider_idgeography_namegeography_descriptiontemporal_aggregationtime_coverageupdate_frequencyis_public_datalangversioncategory_nameprovider_namegeography_idid
                                                0wp_population_704f6b75Population Mosaics - United States of America ...Mosaiced 1km resolution global datasets. The m...demographicsusapopulationworldpopGrid 1km - United States of AmericaGlobal grid at aprox. 1-kilometer resolution (...yearly[2020-01-01, 2021-01-01)yearlyTrueeng2020DemographicsWorldPopcarto-do-public-data.worldpop.geography_usa_gr...carto-do-public-data.worldpop.demographics_pop...
                                                1wp_population_22be8012Population Mosaics - United States of America ...Mosaiced 1km resolution global datasets. The m...demographicsusapopulationworldpopGrid 1km - United States of AmericaGlobal grid at aprox. 1-kilometer resolution (...yearly[2019-01-01, 2020-01-01)yearlyTrueeng2019DemographicsWorldPopcarto-do-public-data.worldpop.geography_usa_gr...carto-do-public-data.worldpop.demographics_pop...
                                                2wp_population_55b9b084Population Mosaics - United States of America ...Mosaiced 1km resolution global datasets. The m...demographicsusapopulationworldpopGrid 1km - United States of AmericaGlobal grid at aprox. 1-kilometer resolution (...yearly[2018-01-01, 2019-01-01)yearlyTrueeng2018DemographicsWorldPopcarto-do-public-data.worldpop.geography_usa_gr...carto-do-public-data.worldpop.demographics_pop...
                                                3wp_population_c506ad15Population Mosaics - United States of America ...Mosaiced 1km resolution global datasets. The m...demographicsusapopulationworldpopGrid 1km - United States of AmericaGlobal grid at aprox. 1-kilometer resolution (...yearly[2017-01-01, 2018-01-01)yearlyTrueeng2017DemographicsWorldPopcarto-do-public-data.worldpop.geography_usa_gr...carto-do-public-data.worldpop.demographics_pop...
                                                4wp_population_b2019d83Population Mosaics - United States of America ...Mosaiced 1km resolution global datasets. The m...demographicsusapopulationworldpopGrid 1km - United States of AmericaGlobal grid at aprox. 1-kilometer resolution (...yearly[2016-01-01, 2017-01-01)yearlyTrueeng2016DemographicsWorldPopcarto-do-public-data.worldpop.geography_usa_gr...carto-do-public-data.worldpop.demographics_pop...
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                                                slugnamedescriptioncategory_idcountry_iddata_source_idprovider_idgeography_namegeography_descriptiontemporal_aggregationtime_coverageupdate_frequencyis_public_datalangversioncategory_nameprovider_namegeography_idid
                                                0acs_sociodemogr_a0c48b07Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCounty - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...yearly[2007-01-01, 2008-01-01)yearlyTrueeng2007DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_count...carto-do-public-data.usa_acs.demographics_soci...
                                                1acs_sociodemogr_a03fb95fSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCongressional District - United States of Amer...Shoreline clipped TIGER/Line boundaries. More ...yearly[2017-01-01, 2018-01-01)yearlyTrueeng2017DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_congr...carto-do-public-data.usa_acs.demographics_soci...
                                                2acs_sociodemogr_e7b702b0Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCore-based Statistical Area - United States of...Shoreline clipped TIGER/Line boundaries. More ...3yrs[2006-01-01, 2009-01-01)yearlyTrueeng20062008DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_cbsa_...carto-do-public-data.usa_acs.demographics_soci...
                                                3acs_sociodemogr_e1e92d8dSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCore-based Statistical Area - United States of...Shoreline clipped TIGER/Line boundaries. More ...yearly[2013-01-01, 2014-01-01)yearlyTrueeng2013DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_cbsa_...carto-do-public-data.usa_acs.demographics_soci...
                                                4acs_sociodemogr_30a865f1Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCore-based Statistical Area - United States of...Shoreline clipped TIGER/Line boundaries. More ...3yrs[2005-01-01, 2008-01-01)yearlyTrueeng20052007DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_cbsa_...carto-do-public-data.usa_acs.demographics_soci...
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                                                " + ], + "text/plain": [ + " slug \\\n", + "0 acs_sociodemogr_a0c48b07 \n", + "1 acs_sociodemogr_a03fb95f \n", + "2 acs_sociodemogr_e7b702b0 \n", + "3 acs_sociodemogr_e1e92d8d \n", + "4 acs_sociodemogr_30a865f1 \n", + "\n", + " name \\\n", + "0 Sociodemographics - United States of America (... \n", + "1 Sociodemographics - United States of America (... \n", + "2 Sociodemographics - United States of America (... \n", + "3 Sociodemographics - United States of America (... \n", + "4 Sociodemographics - United States of America (... \n", + "\n", + " description category_id country_id \\\n", + "0 The American Community Survey (ACS) is an ongo... demographics usa \n", + "1 The American Community Survey (ACS) is an ongo... demographics usa \n", + "2 The American Community Survey (ACS) is an ongo... demographics usa \n", + "3 The American Community Survey (ACS) is an ongo... demographics usa \n", + "4 The American Community Survey (ACS) is an ongo... demographics usa \n", + "\n", + " data_source_id provider_id \\\n", + "0 sociodemographics usa_acs \n", + "1 sociodemographics usa_acs \n", + "2 sociodemographics usa_acs \n", + "3 sociodemographics usa_acs \n", + "4 sociodemographics usa_acs \n", + "\n", + " geography_name \\\n", + "0 County - United States of America (2015) \n", + "1 Congressional District - United States of Amer... \n", + "2 Core-based Statistical Area - United States of... \n", + "3 Core-based Statistical Area - United States of... \n", + "4 Core-based Statistical Area - United States of... \n", + "\n", + " geography_description temporal_aggregation \\\n", + "0 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", + "1 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", + "2 Shoreline clipped TIGER/Line boundaries. More ... 3yrs \n", + "3 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", + "4 Shoreline clipped TIGER/Line boundaries. More ... 3yrs \n", + "\n", + " time_coverage update_frequency is_public_data lang version \\\n", + "0 [2007-01-01, 2008-01-01) yearly True eng 2007 \n", + "1 [2017-01-01, 2018-01-01) yearly True eng 2017 \n", + "2 [2006-01-01, 2009-01-01) yearly True eng 20062008 \n", + "3 [2013-01-01, 2014-01-01) yearly True eng 2013 \n", + "4 [2005-01-01, 2008-01-01) yearly True eng 20052007 \n", + "\n", + " category_name provider_name \\\n", + "0 Demographics American Community Survey \n", + "1 Demographics American Community Survey \n", + "2 Demographics American Community Survey \n", + "3 Demographics American Community Survey \n", + "4 Demographics American Community Survey \n", + "\n", + " geography_id \\\n", + "0 carto-do-public-data.carto.geography_usa_count... \n", + "1 carto-do-public-data.carto.geography_usa_congr... \n", + "2 carto-do-public-data.carto.geography_usa_cbsa_... \n", + "3 carto-do-public-data.carto.geography_usa_cbsa_... \n", + "4 carto-do-public-data.carto.geography_usa_cbsa_... \n", + "\n", + " id \n", + "0 carto-do-public-data.usa_acs.demographics_soci... \n", + "1 carto-do-public-data.usa_acs.demographics_soci... \n", + "2 carto-do-public-data.usa_acs.demographics_soci... \n", + "3 carto-do-public-data.usa_acs.demographics_soci... \n", + "4 carto-do-public-data.usa_acs.demographics_soci... " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "datasets_df = Catalog().country('usa').category('demographics').provider('usa_acs').public().datasets.to_dataframe()\n", + "datasets_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "T4sObEug51WI" + }, + "source": [ + "We are first interested in exploring which datasets provide data at the **county level** and with a **5 year temporal aggregation**.\n", + "\n", + "We select the latest [dataset](https://carto.com/spatial-data-catalog/browser/dataset/acs_sociodemogr_aeb99461/) `acs_sociodemogr_8c2655e0`.\n", + "\n", + "If you'd like to get a description of the dataset, you can use the Dataset functions `to_dict()`, `describe()`, `head()`, and `geom_coverage()`. You can also have access to all its variables as explained on the Explore CARTO Data Observatory notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "-850SlAx5QVg", + "outputId": "a670233b-97b5-4015-d9c1-4529282c0955" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                215acs_sociodemogr_8c2655e0Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCounty - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2013-01-01, 2018-01-01)yearlyTrueeng20132017DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_count...carto-do-public-data.usa_acs.demographics_soci...
                                                156acs_sociodemogr_c6414cc6Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCounty - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2012-01-01, 2017-01-01)yearlyTrueeng20122016DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_count...carto-do-public-data.usa_acs.demographics_soci...
                                                143acs_sociodemogr_18e867acSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCounty - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2011-01-01, 2016-01-01)yearlyTrueeng20112015DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_count...carto-do-public-data.usa_acs.demographics_soci...
                                                182acs_sociodemogr_528f7e8aSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCounty - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2010-01-01, 2015-01-01)yearlyTrueeng20102014DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_count...carto-do-public-data.usa_acs.demographics_soci...
                                                179acs_sociodemogr_aa75afdSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCounty - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2009-01-01, 2014-01-01)yearlyTrueeng20092013DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_count...carto-do-public-data.usa_acs.demographics_soci...
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                                                " + ], + "text/plain": [ + " slug \\\n", + "215 acs_sociodemogr_8c2655e0 \n", + "156 acs_sociodemogr_c6414cc6 \n", + "143 acs_sociodemogr_18e867ac \n", + "182 acs_sociodemogr_528f7e8a \n", + "179 acs_sociodemogr_aa75afd \n", + "\n", + " name \\\n", + "215 Sociodemographics - United States of America (... \n", + "156 Sociodemographics - United States of America (... \n", + "143 Sociodemographics - United States of America (... \n", + "182 Sociodemographics - United States of America (... \n", + "179 Sociodemographics - United States of America (... \n", + "\n", + " description category_id \\\n", + "215 The American Community Survey (ACS) is an ongo... demographics \n", + "156 The American Community Survey (ACS) is an ongo... demographics \n", + "143 The American Community Survey (ACS) is an ongo... demographics \n", + "182 The American Community Survey (ACS) is an ongo... demographics \n", + "179 The American Community Survey (ACS) is an ongo... demographics \n", + "\n", + " country_id data_source_id provider_id \\\n", + "215 usa sociodemographics usa_acs \n", + "156 usa sociodemographics usa_acs \n", + "143 usa sociodemographics usa_acs \n", + "182 usa sociodemographics usa_acs \n", + "179 usa sociodemographics usa_acs \n", + "\n", + " geography_name \\\n", + "215 County - United States of America (2015) \n", + "156 County - United States of America (2015) \n", + "143 County - United States of America (2015) \n", + "182 County - United States of America (2015) \n", + "179 County - United States of America (2015) \n", + "\n", + " geography_description temporal_aggregation \\\n", + "215 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", + "156 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", + "143 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", + "182 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", + "179 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", + "\n", + " time_coverage update_frequency is_public_data lang version \\\n", + "215 [2013-01-01, 2018-01-01) yearly True eng 20132017 \n", + "156 [2012-01-01, 2017-01-01) yearly True eng 20122016 \n", + "143 [2011-01-01, 2016-01-01) yearly True eng 20112015 \n", + "182 [2010-01-01, 2015-01-01) yearly True eng 20102014 \n", + "179 [2009-01-01, 2014-01-01) yearly True eng 20092013 \n", + "\n", + " category_name provider_name \\\n", + "215 Demographics American Community Survey \n", + "156 Demographics American Community Survey \n", + "143 Demographics American Community Survey \n", + "182 Demographics American Community Survey \n", + "179 Demographics American Community Survey \n", + "\n", + " geography_id \\\n", + "215 carto-do-public-data.carto.geography_usa_count... \n", + "156 carto-do-public-data.carto.geography_usa_count... \n", + "143 carto-do-public-data.carto.geography_usa_count... \n", + "182 carto-do-public-data.carto.geography_usa_count... \n", + "179 carto-do-public-data.carto.geography_usa_count... \n", + "\n", + " id \n", + "215 carto-do-public-data.usa_acs.demographics_soci... \n", + "156 carto-do-public-data.usa_acs.demographics_soci... \n", + "143 carto-do-public-data.usa_acs.demographics_soci... \n", + "182 carto-do-public-data.usa_acs.demographics_soci... \n", + "179 carto-do-public-data.usa_acs.demographics_soci... " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "datasets_df[(datasets_df['geography_name'].str.contains('County')) & \n", + " (datasets_df['temporal_aggregation'].str.contains('5yrs'))]\\\n", + " .sort_values('time_coverage', ascending=False).head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NSq6azNp6E6Z" + }, + "source": [ + "We are also interested in exploring which datasets provide data at the **census tract** level and with a **5 year temporal aggregation**.\n", + "\n", + "We select the latest [dataset](https://carto.com/spatial-data-catalog/browser/dataset/acs_sociodemogr_6bf5c7f4/) `acs_sociodemogr_496a0675`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 707 + }, + "id": "rSgq4Ncy5y9r", + "outputId": "24ea831e-6218-41d9-87b6-95dd7ec18e36" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                slugnamedescriptioncategory_idcountry_iddata_source_idprovider_idgeography_namegeography_descriptiontemporal_aggregationtime_coverageupdate_frequencyis_public_datalangversioncategory_nameprovider_namegeography_idid
                                                41acs_sociodemogr_496a0675Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2013-01-01, 2018-01-01)yearlyTrueeng20132017DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                8acs_sociodemogr_30d1f53Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2012-01-01, 2017-01-01)yearlyTrueeng20122016DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                201acs_sociodemogr_dda43439Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2011-01-01, 2016-01-01)yearlyTrueeng20112015DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                169acs_sociodemogr_97c32d1fSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2010-01-01, 2015-01-01)yearlyTrueeng20102014DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                9acs_sociodemogr_cfeb0968Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2009-01-01, 2014-01-01)yearlyTrueeng20092013DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
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                                                " + ], + "text/plain": [ + " slug \\\n", + "41 acs_sociodemogr_496a0675 \n", + "8 acs_sociodemogr_30d1f53 \n", + "201 acs_sociodemogr_dda43439 \n", + "169 acs_sociodemogr_97c32d1f \n", + "9 acs_sociodemogr_cfeb0968 \n", + "\n", + " name \\\n", + "41 Sociodemographics - United States of America (... \n", + "8 Sociodemographics - United States of America (... \n", + "201 Sociodemographics - United States of America (... \n", + "169 Sociodemographics - United States of America (... \n", + "9 Sociodemographics - United States of America (... \n", + "\n", + " description category_id \\\n", + "41 The American Community Survey (ACS) is an ongo... demographics \n", + "8 The American Community Survey (ACS) is an ongo... demographics \n", + "201 The American Community Survey (ACS) is an ongo... demographics \n", + "169 The American Community Survey (ACS) is an ongo... demographics \n", + "9 The American Community Survey (ACS) is an ongo... demographics \n", + "\n", + " country_id data_source_id provider_id \\\n", + "41 usa sociodemographics usa_acs \n", + "8 usa sociodemographics usa_acs \n", + "201 usa sociodemographics usa_acs \n", + "169 usa sociodemographics usa_acs \n", + "9 usa sociodemographics usa_acs \n", + "\n", + " geography_name \\\n", + "41 Census Tract - United States of America (2015) \n", + "8 Census Tract - United States of America (2015) \n", + "201 Census Tract - United States of America (2015) \n", + "169 Census Tract - United States of America (2015) \n", + "9 Census Tract - United States of America (2015) \n", + "\n", + " geography_description temporal_aggregation \\\n", + "41 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", + "8 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", + "201 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", + "169 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", + "9 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", + "\n", + " time_coverage update_frequency is_public_data lang version \\\n", + "41 [2013-01-01, 2018-01-01) yearly True eng 20132017 \n", + "8 [2012-01-01, 2017-01-01) yearly True eng 20122016 \n", + "201 [2011-01-01, 2016-01-01) yearly True eng 20112015 \n", + "169 [2010-01-01, 2015-01-01) yearly True eng 20102014 \n", + "9 [2009-01-01, 2014-01-01) yearly True eng 20092013 \n", + "\n", + " category_name provider_name \\\n", + "41 Demographics American Community Survey \n", + "8 Demographics American Community Survey \n", + "201 Demographics American Community Survey \n", + "169 Demographics American Community Survey \n", + "9 Demographics American Community Survey \n", + "\n", + " geography_id \\\n", + "41 carto-do-public-data.carto.geography_usa_censu... \n", + "8 carto-do-public-data.carto.geography_usa_censu... \n", + "201 carto-do-public-data.carto.geography_usa_censu... \n", + "169 carto-do-public-data.carto.geography_usa_censu... \n", + "9 carto-do-public-data.carto.geography_usa_censu... \n", + "\n", + " id \n", + "41 carto-do-public-data.usa_acs.demographics_soci... \n", + "8 carto-do-public-data.usa_acs.demographics_soci... \n", + "201 carto-do-public-data.usa_acs.demographics_soci... \n", + "169 carto-do-public-data.usa_acs.demographics_soci... \n", + "9 carto-do-public-data.usa_acs.demographics_soci... " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "datasets_df[(datasets_df['geography_name'].str.contains('Census Tract')) & \n", + " (datasets_df['temporal_aggregation'].str.contains('5yrs'))]\\\n", + " .sort_values('time_coverage', ascending=False).head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kVFwrN7BtAgW" + }, + "source": [ + "\n", + "#### Download a selection of variables of a dataset\n", + "\n", + "We'll use the county demographics dataset `acs_sociodemogr_8c2655e0` we identified above to show how to download only a selection of columns from a dataset. In particular, we'll download all counties with their population." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "id": "ItuC51LTs9RI" + }, + "outputs": [], + "source": [ + "dataset = Dataset.get('acs_sociodemogr_8c2655e0')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 367 + }, + "id": "79_zTMLEzijt", + "outputId": "d08c7699-9f1a-4112-c59a-ce56749e190c" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                348307133.0201320171.093.0220.0883.01296.01935.04171.01232.00.0161.039.014.01623.04096.08145.05067.03974.00.44953143.0240.0134.043.5510.0370.0471.06496.0None0.03658.02571.0327.0393.0None263.04302.0212.0220.033.0111.0284.0236.0272.0152.0206.01054.0310.0164.0106.0111.0296.094.0139.042367.0None1.0154.0298.0154.0213.0202.0122.0104.061.0258.0131.066.021.0340.082.0271.0321.0285.0188.0273.0121.0217.0147.0117.0228.0156.0134.0NoneNone0.00.0671.018.00.0867.02.0483.0334.060.05574.0None327.0391.0217.03389.0229.02164.02571.0453.0228.023398.05832.0None671.0535.0412.0410.023.0202.071.0117.046.075.052.0148.0265.0288.0127.0156.0203.0207.0137.0124.0259.0329.0385.01592.02716.0207.03780.01597.0164.0147.0770.03450.03780.0133.081.01482.0424.017.070.0176.0114.069.00.0979.0227.01159.02857.0171.0383.076.0500.0201.01092.064.0100.059.044.046.025.024.019.0809.0None143.0110.016.0214.0458.03143.0203.0889.0809.07863.00.0161.06.018.039.00.0137.06969.01862.03281.023.03498.00.078.017.0850.08140.0None39.093.01969.0396.0589.0261.0679.07.075.035.02398.032.053750.01.0551.0745.07955.031.057.0192.01229.00.0556.0647.0None2.0634.01809.0600.0850.0133.0829.0231.0206.0383.0363.082000.0401.0510.0165.041600.0165300.0602.0719.0214.0265.0721.00.0165.0169.00.0
                                                44612366.0201320178.021.0118.0544.01045.01237.02781.0649.00.00.09.059.01121.02688.05480.04500.02699.00.46332424.0133.0141.046.5639.0135.0341.04362.0None0.02859.018.0140.0496.0None125.03085.0222.0141.050.0127.0129.0143.0155.0116.0164.0881.0238.0205.034.0116.0112.0143.042.048409.0None729.0172.0474.0159.0112.00.052.0132.0223.0203.099.046.065.0122.0123.0133.097.0173.0242.0190.0108.076.087.084.0114.0174.055.0NoneNone0.00.0643.00.00.0878.037.0272.0269.066.05462.0None310.0392.0319.02592.024.01473.018.0273.0239.027613.03858.0None643.0347.0158.0167.0120.092.017.056.039.055.0143.0143.0174.0110.0132.0111.0119.0168.091.0124.0227.0270.0279.01217.01451.027.02911.01367.00.00.0393.02815.02911.0175.032.01422.0247.030.0102.0226.037.032.00.0951.0128.0661.02163.0271.0363.085.0196.031.0571.087.063.058.079.02.037.0104.064.0484.0None233.0108.070.041.0387.02424.0140.0663.0484.05334.00.00.038.052.075.00.0125.04500.01511.02410.016.02281.00.043.032.01217.05425.0None19.0152.01966.0220.0338.0210.0261.083.056.016.01677.027.540040.0747.0255.0747.05294.095.039.0118.0408.00.0580.0674.0None37.0182.01086.0340.01217.0184.0444.092.085.0253.0139.085800.052.0306.026.051600.0156600.0706.0459.0215.0114.0562.00.014.032.06.0
                                                529155308.02013201785.0189.0934.02639.04859.04597.08210.02043.00.04639.0197.0153.04301.08474.017344.011948.09134.00.48626875.0225.0238.037.7871.0566.0344.013251.0None0.06326.0421.0614.0742.0None496.08178.0747.0421.0226.0282.0479.0431.0449.0422.0526.02156.0567.0600.0227.0234.0239.0164.0153.032468.0None18.0671.0388.0202.0483.00.0756.0153.0109.0689.0393.0194.0209.0573.0521.0629.0452.0564.0593.0715.0199.0291.0159.0271.0397.0291.0254.0NoneNone0.00.0815.0239.0230.01273.023.0877.01260.0126.016923.0None824.0828.0660.06067.0517.04342.0421.01082.0956.018883.011212.0None815.0792.0998.0913.0366.0587.078.0167.089.0181.087.0223.0689.0583.0512.0504.0377.0385.0382.0336.0544.0392.0555.02758.06169.097.07082.03602.019.019.01441.06176.07082.0159.072.01904.0594.026.0492.0347.096.0143.06.02533.0641.01303.05259.0194.0741.074.0705.0408.01354.0263.0285.0371.0367.0188.0167.0267.0155.01164.0None316.0513.049.01328.0866.06875.0337.01652.01164.016514.00.04717.090.016.0110.05.0540.012113.04279.05853.054.06098.091.0206.025.01560.017033.0None20.0211.01968.02120.01330.0532.01138.058.0251.00.03631.028.8105750.018.0853.03244.017042.0313.089.0313.02867.00.01066.01780.0None23.01577.02719.02237.01560.0282.01594.0164.0813.0517.0304.073300.0299.01565.089.037000.0120900.0405.0596.0216.0380.0568.02.069.0299.00.0
                                                62115928.02013201769.089.0286.01464.03190.02498.06744.01695.00.0575.083.064.02290.06830.012175.011250.05431.00.44224316.0213.0261.039.0645.0326.0307.09909.0None0.03032.0226.0323.01911.0None342.05240.0432.0217.0125.0481.0485.0720.0476.0592.0430.01631.0433.0294.0123.0142.0247.0166.061.029239.0None9.0295.0226.01441.0225.00.0552.034.08.0334.0229.0101.0118.0303.0308.0384.0316.0401.0419.0466.074.0255.0158.0176.0203.0158.0150.0NoneNone0.00.0373.0159.017.0195.00.0554.0575.039.011949.0None657.0726.0728.02791.036.02940.0226.0481.0782.014914.08547.0None373.0117.0575.0297.0111.0235.0220.0384.0164.0358.0325.094.0288.0476.0430.0203.0138.0224.0365.0182.0271.0373.0356.02311.06325.034.03584.02353.033.017.0428.02799.03584.0447.0122.0267.0460.030.0292.0120.050.058.00.01376.0267.0924.02292.0197.0520.00.0360.0347.01107.0109.022.083.098.0107.049.035.071.0894.0None107.053.0105.045.0736.04316.0218.0958.0894.011860.00.0606.028.018.088.00.0361.011397.03313.02752.037.02976.054.0143.041.0875.012072.0None55.019.01985.0473.0787.0327.0987.0117.0153.00.03114.031.898955.09.0141.01202.010690.063.038.0164.02339.00.0553.0775.0None0.074.02195.01218.0875.0162.01449.085.0184.0603.0183.065500.0165.0319.024.030000.0117600.0392.0516.0275.0226.0483.078.024.0211.039.0
                                                7310050.0201320170.08.00.035.046.0132.0204.046.00.00.01.01.0149.0172.0421.0411.0217.00.4498177.09.03.043.354.027.0388.0298.0None0.0177.00.03.022.0None21.0261.020.04.02.013.08.01.06.018.017.050.08.013.02.05.08.010.017.041250.0None0.08.042.00.015.01.01.013.023.035.014.00.00.06.07.05.013.014.011.025.03.05.09.03.010.015.010.0NoneNone0.00.060.00.00.016.00.032.048.02.0421.0None24.025.033.0146.00.096.00.018.06.021799.0264.0None60.09.03.016.04.012.03.017.014.015.03.01.021.025.013.04.09.08.012.010.014.022.020.087.0120.02.0178.073.00.00.017.0169.0178.06.03.064.024.01.017.07.01.02.00.081.08.084.0109.015.013.03.026.00.034.013.03.01.03.02.05.00.00.020.0None9.00.00.013.021.0177.07.025.020.0415.00.00.00.00.03.00.030.0411.079.0133.010.0229.00.03.01.083.0421.0None0.06.01959.03.027.019.023.015.00.00.0112.025.83040.00.031.065.0421.02.00.03.013.00.059.037.0None0.01.093.06.083.01.054.03.02.025.06.0101500.09.021.02.057000.0162500.067.036.013.06.0658.00.02.012.00.0
                                                801067205.020132017124.074.0427.01953.02310.03581.08208.02346.00.04762.033.0120.03517.08535.017110.011757.08902.00.45296727.0176.0317.043.51373.0459.0398.013972.0None10.06949.0435.0468.02311.0None438.09055.0594.0307.0171.0396.0369.0350.0658.0418.0528.02232.0536.0675.0252.0324.0452.0293.0121.045569.0None5.0452.0628.0289.0835.00.0496.048.0176.0511.0352.0162.0326.0415.0465.0527.0598.0547.0617.0586.0222.0531.0166.0404.0567.0353.0222.0NoneNone0.00.01087.0291.0314.0605.00.0750.0892.0146.016675.0None757.0839.0925.06689.0291.04545.0435.0844.0552.023983.012123.0None1087.0694.0858.01025.0749.01067.0154.0395.0241.0445.0301.0288.0504.0566.0332.0278.0457.0308.0323.0412.0616.0694.0614.03625.06517.0169.07455.03398.013.00.01319.06865.07445.0431.0130.01025.0513.074.0595.0379.0287.0257.010.02182.0185.02328.05900.0386.0769.0148.0828.0237.01843.0149.096.0162.091.035.0109.075.051.01347.0None151.0163.00.0787.0918.06727.0540.02932.01347.016615.00.04762.052.012.0221.00.0243.012144.04105.06413.041.06183.02.0218.0113.02279.016964.0None102.0153.01983.0761.01033.0987.01480.0175.096.00.05531.026.0171195.05.0343.01196.016806.0222.0164.0196.02348.00.01485.01673.0None0.0378.03596.01129.02279.0279.0983.0495.0394.0639.0231.0112600.0788.01238.089.062700.0189100.0353.0766.0386.0697.0603.08.080.0245.00.0
                                                9410214.02013201719.016.025.0176.0188.0426.0962.0330.00.00.00.014.0396.0893.01910.01643.0948.00.4153805.028.061.047.0191.079.0537.01529.0None0.0751.0154.051.0146.0None51.01070.051.044.022.013.018.049.041.032.051.0283.086.057.047.029.077.048.029.039831.0None99.054.083.015.064.00.061.064.055.064.032.01.026.044.058.049.049.069.031.065.061.044.022.032.062.044.022.0NoneNone0.00.0200.00.00.0191.00.0102.0101.014.01756.0None114.0130.077.0684.015.0512.0154.096.052.024178.01373.0None200.0101.081.024.018.034.00.020.020.041.05.054.050.020.073.056.077.077.020.052.050.077.090.0420.0717.030.0812.0426.011.00.0113.0739.0812.024.019.0341.037.014.026.080.020.02.00.0293.026.0265.0575.074.057.09.090.014.0297.032.046.08.034.026.015.013.022.0156.0None0.06.06.022.0111.0805.085.0257.0156.01870.00.00.00.021.020.00.068.01783.0493.0612.07.0863.06.012.015.0233.01903.0None15.040.01957.0109.0128.064.0169.05.013.00.0515.025.411965.099.073.0290.01906.022.038.062.0152.00.0194.0121.0None0.0108.0464.099.0233.011.0114.089.049.079.017.0110900.096.0126.021.083300.0162900.096.0123.010.072.0814.00.021.067.02.0
                                                \n", + "
                                                " + ], + "text/plain": [ + " geoid no_car do_date male_20 male_21 no_cars one_car poverty \\\n", + "0 30007 43.0 20132017 32.0 20.0 65.0 463.0 448.0 \n", + "1 19137 106.0 20132017 17.0 42.0 291.0 1298.0 1653.0 \n", + "2 20101 10.0 20132017 8.0 0.0 36.0 181.0 181.0 \n", + "3 48307 133.0 20132017 1.0 93.0 220.0 883.0 1296.0 \n", + "4 46123 66.0 20132017 8.0 21.0 118.0 544.0 1045.0 \n", + "5 29155 308.0 20132017 85.0 189.0 934.0 2639.0 4859.0 \n", + "6 21159 28.0 20132017 69.0 89.0 286.0 1464.0 3190.0 \n", + "7 31005 0.0 20132017 0.0 8.0 0.0 35.0 46.0 \n", + "8 01067 205.0 20132017 124.0 74.0 427.0 1953.0 2310.0 \n", + "9 41021 4.0 20132017 19.0 16.0 25.0 176.0 188.0 \n", + "\n", + " children male_pop two_cars asian_pop black_pop female_20 female_21 \\\n", + "0 1151.0 2896.0 887.0 0.0 4.0 0.0 0.0 \n", + "1 2349.0 4931.0 1826.0 0.0 0.0 42.0 103.0 \n", + "2 407.0 864.0 350.0 0.0 1.0 1.0 8.0 \n", + "3 1935.0 4171.0 1232.0 0.0 161.0 39.0 14.0 \n", + "4 1237.0 2781.0 649.0 0.0 0.0 9.0 59.0 \n", + "5 4597.0 8210.0 2043.0 0.0 4639.0 197.0 153.0 \n", + "6 2498.0 6744.0 1695.0 0.0 575.0 83.0 64.0 \n", + "7 132.0 204.0 46.0 0.0 0.0 1.0 1.0 \n", + "8 3581.0 8208.0 2346.0 0.0 4762.0 33.0 120.0 \n", + "9 426.0 962.0 330.0 0.0 0.0 0.0 14.0 \n", + "\n", + " in_school pop_25_64 total_pop white_pop female_pop gini_index \\\n", + "0 1017.0 2936.0 5755.0 5432.0 2859.0 0.4452 \n", + "1 2094.0 5037.0 10239.0 9640.0 5308.0 0.4270 \n", + "2 332.0 822.0 1702.0 1539.0 838.0 0.4473 \n", + "3 1623.0 4096.0 8145.0 5067.0 3974.0 0.4495 \n", + "4 1121.0 2688.0 5480.0 4500.0 2699.0 0.4633 \n", + "5 4301.0 8474.0 17344.0 11948.0 9134.0 0.4862 \n", + "6 2290.0 6830.0 12175.0 11250.0 5431.0 0.4422 \n", + "7 149.0 172.0 421.0 411.0 217.0 0.4498 \n", + "8 3517.0 8535.0 17110.0 11757.0 8902.0 0.4529 \n", + "9 396.0 893.0 1910.0 1643.0 948.0 0.4153 \n", + "\n", + " households male_60_61 male_62_64 median_age three_cars male_5_to_9 \\\n", + "0 2405.0 105.0 196.0 46.3 622.0 132.0 \n", + "1 4614.0 149.0 256.0 44.5 774.0 385.0 \n", + "2 828.0 10.0 27.0 41.5 133.0 26.0 \n", + "3 3143.0 240.0 134.0 43.5 510.0 370.0 \n", + "4 2424.0 133.0 141.0 46.5 639.0 135.0 \n", + "5 6875.0 225.0 238.0 37.7 871.0 566.0 \n", + "6 4316.0 213.0 261.0 39.0 645.0 326.0 \n", + "7 177.0 9.0 3.0 43.3 54.0 27.0 \n", + "8 6727.0 176.0 317.0 43.5 1373.0 459.0 \n", + "9 805.0 28.0 61.0 47.0 191.0 79.0 \n", + "\n", + " median_rent pop_16_over pop_widowed armed_forces employed_pop \\\n", + "0 550.0 4762.0 None 0.0 2621.0 \n", + "1 482.0 8243.0 None 11.0 4838.0 \n", + "2 345.0 1379.0 None 0.0 885.0 \n", + "3 471.0 6496.0 None 0.0 3658.0 \n", + "4 341.0 4362.0 None 0.0 2859.0 \n", + "5 344.0 13251.0 None 0.0 6326.0 \n", + "6 307.0 9909.0 None 0.0 3032.0 \n", + "7 388.0 298.0 None 0.0 177.0 \n", + "8 398.0 13972.0 None 10.0 6949.0 \n", + "9 537.0 1529.0 None 0.0 751.0 \n", + "\n", + " hispanic_pop male_under_5 mobile_homes pop_divorced female_5_to_9 \\\n", + "0 152.0 112.0 377.0 None 207.0 \n", + "1 371.0 259.0 178.0 None 310.0 \n", + "2 113.0 74.0 46.0 None 74.0 \n", + "3 2571.0 327.0 393.0 None 263.0 \n", + "4 18.0 140.0 496.0 None 125.0 \n", + "5 421.0 614.0 742.0 None 496.0 \n", + "6 226.0 323.0 1911.0 None 342.0 \n", + "7 0.0 3.0 22.0 None 21.0 \n", + "8 435.0 468.0 2311.0 None 438.0 \n", + "9 154.0 51.0 146.0 None 51.0 \n", + "\n", + " housing_units male_10_to_14 male_15_to_17 male_18_to_19 male_22_to_24 \\\n", + "0 2743.0 196.0 117.0 75.0 54.0 \n", + "1 5231.0 313.0 238.0 115.0 182.0 \n", + "2 991.0 46.0 54.0 22.0 45.0 \n", + "3 4302.0 212.0 220.0 33.0 111.0 \n", + "4 3085.0 222.0 141.0 50.0 127.0 \n", + "5 8178.0 747.0 421.0 226.0 282.0 \n", + "6 5240.0 432.0 217.0 125.0 481.0 \n", + "7 261.0 20.0 4.0 2.0 13.0 \n", + "8 9055.0 594.0 307.0 171.0 396.0 \n", + "9 1070.0 51.0 44.0 22.0 13.0 \n", + "\n", + " male_25_to_29 male_30_to_34 male_35_to_39 male_40_to_44 male_45_to_49 \\\n", + "0 135.0 131.0 160.0 163.0 184.0 \n", + "1 217.0 239.0 252.0 323.0 301.0 \n", + "2 59.0 26.0 76.0 49.0 56.0 \n", + "3 284.0 236.0 272.0 152.0 206.0 \n", + "4 129.0 143.0 155.0 116.0 164.0 \n", + "5 479.0 431.0 449.0 422.0 526.0 \n", + "6 485.0 720.0 476.0 592.0 430.0 \n", + "7 8.0 1.0 6.0 18.0 17.0 \n", + "8 369.0 350.0 658.0 418.0 528.0 \n", + "9 18.0 49.0 41.0 32.0 51.0 \n", + "\n", + " male_45_to_64 male_50_to_54 male_55_to_59 male_65_to_66 male_67_to_69 \\\n", + "0 908.0 210.0 213.0 63.0 171.0 \n", + "1 1449.0 366.0 377.0 148.0 162.0 \n", + "2 226.0 72.0 61.0 19.0 22.0 \n", + "3 1054.0 310.0 164.0 106.0 111.0 \n", + "4 881.0 238.0 205.0 34.0 116.0 \n", + "5 2156.0 567.0 600.0 227.0 234.0 \n", + "6 1631.0 433.0 294.0 123.0 142.0 \n", + "7 50.0 8.0 13.0 2.0 5.0 \n", + "8 2232.0 536.0 675.0 252.0 324.0 \n", + "9 283.0 86.0 57.0 47.0 29.0 \n", + "\n", + " male_70_to_74 male_75_to_79 male_80_to_84 median_income pop_separated \\\n", + "0 191.0 129.0 66.0 55295.0 None \n", + "1 193.0 146.0 137.0 43674.0 None \n", + "2 25.0 40.0 39.0 51765.0 None \n", + "3 296.0 94.0 139.0 42367.0 None \n", + "4 112.0 143.0 42.0 48409.0 None \n", + "5 239.0 164.0 153.0 32468.0 None \n", + "6 247.0 166.0 61.0 29239.0 None \n", + "7 8.0 10.0 17.0 41250.0 None \n", + "8 452.0 293.0 121.0 45569.0 None \n", + "9 77.0 48.0 29.0 39831.0 None \n", + "\n", + " amerindian_pop female_under_5 four_more_cars group_quarters \\\n", + "0 84.0 146.0 368.0 44.0 \n", + "1 40.0 335.0 425.0 210.0 \n", + "2 39.0 48.0 128.0 8.0 \n", + "3 1.0 154.0 298.0 154.0 \n", + "4 729.0 172.0 474.0 159.0 \n", + "5 18.0 671.0 388.0 202.0 \n", + "6 9.0 295.0 226.0 1441.0 \n", + "7 0.0 8.0 42.0 0.0 \n", + "8 5.0 452.0 628.0 289.0 \n", + "9 99.0 54.0 83.0 15.0 \n", + "\n", + " masters_degree other_race_pop unemployed_pop walked_to_work \\\n", + "0 293.0 0.0 112.0 135.0 \n", + "1 227.0 0.0 349.0 194.0 \n", + "2 69.0 0.0 32.0 32.0 \n", + "3 213.0 202.0 122.0 104.0 \n", + "4 112.0 0.0 52.0 132.0 \n", + "5 483.0 0.0 756.0 153.0 \n", + "6 225.0 0.0 552.0 34.0 \n", + "7 15.0 1.0 1.0 13.0 \n", + "8 835.0 0.0 496.0 48.0 \n", + "9 64.0 0.0 61.0 64.0 \n", + "\n", + " worked_at_home female_10_to_14 female_15_to_17 female_18_to_19 \\\n", + "0 205.0 124.0 117.0 123.0 \n", + "1 233.0 302.0 207.0 127.0 \n", + "2 27.0 47.0 38.0 35.0 \n", + "3 61.0 258.0 131.0 66.0 \n", + "4 223.0 203.0 99.0 46.0 \n", + "5 109.0 689.0 393.0 194.0 \n", + "6 8.0 334.0 229.0 101.0 \n", + "7 23.0 35.0 14.0 0.0 \n", + "8 176.0 511.0 352.0 162.0 \n", + "9 55.0 64.0 32.0 1.0 \n", + "\n", + " female_22_to_24 female_25_to_29 female_30_to_34 female_35_to_39 \\\n", + "0 90.0 91.0 113.0 165.0 \n", + "1 119.0 220.0 233.0 262.0 \n", + "2 13.0 40.0 29.0 48.0 \n", + "3 21.0 340.0 82.0 271.0 \n", + "4 65.0 122.0 123.0 133.0 \n", + "5 209.0 573.0 521.0 629.0 \n", + "6 118.0 303.0 308.0 384.0 \n", + "7 0.0 6.0 7.0 5.0 \n", + "8 326.0 415.0 465.0 527.0 \n", + "9 26.0 44.0 58.0 49.0 \n", + "\n", + " female_40_to_44 female_45_to_49 female_50_to_54 female_55_to_59 \\\n", + "0 153.0 218.0 250.0 196.0 \n", + "1 345.0 320.0 373.0 452.0 \n", + "2 53.0 44.0 45.0 63.0 \n", + "3 321.0 285.0 188.0 273.0 \n", + "4 97.0 173.0 242.0 190.0 \n", + "5 452.0 564.0 593.0 715.0 \n", + "6 316.0 401.0 419.0 466.0 \n", + "7 13.0 14.0 11.0 25.0 \n", + "8 598.0 547.0 617.0 586.0 \n", + "9 49.0 69.0 31.0 65.0 \n", + "\n", + " female_60_to_61 female_62_to_64 female_65_to_66 female_67_to_69 \\\n", + "0 116.0 137.0 74.0 114.0 \n", + "1 152.0 200.0 126.0 168.0 \n", + "2 19.0 45.0 22.0 14.0 \n", + "3 121.0 217.0 147.0 117.0 \n", + "4 108.0 76.0 87.0 84.0 \n", + "5 199.0 291.0 159.0 271.0 \n", + "6 74.0 255.0 158.0 176.0 \n", + "7 3.0 5.0 9.0 3.0 \n", + "8 222.0 531.0 166.0 404.0 \n", + "9 61.0 44.0 22.0 32.0 \n", + "\n", + " female_70_to_74 female_75_to_79 female_80_to_84 pop_15_and_over \\\n", + "0 173.0 158.0 52.0 None \n", + "1 273.0 233.0 177.0 None \n", + "2 40.0 42.0 38.0 None \n", + "3 228.0 156.0 134.0 None \n", + "4 114.0 174.0 55.0 None \n", + "5 397.0 291.0 254.0 None \n", + "6 203.0 158.0 150.0 None \n", + "7 10.0 15.0 10.0 None \n", + "8 567.0 353.0 222.0 None \n", + "9 62.0 44.0 22.0 None \n", + "\n", + " pop_now_married asian_male_45_54 asian_male_55_64 bachelors_degree \\\n", + "0 None 0.0 0.0 866.0 \n", + "1 None 0.0 0.0 867.0 \n", + "2 None 0.0 0.0 206.0 \n", + "3 None 0.0 0.0 671.0 \n", + "4 None 0.0 0.0 643.0 \n", + "5 None 0.0 0.0 815.0 \n", + "6 None 0.0 0.0 373.0 \n", + "7 None 0.0 0.0 60.0 \n", + "8 None 0.0 0.0 1087.0 \n", + "9 None 0.0 0.0 200.0 \n", + "\n", + " black_male_45_54 black_male_55_64 commute_5_9_mins commuters_by_bus \\\n", + "0 0.0 0.0 359.0 0.0 \n", + "1 0.0 0.0 1023.0 21.0 \n", + "2 0.0 0.0 178.0 0.0 \n", + "3 18.0 0.0 867.0 2.0 \n", + "4 0.0 0.0 878.0 37.0 \n", + "5 239.0 230.0 1273.0 23.0 \n", + "6 159.0 17.0 195.0 0.0 \n", + "7 0.0 0.0 16.0 0.0 \n", + "8 291.0 314.0 605.0 0.0 \n", + "9 0.0 0.0 191.0 0.0 \n", + "\n", + " in_grades_1_to_4 in_grades_5_to_8 male_85_and_over not_hispanic_pop \\\n", + "0 253.0 252.0 41.0 5603.0 \n", + "1 569.0 512.0 114.0 9868.0 \n", + "2 64.0 79.0 8.0 1589.0 \n", + "3 483.0 334.0 60.0 5574.0 \n", + "4 272.0 269.0 66.0 5462.0 \n", + "5 877.0 1260.0 126.0 16923.0 \n", + "6 554.0 575.0 39.0 11949.0 \n", + "7 32.0 48.0 2.0 421.0 \n", + "8 750.0 892.0 146.0 16675.0 \n", + "9 102.0 101.0 14.0 1756.0 \n", + "\n", + " pop_5_years_over white_male_45_54 white_male_55_64 associates_degree \\\n", + "0 None 369.0 463.0 268.0 \n", + "1 None 628.0 782.0 926.0 \n", + "2 None 121.0 96.0 135.0 \n", + "3 None 327.0 391.0 217.0 \n", + "4 None 310.0 392.0 319.0 \n", + "5 None 824.0 828.0 660.0 \n", + "6 None 657.0 726.0 728.0 \n", + "7 None 24.0 25.0 33.0 \n", + "8 None 757.0 839.0 925.0 \n", + "9 None 114.0 130.0 77.0 \n", + "\n", + " commuters_16_over dwellings_2_units family_households hispanic_any_race \\\n", + "0 2287.0 15.0 1552.0 152.0 \n", + "1 4560.0 177.0 3080.0 371.0 \n", + "2 817.0 0.0 484.0 113.0 \n", + "3 3389.0 229.0 2164.0 2571.0 \n", + "4 2592.0 24.0 1473.0 18.0 \n", + "5 6067.0 517.0 4342.0 421.0 \n", + "6 2791.0 36.0 2940.0 226.0 \n", + "7 146.0 0.0 96.0 0.0 \n", + "8 6689.0 291.0 4545.0 435.0 \n", + "9 684.0 15.0 512.0 154.0 \n", + "\n", + " in_grades_9_to_12 income_less_10000 income_per_capita pop_25_years_over \\\n", + "0 336.0 96.0 32268.0 4210.0 \n", + "1 530.0 282.0 25005.0 7143.0 \n", + "2 67.0 62.0 29768.0 1163.0 \n", + "3 453.0 228.0 23398.0 5832.0 \n", + "4 273.0 239.0 27613.0 3858.0 \n", + "5 1082.0 956.0 18883.0 11212.0 \n", + "6 481.0 782.0 14914.0 8547.0 \n", + "7 18.0 6.0 21799.0 264.0 \n", + "8 844.0 552.0 23983.0 12123.0 \n", + "9 96.0 52.0 24178.0 1373.0 \n", + "\n", + " pop_never_married bachelors_degree_2 commute_10_14_mins \\\n", + "0 None 866.0 337.0 \n", + "1 None 867.0 568.0 \n", + "2 None 206.0 98.0 \n", + "3 None 671.0 535.0 \n", + "4 None 643.0 347.0 \n", + "5 None 815.0 792.0 \n", + "6 None 373.0 117.0 \n", + "7 None 60.0 9.0 \n", + "8 None 1087.0 694.0 \n", + "9 None 200.0 101.0 \n", + "\n", + " commute_15_19_mins commute_20_24_mins commute_25_29_mins \\\n", + "0 132.0 225.0 159.0 \n", + "1 487.0 485.0 205.0 \n", + "2 93.0 64.0 38.0 \n", + "3 412.0 410.0 23.0 \n", + "4 158.0 167.0 120.0 \n", + "5 998.0 913.0 366.0 \n", + "6 575.0 297.0 111.0 \n", + "7 3.0 16.0 4.0 \n", + "8 858.0 1025.0 749.0 \n", + "9 81.0 24.0 18.0 \n", + "\n", + " commute_30_34_mins commute_35_39_mins commute_35_44_mins \\\n", + "0 162.0 214.0 247.0 \n", + "1 236.0 39.0 82.0 \n", + "2 47.0 15.0 15.0 \n", + "3 202.0 71.0 117.0 \n", + "4 92.0 17.0 56.0 \n", + "5 587.0 78.0 167.0 \n", + "6 235.0 220.0 384.0 \n", + "7 12.0 3.0 17.0 \n", + "8 1067.0 154.0 395.0 \n", + "9 34.0 0.0 20.0 \n", + "\n", + " commute_40_44_mins commute_45_59_mins commute_60_89_mins \\\n", + "0 33.0 271.0 150.0 \n", + "1 43.0 292.0 205.0 \n", + "2 0.0 9.0 35.0 \n", + "3 46.0 75.0 52.0 \n", + "4 39.0 55.0 143.0 \n", + "5 89.0 181.0 87.0 \n", + "6 164.0 358.0 325.0 \n", + "7 14.0 15.0 3.0 \n", + "8 241.0 445.0 301.0 \n", + "9 20.0 41.0 5.0 \n", + "\n", + " female_85_and_over income_10000_14999 income_15000_19999 \\\n", + "0 42.0 135.0 96.0 \n", + "1 229.0 419.0 298.0 \n", + "2 32.0 42.0 50.0 \n", + "3 148.0 265.0 288.0 \n", + "4 143.0 174.0 110.0 \n", + "5 223.0 689.0 583.0 \n", + "6 94.0 288.0 476.0 \n", + "7 1.0 21.0 25.0 \n", + "8 288.0 504.0 566.0 \n", + "9 54.0 50.0 20.0 \n", + "\n", + " income_20000_24999 income_25000_29999 income_30000_34999 \\\n", + "0 101.0 88.0 84.0 \n", + "1 345.0 218.0 343.0 \n", + "2 67.0 67.0 35.0 \n", + "3 127.0 156.0 203.0 \n", + "4 132.0 111.0 119.0 \n", + "5 512.0 504.0 377.0 \n", + "6 430.0 203.0 138.0 \n", + "7 13.0 4.0 9.0 \n", + "8 332.0 278.0 457.0 \n", + "9 73.0 56.0 77.0 \n", + "\n", + " income_35000_39999 income_40000_44999 income_45000_49999 \\\n", + "0 210.0 166.0 76.0 \n", + "1 211.0 226.0 222.0 \n", + "2 32.0 35.0 12.0 \n", + "3 207.0 137.0 124.0 \n", + "4 168.0 91.0 124.0 \n", + "5 385.0 382.0 336.0 \n", + "6 224.0 365.0 182.0 \n", + "7 8.0 12.0 10.0 \n", + "8 308.0 323.0 412.0 \n", + "9 77.0 20.0 52.0 \n", + "\n", + " income_50000_59999 income_60000_74999 income_75000_99999 \\\n", + "0 305.0 241.0 267.0 \n", + "1 433.0 515.0 485.0 \n", + "2 79.0 109.0 139.0 \n", + "3 259.0 329.0 385.0 \n", + "4 227.0 270.0 279.0 \n", + "5 544.0 392.0 555.0 \n", + "6 271.0 373.0 356.0 \n", + "7 14.0 22.0 20.0 \n", + "8 616.0 694.0 614.0 \n", + "9 50.0 77.0 90.0 \n", + "\n", + " married_households not_in_labor_force not_us_citizen_pop \\\n", + "0 1348.0 2029.0 20.0 \n", + "1 2221.0 3045.0 136.0 \n", + "2 401.0 462.0 12.0 \n", + "3 1592.0 2716.0 207.0 \n", + "4 1217.0 1451.0 27.0 \n", + "5 2758.0 6169.0 97.0 \n", + "6 2311.0 6325.0 34.0 \n", + "7 87.0 120.0 2.0 \n", + "8 3625.0 6517.0 169.0 \n", + "9 420.0 717.0 30.0 \n", + "\n", + " pop_in_labor_force high_school_diploma hispanic_male_45_54 \\\n", + "0 2733.0 1366.0 0.0 \n", + "1 5198.0 2447.0 39.0 \n", + "2 917.0 302.0 6.0 \n", + "3 3780.0 1597.0 164.0 \n", + "4 2911.0 1367.0 0.0 \n", + "5 7082.0 3602.0 19.0 \n", + "6 3584.0 2353.0 33.0 \n", + "7 178.0 73.0 0.0 \n", + "8 7455.0 3398.0 13.0 \n", + "9 812.0 426.0 11.0 \n", + "\n", + " hispanic_male_55_64 occupation_services workers_16_and_over \\\n", + "0 5.0 461.0 2492.0 \n", + "1 0.0 824.0 4793.0 \n", + "2 2.0 165.0 844.0 \n", + "3 147.0 770.0 3450.0 \n", + "4 0.0 393.0 2815.0 \n", + "5 19.0 1441.0 6176.0 \n", + "6 17.0 428.0 2799.0 \n", + "7 0.0 17.0 169.0 \n", + "8 0.0 1319.0 6865.0 \n", + "9 0.0 113.0 739.0 \n", + "\n", + " civilian_labor_force commute_60_more_mins commute_90_more_mins \\\n", + "0 2733.0 203.0 53.0 \n", + "1 5187.0 319.0 114.0 \n", + "2 917.0 43.0 8.0 \n", + "3 3780.0 133.0 81.0 \n", + "4 2911.0 175.0 32.0 \n", + "5 7082.0 159.0 72.0 \n", + "6 3584.0 447.0 122.0 \n", + "7 178.0 6.0 3.0 \n", + "8 7445.0 431.0 130.0 \n", + "9 812.0 24.0 19.0 \n", + "\n", + " commute_less_10_mins commuters_by_carpool employed_information \\\n", + "0 551.0 267.0 31.0 \n", + "1 1886.0 453.0 153.0 \n", + "2 410.0 42.0 10.0 \n", + "3 1482.0 424.0 17.0 \n", + "4 1422.0 247.0 30.0 \n", + "5 1904.0 594.0 26.0 \n", + "6 267.0 460.0 30.0 \n", + "7 64.0 24.0 1.0 \n", + "8 1025.0 513.0 74.0 \n", + "9 341.0 37.0 14.0 \n", + "\n", + " in_undergrad_college income_100000_124999 income_125000_149999 \\\n", + "0 79.0 170.0 82.0 \n", + "1 176.0 317.0 121.0 \n", + "2 48.0 45.0 11.0 \n", + "3 70.0 176.0 114.0 \n", + "4 102.0 226.0 37.0 \n", + "5 492.0 347.0 96.0 \n", + "6 292.0 120.0 50.0 \n", + "7 17.0 7.0 1.0 \n", + "8 595.0 379.0 287.0 \n", + "9 26.0 80.0 20.0 \n", + "\n", + " income_150000_199999 male_male_households nonfamily_households \\\n", + "0 186.0 0.0 853.0 \n", + "1 93.0 0.0 1534.0 \n", + "2 18.0 0.0 344.0 \n", + "3 69.0 0.0 979.0 \n", + "4 32.0 0.0 951.0 \n", + "5 143.0 6.0 2533.0 \n", + "6 58.0 0.0 1376.0 \n", + "7 2.0 0.0 81.0 \n", + "8 257.0 10.0 2182.0 \n", + "9 2.0 0.0 293.0 \n", + "\n", + " rent_over_50_percent vacant_housing_units commuters_drove_alone \\\n", + "0 47.0 338.0 1852.0 \n", + "1 246.0 617.0 3813.0 \n", + "2 16.0 163.0 732.0 \n", + "3 227.0 1159.0 2857.0 \n", + "4 128.0 661.0 2163.0 \n", + "5 641.0 1303.0 5259.0 \n", + "6 267.0 924.0 2292.0 \n", + "7 8.0 84.0 109.0 \n", + "8 185.0 2328.0 5900.0 \n", + "9 26.0 265.0 575.0 \n", + "\n", + " employed_construction employed_retail_trade income_200000_or_more \\\n", + "0 370.0 180.0 102.0 \n", + "1 309.0 641.0 86.0 \n", + "2 112.0 73.0 25.0 \n", + "3 171.0 383.0 76.0 \n", + "4 271.0 363.0 85.0 \n", + "5 194.0 741.0 74.0 \n", + "6 197.0 520.0 0.0 \n", + "7 15.0 13.0 3.0 \n", + "8 386.0 769.0 148.0 \n", + "9 74.0 57.0 9.0 \n", + "\n", + " less_one_year_college male_45_64_grade_9_12 one_year_more_college \\\n", + "0 261.0 24.0 683.0 \n", + "1 716.0 111.0 935.0 \n", + "2 106.0 7.0 207.0 \n", + "3 500.0 201.0 1092.0 \n", + "4 196.0 31.0 571.0 \n", + "5 705.0 408.0 1354.0 \n", + "6 360.0 347.0 1107.0 \n", + "7 26.0 0.0 34.0 \n", + "8 828.0 237.0 1843.0 \n", + "9 90.0 14.0 297.0 \n", + "\n", + " rent_10_to_15_percent rent_15_to_20_percent rent_20_to_25_percent \\\n", + "0 94.0 22.0 52.0 \n", + "1 180.0 193.0 164.0 \n", + "2 27.0 16.0 18.0 \n", + "3 64.0 100.0 59.0 \n", + "4 87.0 63.0 58.0 \n", + "5 263.0 285.0 371.0 \n", + "6 109.0 22.0 83.0 \n", + "7 13.0 3.0 1.0 \n", + "8 149.0 96.0 162.0 \n", + "9 32.0 46.0 8.0 \n", + "\n", + " rent_25_to_30_percent rent_30_to_35_percent rent_35_to_40_percent \\\n", + "0 15.0 45.0 5.0 \n", + "1 144.0 51.0 77.0 \n", + "2 13.0 19.0 8.0 \n", + "3 44.0 46.0 25.0 \n", + "4 79.0 2.0 37.0 \n", + "5 367.0 188.0 167.0 \n", + "6 98.0 107.0 49.0 \n", + "7 3.0 2.0 5.0 \n", + "8 91.0 35.0 109.0 \n", + "9 34.0 26.0 15.0 \n", + "\n", + " rent_40_to_50_percent rent_under_10_percent sales_office_employed \\\n", + "0 32.0 6.0 404.0 \n", + "1 168.0 75.0 1081.0 \n", + "2 0.0 10.0 185.0 \n", + "3 24.0 19.0 809.0 \n", + "4 104.0 64.0 484.0 \n", + "5 267.0 155.0 1164.0 \n", + "6 35.0 71.0 894.0 \n", + "7 0.0 0.0 20.0 \n", + "8 75.0 51.0 1347.0 \n", + "9 13.0 22.0 156.0 \n", + "\n", + " speak_spanish_at_home two_or_more_races_pop dwellings_3_to_4_units \\\n", + "0 None 83.0 41.0 \n", + "1 None 188.0 291.0 \n", + "2 None 3.0 33.0 \n", + "3 None 143.0 110.0 \n", + "4 None 233.0 108.0 \n", + "5 None 316.0 513.0 \n", + "6 None 107.0 53.0 \n", + "7 None 9.0 0.0 \n", + "8 None 151.0 163.0 \n", + "9 None 0.0 6.0 \n", + "\n", + " dwellings_5_to_9_units employed_manufacturing male_45_64_high_school \\\n", + "0 49.0 215.0 287.0 \n", + "1 101.0 870.0 680.0 \n", + "2 0.0 6.0 82.0 \n", + "3 16.0 214.0 458.0 \n", + "4 70.0 41.0 387.0 \n", + "5 49.0 1328.0 866.0 \n", + "6 105.0 45.0 736.0 \n", + "7 0.0 13.0 21.0 \n", + "8 0.0 787.0 918.0 \n", + "9 6.0 22.0 111.0 \n", + "\n", + " occupied_housing_units male_45_64_some_college mortgaged_housing_units \\\n", + "0 2405.0 247.0 1122.0 \n", + "1 4614.0 264.0 1552.0 \n", + "2 828.0 61.0 306.0 \n", + "3 3143.0 203.0 889.0 \n", + "4 2424.0 140.0 663.0 \n", + "5 6875.0 337.0 1652.0 \n", + "6 4316.0 218.0 958.0 \n", + "7 177.0 7.0 25.0 \n", + "8 6727.0 540.0 2932.0 \n", + "9 805.0 85.0 257.0 \n", + "\n", + " occupation_sales_office population_3_years_over asian_including_hispanic \\\n", + "0 404.0 5553.0 0.0 \n", + "1 1081.0 9869.0 0.0 \n", + "2 185.0 1623.0 0.0 \n", + "3 809.0 7863.0 0.0 \n", + "4 484.0 5334.0 0.0 \n", + "5 1164.0 16514.0 0.0 \n", + "6 894.0 11860.0 0.0 \n", + "7 20.0 415.0 0.0 \n", + "8 1347.0 16615.0 0.0 \n", + "9 156.0 1870.0 0.0 \n", + "\n", + " black_including_hispanic dwellings_10_to_19_units \\\n", + "0 4.0 41.0 \n", + "1 0.0 49.0 \n", + "2 1.0 0.0 \n", + "3 161.0 6.0 \n", + "4 0.0 38.0 \n", + "5 4717.0 90.0 \n", + "6 606.0 28.0 \n", + "7 0.0 0.0 \n", + "8 4762.0 52.0 \n", + "9 0.0 0.0 \n", + "\n", + " dwellings_20_to_49_units employed_wholesale_trade \\\n", + "0 0.0 42.0 \n", + "1 40.0 120.0 \n", + "2 0.0 13.0 \n", + "3 18.0 39.0 \n", + "4 52.0 75.0 \n", + "5 16.0 110.0 \n", + "6 18.0 88.0 \n", + "7 0.0 3.0 \n", + "8 12.0 221.0 \n", + "9 21.0 20.0 \n", + "\n", + " female_female_households rent_burden_not_computed \\\n", + "0 0.0 67.0 \n", + "1 3.0 185.0 \n", + "2 0.0 52.0 \n", + "3 0.0 137.0 \n", + "4 0.0 125.0 \n", + "5 5.0 540.0 \n", + "6 0.0 361.0 \n", + "7 0.0 30.0 \n", + "8 0.0 243.0 \n", + "9 0.0 68.0 \n", + "\n", + " white_including_hispanic high_school_including_ged \\\n", + "0 5533.0 1564.0 \n", + "1 9756.0 2785.0 \n", + "2 1649.0 361.0 \n", + "3 6969.0 1862.0 \n", + "4 4500.0 1511.0 \n", + "5 12113.0 4279.0 \n", + "6 11397.0 3313.0 \n", + "7 411.0 79.0 \n", + "8 12144.0 4105.0 \n", + "9 1783.0 493.0 \n", + "\n", + " commuters_by_car_truck_van dwellings_1_units_attached \\\n", + "0 2119.0 0.0 \n", + "1 4266.0 77.0 \n", + "2 774.0 22.0 \n", + "3 3281.0 23.0 \n", + "4 2410.0 16.0 \n", + "5 5853.0 54.0 \n", + "6 2752.0 37.0 \n", + "7 133.0 10.0 \n", + "8 6413.0 41.0 \n", + "9 612.0 7.0 \n", + "\n", + " dwellings_1_units_detached dwellings_50_or_more_units \\\n", + "0 2208.0 0.0 \n", + "1 4318.0 0.0 \n", + "2 890.0 0.0 \n", + "3 3498.0 0.0 \n", + "4 2281.0 0.0 \n", + "5 6098.0 91.0 \n", + "6 2976.0 54.0 \n", + "7 229.0 0.0 \n", + "8 6183.0 2.0 \n", + "9 863.0 6.0 \n", + "\n", + " housing_built_2000_to_2004 male_45_64_graduate_degree \\\n", + "0 61.0 87.0 \n", + "1 27.0 32.0 \n", + "2 15.0 3.0 \n", + "3 78.0 17.0 \n", + "4 43.0 32.0 \n", + "5 206.0 25.0 \n", + "6 143.0 41.0 \n", + "7 3.0 1.0 \n", + "8 218.0 113.0 \n", + "9 12.0 15.0 \n", + "\n", + " occupation_management_arts population_1_year_and_over \\\n", + "0 955.0 5634.0 \n", + "1 1470.0 10138.0 \n", + "2 264.0 1659.0 \n", + "3 850.0 8140.0 \n", + "4 1217.0 5425.0 \n", + "5 1560.0 17033.0 \n", + "6 875.0 12072.0 \n", + "7 83.0 421.0 \n", + "8 2279.0 16964.0 \n", + "9 233.0 1903.0 \n", + "\n", + " speak_only_english_at_home housing_built_2005_or_later \\\n", + "0 None 16.0 \n", + "1 None 5.0 \n", + "2 None 0.0 \n", + "3 None 39.0 \n", + "4 None 19.0 \n", + "5 None 20.0 \n", + "6 None 55.0 \n", + "7 None 0.0 \n", + "8 None 102.0 \n", + "9 None 15.0 \n", + "\n", + " male_45_64_bachelors_degree median_year_structure_built \\\n", + "0 158.0 1987.0 \n", + "1 178.0 1952.0 \n", + "2 48.0 1957.0 \n", + "3 93.0 1969.0 \n", + "4 152.0 1966.0 \n", + "5 211.0 1968.0 \n", + "6 19.0 1985.0 \n", + "7 6.0 1959.0 \n", + "8 153.0 1983.0 \n", + "9 40.0 1957.0 \n", + "\n", + " children_in_single_female_hh families_with_young_children \\\n", + "0 147.0 335.0 \n", + "1 756.0 662.0 \n", + "2 43.0 137.0 \n", + "3 396.0 589.0 \n", + "4 220.0 338.0 \n", + "5 2120.0 1330.0 \n", + "6 473.0 787.0 \n", + "7 3.0 27.0 \n", + "8 761.0 1033.0 \n", + "9 109.0 128.0 \n", + "\n", + " graduate_professional_degree households_retirement_income \\\n", + "0 337.0 625.0 \n", + "1 281.0 807.0 \n", + "2 72.0 107.0 \n", + "3 261.0 679.0 \n", + "4 210.0 261.0 \n", + "5 532.0 1138.0 \n", + "6 327.0 987.0 \n", + "7 19.0 23.0 \n", + "8 987.0 1480.0 \n", + "9 64.0 169.0 \n", + "\n", + " male_45_64_associates_degree male_45_64_less_than_9_grade \\\n", + "0 92.0 13.0 \n", + "1 151.0 33.0 \n", + "2 22.0 3.0 \n", + "3 7.0 75.0 \n", + "4 83.0 56.0 \n", + "5 58.0 251.0 \n", + "6 117.0 153.0 \n", + "7 15.0 0.0 \n", + "8 175.0 96.0 \n", + "9 5.0 13.0 \n", + "\n", + " million_dollar_housing_units owner_occupied_housing_units \\\n", + "0 26.0 2020.0 \n", + "1 0.0 3131.0 \n", + "2 0.0 649.0 \n", + "3 35.0 2398.0 \n", + "4 16.0 1677.0 \n", + "5 0.0 3631.0 \n", + "6 0.0 3114.0 \n", + "7 0.0 112.0 \n", + "8 0.0 5531.0 \n", + "9 0.0 515.0 \n", + "\n", + " percent_income_spent_on_rent aggregate_travel_time_to_work \\\n", + "0 23.6 60655.0 \n", + "1 26.3 86680.0 \n", + "2 22.9 NaN \n", + "3 32.0 53750.0 \n", + "4 27.5 40040.0 \n", + "5 28.8 105750.0 \n", + "6 31.8 98955.0 \n", + "7 25.8 3040.0 \n", + "8 26.0 171195.0 \n", + "9 25.4 11965.0 \n", + "\n", + " amerindian_including_hispanic housing_built_1939_or_earlier \\\n", + "0 84.0 97.0 \n", + "1 64.0 181.0 \n", + "2 39.0 95.0 \n", + "3 1.0 551.0 \n", + "4 747.0 255.0 \n", + "5 18.0 853.0 \n", + "6 9.0 141.0 \n", + "7 0.0 31.0 \n", + "8 5.0 343.0 \n", + "9 99.0 73.0 \n", + "\n", + " housing_units_renter_occupied pop_determined_poverty_status \\\n", + "0 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"\n", + " management_business_sci_arts_employed \\\n", + "0 955.0 \n", + "1 1470.0 \n", + "2 264.0 \n", + "3 850.0 \n", + "4 1217.0 \n", + "5 1560.0 \n", + "6 875.0 \n", + "7 83.0 \n", + "8 2279.0 \n", + "9 233.0 \n", + "\n", + " employed_finance_insurance_real_estate \\\n", + "0 141.0 \n", + "1 170.0 \n", + "2 30.0 \n", + "3 133.0 \n", + "4 184.0 \n", + "5 282.0 \n", + "6 162.0 \n", + "7 1.0 \n", + "8 279.0 \n", + "9 11.0 \n", + "\n", + " different_house_year_ago_different_city \\\n", + "0 471.0 \n", + "1 839.0 \n", + "2 84.0 \n", + "3 829.0 \n", + "4 444.0 \n", + "5 1594.0 \n", + "6 1449.0 \n", + "7 54.0 \n", + "8 983.0 \n", + "9 114.0 \n", + "\n", + " employed_science_management_admin_waste \\\n", + "0 141.0 \n", + "1 173.0 \n", + "2 34.0 \n", + "3 231.0 \n", + "4 92.0 \n", + "5 164.0 \n", + "6 85.0 \n", + "7 3.0 \n", + "8 495.0 \n", + "9 89.0 \n", + "\n", + " one_parent_families_with_young_children \\\n", + "0 64.0 \n", + "1 327.0 \n", + "2 20.0 \n", + "3 206.0 \n", + "4 85.0 \n", 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occupation_production_transportation_material \\\n", + "0 310.0 \n", + "1 949.0 \n", + "2 73.0 \n", + "3 510.0 \n", + "4 306.0 \n", + "5 1565.0 \n", + "6 319.0 \n", + "7 21.0 \n", + "8 1238.0 \n", + "9 126.0 \n", + "\n", + " father_one_parent_families_with_young_children \\\n", + "0 0.0 \n", + "1 29.0 \n", + "2 13.0 \n", + "3 165.0 \n", + "4 26.0 \n", + "5 89.0 \n", + "6 24.0 \n", + "7 2.0 \n", + "8 89.0 \n", + "9 21.0 \n", + "\n", + " owner_occupied_housing_units_lower_value_quartile \\\n", + "0 119800.0 \n", + "1 49500.0 \n", + "2 44300.0 \n", + "3 41600.0 \n", + "4 51600.0 \n", + "5 37000.0 \n", + "6 30000.0 \n", + "7 57000.0 \n", + "8 62700.0 \n", + "9 83300.0 \n", + "\n", + " owner_occupied_housing_units_upper_value_quartile \\\n", + "0 301300.0 \n", + "1 126300.0 \n", + "2 124400.0 \n", + "3 165300.0 \n", + "4 156600.0 \n", + "5 120900.0 \n", + "6 117600.0 \n", + "7 162500.0 \n", + "8 189100.0 \n", + "9 162900.0 \n", + "\n", + " 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                                                slugnamedescriptiondb_typeagg_methodcolumn_namevariable_group_iddataset_idid
                                                0geoid_9d24d904geoidUS Census Block Groups GeoidsSTRINGNonegeoidNonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                1do_date_69c292c1do_dateFirst day of the year the survey was issuedDATENonedo_dateNonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                2total_pop_9ba05abfTotal PopulationTotal Population. The total number of all peop...FLOATSUMtotal_popNonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                3households_51ae36c8Number of householdsHouseholds. A count of the number of household...FLOATSUMhouseholdsNonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                4male_pop_73e15d04male_popMale Population. The number of people within e...FLOATSUMmale_popcarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
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                                                " + ], + "text/plain": [ + " slug name \\\n", + "0 geoid_9d24d904 geoid \n", + "1 do_date_69c292c1 do_date \n", + "2 total_pop_9ba05abf Total Population \n", + "3 households_51ae36c8 Number of households \n", + "4 male_pop_73e15d04 male_pop \n", + "\n", + " description db_type agg_method \\\n", + "0 US Census Block Groups Geoids STRING None \n", + "1 First day of the year the survey was issued DATE None \n", + "2 Total Population. The total number of all peop... FLOAT SUM \n", + "3 Households. A count of the number of household... FLOAT SUM \n", + "4 Male Population. The number of people within e... FLOAT SUM \n", + "\n", + " column_name variable_group_id \\\n", + "0 geoid None \n", + "1 do_date None \n", + "2 total_pop None \n", + "3 households None \n", + "4 male_pop carto-do-public-data.usa_acs.demographics_soci... \n", + "\n", + " dataset_id \\\n", + "0 carto-do-public-data.usa_acs.demographics_soci... \n", + "1 carto-do-public-data.usa_acs.demographics_soci... \n", + "2 carto-do-public-data.usa_acs.demographics_soci... \n", + "3 carto-do-public-data.usa_acs.demographics_soci... \n", + "4 carto-do-public-data.usa_acs.demographics_soci... \n", + "\n", + " id \n", + "0 carto-do-public-data.usa_acs.demographics_soci... \n", + "1 carto-do-public-data.usa_acs.demographics_soci... \n", + "2 carto-do-public-data.usa_acs.demographics_soci... \n", + "3 carto-do-public-data.usa_acs.demographics_soci... \n", + "4 carto-do-public-data.usa_acs.demographics_soci... " + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.variables.to_dataframe().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can explore the variable names in the previous dataframe using the column `column_name`. The variable names we are interested in are `total_pop` and `geom`.\n", + "\n", + "In order to download a dataset, we will use the Dataset function `to_dataframe()`. Since we are only interested in downloading two variables of the dataset, we can use a SQL query to express this as shown in the following line of code." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "Jce9Hfys0td6" + }, + "outputs": [], + "source": [ + "county_pop_df = dataset.to_dataframe(sql_query=\"select total_pop, geom from $dataset$\")" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 197 + }, + "id": "Zu1A2UET2fY7", + "outputId": "ce64b55c-1b7b-4ecc-cac6-6d1acd34683a" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                091518.0POLYGON ((-97.03319 29.06433, -97.25027 28.905...
                                                116275.0POLYGON ((-116.15359 46.34831, -116.12716 46.2...
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                                                " + ], + "text/plain": [ + " total_pop geom\n", + "0 91518.0 POLYGON ((-97.03319 29.06433, -97.25027 28.905...\n", + "1 16275.0 POLYGON ((-116.15359 46.34831, -116.12716 46.2...\n", + "2 7113.0 POLYGON ((-79.45176 37.76645, -79.42082 37.789...\n", + "3 13843.0 POLYGON ((-77.18885 38.89627, -77.18972 38.878...\n", + "4 24741.0 POLYGON ((-92.55449 42.64231, -92.31808 42.642..." + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "county_pop_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Xvmj4nXV_p-w" + }, + "source": [ + "##### Visualize dataset\n", + "\n", + "We can now visualize the data." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "LXld0r-G2mS7", + "outputId": "276dce9d-65af-4de1-f0b8-ec4ca407fc91" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                                                  7250259901010.0201320170.00.00.00.00.00.058.00.00.00.00.00.029.023.071.00.013.0NaN13.00.00.024.013.00.0NaN71.0None0.071.071.00.00.0None0.013.00.00.016.019.00.00.00.00.00.023.00.023.00.00.00.00.00.0NaNNone0.00.00.00.00.00.00.00.00.00.00.00.013.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NoneNone0.00.00.00.00.00.071.00.00.00.00.0None0.00.00.071.00.013.071.00.00.017785.023.0None0.00.00.00.00.036.00.00.00.00.035.00.00.00.00.00.00.00.00.00.00.013.00.00.00.071.071.023.00.023.013.071.071.035.00.00.00.00.029.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.013.00.00.00.00.00.00.00.0None0.013.00.00.023.013.00.00.00.071.00.019.00.00.00.00.00.052.023.00.00.00.00.00.00.016.071.0None0.00.0NaN0.00.00.00.00.00.00.00.0NaNNone0.00.013.071.00.00.00.00.00.00.029.0None71.00.00.013.016.00.019.00.00.00.023.0None19.019.00.0NaNNaN0.023.00.00.0NaN0.00.00.00.0
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                                                  9260419900000.0201320170.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaN0.00.00.0NaN0.00.0NaN0.0None0.00.00.00.00.0None0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NaNNone0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0NoneNone0.00.00.00.00.00.00.00.00.00.00.0None0.00.00.00.00.00.00.00.00.0NaN0.0None0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0None0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0None0.00.0NaN0.00.00.00.00.00.00.00.0NaNNone0.00.00.00.00.00.00.00.00.00.00.0None0.00.00.00.00.00.00.00.00.00.00.0None0.00.00.0NaNNaN0.00.00.00.0NaN0.00.00.00.0
                                                  \n", + "
                                                  " + ], + "text/plain": [ + " geoid no_car do_date male_20 male_21 no_cars one_car poverty \\\n", + "0 47157005000 76.0 20132017 0.0 0.0 151.0 122.0 552.0 \n", + "1 72051990021 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "2 72071990000 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "3 01097990000 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "4 18127980001 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "5 47157980100 0.0 20132017 0.0 6.0 0.0 0.0 58.0 \n", + "6 55071990000 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "7 25025990101 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "8 55025991703 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "9 26041990000 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " children male_pop two_cars asian_pop black_pop female_20 female_21 \\\n", + "0 316.0 415.0 64.0 0.0 1004.0 19.0 42.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "5 0.0 38.0 0.0 0.0 4.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "7 0.0 58.0 0.0 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " in_school pop_25_64 total_pop white_pop female_pop gini_index \\\n", + "0 331.0 328.0 1042.0 16.0 627.0 0.4372 \n", + "1 0.0 0.0 0.0 0.0 0.0 NaN \n", + "2 0.0 0.0 0.0 0.0 0.0 NaN \n", + "3 0.0 0.0 0.0 0.0 0.0 NaN \n", + "4 0.0 0.0 0.0 0.0 0.0 NaN \n", + "5 0.0 54.0 74.0 70.0 36.0 NaN \n", + "6 0.0 0.0 0.0 0.0 0.0 NaN \n", + "7 29.0 23.0 71.0 0.0 13.0 NaN \n", + "8 0.0 0.0 0.0 0.0 0.0 NaN \n", + "9 0.0 0.0 0.0 0.0 0.0 NaN \n", + "\n", + " households male_60_61 male_62_64 median_age three_cars male_5_to_9 \\\n", + "0 343.0 0.0 2.0 28.9 0.0 38.0 \n", + "1 0.0 0.0 0.0 NaN 0.0 0.0 \n", + "2 0.0 0.0 0.0 NaN 0.0 0.0 \n", + "3 0.0 0.0 0.0 NaN 0.0 0.0 \n", + "4 0.0 0.0 0.0 NaN 0.0 0.0 \n", + "5 0.0 0.0 0.0 31.3 0.0 0.0 \n", + "6 0.0 0.0 0.0 NaN 0.0 0.0 \n", + "7 13.0 0.0 0.0 24.0 13.0 0.0 \n", + "8 0.0 0.0 0.0 NaN 0.0 0.0 \n", + "9 0.0 0.0 0.0 NaN 0.0 0.0 \n", + "\n", + " median_rent pop_16_over pop_widowed armed_forces employed_pop \\\n", + "0 391.0 749.0 None 0.0 233.0 \n", + "1 NaN 0.0 None 0.0 0.0 \n", + "2 NaN 0.0 None 0.0 0.0 \n", + "3 NaN 0.0 None 0.0 0.0 \n", + "4 NaN 0.0 None 0.0 0.0 \n", + "5 NaN 74.0 None 0.0 0.0 \n", + "6 NaN 0.0 None 0.0 0.0 \n", + "7 NaN 71.0 None 0.0 71.0 \n", + "8 NaN 0.0 None 0.0 0.0 \n", + "9 NaN 0.0 None 0.0 0.0 \n", + "\n", + " hispanic_pop male_under_5 mobile_homes pop_divorced female_5_to_9 \\\n", + "0 0.0 44.0 5.0 None 76.0 \n", + "1 0.0 0.0 0.0 None 0.0 \n", + "2 0.0 0.0 0.0 None 0.0 \n", + "3 0.0 0.0 0.0 None 0.0 \n", + "4 0.0 0.0 0.0 None 0.0 \n", + "5 0.0 0.0 0.0 None 0.0 \n", + "6 0.0 0.0 0.0 None 0.0 \n", + "7 71.0 0.0 0.0 None 0.0 \n", + "8 0.0 0.0 0.0 None 0.0 \n", + "9 0.0 0.0 0.0 None 0.0 \n", + "\n", + " housing_units male_10_to_14 male_15_to_17 male_18_to_19 male_22_to_24 \\\n", + "0 465.0 27.0 26.0 30.0 22.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 6.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 0.0 \n", + "7 13.0 0.0 0.0 16.0 19.0 \n", + "8 0.0 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " male_25_to_29 male_30_to_34 male_35_to_39 male_40_to_44 male_45_to_49 \\\n", + "0 11.0 13.0 31.0 11.0 15.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 0.0 \n", + "5 0.0 6.0 12.0 4.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " male_45_to_64 male_50_to_54 male_55_to_59 male_65_to_66 male_67_to_69 \\\n", + "0 79.0 21.0 41.0 0.0 5.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 0.0 \n", + "7 23.0 0.0 23.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " male_70_to_74 male_75_to_79 male_80_to_84 median_income pop_separated \\\n", + "0 26.0 5.0 31.0 14410.0 None \n", + "1 0.0 0.0 0.0 NaN None \n", + "2 0.0 0.0 0.0 NaN None \n", + "3 0.0 0.0 0.0 NaN None \n", + "4 0.0 0.0 0.0 NaN None \n", + "5 0.0 4.0 0.0 NaN None \n", + "6 0.0 0.0 0.0 NaN None \n", + "7 0.0 0.0 0.0 NaN None \n", + "8 0.0 0.0 0.0 NaN None \n", + "9 0.0 0.0 0.0 NaN None \n", + "\n", + " amerindian_pop female_under_5 four_more_cars group_quarters \\\n", + "0 0.0 24.0 6.0 45.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 74.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " masters_degree other_race_pop unemployed_pop walked_to_work \\\n", + "0 13.0 0.0 71.0 7.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " worked_at_home female_10_to_14 female_15_to_17 female_18_to_19 \\\n", + "0 0.0 47.0 34.0 17.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " female_22_to_24 female_25_to_29 female_30_to_34 female_35_to_39 \\\n", + "0 32.0 34.0 13.0 38.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 4.0 18.0 6.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 13.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " female_40_to_44 female_45_to_49 female_50_to_54 female_55_to_59 \\\n", + "0 19.0 25.0 26.0 22.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 5.0 3.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " female_60_to_61 female_62_to_64 female_65_to_66 female_67_to_69 \\\n", + "0 0.0 6.0 22.0 38.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " female_70_to_74 female_75_to_79 female_80_to_84 pop_15_and_over \\\n", + "0 36.0 13.0 6.0 None \n", + "1 0.0 0.0 0.0 None \n", + "2 0.0 0.0 0.0 None \n", + "3 0.0 0.0 0.0 None \n", + "4 0.0 0.0 0.0 None \n", + "5 0.0 0.0 0.0 None \n", + "6 0.0 0.0 0.0 None \n", + "7 0.0 0.0 0.0 None \n", + "8 0.0 0.0 0.0 None \n", + "9 0.0 0.0 0.0 None \n", + "\n", + " pop_now_married asian_male_45_54 asian_male_55_64 bachelors_degree \\\n", + "0 None 0.0 0.0 0.0 \n", + "1 None 0.0 0.0 0.0 \n", + "2 None 0.0 0.0 0.0 \n", + "3 None 0.0 0.0 0.0 \n", + "4 None 0.0 0.0 0.0 \n", + "5 None 0.0 0.0 0.0 \n", + "6 None 0.0 0.0 0.0 \n", + "7 None 0.0 0.0 0.0 \n", + "8 None 0.0 0.0 0.0 \n", + "9 None 0.0 0.0 0.0 \n", + "\n", + " black_male_45_54 black_male_55_64 commute_5_9_mins commuters_by_bus \\\n", + "0 23.0 35.0 7.0 52.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 71.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " in_grades_1_to_4 in_grades_5_to_8 male_85_and_over not_hispanic_pop \\\n", + "0 90.0 41.0 16.0 1042.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 74.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " pop_5_years_over white_male_45_54 white_male_55_64 associates_degree \\\n", + "0 None 13.0 3.0 28.0 \n", + "1 None 0.0 0.0 0.0 \n", + "2 None 0.0 0.0 0.0 \n", + "3 None 0.0 0.0 0.0 \n", + "4 None 0.0 0.0 0.0 \n", + "5 None 0.0 0.0 0.0 \n", + "6 None 0.0 0.0 0.0 \n", + "7 None 0.0 0.0 0.0 \n", + "8 None 0.0 0.0 0.0 \n", + "9 None 0.0 0.0 0.0 \n", + "\n", + " commuters_16_over dwellings_2_units family_households hispanic_any_race \\\n", + "0 233.0 13.0 183.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 71.0 0.0 13.0 71.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " in_grades_9_to_12 income_less_10000 income_per_capita pop_25_years_over \\\n", + "0 111.0 79.0 8747.0 564.0 \n", + "1 0.0 0.0 NaN 0.0 \n", + "2 0.0 0.0 NaN 0.0 \n", + "3 0.0 0.0 NaN 0.0 \n", + "4 0.0 0.0 NaN 0.0 \n", + "5 0.0 0.0 5896.0 58.0 \n", + "6 0.0 0.0 NaN 0.0 \n", + "7 0.0 0.0 17785.0 23.0 \n", + "8 0.0 0.0 NaN 0.0 \n", + "9 0.0 0.0 NaN 0.0 \n", + "\n", + " pop_never_married bachelors_degree_2 commute_10_14_mins \\\n", + "0 None 0.0 4.0 \n", + "1 None NaN 0.0 \n", + "2 None NaN 0.0 \n", + "3 None 0.0 0.0 \n", + "4 None 0.0 0.0 \n", + "5 None 0.0 0.0 \n", + "6 None 0.0 0.0 \n", + "7 None 0.0 0.0 \n", + "8 None 0.0 0.0 \n", + "9 None 0.0 0.0 \n", + "\n", + " commute_15_19_mins commute_20_24_mins commute_25_29_mins \\\n", + "0 59.0 108.0 10.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " commute_30_34_mins commute_35_39_mins commute_35_44_mins \\\n", + "0 20.0 0.0 7.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 36.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " commute_40_44_mins commute_45_59_mins commute_60_89_mins \\\n", + "0 7.0 13.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 35.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " female_85_and_over income_10000_14999 income_15000_19999 \\\n", + "0 38.0 105.0 44.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " income_20000_24999 income_25000_29999 income_30000_34999 \\\n", + "0 38.0 13.0 18.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " income_35000_39999 income_40000_44999 income_45000_49999 \\\n", + "0 11.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " income_50000_59999 income_60000_74999 income_75000_99999 \\\n", + "0 7.0 10.0 18.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 13.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " married_households not_in_labor_force not_us_citizen_pop \\\n", + "0 26.0 445.0 0.0 \n", + "1 0.0 0.0 NaN \n", + "2 0.0 0.0 NaN \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 74.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 71.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " pop_in_labor_force high_school_diploma hispanic_male_45_54 \\\n", + "0 304.0 188.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 6.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 71.0 23.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " hispanic_male_55_64 occupation_services workers_16_and_over \\\n", + "0 0.0 58.0 233.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 23.0 13.0 71.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " civilian_labor_force commute_60_more_mins commute_90_more_mins \\\n", + "0 304.0 5.0 5.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 71.0 35.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " commute_less_10_mins commuters_by_carpool employed_information \\\n", + "0 7.0 21.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " in_undergrad_college income_100000_124999 income_125000_149999 \\\n", + "0 63.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 29.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " income_150000_199999 male_male_households nonfamily_households \\\n", + "0 0.0 0.0 160.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " rent_over_50_percent vacant_housing_units commuters_drove_alone \\\n", + "0 38.0 122.0 141.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " employed_construction employed_retail_trade income_200000_or_more \\\n", + "0 24.0 19.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " less_one_year_college male_45_64_grade_9_12 one_year_more_college \\\n", + "0 22.0 18.0 85.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 18.0 0.0 4.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " rent_10_to_15_percent rent_15_to_20_percent rent_20_to_25_percent \\\n", + "0 11.0 42.0 39.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 13.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " rent_25_to_30_percent rent_30_to_35_percent rent_35_to_40_percent \\\n", + "0 33.0 47.0 9.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " rent_40_to_50_percent rent_under_10_percent sales_office_employed \\\n", + "0 41.0 0.0 30.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " speak_spanish_at_home two_or_more_races_pop dwellings_3_to_4_units \\\n", + "0 None 22.0 63.0 \n", + "1 None 0.0 0.0 \n", + "2 None 0.0 0.0 \n", + "3 None 0.0 0.0 \n", + "4 None 0.0 0.0 \n", + "5 None 0.0 0.0 \n", + "6 None 0.0 0.0 \n", + "7 None 0.0 13.0 \n", + "8 None 0.0 0.0 \n", + "9 None 0.0 0.0 \n", + "\n", + " dwellings_5_to_9_units employed_manufacturing male_45_64_high_school \\\n", + "0 69.0 16.0 41.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 23.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " occupied_housing_units male_45_64_some_college mortgaged_housing_units \\\n", + "0 343.0 10.0 20.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 13.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " occupation_sales_office population_3_years_over asian_including_hispanic \\\n", + "0 30.0 999.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 74.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 71.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " black_including_hispanic dwellings_10_to_19_units \\\n", + "0 1004.0 40.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 4.0 0.0 \n", + "6 0.0 0.0 \n", + "7 19.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " dwellings_20_to_49_units employed_wholesale_trade \\\n", + "0 25.0 2.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " female_female_households rent_burden_not_computed \\\n", + "0 0.0 18.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " white_including_hispanic high_school_including_ged \\\n", + "0 16.0 235.0 \n", + "1 0.0 NaN \n", + "2 0.0 NaN \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 70.0 18.0 \n", + "6 0.0 0.0 \n", + "7 52.0 23.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " commuters_by_car_truck_van dwellings_1_units_attached \\\n", + "0 162.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " dwellings_1_units_detached dwellings_50_or_more_units 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+ "3 0.0 \n", + "4 0.0 \n", + "5 0.0 \n", + "6 0.0 \n", + "7 0.0 \n", + "8 0.0 \n", + "9 0.0 \n", + "\n", + " two_parents_mother_in_labor_force_families_with_young_children \n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "5 0.0 \n", + "6 0.0 \n", + "7 0.0 \n", + "8 0.0 \n", + "9 0.0 " + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_o3YCUYnAJQw" + }, + "source": [ + "Just like we did in the previous section, we can use SQL queries to specify the bounding box or polygon we are interested in.\n", + "\n", + "- If you'd like to filter by bounding box, you need to use the SQL geography function `ST_IntersectsBox`.\n", + "- If you'd like to filter by polygon, you need to use the SQL geography function `ST_Intersects`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": 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                                                  0360470535002013-01-014053.01052.02072.01981.020.2554.0258.0120.079.061.00.051.0192.0185.067.049.057.016.031.0140.06.051.031.056.00.059.00.09.0466.0308.086.070.016.039.033.0226.0140.088.020.060.027.044.0139.013.051.053.036.00.052.014.00.03705.03854.03407.0NaNNaN2241.01494.01184.0NaNNaNNaNNaNNaN136.0197.00.0137.014.00.00.03745.0197.00.014.0137.03916.00.00.00.00.00.034.047.0163.027027.09140.034.0243.076.0113.092.0105.0118.052.021.035.069.041.035.018.00.00.047.04053.02042.00.38011110.01150.0312500.0740400.0905400.01052.0953.058.037.00.012.012.0138.0357.047.0101.0352.091.00.00.00.066.01939.0773.0170.0882.0633.00.00.01941.0126.01053.049.734.0456.0117.079.032.052.099.038.046.00.099.010.014.038.0112.01211.01064.0223.0732.0109.00.0147.00.00.0128.0140.0299.0235.0161.024.064.014.020.034.090.037.012.025.01084.0674.0101.0909.0797.0214.041.00.00.0NaN135.036.0275.064.099.0211.00.00.023.0611.083.071.0150.0486.0656.023.0110.096.0233.00.00.012.037.041.0104.050.0244.01196.055.01251.0990.01185.00.01251.00.0179.045.0301.034.011.058.0133.00.0225.066.077.067.0351.044.0110.0527.0164.0351.0527.0427.0170.0200.01462.0135.0NaNNaNNaN535.01588.147129710.3728POLYGON ((-73.96328 40.70773, -73.96485 40.707...
                                                  1360610113002013-01-01115.062.090.025.030.80.00.00.00.00.00.00.022.015.019.06.010.00.04.07.07.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.08.08.05.04.00.00.00.00.00.00.00.00.00.00.00.021.0115.0115.0NaNNaN115.093.093.0NaNNaNNaNNaNNaN37.026.065.00.00.00.03.021.026.065.00.00.0115.00.00.00.014.00.00.04.00.0110000.0108737.03.04.00.00.00.00.00.00.09.00.04.011.00.010.05.016.00.0115.028.00.567678.03021.0797800.0904400.01023400.062.033.016.00.00.00.00.00.03.05.04.08.058.00.00.05.00.02001.00.062.00.03.00.00.00.00.03021.022.50.03.04.00.00.08.03.011.00.04.029.08.012.05.019.00.00.00.00.00.00.00.00.00.05.05.027.021.015.014.04.00.00.00.00.00.00.00.086.052.04.069.054.08.00.00.00.0NaN34.00.00.00.034.00.032.03.048.00.07.03.00.019.06.048.058.010.010.00.04.00.07.00.00.07.018.090.06.096.019.090.00.096.00.013.04.04.020.07.00.00.00.04.027.011.00.067.00.04.016.03.067.016.00.00.00.08.00.0NaNNaNNaN113.01737.382175691.8375POLYGON ((-73.99163 40.75471, -73.98595 40.752...
                                                  2360810199002013-01-01830.0193.0652.0178.036.25.00.026.00.016.013.018.022.096.0102.057.089.051.062.025.010.05.026.09.00.011.05.04.00.013.031.08.00.00.03.016.027.05.020.00.047.00.00.04.00.00.04.00.00.00.00.0140.0830.0825.0NaNNaN755.0659.0600.0NaNNaNNaNNaNNaN103.0196.068.0426.00.00.00.0241.0224.068.08.0426.0404.06.00.026.09.032.023.049.08.038977.015956.08.013.00.012.012.023.033.00.05.05.013.046.00.015.00.08.021.0467.039.00.4302287.01309.0327100.0387500.0659700.0193.0164.094.013.00.00.027.041.0116.084.05.010.04.00.00.00.051.01939.066.073.0120.021.00.00.083.00.01196.026.813.045.00.011.05.023.05.038.020.04.029.00.012.0203.088.05.05.00.05.00.00.00.00.00.021.021.055.014.023.00.060.026.028.054.029.042.022.020.0298.043.00.0118.094.089.010.00.00.0NaN173.039.082.00.0134.082.0363.035.052.0169.027.04.093.0184.0264.052.056.04.0155.011.020.00.06.029.069.018.0153.0298.036.0334.0421.0298.00.0334.00.060.04.066.010.04.05.065.00.06.00.078.00.031.042.085.033.0107.031.033.018.039.034.0124.020.0NaNNaNNaN199.06528.5381679379.83729POLYGON ((-73.94216 40.73568, -73.94037 40.733...
                                                  \n", + "
                                                  " + ], + "text/plain": [ + " geoid do_date total_pop households male_pop female_pop \\\n", + "0 36047053500 2013-01-01 4053.0 1052.0 2072.0 1981.0 \n", + "1 36061011300 2013-01-01 115.0 62.0 90.0 25.0 \n", + "2 36081019900 2013-01-01 830.0 193.0 652.0 178.0 \n", + "\n", + " median_age male_under_5 male_5_to_9 male_10_to_14 male_15_to_17 \\\n", + "0 20.2 554.0 258.0 120.0 79.0 \n", + "1 30.8 0.0 0.0 0.0 0.0 \n", + "2 36.2 5.0 0.0 26.0 0.0 \n", + "\n", + " male_18_to_19 male_20 male_21 male_22_to_24 male_25_to_29 \\\n", + "0 61.0 0.0 51.0 192.0 185.0 \n", + "1 0.0 0.0 0.0 22.0 15.0 \n", + "2 16.0 13.0 18.0 22.0 96.0 \n", + "\n", + " male_30_to_34 male_35_to_39 male_40_to_44 male_45_to_49 male_50_to_54 \\\n", + "0 67.0 49.0 57.0 16.0 31.0 \n", + "1 19.0 6.0 10.0 0.0 4.0 \n", + "2 102.0 57.0 89.0 51.0 62.0 \n", + "\n", + " male_55_to_59 male_60_to_61 male_62_to_64 male_65_to_66 male_67_to_69 \\\n", + "0 140.0 6.0 51.0 31.0 56.0 \n", + "1 7.0 7.0 0.0 0.0 0.0 \n", + "2 25.0 10.0 5.0 26.0 9.0 \n", + "\n", + " male_70_to_74 male_75_to_79 male_80_to_84 male_85_and_over \\\n", + "0 0.0 59.0 0.0 9.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 11.0 5.0 4.0 \n", + "\n", + " female_under_5 female_5_to_9 female_10_to_14 female_15_to_17 \\\n", + "0 466.0 308.0 86.0 70.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 13.0 31.0 8.0 \n", + "\n", + " female_18_to_19 female_20 female_21 female_22_to_24 female_25_to_29 \\\n", + "0 16.0 39.0 33.0 226.0 140.0 \n", + "1 0.0 0.0 0.0 0.0 8.0 \n", + "2 0.0 0.0 3.0 16.0 27.0 \n", + "\n", + " female_30_to_34 female_35_to_39 female_40_to_44 female_45_to_49 \\\n", + "0 88.0 20.0 60.0 27.0 \n", + "1 8.0 5.0 4.0 0.0 \n", + "2 5.0 20.0 0.0 47.0 \n", + "\n", + " female_50_to_54 female_55_to_59 female_60_to_61 female_62_to_64 \\\n", + "0 44.0 139.0 13.0 51.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 4.0 0.0 \n", + "\n", + " female_65_to_66 female_67_to_69 female_70_to_74 female_75_to_79 \\\n", + "0 53.0 36.0 0.0 52.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 4.0 0.0 0.0 \n", + "\n", + " female_80_to_84 female_85_and_over white_pop population_1_year_and_over \\\n", + "0 14.0 0.0 3705.0 3854.0 \n", + "1 0.0 0.0 21.0 115.0 \n", + "2 0.0 0.0 140.0 830.0 \n", + "\n", + " population_3_years_over pop_5_years_over pop_15_and_over pop_16_over \\\n", + "0 3407.0 NaN NaN 2241.0 \n", + "1 115.0 NaN NaN 115.0 \n", + "2 825.0 NaN NaN 755.0 \n", + "\n", + " pop_25_years_over pop_25_64 pop_never_married pop_now_married \\\n", + "0 1494.0 1184.0 NaN NaN \n", + "1 93.0 93.0 NaN NaN \n", + "2 659.0 600.0 NaN NaN \n", + "\n", + " pop_separated pop_widowed pop_divorced not_us_citizen_pop black_pop \\\n", + "0 NaN NaN NaN 136.0 197.0 \n", + "1 NaN NaN NaN 37.0 26.0 \n", + "2 NaN NaN NaN 103.0 196.0 \n", + "\n", + " asian_pop hispanic_pop amerindian_pop other_race_pop \\\n", + "0 0.0 137.0 14.0 0.0 \n", + "1 65.0 0.0 0.0 0.0 \n", + "2 68.0 426.0 0.0 0.0 \n", + "\n", + " two_or_more_races_pop white_including_hispanic black_including_hispanic \\\n", + "0 0.0 3745.0 197.0 \n", + "1 3.0 21.0 26.0 \n", + "2 0.0 241.0 224.0 \n", + "\n", + " asian_including_hispanic amerindian_including_hispanic hispanic_any_race \\\n", + "0 0.0 14.0 137.0 \n", + "1 65.0 0.0 0.0 \n", + "2 68.0 8.0 426.0 \n", + "\n", + " not_hispanic_pop asian_male_45_54 asian_male_55_64 black_male_45_54 \\\n", + "0 3916.0 0.0 0.0 0.0 \n", + "1 115.0 0.0 0.0 0.0 \n", + "2 404.0 6.0 0.0 26.0 \n", + "\n", + " black_male_55_64 hispanic_male_45_54 hispanic_male_55_64 \\\n", + "0 0.0 0.0 34.0 \n", + "1 14.0 0.0 0.0 \n", + "2 9.0 32.0 23.0 \n", + "\n", + " white_male_45_54 white_male_55_64 median_income income_per_capita \\\n", + "0 47.0 163.0 27027.0 9140.0 \n", + "1 4.0 0.0 110000.0 108737.0 \n", + "2 49.0 8.0 38977.0 15956.0 \n", + "\n", + " income_less_10000 income_10000_14999 income_15000_19999 \\\n", + "0 34.0 243.0 76.0 \n", + "1 3.0 4.0 0.0 \n", + "2 8.0 13.0 0.0 \n", + "\n", + " income_20000_24999 income_25000_29999 income_30000_34999 \\\n", + "0 113.0 92.0 105.0 \n", + "1 0.0 0.0 0.0 \n", + "2 12.0 12.0 23.0 \n", + "\n", + " income_35000_39999 income_40000_44999 income_45000_49999 \\\n", + "0 118.0 52.0 21.0 \n", + "1 0.0 0.0 9.0 \n", + "2 33.0 0.0 5.0 \n", + "\n", + " income_50000_59999 income_60000_74999 income_75000_99999 \\\n", + "0 35.0 69.0 41.0 \n", + "1 0.0 4.0 11.0 \n", + "2 5.0 13.0 46.0 \n", + "\n", + " income_100000_124999 income_125000_149999 income_150000_199999 \\\n", + "0 35.0 18.0 0.0 \n", + "1 0.0 10.0 5.0 \n", + "2 0.0 15.0 0.0 \n", + "\n", + " income_200000_or_more households_retirement_income \\\n", + "0 0.0 47.0 \n", + "1 16.0 0.0 \n", + "2 8.0 21.0 \n", + "\n", + " pop_determined_poverty_status poverty gini_index housing_units \\\n", + "0 4053.0 2042.0 0.3801 1110.0 \n", + "1 115.0 28.0 0.5676 78.0 \n", + "2 467.0 39.0 0.4302 287.0 \n", + "\n", + " renter_occupied_housing_units_paying_cash_median_gross_rent \\\n", + "0 1150.0 \n", + "1 3021.0 \n", + "2 1309.0 \n", + "\n", + " owner_occupied_housing_units_lower_value_quartile \\\n", + "0 312500.0 \n", + "1 797800.0 \n", + "2 327100.0 \n", + "\n", + " owner_occupied_housing_units_median_value \\\n", + "0 740400.0 \n", + "1 904400.0 \n", + "2 387500.0 \n", + "\n", + " owner_occupied_housing_units_upper_value_quartile occupied_housing_units \\\n", + "0 905400.0 1052.0 \n", + "1 1023400.0 62.0 \n", + "2 659700.0 193.0 \n", + "\n", + " housing_units_renter_occupied vacant_housing_units \\\n", + "0 953.0 58.0 \n", + "1 33.0 16.0 \n", + "2 164.0 94.0 \n", + "\n", + " vacant_housing_units_for_rent vacant_housing_units_for_sale \\\n", + "0 37.0 0.0 \n", + "1 0.0 0.0 \n", + "2 13.0 0.0 \n", + "\n", + " dwellings_1_units_detached dwellings_1_units_attached dwellings_2_units \\\n", + "0 12.0 12.0 138.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 27.0 41.0 \n", + "\n", + " dwellings_3_to_4_units dwellings_5_to_9_units dwellings_10_to_19_units \\\n", + "0 357.0 47.0 101.0 \n", + "1 3.0 5.0 4.0 \n", + "2 116.0 84.0 5.0 \n", + "\n", + " dwellings_20_to_49_units dwellings_50_or_more_units mobile_homes \\\n", + "0 352.0 91.0 0.0 \n", + "1 8.0 58.0 0.0 \n", + "2 10.0 4.0 0.0 \n", + "\n", + " housing_built_2005_or_later housing_built_2000_to_2004 \\\n", + "0 0.0 0.0 \n", + "1 0.0 5.0 \n", + "2 0.0 0.0 \n", + "\n", + " housing_built_1939_or_earlier median_year_structure_built \\\n", + "0 66.0 1939.0 \n", + "1 0.0 2001.0 \n", + "2 51.0 1939.0 \n", + "\n", + " married_households nonfamily_households family_households \\\n", + "0 773.0 170.0 882.0 \n", + "1 0.0 62.0 0.0 \n", + "2 66.0 73.0 120.0 \n", + "\n", + " households_public_asst_or_food_stamps male_male_households \\\n", + "0 633.0 0.0 \n", + "1 3.0 0.0 \n", + "2 21.0 0.0 \n", + "\n", + " female_female_households children children_in_single_female_hh \\\n", + "0 0.0 1941.0 126.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 83.0 0.0 \n", + "\n", + " median_rent percent_income_spent_on_rent rent_burden_not_computed \\\n", + "0 1053.0 49.7 34.0 \n", + "1 3021.0 22.5 0.0 \n", + "2 1196.0 26.8 13.0 \n", + "\n", + " rent_over_50_percent rent_40_to_50_percent rent_35_to_40_percent \\\n", + "0 456.0 117.0 79.0 \n", + "1 3.0 4.0 0.0 \n", + "2 45.0 0.0 11.0 \n", + "\n", + " rent_30_to_35_percent rent_25_to_30_percent rent_20_to_25_percent \\\n", + "0 32.0 52.0 99.0 \n", + "1 0.0 8.0 3.0 \n", + "2 5.0 23.0 5.0 \n", + "\n", + " rent_15_to_20_percent rent_10_to_15_percent rent_under_10_percent \\\n", + "0 38.0 46.0 0.0 \n", + "1 11.0 0.0 4.0 \n", + "2 38.0 20.0 4.0 \n", + "\n", + " owner_occupied_housing_units million_dollar_housing_units \\\n", + "0 99.0 10.0 \n", + "1 29.0 8.0 \n", + "2 29.0 0.0 \n", + "\n", + " mortgaged_housing_units different_house_year_ago_different_city \\\n", + "0 14.0 38.0 \n", + "1 12.0 5.0 \n", + "2 12.0 203.0 \n", + "\n", + " different_house_year_ago_same_city families_with_young_children \\\n", + "0 112.0 1211.0 \n", + "1 19.0 0.0 \n", + "2 88.0 5.0 \n", + "\n", + " two_parent_families_with_young_children \\\n", + "0 1064.0 \n", + "1 0.0 \n", + "2 5.0 \n", + "\n", + " two_parents_in_labor_force_families_with_young_children \\\n", + "0 223.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "\n", + " two_parents_father_in_labor_force_families_with_young_children \\\n", + "0 732.0 \n", + "1 0.0 \n", + "2 5.0 \n", + "\n", + " two_parents_mother_in_labor_force_families_with_young_children \\\n", + "0 109.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "\n", + " two_parents_not_in_labor_force_families_with_young_children \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "\n", + " one_parent_families_with_young_children \\\n", + "0 147.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "\n", + " father_one_parent_families_with_young_children \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "\n", + " father_in_labor_force_one_parent_families_with_young_children \\\n", + "0 0.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "\n", + " commute_5_9_mins commute_less_10_mins commute_10_14_mins \\\n", + "0 128.0 140.0 299.0 \n", + "1 5.0 5.0 27.0 \n", + "2 21.0 21.0 55.0 \n", + "\n", + " commute_15_19_mins commute_20_24_mins commute_25_29_mins \\\n", + "0 235.0 161.0 24.0 \n", + "1 21.0 15.0 14.0 \n", + "2 14.0 23.0 0.0 \n", + "\n", + " commute_30_34_mins commute_35_39_mins commute_40_44_mins \\\n", + "0 64.0 14.0 20.0 \n", + "1 4.0 0.0 0.0 \n", + "2 60.0 26.0 28.0 \n", + "\n", + " commute_35_44_mins commute_45_59_mins commute_60_more_mins \\\n", + "0 34.0 90.0 37.0 \n", + "1 0.0 0.0 0.0 \n", + "2 54.0 29.0 42.0 \n", + "\n", + " commute_60_89_mins commute_90_more_mins commuters_16_over \\\n", + "0 12.0 25.0 1084.0 \n", + "1 0.0 0.0 86.0 \n", + "2 22.0 20.0 298.0 \n", + "\n", + " walked_to_work worked_at_home no_car no_cars one_car two_cars \\\n", + "0 674.0 101.0 909.0 797.0 214.0 41.0 \n", + "1 52.0 4.0 69.0 54.0 8.0 0.0 \n", + "2 43.0 0.0 118.0 94.0 89.0 10.0 \n", + "\n", + " three_cars four_more_cars aggregate_travel_time_to_work \\\n", + "0 0.0 0.0 NaN \n", + "1 0.0 0.0 NaN \n", + "2 0.0 0.0 NaN \n", + "\n", + " commuters_by_public_transportation commuters_by_bus \\\n", + "0 135.0 36.0 \n", + "1 34.0 0.0 \n", + "2 173.0 39.0 \n", + "\n", + " commuters_by_car_truck_van commuters_by_carpool \\\n", + "0 275.0 64.0 \n", + "1 0.0 0.0 \n", + "2 82.0 0.0 \n", + "\n", + " commuters_by_subway_or_elevated commuters_drove_alone group_quarters \\\n", + "0 99.0 211.0 0.0 \n", + "1 34.0 0.0 32.0 \n", + "2 134.0 82.0 363.0 \n", + "\n", + " associates_degree bachelors_degree high_school_diploma \\\n", + "0 0.0 23.0 611.0 \n", + "1 3.0 48.0 0.0 \n", + "2 35.0 52.0 169.0 \n", + "\n", + " less_one_year_college masters_degree one_year_more_college \\\n", + "0 83.0 71.0 150.0 \n", + "1 7.0 3.0 0.0 \n", + "2 27.0 4.0 93.0 \n", + "\n", + " less_than_high_school_graduate high_school_including_ged \\\n", + "0 486.0 656.0 \n", + "1 19.0 6.0 \n", + "2 184.0 264.0 \n", + "\n", + " bachelors_degree_2 bachelors_degree_or_higher_25_64 \\\n", + "0 23.0 110.0 \n", + "1 48.0 58.0 \n", + "2 52.0 56.0 \n", + "\n", + " graduate_professional_degree some_college_and_associates_degree \\\n", + "0 96.0 233.0 \n", + "1 10.0 10.0 \n", + "2 4.0 155.0 \n", + "\n", + " male_45_64_associates_degree male_45_64_bachelors_degree \\\n", + "0 0.0 0.0 \n", + "1 0.0 4.0 \n", + "2 11.0 20.0 \n", + "\n", + " male_45_64_graduate_degree male_45_64_less_than_9_grade \\\n", + "0 12.0 37.0 \n", + "1 0.0 7.0 \n", + "2 0.0 6.0 \n", + "\n", + " male_45_64_grade_9_12 male_45_64_high_school male_45_64_some_college \\\n", + "0 41.0 104.0 50.0 \n", + "1 0.0 0.0 7.0 \n", + "2 29.0 69.0 18.0 \n", + "\n", + " male_45_to_64 employed_pop unemployed_pop pop_in_labor_force \\\n", + "0 244.0 1196.0 55.0 1251.0 \n", + "1 18.0 90.0 6.0 96.0 \n", + "2 153.0 298.0 36.0 334.0 \n", + "\n", + " not_in_labor_force workers_16_and_over armed_forces \\\n", + "0 990.0 1185.0 0.0 \n", + "1 19.0 90.0 0.0 \n", + "2 421.0 298.0 0.0 \n", + "\n", + " civilian_labor_force employed_agriculture_forestry_fishing_hunting_mining \\\n", + "0 1251.0 0.0 \n", + "1 96.0 0.0 \n", + "2 334.0 0.0 \n", + "\n", + " employed_arts_entertainment_recreation_accommodation_food \\\n", + "0 179.0 \n", + "1 13.0 \n", + "2 60.0 \n", + "\n", + " employed_construction employed_education_health_social \\\n", + "0 45.0 301.0 \n", + "1 4.0 4.0 \n", + "2 4.0 66.0 \n", + "\n", + " employed_finance_insurance_real_estate employed_information \\\n", + "0 34.0 11.0 \n", + "1 20.0 7.0 \n", + "2 10.0 4.0 \n", + "\n", + " employed_manufacturing employed_other_services_not_public_admin \\\n", + "0 58.0 133.0 \n", + "1 0.0 0.0 \n", + "2 5.0 65.0 \n", + "\n", + " employed_public_administration employed_retail_trade \\\n", + "0 0.0 225.0 \n", + "1 0.0 4.0 \n", + "2 0.0 6.0 \n", + "\n", + " employed_science_management_admin_waste \\\n", + "0 66.0 \n", + "1 27.0 \n", + "2 0.0 \n", + "\n", + " employed_transportation_warehousing_utilities employed_wholesale_trade \\\n", + "0 77.0 67.0 \n", + "1 11.0 0.0 \n", + "2 78.0 0.0 \n", + "\n", + " occupation_management_arts \\\n", + "0 351.0 \n", + "1 67.0 \n", + "2 31.0 \n", + "\n", + " occupation_natural_resources_construction_maintenance \\\n", + "0 44.0 \n", + "1 0.0 \n", + "2 42.0 \n", + "\n", + " occupation_production_transportation_material occupation_sales_office \\\n", + "0 110.0 527.0 \n", + "1 4.0 16.0 \n", + "2 85.0 33.0 \n", + "\n", + " occupation_services management_business_sci_arts_employed \\\n", + "0 164.0 351.0 \n", + "1 3.0 67.0 \n", + "2 107.0 31.0 \n", + "\n", + " sales_office_employed in_grades_1_to_4 in_grades_5_to_8 \\\n", + "0 527.0 427.0 170.0 \n", + "1 16.0 0.0 0.0 \n", + "2 33.0 18.0 39.0 \n", + "\n", + " in_grades_9_to_12 in_school in_undergrad_college \\\n", + "0 200.0 1462.0 135.0 \n", + "1 0.0 8.0 0.0 \n", + "2 34.0 124.0 20.0 \n", + "\n", + " speak_only_english_at_home speak_spanish_at_home \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "\n", + " speak_spanish_at_home_low_english do_label do_perimeter do_area \\\n", + "0 NaN 535.0 1588.147 129710.372 \n", + "1 NaN 113.0 1737.382 175691.837 \n", + "2 NaN 199.0 6528.538 1679379.837 \n", + "\n", + " do_num_vertices geom \n", + "0 8 POLYGON ((-73.96328 40.70773, -73.96485 40.707... \n", + "1 5 POLYGON ((-73.99163 40.75471, -73.98595 40.752... \n", + "2 29 POLYGON ((-73.94216 40.73568, -73.94037 40.733... " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sql_query = \"SELECT * FROM $dataset$ WHERE ST_IntersectsBox(geom, -74.044467,40.706128,-73.891345,40.837690)\"\n", + "ct_demog_df = dataset.to_dataframe(sql_query=sql_query)\n", + "ct_demog_df.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "I85IxaWNASib" + }, + "source": [ + "##### Visualize dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "jdpXmibP34Yx", + "outputId": "e8276b0c-a1d8-4625-9e84-0ce14fcbacd3" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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How to explore the data catalog. Data discovery.\n", + "\n", + "This notebook shows how to use CARTOframes to discover and explore datasets from CARTO's [Data Observatory](https://carto.com/spatial-data-catalog/).\n", + "\n", + "If you haven't installed CARTOframes yet, please visit our [installation guide](https://carto.com/developers/cartoframes/guides/Installation/).\n", + "\n", + "The notebook is organized in the following sections:\n", + "\n", + "0. [Setup](#section0)\n", + "\n", + " 0.1. [Import packages](#section0.1)\n", + " \n", + " 0.2. [Set CARTO default credentials](#section0.2)\n", + "\n", + "\n", + "1. [Data discovery](#section1)\n", + " \n", + " 1.1. [Data catalog structure](#section1.1)\n", + " \n", + " 1.2. [Combining filters](#section1.2)\n", + " \n", + " 1.3. [Filter datasets by the type of geography](#section1.3)\n", + " \n", + " 1.4. [Get a first glimpse of a dataset](#section1.3)\n", + " \n", + " \n", + "**Want to learn more?** Learn how to access and download datasets on the following notebooks Access Public Data and Access Premium Data." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "V4-0hJjiVsen" + }, + "source": [ + "\n", + "### 0. Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 0.1. Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "6vLRH4fEEkms" + }, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import pandas as pd\n", + "pd.set_option('display.max_columns', None)\n", + "\n", + "from cartoframes.auth import set_default_credentials\n", + "from cartoframes.data.observatory import *" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 0.2. Set CARTO default credentials\n", + "\n", + "In order to be able to use the Data Observatory via CARTOframes, you need to set your CARTO account credentials first.\n", + "\n", + "Please, visit the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/) for further detail." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "id": "2E7lg3CDV2ge" + }, + "outputs": [], + "source": [ + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note about credentials**\n", + "\n", + "For security reasons, we recommend storing your credentials in an external file to prevent publishing them by accident when sharing your notebooks. You can get more information in the section _Setting your credentials_ of the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uwAtCjyeV3QI" + }, + "source": [ + "\n", + "### 1. Data Discovery\n", + "\n", + "CARTO's data catalog consists of a set of datasets organized by:\n", + " - Country\n", + " - Category\n", + " - Provider\n", + " - Geography\n", + " \n", + "In addition, datasets are classified in public and premium data.\n", + "\n", + "This classification can be used to explore the Catalog and narrow down your search for the dataset most suitable for your analysis.\n", + "\n", + "For example, you may start exploring by country, then filter by category and finally select a dataset based on the provider. Alternatively, you could start exploring by category or provider." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "#### 1.1. Data catalog structure\n", + "\n", + "In this subsection we explore each of the four classification categories.\n", + "\n", + "**Note** results can be displayed in Python list format or in Pandas DataFrame format." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 1.1.1. Countries" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "bowL1MIBgLbX", + "outputId": "6f897e48-0238-48bc-f9b9-12b7c0c87d6f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                    3mbi_parishes_309be8bcParishes - JamaicaMBI Digital Boundaries for Jamaica at Parishes...jammbiMULTIPOLYGONNoneyearlyFalseeng2020Michael Bauer Internationalcarto-do.mbi.geography_jam_parishes_2020
                                                    4tigr_county_c781b68eCounty - United States of America (2017)County TIGER/Line Shapefiles from the United S...usausa_tigerMULTIPOLYGONNoneNoneTrueeng2017Tiger/Line geographic data from the U.S. Censu...carto-do-public-data.usa_tiger.geography_usa_c...
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                                                    " + ], + "text/plain": [ + " slug \\\n", + "0 ne_ports_e4ee35c9 \n", + "1 ne_parksandpro_dc9d5ce5 \n", + "2 mbi_small_areas_f4ab4f8e \n", + "3 mbi_parishes_309be8bc \n", + "4 tigr_county_c781b68e \n", + "\n", + " name \\\n", + "0 Ports - Global \n", + "1 Parks And Protected Lands Area - United States... \n", + "2 Small Areas - South Africa \n", + "3 Parishes - Jamaica \n", + "4 County - United States of America (2017) \n", + "\n", + " description country_id \\\n", + "0 Derives from High Seas, a detailed GIS compila... glo \n", + "1 Data includes the 398 authorized National Park... usa \n", + "2 MBI Digital Boundaries for South Africa at Sma... zaf \n", + "3 MBI Digital Boundaries for Jamaica at Parishes... jam \n", + "4 County TIGER/Line Shapefiles from the United S... usa \n", + "\n", + " provider_id geom_type geom_coverage update_frequency is_public_data \\\n", + "0 natural_earth POINT None None True \n", + "1 natural_earth MULTIPOLYGON None None True \n", + "2 mbi MULTIPOLYGON None yearly False \n", + "3 mbi MULTIPOLYGON None yearly False \n", + "4 usa_tiger MULTIPOLYGON None None True \n", + "\n", + " lang version provider_name \\\n", + "0 eng 410 Natural Earth \n", + "1 eng 410 Natural Earth \n", + "2 eng 2020 Michael Bauer International \n", + "3 eng 2020 Michael Bauer International \n", + "4 eng 2017 Tiger/Line geographic data from the U.S. Censu... \n", + "\n", + " id \n", + "0 carto-do-public-data.natural_earth.geography_g... \n", + "1 carto-do-public-data.natural_earth.geography_u... \n", + "2 carto-do.mbi.geography_zaf_smallareas_2020 \n", + "3 carto-do.mbi.geography_jam_parishes_2020 \n", + "4 carto-do-public-data.usa_tiger.geography_usa_c... " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Catalog().geographies.to_dataframe().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "asbQxIHQi-9Z" + }, + "source": [ + "\n", + "#### 1.2. Combining filters\n", + "Let's now take a look at how to combine category and provider filters to get the public demographics datasets available in the US from ACS at the census tract level." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Categories available in the US." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 194 + }, + "id": "soKtHxhso5_l", + "outputId": "84a21fad-7a99-4548-aa7c-57f6152de6c2" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can find more entities with the Global country filter. To apply that filter run:\n", + "\tCatalog().country('glo')\n" + ] + }, + { + "data": { + "text/html": [ + "
                                                    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
                                                    idname
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                                                    2demographicsDemographics
                                                    3derivedDerived
                                                    4environmentalEnvironmental
                                                    5financialFinancial
                                                    6housingHousing
                                                    7human_mobilityHuman Mobility
                                                    8points_of_interestPoints of Interest
                                                    9road_trafficRoad Traffic
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                                                    " + ], + "text/plain": [ + " id name\n", + "0 behavioral Behavioral\n", + "1 covid19 Covid-19\n", + "2 demographics Demographics\n", + "3 derived Derived\n", + "4 environmental Environmental\n", + "5 financial Financial\n", + "6 housing Housing\n", + "7 human_mobility Human Mobility\n", + "8 points_of_interest Points of Interest\n", + "9 road_traffic Road Traffic" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Catalog().country('usa').categories.to_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Providers available in the US." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 301 + }, + "id": "FNlJMzn3kfIK", + "outputId": "46cd0f10-2dad-4e1c-f84a-77b368e96c75" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can find more entities with the Global country filter. To apply that filter run:\n", + "\tCatalog().country('glo')\n" + ] + }, + { + "data": { + "text/html": [ + "
                                                    \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
                                                    idname
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                                                    " + ], + "text/plain": [ + " id name\n", + "0 lifesight Lifesight\n", + "1 tomtom TomTom\n", + "2 home_junction Home Junction\n", + "3 mastercard Mastercard\n", + "4 usa_bls Bureau of Labor Statistics" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Catalog().country('usa').providers.to_dataframe().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "List of providers available in the US offering demographics data." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 105 + }, + "id": "0TGBWgPwkjDY", + "outputId": "145812ec-eeca-4ca3-d39c-05e35484f11a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can find more entities with the Global country filter. To apply that filter run:\n", + "\tCatalog().country('glo')\n" + ] + }, + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Catalog().country('usa').category('demographics').providers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "List of providers available in the US offering **public** demographics data." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can find more entities with the Global country filter. To apply that filter run:\n", + "\tCatalog().country('glo')\n" + ] + }, + { + "data": { + "text/plain": [ + "[,\n", + " ,\n", + " ,\n", + " ]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Catalog().country('usa').category('demographics').public().providers" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now take a look at all the demographics datasets provided by ACS in the US." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 926 + }, + "id": "txVEvD6ylQoT", + "outputId": "1007d1d4-b67e-430e-a511-05e47669c300" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can find more entities with the Global country filter. To apply that filter run:\n", + "\tCatalog().country('glo')" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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                                                    slugnamedescriptioncategory_idcountry_iddata_source_idprovider_idgeography_namegeography_descriptiontemporal_aggregationtime_coverageupdate_frequencyis_public_datalangversioncategory_nameprovider_namegeography_idid
                                                    0acs_sociodemogr_a0c48b07Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCounty - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...yearly[2007-01-01, 2008-01-01)yearlyTrueeng2007DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_count...carto-do-public-data.usa_acs.demographics_soci...
                                                    1acs_sociodemogr_a03fb95fSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCongressional District - United States of Amer...Shoreline clipped TIGER/Line boundaries. More ...yearly[2017-01-01, 2018-01-01)yearlyTrueeng2017DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_congr...carto-do-public-data.usa_acs.demographics_soci...
                                                    2acs_sociodemogr_e7b702b0Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCore-based Statistical Area - United States of...Shoreline clipped TIGER/Line boundaries. More ...3yrs[2006-01-01, 2009-01-01)yearlyTrueeng20062008DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_cbsa_...carto-do-public-data.usa_acs.demographics_soci...
                                                    3acs_sociodemogr_e1e92d8dSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCore-based Statistical Area - United States of...Shoreline clipped TIGER/Line boundaries. More ...yearly[2013-01-01, 2014-01-01)yearlyTrueeng2013DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_cbsa_...carto-do-public-data.usa_acs.demographics_soci...
                                                    4acs_sociodemogr_30a865f1Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCore-based Statistical Area - United States of...Shoreline clipped TIGER/Line boundaries. More ...3yrs[2005-01-01, 2008-01-01)yearlyTrueeng20052007DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_cbsa_...carto-do-public-data.usa_acs.demographics_soci...
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                                                    " + ], + "text/plain": [ + " slug \\\n", + "0 acs_sociodemogr_a0c48b07 \n", + "1 acs_sociodemogr_a03fb95f \n", + "2 acs_sociodemogr_e7b702b0 \n", + "3 acs_sociodemogr_e1e92d8d \n", + "4 acs_sociodemogr_30a865f1 \n", + "\n", + " name \\\n", + "0 Sociodemographics - United States of America (... \n", + "1 Sociodemographics - United States of America (... \n", + "2 Sociodemographics - United States of America (... \n", + "3 Sociodemographics - United States of America (... \n", + "4 Sociodemographics - United States of America (... \n", + "\n", + " description category_id country_id \\\n", + "0 The American Community Survey (ACS) is an ongo... demographics usa \n", + "1 The American Community Survey (ACS) is an ongo... demographics usa \n", + "2 The American Community Survey (ACS) is an ongo... demographics usa \n", + "3 The American Community Survey (ACS) is an ongo... demographics usa \n", + "4 The American Community Survey (ACS) is an ongo... demographics usa \n", + "\n", + " data_source_id provider_id \\\n", + "0 sociodemographics usa_acs \n", + "1 sociodemographics usa_acs \n", + "2 sociodemographics usa_acs \n", + "3 sociodemographics usa_acs \n", + "4 sociodemographics usa_acs \n", + "\n", + " geography_name \\\n", + "0 County - United States of America (2015) \n", + "1 Congressional District - United States of Amer... \n", + "2 Core-based Statistical Area - United States of... \n", + "3 Core-based Statistical Area - United States of... \n", + "4 Core-based Statistical Area - United States of... \n", + "\n", + " geography_description temporal_aggregation \\\n", + "0 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", + "1 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", + "2 Shoreline clipped TIGER/Line boundaries. More ... 3yrs \n", + "3 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", + "4 Shoreline clipped TIGER/Line boundaries. More ... 3yrs \n", + "\n", + " time_coverage update_frequency is_public_data lang version \\\n", + "0 [2007-01-01, 2008-01-01) yearly True eng 2007 \n", + "1 [2017-01-01, 2018-01-01) yearly True eng 2017 \n", + "2 [2006-01-01, 2009-01-01) yearly True eng 20062008 \n", + "3 [2013-01-01, 2014-01-01) yearly True eng 2013 \n", + "4 [2005-01-01, 2008-01-01) yearly True eng 20052007 \n", + "\n", + " category_name provider_name \\\n", + "0 Demographics American Community Survey \n", + "1 Demographics American Community Survey \n", + "2 Demographics American Community Survey \n", + "3 Demographics American Community Survey \n", + "4 Demographics American Community Survey \n", + "\n", + " geography_id \\\n", + "0 carto-do-public-data.carto.geography_usa_count... \n", + "1 carto-do-public-data.carto.geography_usa_congr... \n", + "2 carto-do-public-data.carto.geography_usa_cbsa_... \n", + "3 carto-do-public-data.carto.geography_usa_cbsa_... \n", + "4 carto-do-public-data.carto.geography_usa_cbsa_... \n", + "\n", + " id \n", + "0 carto-do-public-data.usa_acs.demographics_soci... \n", + "1 carto-do-public-data.usa_acs.demographics_soci... \n", + "2 carto-do-public-data.usa_acs.demographics_soci... \n", + "3 carto-do-public-data.usa_acs.demographics_soci... \n", + "4 carto-do-public-data.usa_acs.demographics_soci... " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Catalog().country('usa').category('demographics').provider('usa_acs').datasets.to_dataframe().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KQv9beU-16bi" + }, + "source": [ + "\n", + "#### 1.3. Filter datasets by the type of geography\n", + "\n", + "We can explore the types of geographies for which the datasets are available. In order to filter by a specific type of geography, we have to apply a filter to the geography_name column just like we would for a string column on a Pandas DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can find more entities with the Global country filter. To apply that filter run:\n", + "\tCatalog().country('glo')\n" + ] + } + ], + "source": [ + "datasets_acs_df = Catalog().country('usa').category('demographics').provider('usa_acs').datasets.to_dataframe()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 247 + }, + "id": "vI6u6tJ9q4AG", + "outputId": "3d1dbc5d-5c04-43fa-c0b2-08fc1765f626" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['County - United States of America (2015)',\n", + " 'Congressional District - United States of America (2019)',\n", + " 'Core-based Statistical Area - United States of America (2019)',\n", + " 'Census Tract - United States of America (2015)',\n", + " 'Public Use Microdata Area - United States of America (2019)',\n", + " 'Census Place - United States of America (2019)',\n", + " 'State - United States of America (2015)',\n", + " 'Census Block Group - United States of America (2015)',\n", + " '5-digit Zip Code Tabulation Area - United States of America (2015)',\n", + " 'School District (secondary) - United States of America (2019)',\n", + " 'School District (elementary) - United States of America (2019)',\n", + " 'School District (unified) - United States of America (2019)'],\n", + " dtype=object)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "datasets_acs_df['geography_name'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 707 + }, + "id": "bMin3NRkrBOz", + "outputId": "e79d6841-3ed1-4912-e34a-8aaea416b2ef" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                    slugnamedescriptioncategory_idcountry_iddata_source_idprovider_idgeography_namegeography_descriptiontemporal_aggregationtime_coverageupdate_frequencyis_public_datalangversioncategory_nameprovider_namegeography_idid
                                                    8acs_sociodemogr_30d1f53Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2012-01-01, 2017-01-01)yearlyTrueeng20122016DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                    9acs_sociodemogr_cfeb0968Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2009-01-01, 2014-01-01)yearlyTrueeng20092013DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                    35acs_sociodemogr_9ed5d625Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2007-01-01, 2012-01-01)yearlyTrueeng20072011DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                    41acs_sociodemogr_496a0675Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2013-01-01, 2018-01-01)yearlyTrueeng20132017DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                    42acs_sociodemogr_d4b2cf03Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2006-01-01, 2011-01-01)yearlyTrueeng20062010DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                    169acs_sociodemogr_97c32d1fSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2010-01-01, 2015-01-01)yearlyTrueeng20102014DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                    201acs_sociodemogr_dda43439Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2011-01-01, 2016-01-01)yearlyTrueeng20112015DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                    231acs_sociodemogr_6bf5c7f4Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrsNoneyearlyTrueeng20142018DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                    246acs_sociodemogr_858c104eSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of America (2015)Shoreline clipped TIGER/Line boundaries. More ...5yrs[2008-01-01, 2013-01-01)yearlyTrueeng20082012DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
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" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "datasets_acs_df[datasets_acs_df['geography_name'].str.contains('Census Tract')]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZEzr-AfG6uSM" + }, + "source": [ + "\n", + "#### 1.4. Get a first glimpse of a dataset\n", + "\n", + "We select the dataset acs_sociodemogr_496a0675 from the list of datasets above because it is the one with the latest data update.\n", + "\n", + "CARTOframes allows you to get a first glimpse of the dataset so that you can make sure it's the right dataset for your analysis. This includes:\n", + " - Information about the dataset. This includes a description, provider, temporal aggregation, if it is public or premium, etc.\n", + " - Information about its variables. This includes a name, description, aggregation method, etc.\n", + " - Access to the first 10 rows of the dataset.\n", + " - A statistical description of all numerical variables, just like the `describe()` function in Pandas.\n", + " - A map with the geometric coverage of the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "ty7maPPM66kD" + }, + "outputs": [], + "source": [ + "sample_ds = Dataset.get('acs_sociodemogr_496a0675')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 1.4.1. Information about the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 374 + }, + "id": "A7DIGyXj9Otw", + "outputId": "43fcb060-0f9a-42e4-c3b4-4c44cdc152fe" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'slug': 'acs_sociodemogr_496a0675',\n", + " 'name': 'Sociodemographics - United States of America (Census Tract, 2017, 5yrs)',\n", + " 'description': 'The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about the USA and its people. This dataset contains only a subset of the variables that have been deemed most relevant. More info: https://www.census.gov/programs-surveys/acs/about.html',\n", + " 'category_id': 'demographics',\n", + " 'country_id': 'usa',\n", + " 'data_source_id': 'sociodemographics',\n", + " 'provider_id': 'usa_acs',\n", + " 'geography_name': 'Census Tract - United States of America (2015)',\n", + " 'geography_description': 'Shoreline clipped TIGER/Line boundaries. More info: https://carto.com/blog/tiger-shoreline-clip/',\n", + " 'temporal_aggregation': '5yrs',\n", + " 'time_coverage': '[2013-01-01, 2018-01-01)',\n", + " 'update_frequency': 'yearly',\n", + " 'is_public_data': True,\n", + " 'lang': 'eng',\n", + " 'version': '20132017',\n", + " 'category_name': 'Demographics',\n", + " 'provider_name': 'American Community Survey',\n", + " 'geography_id': 'carto-do-public-data.carto.geography_usa_censustract_2015',\n", + " 'id': 'carto-do-public-data.usa_acs.demographics_sociodemographics_usa_censustract_2015_5yrs_20132017'}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_ds.to_dict()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 1.4.2. Information about the dataset variables" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "6eufahA97EOg", + "outputId": "da142ec2-29b0-4efb-e45c-715b900bdf29" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                    slugnamedescriptiondb_typeagg_methodcolumn_namevariable_group_iddataset_idid
                                                    0geoid_e99a58c1geoidUS Census Block Groups GeoidsSTRINGNonegeoidNonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                    1do_date_45f076c2do_dateFirst day of the year the survey was issuedDATENonedo_dateNonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                    2total_pop_17ea032fTotal PopulationTotal Population. The total number of all peop...FLOATSUMtotal_popNonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                    3households_a12defd5Number of householdsHouseholds. A count of the number of household...FLOATSUMhouseholdsNonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                    4male_pop_eac43e5amale_popMale Population. The number of people within e...FLOATSUMmale_popcarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
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                                                    " + ], + "text/plain": [ + " slug name \\\n", + "0 geoid_e99a58c1 geoid \n", + "1 do_date_45f076c2 do_date \n", + "2 total_pop_17ea032f Total Population \n", + "3 households_a12defd5 Number of households \n", + "4 male_pop_eac43e5a male_pop \n", + "\n", + " description db_type agg_method \\\n", + "0 US Census Block Groups Geoids STRING None \n", + "1 First day of the year the survey was issued DATE None \n", + "2 Total Population. The total number of all peop... FLOAT SUM \n", + "3 Households. A count of the number of household... FLOAT SUM \n", + "4 Male Population. The number of people within e... FLOAT SUM \n", + "\n", + " column_name variable_group_id \\\n", + "0 geoid None \n", + "1 do_date None \n", + "2 total_pop None \n", + "3 households None \n", + "4 male_pop carto-do-public-data.usa_acs.demographics_soci... \n", + "\n", + " dataset_id \\\n", + "0 carto-do-public-data.usa_acs.demographics_soci... \n", + "1 carto-do-public-data.usa_acs.demographics_soci... \n", + "2 carto-do-public-data.usa_acs.demographics_soci... \n", + "3 carto-do-public-data.usa_acs.demographics_soci... \n", + "4 carto-do-public-data.usa_acs.demographics_soci... \n", + "\n", + " id \n", + "0 carto-do-public-data.usa_acs.demographics_soci... \n", + "1 carto-do-public-data.usa_acs.demographics_soci... \n", + "2 carto-do-public-data.usa_acs.demographics_soci... \n", + "3 carto-do-public-data.usa_acs.demographics_soci... \n", + "4 carto-do-public-data.usa_acs.demographics_soci... " + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_ds.variables.to_dataframe().head(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 1.4.3. Access to the ten first rows of the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 367 + }, + "id": "QJqYk6r886_J", + "outputId": "fcd88f75-2146-4a61-b176-dc07a1c5a56c" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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geoidno_cardo_datemale_20male_21no_carsone_carpovertychildrenmale_poptwo_carsasian_popblack_popfemale_20female_21in_schoolpop_25_64total_popwhite_popfemale_popgini_indexhouseholdsmale_60_61male_62_64median_agethree_carsmale_5_to_9median_rentpop_16_overpop_widowedarmed_forcesemployed_pophispanic_popmale_under_5mobile_homespop_divorcedfemale_5_to_9housing_unitsmale_10_to_14male_15_to_17male_18_to_19male_22_to_24male_25_to_29male_30_to_34male_35_to_39male_40_to_44male_45_to_49male_45_to_64male_50_to_54male_55_to_59male_65_to_66male_67_to_69male_70_to_74male_75_to_79male_80_to_84median_incomepop_separatedamerindian_popfemale_under_5four_more_carsgroup_quartersmasters_degreeother_race_popunemployed_popwalked_to_workworked_at_homefemale_10_to_14female_15_to_17female_18_to_19female_22_to_24female_25_to_29female_30_to_34female_35_to_39female_40_to_44female_45_to_49female_50_to_54female_55_to_59female_60_to_61female_62_to_64female_65_to_66female_67_to_69female_70_to_74female_75_to_79female_80_to_84pop_15_and_overpop_now_marriedasian_male_45_54asian_male_55_64bachelors_degreeblack_male_45_54black_male_55_64commute_5_9_minscommuters_by_busin_grades_1_to_4in_grades_5_to_8male_85_and_overnot_hispanic_poppop_5_years_overwhite_male_45_54white_male_55_64associates_degreecommuters_16_overdwellings_2_unitsfamily_householdshispanic_any_racein_grades_9_to_12income_less_10000income_per_capitapop_25_years_overpop_never_marriedbachelors_degree_2commute_10_14_minscommute_15_19_minscommute_20_24_minscommute_25_29_minscommute_30_34_minscommute_35_39_minscommute_35_44_minscommute_40_44_minscommute_45_59_minscommute_60_89_minsfemale_85_and_overincome_10000_14999income_15000_19999income_20000_24999income_25000_29999income_30000_34999income_35000_39999income_40000_44999income_45000_49999income_50000_59999income_60000_74999income_75000_99999married_householdsnot_in_labor_forcenot_us_citizen_poppop_in_labor_forcehigh_school_diplomahispanic_male_45_54hispanic_male_55_64occupation_servicesworkers_16_and_overcivilian_labor_forcecommute_60_more_minscommute_90_more_minscommute_less_10_minscommuters_by_carpoolemployed_informationin_undergrad_collegeincome_100000_124999income_125000_149999income_150000_199999male_male_householdsnonfamily_householdsrent_over_50_percentvacant_housing_unitscommuters_drove_aloneemployed_constructionemployed_retail_tradeincome_200000_or_moreless_one_year_collegemale_45_64_grade_9_12one_year_more_collegerent_10_to_15_percentrent_15_to_20_percentrent_20_to_25_percentrent_25_to_30_percentrent_30_to_35_percentrent_35_to_40_percentrent_40_to_50_percentrent_under_10_percentsales_office_employedspeak_spanish_at_hometwo_or_more_races_popdwellings_3_to_4_unitsdwellings_5_to_9_unitsemployed_manufacturingmale_45_64_high_schooloccupied_housing_unitsmale_45_64_some_collegemortgaged_housing_unitsoccupation_sales_officepopulation_3_years_overasian_including_hispanicblack_including_hispanicdwellings_10_to_19_unitsdwellings_20_to_49_unitsemployed_wholesale_tradefemale_female_householdsrent_burden_not_computedwhite_including_hispanichigh_school_including_gedcommuters_by_car_truck_vandwellings_1_units_attacheddwellings_1_units_detacheddwellings_50_or_more_unitshousing_built_2000_to_2004male_45_64_graduate_degreeoccupation_management_artspopulation_1_year_and_overspeak_only_english_at_homehousing_built_2005_or_latermale_45_64_bachelors_degreemedian_year_structure_builtchildren_in_single_female_hhfamilies_with_young_childrengraduate_professional_degreehouseholds_retirement_incomemale_45_64_associates_degreemale_45_64_less_than_9_grademillion_dollar_housing_unitsowner_occupied_housing_unitspercent_income_spent_on_rentaggregate_travel_time_to_workamerindian_including_hispanichousing_built_1939_or_earlierhousing_units_renter_occupiedpop_determined_poverty_statusvacant_housing_units_for_rentvacant_housing_units_for_saleemployed_public_administrationless_than_high_school_graduatecommuters_by_subway_or_elevatedbachelors_degree_or_higher_25_64employed_education_health_socialspeak_spanish_at_home_low_englishcommuters_by_public_transportationdifferent_house_year_ago_same_citysome_college_and_associates_degreehouseholds_public_asst_or_food_stampsmanagement_business_sci_arts_employedemployed_finance_insurance_real_estatedifferent_house_year_ago_different_cityemployed_science_management_admin_wasteone_parent_families_with_young_childrentwo_parent_families_with_young_childrenemployed_other_services_not_public_adminowner_occupied_housing_units_median_valueemployed_transportation_warehousing_utilitiesoccupation_production_transportation_materialfather_one_parent_families_with_young_childrenowner_occupied_housing_units_lower_value_quartileowner_occupied_housing_units_upper_value_quartileemployed_agriculture_forestry_fishing_hunting_miningoccupation_natural_resources_construction_maintenancetwo_parents_in_labor_force_families_with_young_childrenemployed_arts_entertainment_recreation_accommodation_foodrenter_occupied_housing_units_paying_cash_median_gross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                                                    \n", + "
                                                    " + ], + "text/plain": [ + " geoid no_car do_date male_20 male_21 no_cars one_car poverty \\\n", + "0 47157005000 76.0 20132017 0.0 0.0 151.0 122.0 552.0 \n", + "1 72051990021 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "2 72071990000 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "3 01097990000 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "4 18127980001 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "5 47157980100 0.0 20132017 0.0 6.0 0.0 0.0 58.0 \n", + "6 55071990000 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "7 25025990101 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "8 55025991703 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "9 26041990000 0.0 20132017 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " children male_pop two_cars asian_pop black_pop female_20 female_21 \\\n", + "0 316.0 415.0 64.0 0.0 1004.0 19.0 42.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "5 0.0 38.0 0.0 0.0 4.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "7 0.0 58.0 0.0 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " in_school pop_25_64 total_pop white_pop female_pop gini_index \\\n", + "0 331.0 328.0 1042.0 16.0 627.0 0.4372 \n", + "1 0.0 0.0 0.0 0.0 0.0 NaN \n", + "2 0.0 0.0 0.0 0.0 0.0 NaN \n", + "3 0.0 0.0 0.0 0.0 0.0 NaN \n", + "4 0.0 0.0 0.0 0.0 0.0 NaN \n", + "5 0.0 54.0 74.0 70.0 36.0 NaN \n", + "6 0.0 0.0 0.0 0.0 0.0 NaN \n", + "7 29.0 23.0 71.0 0.0 13.0 NaN \n", + "8 0.0 0.0 0.0 0.0 0.0 NaN \n", + "9 0.0 0.0 0.0 0.0 0.0 NaN \n", + "\n", + " households male_60_61 male_62_64 median_age three_cars male_5_to_9 \\\n", + "0 343.0 0.0 2.0 28.9 0.0 38.0 \n", + "1 0.0 0.0 0.0 NaN 0.0 0.0 \n", + "2 0.0 0.0 0.0 NaN 0.0 0.0 \n", + "3 0.0 0.0 0.0 NaN 0.0 0.0 \n", + "4 0.0 0.0 0.0 NaN 0.0 0.0 \n", + "5 0.0 0.0 0.0 31.3 0.0 0.0 \n", + "6 0.0 0.0 0.0 NaN 0.0 0.0 \n", + "7 13.0 0.0 0.0 24.0 13.0 0.0 \n", + "8 0.0 0.0 0.0 NaN 0.0 0.0 \n", + "9 0.0 0.0 0.0 NaN 0.0 0.0 \n", + "\n", + " median_rent pop_16_over pop_widowed armed_forces employed_pop \\\n", + "0 391.0 749.0 None 0.0 233.0 \n", + "1 NaN 0.0 None 0.0 0.0 \n", + "2 NaN 0.0 None 0.0 0.0 \n", + "3 NaN 0.0 None 0.0 0.0 \n", + "4 NaN 0.0 None 0.0 0.0 \n", + "5 NaN 74.0 None 0.0 0.0 \n", + "6 NaN 0.0 None 0.0 0.0 \n", + "7 NaN 71.0 None 0.0 71.0 \n", + "8 NaN 0.0 None 0.0 0.0 \n", + "9 NaN 0.0 None 0.0 0.0 \n", + "\n", + " hispanic_pop male_under_5 mobile_homes pop_divorced female_5_to_9 \\\n", + "0 0.0 44.0 5.0 None 76.0 \n", + "1 0.0 0.0 0.0 None 0.0 \n", + "2 0.0 0.0 0.0 None 0.0 \n", + "3 0.0 0.0 0.0 None 0.0 \n", + "4 0.0 0.0 0.0 None 0.0 \n", + "5 0.0 0.0 0.0 None 0.0 \n", + "6 0.0 0.0 0.0 None 0.0 \n", + "7 71.0 0.0 0.0 None 0.0 \n", + "8 0.0 0.0 0.0 None 0.0 \n", + "9 0.0 0.0 0.0 None 0.0 \n", + "\n", + " housing_units male_10_to_14 male_15_to_17 male_18_to_19 male_22_to_24 \\\n", + "0 465.0 27.0 26.0 30.0 22.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 6.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 0.0 \n", + "7 13.0 0.0 0.0 16.0 19.0 \n", + "8 0.0 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " male_25_to_29 male_30_to_34 male_35_to_39 male_40_to_44 male_45_to_49 \\\n", + "0 11.0 13.0 31.0 11.0 15.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 0.0 \n", + "5 0.0 6.0 12.0 4.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " male_45_to_64 male_50_to_54 male_55_to_59 male_65_to_66 male_67_to_69 \\\n", + "0 79.0 21.0 41.0 0.0 5.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 0.0 \n", + "7 23.0 0.0 23.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " male_70_to_74 male_75_to_79 male_80_to_84 median_income pop_separated \\\n", + "0 26.0 5.0 31.0 14410.0 None \n", + "1 0.0 0.0 0.0 NaN None \n", + "2 0.0 0.0 0.0 NaN None \n", + "3 0.0 0.0 0.0 NaN None \n", + "4 0.0 0.0 0.0 NaN None \n", + "5 0.0 4.0 0.0 NaN None \n", + "6 0.0 0.0 0.0 NaN None \n", + "7 0.0 0.0 0.0 NaN None \n", + "8 0.0 0.0 0.0 NaN None \n", + "9 0.0 0.0 0.0 NaN None \n", + "\n", + " amerindian_pop female_under_5 four_more_cars group_quarters \\\n", + "0 0.0 24.0 6.0 45.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 74.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " masters_degree other_race_pop unemployed_pop walked_to_work \\\n", + "0 13.0 0.0 71.0 7.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " worked_at_home female_10_to_14 female_15_to_17 female_18_to_19 \\\n", + "0 0.0 47.0 34.0 17.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " female_22_to_24 female_25_to_29 female_30_to_34 female_35_to_39 \\\n", + "0 32.0 34.0 13.0 38.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 4.0 18.0 6.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 13.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " female_40_to_44 female_45_to_49 female_50_to_54 female_55_to_59 \\\n", + "0 19.0 25.0 26.0 22.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 5.0 3.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " female_60_to_61 female_62_to_64 female_65_to_66 female_67_to_69 \\\n", + "0 0.0 6.0 22.0 38.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " female_70_to_74 female_75_to_79 female_80_to_84 pop_15_and_over \\\n", + "0 36.0 13.0 6.0 None \n", + "1 0.0 0.0 0.0 None \n", + "2 0.0 0.0 0.0 None \n", + "3 0.0 0.0 0.0 None \n", + "4 0.0 0.0 0.0 None \n", + "5 0.0 0.0 0.0 None \n", + "6 0.0 0.0 0.0 None \n", + "7 0.0 0.0 0.0 None \n", + "8 0.0 0.0 0.0 None \n", + "9 0.0 0.0 0.0 None \n", + "\n", + " pop_now_married asian_male_45_54 asian_male_55_64 bachelors_degree \\\n", + "0 None 0.0 0.0 0.0 \n", + "1 None 0.0 0.0 0.0 \n", + "2 None 0.0 0.0 0.0 \n", + "3 None 0.0 0.0 0.0 \n", + "4 None 0.0 0.0 0.0 \n", + "5 None 0.0 0.0 0.0 \n", + "6 None 0.0 0.0 0.0 \n", + "7 None 0.0 0.0 0.0 \n", + "8 None 0.0 0.0 0.0 \n", + "9 None 0.0 0.0 0.0 \n", + "\n", + " black_male_45_54 black_male_55_64 commute_5_9_mins commuters_by_bus \\\n", + "0 23.0 35.0 7.0 52.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 71.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " in_grades_1_to_4 in_grades_5_to_8 male_85_and_over not_hispanic_pop \\\n", + "0 90.0 41.0 16.0 1042.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 74.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " pop_5_years_over white_male_45_54 white_male_55_64 associates_degree \\\n", + "0 None 13.0 3.0 28.0 \n", + "1 None 0.0 0.0 0.0 \n", + "2 None 0.0 0.0 0.0 \n", + "3 None 0.0 0.0 0.0 \n", + "4 None 0.0 0.0 0.0 \n", + "5 None 0.0 0.0 0.0 \n", + "6 None 0.0 0.0 0.0 \n", + "7 None 0.0 0.0 0.0 \n", + "8 None 0.0 0.0 0.0 \n", + "9 None 0.0 0.0 0.0 \n", + "\n", + " commuters_16_over dwellings_2_units family_households hispanic_any_race \\\n", + "0 233.0 13.0 183.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 0.0 \n", + "7 71.0 0.0 13.0 71.0 \n", + "8 0.0 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 0.0 \n", + "\n", + " in_grades_9_to_12 income_less_10000 income_per_capita pop_25_years_over \\\n", + "0 111.0 79.0 8747.0 564.0 \n", + "1 0.0 0.0 NaN 0.0 \n", + "2 0.0 0.0 NaN 0.0 \n", + "3 0.0 0.0 NaN 0.0 \n", + "4 0.0 0.0 NaN 0.0 \n", + "5 0.0 0.0 5896.0 58.0 \n", + "6 0.0 0.0 NaN 0.0 \n", + "7 0.0 0.0 17785.0 23.0 \n", + "8 0.0 0.0 NaN 0.0 \n", + "9 0.0 0.0 NaN 0.0 \n", + "\n", + " pop_never_married bachelors_degree_2 commute_10_14_mins \\\n", + "0 None 0.0 4.0 \n", + "1 None NaN 0.0 \n", + "2 None NaN 0.0 \n", + "3 None 0.0 0.0 \n", + "4 None 0.0 0.0 \n", + "5 None 0.0 0.0 \n", + "6 None 0.0 0.0 \n", + "7 None 0.0 0.0 \n", + "8 None 0.0 0.0 \n", + "9 None 0.0 0.0 \n", + "\n", + " commute_15_19_mins commute_20_24_mins commute_25_29_mins \\\n", + "0 59.0 108.0 10.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " commute_30_34_mins commute_35_39_mins commute_35_44_mins \\\n", + "0 20.0 0.0 7.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 36.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " commute_40_44_mins commute_45_59_mins commute_60_89_mins \\\n", + "0 7.0 13.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 35.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " female_85_and_over income_10000_14999 income_15000_19999 \\\n", + "0 38.0 105.0 44.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " income_20000_24999 income_25000_29999 income_30000_34999 \\\n", + "0 38.0 13.0 18.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " income_35000_39999 income_40000_44999 income_45000_49999 \\\n", + "0 11.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " income_50000_59999 income_60000_74999 income_75000_99999 \\\n", + "0 7.0 10.0 18.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 13.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " married_households not_in_labor_force not_us_citizen_pop \\\n", + "0 26.0 445.0 0.0 \n", + "1 0.0 0.0 NaN \n", + "2 0.0 0.0 NaN \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 74.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 71.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " pop_in_labor_force high_school_diploma hispanic_male_45_54 \\\n", + "0 304.0 188.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 6.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 71.0 23.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " hispanic_male_55_64 occupation_services workers_16_and_over \\\n", + "0 0.0 58.0 233.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 23.0 13.0 71.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " civilian_labor_force commute_60_more_mins commute_90_more_mins \\\n", + "0 304.0 5.0 5.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 71.0 35.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " commute_less_10_mins commuters_by_carpool employed_information \\\n", + "0 7.0 21.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " in_undergrad_college income_100000_124999 income_125000_149999 \\\n", + "0 63.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 29.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " income_150000_199999 male_male_households nonfamily_households \\\n", + "0 0.0 0.0 160.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " rent_over_50_percent vacant_housing_units commuters_drove_alone \\\n", + "0 38.0 122.0 141.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " employed_construction employed_retail_trade income_200000_or_more \\\n", + "0 24.0 19.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " less_one_year_college male_45_64_grade_9_12 one_year_more_college \\\n", + "0 22.0 18.0 85.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 18.0 0.0 4.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " rent_10_to_15_percent rent_15_to_20_percent rent_20_to_25_percent \\\n", + "0 11.0 42.0 39.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 13.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " rent_25_to_30_percent rent_30_to_35_percent rent_35_to_40_percent \\\n", + "0 33.0 47.0 9.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " rent_40_to_50_percent rent_under_10_percent sales_office_employed \\\n", + "0 41.0 0.0 30.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " speak_spanish_at_home two_or_more_races_pop dwellings_3_to_4_units \\\n", + "0 None 22.0 63.0 \n", + "1 None 0.0 0.0 \n", + "2 None 0.0 0.0 \n", + "3 None 0.0 0.0 \n", + "4 None 0.0 0.0 \n", + "5 None 0.0 0.0 \n", + "6 None 0.0 0.0 \n", + "7 None 0.0 13.0 \n", + "8 None 0.0 0.0 \n", + "9 None 0.0 0.0 \n", + "\n", + " dwellings_5_to_9_units employed_manufacturing male_45_64_high_school \\\n", + "0 69.0 16.0 41.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 0.0 23.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " occupied_housing_units male_45_64_some_college mortgaged_housing_units \\\n", + "0 343.0 10.0 20.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 0.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 13.0 0.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " occupation_sales_office population_3_years_over asian_including_hispanic \\\n", + "0 30.0 999.0 0.0 \n", + "1 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 \n", + "3 0.0 0.0 0.0 \n", + "4 0.0 0.0 0.0 \n", + "5 0.0 74.0 0.0 \n", + "6 0.0 0.0 0.0 \n", + "7 0.0 71.0 0.0 \n", + "8 0.0 0.0 0.0 \n", + "9 0.0 0.0 0.0 \n", + "\n", + " black_including_hispanic dwellings_10_to_19_units \\\n", + "0 1004.0 40.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 4.0 0.0 \n", + "6 0.0 0.0 \n", + "7 19.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " dwellings_20_to_49_units employed_wholesale_trade \\\n", + "0 25.0 2.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " female_female_households rent_burden_not_computed \\\n", + "0 0.0 18.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " white_including_hispanic high_school_including_ged \\\n", + "0 16.0 235.0 \n", + "1 0.0 NaN \n", + "2 0.0 NaN \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 70.0 18.0 \n", + "6 0.0 0.0 \n", + "7 52.0 23.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " commuters_by_car_truck_van dwellings_1_units_attached \\\n", + "0 162.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " dwellings_1_units_detached dwellings_50_or_more_units \\\n", + "0 195.0 55.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " housing_built_2000_to_2004 male_45_64_graduate_degree \\\n", + "0 10.0 1.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " occupation_management_arts population_1_year_and_over \\\n", + "0 54.0 1019.0 \n", + "1 0.0 NaN \n", + "2 0.0 NaN \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 74.0 \n", + "6 0.0 0.0 \n", + "7 16.0 71.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " speak_only_english_at_home housing_built_2005_or_later \\\n", + "0 None 0.0 \n", + "1 None 0.0 \n", + "2 None 0.0 \n", + "3 None 0.0 \n", + "4 None 0.0 \n", + "5 None 0.0 \n", + "6 None 0.0 \n", + "7 None 0.0 \n", + "8 None 0.0 \n", + "9 None 0.0 \n", + "\n", + " male_45_64_bachelors_degree median_year_structure_built \\\n", + "0 0.0 1981.0 \n", + "1 0.0 NaN \n", + "2 0.0 NaN \n", + "3 0.0 NaN \n", + "4 0.0 NaN \n", + "5 0.0 NaN \n", + "6 0.0 NaN \n", + "7 0.0 NaN \n", + "8 0.0 NaN \n", + "9 0.0 NaN \n", + "\n", + " children_in_single_female_hh families_with_young_children \\\n", + "0 278.0 75.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " graduate_professional_degree households_retirement_income \\\n", + "0 13.0 47.0 \n", + "1 NaN 0.0 \n", + "2 NaN 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " male_45_64_associates_degree male_45_64_less_than_9_grade \\\n", + "0 7.0 2.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " million_dollar_housing_units owner_occupied_housing_units \\\n", + "0 0.0 65.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " percent_income_spent_on_rent aggregate_travel_time_to_work \\\n", + "0 30.5 None \n", + "1 NaN None \n", + "2 NaN None \n", + "3 NaN None \n", + "4 NaN None \n", + "5 NaN None \n", + "6 NaN None \n", + "7 NaN None \n", + "8 NaN None \n", + "9 NaN None \n", + "\n", + " amerindian_including_hispanic housing_built_1939_or_earlier \\\n", + "0 0.0 81.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " housing_units_renter_occupied pop_determined_poverty_status \\\n", + "0 278.0 1042.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 74.0 \n", + "6 0.0 0.0 \n", + "7 13.0 71.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " vacant_housing_units_for_rent vacant_housing_units_for_sale \\\n", + "0 14.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " employed_public_administration less_than_high_school_graduate \\\n", + "0 4.0 181.0 \n", + "1 0.0 NaN \n", + "2 0.0 NaN \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 18.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " commuters_by_subway_or_elevated bachelors_degree_or_higher_25_64 \\\n", + "0 0.0 1.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " employed_education_health_social speak_spanish_at_home_low_english \\\n", + "0 57.0 None \n", + "1 0.0 None \n", + "2 0.0 None \n", + "3 0.0 None \n", + "4 0.0 None \n", + "5 0.0 None \n", + "6 0.0 None \n", + "7 29.0 None \n", + "8 0.0 None \n", + "9 0.0 None \n", + "\n", + " commuters_by_public_transportation different_house_year_ago_same_city \\\n", + "0 52.0 38.0 \n", + "1 0.0 NaN \n", + "2 0.0 NaN \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 71.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " some_college_and_associates_degree households_public_asst_or_food_stamps \\\n", + "0 135.0 147.0 \n", + "1 NaN 0.0 \n", + "2 NaN 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 22.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 13.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "\n", + " management_business_sci_arts_employed \\\n", + "0 54.0 \n", + "1 0.0 \n", + "2 0.0 \n", + "3 0.0 \n", + "4 0.0 \n", + "5 0.0 \n", + "6 0.0 \n", + "7 16.0 \n", + "8 0.0 \n", + "9 0.0 \n", + "\n", + " employed_finance_insurance_real_estate \\\n", + "0 0.0 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                                                    total_pophouseholdsmale_popfemale_popmedian_agemale_under_5male_5_to_9male_10_to_14male_15_to_17male_18_to_19male_20male_21male_22_to_24male_25_to_29male_30_to_34male_35_to_39male_40_to_44male_45_to_49male_50_to_54male_55_to_59male_65_to_66male_67_to_69male_70_to_74male_75_to_79male_80_to_84male_85_and_overfemale_under_5female_5_to_9female_10_to_14female_15_to_17female_18_to_19female_20female_21female_22_to_24female_25_to_29female_30_to_34female_35_to_39female_40_to_44female_45_to_49female_50_to_54female_55_to_59female_60_to_61female_62_to_64female_65_to_66female_67_to_69female_70_to_74female_75_to_79female_80_to_84female_85_and_overwhite_poppopulation_1_year_and_overpopulation_3_years_overpop_5_years_overpop_15_and_overpop_16_overpop_25_years_overpop_25_64pop_never_marriedpop_now_marriedpop_separatedpop_widowedpop_divorcednot_us_citizen_popblack_popasian_pophispanic_popamerindian_popother_race_poptwo_or_more_races_pophispanic_any_racenot_hispanic_popasian_male_45_54asian_male_55_64black_male_45_54black_male_55_64hispanic_male_45_54hispanic_male_55_64white_male_45_54white_male_55_64median_incomeincome_per_capitaincome_less_10000income_10000_14999income_15000_19999income_20000_24999income_25000_29999income_30000_34999income_35000_39999income_40000_44999income_45000_49999income_50000_59999income_60000_74999income_75000_99999income_100000_124999income_125000_149999income_150000_199999income_200000_or_morepop_determined_poverty_statuspovertygini_indexhousing_unitsrenter_occupied_housing_units_paying_cash_median_gross_rentowner_occupied_housing_units_lower_value_quartileowner_occupied_housing_units_median_valueowner_occupied_housing_units_upper_value_quartileoccupied_housing_unitshousing_units_renter_occupiedvacant_housing_unitsvacant_housing_units_for_rentvacant_housing_units_for_saledwellings_1_units_detacheddwellings_1_units_attacheddwellings_2_unitsdwellings_3_to_4_unitsdwellings_5_to_9_unitsdwellings_10_to_19_unitsdwellings_20_to_49_unitsdwellings_50_or_more_unitsmobile_homeshousing_built_2005_or_laterhousing_built_2000_to_2004housing_built_1939_or_earliermedian_year_structure_builtmarried_householdsnonfamily_householdsfamily_householdshouseholds_public_asst_or_food_stampsmale_male_householdsfemale_female_householdschildrenchildren_in_single_female_hhmedian_rentpercent_income_spent_on_rentrent_burden_not_computedrent_over_50_percentrent_40_to_50_percentrent_35_to_40_percentrent_30_to_35_percentrent_25_to_30_percentrent_20_to_25_percentrent_15_to_20_percentrent_10_to_15_percentrent_under_10_percentowner_occupied_housing_unitsmillion_dollar_housing_unitsmortgaged_housing_unitsdifferent_house_year_ago_different_citydifferent_house_year_ago_same_cityfamilies_with_young_childrentwo_parent_families_with_young_childrentwo_parents_in_labor_force_families_with_young_childrentwo_parents_father_in_labor_force_families_with_young_childrentwo_parents_mother_in_labor_force_families_with_young_childrentwo_parents_not_in_labor_force_families_with_young_childrenone_parent_families_with_young_childrenfather_one_parent_families_with_young_childrenfather_in_labor_force_one_parent_families_with_young_childrencommute_less_10_minscommute_10_14_minscommute_15_19_minscommute_20_24_minscommute_25_29_minscommute_30_34_minscommute_35_44_minscommute_45_59_minscommute_60_more_minscommuters_16_overwalked_to_workworked_at_homeno_carno_carsone_cartwo_carsthree_carsfour_more_carsaggregate_travel_time_to_workcommuters_by_public_transportationcommuters_by_buscommuters_by_car_truck_vancommuters_by_carpoolcommuters_by_subway_or_elevatedcommuters_drove_alonegroup_quartersassociates_degreebachelors_degreehigh_school_diplomaless_one_year_collegemasters_degreeone_year_more_collegeless_than_high_school_graduatehigh_school_including_gedbachelors_degree_2bachelors_degree_or_higher_25_64graduate_professional_degreesome_college_and_associates_degreemale_45_64_associates_degreemale_45_64_bachelors_degreemale_45_64_graduate_degreemale_45_64_less_than_9_grademale_45_64_grade_9_12male_45_64_high_schoolmale_45_64_some_collegemale_45_to_64employed_popunemployed_poppop_in_labor_forcenot_in_labor_forceworkers_16_and_overarmed_forcescivilian_labor_forceemployed_agriculture_forestry_fishing_hunting_miningemployed_arts_entertainment_recreation_accommodation_foodemployed_constructionemployed_education_health_socialemployed_finance_insurance_real_estateemployed_informationemployed_manufacturingemployed_other_services_not_public_adminemployed_public_administrationemployed_retail_tradeemployed_science_management_admin_wasteemployed_transportation_warehousing_utilitiesemployed_wholesale_tradeoccupation_management_artsoccupation_natural_resources_construction_maintenanceoccupation_production_transportation_materialoccupation_sales_officeoccupation_servicesmanagement_business_sci_arts_employedsales_office_employedin_grades_1_to_4in_grades_5_to_8in_grades_9_to_12in_schoolin_undergrad_collegespeak_only_english_at_homespeak_spanish_at_homespeak_spanish_at_home_low_english
                                                    avg4.384716e+031.622255e+032.157711e+032.227005e+033.930112e+011.383831e+021.423078e+021.445751e+028.804836e+016.037216e+013.276450e+013.217450e+019.285125e+011.552524e+021.483501e+021.388303e+021.375463e+021.416246e+021.482727e+021.423548e+024.490084e+015.796503e+017.306170e+015.008765e+013.334291e+012.886338e+011.322310e+021.365999e+021.383163e+028.401951e+015.756660e+013.088993e+013.041858e+018.833956e+011.505691e+021.468783e+021.396495e+021.393065e+021.446716e+021.538896e+021.514543e+025.749201e+017.940259e+014.982987e+016.520221e+018.553553e+016.253888e+014.708153e+015.512231e+012.666237e+034.342539e+034.225816e+03NoneNone3.495006e+032.954858e+032.301326e+03NoneNoneNoneNoneNone3.057622e+025.330768e+022.296153e+028.100321e+022.836241e+019.6943691.007309e+028.100321e+023.574684e+031.491637e+0111.6937883.440143e+012.904223e+014.700662e+013.048543e+011.883150e+021.926332e+026.120650e+043.065180e+041.120286e+027.990310e+017.906576e+018.121069e+017.699035e+017.811121e+017.282273e+017.298924e+016.437017e+011.248865e+021.605002e+021.984182e+021.391279e+028.701426e+019.380035e+011.010161e+024.276775e+036.377423e+020.4259111.850797e+031.058678e+031.765685e+052.443280e+053.329604e+051.622255e+035.861794e+022.285420e+023.884688e+011.863187e+011.143346e+031.092662e+026.762999e+018.112938e+018.797462e+018.248062e+016.591754e+019.643179e+011.150671e+021.610305e+014.221539e+019.403019e+011.971950e+037.829447e+025.526839e+021.069572e+032.201474e+022.8475432.9098661.004481e+032.549481e+028.960611e+023.075703e+014.549625e+011.383322e+024.980808e+013.622229e+014.936739e+016.223202e+016.874649e+016.792566e+014.691335e+012.113569e+011.036076e+031.498115e+016.556553e+024.115417e+021.951105e+023.127425e+022.018102e+021.182977e+027.417954e+016.3409412.9919871.109323e+022.560057e+012.281514e+012.433403e+022.615797e+022.943585e+022.800684e+021.222793e+022.636407e+021.311400e+021.560668e+021.717179e+021.924192e+035.515674e+019.526220e+018.728505e+011.442299e+025.405044e+026.048036e+022.295713e+021.031463e+025.934150e+041.031013e+025.114798e+011.729272e+031.847843e+023.818269e+011.544488e+031.098196e+022.452862e+025.649029e+026.904443e+021.821747e+022.468936e+024.290593e+023.755628e+028.088810e+025.663747e+027.435482e+023.491915e+028.603443e+024.302832e+011.000650e+026.498545e+013.009663e+014.223971e+011.648105e+021.128077e+025.580332e+022.049152e+031.456859e+022.208707e+031.286299e+032.019454e+031.386930e+012.194838e+033.827483e+011.984437e+021.299722e+024.733737e+021.346475e+024.315308e+012.104029e+021.003838e+029.616204e+012.338625e+022.311254e+021.043314e+025.501904e+017.665854e+021.820007e+022.491306e+024.828023e+023.686331e+027.665854e+024.828023e+022.228999e+022.253273e+022.322261e+021.116528e+032.557242e+02NoneNoneNone
                                                    max6.552800e+042.142900e+043.226600e+043.326200e+048.570000e+013.297000e+033.464000e+033.137000e+032.416000e+033.909000e+032.845000e+033.025000e+036.083000e+033.956000e+032.143000e+033.146000e+033.718000e+032.652000e+032.300000e+031.741000e+031.244000e+033.225000e+034.708000e+033.287000e+031.666000e+036.670000e+022.829000e+033.509000e+033.667000e+031.601000e+034.815000e+031.586000e+031.330000e+032.045000e+032.410000e+032.840000e+033.450000e+033.586000e+033.628000e+031.756000e+031.445000e+038.730000e+022.105000e+032.147000e+033.661000e+035.268000e+032.481000e+031.166000e+031.337000e+033.868400e+046.480300e+046.243800e+04NoneNone4.464200e+043.948600e+043.425800e+04NoneNoneNoneNoneNone1.223200e+041.665500e+041.371200e+042.868300e+049.644000e+031385.0000003.611000e+032.868300e+045.047700e+041.301000e+03745.0000001.094000e+031.122000e+031.464000e+037.770000e+022.189000e+032.545000e+032.500010e+052.202530e+051.550000e+039.300000e+027.610000e+021.009000e+038.760000e+028.640000e+021.530000e+031.118000e+038.610000e+022.054000e+032.789000e+033.368000e+032.704000e+032.157000e+033.878000e+035.386000e+036.533200e+049.440000e+030.8290002.652600e+043.501000e+032.000001e+062.000001e+062.000001e+062.142900e+048.039000e+031.192400e+044.472000e+035.470000e+022.552700e+044.280000e+031.999000e+031.629000e+031.863000e+035.280000e+034.283000e+031.151800e+043.314000e+032.502000e+037.206000e+036.177000e+032.016000e+031.510500e+046.883000e+031.681300e+042.325000e+03355.000000174.0000002.360800e+042.863000e+033.501000e+035.000000e+011.100000e+033.487000e+039.720000e+026.410000e+028.600000e+029.990000e+021.382000e+031.879000e+031.098000e+039.120000e+022.047300e+041.453000e+031.267200e+041.306400e+045.347000e+036.700000e+036.517000e+033.989000e+033.888000e+031308.000000285.0000001.418000e+035.020000e+025.020000e+021.162100e+045.838000e+037.095000e+034.886000e+032.205000e+034.880000e+033.178000e+035.390000e+036.070000e+032.648200e+041.162100e+047.043000e+038.111000e+035.847000e+031.554800e+041.201200e+043.231000e+031.107000e+039.731600e+057.759000e+033.940000e+032.501200e+043.023000e+035.945000e+032.198900e+041.642100e+044.070000e+031.376700e+048.215000e+034.037000e+037.767000e+035.621000e+035.363000e+038.929000e+031.376700e+042.285600e+041.037400e+041.274500e+048.550000e+022.547000e+032.448000e+039.000000e+021.626000e+032.084000e+031.628000e+036.993000e+032.894500e+041.607000e+033.055200e+043.414200e+042.825200e+042.121400e+043.055200e+044.197000e+035.211000e+031.950000e+039.127000e+032.853000e+031.141000e+033.993000e+031.175000e+031.816000e+032.808000e+035.115000e+031.465000e+031.294000e+031.909700e+043.973000e+032.495000e+036.162000e+033.948000e+031.909700e+046.162000e+035.927000e+035.674000e+034.965000e+032.398900e+041.298500e+04NoneNoneNone
                                                    min0.000000e+000.000000e+000.000000e+000.000000e+007.700000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+00NoneNone0.000000e+000.000000e+000.000000e+00NoneNoneNoneNoneNone0.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.0000000.000000e+000.000000e+000.000000e+000.000000e+000.0000000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+002.499000e+033.200000e+010.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.0007000.000000e+009.900000e+019.999000e+039.999000e+039.999000e+030.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+001.939000e+030.000000e+000.000000e+000.000000e+000.000000e+000.0000000.0000000.000000e+000.000000e+009.900000e+011.000000e+010.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.0000000.0000000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+006.500000e+010.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+00NoneNoneNone
                                                    sum3.244734e+081.200485e+081.596728e+081.648006e+082.879751e+061.024049e+071.053092e+071.069870e+076.515667e+064.467600e+062.424606e+062.380945e+066.871085e+061.148883e+071.097806e+071.027358e+071.017856e+071.048036e+071.097233e+071.053440e+073.322707e+064.289470e+065.406639e+063.706536e+062.467409e+062.135919e+069.785223e+061.010853e+071.023555e+076.217528e+064.259986e+062.285886e+062.251005e+066.537216e+061.114227e+071.086914e+071.033420e+071.030882e+071.070584e+071.138799e+071.120777e+074.254466e+065.875871e+063.687460e+064.825029e+066.329715e+064.627940e+063.484080e+064.079106e+061.973042e+083.172486e+083.127146e+08NoneNone2.586340e+082.186624e+081.703004e+08NoneNoneNoneNoneNone2.233776e+073.944822e+071.699176e+075.994318e+072.098847e+06717393.0000007.454186e+065.994318e+072.645302e+081.103826e+06865352.0000002.545740e+062.149154e+063.478537e+062.255952e+061.393550e+071.425505e+074.464586e+092.245429e+098.290228e+065.912909e+065.850945e+066.009672e+065.697363e+065.780308e+065.388955e+065.401277e+064.763457e+069.241728e+061.187718e+071.468315e+071.029560e+076.439142e+066.941320e+067.475296e+063.164856e+084.719356e+0731111.1005001.369609e+087.612849e+071.258933e+101.758746e+102.396882e+101.200485e+084.337786e+071.691234e+072.874708e+061.378777e+068.460878e+078.085807e+065.004687e+066.003655e+066.510210e+066.103648e+064.877964e+067.136049e+068.515077e+061.191642e+063.123981e+066.958328e+061.429487e+085.793869e+074.089916e+077.914937e+071.629113e+07210721.000000215333.0000007.433261e+071.886641e+076.427357e+072.225702e+063.366768e+061.023672e+073.685848e+062.680486e+063.653236e+064.605232e+065.087309e+065.026567e+063.471635e+061.564062e+067.667066e+071.108620e+064.851915e+073.006559e+071.425400e+072.314326e+071.493416e+078.754150e+065.489360e+06469236.000000221410.0000008.209099e+061.894468e+061.688343e+061.800742e+071.935716e+072.178282e+072.072534e+079.048793e+061.950968e+079.704493e+061.154910e+071.270730e+071.423921e+084.081654e+067.049498e+066.459181e+061.067316e+073.999786e+074.475607e+071.698850e+077.632928e+062.970754e+097.629599e+063.785002e+061.279679e+081.367422e+072.825557e+061.142936e+088.126758e+061.815143e+074.180338e+075.109357e+071.348111e+071.827037e+073.175082e+072.743711e+075.909361e+074.137707e+075.502331e+072.551054e+076.285332e+073.184139e+067.404907e+064.808988e+062.227181e+063.125781e+061.219614e+078.347879e+064.129502e+071.516393e+081.078090e+071.634465e+089.518743e+071.494416e+081.026342e+061.624202e+082.832376e+061.468503e+079.618071e+063.503013e+079.964050e+063.193371e+061.557002e+077.428501e+067.116087e+061.730606e+071.710351e+077.720629e+064.071464e+065.672809e+071.346823e+071.843592e+073.572785e+072.727922e+075.672809e+073.572785e+071.649482e+071.667444e+071.718497e+078.262417e+071.892385e+07NoneNoneNone
                                                    range6.552800e+042.142900e+043.226600e+043.326200e+047.800000e+013.297000e+033.464000e+033.137000e+032.416000e+033.909000e+032.845000e+033.025000e+036.083000e+033.956000e+032.143000e+033.146000e+033.718000e+032.652000e+032.300000e+031.741000e+031.244000e+033.225000e+034.708000e+033.287000e+031.666000e+036.670000e+022.829000e+033.509000e+033.667000e+031.601000e+034.815000e+031.586000e+031.330000e+032.045000e+032.410000e+032.840000e+033.450000e+033.586000e+033.628000e+031.756000e+031.445000e+038.730000e+022.105000e+032.147000e+033.661000e+035.268000e+032.481000e+031.166000e+031.337000e+033.868400e+046.480300e+046.243800e+04NoneNone4.464200e+043.948600e+043.425800e+04NoneNoneNoneNoneNone1.223200e+041.665500e+041.371200e+042.868300e+049.644000e+031385.0000003.611000e+032.868300e+045.047700e+041.301000e+03745.0000001.094000e+031.122000e+031.464000e+037.770000e+022.189000e+032.545000e+032.475020e+052.202210e+051.550000e+039.300000e+027.610000e+021.009000e+038.760000e+028.640000e+021.530000e+031.118000e+038.610000e+022.054000e+032.789000e+033.368000e+032.704000e+032.157000e+033.878000e+035.386000e+036.533200e+049.440000e+030.8283002.652600e+043.402000e+031.990002e+061.990002e+061.990002e+062.142900e+048.039000e+031.192400e+044.472000e+035.470000e+022.552700e+044.280000e+031.999000e+031.629000e+031.863000e+035.280000e+034.283000e+031.151800e+043.314000e+032.502000e+037.206000e+036.177000e+037.700000e+011.510500e+046.883000e+031.681300e+042.325000e+03355.000000174.0000002.360800e+042.863000e+033.402000e+034.000000e+011.100000e+033.487000e+039.720000e+026.410000e+028.600000e+029.990000e+021.382000e+031.879000e+031.098000e+039.120000e+022.047300e+041.453000e+031.267200e+041.306400e+045.347000e+036.700000e+036.517000e+033.989000e+033.888000e+031308.000000285.0000001.418000e+035.020000e+025.020000e+021.162100e+045.838000e+037.095000e+034.886000e+032.205000e+034.880000e+033.178000e+035.390000e+036.070000e+032.648200e+041.162100e+047.043000e+038.111000e+035.847000e+031.554800e+041.201200e+043.231000e+031.107000e+039.730950e+057.759000e+033.940000e+032.501200e+043.023000e+035.945000e+032.198900e+041.642100e+044.070000e+031.376700e+048.215000e+034.037000e+037.767000e+035.621000e+035.363000e+038.929000e+031.376700e+042.285600e+041.037400e+041.274500e+048.550000e+022.547000e+032.448000e+039.000000e+021.626000e+032.084000e+031.628000e+036.993000e+032.894500e+041.607000e+033.055200e+043.414200e+042.825200e+042.121400e+043.055200e+044.197000e+035.211000e+031.950000e+039.127000e+032.853000e+031.141000e+033.993000e+031.175000e+031.816000e+032.808000e+035.115000e+031.465000e+031.294000e+031.909700e+043.973000e+032.495000e+036.162000e+033.948000e+031.909700e+046.162000e+035.927000e+035.674000e+034.965000e+032.398900e+041.298500e+04NoneNoneNone
                                                    stdev2.228937e+037.936172e+021.120561e+031.146240e+037.750633e+001.104914e+021.121643e+021.132003e+027.188730e+011.075927e+025.489900e+015.251894e+019.475956e+011.251577e+021.155036e+021.057082e+021.034058e+029.913924e+019.575753e+018.738622e+013.685899e+014.708550e+015.993755e+014.641712e+013.390149e+013.303075e+011.062496e+021.096859e+021.100421e+026.923133e+011.222020e+025.400368e+015.017502e+017.957206e+011.181003e+021.097178e+021.040098e+021.022617e+029.984035e+019.595609e+019.064753e+014.279481e+015.533706e+014.018353e+015.125616e+016.700952e+015.231319e+014.266777e+015.793785e+011.907504e+032.201732e+032.139467e+03NoneNone1.719905e+031.474748e+031.216138e+03NoneNoneNoneNoneNone4.535513e+029.093226e+025.036082e+021.282778e+031.789563e+0234.6878471.270444e+021.282778e+032.056417e+033.975201e+0131.8277246.303262e+015.395722e+018.368872e+015.878341e+011.523871e+021.455158e+023.090106e+041.612694e+041.063003e+026.984943e+016.359047e+016.205942e+015.829994e+015.862582e+015.505314e+015.498863e+014.950700e+018.417410e+011.059809e+021.372959e+021.147172e+028.513799e+011.078177e+021.647825e+022.216522e+035.618142e+020.0635778.958362e+024.642559e+021.648026e+052.189567e+052.866141e+057.936172e+024.791495e+023.131224e+026.702031e+012.830720e+017.543086e+021.952911e+021.133588e+021.165817e+021.324159e+021.511113e+021.440865e+022.991683e+022.250775e+024.732752e+011.038676e+021.166008e+021.755339e+015.050110e+023.637558e+025.859720e+021.966127e+028.8992657.6528336.760134e+022.402736e+024.587204e+027.795312e+004.974370e+011.432986e+025.574670e+014.303120e+015.598719e+016.694950e+017.252401e+017.206055e+015.312190e+013.129274e+016.543429e+025.537075e+014.790928e+023.932843e+022.492432e+022.316744e+021.829281e+021.148169e+029.050640e+0118.14501111.5236131.141550e+023.774419e+013.534531e+012.257531e+021.967251e+022.132096e+022.089584e+021.102167e+022.155892e+021.292811e+021.602168e+021.904055e+021.072317e+031.404476e+021.047340e+022.315210e+022.298423e+023.314566e+023.799984e+021.711299e+029.448405e+013.422968e+042.647627e+021.086306e+021.041140e+031.447731e+021.967409e+029.474669e+024.446641e+021.694405e+024.828823e+024.093667e+021.267778e+022.557686e+022.629695e+023.540041e+024.791437e+024.842803e+027.062823e+023.801689e+025.062105e+024.047851e+011.009911e+028.698075e+014.965424e+014.571015e+011.159105e+028.115282e+013.033556e+021.138865e+031.092197e+021.203354e+037.150048e+021.129133e+031.616169e+021.192208e+038.874869e+011.600300e+021.116914e+023.015791e+021.305728e+025.400275e+011.826280e+027.619403e+019.987631e+011.551951e+022.015002e+029.018655e+015.451432e+016.156759e+021.512020e+021.862018e+022.988008e+022.366780e+026.156759e+022.988008e+021.687454e+021.692225e+021.687234e+027.662855e+024.083119e+02NoneNoneNone
                                                    q12.627000e+039.980000e+021.276000e+031.318000e+033.280000e+015.600000e+015.700000e+015.900000e+013.200000e+011.400000e+010.000000e+000.000000e+003.100000e+016.100000e+016.100000e+016.000000e+016.000000e+016.600000e+017.200000e+017.100000e+011.500000e+012.200000e+012.800000e+011.600000e+018.000000e+004.000000e+005.300000e+015.400000e+015.500000e+013.000000e+011.200000e+010.000000e+000.000000e+003.000000e+016.000000e+016.400000e+016.200000e+016.200000e+016.800000e+017.700000e+017.700000e+012.300000e+013.500000e+011.800000e+012.600000e+013.600000e+012.200000e+011.400000e+011.400000e+019.510000e+022.604000e+032.519000e+03NoneNone2.125000e+031.782000e+031.328000e+03NoneNoneNoneNoneNone2.200000e+011.600000e+010.000000e+006.600000e+010.000000e+000.0000001.600000e+016.600000e+011.943000e+030.000000e+000.0000000.000000e+000.000000e+000.000000e+000.000000e+005.300000e+016.000000e+013.697400e+041.902300e+043.400000e+012.300000e+012.600000e+013.000000e+012.900000e+013.000000e+012.800000e+012.900000e+012.400000e+015.800000e+017.600000e+018.600000e+014.700000e+011.900000e+011.400000e+018.000000e+002.559000e+032.120000e+020.3738001.153000e+036.920000e+026.570000e+049.880000e+041.491000e+059.980000e+022.090000e+026.400000e+010.000000e+000.000000e+005.220000e+027.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+009.000000e+001.956000e+033.810000e+022.730000e+026.120000e+026.200000e+010.0000000.0000005.080000e+027.300000e+015.290000e+022.420000e+019.000000e+002.900000e+017.000000e+000.000000e+007.000000e+001.100000e+011.400000e+011.500000e+018.000000e+000.000000e+004.960000e+020.000000e+002.740000e+021.400000e+026.000000e+001.410000e+027.000000e+013.400000e+011.200000e+010.0000000.0000002.300000e+010.000000e+000.000000e+008.400000e+011.040000e+021.260000e+021.100000e+023.600000e+019.500000e+013.300000e+013.800000e+014.200000e+011.072000e+030.000000e+002.300000e+017.000000e+002.900000e+012.780000e+023.010000e+028.500000e+012.400000e+013.315000e+040.000000e+000.000000e+008.940000e+027.400000e+010.000000e+007.800000e+020.000000e+001.090000e+021.910000e+023.420000e+028.000000e+016.200000e+012.220000e+021.150000e+024.000000e+021.920000e+022.200000e+028.100000e+014.570000e+021.100000e+012.400000e+018.000000e+000.000000e+007.000000e+006.700000e+014.700000e+013.160000e+021.143000e+036.000000e+011.245000e+037.460000e+021.115000e+030.000000e+001.235000e+030.000000e+008.200000e+014.500000e+012.340000e+024.100000e+017.000000e+006.500000e+014.000000e+012.900000e+011.110000e+028.100000e+013.700000e+011.300000e+012.900000e+026.300000e+019.600000e+012.430000e+021.860000e+022.900000e+022.430000e+029.700000e+019.700000e+011.020000e+025.600000e+028.400000e+01NoneNoneNone
                                                    q34.606000e+031.714000e+032.254000e+032.334000e+034.110000e+011.380000e+021.420000e+021.450000e+028.800000e+015.200000e+012.600000e+012.600000e+018.700000e+011.510000e+021.460000e+021.380000e+021.380000e+021.440000e+021.520000e+021.480000e+024.500000e+015.900000e+017.400000e+015.000000e+013.300000e+012.700000e+011.320000e+021.370000e+021.390000e+028.500000e+014.600000e+012.300000e+012.400000e+018.400000e+011.490000e+021.460000e+021.400000e+021.410000e+021.480000e+021.590000e+021.590000e+025.800000e+018.100000e+015.000000e+016.600000e+018.700000e+016.300000e+014.600000e+015.100000e+012.942000e+034.525000e+034.421000e+03NoneNone3.666000e+033.115000e+032.398000e+03NoneNoneNoneNoneNone1.980000e+022.690000e+029.700000e+014.480000e+022.000000e+000.0000008.800000e+014.480000e+023.809000e+030.000000e+000.0000001.600000e+011.300000e+012.300000e+011.300000e+012.000000e+022.100000e+026.082500e+043.022100e+041.040000e+027.900000e+018.000000e+018.200000e+017.800000e+018.000000e+017.400000e+017.400000e+016.500000e+011.280000e+021.670000e+022.070000e+021.410000e+028.300000e+018.100000e+016.000000e+014.496000e+036.050000e+020.4347001.949000e+031.055000e+031.533000e+052.108000e+052.867000e+051.714000e+035.700000e+021.880000e+023.100000e+011.500000e+011.248000e+036.200000e+014.100000e+015.900000e+016.000000e+014.300000e+012.700000e+012.000000e+012.700000e+010.000000e+002.100000e+018.100000e+011.978000e+038.130000e+025.550000e+021.126000e+032.100000e+020.0000000.0000001.019000e+032.330000e+028.890000e+023.140000e+014.100000e+011.260000e+024.500000e+013.200000e+014.400000e+015.700000e+016.300000e+016.200000e+014.200000e+011.700000e+011.105000e+030.000000e+006.790000e+023.830000e+021.660000e+023.130000e+021.970000e+021.130000e+026.600000e+010.0000000.0000001.000000e+021.900000e+011.700000e+012.310000e+022.610000e+022.970000e+022.830000e+021.210000e+022.610000e+021.240000e+021.450000e+021.450000e+022.014000e+033.400000e+018.700000e+014.800000e+011.090000e+025.560000e+026.360000e+022.400000e+021.020000e+026.035000e+043.400000e+012.000000e+011.824000e+031.830000e+020.000000e+001.637000e+032.000000e+012.520000e+025.500000e+027.280000e+021.890000e+022.160000e+024.380000e+023.390000e+028.500000e+025.470000e+026.830000e+022.950000e+029.020000e+024.200000e+019.200000e+014.900000e+011.900000e+013.900000e+011.720000e+021.150000e+025.820000e+022.156000e+031.450000e+022.319000e+031.325000e+032.115000e+030.000000e+002.300000e+031.700000e+011.940000e+021.260000e+024.870000e+021.250000e+023.700000e+012.040000e+021.010000e+028.700000e+012.380000e+022.200000e+021.010000e+025.300000e+017.470000e+021.810000e+022.530000e+025.010000e+023.740000e+027.470000e+025.010000e+022.240000e+022.260000e+022.360000e+021.120000e+032.120000e+02NoneNoneNone
                                                    median3.627000e+031.357000e+031.773000e+031.842000e+033.710000e+019.400000e+019.800000e+019.900000e+015.900000e+013.100000e+011.300000e+011.300000e+015.600000e+011.030000e+021.010000e+029.600000e+019.700000e+011.030000e+021.110000e+021.080000e+022.900000e+013.900000e+015.000000e+013.200000e+011.900000e+011.500000e+019.000000e+019.400000e+019.400000e+015.600000e+012.700000e+011.100000e+011.100000e+015.600000e+011.020000e+021.020000e+021.000000e+029.900000e+011.060000e+021.180000e+021.170000e+024.000000e+015.800000e+013.400000e+014.500000e+016.000000e+014.100000e+012.900000e+013.000000e+011.989000e+033.590000e+033.499000e+03NoneNone2.921000e+032.453000e+031.880000e+03NoneNoneNoneNoneNone8.200000e+018.200000e+012.800000e+011.870000e+020.000000e+000.0000004.700000e+011.870000e+022.906000e+030.000000e+000.0000000.000000e+000.000000e+005.000000e+000.000000e+001.270000e+021.350000e+024.881100e+042.455200e+046.400000e+014.800000e+015.100000e+015.500000e+015.200000e+015.300000e+014.900000e+015.000000e+014.400000e+019.200000e+011.200000e+021.450000e+029.200000e+014.800000e+014.000000e+012.600000e+013.523000e+033.830000e+020.4050001.547000e+038.510000e+021.011000e+051.479000e+052.031000e+051.357000e+033.740000e+021.200000e+021.000000e+010.000000e+009.150000e+022.600000e+011.500000e+012.300000e+011.900000e+019.000000e+000.000000e+000.000000e+000.000000e+000.000000e+004.000000e+004.000000e+011.969000e+035.990000e+024.090000e+028.700000e+021.290000e+020.0000000.0000007.650000e+021.440000e+026.910000e+022.810000e+012.400000e+017.000000e+012.200000e+011.500000e+012.200000e+013.000000e+013.500000e+013.500000e+012.300000e+017.000000e+008.120000e+020.000000e+004.720000e+022.510000e+026.700000e+012.230000e+021.300000e+027.100000e+013.500000e+010.0000000.0000005.700000e+015.000000e+000.000000e+001.500000e+021.800000e+022.070000e+021.930000e+027.400000e+011.740000e+027.300000e+018.200000e+018.400000e+011.548000e+031.600000e+015.200000e+012.300000e+016.200000e+014.110000e+024.710000e+021.620000e+026.000000e+014.599500e+048.000000e+002.000000e+001.362000e+031.240000e+020.000000e+001.211000e+035.000000e+001.780000e+023.420000e+025.340000e+021.320000e+021.260000e+023.250000e+022.140000e+026.300000e+023.450000e+024.080000e+021.650000e+026.720000e+022.500000e+015.200000e+012.400000e+016.000000e+002.100000e+011.180000e+027.900000e+014.500000e+021.651000e+031.000000e+021.793000e+031.026000e+031.622000e+030.000000e+001.780000e+030.000000e+001.330000e+028.200000e+013.580000e+027.800000e+011.900000e+011.290000e+026.900000e+015.500000e+011.720000e+021.410000e+026.600000e+013.100000e+014.950000e+021.200000e+021.680000e+023.690000e+022.740000e+024.950000e+023.690000e+021.580000e+021.610000e+021.650000e+028.330000e+021.420000e+02NoneNoneNone
                                                    interquartile_range1.979000e+037.160000e+029.780000e+021.016000e+038.300000e+008.200000e+018.500000e+018.600000e+015.600000e+013.800000e+012.600000e+012.600000e+015.600000e+019.000000e+018.500000e+017.800000e+017.800000e+017.800000e+018.000000e+017.700000e+013.000000e+013.700000e+014.600000e+013.400000e+012.500000e+012.300000e+017.900000e+018.300000e+018.400000e+015.500000e+013.400000e+012.300000e+012.400000e+015.400000e+018.900000e+018.200000e+017.800000e+017.900000e+018.000000e+018.200000e+018.200000e+013.500000e+014.600000e+013.200000e+014.000000e+015.100000e+014.100000e+013.200000e+013.700000e+011.991000e+031.921000e+031.902000e+03NoneNone1.541000e+031.333000e+031.070000e+03NoneNoneNoneNoneNone1.760000e+022.530000e+029.700000e+013.820000e+022.000000e+000.0000007.200000e+013.820000e+021.866000e+030.000000e+000.0000001.600000e+011.300000e+012.300000e+011.300000e+011.470000e+021.500000e+022.385100e+041.119800e+047.000000e+015.600000e+015.400000e+015.200000e+014.900000e+015.000000e+014.600000e+014.500000e+014.100000e+017.000000e+019.100000e+011.210000e+029.400000e+016.400000e+016.700000e+015.200000e+011.937000e+033.930000e+020.0609007.960000e+023.630000e+028.760000e+041.120000e+051.376000e+057.160000e+023.610000e+021.240000e+023.100000e+011.500000e+017.260000e+025.500000e+014.100000e+015.900000e+016.000000e+014.300000e+012.700000e+012.000000e+012.700000e+010.000000e+002.100000e+017.200000e+012.200000e+014.320000e+022.820000e+025.140000e+021.480000e+020.0000000.0000005.110000e+021.600000e+023.600000e+027.200000e+003.200000e+019.700000e+013.800000e+013.200000e+013.700000e+014.600000e+014.900000e+014.700000e+013.400000e+011.700000e+016.090000e+020.000000e+004.050000e+022.430000e+021.600000e+021.720000e+021.270000e+027.900000e+015.400000e+010.0000000.0000007.700000e+011.900000e+011.700000e+011.470000e+021.570000e+021.710000e+021.730000e+028.500000e+011.660000e+029.100000e+011.070000e+021.030000e+029.420000e+023.400000e+016.400000e+014.100000e+018.000000e+012.780000e+023.350000e+021.550000e+027.800000e+012.720000e+043.400000e+012.000000e+019.300000e+021.090000e+020.000000e+008.570000e+022.000000e+011.430000e+023.590000e+023.860000e+021.090000e+021.540000e+022.160000e+022.240000e+024.500000e+023.550000e+024.630000e+022.140000e+024.450000e+023.100000e+016.800000e+014.100000e+011.900000e+013.200000e+011.050000e+026.800000e+012.660000e+021.013000e+038.500000e+011.074000e+035.790000e+021.000000e+030.000000e+001.065000e+031.700000e+011.120000e+028.100000e+012.530000e+028.400000e+013.000000e+011.390000e+026.100000e+015.800000e+011.270000e+021.390000e+026.400000e+014.000000e+014.570000e+021.180000e+021.570000e+022.580000e+021.880000e+024.570000e+022.580000e+021.270000e+021.290000e+021.340000e+025.600000e+021.280000e+02NoneNoneNone
                                                    \n", + "
                                                    " + ], + "text/plain": [ + " total_pop households male_pop female_pop \\\n", + "avg 4.384716e+03 1.622255e+03 2.157711e+03 2.227005e+03 \n", + "max 6.552800e+04 2.142900e+04 3.226600e+04 3.326200e+04 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 3.244734e+08 1.200485e+08 1.596728e+08 1.648006e+08 \n", + "range 6.552800e+04 2.142900e+04 3.226600e+04 3.326200e+04 \n", + "stdev 2.228937e+03 7.936172e+02 1.120561e+03 1.146240e+03 \n", + "q1 2.627000e+03 9.980000e+02 1.276000e+03 1.318000e+03 \n", + "q3 4.606000e+03 1.714000e+03 2.254000e+03 2.334000e+03 \n", + "median 3.627000e+03 1.357000e+03 1.773000e+03 1.842000e+03 \n", + "interquartile_range 1.979000e+03 7.160000e+02 9.780000e+02 1.016000e+03 \n", + "\n", + " median_age male_under_5 male_5_to_9 male_10_to_14 \\\n", + "avg 3.930112e+01 1.383831e+02 1.423078e+02 1.445751e+02 \n", + "max 8.570000e+01 3.297000e+03 3.464000e+03 3.137000e+03 \n", + "min 7.700000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 2.879751e+06 1.024049e+07 1.053092e+07 1.069870e+07 \n", + "range 7.800000e+01 3.297000e+03 3.464000e+03 3.137000e+03 \n", + "stdev 7.750633e+00 1.104914e+02 1.121643e+02 1.132003e+02 \n", + "q1 3.280000e+01 5.600000e+01 5.700000e+01 5.900000e+01 \n", + "q3 4.110000e+01 1.380000e+02 1.420000e+02 1.450000e+02 \n", + "median 3.710000e+01 9.400000e+01 9.800000e+01 9.900000e+01 \n", + "interquartile_range 8.300000e+00 8.200000e+01 8.500000e+01 8.600000e+01 \n", + "\n", + " male_15_to_17 male_18_to_19 male_20 male_21 \\\n", + "avg 8.804836e+01 6.037216e+01 3.276450e+01 3.217450e+01 \n", + "max 2.416000e+03 3.909000e+03 2.845000e+03 3.025000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 6.515667e+06 4.467600e+06 2.424606e+06 2.380945e+06 \n", + "range 2.416000e+03 3.909000e+03 2.845000e+03 3.025000e+03 \n", + "stdev 7.188730e+01 1.075927e+02 5.489900e+01 5.251894e+01 \n", + "q1 3.200000e+01 1.400000e+01 0.000000e+00 0.000000e+00 \n", + "q3 8.800000e+01 5.200000e+01 2.600000e+01 2.600000e+01 \n", + "median 5.900000e+01 3.100000e+01 1.300000e+01 1.300000e+01 \n", + "interquartile_range 5.600000e+01 3.800000e+01 2.600000e+01 2.600000e+01 \n", + "\n", + " male_22_to_24 male_25_to_29 male_30_to_34 \\\n", + "avg 9.285125e+01 1.552524e+02 1.483501e+02 \n", + "max 6.083000e+03 3.956000e+03 2.143000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 6.871085e+06 1.148883e+07 1.097806e+07 \n", + "range 6.083000e+03 3.956000e+03 2.143000e+03 \n", + "stdev 9.475956e+01 1.251577e+02 1.155036e+02 \n", + "q1 3.100000e+01 6.100000e+01 6.100000e+01 \n", + "q3 8.700000e+01 1.510000e+02 1.460000e+02 \n", + "median 5.600000e+01 1.030000e+02 1.010000e+02 \n", + "interquartile_range 5.600000e+01 9.000000e+01 8.500000e+01 \n", + "\n", + " male_35_to_39 male_40_to_44 male_45_to_49 \\\n", + "avg 1.388303e+02 1.375463e+02 1.416246e+02 \n", + "max 3.146000e+03 3.718000e+03 2.652000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 1.027358e+07 1.017856e+07 1.048036e+07 \n", + "range 3.146000e+03 3.718000e+03 2.652000e+03 \n", + "stdev 1.057082e+02 1.034058e+02 9.913924e+01 \n", + "q1 6.000000e+01 6.000000e+01 6.600000e+01 \n", + "q3 1.380000e+02 1.380000e+02 1.440000e+02 \n", + "median 9.600000e+01 9.700000e+01 1.030000e+02 \n", + "interquartile_range 7.800000e+01 7.800000e+01 7.800000e+01 \n", + "\n", + " male_50_to_54 male_55_to_59 male_65_to_66 \\\n", + "avg 1.482727e+02 1.423548e+02 4.490084e+01 \n", + "max 2.300000e+03 1.741000e+03 1.244000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 1.097233e+07 1.053440e+07 3.322707e+06 \n", + "range 2.300000e+03 1.741000e+03 1.244000e+03 \n", + "stdev 9.575753e+01 8.738622e+01 3.685899e+01 \n", + "q1 7.200000e+01 7.100000e+01 1.500000e+01 \n", + "q3 1.520000e+02 1.480000e+02 4.500000e+01 \n", + "median 1.110000e+02 1.080000e+02 2.900000e+01 \n", + "interquartile_range 8.000000e+01 7.700000e+01 3.000000e+01 \n", + "\n", + " male_67_to_69 male_70_to_74 male_75_to_79 \\\n", + "avg 5.796503e+01 7.306170e+01 5.008765e+01 \n", + "max 3.225000e+03 4.708000e+03 3.287000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 4.289470e+06 5.406639e+06 3.706536e+06 \n", + "range 3.225000e+03 4.708000e+03 3.287000e+03 \n", + "stdev 4.708550e+01 5.993755e+01 4.641712e+01 \n", + "q1 2.200000e+01 2.800000e+01 1.600000e+01 \n", + "q3 5.900000e+01 7.400000e+01 5.000000e+01 \n", + "median 3.900000e+01 5.000000e+01 3.200000e+01 \n", + "interquartile_range 3.700000e+01 4.600000e+01 3.400000e+01 \n", + "\n", + " male_80_to_84 male_85_and_over female_under_5 \\\n", + "avg 3.334291e+01 2.886338e+01 1.322310e+02 \n", + "max 1.666000e+03 6.670000e+02 2.829000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 2.467409e+06 2.135919e+06 9.785223e+06 \n", + "range 1.666000e+03 6.670000e+02 2.829000e+03 \n", + "stdev 3.390149e+01 3.303075e+01 1.062496e+02 \n", + "q1 8.000000e+00 4.000000e+00 5.300000e+01 \n", + "q3 3.300000e+01 2.700000e+01 1.320000e+02 \n", + "median 1.900000e+01 1.500000e+01 9.000000e+01 \n", + "interquartile_range 2.500000e+01 2.300000e+01 7.900000e+01 \n", + "\n", + " female_5_to_9 female_10_to_14 female_15_to_17 \\\n", + "avg 1.365999e+02 1.383163e+02 8.401951e+01 \n", + "max 3.509000e+03 3.667000e+03 1.601000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 1.010853e+07 1.023555e+07 6.217528e+06 \n", + "range 3.509000e+03 3.667000e+03 1.601000e+03 \n", + "stdev 1.096859e+02 1.100421e+02 6.923133e+01 \n", + "q1 5.400000e+01 5.500000e+01 3.000000e+01 \n", + "q3 1.370000e+02 1.390000e+02 8.500000e+01 \n", + "median 9.400000e+01 9.400000e+01 5.600000e+01 \n", + "interquartile_range 8.300000e+01 8.400000e+01 5.500000e+01 \n", + "\n", + " female_18_to_19 female_20 female_21 \\\n", + "avg 5.756660e+01 3.088993e+01 3.041858e+01 \n", + "max 4.815000e+03 1.586000e+03 1.330000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 4.259986e+06 2.285886e+06 2.251005e+06 \n", + "range 4.815000e+03 1.586000e+03 1.330000e+03 \n", + "stdev 1.222020e+02 5.400368e+01 5.017502e+01 \n", + "q1 1.200000e+01 0.000000e+00 0.000000e+00 \n", + "q3 4.600000e+01 2.300000e+01 2.400000e+01 \n", + "median 2.700000e+01 1.100000e+01 1.100000e+01 \n", + "interquartile_range 3.400000e+01 2.300000e+01 2.400000e+01 \n", + "\n", + " female_22_to_24 female_25_to_29 female_30_to_34 \\\n", + "avg 8.833956e+01 1.505691e+02 1.468783e+02 \n", + "max 2.045000e+03 2.410000e+03 2.840000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 6.537216e+06 1.114227e+07 1.086914e+07 \n", + "range 2.045000e+03 2.410000e+03 2.840000e+03 \n", + "stdev 7.957206e+01 1.181003e+02 1.097178e+02 \n", + "q1 3.000000e+01 6.000000e+01 6.400000e+01 \n", + "q3 8.400000e+01 1.490000e+02 1.460000e+02 \n", + "median 5.600000e+01 1.020000e+02 1.020000e+02 \n", + "interquartile_range 5.400000e+01 8.900000e+01 8.200000e+01 \n", + "\n", + " female_35_to_39 female_40_to_44 female_45_to_49 \\\n", + "avg 1.396495e+02 1.393065e+02 1.446716e+02 \n", + "max 3.450000e+03 3.586000e+03 3.628000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 1.033420e+07 1.030882e+07 1.070584e+07 \n", + "range 3.450000e+03 3.586000e+03 3.628000e+03 \n", + "stdev 1.040098e+02 1.022617e+02 9.984035e+01 \n", + "q1 6.200000e+01 6.200000e+01 6.800000e+01 \n", + "q3 1.400000e+02 1.410000e+02 1.480000e+02 \n", + "median 1.000000e+02 9.900000e+01 1.060000e+02 \n", + "interquartile_range 7.800000e+01 7.900000e+01 8.000000e+01 \n", + "\n", + " female_50_to_54 female_55_to_59 female_60_to_61 \\\n", + "avg 1.538896e+02 1.514543e+02 5.749201e+01 \n", + "max 1.756000e+03 1.445000e+03 8.730000e+02 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 1.138799e+07 1.120777e+07 4.254466e+06 \n", + "range 1.756000e+03 1.445000e+03 8.730000e+02 \n", + "stdev 9.595609e+01 9.064753e+01 4.279481e+01 \n", + "q1 7.700000e+01 7.700000e+01 2.300000e+01 \n", + "q3 1.590000e+02 1.590000e+02 5.800000e+01 \n", + "median 1.180000e+02 1.170000e+02 4.000000e+01 \n", + "interquartile_range 8.200000e+01 8.200000e+01 3.500000e+01 \n", + "\n", + " female_62_to_64 female_65_to_66 female_67_to_69 \\\n", + "avg 7.940259e+01 4.982987e+01 6.520221e+01 \n", + "max 2.105000e+03 2.147000e+03 3.661000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 5.875871e+06 3.687460e+06 4.825029e+06 \n", + "range 2.105000e+03 2.147000e+03 3.661000e+03 \n", + "stdev 5.533706e+01 4.018353e+01 5.125616e+01 \n", + "q1 3.500000e+01 1.800000e+01 2.600000e+01 \n", + "q3 8.100000e+01 5.000000e+01 6.600000e+01 \n", + "median 5.800000e+01 3.400000e+01 4.500000e+01 \n", + "interquartile_range 4.600000e+01 3.200000e+01 4.000000e+01 \n", + "\n", + " female_70_to_74 female_75_to_79 female_80_to_84 \\\n", + "avg 8.553553e+01 6.253888e+01 4.708153e+01 \n", + "max 5.268000e+03 2.481000e+03 1.166000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 6.329715e+06 4.627940e+06 3.484080e+06 \n", + "range 5.268000e+03 2.481000e+03 1.166000e+03 \n", + "stdev 6.700952e+01 5.231319e+01 4.266777e+01 \n", + "q1 3.600000e+01 2.200000e+01 1.400000e+01 \n", + "q3 8.700000e+01 6.300000e+01 4.600000e+01 \n", + "median 6.000000e+01 4.100000e+01 2.900000e+01 \n", + "interquartile_range 5.100000e+01 4.100000e+01 3.200000e+01 \n", + "\n", + " female_85_and_over white_pop \\\n", + "avg 5.512231e+01 2.666237e+03 \n", + "max 1.337000e+03 3.868400e+04 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 4.079106e+06 1.973042e+08 \n", + "range 1.337000e+03 3.868400e+04 \n", + "stdev 5.793785e+01 1.907504e+03 \n", + "q1 1.400000e+01 9.510000e+02 \n", + "q3 5.100000e+01 2.942000e+03 \n", + "median 3.000000e+01 1.989000e+03 \n", + "interquartile_range 3.700000e+01 1.991000e+03 \n", + "\n", + " population_1_year_and_over population_3_years_over \\\n", + "avg 4.342539e+03 4.225816e+03 \n", + "max 6.480300e+04 6.243800e+04 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 3.172486e+08 3.127146e+08 \n", + "range 6.480300e+04 6.243800e+04 \n", + "stdev 2.201732e+03 2.139467e+03 \n", + "q1 2.604000e+03 2.519000e+03 \n", + "q3 4.525000e+03 4.421000e+03 \n", + "median 3.590000e+03 3.499000e+03 \n", + "interquartile_range 1.921000e+03 1.902000e+03 \n", + "\n", + " pop_5_years_over pop_15_and_over pop_16_over \\\n", + "avg None None 3.495006e+03 \n", + "max None None 4.464200e+04 \n", + "min None None 0.000000e+00 \n", + "sum None None 2.586340e+08 \n", + "range None None 4.464200e+04 \n", + "stdev None None 1.719905e+03 \n", + "q1 None None 2.125000e+03 \n", + "q3 None None 3.666000e+03 \n", + "median None None 2.921000e+03 \n", + "interquartile_range None None 1.541000e+03 \n", + "\n", + " pop_25_years_over pop_25_64 pop_never_married \\\n", + "avg 2.954858e+03 2.301326e+03 None \n", + "max 3.948600e+04 3.425800e+04 None \n", + "min 0.000000e+00 0.000000e+00 None \n", + "sum 2.186624e+08 1.703004e+08 None \n", + "range 3.948600e+04 3.425800e+04 None \n", + "stdev 1.474748e+03 1.216138e+03 None \n", + "q1 1.782000e+03 1.328000e+03 None \n", + "q3 3.115000e+03 2.398000e+03 None \n", + "median 2.453000e+03 1.880000e+03 None \n", + "interquartile_range 1.333000e+03 1.070000e+03 None \n", + "\n", + " pop_now_married pop_separated pop_widowed pop_divorced \\\n", + "avg None None None None \n", + "max None None None None \n", + "min None None None None \n", + "sum None None None None \n", + "range None None None None \n", + "stdev None None None None \n", + "q1 None None None None \n", + "q3 None None None None \n", + "median None None None None \n", + "interquartile_range None None None None \n", + "\n", + " not_us_citizen_pop black_pop asian_pop \\\n", + "avg 3.057622e+02 5.330768e+02 2.296153e+02 \n", + "max 1.223200e+04 1.665500e+04 1.371200e+04 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 2.233776e+07 3.944822e+07 1.699176e+07 \n", + "range 1.223200e+04 1.665500e+04 1.371200e+04 \n", + "stdev 4.535513e+02 9.093226e+02 5.036082e+02 \n", + "q1 2.200000e+01 1.600000e+01 0.000000e+00 \n", + "q3 1.980000e+02 2.690000e+02 9.700000e+01 \n", + "median 8.200000e+01 8.200000e+01 2.800000e+01 \n", + "interquartile_range 1.760000e+02 2.530000e+02 9.700000e+01 \n", + "\n", + " hispanic_pop amerindian_pop other_race_pop \\\n", + "avg 8.100321e+02 2.836241e+01 9.694369 \n", + "max 2.868300e+04 9.644000e+03 1385.000000 \n", + "min 0.000000e+00 0.000000e+00 0.000000 \n", + "sum 5.994318e+07 2.098847e+06 717393.000000 \n", + "range 2.868300e+04 9.644000e+03 1385.000000 \n", + "stdev 1.282778e+03 1.789563e+02 34.687847 \n", + "q1 6.600000e+01 0.000000e+00 0.000000 \n", + "q3 4.480000e+02 2.000000e+00 0.000000 \n", + "median 1.870000e+02 0.000000e+00 0.000000 \n", + "interquartile_range 3.820000e+02 2.000000e+00 0.000000 \n", + "\n", + " two_or_more_races_pop hispanic_any_race \\\n", + "avg 1.007309e+02 8.100321e+02 \n", + "max 3.611000e+03 2.868300e+04 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 7.454186e+06 5.994318e+07 \n", + "range 3.611000e+03 2.868300e+04 \n", + "stdev 1.270444e+02 1.282778e+03 \n", + "q1 1.600000e+01 6.600000e+01 \n", + "q3 8.800000e+01 4.480000e+02 \n", + "median 4.700000e+01 1.870000e+02 \n", + "interquartile_range 7.200000e+01 3.820000e+02 \n", + "\n", + " not_hispanic_pop asian_male_45_54 asian_male_55_64 \\\n", + "avg 3.574684e+03 1.491637e+01 11.693788 \n", + "max 5.047700e+04 1.301000e+03 745.000000 \n", + "min 0.000000e+00 0.000000e+00 0.000000 \n", + "sum 2.645302e+08 1.103826e+06 865352.000000 \n", + "range 5.047700e+04 1.301000e+03 745.000000 \n", + "stdev 2.056417e+03 3.975201e+01 31.827724 \n", + "q1 1.943000e+03 0.000000e+00 0.000000 \n", + "q3 3.809000e+03 0.000000e+00 0.000000 \n", + "median 2.906000e+03 0.000000e+00 0.000000 \n", + "interquartile_range 1.866000e+03 0.000000e+00 0.000000 \n", + "\n", + " black_male_45_54 black_male_55_64 hispanic_male_45_54 \\\n", + "avg 3.440143e+01 2.904223e+01 4.700662e+01 \n", + "max 1.094000e+03 1.122000e+03 1.464000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 2.545740e+06 2.149154e+06 3.478537e+06 \n", + "range 1.094000e+03 1.122000e+03 1.464000e+03 \n", + "stdev 6.303262e+01 5.395722e+01 8.368872e+01 \n", + "q1 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "q3 1.600000e+01 1.300000e+01 2.300000e+01 \n", + "median 0.000000e+00 0.000000e+00 5.000000e+00 \n", + "interquartile_range 1.600000e+01 1.300000e+01 2.300000e+01 \n", + "\n", + " hispanic_male_55_64 white_male_45_54 white_male_55_64 \\\n", + "avg 3.048543e+01 1.883150e+02 1.926332e+02 \n", + "max 7.770000e+02 2.189000e+03 2.545000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 2.255952e+06 1.393550e+07 1.425505e+07 \n", + "range 7.770000e+02 2.189000e+03 2.545000e+03 \n", + "stdev 5.878341e+01 1.523871e+02 1.455158e+02 \n", + "q1 0.000000e+00 5.300000e+01 6.000000e+01 \n", + "q3 1.300000e+01 2.000000e+02 2.100000e+02 \n", + "median 0.000000e+00 1.270000e+02 1.350000e+02 \n", + "interquartile_range 1.300000e+01 1.470000e+02 1.500000e+02 \n", + "\n", + " median_income income_per_capita income_less_10000 \\\n", + "avg 6.120650e+04 3.065180e+04 1.120286e+02 \n", + "max 2.500010e+05 2.202530e+05 1.550000e+03 \n", + "min 2.499000e+03 3.200000e+01 0.000000e+00 \n", + "sum 4.464586e+09 2.245429e+09 8.290228e+06 \n", + "range 2.475020e+05 2.202210e+05 1.550000e+03 \n", + "stdev 3.090106e+04 1.612694e+04 1.063003e+02 \n", + "q1 3.697400e+04 1.902300e+04 3.400000e+01 \n", + "q3 6.082500e+04 3.022100e+04 1.040000e+02 \n", + "median 4.881100e+04 2.455200e+04 6.400000e+01 \n", + "interquartile_range 2.385100e+04 1.119800e+04 7.000000e+01 \n", + "\n", + " income_10000_14999 income_15000_19999 \\\n", + "avg 7.990310e+01 7.906576e+01 \n", + "max 9.300000e+02 7.610000e+02 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 5.912909e+06 5.850945e+06 \n", + "range 9.300000e+02 7.610000e+02 \n", + "stdev 6.984943e+01 6.359047e+01 \n", + "q1 2.300000e+01 2.600000e+01 \n", + "q3 7.900000e+01 8.000000e+01 \n", + "median 4.800000e+01 5.100000e+01 \n", + "interquartile_range 5.600000e+01 5.400000e+01 \n", + "\n", + " income_20000_24999 income_25000_29999 \\\n", + "avg 8.121069e+01 7.699035e+01 \n", + "max 1.009000e+03 8.760000e+02 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 6.009672e+06 5.697363e+06 \n", + "range 1.009000e+03 8.760000e+02 \n", + "stdev 6.205942e+01 5.829994e+01 \n", + "q1 3.000000e+01 2.900000e+01 \n", + "q3 8.200000e+01 7.800000e+01 \n", + "median 5.500000e+01 5.200000e+01 \n", + "interquartile_range 5.200000e+01 4.900000e+01 \n", + "\n", + " income_30000_34999 income_35000_39999 \\\n", + "avg 7.811121e+01 7.282273e+01 \n", + "max 8.640000e+02 1.530000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 5.780308e+06 5.388955e+06 \n", + "range 8.640000e+02 1.530000e+03 \n", + "stdev 5.862582e+01 5.505314e+01 \n", + "q1 3.000000e+01 2.800000e+01 \n", + "q3 8.000000e+01 7.400000e+01 \n", + "median 5.300000e+01 4.900000e+01 \n", + "interquartile_range 5.000000e+01 4.600000e+01 \n", + "\n", + " income_40000_44999 income_45000_49999 \\\n", + "avg 7.298924e+01 6.437017e+01 \n", + "max 1.118000e+03 8.610000e+02 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 5.401277e+06 4.763457e+06 \n", + "range 1.118000e+03 8.610000e+02 \n", + "stdev 5.498863e+01 4.950700e+01 \n", + "q1 2.900000e+01 2.400000e+01 \n", + "q3 7.400000e+01 6.500000e+01 \n", + "median 5.000000e+01 4.400000e+01 \n", + "interquartile_range 4.500000e+01 4.100000e+01 \n", + "\n", + " income_50000_59999 income_60000_74999 \\\n", + "avg 1.248865e+02 1.605002e+02 \n", + "max 2.054000e+03 2.789000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 9.241728e+06 1.187718e+07 \n", + "range 2.054000e+03 2.789000e+03 \n", + "stdev 8.417410e+01 1.059809e+02 \n", + "q1 5.800000e+01 7.600000e+01 \n", + "q3 1.280000e+02 1.670000e+02 \n", + "median 9.200000e+01 1.200000e+02 \n", + "interquartile_range 7.000000e+01 9.100000e+01 \n", + "\n", + " income_75000_99999 income_100000_124999 \\\n", + "avg 1.984182e+02 1.391279e+02 \n", + "max 3.368000e+03 2.704000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 1.468315e+07 1.029560e+07 \n", + "range 3.368000e+03 2.704000e+03 \n", + "stdev 1.372959e+02 1.147172e+02 \n", + "q1 8.600000e+01 4.700000e+01 \n", + "q3 2.070000e+02 1.410000e+02 \n", + "median 1.450000e+02 9.200000e+01 \n", + "interquartile_range 1.210000e+02 9.400000e+01 \n", + "\n", + " income_125000_149999 income_150000_199999 \\\n", + "avg 8.701426e+01 9.380035e+01 \n", + "max 2.157000e+03 3.878000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 6.439142e+06 6.941320e+06 \n", + "range 2.157000e+03 3.878000e+03 \n", + "stdev 8.513799e+01 1.078177e+02 \n", + "q1 1.900000e+01 1.400000e+01 \n", + "q3 8.300000e+01 8.100000e+01 \n", + "median 4.800000e+01 4.000000e+01 \n", + "interquartile_range 6.400000e+01 6.700000e+01 \n", + "\n", + " income_200000_or_more pop_determined_poverty_status \\\n", + "avg 1.010161e+02 4.276775e+03 \n", + "max 5.386000e+03 6.533200e+04 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 7.475296e+06 3.164856e+08 \n", + "range 5.386000e+03 6.533200e+04 \n", + "stdev 1.647825e+02 2.216522e+03 \n", + "q1 8.000000e+00 2.559000e+03 \n", + "q3 6.000000e+01 4.496000e+03 \n", + "median 2.600000e+01 3.523000e+03 \n", + "interquartile_range 5.200000e+01 1.937000e+03 \n", + "\n", + " poverty gini_index housing_units \\\n", + "avg 6.377423e+02 0.425911 1.850797e+03 \n", + "max 9.440000e+03 0.829000 2.652600e+04 \n", + "min 0.000000e+00 0.000700 0.000000e+00 \n", + "sum 4.719356e+07 31111.100500 1.369609e+08 \n", + "range 9.440000e+03 0.828300 2.652600e+04 \n", + "stdev 5.618142e+02 0.063577 8.958362e+02 \n", + "q1 2.120000e+02 0.373800 1.153000e+03 \n", + "q3 6.050000e+02 0.434700 1.949000e+03 \n", + "median 3.830000e+02 0.405000 1.547000e+03 \n", + "interquartile_range 3.930000e+02 0.060900 7.960000e+02 \n", + "\n", + " renter_occupied_housing_units_paying_cash_median_gross_rent \\\n", + "avg 1.058678e+03 \n", + "max 3.501000e+03 \n", + "min 9.900000e+01 \n", + "sum 7.612849e+07 \n", + "range 3.402000e+03 \n", + "stdev 4.642559e+02 \n", + "q1 6.920000e+02 \n", + "q3 1.055000e+03 \n", + "median 8.510000e+02 \n", + "interquartile_range 3.630000e+02 \n", + "\n", + " owner_occupied_housing_units_lower_value_quartile \\\n", + "avg 1.765685e+05 \n", + "max 2.000001e+06 \n", + "min 9.999000e+03 \n", + "sum 1.258933e+10 \n", + "range 1.990002e+06 \n", + "stdev 1.648026e+05 \n", + "q1 6.570000e+04 \n", + "q3 1.533000e+05 \n", + "median 1.011000e+05 \n", + "interquartile_range 8.760000e+04 \n", + "\n", + " owner_occupied_housing_units_median_value \\\n", + "avg 2.443280e+05 \n", + "max 2.000001e+06 \n", + "min 9.999000e+03 \n", + "sum 1.758746e+10 \n", + "range 1.990002e+06 \n", + "stdev 2.189567e+05 \n", + "q1 9.880000e+04 \n", + "q3 2.108000e+05 \n", + "median 1.479000e+05 \n", + "interquartile_range 1.120000e+05 \n", + "\n", + " owner_occupied_housing_units_upper_value_quartile \\\n", + "avg 3.329604e+05 \n", + "max 2.000001e+06 \n", + "min 9.999000e+03 \n", + "sum 2.396882e+10 \n", + "range 1.990002e+06 \n", + "stdev 2.866141e+05 \n", + "q1 1.491000e+05 \n", + "q3 2.867000e+05 \n", + "median 2.031000e+05 \n", + "interquartile_range 1.376000e+05 \n", + "\n", + " occupied_housing_units housing_units_renter_occupied \\\n", + "avg 1.622255e+03 5.861794e+02 \n", + "max 2.142900e+04 8.039000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 1.200485e+08 4.337786e+07 \n", + "range 2.142900e+04 8.039000e+03 \n", + "stdev 7.936172e+02 4.791495e+02 \n", + "q1 9.980000e+02 2.090000e+02 \n", + "q3 1.714000e+03 5.700000e+02 \n", + "median 1.357000e+03 3.740000e+02 \n", + "interquartile_range 7.160000e+02 3.610000e+02 \n", + "\n", + " vacant_housing_units vacant_housing_units_for_rent \\\n", + "avg 2.285420e+02 3.884688e+01 \n", + "max 1.192400e+04 4.472000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 1.691234e+07 2.874708e+06 \n", + "range 1.192400e+04 4.472000e+03 \n", + "stdev 3.131224e+02 6.702031e+01 \n", + "q1 6.400000e+01 0.000000e+00 \n", + "q3 1.880000e+02 3.100000e+01 \n", + "median 1.200000e+02 1.000000e+01 \n", + "interquartile_range 1.240000e+02 3.100000e+01 \n", + "\n", + " vacant_housing_units_for_sale \\\n", + "avg 1.863187e+01 \n", + "max 5.470000e+02 \n", + "min 0.000000e+00 \n", + "sum 1.378777e+06 \n", + "range 5.470000e+02 \n", + "stdev 2.830720e+01 \n", + "q1 0.000000e+00 \n", + "q3 1.500000e+01 \n", + "median 0.000000e+00 \n", + "interquartile_range 1.500000e+01 \n", + "\n", + " dwellings_1_units_detached dwellings_1_units_attached \\\n", + "avg 1.143346e+03 1.092662e+02 \n", + "max 2.552700e+04 4.280000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 8.460878e+07 8.085807e+06 \n", + "range 2.552700e+04 4.280000e+03 \n", + "stdev 7.543086e+02 1.952911e+02 \n", + "q1 5.220000e+02 7.000000e+00 \n", + "q3 1.248000e+03 6.200000e+01 \n", + "median 9.150000e+02 2.600000e+01 \n", + "interquartile_range 7.260000e+02 5.500000e+01 \n", + "\n", + " dwellings_2_units dwellings_3_to_4_units \\\n", + "avg 6.762999e+01 8.112938e+01 \n", + "max 1.999000e+03 1.629000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 5.004687e+06 6.003655e+06 \n", + "range 1.999000e+03 1.629000e+03 \n", + "stdev 1.133588e+02 1.165817e+02 \n", + "q1 0.000000e+00 0.000000e+00 \n", + "q3 4.100000e+01 5.900000e+01 \n", + "median 1.500000e+01 2.300000e+01 \n", + "interquartile_range 4.100000e+01 5.900000e+01 \n", + "\n", + " dwellings_5_to_9_units dwellings_10_to_19_units \\\n", + "avg 8.797462e+01 8.248062e+01 \n", + "max 1.863000e+03 5.280000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 6.510210e+06 6.103648e+06 \n", + "range 1.863000e+03 5.280000e+03 \n", + "stdev 1.324159e+02 1.511113e+02 \n", + "q1 0.000000e+00 0.000000e+00 \n", + "q3 6.000000e+01 4.300000e+01 \n", + "median 1.900000e+01 9.000000e+00 \n", + "interquartile_range 6.000000e+01 4.300000e+01 \n", + "\n", + " dwellings_20_to_49_units dwellings_50_or_more_units \\\n", + "avg 6.591754e+01 9.643179e+01 \n", + "max 4.283000e+03 1.151800e+04 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 4.877964e+06 7.136049e+06 \n", + "range 4.283000e+03 1.151800e+04 \n", + "stdev 1.440865e+02 2.991683e+02 \n", + "q1 0.000000e+00 0.000000e+00 \n", + "q3 2.700000e+01 2.000000e+01 \n", + "median 0.000000e+00 0.000000e+00 \n", + "interquartile_range 2.700000e+01 2.000000e+01 \n", + "\n", + " mobile_homes housing_built_2005_or_later \\\n", + "avg 1.150671e+02 1.610305e+01 \n", + "max 3.314000e+03 2.502000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 8.515077e+06 1.191642e+06 \n", + "range 3.314000e+03 2.502000e+03 \n", + "stdev 2.250775e+02 4.732752e+01 \n", + "q1 0.000000e+00 0.000000e+00 \n", + "q3 2.700000e+01 0.000000e+00 \n", + "median 0.000000e+00 0.000000e+00 \n", + "interquartile_range 2.700000e+01 0.000000e+00 \n", + "\n", + " housing_built_2000_to_2004 \\\n", + "avg 4.221539e+01 \n", + "max 7.206000e+03 \n", + "min 0.000000e+00 \n", + "sum 3.123981e+06 \n", + "range 7.206000e+03 \n", + "stdev 1.038676e+02 \n", + "q1 0.000000e+00 \n", + "q3 2.100000e+01 \n", + "median 4.000000e+00 \n", + "interquartile_range 2.100000e+01 \n", + "\n", + " housing_built_1939_or_earlier \\\n", + "avg 9.403019e+01 \n", + "max 6.177000e+03 \n", + "min 0.000000e+00 \n", + "sum 6.958328e+06 \n", + "range 6.177000e+03 \n", + "stdev 1.166008e+02 \n", + "q1 9.000000e+00 \n", + "q3 8.100000e+01 \n", + "median 4.000000e+01 \n", + "interquartile_range 7.200000e+01 \n", + "\n", + " median_year_structure_built married_households \\\n", + "avg 1.971950e+03 7.829447e+02 \n", + "max 2.016000e+03 1.510500e+04 \n", + "min 1.939000e+03 0.000000e+00 \n", + "sum 1.429487e+08 5.793869e+07 \n", + "range 7.700000e+01 1.510500e+04 \n", + "stdev 1.755339e+01 5.050110e+02 \n", + "q1 1.956000e+03 3.810000e+02 \n", + "q3 1.978000e+03 8.130000e+02 \n", + "median 1.969000e+03 5.990000e+02 \n", + "interquartile_range 2.200000e+01 4.320000e+02 \n", + "\n", + " nonfamily_households family_households \\\n", + "avg 5.526839e+02 1.069572e+03 \n", + "max 6.883000e+03 1.681300e+04 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 4.089916e+07 7.914937e+07 \n", + "range 6.883000e+03 1.681300e+04 \n", + "stdev 3.637558e+02 5.859720e+02 \n", + "q1 2.730000e+02 6.120000e+02 \n", + "q3 5.550000e+02 1.126000e+03 \n", + "median 4.090000e+02 8.700000e+02 \n", + "interquartile_range 2.820000e+02 5.140000e+02 \n", + "\n", + " households_public_asst_or_food_stamps \\\n", + "avg 2.201474e+02 \n", + "max 2.325000e+03 \n", + "min 0.000000e+00 \n", + "sum 1.629113e+07 \n", + "range 2.325000e+03 \n", + "stdev 1.966127e+02 \n", + "q1 6.200000e+01 \n", + "q3 2.100000e+02 \n", + "median 1.290000e+02 \n", + "interquartile_range 1.480000e+02 \n", + "\n", + " male_male_households female_female_households \\\n", + "avg 2.847543 2.909866 \n", + "max 355.000000 174.000000 \n", + "min 0.000000 0.000000 \n", + "sum 210721.000000 215333.000000 \n", + "range 355.000000 174.000000 \n", + "stdev 8.899265 7.652833 \n", + "q1 0.000000 0.000000 \n", + "q3 0.000000 0.000000 \n", + "median 0.000000 0.000000 \n", + "interquartile_range 0.000000 0.000000 \n", + "\n", + " children children_in_single_female_hh median_rent \\\n", + "avg 1.004481e+03 2.549481e+02 8.960611e+02 \n", + "max 2.360800e+04 2.863000e+03 3.501000e+03 \n", + "min 0.000000e+00 0.000000e+00 9.900000e+01 \n", + "sum 7.433261e+07 1.886641e+07 6.427357e+07 \n", + "range 2.360800e+04 2.863000e+03 3.402000e+03 \n", + "stdev 6.760134e+02 2.402736e+02 4.587204e+02 \n", + "q1 5.080000e+02 7.300000e+01 5.290000e+02 \n", + "q3 1.019000e+03 2.330000e+02 8.890000e+02 \n", + "median 7.650000e+02 1.440000e+02 6.910000e+02 \n", + "interquartile_range 5.110000e+02 1.600000e+02 3.600000e+02 \n", + "\n", + " percent_income_spent_on_rent rent_burden_not_computed \\\n", + "avg 3.075703e+01 4.549625e+01 \n", + "max 5.000000e+01 1.100000e+03 \n", + "min 1.000000e+01 0.000000e+00 \n", + "sum 2.225702e+06 3.366768e+06 \n", + "range 4.000000e+01 1.100000e+03 \n", + "stdev 7.795312e+00 4.974370e+01 \n", + "q1 2.420000e+01 9.000000e+00 \n", + "q3 3.140000e+01 4.100000e+01 \n", + "median 2.810000e+01 2.400000e+01 \n", + "interquartile_range 7.200000e+00 3.200000e+01 \n", + "\n", + " rent_over_50_percent rent_40_to_50_percent \\\n", + "avg 1.383322e+02 4.980808e+01 \n", + "max 3.487000e+03 9.720000e+02 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 1.023672e+07 3.685848e+06 \n", + "range 3.487000e+03 9.720000e+02 \n", + "stdev 1.432986e+02 5.574670e+01 \n", + "q1 2.900000e+01 7.000000e+00 \n", + "q3 1.260000e+02 4.500000e+01 \n", + "median 7.000000e+01 2.200000e+01 \n", + "interquartile_range 9.700000e+01 3.800000e+01 \n", + "\n", + " rent_35_to_40_percent rent_30_to_35_percent \\\n", + "avg 3.622229e+01 4.936739e+01 \n", + "max 6.410000e+02 8.600000e+02 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 2.680486e+06 3.653236e+06 \n", + "range 6.410000e+02 8.600000e+02 \n", + "stdev 4.303120e+01 5.598719e+01 \n", + "q1 0.000000e+00 7.000000e+00 \n", + "q3 3.200000e+01 4.400000e+01 \n", + "median 1.500000e+01 2.200000e+01 \n", + "interquartile_range 3.200000e+01 3.700000e+01 \n", + "\n", + " rent_25_to_30_percent rent_20_to_25_percent \\\n", + "avg 6.223202e+01 6.874649e+01 \n", + "max 9.990000e+02 1.382000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 4.605232e+06 5.087309e+06 \n", + "range 9.990000e+02 1.382000e+03 \n", + "stdev 6.694950e+01 7.252401e+01 \n", + "q1 1.100000e+01 1.400000e+01 \n", + "q3 5.700000e+01 6.300000e+01 \n", + "median 3.000000e+01 3.500000e+01 \n", + "interquartile_range 4.600000e+01 4.900000e+01 \n", + "\n", + " rent_15_to_20_percent rent_10_to_15_percent \\\n", + "avg 6.792566e+01 4.691335e+01 \n", + "max 1.879000e+03 1.098000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 5.026567e+06 3.471635e+06 \n", + "range 1.879000e+03 1.098000e+03 \n", + "stdev 7.206055e+01 5.312190e+01 \n", + "q1 1.500000e+01 8.000000e+00 \n", + "q3 6.200000e+01 4.200000e+01 \n", + "median 3.500000e+01 2.300000e+01 \n", + "interquartile_range 4.700000e+01 3.400000e+01 \n", + "\n", + " rent_under_10_percent owner_occupied_housing_units \\\n", + "avg 2.113569e+01 1.036076e+03 \n", + "max 9.120000e+02 2.047300e+04 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 1.564062e+06 7.667066e+07 \n", + "range 9.120000e+02 2.047300e+04 \n", + "stdev 3.129274e+01 6.543429e+02 \n", + "q1 0.000000e+00 4.960000e+02 \n", + "q3 1.700000e+01 1.105000e+03 \n", + "median 7.000000e+00 8.120000e+02 \n", + "interquartile_range 1.700000e+01 6.090000e+02 \n", + "\n", + " million_dollar_housing_units mortgaged_housing_units \\\n", + "avg 1.498115e+01 6.556553e+02 \n", + "max 1.453000e+03 1.267200e+04 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 1.108620e+06 4.851915e+07 \n", + "range 1.453000e+03 1.267200e+04 \n", + "stdev 5.537075e+01 4.790928e+02 \n", + "q1 0.000000e+00 2.740000e+02 \n", + "q3 0.000000e+00 6.790000e+02 \n", + "median 0.000000e+00 4.720000e+02 \n", + "interquartile_range 0.000000e+00 4.050000e+02 \n", + "\n", + " different_house_year_ago_different_city \\\n", + "avg 4.115417e+02 \n", + "max 1.306400e+04 \n", + "min 0.000000e+00 \n", + "sum 3.006559e+07 \n", + "range 1.306400e+04 \n", + "stdev 3.932843e+02 \n", + "q1 1.400000e+02 \n", + "q3 3.830000e+02 \n", + "median 2.510000e+02 \n", + "interquartile_range 2.430000e+02 \n", + "\n", + " different_house_year_ago_same_city \\\n", + "avg 1.951105e+02 \n", + "max 5.347000e+03 \n", + "min 0.000000e+00 \n", + "sum 1.425400e+07 \n", + "range 5.347000e+03 \n", + "stdev 2.492432e+02 \n", + "q1 6.000000e+00 \n", + "q3 1.660000e+02 \n", + "median 6.700000e+01 \n", + "interquartile_range 1.600000e+02 \n", + "\n", + " families_with_young_children \\\n", + "avg 3.127425e+02 \n", + "max 6.700000e+03 \n", + "min 0.000000e+00 \n", + "sum 2.314326e+07 \n", + "range 6.700000e+03 \n", + "stdev 2.316744e+02 \n", + "q1 1.410000e+02 \n", + "q3 3.130000e+02 \n", + "median 2.230000e+02 \n", + "interquartile_range 1.720000e+02 \n", + "\n", + " two_parent_families_with_young_children \\\n", + "avg 2.018102e+02 \n", + "max 6.517000e+03 \n", + "min 0.000000e+00 \n", + "sum 1.493416e+07 \n", + "range 6.517000e+03 \n", + "stdev 1.829281e+02 \n", + "q1 7.000000e+01 \n", + "q3 1.970000e+02 \n", + "median 1.300000e+02 \n", + "interquartile_range 1.270000e+02 \n", + "\n", + " two_parents_in_labor_force_families_with_young_children \\\n", + "avg 1.182977e+02 \n", + "max 3.989000e+03 \n", + "min 0.000000e+00 \n", + "sum 8.754150e+06 \n", + "range 3.989000e+03 \n", + "stdev 1.148169e+02 \n", + "q1 3.400000e+01 \n", + "q3 1.130000e+02 \n", + "median 7.100000e+01 \n", + "interquartile_range 7.900000e+01 \n", + "\n", + " two_parents_father_in_labor_force_families_with_young_children \\\n", + "avg 7.417954e+01 \n", + "max 3.888000e+03 \n", + "min 0.000000e+00 \n", + "sum 5.489360e+06 \n", + "range 3.888000e+03 \n", + "stdev 9.050640e+01 \n", + "q1 1.200000e+01 \n", + "q3 6.600000e+01 \n", + "median 3.500000e+01 \n", + "interquartile_range 5.400000e+01 \n", + "\n", + " two_parents_mother_in_labor_force_families_with_young_children \\\n", + "avg 6.340941 \n", + "max 1308.000000 \n", + "min 0.000000 \n", + "sum 469236.000000 \n", + "range 1308.000000 \n", + "stdev 18.145011 \n", + "q1 0.000000 \n", + "q3 0.000000 \n", + "median 0.000000 \n", + "interquartile_range 0.000000 \n", + "\n", + " two_parents_not_in_labor_force_families_with_young_children \\\n", + "avg 2.991987 \n", + "max 285.000000 \n", + "min 0.000000 \n", + "sum 221410.000000 \n", + "range 285.000000 \n", + "stdev 11.523613 \n", + "q1 0.000000 \n", + "q3 0.000000 \n", + "median 0.000000 \n", + "interquartile_range 0.000000 \n", + "\n", + " one_parent_families_with_young_children \\\n", + "avg 1.109323e+02 \n", + "max 1.418000e+03 \n", + "min 0.000000e+00 \n", + "sum 8.209099e+06 \n", + "range 1.418000e+03 \n", + "stdev 1.141550e+02 \n", + "q1 2.300000e+01 \n", + "q3 1.000000e+02 \n", + "median 5.700000e+01 \n", + "interquartile_range 7.700000e+01 \n", + "\n", + " father_one_parent_families_with_young_children \\\n", + "avg 2.560057e+01 \n", + "max 5.020000e+02 \n", + "min 0.000000e+00 \n", + "sum 1.894468e+06 \n", + "range 5.020000e+02 \n", + "stdev 3.774419e+01 \n", + "q1 0.000000e+00 \n", + "q3 1.900000e+01 \n", + "median 5.000000e+00 \n", + "interquartile_range 1.900000e+01 \n", + "\n", + " father_in_labor_force_one_parent_families_with_young_children \\\n", + "avg 2.281514e+01 \n", + "max 5.020000e+02 \n", + "min 0.000000e+00 \n", + "sum 1.688343e+06 \n", + "range 5.020000e+02 \n", + "stdev 3.534531e+01 \n", + "q1 0.000000e+00 \n", + "q3 1.700000e+01 \n", + "median 0.000000e+00 \n", + "interquartile_range 1.700000e+01 \n", + "\n", + " commute_less_10_mins commute_10_14_mins \\\n", + "avg 2.433403e+02 2.615797e+02 \n", + "max 1.162100e+04 5.838000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 1.800742e+07 1.935716e+07 \n", + "range 1.162100e+04 5.838000e+03 \n", + "stdev 2.257531e+02 1.967251e+02 \n", + "q1 8.400000e+01 1.040000e+02 \n", + "q3 2.310000e+02 2.610000e+02 \n", + "median 1.500000e+02 1.800000e+02 \n", + "interquartile_range 1.470000e+02 1.570000e+02 \n", + "\n", + " commute_15_19_mins commute_20_24_mins \\\n", + "avg 2.943585e+02 2.800684e+02 \n", + "max 7.095000e+03 4.886000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 2.178282e+07 2.072534e+07 \n", + "range 7.095000e+03 4.886000e+03 \n", + "stdev 2.132096e+02 2.089584e+02 \n", + "q1 1.260000e+02 1.100000e+02 \n", + "q3 2.970000e+02 2.830000e+02 \n", + "median 2.070000e+02 1.930000e+02 \n", + "interquartile_range 1.710000e+02 1.730000e+02 \n", + "\n", + " commute_25_29_mins commute_30_34_mins \\\n", + "avg 1.222793e+02 2.636407e+02 \n", + "max 2.205000e+03 4.880000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 9.048793e+06 1.950968e+07 \n", + "range 2.205000e+03 4.880000e+03 \n", + "stdev 1.102167e+02 2.155892e+02 \n", + "q1 3.600000e+01 9.500000e+01 \n", + "q3 1.210000e+02 2.610000e+02 \n", + "median 7.400000e+01 1.740000e+02 \n", + "interquartile_range 8.500000e+01 1.660000e+02 \n", + "\n", + " commute_35_44_mins commute_45_59_mins \\\n", + "avg 1.311400e+02 1.560668e+02 \n", + "max 3.178000e+03 5.390000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 9.704493e+06 1.154910e+07 \n", + "range 3.178000e+03 5.390000e+03 \n", + "stdev 1.292811e+02 1.602168e+02 \n", + "q1 3.300000e+01 3.800000e+01 \n", + "q3 1.240000e+02 1.450000e+02 \n", + "median 7.300000e+01 8.200000e+01 \n", + "interquartile_range 9.100000e+01 1.070000e+02 \n", + "\n", + " commute_60_more_mins commuters_16_over walked_to_work \\\n", + "avg 1.717179e+02 1.924192e+03 5.515674e+01 \n", + "max 6.070000e+03 2.648200e+04 1.162100e+04 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 1.270730e+07 1.423921e+08 4.081654e+06 \n", + "range 6.070000e+03 2.648200e+04 1.162100e+04 \n", + "stdev 1.904055e+02 1.072317e+03 1.404476e+02 \n", + "q1 4.200000e+01 1.072000e+03 0.000000e+00 \n", + "q3 1.450000e+02 2.014000e+03 3.400000e+01 \n", + "median 8.400000e+01 1.548000e+03 1.600000e+01 \n", + "interquartile_range 1.030000e+02 9.420000e+02 3.400000e+01 \n", + "\n", + " worked_at_home no_car no_cars one_car \\\n", + "avg 9.526220e+01 8.728505e+01 1.442299e+02 5.405044e+02 \n", + "max 7.043000e+03 8.111000e+03 5.847000e+03 1.554800e+04 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 7.049498e+06 6.459181e+06 1.067316e+07 3.999786e+07 \n", + "range 7.043000e+03 8.111000e+03 5.847000e+03 1.554800e+04 \n", + "stdev 1.047340e+02 2.315210e+02 2.298423e+02 3.314566e+02 \n", + "q1 2.300000e+01 7.000000e+00 2.900000e+01 2.780000e+02 \n", + "q3 8.700000e+01 4.800000e+01 1.090000e+02 5.560000e+02 \n", + "median 5.200000e+01 2.300000e+01 6.200000e+01 4.110000e+02 \n", + "interquartile_range 6.400000e+01 4.100000e+01 8.000000e+01 2.780000e+02 \n", + "\n", + " two_cars three_cars four_more_cars \\\n", + "avg 6.048036e+02 2.295713e+02 1.031463e+02 \n", + "max 1.201200e+04 3.231000e+03 1.107000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 4.475607e+07 1.698850e+07 7.632928e+06 \n", + "range 1.201200e+04 3.231000e+03 1.107000e+03 \n", + "stdev 3.799984e+02 1.711299e+02 9.448405e+01 \n", + "q1 3.010000e+02 8.500000e+01 2.400000e+01 \n", + "q3 6.360000e+02 2.400000e+02 1.020000e+02 \n", + "median 4.710000e+02 1.620000e+02 6.000000e+01 \n", + "interquartile_range 3.350000e+02 1.550000e+02 7.800000e+01 \n", + "\n", + " aggregate_travel_time_to_work \\\n", + "avg 5.934150e+04 \n", + "max 9.731600e+05 \n", + "min 6.500000e+01 \n", + "sum 2.970754e+09 \n", + "range 9.730950e+05 \n", + "stdev 3.422968e+04 \n", + "q1 3.315000e+04 \n", + "q3 6.035000e+04 \n", + "median 4.599500e+04 \n", + "interquartile_range 2.720000e+04 \n", + "\n", + " commuters_by_public_transportation commuters_by_bus \\\n", + "avg 1.031013e+02 5.114798e+01 \n", + "max 7.759000e+03 3.940000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 7.629599e+06 3.785002e+06 \n", + "range 7.759000e+03 3.940000e+03 \n", + "stdev 2.647627e+02 1.086306e+02 \n", + "q1 0.000000e+00 0.000000e+00 \n", + "q3 3.400000e+01 2.000000e+01 \n", + "median 8.000000e+00 2.000000e+00 \n", + "interquartile_range 3.400000e+01 2.000000e+01 \n", + "\n", + " commuters_by_car_truck_van commuters_by_carpool \\\n", + "avg 1.729272e+03 1.847843e+02 \n", + "max 2.501200e+04 3.023000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 1.279679e+08 1.367422e+07 \n", + "range 2.501200e+04 3.023000e+03 \n", + "stdev 1.041140e+03 1.447731e+02 \n", + "q1 8.940000e+02 7.400000e+01 \n", + "q3 1.824000e+03 1.830000e+02 \n", + "median 1.362000e+03 1.240000e+02 \n", + "interquartile_range 9.300000e+02 1.090000e+02 \n", + "\n", + " commuters_by_subway_or_elevated commuters_drove_alone \\\n", + "avg 3.818269e+01 1.544488e+03 \n", + "max 5.945000e+03 2.198900e+04 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 2.825557e+06 1.142936e+08 \n", + "range 5.945000e+03 2.198900e+04 \n", + "stdev 1.967409e+02 9.474669e+02 \n", + "q1 0.000000e+00 7.800000e+02 \n", + "q3 0.000000e+00 1.637000e+03 \n", + "median 0.000000e+00 1.211000e+03 \n", + "interquartile_range 0.000000e+00 8.570000e+02 \n", + "\n", + " group_quarters associates_degree bachelors_degree \\\n", + "avg 1.098196e+02 2.452862e+02 5.649029e+02 \n", + "max 1.642100e+04 4.070000e+03 1.376700e+04 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 8.126758e+06 1.815143e+07 4.180338e+07 \n", + "range 1.642100e+04 4.070000e+03 1.376700e+04 \n", + "stdev 4.446641e+02 1.694405e+02 4.828823e+02 \n", + "q1 0.000000e+00 1.090000e+02 1.910000e+02 \n", + "q3 2.000000e+01 2.520000e+02 5.500000e+02 \n", + "median 5.000000e+00 1.780000e+02 3.420000e+02 \n", + "interquartile_range 2.000000e+01 1.430000e+02 3.590000e+02 \n", + "\n", + " high_school_diploma less_one_year_college \\\n", + "avg 6.904443e+02 1.821747e+02 \n", + "max 8.215000e+03 4.037000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 5.109357e+07 1.348111e+07 \n", + "range 8.215000e+03 4.037000e+03 \n", + "stdev 4.093667e+02 1.267778e+02 \n", + "q1 3.420000e+02 8.000000e+01 \n", + "q3 7.280000e+02 1.890000e+02 \n", + "median 5.340000e+02 1.320000e+02 \n", + "interquartile_range 3.860000e+02 1.090000e+02 \n", + "\n", + " masters_degree one_year_more_college \\\n", + "avg 2.468936e+02 4.290593e+02 \n", + "max 7.767000e+03 5.621000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 1.827037e+07 3.175082e+07 \n", + "range 7.767000e+03 5.621000e+03 \n", + "stdev 2.557686e+02 2.629695e+02 \n", + "q1 6.200000e+01 2.220000e+02 \n", + "q3 2.160000e+02 4.380000e+02 \n", + "median 1.260000e+02 3.250000e+02 \n", + "interquartile_range 1.540000e+02 2.160000e+02 \n", + "\n", + " less_than_high_school_graduate \\\n", + "avg 3.755628e+02 \n", + "max 5.363000e+03 \n", + "min 0.000000e+00 \n", + "sum 2.743711e+07 \n", + "range 5.363000e+03 \n", + "stdev 3.540041e+02 \n", + "q1 1.150000e+02 \n", + "q3 3.390000e+02 \n", + "median 2.140000e+02 \n", + "interquartile_range 2.240000e+02 \n", + "\n", + " high_school_including_ged bachelors_degree_2 \\\n", + "avg 8.088810e+02 5.663747e+02 \n", + "max 8.929000e+03 1.376700e+04 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 5.909361e+07 4.137707e+07 \n", + "range 8.929000e+03 1.376700e+04 \n", + "stdev 4.791437e+02 4.842803e+02 \n", + "q1 4.000000e+02 1.920000e+02 \n", + "q3 8.500000e+02 5.470000e+02 \n", + "median 6.300000e+02 3.450000e+02 \n", + "interquartile_range 4.500000e+02 3.550000e+02 \n", + "\n", + " bachelors_degree_or_higher_25_64 \\\n", + "avg 7.435482e+02 \n", + "max 2.285600e+04 \n", + "min 0.000000e+00 \n", + "sum 5.502331e+07 \n", + "range 2.285600e+04 \n", + "stdev 7.062823e+02 \n", + "q1 2.200000e+02 \n", + "q3 6.830000e+02 \n", + "median 4.080000e+02 \n", + "interquartile_range 4.630000e+02 \n", + "\n", + " graduate_professional_degree \\\n", + "avg 3.491915e+02 \n", + "max 1.037400e+04 \n", + "min 0.000000e+00 \n", + "sum 2.551054e+07 \n", + "range 1.037400e+04 \n", + "stdev 3.801689e+02 \n", + "q1 8.100000e+01 \n", + "q3 2.950000e+02 \n", + "median 1.650000e+02 \n", + "interquartile_range 2.140000e+02 \n", + "\n", + " some_college_and_associates_degree \\\n", + "avg 8.603443e+02 \n", + "max 1.274500e+04 \n", + "min 0.000000e+00 \n", + "sum 6.285332e+07 \n", + "range 1.274500e+04 \n", + "stdev 5.062105e+02 \n", + "q1 4.570000e+02 \n", + "q3 9.020000e+02 \n", + "median 6.720000e+02 \n", + "interquartile_range 4.450000e+02 \n", + "\n", + " male_45_64_associates_degree \\\n", + "avg 4.302832e+01 \n", + "max 8.550000e+02 \n", + "min 0.000000e+00 \n", + "sum 3.184139e+06 \n", + "range 8.550000e+02 \n", + "stdev 4.047851e+01 \n", + "q1 1.100000e+01 \n", + "q3 4.200000e+01 \n", + "median 2.500000e+01 \n", + "interquartile_range 3.100000e+01 \n", + "\n", + " male_45_64_bachelors_degree male_45_64_graduate_degree \\\n", + "avg 1.000650e+02 6.498545e+01 \n", + "max 2.547000e+03 2.448000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 7.404907e+06 4.808988e+06 \n", + "range 2.547000e+03 2.448000e+03 \n", + "stdev 1.009911e+02 8.698075e+01 \n", + "q1 2.400000e+01 8.000000e+00 \n", + "q3 9.200000e+01 4.900000e+01 \n", + "median 5.200000e+01 2.400000e+01 \n", + "interquartile_range 6.800000e+01 4.100000e+01 \n", + "\n", + " male_45_64_less_than_9_grade male_45_64_grade_9_12 \\\n", + "avg 3.009663e+01 4.223971e+01 \n", + "max 9.000000e+02 1.626000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 2.227181e+06 3.125781e+06 \n", + "range 9.000000e+02 1.626000e+03 \n", + "stdev 4.965424e+01 4.571015e+01 \n", + "q1 0.000000e+00 7.000000e+00 \n", + "q3 1.900000e+01 3.900000e+01 \n", + "median 6.000000e+00 2.100000e+01 \n", + "interquartile_range 1.900000e+01 3.200000e+01 \n", + "\n", + " male_45_64_high_school male_45_64_some_college \\\n", + "avg 1.648105e+02 1.128077e+02 \n", + "max 2.084000e+03 1.628000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 1.219614e+07 8.347879e+06 \n", + "range 2.084000e+03 1.628000e+03 \n", + "stdev 1.159105e+02 8.115282e+01 \n", + "q1 6.700000e+01 4.700000e+01 \n", + "q3 1.720000e+02 1.150000e+02 \n", + "median 1.180000e+02 7.900000e+01 \n", + "interquartile_range 1.050000e+02 6.800000e+01 \n", + "\n", + " male_45_to_64 employed_pop unemployed_pop \\\n", + "avg 5.580332e+02 2.049152e+03 1.456859e+02 \n", + "max 6.993000e+03 2.894500e+04 1.607000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 4.129502e+07 1.516393e+08 1.078090e+07 \n", + "range 6.993000e+03 2.894500e+04 1.607000e+03 \n", + "stdev 3.033556e+02 1.138865e+03 1.092197e+02 \n", + "q1 3.160000e+02 1.143000e+03 6.000000e+01 \n", + "q3 5.820000e+02 2.156000e+03 1.450000e+02 \n", + "median 4.500000e+02 1.651000e+03 1.000000e+02 \n", + "interquartile_range 2.660000e+02 1.013000e+03 8.500000e+01 \n", + "\n", + " pop_in_labor_force not_in_labor_force \\\n", + "avg 2.208707e+03 1.286299e+03 \n", + "max 3.055200e+04 3.414200e+04 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 1.634465e+08 9.518743e+07 \n", + "range 3.055200e+04 3.414200e+04 \n", + "stdev 1.203354e+03 7.150048e+02 \n", + "q1 1.245000e+03 7.460000e+02 \n", + "q3 2.319000e+03 1.325000e+03 \n", + "median 1.793000e+03 1.026000e+03 \n", + "interquartile_range 1.074000e+03 5.790000e+02 \n", + "\n", + " workers_16_and_over armed_forces civilian_labor_force \\\n", + "avg 2.019454e+03 1.386930e+01 2.194838e+03 \n", + "max 2.825200e+04 2.121400e+04 3.055200e+04 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 1.494416e+08 1.026342e+06 1.624202e+08 \n", + "range 2.825200e+04 2.121400e+04 3.055200e+04 \n", + "stdev 1.129133e+03 1.616169e+02 1.192208e+03 \n", + "q1 1.115000e+03 0.000000e+00 1.235000e+03 \n", + "q3 2.115000e+03 0.000000e+00 2.300000e+03 \n", + "median 1.622000e+03 0.000000e+00 1.780000e+03 \n", + "interquartile_range 1.000000e+03 0.000000e+00 1.065000e+03 \n", + "\n", + " employed_agriculture_forestry_fishing_hunting_mining \\\n", + "avg 3.827483e+01 \n", + "max 4.197000e+03 \n", + "min 0.000000e+00 \n", + "sum 2.832376e+06 \n", + "range 4.197000e+03 \n", + "stdev 8.874869e+01 \n", + "q1 0.000000e+00 \n", + "q3 1.700000e+01 \n", + "median 0.000000e+00 \n", + "interquartile_range 1.700000e+01 \n", + "\n", + " employed_arts_entertainment_recreation_accommodation_food \\\n", + "avg 1.984437e+02 \n", + "max 5.211000e+03 \n", + "min 0.000000e+00 \n", + "sum 1.468503e+07 \n", + "range 5.211000e+03 \n", + "stdev 1.600300e+02 \n", + "q1 8.200000e+01 \n", + "q3 1.940000e+02 \n", + "median 1.330000e+02 \n", + "interquartile_range 1.120000e+02 \n", + "\n", + " employed_construction employed_education_health_social \\\n", + "avg 1.299722e+02 4.733737e+02 \n", + "max 1.950000e+03 9.127000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 9.618071e+06 3.503013e+07 \n", + "range 1.950000e+03 9.127000e+03 \n", + "stdev 1.116914e+02 3.015791e+02 \n", + "q1 4.500000e+01 2.340000e+02 \n", + "q3 1.260000e+02 4.870000e+02 \n", + "median 8.200000e+01 3.580000e+02 \n", + "interquartile_range 8.100000e+01 2.530000e+02 \n", + "\n", + " employed_finance_insurance_real_estate \\\n", + "avg 1.346475e+02 \n", + "max 2.853000e+03 \n", + "min 0.000000e+00 \n", + "sum 9.964050e+06 \n", + "range 2.853000e+03 \n", + "stdev 1.305728e+02 \n", + "q1 4.100000e+01 \n", + "q3 1.250000e+02 \n", + "median 7.800000e+01 \n", + "interquartile_range 8.400000e+01 \n", + "\n", + " employed_information employed_manufacturing \\\n", + "avg 4.315308e+01 2.104029e+02 \n", + "max 1.141000e+03 3.993000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 3.193371e+06 1.557002e+07 \n", + "range 1.141000e+03 3.993000e+03 \n", + "stdev 5.400275e+01 1.826280e+02 \n", + "q1 7.000000e+00 6.500000e+01 \n", + "q3 3.700000e+01 2.040000e+02 \n", + "median 1.900000e+01 1.290000e+02 \n", + "interquartile_range 3.000000e+01 1.390000e+02 \n", + "\n", + " employed_other_services_not_public_admin \\\n", + "avg 1.003838e+02 \n", + "max 1.175000e+03 \n", + "min 0.000000e+00 \n", + "sum 7.428501e+06 \n", + "range 1.175000e+03 \n", + "stdev 7.619403e+01 \n", + "q1 4.000000e+01 \n", + "q3 1.010000e+02 \n", + "median 6.900000e+01 \n", + "interquartile_range 6.100000e+01 \n", + "\n", + " employed_public_administration employed_retail_trade \\\n", + "avg 9.616204e+01 2.338625e+02 \n", + "max 1.816000e+03 2.808000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 7.116087e+06 1.730606e+07 \n", + "range 1.816000e+03 2.808000e+03 \n", + "stdev 9.987631e+01 1.551951e+02 \n", + "q1 2.900000e+01 1.110000e+02 \n", + "q3 8.700000e+01 2.380000e+02 \n", + "median 5.500000e+01 1.720000e+02 \n", + "interquartile_range 5.800000e+01 1.270000e+02 \n", + "\n", + " employed_science_management_admin_waste \\\n", + "avg 2.311254e+02 \n", + "max 5.115000e+03 \n", + "min 0.000000e+00 \n", + "sum 1.710351e+07 \n", + "range 5.115000e+03 \n", + "stdev 2.015002e+02 \n", + "q1 8.100000e+01 \n", + "q3 2.200000e+02 \n", + "median 1.410000e+02 \n", + "interquartile_range 1.390000e+02 \n", + "\n", + " employed_transportation_warehousing_utilities \\\n", + "avg 1.043314e+02 \n", + "max 1.465000e+03 \n", + "min 0.000000e+00 \n", + "sum 7.720629e+06 \n", + "range 1.465000e+03 \n", + "stdev 9.018655e+01 \n", + "q1 3.700000e+01 \n", + "q3 1.010000e+02 \n", + "median 6.600000e+01 \n", + "interquartile_range 6.400000e+01 \n", + "\n", + " employed_wholesale_trade occupation_management_arts \\\n", + "avg 5.501904e+01 7.665854e+02 \n", + "max 1.294000e+03 1.909700e+04 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 4.071464e+06 5.672809e+07 \n", + "range 1.294000e+03 1.909700e+04 \n", + "stdev 5.451432e+01 6.156759e+02 \n", + "q1 1.300000e+01 2.900000e+02 \n", + "q3 5.300000e+01 7.470000e+02 \n", + "median 3.100000e+01 4.950000e+02 \n", + "interquartile_range 4.000000e+01 4.570000e+02 \n", + "\n", + " occupation_natural_resources_construction_maintenance \\\n", + "avg 1.820007e+02 \n", + "max 3.973000e+03 \n", + "min 0.000000e+00 \n", + "sum 1.346823e+07 \n", + "range 3.973000e+03 \n", + "stdev 1.512020e+02 \n", + "q1 6.300000e+01 \n", + "q3 1.810000e+02 \n", + "median 1.200000e+02 \n", + "interquartile_range 1.180000e+02 \n", + "\n", + " occupation_production_transportation_material \\\n", + "avg 2.491306e+02 \n", + "max 2.495000e+03 \n", + "min 0.000000e+00 \n", + "sum 1.843592e+07 \n", + "range 2.495000e+03 \n", + "stdev 1.862018e+02 \n", + "q1 9.600000e+01 \n", + "q3 2.530000e+02 \n", + "median 1.680000e+02 \n", + "interquartile_range 1.570000e+02 \n", + "\n", + " occupation_sales_office occupation_services \\\n", + "avg 4.828023e+02 3.686331e+02 \n", + "max 6.162000e+03 3.948000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 3.572785e+07 2.727922e+07 \n", + "range 6.162000e+03 3.948000e+03 \n", + "stdev 2.988008e+02 2.366780e+02 \n", + "q1 2.430000e+02 1.860000e+02 \n", + "q3 5.010000e+02 3.740000e+02 \n", + "median 3.690000e+02 2.740000e+02 \n", + "interquartile_range 2.580000e+02 1.880000e+02 \n", + "\n", + " management_business_sci_arts_employed \\\n", + "avg 7.665854e+02 \n", + "max 1.909700e+04 \n", + "min 0.000000e+00 \n", + "sum 5.672809e+07 \n", + "range 1.909700e+04 \n", + "stdev 6.156759e+02 \n", + "q1 2.900000e+02 \n", + "q3 7.470000e+02 \n", + "median 4.950000e+02 \n", + "interquartile_range 4.570000e+02 \n", + "\n", + " sales_office_employed in_grades_1_to_4 \\\n", + "avg 4.828023e+02 2.228999e+02 \n", + "max 6.162000e+03 5.927000e+03 \n", + "min 0.000000e+00 0.000000e+00 \n", + "sum 3.572785e+07 1.649482e+07 \n", + "range 6.162000e+03 5.927000e+03 \n", + "stdev 2.988008e+02 1.687454e+02 \n", + "q1 2.430000e+02 9.700000e+01 \n", + "q3 5.010000e+02 2.240000e+02 \n", + "median 3.690000e+02 1.580000e+02 \n", + "interquartile_range 2.580000e+02 1.270000e+02 \n", + "\n", + " in_grades_5_to_8 in_grades_9_to_12 in_school \\\n", + "avg 2.253273e+02 2.322261e+02 1.116528e+03 \n", + "max 5.674000e+03 4.965000e+03 2.398900e+04 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 1.667444e+07 1.718497e+07 8.262417e+07 \n", + "range 5.674000e+03 4.965000e+03 2.398900e+04 \n", + "stdev 1.692225e+02 1.687234e+02 7.662855e+02 \n", + "q1 9.700000e+01 1.020000e+02 5.600000e+02 \n", + "q3 2.260000e+02 2.360000e+02 1.120000e+03 \n", + "median 1.610000e+02 1.650000e+02 8.330000e+02 \n", + "interquartile_range 1.290000e+02 1.340000e+02 5.600000e+02 \n", + "\n", + " in_undergrad_college speak_only_english_at_home \\\n", + "avg 2.557242e+02 None \n", + "max 1.298500e+04 None \n", + "min 0.000000e+00 None \n", + "sum 1.892385e+07 None \n", + "range 1.298500e+04 None \n", + "stdev 4.083119e+02 None \n", + "q1 8.400000e+01 None \n", + "q3 2.120000e+02 None \n", + "median 1.420000e+02 None \n", + "interquartile_range 1.280000e+02 None \n", + "\n", + " speak_spanish_at_home speak_spanish_at_home_low_english \n", + "avg None None \n", + "max None None \n", + "min None None \n", + "sum None None \n", + "range None None \n", + "stdev None None \n", + "q1 None None \n", + "q3 None None \n", + "median None None \n", + "interquartile_range None None " + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample_ds.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### 1.4.5. Visualization of dataset coverage" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "vLWOeAhn9Wf1", + "outputId": "e4e46f9f-c9e2-4eb4-cd24-9ab08be22a71" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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Please, visit [CARTOframes Guides](https://carto.com/developers/cartoframes/guides/) to learn more about the enrichment functionality.\n", + "\n", + "We will show CARTOframes enrichment functionality with an example in which we will quantify the number of eating places within a 5-minute isochrone for all sports POI's in Madrid downtown.\n", + "\n", + "The notebook is organized as follows:\n", + "1. [Download sports POIs](#section1)\n", + "2. [Calculate isochrones](#section2)\n", + "3. [Enrich isochrones](#section3)\n", + " - [Simple enrichment: Counting the number of POI's within isochrones](#section31)\n", + " - [Enrichment applying filters: Counting the number of eating places](#section32)\n", + " - [Brief analysis](#section33)\n", + "\n", + "**Note** for this notebook we are using the premium [dataset of Pitney Bowes POI's in Spain](https://carto.com/spatial-data-catalog/browser/dataset/pb_points_of_i_94bda91b/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z7-NfDHvhgRc" + }, + "source": [ + "### Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + }, + "id": "tamaCNqjlJ3u", + "outputId": "12fefed4-74bc-4ab2-9f35-9b4493db346c" + }, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from cartoframes.auth import set_default_credentials\n", + "from cartoframes.data.observatory import *\n", + "from cartoframes.data.services import Isolines\n", + "from cartoframes.viz import *\n", + "\n", + "sns.set_style('whitegrid')\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Set CARTO default credentials\n", + "\n", + "In order to be able to use the Data Observatory via CARTOframes, you need to set your CARTO account credentials first.\n", + "\n", + "Please, visit the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/) for further detail." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note about credentials**\n", + "\n", + "For security reasons, we recommend storing your credentials in an external file to prevent publishing them by accident when sharing your notebooks. You can get more information in the section _Setting your credentials_ of the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "dq4_-q83lchv" + }, + "source": [ + "\n", + "### 1. Download sports POIs\n", + "\n", + "We need to start with the initial DataFrame that we would like to enrich. Normally, this initial DataFrame contains your own data that you later enrich with data from the Data Observatory. In this case, we will download all sports POI's and use it as our initial DataFrame.\n", + "\n", + "We first check that we are subscribed to PB POIs dataset in Spain and download the sports POI's within a bounding box covering Madrid downtown. You can calculate your bounding box of interest using [bboxfinder](http://bboxfinder.com).\n", + "\n", + "For a step by step description on how to discover and download premium datasets, take a look at templates: Data Discovery and Access Premium Data." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "id": "nugq6FQJlYFy", + "outputId": "3a5f3fe1-5a5f-4f43-fd1c-866104998b9e" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                      slugnamedescriptioncategory_idcountry_iddata_source_idprovider_idgeography_namegeography_descriptiontemporal_aggregationtime_coverageupdate_frequencyis_public_datalangversioncategory_nameprovider_namegeography_idid
                                                      0ags_sociodemogr_a7e14220Sociodemographics - United States of America (...Census and ACS sociodemographic data estimated...demographicsusasociodemographicsagsCensus Block Group - United States of AmericaNoneyearlyNoneyearlyFalseeng2020DemographicsApplied Geographic Solutionscarto-do.ags.geography_usa_blockgroup_2015carto-do.ags.demographics_sociodemographics_us...
                                                      1ags_retailpoten_aaf25a8cRetail Potential - United States of America (C...The retail potential database consists of aver...demographicsusaretailpotentialagsCensus Block Group - United States of America ...Shoreline clipped TIGER/Line boundaries. More ...yearly[2018-01-01, 2019-01-01)yearlyFalseeng2019DemographicsApplied Geographic Solutionscarto-do-public-data.carto.geography_usa_block...carto-do.ags.demographics_retailpotential_usa_...
                                                      2pb_consumer_po_62cddc04Points Of Interest - Consumer - United States ...Consumer Point of interest database per catego...points_of_interestusaconsumer_points_of_interestpitney_bowesLatitude/Longitude - United States of AmericaLocation of Points of InterestmonthlyNonemonthlyFalseengv1Points of InterestPitney Bowescarto-do.pitney_bowes.geography_usa_latlon_v1carto-do.pitney_bowes.pointsofinterest_consume...
                                                      3ags_sociodemogr_f510a947Sociodemographics - United States of America (...Census and ACS sociodemographic data estimated...demographicsusasociodemographicsagsCensus Block Group - United States of America ...Shoreline clipped TIGER/Line boundaries. More ...yearly[2019-01-01, 2020-01-01)yearlyFalseeng2019DemographicsApplied Geographic Solutionscarto-do-public-data.carto.geography_usa_block...carto-do.ags.demographics_sociodemographics_us...
                                                      4ags_consumer_sp_dbabddfbConsumer Spending - United States of America (...The Consumer Expenditure database consists of ...demographicsusaconsumer_spendingagsCensus Block Group - United States of AmericaNoneyearlyNoneyearlyFalseeng2020DemographicsApplied Geographic Solutionscarto-do.ags.geography_usa_blockgroup_2015carto-do.ags.demographics_consumerspending_usa...
                                                      5spa_geosocial_s_d5dc42aeGeosocial Segments - United States of America ...By analysing feeds from Twitter, Instagram, Me...behavioralusageosocial_segmentsspatial_aiCensus Block Group - United States of America ...Shoreline clipped TIGER/Line boundaries. More ...quarterly[2020-01-01, 2020-04-01)quarterlyFalseengv1BehavioralSpatial.aicarto-do-public-data.carto.geography_usa_block...carto-do.spatial_ai.behavioral_geosocialsegmen...
                                                      6mc_geographic__7980c5c3Geographic Insights - United States of America...Geographic Insights validate, evaluate and ben...financialusageographic_insightsmastercardCensus Block Group - United States of America ...Shoreline clipped TIGER/Line boundaries. More ...monthly[2019-01-01, 2020-01-01)monthlyFalseengv1FinancialMastercardcarto-do-public-data.carto.geography_usa_block...carto-do.mastercard.financial_geographicinsigh...
                                                      7pb_points_of_i_94bda91bPoints Of Interest - Spain (Latitude/Longitude)Point of interest database per categoriespoints_of_interestesppoints_of_interestpitney_bowesLatitude/Longitude - SpainLocation of Points of InterestmonthlyNonemonthlyFalseengv1Points of InterestPitney Bowescarto-do.pitney_bowes.geography_esp_latlon_v1carto-do.pitney_bowes.pointsofinterest_pointso...
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                                                      " + ], + "text/plain": [ + " NAME geoid \\\n", + "0 ACQUAPLAYA SPA 2204263540#-3.7095628#40.4226394 \n", + "1 ALAMBIQUE TIENDA Y ESCUELA DE COCINA 2157202351#-3.7109121#40.4198204 \n", + "2 ALCÁZAR NIGHT 2137823204#-3.69905#40.4178 \n", + "3 ALICIA PRODUCE 2181768913#-3.7112#40.42404 \n", + "4 ALMA PILATES 2197072938#-3.7033635#40.4141477 \n", + "\n", + " do_date BRANDNAME PB_ID TRADE_NAME FRANCHISE_NAME ISO3 \\\n", + "0 2020-04-01 NaN 2204263540 NaN NaN ESP \n", + "1 2019-12-01 NaN 2157202351 NaN NaN ESP \n", + "2 2019-12-01 NaN 2137823204 NaN NaN ESP \n", + "3 2020-02-01 NaN 2181768913 NaN NaN ESP \n", + "4 2020-04-01 NaN 2197072938 NaN NaN ESP \n", + "\n", + " AREANAME4 AREANAME3 ... GLOBAL_ULTIMATE_STREET_ADDRESS \\\n", + "0 NaN MADRID ... NaN \n", + "1 NaN MADRID ... NaN \n", + "2 NaN MADRID ... NaN \n", + "3 NaN MADRID ... NaN \n", + "4 NaN MADRID ... NaN \n", + "\n", + " GLOBAL_ULTIMATE_AREANAME3 GLOBAL_ULTIMATE_AREANAME1 \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " GLOBAL_ULTIMATE_COUNTRY GLOBAL_ULTIMATE_POSTCODE FAMILY_MEMBERS \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " HIERARCHY_CODE TICKER_SYMBOL EXCHANGE_NAME geom \n", + "0 NaN NaN NaN POINT (-3.70956 40.42264) \n", + "1 NaN NaN NaN POINT (-3.71091 40.41982) \n", + "2 NaN NaN NaN POINT (-3.69905 40.41780) \n", + "3 NaN NaN NaN POINT (-3.71120 40.42404) \n", + "4 NaN NaN NaN POINT (-3.70336 40.41415) \n", + "\n", + "[5 rows x 74 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sql_query = \"\"\"\n", + " SELECT * except(do_label) FROM $dataset$ \n", + " WHERE TRADE_DIVISION = 'DIVISION M. - SPORTS' \n", + " AND ST_IntersectsBox(geom, -3.716398,40.407437,-3.690477,40.425277)\n", + "\"\"\"\n", + "\n", + "pois_df = pois_ds.to_dataframe(sql_query=sql_query)\n", + "\n", + "# To keep only the latest version of POI's\n", + "pois_df = pois_df.sort_values(['NAME', 'do_date']).groupby('NAME').first().reset_index()\n", + "\n", + "pois_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "P-au_zH5r2BY" + }, + "source": [ + "\n", + "### 2. Calculate isochrones\n", + "\n", + "For this analysis, we are interested in knowing the number of eating places reachable within 5 minutes for every sport POI. We'll now proceed to calculate 5-minute isochrones for every POI, which represent the area reachable within 5 minutes.\n", + "\n", + "You can read more regarding isochrones on [CARTOframes Guides](https://carto.com/developers/cartoframes/guides/)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "id": "8UHM-TzmnmJL", + "outputId": "bee85084-45b2-41df-e0b5-41c2fcbdb7aa" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success! Isolines created correctly\n" + ] + } + ], + "source": [ + "iso_service = Isolines()\n", + "isochrones_gdf, isochrones_metadata = iso_service.isochrones(pois_df, [300], mode='walk', geom_col='geom')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 197 + }, + "id": "PwpFB32KaWPo", + "outputId": "870b06e0-e555-464a-9d25-59aff9a9c3b6" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                      NAMEgeoiddo_dateBRANDNAMEPB_IDTRADE_NAMEFRANCHISE_NAMEISO3AREANAME4AREANAME3...GLOBAL_ULTIMATE_AREANAME3GLOBAL_ULTIMATE_AREANAME1GLOBAL_ULTIMATE_COUNTRYGLOBAL_ULTIMATE_POSTCODEFAMILY_MEMBERSHIERARCHY_CODETICKER_SYMBOLEXCHANGE_NAMEgeomisochrone
                                                      0ACQUAPLAYA SPA2204263540#-3.7095628#40.42263942020-04-01NaN2204263540NaNNaNESPNaNMADRID...NaNNaNNaNNaNNaNNaNNaNNaNPOINT (-3.70956 40.42264)MULTIPOLYGON (((-3.71192 40.42488, -3.71149 40...
                                                      1ALAMBIQUE TIENDA Y ESCUELA DE COCINA2157202351#-3.7109121#40.41982042019-12-01NaN2157202351NaNNaNESPNaNMADRID...NaNNaNNaNNaNNaNNaNNaNNaNPOINT (-3.71091 40.41982)MULTIPOLYGON (((-3.71346 40.42093, -3.71321 40...
                                                      2ALCÁZAR NIGHT2137823204#-3.69905#40.41782019-12-01NaN2137823204NaNNaNESPNaNMADRID...NaNNaNNaNNaNNaNNaNNaNNaNPOINT (-3.69905 40.41780)MULTIPOLYGON (((-3.70248 40.41750, -3.70222 40...
                                                      3ALICIA PRODUCE2181768913#-3.7112#40.424042020-02-01NaN2181768913NaNNaNESPNaNMADRID...NaNNaNNaNNaNNaNNaNNaNNaNPOINT (-3.71120 40.42404)MULTIPOLYGON (((-3.71346 40.42437, -3.71338 40...
                                                      4ALMA PILATES2197072938#-3.7033635#40.41414772020-04-01NaN2197072938NaNNaNESPNaNMADRID...NaNNaNNaNNaNNaNNaNNaNNaNPOINT (-3.70336 40.41415)MULTIPOLYGON (((-3.70660 40.41544, -3.70634 40...
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                                                      " + ], + "text/plain": [ + " NAME geoid \\\n", + "0 ACQUAPLAYA SPA 2204263540#-3.7095628#40.4226394 \n", + "1 ALAMBIQUE TIENDA Y ESCUELA DE COCINA 2157202351#-3.7109121#40.4198204 \n", + "2 ALCÁZAR NIGHT 2137823204#-3.69905#40.4178 \n", + "3 ALICIA PRODUCE 2181768913#-3.7112#40.42404 \n", + "4 ALMA PILATES 2197072938#-3.7033635#40.4141477 \n", + "\n", + " do_date BRANDNAME PB_ID TRADE_NAME FRANCHISE_NAME ISO3 \\\n", + "0 2020-04-01 NaN 2204263540 NaN NaN ESP \n", + "1 2019-12-01 NaN 2157202351 NaN NaN ESP \n", + "2 2019-12-01 NaN 2137823204 NaN NaN ESP \n", + "3 2020-02-01 NaN 2181768913 NaN NaN ESP \n", + "4 2020-04-01 NaN 2197072938 NaN NaN ESP \n", + "\n", + " AREANAME4 AREANAME3 ... GLOBAL_ULTIMATE_AREANAME3 \\\n", + "0 NaN MADRID ... NaN \n", + "1 NaN MADRID ... NaN \n", + "2 NaN MADRID ... NaN \n", + "3 NaN MADRID ... NaN \n", + "4 NaN MADRID ... NaN \n", + "\n", + " GLOBAL_ULTIMATE_AREANAME1 GLOBAL_ULTIMATE_COUNTRY GLOBAL_ULTIMATE_POSTCODE \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " FAMILY_MEMBERS HIERARCHY_CODE TICKER_SYMBOL EXCHANGE_NAME \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " geom \\\n", + "0 POINT (-3.70956 40.42264) \n", + "1 POINT (-3.71091 40.41982) \n", + "2 POINT (-3.69905 40.41780) \n", + "3 POINT (-3.71120 40.42404) \n", + "4 POINT (-3.70336 40.41415) \n", + "\n", + " isochrone \n", + "0 MULTIPOLYGON (((-3.71192 40.42488, -3.71149 40... \n", + "1 MULTIPOLYGON (((-3.71346 40.42093, -3.71321 40... \n", + "2 MULTIPOLYGON (((-3.70248 40.41750, -3.70222 40... \n", + "3 MULTIPOLYGON (((-3.71346 40.42437, -3.71338 40... \n", + "4 MULTIPOLYGON (((-3.70660 40.41544, -3.70634 40... \n", + "\n", + "[5 rows x 75 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pois_df['isochrone'] = isochrones_gdf.sort_values('source_id')['the_geom'].values\n", + "pois_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Visualize isochrones" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "syg5-YSopMDE", + "outputId": "2c86f83f-78c6-4cae-a57e-029d8c2a24fb" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                                                          NAMEgeoiddo_dateBRANDNAMEPB_IDTRADE_NAMEFRANCHISE_NAMEISO3AREANAME4AREANAME3...GLOBAL_ULTIMATE_COUNTRYGLOBAL_ULTIMATE_POSTCODEFAMILY_MEMBERSHIERARCHY_CODETICKER_SYMBOLEXCHANGE_NAMEgeomisochronen_poisn_pois_eating
                                                          0ACQUAPLAYA SPA2204263540#-3.7095628#40.42263942020-04-01NaN2204263540NaNNaNESPNaNMADRID...NaNNaNNaNNaNNaNNaNPOINT (-3.70956 40.42264)MULTIPOLYGON (((-3.71192 40.42488, -3.71149 40...319772052
                                                          1ALAMBIQUE TIENDA Y ESCUELA DE COCINA2157202351#-3.7109121#40.41982042019-12-01NaN2157202351NaNNaNESPNaNMADRID...NaNNaNNaNNaNNaNNaNPOINT (-3.71091 40.41982)MULTIPOLYGON (((-3.71346 40.42093, -3.71321 40...101311009
                                                          2ALCÁZAR NIGHT2137823204#-3.69905#40.41782019-12-01NaN2137823204NaNNaNESPNaNMADRID...NaNNaNNaNNaNNaNNaNPOINT (-3.69905 40.41780)MULTIPOLYGON (((-3.70248 40.41750, -3.70222 40...199471534
                                                          3ALICIA PRODUCE2181768913#-3.7112#40.424042020-02-01NaN2181768913NaNNaNESPNaNMADRID...NaNNaNNaNNaNNaNNaNPOINT (-3.71120 40.42404)MULTIPOLYGON (((-3.71346 40.42437, -3.71338 40...244802125
                                                          4ALMA PILATES2197072938#-3.7033635#40.41414772020-04-01NaN2197072938NaNNaNESPNaNMADRID...NaNNaNNaNNaNNaNNaNPOINT (-3.70336 40.41415)MULTIPOLYGON (((-3.70660 40.41544, -3.70634 40...303482744
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"outputId": "ef1550b8-02ac-4768-bdc0-4d0cbf6f6f99" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
                                                              " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(12,5))\n", + "sns.regplot(pois_df['n_pois'], pois_df['n_pois_eating'], \n", + " scatter_kws={'color':'blue', 'alpha':0.5}, line_kws={'color':'red'})" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "CARTO | Data Observatory v2.0 - Data Enrichment.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/data_observatory/do_notebook_template.ipynb b/docs/examples/templates/do_dataset_notebook_template.ipynb similarity index 93% rename from examples/data_observatory/do_notebook_template.ipynb rename to docs/examples/templates/do_dataset_notebook_template.ipynb index 2fdaad592..b67c44bb1 100644 --- a/examples/data_observatory/do_notebook_template.ipynb +++ b/docs/examples/templates/do_dataset_notebook_template.ipynb @@ -77,7 +77,7 @@ "from cartoframes.auth import set_default_credentials\n", "\n", "username = 'YOUR_USERNAME'\n", - "api_key = 'YOUR_API_KEY'\n", + "api_key = 'YOUR_API_KEY' # Master API key. Do not make this file public!\n", "\n", "set_default_credentials(username, api_key)" ] @@ -86,7 +86,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "**Note about credentials**\n", + "**⚠️ Note about credentials**\n", "\n", "For security reasons, we recommend storing your credentials in an external file preventing publishing them by accident within your notebook. You can get more information in the section *Setting your credentials* of the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/)." ] @@ -106,7 +106,7 @@ "\n", "In this section we will explore some basic information regarding the Dataset you have licensed. More information on how to explore metadata associated to a Dataset is available in the [Data discovery guide](https://carto.com/developers/cartoframes/guides/Data-discovery/).\n", "\n", - "In order to access the Dataset and its associeted metadata, you need to provide the \"Slug ID\" which is a unique identifier of that Dataset. The Slug IDs of your Datasets are available from Your Subscriptions page in the CARTO Dashboard and via the Discovery methods in CARTOFrames." + "In order to access the Dataset and its associeted metadata, you need to provide the \"ID\" which is a unique identifier of that Dataset. The IDs of your Datasets are available from Your Subscriptions page in the CARTO Dashboard and via the Discovery methods in CARTOFrames." ] }, { @@ -117,7 +117,7 @@ "source": [ "from cartoframes.data.observatory import Dataset\n", "\n", - "dataset = Dataset.get('YOUR_SLUG_ID')" + "dataset = Dataset.get('YOUR_ID')" ] }, { @@ -198,7 +198,7 @@ "outputs": [], "source": [ "# Filter by SQL query\n", - "query = \"SELECT * FROM $dataset$ LIMIT 100\"\n", + "query = \"SELECT * FROM $dataset$ LIMIT 50\"\n", "\n", "dataset_df = dataset.to_dataframe(sql_query=query)" ] @@ -273,7 +273,7 @@ "source": [ "from cartoframes.viz import color_bins_style\n", "\n", - "Layer(dataset_df, color_bins_style('YOUR_VARIABLE'),geom_col='geom')" + "Layer(dataset_df, color_bins_style('YOUR_VARIABLE_ID'), geom_col='geom')" ] }, { @@ -316,7 +316,7 @@ "\n", "Enrichment is the process of adding variables to a geometry, which we call the target (point, line, polygon…) from a spatial Dataset, which we call the source. CARTOFrames has a set of methods for you to augment your data with new variables from a Dataset in the Data Observatory.\n", "\n", - "In this example, you will need to load a dataframe with the geometries that you want to enrich with a variable or a group of variables from the Dataset. You can detail the variables to get from the Dataset by passing the variable's Slug ID. You can get the variables' Slug IDs with the metadata methods.\n", + "In this example, you will need to load a dataframe with the geometries that you want to enrich with a variable or a group of variables from the Dataset. You can detail the variables to get from the Dataset by passing the variable's ID. You can get the variables IDs with the metadata methods.\n", "\n", "More info in the [Data enrichment guide](https://carto.com/developers/cartoframes/guides/Data-enrichment/)." ] @@ -331,7 +331,7 @@ "\n", "enriched_df = Enrichment().enrich_polygons(\n", " df, # Insert here the DataFrame to be enriched\n", - " variables=['YOUR_DATASET_VARIABLE_SLUG_ID']\n", + " variables=['YOUR_VARIABLE_ID']\n", ")" ] }, @@ -351,7 +351,7 @@ "outputs": [], "source": [ "# Filter by SQL query\n", - "query = \"SELECT * FROM $dataset$ LIMIT 100\"\n", + "query = \"SELECT * FROM $dataset$ LIMIT 50\"\n", "\n", "dataset.to_csv('my_dataset.csv', sql_query=query)" ] @@ -379,3 +379,4 @@ "nbformat": 4, "nbformat_minor": 2 } + diff --git a/docs/examples/templates/do_geography_notebook_template.ipynb b/docs/examples/templates/do_geography_notebook_template.ipynb new file mode 100644 index 000000000..bb9d540da --- /dev/null +++ b/docs/examples/templates/do_geography_notebook_template.ipynb @@ -0,0 +1,304 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# CARTO Data Observatory" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a basic template notebook to start exploring your new Geography from CARTO's Data Observatory via the Python library [CARTOframes](https://carto.com/cartoframes).\n", + "\n", + "You can find more details about how to use CARTOframes in the [Quickstart guide](https://carto.com/developers/cartoframes/guides/Quickstart/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installation\n", + "\n", + "Make sure that you have the latest version installed. Please, find more information in the [Installation guide](https://carto.com/developers/cartoframes/guides/Installation/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install -U cartoframes" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Note: a kernel restart is required after installing the library\n", + "\n", + "import cartoframes\n", + "\n", + "cartoframes.__version__" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Credentials" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to be able to use the Data Observatory via CARTOframes, you need to set your CARTO account credentials first." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "username = 'YOUR_USERNAME'\n", + "api_key = 'YOUR_API_KEY' # Master API key. Do not make this file public!\n", + "\n", + "set_default_credentials(username, api_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**⚠️ Note about credentials**\n", + "\n", + "For security reasons, we recommend storing your credentials in an external file preventing publishing them by accident within your notebook. You can get more information in the section *Setting your credentials* of the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Geography operations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Metadata exploration\n", + "\n", + "In this section we will explore some basic information regarding the Geography you have licensed. More information on how to explore metadata associated to a Geography is available in the [Data discovery guide](https://carto.com/developers/cartoframes/guides/Data-discovery/).\n", + "\n", + "In order to access the Geography and its associeted metadata, you need to provide the \"ID\" which is a unique identifier of that Geography. The IDs of your Geography are available from Your Subscriptions page in the CARTO Dashboard and via the Discovery methods in CARTOFrames." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.data.observatory import Geography\n", + "\n", + "geography = Geography.get('YOUR_ID')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Retrieve some general metadata about the Geography\n", + "geography.to_dict()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Access the data\n", + "\n", + "Now that we have explored some basic information about the Geography, we will proceed to download a sample of the Geography into a dataframe so we can operate it in Python. \n", + "\n", + "Geographies can be downloaded in full or by applying a filter with a SQL query. More info on how to download the Geography or portions of it is available in the [Data discovery guide](https://carto.com/developers/cartoframes/guides/Data-discovery/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Filter by SQL query\n", + "query = \"SELECT * FROM $geography$ LIMIT 50\"\n", + "\n", + "geography_df = geography.to_dataframe(sql_query=query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note about SQL filters**\n", + "\n", + "Our SQL filtering queries allow for any PostgreSQL and PostGIS operation, so you can filter the rows (by a WHERE condition) or the columns (using the SELECT). Some common examples are filtering the Geography by bounding box or filtering by column value: \n", + "\n", + "```\n", + "SELECT * FROM $geography$ WHERE ST_IntersectsBox(geom, -74.044467,40.706128,-73.891345,40.837690)\n", + "```\n", + "\n", + "```\n", + "SELECT do_area, geom FROM $geography$\n", + "```\n", + "\n", + "A good tool to get the bounding box details for a specific area is [bboxfinder.com](http://bboxfinder.com/#0.000000,0.000000,0.000000,0.000000)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# First 10 rows of the Geography sample\n", + "geography_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Visualization\n", + "\n", + "Now that we have downloaded some data into a dataframe we can leverage the visualization capabilities of CARTOframes to build an interactive map.\n", + "\n", + "More info about building visualizations with CARTOframes is available in the [Visualization guide](https://carto.com/developers/cartoframes/guides/Visualization/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.viz import Layer\n", + "\n", + "Layer(geography_df, geom_col='geom')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note about variables**\n", + "\n", + "CARTOframes allows you to make data-driven visualizations from your Geography variables (columns) via the Style helpers. These functions provide out-of-the-box cartographic styles, legends, popups and widgets.\n", + "\n", + "Style helpers are also highly customizable to reach your desired visualization setting simple parameters. The helpers collection contains functions to visualize by color and size, and also by type: category, bins and continuous, depending on the type of the variable." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.viz import color_bins_style\n", + "\n", + "Layer(geography_df, color_bins_style('do_area'), geom_col='geom')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Upload to CARTO account\n", + "\n", + "In order to operate with the data in CARTO Builder or to build a CARTOFrames visualization reading the data from a table in the Cloud instead of having it in your local environment (with its benefits in performance), you can load the dataframe as a table in your CARTO account.\n", + "\n", + "More info in the [Data Management guide](https://carto.com/developers/cartoframes/guides/Data-management/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes import to_carto\n", + "\n", + "to_carto(geography_df, 'my_geography', geom_col='geom')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Build a visualization reading the data from your CARTO account\n", + "Layer('my_geography')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Save to file\n", + "\n", + "Finally, you can also export the data into a CSV file. More info in the [Data discovery guide](https://carto.com/developers/cartoframes/guides/Data-discovery/)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Filter by SQL query\n", + "query = \"SELECT * FROM $geography$ LIMIT 50\"\n", + "\n", + "geography.to_csv('my_geography.csv', sql_query=query)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} \ No newline at end of file diff --git a/docs/examples/use_cases/building_a_dashboard.ipynb b/docs/examples/use_cases/building_a_dashboard.ipynb new file mode 100644 index 000000000..ddd7ba9a5 --- /dev/null +++ b/docs/examples/use_cases/building_a_dashboard.ipynb @@ -0,0 +1,10908 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "g0pFW7zACCPt" + }, + "source": [ + "## Building a dashboard to plan a marketing campaign leveraging CARTO Data Observatory\n", + "\n", + "Combining different data sources to identify some patterns or understand some behavior in a specific location is a very typical use case in Spatial Data Science.\n", + "\n", + "In this notebook, we will build a dashboard combining different data from CARTO's Data Observatory to help identify the locations with specific characteristics described below.\n", + "\n", + "**Note** this use case leverages premium datasets from [CARTO Data Observatory](https://carto.com/spatial-data-catalog/).\n", + "\n", + "\n", + "### Use case description\n", + "\n", + "A pharmaceutical lab wants to launch a new marketing campaign to promote a new line of personal care products for senior people in the city of Philadelphia, PA. They know their target group is characterized by:\n", + " - People over 60\n", + " - Medium-high to high income\n", + " - High expenditure in personal care products and services\n", + "\n", + "\n", + "Given these characteristics, they would like to know which pharmacies and drug stores in the city of Philadelphia they should focus their efforts on.\n", + "\n", + "In order to identify the target drug stores and pharmacies, we will follow the following steps:\n", + " - [Get all pharmacies in Philadelphia](#section1)\n", + " - [Calculate their cathment areas using isochrones](#section2)\n", + " - [Enrich the isochrones with demographic, POI's, and consumption data](#section3)\n", + " - [Build the dashboard to help identify the pharmacies where the campaign can be more successful given the characteristics of the population within their catchment area](#section4)\n", + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UtO6XoGqCCPu" + }, + "source": [ + "### 0. Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aGwjaIPPpnMU" + }, + "source": [ + "Import the packages we'll use." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "id": "AgYjVJHmCCPv" + }, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import pandas as pd\n", + "\n", + "from cartoframes.auth import set_default_credentials\n", + "from cartoframes.data.services import Isolines\n", + "from cartoframes.data.observatory import *\n", + "from cartoframes.viz import *\n", + "from shapely.geometry import box\n", + "\n", + "pd.set_option('display.max_columns', None)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7JpDF3DKpsfT" + }, + "source": [ + "In order to be able to use the Data Observatory via CARTOframes, you need to set your CARTO account credentials first.\n", + "\n", + "Please, visit the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/) for further detail." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note about credentials**\n", + "\n", + "For security reasons, we recommend storing your credentials in an external file to prevent publishing them by accident when sharing your notebooks. You can get more information in the section _Setting your credentials_ of the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gBl3tk8Cpw0B" + }, + "source": [ + "\n", + "### 1. Download all pharmacies in Philadelphia from the Data Observatory" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NSbXgVDEp5_h" + }, + "source": [ + "Below is the bounding box of the area of study." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "B9FHBLrbCCP5" + }, + "outputs": [], + "source": [ + "dem_bbox = box(-75.229353,39.885501,-75.061124,39.997898)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Ymtjq8sgqCja" + }, + "source": [ + "We can get the pharmacies from [Pitney Bowes' Consumer Points of Interest](https://carto.com/spatial-data-catalog/browser/dataset/pb_consumer_po_62cddc04/) dataset. This is a premium dataset, so we first need to check that we are subscribed to it.\n", + "\n", + "Take a look at this template for more details on how to access and download a premium dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 72 + }, + "id": "mRXdJjZMVmtu", + "outputId": "5cf2ebe8-3ad0-4038-a0ec-a832d5bed7fb" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                              slugnamedescriptioncategory_idcountry_iddata_source_idprovider_idgeography_namegeography_descriptiontemporal_aggregationtime_coverageupdate_frequencyis_public_datalangversioncategory_nameprovider_namegeography_idid
                                                              0ags_sociodemogr_a7e14220Sociodemographics - United States of America (...Census and ACS sociodemographic data estimated...demographicsusasociodemographicsagsCensus Block Group - United States of AmericaNoneyearlyNoneyearlyFalseeng2020DemographicsApplied Geographic Solutionscarto-do.ags.geography_usa_blockgroup_2015carto-do.ags.demographics_sociodemographics_us...
                                                              1ags_retailpoten_aaf25a8cRetail Potential - United States of America (C...The retail potential database consists of aver...demographicsusaretailpotentialagsCensus Block Group - United States of America ...Shoreline clipped TIGER/Line boundaries. More ...yearly[2018-01-01, 2019-01-01)yearlyFalseeng2019DemographicsApplied Geographic Solutionscarto-do-public-data.carto.geography_usa_block...carto-do.ags.demographics_retailpotential_usa_...
                                                              2pb_consumer_po_62cddc04Points Of Interest - Consumer - United States ...Consumer Point of interest database per catego...points_of_interestusaconsumer_points_of_interestpitney_bowesLatitude/Longitude - United States of AmericaLocation of Points of InterestmonthlyNonemonthlyFalseengv1Points of InterestPitney Bowescarto-do.pitney_bowes.geography_usa_latlon_v1carto-do.pitney_bowes.pointsofinterest_consume...
                                                              \n", + "
                                                              " + ], + "text/plain": [ + " slug \\\n", + "0 ags_sociodemogr_a7e14220 \n", + "1 ags_retailpoten_aaf25a8c \n", + "2 pb_consumer_po_62cddc04 \n", + "\n", + " name \\\n", + "0 Sociodemographics - United States of America (... \n", + "1 Retail Potential - United States of America (C... \n", + "2 Points Of Interest - Consumer - United States ... \n", + "\n", + " description category_id \\\n", + "0 Census and ACS sociodemographic data estimated... demographics \n", + "1 The retail potential database consists of aver... demographics \n", + "2 Consumer Point of interest database per catego... points_of_interest \n", + "\n", + " country_id data_source_id provider_id \\\n", + "0 usa sociodemographics ags \n", + "1 usa retailpotential ags \n", + "2 usa consumer_points_of_interest pitney_bowes \n", + "\n", + " geography_name \\\n", + "0 Census Block Group - United States of America \n", + "1 Census Block Group - United States of America ... \n", + "2 Latitude/Longitude - United States of America \n", + "\n", + " geography_description temporal_aggregation \\\n", + "0 None yearly \n", + "1 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", + "2 Location of Points of Interest monthly \n", + "\n", + " time_coverage update_frequency is_public_data lang version \\\n", + "0 None yearly False eng 2020 \n", + "1 [2018-01-01, 2019-01-01) yearly False eng 2019 \n", + "2 None monthly False eng v1 \n", + "\n", + " category_name provider_name \\\n", + "0 Demographics Applied Geographic Solutions \n", + "1 Demographics Applied Geographic Solutions \n", + "2 Points of Interest Pitney Bowes \n", + "\n", + " geography_id \\\n", + "0 carto-do.ags.geography_usa_blockgroup_2015 \n", + "1 carto-do-public-data.carto.geography_usa_block... \n", + "2 carto-do.pitney_bowes.geography_usa_latlon_v1 \n", + "\n", + " id \n", + "0 carto-do.ags.demographics_sociodemographics_us... \n", + "1 carto-do.ags.demographics_retailpotential_usa_... \n", + "2 carto-do.pitney_bowes.pointsofinterest_consume... " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Catalog().subscriptions().datasets.to_dataframe()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "uvOsBkncqwCT" + }, + "source": [ + "#### Download and explore sample\n", + "\n", + "Pitney Bowes POI's are hierarchically classified (levels: trade division, group, class, sub class). \n", + "\n", + "Since we might not know which level can help us identify all pharmacies, we can start by downloading a sample for a smaller area to explore the dataset. For calculating the bounding box we use [bboxfinder](http://bboxfinder.com).\n", + "\n", + "We start by selecting our dataset and taking a quick look at its first 10 rows." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "rRCz-jiYVmrg" + }, + "outputs": [], + "source": [ + "dataset = Dataset.get('pb_consumer_po_62cddc04')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 354 + }, + "id": "1o4TmFUAVmo7", + "outputId": "f2e15514-5946-4523-b86e-2015831d7afb" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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None \n", + "7 None IA None GALT \n", + "8 None IA None None \n", + "9 None IA None None \n", + "\n", + " SIC8_DESCRIPTION ALT_INDUSTRY_CODE \\\n", + "0 LIFE INSURANCE CARRIERS 812930 \n", + "1 MORTGAGE BANKERS 812930 \n", + "2 LOAN AGENTS 812930 \n", + "3 ASSESSMENT ASSOCIATIONS, ACCIDENT AND HEALTH I... 812930 \n", + "4 PENSION FUNDS 812930 \n", + "5 WARRANTY INSURANCE, AUTOMOBILE 812930 \n", + "6 ACCIDENT INSURANCE CARRIERS 812930 \n", + "7 HEALTH INSURANCE CARRIERS 812930 \n", + "8 PENSION FUNDS 812930 \n", + "9 LOAN AGENTS 812930 \n", + "\n", + " LEGAL_STATUS_CODE SALES_VOLUME_LOCAL COUNTRY_ACCESS_CODE \\\n", + "0 003 706388 None \n", + "1 003 600000 None \n", + "2 003 4230106 None \n", + "3 000 508581 None \n", + "4 000 269036 None \n", + "5 003 218834 None \n", + "6 000 1179493 None \n", + "7 003 702600 None \n", + "8 003 176397 None \n", + "9 003 1806921 None \n", + "\n", + " PARENT_BUSINESS_NAME SUBSIDIARY_INDICATOR PARENT_STREET_ADDRESS \\\n", + "0 None 0 None \n", + "1 None 0 None \n", + "2 None 0 None \n", + "3 None 0 None \n", + "4 None 0 None \n", + "5 None 0 None \n", + "6 None 0 None \n", + "7 None 0 None \n", + "8 None 0 None \n", + "9 None 0 None \n", + "\n", + " GLOBAL_ULTIMATE_ADDRESS GLOBAL_ULTIMATE_COUNTRY \\\n", + "0 6000 EXECUTIVE BLVD # 700, ROCKVILLE, MARYLAND... None \n", + "1 1101 ELLER DR, FORT LAUDERDALE, FLORIDA, 33316... None \n", + "2 None None \n", + "3 222 3RD AVE SE STE 285, CEDAR RAPIDS, IOWA, 52... None \n", + "4 None USA \n", + "5 None None \n", + "6 1050 THMAS JFFERSON ST NW, WASHINGTON, DISTRIC... None \n", + "7 None None \n", + "8 1325 N 10TH ST, SAINT LOUIS, MISSOURI, 6310645... None \n", + "9 None USA \n", + "\n", + " SALES_VOLUME_US_DOLLARS GLOBAL_ULTIMATE_POSTCODE DOMESTIC_ULTIMATE_ADDRESS \\\n", + "0 None None None \n", + "1 None None None \n", + "2 None None None \n", + "3 None None None \n", + "4 None None None \n", + "5 None None None \n", + "6 None None None \n", + "7 None None None \n", + "8 None None None \n", + "9 None None None \n", + "\n", + " GLOBAL_ULTIMATE_AREANAME1 GLOBAL_ULTIMATE_AREANAME3 \\\n", + "0 None None \n", + "1 None None \n", + "2 None None \n", + "3 None None \n", + "4 None None \n", + "5 None None \n", + "6 None None \n", + "7 None None \n", + "8 None None \n", + "9 None None \n", + "\n", + " GLOBAL_ULTIMATE_INDICATOR DOMESTIC_ULTIMATE_POSTCODE \\\n", + "0 N None \n", + "1 N None \n", + "2 N None \n", + "3 Y None \n", + "4 N None \n", + "5 N None \n", + "6 N None \n", + "7 N None \n", + "8 N None \n", + "9 N None \n", + "\n", + " DOMESTIC_ULTIMATE_AREANAME1 DOMESTIC_ULTIMATE_AREANAME3 \\\n", + "0 None None \n", + "1 None None \n", + "2 None None \n", + "3 None None \n", + "4 None None \n", + "5 None None \n", + "6 None None \n", + "7 None None \n", + "8 None None \n", + "9 None None \n", + "\n", + " GLOBAL_ULTIMATE_BUSINESS_NAME GLOBAL_ULTIMATE_STREET_ADDRESS \\\n", + "0 None None \n", + "1 None None \n", + "2 None 711 D ST NW STE 200 \n", + "3 None None \n", + "4 None None \n", + "5 None None \n", + "6 None None \n", + "7 None None \n", + "8 None None \n", + "9 None 820 1ST ST NE STE 740 \n", + "\n", + " DOMESTIC_ULTIMATE_BUSINESS_NAME DOMESTIC_ULTIMATE_STREET_ADDRESS \n", + "0 None None \n", + "1 None None \n", + "2 None None \n", + "3 None None \n", + "4 None None \n", + "5 None None \n", + "6 None None \n", + "7 None None \n", + "8 None None \n", + "9 None None " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now download a small sample to help us identify which of the four hierarchy variables gives us the pharmacies." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "0sMcmlbEVmmN" + }, + "outputs": [], + "source": [ + "sql_query = \"SELECT * except(do_label) FROM $dataset$ WHERE ST_IntersectsBox(geom, -75.161723,39.962019,-75.149535,39.968071)\"\n", + "sample = dataset.to_dataframe(sql_query=sql_query)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 417 + }, + "id": "7LDBunMhVmjQ", + "outputId": "d989406d-49c0-478c-99b6-70fac54eae67" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                              01116489911#-75.157314#39.9628572019-11-01DRINKER DOWNS, INC.NaN1116489911NaNNaNUSANaNPHILADELPHIANaNPENNSYLVANIAPA191233332549 N 12TH ST, PHILADELPHIA, PA, 191233332549 N 12TH STPHILADELPHIA, PA, 191233332-75.15731439.962857S8HPNTSCZAHIGH1.0(267) 318-7772NaNNaNWWW.INSTITUTEBAR.COM/NaNDRINKING PLACES, NSK5813.0NaN58130200.0NIGHT CLUBS722410.010130261DIVISION G. - RETAIL TRADEEATING AND DRINKING PLACESDRINKING PLACESDRINKING PLACES/CAFE, PUB4.04.02008.0165700.0165700.020.0G3.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNPOINT (-75.15731 39.96286)
                                                              12019661767#-75.160361#39.965872019-10-01DIVING BELL SCUBA SHOPNaN2019661767NaNNaNUSANaNPHILADELPHIAPHILADELPHIAPENNSYLVANIAPA19123681 N BROAD ST, PHILADELPHIA, PA, 19123681 N BROAD STPHILADELPHIA, PA, 19123-75.16036139.965870S8HPNTSCZAHIGHNaN(215) 763-6868NaNHOWARD_PRUYN@DIVINGBELL.COMNaNNaNNaNNaNNaNNaNWATER SPORTNaN10120300DIVISION M. - SPORTSSPORTSWATER SPORTWATER SPORTNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPOINT (-75.16036 39.96587)
                                                              21116489911#-75.157314#39.9628572019-09-01DRINKER DOWNS, INC.NaN1116489911NaNNaNUSANaNPHILADELPHIANaNPENNSYLVANIAPA191233332549 N 12TH ST, PHILADELPHIA, PA 191233332549 N 12TH STPHILADELPHIA, PA 191233332-75.15731439.962857S8HPNTSCZAHIGH1.0(267) 318-7772NaNNaNWWW.INSTITUTEBAR.COM/NaNDRINKING PLACES, NSK5813.0NaN58130200.0NIGHT CLUBS722410.010130261DIVISION G. - RETAIL TRADEEATING AND DRINKING PLACESDRINKING PLACESDRINKING PLACES/CAFE, PUB4.04.02008.0165700.0165700.020.0G3.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNPOINT (-75.15731 39.96286)
                                                              31249192804#-75.157314#39.9628572019-11-01THE INSTITUTE BARNaN1249192804NaNNaNUSANaNPHILADELPHIANaNPENNSYLVANIAPA191233332549 N 12TH ST, PHILADELPHIA, PA, 191233332549 N 12TH STPHILADELPHIA, PA, 191233332-75.15731439.962857S8HPNTSCZAHIGH1.0(215) 787-0888NaNCHARLIE@INSTITUTEBAR.COMWWW.INSTITUTEBAR.COMNaNDRINKING PLACES, NSK5813.0NaN58130105.0TAVERN (DRINKING PLACES)722410.010786105DIVISION G. - RETAIL TRADEEATING AND DRINKING PLACESDRINKING PLACESDRINKING PLACES/CAFE, PUB4.04.02013.0101129.0101129.020.0G0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNPOINT (-75.15731 39.96286)
                                                              41243566100#-75.15985#39.964912019-09-01LIUNA LOCAL UNION 332NaN1243566100NaNNaNUSANaNPHILADELPHIANaNPENNSYLVANIAPA1912324111310 WALLACE ST, PHILADELPHIA, PA 1912324111310 WALLACE STPHILADELPHIA, PA 191232411-75.15985039.964910S5HPNTSCZAHIGH1.0(215) 765-6272NaNNaNWWW.LDC-PHILA-VIC.ORGNaNLABOR ORGANIZATIONS, NSK8631.0NaN86310000.0LABOR ORGANIZATIONS813930.010248631DIVISION I. - SERVICESMEMBERSHIP ORGANIZATIONSLABOR UNIONS AND SIMILAR LABOR ORGANIZATIONSLABOR ORGANIZATIONS3.03.02010.03033044.03033044.020.0G0.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNPOINT (-75.15985 39.96491)
                                                              \n", + "
                                                              " + ], + "text/plain": [ + " geoid do_date NAME \\\n", + "0 1116489911#-75.157314#39.962857 2019-11-01 DRINKER DOWNS, INC. \n", + "1 2019661767#-75.160361#39.96587 2019-10-01 DIVING BELL SCUBA SHOP \n", + "2 1116489911#-75.157314#39.962857 2019-09-01 DRINKER DOWNS, INC. \n", + "3 1249192804#-75.157314#39.962857 2019-11-01 THE INSTITUTE BAR \n", + "4 1243566100#-75.15985#39.96491 2019-09-01 LIUNA LOCAL UNION 332 \n", + "\n", + " BRANDNAME PB_ID TRADE_NAME FRANCHISE_NAME ISO3 AREANAME4 \\\n", + "0 NaN 1116489911 NaN NaN USA NaN \n", + "1 NaN 2019661767 NaN NaN USA NaN \n", + "2 NaN 1116489911 NaN NaN USA NaN \n", + "3 NaN 1249192804 NaN NaN USA NaN \n", + "4 NaN 1243566100 NaN NaN USA NaN \n", + "\n", + " AREANAME3 AREANAME2 AREANAME1 STABB POSTCODE \\\n", + "0 PHILADELPHIA NaN PENNSYLVANIA PA 191233332 \n", + "1 PHILADELPHIA PHILADELPHIA PENNSYLVANIA PA 19123 \n", + "2 PHILADELPHIA NaN PENNSYLVANIA PA 191233332 \n", + "3 PHILADELPHIA NaN PENNSYLVANIA PA 191233332 \n", + "4 PHILADELPHIA NaN PENNSYLVANIA PA 191232411 \n", + "\n", + " FORMATTEDADDRESS MAINADDRESSLINE \\\n", + "0 549 N 12TH ST, PHILADELPHIA, PA, 191233332 549 N 12TH ST \n", + "1 681 N BROAD ST, PHILADELPHIA, PA, 19123 681 N BROAD ST \n", + "2 549 N 12TH ST, PHILADELPHIA, PA 191233332 549 N 12TH ST \n", + "3 549 N 12TH ST, PHILADELPHIA, PA, 191233332 549 N 12TH ST \n", + "4 1310 WALLACE ST, PHILADELPHIA, PA 191232411 1310 WALLACE ST \n", + "\n", + " ADDRESSLASTLINE LONGITUDE LATITUDE GEORESULT \\\n", + "0 PHILADELPHIA, PA, 191233332 -75.157314 39.962857 S8HPNTSCZA \n", + "1 PHILADELPHIA, PA, 19123 -75.160361 39.965870 S8HPNTSCZA \n", + "2 PHILADELPHIA, PA 191233332 -75.157314 39.962857 S8HPNTSCZA \n", + "3 PHILADELPHIA, PA, 191233332 -75.157314 39.962857 S8HPNTSCZA \n", + "4 PHILADELPHIA, PA 191232411 -75.159850 39.964910 S5HPNTSCZA \n", + "\n", + " CONFIDENCE_CODE COUNTRY_ACCESS_CODE TEL_NUM FAXNUM \\\n", + "0 HIGH 1.0 (267) 318-7772 NaN \n", + "1 HIGH NaN (215) 763-6868 NaN \n", + "2 HIGH 1.0 (267) 318-7772 NaN \n", + "3 HIGH 1.0 (215) 787-0888 NaN \n", + "4 HIGH 1.0 (215) 765-6272 NaN \n", + "\n", + " EMAIL HTTP OPEN_24H \\\n", + "0 NaN WWW.INSTITUTEBAR.COM/ NaN \n", + "1 HOWARD_PRUYN@DIVINGBELL.COM NaN NaN \n", + "2 NaN WWW.INSTITUTEBAR.COM/ NaN \n", + "3 CHARLIE@INSTITUTEBAR.COM WWW.INSTITUTEBAR.COM NaN \n", + "4 NaN WWW.LDC-PHILA-VIC.ORG NaN \n", + "\n", + " BUSINESS_LINE SIC1 SIC2 SIC8 \\\n", + "0 DRINKING PLACES, NSK 5813.0 NaN 58130200.0 \n", + "1 NaN NaN NaN NaN \n", + "2 DRINKING PLACES, NSK 5813.0 NaN 58130200.0 \n", + "3 DRINKING PLACES, NSK 5813.0 NaN 58130105.0 \n", + "4 LABOR ORGANIZATIONS, NSK 8631.0 NaN 86310000.0 \n", + "\n", + " SIC8_DESCRIPTION ALT_INDUSTRY_CODE MICODE \\\n", + "0 NIGHT CLUBS 722410.0 10130261 \n", + "1 WATER SPORT NaN 10120300 \n", + "2 NIGHT CLUBS 722410.0 10130261 \n", + "3 TAVERN (DRINKING PLACES) 722410.0 10786105 \n", + "4 LABOR ORGANIZATIONS 813930.0 10248631 \n", + "\n", + " TRADE_DIVISION GROUP \\\n", + "0 DIVISION G. - RETAIL TRADE EATING AND DRINKING PLACES \n", + "1 DIVISION M. - SPORTS SPORTS \n", + "2 DIVISION G. - RETAIL TRADE EATING AND DRINKING PLACES \n", + "3 DIVISION G. - RETAIL TRADE EATING AND DRINKING PLACES \n", + "4 DIVISION I. - SERVICES MEMBERSHIP ORGANIZATIONS \n", + "\n", + " CLASS SUB_CLASS \\\n", + "0 DRINKING PLACES DRINKING PLACES/CAFE, PUB \n", + "1 WATER SPORT WATER SPORT \n", + "2 DRINKING PLACES DRINKING PLACES/CAFE, PUB \n", + "3 DRINKING PLACES DRINKING PLACES/CAFE, PUB \n", + "4 LABOR UNIONS AND SIMILAR LABOR ORGANIZATIONS LABOR ORGANIZATIONS \n", + "\n", + " EMPLOYEE_HERE EMPLOYEE_COUNT YEAR_START SALES_VOLUME_LOCAL \\\n", + "0 4.0 4.0 2008.0 165700.0 \n", + "1 NaN NaN NaN NaN \n", + "2 4.0 4.0 2008.0 165700.0 \n", + "3 4.0 4.0 2013.0 101129.0 \n", + "4 3.0 3.0 2010.0 3033044.0 \n", + "\n", + " SALES_VOLUME_US_DOLLARS CURRENCY_CODE AGENT_CODE LEGAL_STATUS_CODE \\\n", + "0 165700.0 20.0 G 3.0 \n", + "1 NaN NaN NaN NaN \n", + "2 165700.0 20.0 G 3.0 \n", + "3 101129.0 20.0 G 0.0 \n", + "4 3033044.0 20.0 G 0.0 \n", + "\n", + " STATUS_CODE SUBSIDIARY_INDICATOR PARENT_BUSINESS_NAME PARENT_ADDRESS \\\n", + "0 0.0 0.0 NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 0.0 0.0 NaN NaN \n", + "3 0.0 0.0 NaN NaN \n", + "4 0.0 0.0 NaN NaN \n", + "\n", + " PARENT_STREET_ADDRESS PARENT_AREANAME3 PARENT_AREANAME1 PARENT_COUNTRY \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " PARENT_POSTCODE DOMESTIC_ULTIMATE_BUSINESS_NAME DOMESTIC_ULTIMATE_ADDRESS \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " DOMESTIC_ULTIMATE_STREET_ADDRESS DOMESTIC_ULTIMATE_AREANAME3 \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " DOMESTIC_ULTIMATE_AREANAME1 DOMESTIC_ULTIMATE_POSTCODE \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " GLOBAL_ULTIMATE_INDICATOR GLOBAL_ULTIMATE_BUSINESS_NAME \\\n", + "0 N NaN \n", + "1 NaN NaN \n", + "2 N NaN \n", + "3 N NaN \n", + "4 N NaN \n", + "\n", + " GLOBAL_ULTIMATE_ADDRESS GLOBAL_ULTIMATE_STREET_ADDRESS \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " GLOBAL_ULTIMATE_AREANAME3 GLOBAL_ULTIMATE_AREANAME1 GLOBAL_ULTIMATE_COUNTRY \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " GLOBAL_ULTIMATE_POSTCODE FAMILY_MEMBERS HIERARCHY_CODE TICKER_SYMBOL \\\n", + "0 NaN 0.0 0.0 NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN 0.0 0.0 NaN \n", + "3 NaN 0.0 0.0 NaN \n", + "4 NaN 0.0 0.0 NaN \n", + "\n", + " EXCHANGE_NAME geom \n", + "0 NaN POINT (-75.15731 39.96286) \n", + "1 NaN POINT (-75.16036 39.96587) \n", + "2 NaN POINT (-75.15731 39.96286) \n", + "3 NaN POINT (-75.15731 39.96286) \n", + "4 NaN POINT (-75.15985 39.96491) " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 141 + }, + "id": "V0nvkfg6ZKdF", + "outputId": "88969105-7029-412a-e38e-75622fc0eb82" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['DIVISION G. - RETAIL TRADE', 'DIVISION M. - SPORTS',\n", + " 'DIVISION I. - SERVICES',\n", + " 'DIVISION H. - FINANCE, INSURANCE, AND REAL ESTATE',\n", + " 'DIVISION C. - CONSTRUCTION',\n", + " 'DIVISION J. - PUBLIC ADMINISTRATION',\n", + " 'DIVISION E. - TRANSPORTATION AND PUBLIC UTILITIES',\n", + " 'DIVISION L. - TOURISM'], dtype=object)" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample['TRADE_DIVISION'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 123 + }, + "id": "maJzFw3pZKaX", + "outputId": "e3162bc9-b919-4b7d-b25d-c5f1adb9b2b9" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['EATING AND DRINKING PLACES',\n", + " 'AUTOMOTIVE DEALERS AND GASOLINE SERVICE STATIONS',\n", + " 'MISCELLANEOUS RETAIL',\n", + " 'BUILDING MATERIALS, HARDWARE, GARDEN SUPPLIES AND MOBILE HOMES',\n", + " 'APPAREL AND ACCESSORY STORES',\n", + " 'HOME FURNITURE, FURNISHINGS AND EQUIPMENT STORES', 'FOOD STORES'],\n", + " dtype=object)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample.loc[sample['TRADE_DIVISION'] == 'DIVISION G. - RETAIL TRADE', 'GROUP'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 212 + }, + "id": "feTf6hYZZKXs", + "outputId": "601305d0-3f70-4a16-aceb-f8743257e53b" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['DRINKING PLACES', 'EATING PLACES/RESTAURANTS',\n", + " 'AUTO AND HOME SUPPLY STORES', 'GASOLINE SERVICE STATIONS',\n", + " 'MOTOR VEHICLE DEALERS (NEW AND USED)',\n", + " 'RETAIL STORES, NOT ELSEWHERE CLASSIFIED',\n", + " 'PAINT, GLASS, AND WALLPAPER STORES',\n", + " 'MISCELLANEOUS APPAREL AND ACCESSORY STORES',\n", + " 'FAMILY CLOTHING STORES', 'MISCELLANEOUS SHOPPING GOODS STORES',\n", + " 'LUMBER AND OTHER BUILDING MATERIALS DEALERS',\n", + " 'HOME FURNITURE AND FURNISHINGS STORES',\n", + " 'DRUG STORES AND PROPRIETARY STORES', 'NONSTORE RETAILERS',\n", + " 'GROCERY STORES', 'FRUIT AND VEGETABLE MARKETS', 'LIQUOR STORES'],\n", + " dtype=object)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample.loc[sample['TRADE_DIVISION'] == 'DIVISION G. - RETAIL TRADE', 'CLASS'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The class `DRUG STORES AND PROPRIETARY STORES` is the one we're looking for." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "id": "HGNBhEtzZKUp", + "outputId": "2841f1bb-0c24-4eb2-fcec-429312ac4157" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['DRUG STORES AND PROPRIETARY STORES/PHARMACY'], dtype=object)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample.loc[sample['CLASS'] == 'DRUG STORES AND PROPRIETARY STORES', 'SUB_CLASS'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_hefGvb2r6cJ" + }, + "source": [ + "#### Download all pharmacies in the area of study" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "giSmBp0harWJ" + }, + "outputs": [], + "source": [ + "sql_query = \"\"\"SELECT * except(do_label) \n", + " FROM $dataset$\n", + " WHERE CLASS = 'DRUG STORES AND PROPRIETARY STORES'\n", + " AND ST_IntersectsBox(geom, -75.229353,39.885501,-75.061124,39.997898)\"\"\"\n", + "\n", + "ph_pharmacies = dataset.to_dataframe(sql_query=sql_query)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 467 + }, + "id": "a1B66RyjbotY", + "outputId": "44176523-8e36-4111-b5e1-10bf10e96f61" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                              geoiddo_dateNAMEBRANDNAMEPB_IDTRADE_NAMEFRANCHISE_NAMEISO3AREANAME4AREANAME3AREANAME2AREANAME1STABBPOSTCODEFORMATTEDADDRESSMAINADDRESSLINEADDRESSLASTLINELONGITUDELATITUDEGEORESULTCONFIDENCE_CODECOUNTRY_ACCESS_CODETEL_NUMFAXNUMEMAILHTTPOPEN_24HBUSINESS_LINESIC1SIC2SIC8SIC8_DESCRIPTIONALT_INDUSTRY_CODEMICODETRADE_DIVISIONGROUPCLASSSUB_CLASSEMPLOYEE_HEREEMPLOYEE_COUNTYEAR_STARTSALES_VOLUME_LOCALSALES_VOLUME_US_DOLLARSCURRENCY_CODEAGENT_CODELEGAL_STATUS_CODESTATUS_CODESUBSIDIARY_INDICATORPARENT_BUSINESS_NAMEPARENT_ADDRESSPARENT_STREET_ADDRESSPARENT_AREANAME3PARENT_AREANAME1PARENT_COUNTRYPARENT_POSTCODEDOMESTIC_ULTIMATE_BUSINESS_NAMEDOMESTIC_ULTIMATE_ADDRESSDOMESTIC_ULTIMATE_STREET_ADDRESSDOMESTIC_ULTIMATE_AREANAME3DOMESTIC_ULTIMATE_AREANAME1DOMESTIC_ULTIMATE_POSTCODEGLOBAL_ULTIMATE_INDICATORGLOBAL_ULTIMATE_BUSINESS_NAMEGLOBAL_ULTIMATE_ADDRESSGLOBAL_ULTIMATE_STREET_ADDRESSGLOBAL_ULTIMATE_AREANAME3GLOBAL_ULTIMATE_AREANAME1GLOBAL_ULTIMATE_COUNTRYGLOBAL_ULTIMATE_POSTCODEFAMILY_MEMBERSHIERARCHY_CODETICKER_SYMBOLEXCHANGE_NAMEgeom
                                                              02128682600#-75.156071#39.9537382019-08-01HERBALIFENaN2128682600NaNNaNUSANaNPHILADELPHIAPHILADELPHIAPENNSYLVANIAPA19107933 ARCH ST, PHILADELPHIA, PA, 19107933 ARCH STPHILADELPHIA, PA, 19107-75.15607139.953738S8HPNTSCZAHIGHNaN(267) 687-8065NaNNaNWWW.HERBALIFE.COMNaNNaN5912NaN59129901DRUG STORESNaN10010700DIVISION G. - RETAIL TRADEMISCELLANEOUS RETAILDRUG STORES AND PROPRIETARY STORESDRUG STORES AND PROPRIETARY STORES/PHARMACYNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPOINT (-75.15607 39.95374)
                                                              12154275062#-75.15871#39.9358682019-08-01HERBALIFENaN2154275062NaNNaNUSANaNPHILADELPHIAPHILADELPHIAPENNSYLVANIAPA191471116 S 9TH ST, PHILADELPHIA, PA, 191471116 S 9TH STPHILADELPHIA, PA, 19147-75.15871039.935868S7HPNTSCZAHIGHNaN(267) 519-3122NaNNaNWWW.HERBALIFE.COMNaNNaN5912NaN59129901DRUG STORESNaN10010700DIVISION G. - RETAIL TRADEMISCELLANEOUS RETAILDRUG STORES AND PROPRIETARY STORESDRUG STORES AND PROPRIETARY STORES/PHARMACYNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPOINT (-75.15871 39.93587)
                                                              22038322702#-75.063221#39.9465452019-08-01SHAKLEE DISTRIBUTORNaN2038322702NaNNaNUSANaNMERCHANTVILLECAMDENNEW JERSEYNJ08109226 POPLAR AVE, MERCHANTVILLE, NJ, 08109226 POPLAR AVEMERCHANTVILLE, NJ, 08109-75.06322139.946545S7HPNTSCZAHIGHNaN(856) 767-9203NaNNaNWWW.SHAKLEE.COMNaNNaN5912NaN59129901DRUG STORESNaN10010700DIVISION G. - RETAIL TRADEMISCELLANEOUS RETAILDRUG STORES AND PROPRIETARY STORESDRUG STORES AND PROPRIETARY STORES/PHARMACYNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPOINT (-75.06322 39.94655)
                                                              32038337811#-75.063855#39.9492019-08-01RYLA COMMUNITY PHARMACYNaN2038337811NaNNaNUSANaNMERCHANTVILLECAMDENNEW JERSEYNJ081094301 MAPLE AVE, MERCHANTVILLE, NJ, 081094301 MAPLE AVEMERCHANTVILLE, NJ, 08109-75.06385539.949000S8HPNTSCZAHIGHNaN(856) 320-2609NaNNaNNaNNaNNaN5912NaN59120000DRUG STORES AND PROPRIETARY STORESNaN10230030DIVISION G. - RETAIL TRADEMISCELLANEOUS RETAILDRUG STORES AND PROPRIETARY STORESDRUG STORES AND PROPRIETARY STORES/PHARMACYNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPOINT (-75.06386 39.94900)
                                                              42017798948#-75.119533#39.9417572019-08-01LINCOLN DRUG CONaN2017798948NaNNaNUSANaNCAMDENCAMDENNEW JERSEYNJ08103221 S BROADWAY, CAMDEN, NJ, 08103221 S BROADWAYCAMDEN, NJ, 08103-75.11953339.941757T16MEDIUMNaN(856) 365-3400NaNNaNNaNNaNNaN5912NaN59120000DRUG STORES AND PROPRIETARY STORESNaN10230030DIVISION G. - RETAIL TRADEMISCELLANEOUS RETAILDRUG STORES AND PROPRIETARY STORESDRUG STORES AND PROPRIETARY STORES/PHARMACYNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNPOINT (-75.11953 39.94176)
                                                              \n", + "
                                                              " + ], + "text/plain": [ + " geoid do_date NAME \\\n", + "0 2128682600#-75.156071#39.953738 2019-08-01 HERBALIFE \n", + "1 2154275062#-75.15871#39.935868 2019-08-01 HERBALIFE \n", + "2 2038322702#-75.063221#39.946545 2019-08-01 SHAKLEE DISTRIBUTOR \n", + "3 2038337811#-75.063855#39.949 2019-08-01 RYLA COMMUNITY PHARMACY \n", + "4 2017798948#-75.119533#39.941757 2019-08-01 LINCOLN DRUG CO \n", + "\n", + " BRANDNAME PB_ID TRADE_NAME FRANCHISE_NAME ISO3 AREANAME4 \\\n", + "0 NaN 2128682600 NaN NaN USA NaN \n", + "1 NaN 2154275062 NaN NaN USA NaN \n", + "2 NaN 2038322702 NaN NaN USA NaN \n", + "3 NaN 2038337811 NaN NaN USA NaN \n", + "4 NaN 2017798948 NaN NaN USA NaN \n", + "\n", + " AREANAME3 AREANAME2 AREANAME1 STABB POSTCODE \\\n", + "0 PHILADELPHIA PHILADELPHIA PENNSYLVANIA PA 19107 \n", + "1 PHILADELPHIA PHILADELPHIA PENNSYLVANIA PA 19147 \n", + "2 MERCHANTVILLE CAMDEN NEW JERSEY NJ 08109 \n", + "3 MERCHANTVILLE CAMDEN NEW JERSEY NJ 08109 \n", + "4 CAMDEN CAMDEN NEW JERSEY NJ 08103 \n", + "\n", + " FORMATTEDADDRESS MAINADDRESSLINE \\\n", + "0 933 ARCH ST, PHILADELPHIA, PA, 19107 933 ARCH ST \n", + "1 1116 S 9TH ST, PHILADELPHIA, PA, 19147 1116 S 9TH ST \n", + "2 226 POPLAR AVE, MERCHANTVILLE, NJ, 08109 226 POPLAR AVE \n", + "3 4301 MAPLE AVE, MERCHANTVILLE, NJ, 08109 4301 MAPLE AVE \n", + "4 221 S BROADWAY, CAMDEN, NJ, 08103 221 S BROADWAY \n", + "\n", + " ADDRESSLASTLINE LONGITUDE LATITUDE GEORESULT CONFIDENCE_CODE \\\n", + "0 PHILADELPHIA, PA, 19107 -75.156071 39.953738 S8HPNTSCZA HIGH \n", + "1 PHILADELPHIA, PA, 19147 -75.158710 39.935868 S7HPNTSCZA HIGH \n", + "2 MERCHANTVILLE, NJ, 08109 -75.063221 39.946545 S7HPNTSCZA HIGH \n", + "3 MERCHANTVILLE, NJ, 08109 -75.063855 39.949000 S8HPNTSCZA HIGH \n", + "4 CAMDEN, NJ, 08103 -75.119533 39.941757 T16 MEDIUM \n", + "\n", + " COUNTRY_ACCESS_CODE TEL_NUM FAXNUM EMAIL HTTP \\\n", + "0 NaN (267) 687-8065 NaN NaN WWW.HERBALIFE.COM \n", + "1 NaN (267) 519-3122 NaN NaN WWW.HERBALIFE.COM \n", + "2 NaN (856) 767-9203 NaN NaN WWW.SHAKLEE.COM \n", + "3 NaN (856) 320-2609 NaN NaN NaN \n", + "4 NaN (856) 365-3400 NaN NaN NaN \n", + "\n", + " OPEN_24H BUSINESS_LINE SIC1 SIC2 SIC8 \\\n", + "0 NaN NaN 5912 NaN 59129901 \n", + "1 NaN NaN 5912 NaN 59129901 \n", + "2 NaN NaN 5912 NaN 59129901 \n", + "3 NaN NaN 5912 NaN 59120000 \n", + "4 NaN NaN 5912 NaN 59120000 \n", + "\n", + " SIC8_DESCRIPTION ALT_INDUSTRY_CODE MICODE \\\n", + "0 DRUG STORES NaN 10010700 \n", + "1 DRUG STORES NaN 10010700 \n", + "2 DRUG STORES NaN 10010700 \n", + "3 DRUG STORES AND PROPRIETARY STORES NaN 10230030 \n", + "4 DRUG STORES AND PROPRIETARY STORES NaN 10230030 \n", + "\n", + " TRADE_DIVISION GROUP \\\n", + "0 DIVISION G. - RETAIL TRADE MISCELLANEOUS RETAIL \n", + "1 DIVISION G. - RETAIL TRADE MISCELLANEOUS RETAIL \n", + "2 DIVISION G. - RETAIL TRADE MISCELLANEOUS RETAIL \n", + "3 DIVISION G. - RETAIL TRADE MISCELLANEOUS RETAIL \n", + "4 DIVISION G. - RETAIL TRADE MISCELLANEOUS RETAIL \n", + "\n", + " CLASS \\\n", + "0 DRUG STORES AND PROPRIETARY STORES \n", + "1 DRUG STORES AND PROPRIETARY STORES \n", + "2 DRUG STORES AND PROPRIETARY STORES \n", + "3 DRUG STORES AND PROPRIETARY STORES \n", + "4 DRUG STORES AND PROPRIETARY STORES \n", + "\n", + " SUB_CLASS EMPLOYEE_HERE EMPLOYEE_COUNT \\\n", + "0 DRUG STORES AND PROPRIETARY STORES/PHARMACY NaN NaN \n", + "1 DRUG STORES AND PROPRIETARY STORES/PHARMACY NaN NaN \n", + "2 DRUG STORES AND PROPRIETARY STORES/PHARMACY NaN NaN \n", + "3 DRUG STORES AND PROPRIETARY STORES/PHARMACY NaN NaN \n", + "4 DRUG STORES AND PROPRIETARY STORES/PHARMACY NaN NaN \n", + "\n", + " YEAR_START SALES_VOLUME_LOCAL SALES_VOLUME_US_DOLLARS CURRENCY_CODE \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " AGENT_CODE LEGAL_STATUS_CODE STATUS_CODE SUBSIDIARY_INDICATOR \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " PARENT_BUSINESS_NAME PARENT_ADDRESS PARENT_STREET_ADDRESS PARENT_AREANAME3 \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " PARENT_AREANAME1 PARENT_COUNTRY PARENT_POSTCODE \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " DOMESTIC_ULTIMATE_BUSINESS_NAME DOMESTIC_ULTIMATE_ADDRESS \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " DOMESTIC_ULTIMATE_STREET_ADDRESS DOMESTIC_ULTIMATE_AREANAME3 \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " DOMESTIC_ULTIMATE_AREANAME1 DOMESTIC_ULTIMATE_POSTCODE \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " GLOBAL_ULTIMATE_INDICATOR GLOBAL_ULTIMATE_BUSINESS_NAME \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " GLOBAL_ULTIMATE_ADDRESS GLOBAL_ULTIMATE_STREET_ADDRESS \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " GLOBAL_ULTIMATE_AREANAME3 GLOBAL_ULTIMATE_AREANAME1 GLOBAL_ULTIMATE_COUNTRY \\\n", + "0 NaN NaN NaN \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "\n", + " GLOBAL_ULTIMATE_POSTCODE FAMILY_MEMBERS HIERARCHY_CODE TICKER_SYMBOL \\\n", + "0 NaN NaN NaN NaN \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 NaN NaN NaN NaN \n", + "4 NaN NaN NaN NaN \n", + "\n", + " EXCHANGE_NAME geom \n", + "0 NaN POINT (-75.15607 39.95374) \n", + "1 NaN POINT (-75.15871 39.93587) \n", + "2 NaN POINT (-75.06322 39.94655) \n", + "3 NaN POINT (-75.06386 39.94900) \n", + "4 NaN POINT (-75.11953 39.94176) " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ph_pharmacies.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7k0cIht6sDUR" + }, + "source": [ + "The dataset contains different versions of the POI's tagged by the _do_date_ column. We are only inetrested in the latest version of each POI." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "rVwNF4cakE1W" + }, + "outputs": [], + "source": [ + "ph_pharmacies = ph_pharmacies.sort_values(by='do_date', ascending=False).groupby('PB_ID').first().reset_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "id": "qmN37R821xuA", + "outputId": "8714967b-d02d-4c4e-c77b-2285992800cb" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(477, 74)" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ph_pharmacies.shape" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KBCirDMtsYjT" + }, + "source": [ + "#### Visualize the dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "HeZ9LEkibjWM", + "outputId": "19b69a5d-ff91-4293-9196-5e0366b98699" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                                                                  slugnamedescriptioncategory_idcountry_iddata_source_idprovider_idgeography_namegeography_descriptiontemporal_aggregationtime_coverageupdate_frequencyis_public_datalangversioncategory_nameprovider_namegeography_idid
                                                                  0ags_consumer_pr_9f337eb8Consumer Profiles - United States of America (...Segmentation of the population in sixty-eight ...demographicsusaconsumer_profilesagsCensus Block Group - United States of America ...Shoreline clipped TIGER/Line boundaries. More ...yearly[2018-01-01, 2019-01-01)yearlyFalseeng2019DemographicsApplied Geographic Solutionscarto-do-public-data.carto.geography_usa_block...carto-do.ags.demographics_consumerprofiles_usa...
                                                                  1ags_sociodemogr_f510a947Sociodemographics - United States of America (...Census and ACS sociodemographic data estimated...demographicsusasociodemographicsagsCensus Block Group - United States of America ...Shoreline clipped TIGER/Line boundaries. More ...yearly[2019-01-01, 2020-01-01)yearlyFalseeng2019DemographicsApplied Geographic Solutionscarto-do-public-data.carto.geography_usa_block...carto-do.ags.demographics_sociodemographics_us...
                                                                  2ags_businesscou_df363a87Business Counts - United States of America (Ce...Business Counts database is a geographic summa...demographicsusabusinesscountsagsCensus Block Group - United States of America ...Shoreline clipped TIGER/Line boundaries. More ...yearly[2018-01-01, 2019-01-01)yearlyFalseeng2019DemographicsApplied Geographic Solutionscarto-do-public-data.carto.geography_usa_block...carto-do.ags.demographics_businesscounts_usa_b...
                                                                  3ags_consumer_sp_895a369cConsumer Spending - United States of America (...The Consumer Expenditure database consists of ...demographicsusaconsumer_spendingagsCensus Block Group - United States of America ...Shoreline clipped TIGER/Line boundaries. More ...yearly[2018-01-01, 2019-01-01)yearlyFalseeng2019DemographicsApplied Geographic Solutionscarto-do-public-data.carto.geography_usa_block...carto-do.ags.demographics_consumerspending_usa...
                                                                  4ags_crimerisk_bb3e4fb3Crime Risk - United States of America (Census ...Using advanced statistical methodologies and a...demographicsusacrimeriskagsCensus Block Group - United States of AmericaNoneyearlyNoneyearlyFalseeng2020DemographicsApplied Geographic Solutionscarto-do.ags.geography_usa_blockgroup_2015carto-do.ags.demographics_crimerisk_usa_blockg...
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                                                                  " + ], + "text/plain": [ + " slug \\\n", + "0 ags_consumer_pr_9f337eb8 \n", + "1 ags_sociodemogr_f510a947 \n", + "2 ags_businesscou_df363a87 \n", + "3 ags_consumer_sp_895a369c \n", + "4 ags_crimerisk_bb3e4fb3 \n", + "\n", + " name \\\n", + "0 Consumer Profiles - United States of America (... \n", + "1 Sociodemographics - United States of America (... \n", + "2 Business Counts - United States of America (Ce... \n", + "3 Consumer Spending - United States of America (... \n", + "4 Crime Risk - United States of America (Census ... \n", + "\n", + " description category_id country_id \\\n", + "0 Segmentation of the population in sixty-eight ... demographics usa \n", + "1 Census and ACS sociodemographic data estimated... demographics usa \n", + "2 Business Counts database is a geographic summa... demographics usa \n", + "3 The Consumer Expenditure database consists of ... demographics usa \n", + "4 Using advanced statistical methodologies and a... demographics usa \n", + "\n", + " data_source_id provider_id \\\n", + "0 consumer_profiles ags \n", + "1 sociodemographics ags \n", + "2 businesscounts ags \n", + "3 consumer_spending ags \n", + "4 crimerisk ags \n", + "\n", + " geography_name \\\n", + "0 Census Block Group - United States of America ... \n", + "1 Census Block Group - United States of America ... \n", + "2 Census Block Group - United States of America ... \n", + "3 Census Block Group - United States of America ... \n", + "4 Census Block Group - United States of America \n", + "\n", + " geography_description temporal_aggregation \\\n", + "0 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", + "1 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", + "2 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", + "3 Shoreline clipped TIGER/Line boundaries. More ... yearly \n", + "4 None yearly \n", + "\n", + " time_coverage update_frequency is_public_data lang version \\\n", + "0 [2018-01-01, 2019-01-01) yearly False eng 2019 \n", + "1 [2019-01-01, 2020-01-01) yearly False eng 2019 \n", + "2 [2018-01-01, 2019-01-01) yearly False eng 2019 \n", + "3 [2018-01-01, 2019-01-01) yearly False eng 2019 \n", + "4 None yearly False eng 2020 \n", + "\n", + " category_name provider_name \\\n", + "0 Demographics Applied Geographic Solutions \n", + "1 Demographics Applied Geographic Solutions \n", + "2 Demographics Applied Geographic Solutions \n", + "3 Demographics Applied Geographic Solutions \n", + "4 Demographics Applied Geographic Solutions \n", + "\n", + " geography_id \\\n", + "0 carto-do-public-data.carto.geography_usa_block... \n", + "1 carto-do-public-data.carto.geography_usa_block... \n", + "2 carto-do-public-data.carto.geography_usa_block... \n", + "3 carto-do-public-data.carto.geography_usa_block... \n", + "4 carto-do.ags.geography_usa_blockgroup_2015 \n", + "\n", + " id \n", + "0 carto-do.ags.demographics_consumerprofiles_usa... \n", + "1 carto-do.ags.demographics_sociodemographics_us... \n", + "2 carto-do.ags.demographics_businesscounts_usa_b... \n", + "3 carto-do.ags.demographics_consumerspending_usa... \n", + "4 carto-do.ags.demographics_crimerisk_usa_blockg... " + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Catalog().country('usa').category('demographics').provider('ags').datasets.to_dataframe().head()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "id": "YBMKA2m2IvW4" + }, + "outputs": [], + "source": [ + "dataset = Dataset.get('ags_sociodemogr_f510a947')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 367 + }, + "id": "hjUYGb3s4DLJ", + "outputId": "d73b3c91-02a3-44ac-8882-daaf750716c5" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                  " + ], + "text/plain": [ + " DWLCY HHDCY POPCY VPHCY1 AGECYMED HHDCYFAM HOOEXMED HUSEXAPT \\\n", + "0 5 5 6 0 64.00 1 63749 0 \n", + "1 2 2 5 1 36.50 2 124999 0 \n", + "2 0 0 0 0 0.00 0 0 0 \n", + "3 21 11 22 4 64.00 6 74999 0 \n", + "4 0 0 959 0 18.91 0 0 0 \n", + "5 0 0 0 0 0.00 0 0 0 \n", + "6 0 0 0 0 0.00 0 0 0 \n", + "7 0 0 0 0 0.00 0 0 0 \n", + "8 0 0 0 0 0.00 0 0 0 \n", + "9 0 0 0 0 0.00 0 0 0 \n", + "\n", + " LBFCYARM LBFCYLBF LBFCYNLF MARCYSEP POPCYGRP RNTEXMED SEXCYFEM \\\n", + "0 0 0 6 0 0 0 5 \n", + "1 0 2 1 0 0 0 3 \n", + "2 0 0 0 0 0 0 0 \n", + "3 0 10 10 1 0 449 9 \n", + "4 0 378 581 0 959 0 585 \n", + "5 0 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 0 \n", + "7 0 0 0 0 0 0 0 \n", + "8 0 0 0 0 0 0 0 \n", + "9 0 0 0 0 0 0 0 \n", + "\n", + " SEXCYMAL VPHCYGT1 AGECY0004 AGECY0509 AGECY1014 AGECY1519 AGECY2024 \\\n", + "0 1 5 0 0 0 0 0 \n", + "1 2 1 0 0 2 0 0 \n", + "2 0 0 0 0 0 0 0 \n", + "3 13 5 0 1 1 1 1 \n", + "4 374 0 0 0 0 488 430 \n", + "5 0 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 0 \n", + "7 0 0 0 0 0 0 0 \n", + "8 0 0 0 0 0 0 0 \n", + "9 0 0 0 0 0 0 0 \n", + "\n", + " AGECY2529 AGECY3034 AGECY3539 AGECY4044 AGECY4549 AGECY5054 \\\n", + "0 0 0 0 0 1 0 \n", + "1 0 0 1 0 0 0 \n", + "2 0 0 0 0 0 0 \n", + "3 0 0 1 0 3 1 \n", + "4 31 5 0 0 3 0 \n", + "5 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 \n", + "7 0 0 0 0 0 0 \n", + "8 0 0 0 0 0 0 \n", + "9 0 0 0 0 0 0 \n", + "\n", + " AGECY5559 AGECY6064 AGECY6569 AGECY7074 AGECY7579 AGECY8084 \\\n", + "0 1 1 2 0 0 0 \n", + "1 0 2 0 0 0 0 \n", + "2 0 0 0 0 0 0 \n", + "3 2 0 7 1 1 0 \n", + "4 0 0 0 0 0 0 \n", + "5 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 \n", + "7 0 0 0 0 0 0 \n", + "8 0 0 0 0 0 0 \n", + "9 0 0 0 0 0 0 \n", + "\n", + " AGECYGT15 AGECYGT25 AGECYGT85 DWLCYRENT EDUCYBACH EDUCYGRAD \\\n", + "0 6 6 1 0 0 0 \n", + "1 3 3 0 0 0 0 \n", + "2 0 0 0 0 0 0 \n", + "3 20 18 2 6 1 1 \n", + "4 959 41 2 0 7 3 \n", + "5 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 \n", + "7 0 0 0 0 0 0 \n", + "8 0 0 0 0 0 0 \n", + "9 0 0 0 0 0 0 \n", + "\n", + " EDUCYHSCH HINCY1015 HINCY1520 HINCY2025 HINCY2530 HINCY3035 \\\n", + "0 5 0 0 0 0 0 \n", + "1 0 0 0 0 0 0 \n", + "2 0 0 0 0 0 0 \n", + "3 8 1 1 1 1 1 \n", + "4 14 0 0 0 0 0 \n", + "5 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 \n", + "7 0 0 0 0 0 0 \n", + "8 0 0 0 0 0 0 \n", + "9 0 0 0 0 0 0 \n", + "\n", + " HINCY3540 HINCY4045 HINCY4550 HINCY5060 HINCY6075 HINCYLT10 \\\n", + "0 0 0 0 0 5 0 \n", + "1 0 0 0 0 0 0 \n", + "2 0 0 0 0 0 0 \n", + "3 0 0 0 2 2 2 \n", + "4 0 0 0 0 0 0 \n", + "5 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 \n", + "7 0 0 0 0 0 0 \n", + "8 0 0 0 0 0 0 \n", + "9 0 0 0 0 0 0 \n", + "\n", + " HISCYHISP HUSEX1DET INCCYPCAP LBFCYEMPL LBFCYUNEM LNIEXISOL \\\n", + "0 0 2 53754 0 0 0 \n", + "1 0 2 33026 2 0 0 \n", + "2 0 0 0 0 0 0 \n", + "3 0 9 16647 10 0 0 \n", + "4 36 0 3753 346 32 0 \n", + "5 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 \n", + "7 0 0 0 0 0 0 \n", + "8 0 0 0 0 0 0 \n", + "9 0 0 0 0 0 0 \n", + "\n", + " LNIEXSPAN MARCYMARR POPCYGRPI UNECYRATE VPHCYNONE BLOCKGROUP \\\n", + "0 0 6 0 0.00 0 010159819011 \n", + "1 0 2 0 0.00 0 010159819021 \n", + "2 0 0 0 0.00 0 010159819031 \n", + "3 0 0 0 0.00 2 010970004011 \n", + "4 0 0 0 8.47 0 010970036051 \n", + "5 0 0 0 0.00 0 020900018001 \n", + "6 0 0 0 0.00 0 040030014021 \n", + "7 0 0 0 0.00 0 040030014022 \n", + "8 0 0 0 0.00 0 040129800001 \n", + "9 0 0 0 0.00 0 040139801001 \n", + "\n", + " DWLCYOWNED DWLCYVACNT EDUCYASSOC EDUCYLTGR9 EDUCYSCOLL EDUCYSHSCH \\\n", + "0 5 0 0 1 0 0 \n", + "1 2 0 0 1 2 0 \n", + "2 0 0 0 0 0 0 \n", + "3 5 10 1 0 3 4 \n", + "4 0 0 3 0 10 4 \n", + "5 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 \n", + "7 0 0 0 0 0 0 \n", + "8 0 0 0 0 0 0 \n", + "9 0 0 0 0 0 0 \n", + "\n", + " HHDCYAVESZ HHDCYMEDAG HHSCYLPFCH HHSCYLPMCH HHSCYMCFCH HINCY10025 \\\n", + "0 1.2 61.5 0 0 0 0 \n", + "1 2.5 54.0 1 0 0 0 \n", + "2 0.0 0.0 0 0 0 0 \n", + "3 2.0 61.5 1 0 0 0 \n", + "4 0.0 0.0 0 0 0 0 \n", + "5 0.0 0.0 0 0 0 0 \n", + "6 0.0 0.0 0 0 0 0 \n", + "7 0.0 0.0 0 0 0 0 \n", + "8 0.0 0.0 0 0 0 0 \n", + "9 0.0 0.0 0 0 0 0 \n", + "\n", + " HINCY12550 HINCY15020 HINCY75100 HINCYGT200 HINCYMED24 HINCYMED25 \\\n", + "0 0 0 0 0 0 0 \n", + "1 0 0 2 0 0 0 \n", + "2 0 0 0 0 0 0 \n", + "3 0 0 0 0 0 0 \n", + "4 0 0 0 0 0 0 \n", + "5 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 \n", + "7 0 0 0 0 0 0 \n", + "8 0 0 0 0 0 0 \n", + "9 0 0 0 0 0 0 \n", + "\n", + " HINCYMED35 HINCYMED45 HINCYMED55 HINCYMED65 HINCYMED75 INCCYAVEHH \\\n", + "0 0 67500 67500 67500 67500 64504 \n", + "1 87500 0 87500 0 0 82566 \n", + "2 0 0 0 0 0 0 \n", + "3 5000 60000 30000 50000 12500 33294 \n", + "4 0 0 0 0 0 0 \n", + "5 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 \n", + "7 0 0 0 0 0 0 \n", + "8 0 0 0 0 0 0 \n", + "9 0 0 0 0 0 0 \n", + "\n", + " INCCYMEDFA INCCYMEDHH LBFCYPOP16 MARCYDIVOR MARCYNEVER MARCYWIDOW \\\n", + "0 67499 67499 6 0 0 0 \n", + "1 87499 87499 3 0 1 0 \n", + "2 0 0 0 0 0 0 \n", + "3 49999 27499 20 4 13 2 \n", + "4 0 0 959 0 959 0 \n", + "5 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 \n", + "7 0 0 0 0 0 0 \n", + "8 0 0 0 0 0 0 \n", + "9 0 0 0 0 0 0 \n", + "\n", + " RCHCYAMNHS RCHCYASNHS RCHCYBLNHS RCHCYHANHS RCHCYMUNHS RCHCYOTNHS \\\n", + "0 0 0 0 0 0 0 \n", + "1 0 0 3 0 0 0 \n", + "2 0 0 0 0 0 0 \n", + "3 0 0 22 0 0 0 \n", + "4 5 53 230 0 25 0 \n", + "5 0 0 0 0 0 0 \n", + "6 0 0 0 0 0 0 \n", + "7 0 0 0 0 0 0 \n", + "8 0 0 0 0 0 0 \n", + "9 0 0 0 0 0 0 \n", + "\n", + " RCHCYWHNHS \n", + "0 6 \n", + "1 2 \n", + "2 0 \n", + "3 0 \n", + "4 609 \n", + "5 0 \n", + "6 0 \n", + "7 0 \n", + "8 0 \n", + "9 0 " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We explore the variables to identify the ones we're interested in. \n", + "\n", + "Variables in a dataset are uniquely identified by their slug." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "BQ7HvT5L4PSA", + "outputId": "b86413c0-a0c4-4e70-ec47-836ca615bd43" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                  slugnamedescriptiondb_typeagg_methodcolumn_namevariable_group_iddataset_idid
                                                                  0BLOCKGROUP_108673f9BLOCKGROUPGeographic IdentifierSTRINGNoneBLOCKGROUPNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                  1POPCY_5e23b8f4Total PopulationPopulation (2019A)INTEGERSUMPOPCYNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                  2POPCYGRP_55c4a2e5POPCYGRPPopulation in Group Quarters (2019A)INTEGERSUMPOPCYGRPNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                  3POPCYGRPI_d37c4ecPOPCYGRPIInstitutional Group Quarters Population (2019A)INTEGERSUMPOPCYGRPINonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                  4AGECY0004_a67ddb4fAGECY0004Population age 0-4 (2019A)INTEGERSUMAGECY0004carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                  \n", + "
                                                                  " + ], + "text/plain": [ + " slug name \\\n", + "0 BLOCKGROUP_108673f9 BLOCKGROUP \n", + "1 POPCY_5e23b8f4 Total Population \n", + "2 POPCYGRP_55c4a2e5 POPCYGRP \n", + "3 POPCYGRPI_d37c4ec POPCYGRPI \n", + "4 AGECY0004_a67ddb4f AGECY0004 \n", + "\n", + " description db_type agg_method \\\n", + "0 Geographic Identifier STRING None \n", + "1 Population (2019A) INTEGER SUM \n", + "2 Population in Group Quarters (2019A) INTEGER SUM \n", + "3 Institutional Group Quarters Population (2019A) INTEGER SUM \n", + "4 Population age 0-4 (2019A) INTEGER SUM \n", + "\n", + " column_name variable_group_id \\\n", + "0 BLOCKGROUP None \n", + "1 POPCY None \n", + "2 POPCYGRP None \n", + "3 POPCYGRPI None \n", + "4 AGECY0004 carto-do.ags.demographics_sociodemographics_us... \n", + "\n", + " dataset_id \\\n", + "0 carto-do.ags.demographics_sociodemographics_us... \n", + "1 carto-do.ags.demographics_sociodemographics_us... \n", + "2 carto-do.ags.demographics_sociodemographics_us... \n", + "3 carto-do.ags.demographics_sociodemographics_us... \n", + "4 carto-do.ags.demographics_sociodemographics_us... \n", + "\n", + " id \n", + "0 carto-do.ags.demographics_sociodemographics_us... \n", + "1 carto-do.ags.demographics_sociodemographics_us... \n", + "2 carto-do.ags.demographics_sociodemographics_us... \n", + "3 carto-do.ags.demographics_sociodemographics_us... \n", + "4 carto-do.ags.demographics_sociodemographics_us... " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.variables.to_dataframe().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll select:\n", + " - Population and population by age variables to identify number of people aged 60+ as a percentage of total population\n", + " - Average household income\n", + " - Average household income for porpulation aged 65+" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "id": "tZQQJkh34DX2" + }, + "outputs": [], + "source": [ + "vars_enrichment = ['POPCY_5e23b8f4', 'AGECY6064_d54c2315', 'AGECY6569_ad369d43', 'AGECY7074_74eb7531',\n", + " 'AGECY7579_c91cb67', 'AGECY8084_ab1079a8', 'AGECYGT85_a0959a08', 'INCCYMEDHH_b80a7a7b',\n", + " 'HINCYMED65_37a430a4', 'HINCYMED75_2ebf01e5']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Isochrone enrichment" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "id": "khyZ9U9G4Dla" + }, + "outputs": [], + "source": [ + "ph_pharmacies_enriched = enrichment.enrich_polygons(\n", + " ph_pharmacies,\n", + " variables=vars_enrichment,\n", + " geom_col='iso_5car'\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 467 + }, + "id": "eYUGxuLtDfjK", + "outputId": "ac957f77-a2b2-4f7c-fde7-a3b1910d472f" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                  PB_IDgeoiddo_dateNAMEBRANDNAMETRADE_NAMEFRANCHISE_NAMEISO3AREANAME4AREANAME3AREANAME2AREANAME1STABBPOSTCODEFORMATTEDADDRESSMAINADDRESSLINEADDRESSLASTLINELONGITUDELATITUDEGEORESULTCONFIDENCE_CODECOUNTRY_ACCESS_CODETEL_NUMFAXNUMEMAILHTTPOPEN_24HBUSINESS_LINESIC1SIC2SIC8SIC8_DESCRIPTIONALT_INDUSTRY_CODEMICODETRADE_DIVISIONGROUPCLASSSUB_CLASSEMPLOYEE_HEREEMPLOYEE_COUNTYEAR_STARTSALES_VOLUME_LOCALSALES_VOLUME_US_DOLLARSCURRENCY_CODEAGENT_CODELEGAL_STATUS_CODESTATUS_CODESUBSIDIARY_INDICATORPARENT_BUSINESS_NAMEPARENT_ADDRESSPARENT_STREET_ADDRESSPARENT_AREANAME3PARENT_AREANAME1PARENT_COUNTRYPARENT_POSTCODEDOMESTIC_ULTIMATE_BUSINESS_NAMEDOMESTIC_ULTIMATE_ADDRESSDOMESTIC_ULTIMATE_STREET_ADDRESSDOMESTIC_ULTIMATE_AREANAME3DOMESTIC_ULTIMATE_AREANAME1DOMESTIC_ULTIMATE_POSTCODEGLOBAL_ULTIMATE_INDICATORGLOBAL_ULTIMATE_BUSINESS_NAMEGLOBAL_ULTIMATE_ADDRESSGLOBAL_ULTIMATE_STREET_ADDRESSGLOBAL_ULTIMATE_AREANAME3GLOBAL_ULTIMATE_AREANAME1GLOBAL_ULTIMATE_COUNTRYGLOBAL_ULTIMATE_POSTCODEFAMILY_MEMBERSHIERARCHY_CODETICKER_SYMBOLEXCHANGE_NAMEgeomiso_5carPOPCYAGECY8084AGECYGT85AGECY6569AGECY7579AGECY7074AGECY6064HINCYMED65HINCYMED75INCCYMEDHH
                                                                  011146893271114689327#-75.186601#39.9183012020-08-01WALGREEN EASTERN CO., INC.WALGREENSWALGREENSNaNUSANaNPHILADELPHIAPHILADELPHIAPENNSYLVANIAPA19145-41222310 W OREGON AVE, PHILADELPHIA, PA, 19145-41222310 W OREGON AVEPHILADELPHIA, PA, 19145-4122-75.18660139.918301S8HPNTSCZAHIGH1.0(215) 468-2481NaNNaNWWW.WALLGREENSBOOTSALLIANCE.COMNaNDRUG STORES AND PROPRIETARY STORES5912NaN59129901DRUG STORES446110.010010700DIVISION G. - RETAIL TRADEMISCELLANEOUS RETAILDRUG STORES AND PROPRIETARY STORESDRUG STORES AND PROPRIETARY STORES/PHARMACY40.00.00.00.00.0NaNG0.02.00.0WALGREEN EASTERN CO., INC.200 WILMOT RD, DEERFIELD, ILLINOIS, 600154620,...200 WILMOT RDDEERFIELDILLINOISUSA600154620.0WALGREENS BOOTS ALLIANCE, INC.108 WILMOT RD, DEERFIELD, ILLINOIS, 600155145108 WILMOT RDDEERFIELDILLINOIS600155145.0NWALGREENS BOOTS ALLIANCE, INC.108 WILMOT RD, DEERFIELD, ILLINOIS, 600155145,...108 WILMOT RDDEERFIELDILLINOISUSA600155145.09551.03.0NaNNaNPOINT (-75.18660 39.91830)MULTIPOLYGON (((-75.19996 39.93393, -75.19953 ...15037.340234293.522613310.290651783.930533454.807673599.859593922.26417050981.22727338971.63636455397.409091
                                                                  111149424691114942469#-75.160179#39.9704782020-08-01TANG PHARMACY IVNaNTANG PHARMACY IVNaNUSANaNPHILADELPHIAPHILADELPHIAPENNSYLVANIAPA19130-1604900 N BROAD ST, PHILADELPHIA, PA, 19130-1604900 N BROAD STPHILADELPHIA, PA, 19130-1604-75.16017939.970478S8HPNTSCZAHIGH1.0(215) 235-1400NaNNaNNaNNaNDRUG STORES AND PROPRIETARY STORES5912NaN59120000DRUG STORES AND PROPRIETARY STORES446110.010230030DIVISION G. - RETAIL TRADEMISCELLANEOUS RETAILDRUG STORES AND PROPRIETARY STORESDRUG STORES AND PROPRIETARY STORES/PHARMACY4.04.02012.0610000.0610000.020.0G3.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNPOINT (-75.16018 39.97048)MULTIPOLYGON (((-75.17592 39.96826, -75.17550 ...54623.530980813.192570983.7033762391.8635571246.6551191799.5278052877.78271546537.36842137788.38596549895.807018
                                                                  211150246091115024609#-75.067035#39.9357052020-08-01AMERICARE 1 PHARMACYNaNNaNNaNUSANaNMERCHANTVILLECAMDENNEW JERSEYNJ08109-48035115 ROUTE 38, MERCHANTVILLE, NJ, 08109-48035115 ROUTE 38MERCHANTVILLE, NJ, 08109-4803-75.06703539.935705S8HPNTSCZAHIGH1.0(215) 821-2720NaNNaNWWW.ACAREPHARMACY.COMNaNDRUG STORES AND PROPRIETARY STORES5912NaN59129901DRUG STORES446110.010010700DIVISION G. - RETAIL TRADEMISCELLANEOUS RETAILDRUG STORES AND PROPRIETARY STORESDRUG STORES AND PROPRIETARY STORES/PHARMACY5.05.02011.0616316.0616316.020.0G13.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNPOINT (-75.06704 39.93570)MULTIPOLYGON (((-75.10743 39.94440, -75.10717 ...11454.933682194.094570219.712464510.703182295.695152426.563198636.48608241940.34615434227.65384650875.269231
                                                                  311152893801115289380#-75.148596#39.9701772020-08-01GET WELL PHARMACYLEADER DRUG STORELEADER DRUG STORELEADER DRUG STORESUSANaNPHILADELPHIAPHILADELPHIAPENNSYLVANIAPA19123-1313708 W GIRARD AVE, PHILADELPHIA, PA, 19123-1313708 W GIRARD AVEPHILADELPHIA, PA, 19123-1313-75.14859639.970177S8HPNTSCZAHIGH1.0(215) 629-5566(215) 629-5567NaNNaNNaNDRUG STORES AND PROPRIETARY STORES5912NaN59129901DRUG STORES446110.010010700DIVISION G. - RETAIL TRADEMISCELLANEOUS RETAILDRUG STORES AND PROPRIETARY STORESDRUG STORES AND PROPRIETARY STORES/PHARMACY6.00.00.00.00.0NaNG0.02.00.0GET WELL PHARMACY4511 N 5TH ST, PHILADELPHIA, PENNSYLVANIA, 191...4511 N 5TH STPHILADELPHIAPENNSYLVANIAUSA191402309.0GET WELL PHARMACY4511 N 5TH ST, PHILADELPHIA, PENNSYLVANIA, 191...4511 N 5TH STPHILADELPHIAPENNSYLVANIA191402309.0NGET WELL PHARMACY4511 N 5TH ST, PHILADELPHIA, PENNSYLVANIA, 191...4511 N 5TH STPHILADELPHIAPENNSYLVANIAUSA191402309.02.01.0NaNNaNPOINT (-75.14860 39.97018)MULTIPOLYGON (((-75.17043 39.97375, -75.16983 ...47722.338755754.767397894.0952942154.8511891133.5203121662.0609432561.43392845865.36734736400.42857147234.571429
                                                                  411160030621116003062#-75.096649#39.9066292020-08-01CVS PHARMACY, INC.CVSCVSNaNUSANaNHADDON TOWNSHIPCAMDENNEW JERSEYNJ081043002 MOUNT EPHRAIM AVE, HADDON TOWNSHIP, NJ, 0...3002 MOUNT EPHRAIM AVEHADDON TOWNSHIP, NJ, 08104-75.09664939.906629S5HPNTSCZAHIGH1.0(856) 854-9163NaNNaNWWW.CVS.COMNaNDRUG STORES AND PROPRIETARY STORES5912NaN59120000DRUG STORES AND PROPRIETARY STORES446110.010230030DIVISION G. - RETAIL TRADEMISCELLANEOUS RETAILDRUG STORES AND PROPRIETARY STORESDRUG STORES AND PROPRIETARY STORES/PHARMACY25.00.00.00.00.0NaNG0.02.00.0CVS PHARMACY, INC.1 CVS DR, WOONSOCKET, RHODE ISLAND, 028956195,...1 CVS DRWOONSOCKETRHODE ISLANDUSA28956195.0CVS HEALTH CORPORATION1 CVS DR, WOONSOCKET, RHODE ISLAND, 0289561951 CVS DRWOONSOCKETRHODE ISLAND28956195.0NCVS HEALTH CORPORATION1 CVS DR, WOONSOCKET, RHODE ISLAND, 028956195,...1 CVS DRWOONSOCKETRHODE ISLANDUSA28956195.013290.02.0NaNNaNPOINT (-75.09665 39.90663)MULTIPOLYGON (((-75.12031 39.90440, -75.11971 ...13244.490216153.927121179.820079533.225761236.796307409.091195686.86169148903.48484840511.36363653548.757576
                                                                  \n", + "
                                                                  " + ], + "text/plain": [ + " PB_ID geoid do_date \\\n", + "0 1114689327 1114689327#-75.186601#39.918301 2020-08-01 \n", + "1 1114942469 1114942469#-75.160179#39.970478 2020-08-01 \n", + "2 1115024609 1115024609#-75.067035#39.935705 2020-08-01 \n", + "3 1115289380 1115289380#-75.148596#39.970177 2020-08-01 \n", + "4 1116003062 1116003062#-75.096649#39.906629 2020-08-01 \n", + "\n", + " NAME BRANDNAME TRADE_NAME \\\n", + "0 WALGREEN EASTERN CO., INC. WALGREENS WALGREENS \n", + "1 TANG PHARMACY IV NaN TANG PHARMACY IV \n", + "2 AMERICARE 1 PHARMACY NaN NaN \n", + "3 GET WELL PHARMACY LEADER DRUG STORE LEADER DRUG STORE \n", + "4 CVS PHARMACY, INC. CVS CVS \n", + "\n", + " FRANCHISE_NAME ISO3 AREANAME4 AREANAME3 AREANAME2 \\\n", + "0 NaN USA NaN PHILADELPHIA PHILADELPHIA \n", + "1 NaN USA NaN PHILADELPHIA PHILADELPHIA \n", + "2 NaN USA NaN MERCHANTVILLE CAMDEN \n", + "3 LEADER DRUG STORES USA NaN PHILADELPHIA PHILADELPHIA \n", + "4 NaN USA NaN HADDON TOWNSHIP CAMDEN \n", + "\n", + " AREANAME1 STABB POSTCODE \\\n", + "0 PENNSYLVANIA PA 19145-4122 \n", + "1 PENNSYLVANIA PA 19130-1604 \n", + "2 NEW JERSEY NJ 08109-4803 \n", + "3 PENNSYLVANIA PA 19123-1313 \n", + "4 NEW JERSEY NJ 08104 \n", + "\n", + " FORMATTEDADDRESS MAINADDRESSLINE \\\n", + "0 2310 W OREGON AVE, PHILADELPHIA, PA, 19145-4122 2310 W OREGON AVE \n", + "1 900 N BROAD ST, PHILADELPHIA, PA, 19130-1604 900 N BROAD ST \n", + "2 5115 ROUTE 38, MERCHANTVILLE, NJ, 08109-4803 5115 ROUTE 38 \n", + "3 708 W GIRARD AVE, PHILADELPHIA, PA, 19123-1313 708 W GIRARD AVE \n", + "4 3002 MOUNT EPHRAIM AVE, HADDON TOWNSHIP, NJ, 0... 3002 MOUNT EPHRAIM AVE \n", + "\n", + " ADDRESSLASTLINE LONGITUDE LATITUDE GEORESULT \\\n", + "0 PHILADELPHIA, PA, 19145-4122 -75.186601 39.918301 S8HPNTSCZA \n", + "1 PHILADELPHIA, PA, 19130-1604 -75.160179 39.970478 S8HPNTSCZA \n", + "2 MERCHANTVILLE, NJ, 08109-4803 -75.067035 39.935705 S8HPNTSCZA \n", + "3 PHILADELPHIA, PA, 19123-1313 -75.148596 39.970177 S8HPNTSCZA \n", + "4 HADDON TOWNSHIP, NJ, 08104 -75.096649 39.906629 S5HPNTSCZA \n", + "\n", + " CONFIDENCE_CODE COUNTRY_ACCESS_CODE TEL_NUM FAXNUM EMAIL \\\n", + "0 HIGH 1.0 (215) 468-2481 NaN NaN \n", + "1 HIGH 1.0 (215) 235-1400 NaN NaN \n", + "2 HIGH 1.0 (215) 821-2720 NaN NaN \n", + "3 HIGH 1.0 (215) 629-5566 (215) 629-5567 NaN \n", + "4 HIGH 1.0 (856) 854-9163 NaN NaN \n", + "\n", + " HTTP OPEN_24H \\\n", + "0 WWW.WALLGREENSBOOTSALLIANCE.COM NaN \n", + "1 NaN NaN \n", + "2 WWW.ACAREPHARMACY.COM NaN \n", + "3 NaN NaN \n", + "4 WWW.CVS.COM NaN \n", + "\n", + " BUSINESS_LINE SIC1 SIC2 SIC8 \\\n", + "0 DRUG STORES AND PROPRIETARY STORES 5912 NaN 59129901 \n", + "1 DRUG STORES AND PROPRIETARY STORES 5912 NaN 59120000 \n", + "2 DRUG STORES AND PROPRIETARY STORES 5912 NaN 59129901 \n", + "3 DRUG STORES AND PROPRIETARY STORES 5912 NaN 59129901 \n", + "4 DRUG STORES AND PROPRIETARY STORES 5912 NaN 59120000 \n", + "\n", + " SIC8_DESCRIPTION ALT_INDUSTRY_CODE MICODE \\\n", + "0 DRUG STORES 446110.0 10010700 \n", + "1 DRUG STORES AND PROPRIETARY STORES 446110.0 10230030 \n", + "2 DRUG STORES 446110.0 10010700 \n", + "3 DRUG STORES 446110.0 10010700 \n", + "4 DRUG STORES AND PROPRIETARY STORES 446110.0 10230030 \n", + "\n", + " TRADE_DIVISION GROUP \\\n", + "0 DIVISION G. - RETAIL TRADE MISCELLANEOUS RETAIL \n", + "1 DIVISION G. - RETAIL TRADE MISCELLANEOUS RETAIL \n", + "2 DIVISION G. - RETAIL TRADE MISCELLANEOUS RETAIL \n", + "3 DIVISION G. - RETAIL TRADE MISCELLANEOUS RETAIL \n", + "4 DIVISION G. - RETAIL TRADE MISCELLANEOUS RETAIL \n", + "\n", + " CLASS \\\n", + "0 DRUG STORES AND PROPRIETARY STORES \n", + "1 DRUG STORES AND PROPRIETARY STORES \n", + "2 DRUG STORES AND PROPRIETARY STORES \n", + "3 DRUG STORES AND PROPRIETARY STORES \n", + "4 DRUG STORES AND PROPRIETARY STORES \n", + "\n", + " SUB_CLASS EMPLOYEE_HERE EMPLOYEE_COUNT \\\n", + "0 DRUG STORES AND PROPRIETARY STORES/PHARMACY 40.0 0.0 \n", + "1 DRUG STORES AND PROPRIETARY STORES/PHARMACY 4.0 4.0 \n", + "2 DRUG STORES AND PROPRIETARY STORES/PHARMACY 5.0 5.0 \n", + "3 DRUG STORES AND PROPRIETARY STORES/PHARMACY 6.0 0.0 \n", + "4 DRUG STORES AND PROPRIETARY STORES/PHARMACY 25.0 0.0 \n", + "\n", + " YEAR_START SALES_VOLUME_LOCAL SALES_VOLUME_US_DOLLARS CURRENCY_CODE \\\n", + "0 0.0 0.0 0.0 NaN \n", + "1 2012.0 610000.0 610000.0 20.0 \n", + "2 2011.0 616316.0 616316.0 20.0 \n", + "3 0.0 0.0 0.0 NaN \n", + "4 0.0 0.0 0.0 NaN \n", + "\n", + " AGENT_CODE LEGAL_STATUS_CODE STATUS_CODE SUBSIDIARY_INDICATOR \\\n", + "0 G 0.0 2.0 0.0 \n", + "1 G 3.0 0.0 0.0 \n", + "2 G 13.0 0.0 0.0 \n", + "3 G 0.0 2.0 0.0 \n", + "4 G 0.0 2.0 0.0 \n", + "\n", + " PARENT_BUSINESS_NAME \\\n", + "0 WALGREEN EASTERN CO., INC. \n", + "1 NaN \n", + "2 NaN \n", + "3 GET WELL PHARMACY \n", + "4 CVS PHARMACY, INC. \n", + "\n", + " PARENT_ADDRESS PARENT_STREET_ADDRESS \\\n", + "0 200 WILMOT RD, DEERFIELD, ILLINOIS, 600154620,... 200 WILMOT RD \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 4511 N 5TH ST, PHILADELPHIA, PENNSYLVANIA, 191... 4511 N 5TH ST \n", + "4 1 CVS DR, WOONSOCKET, RHODE ISLAND, 028956195,... 1 CVS DR \n", + "\n", + " PARENT_AREANAME3 PARENT_AREANAME1 PARENT_COUNTRY PARENT_POSTCODE \\\n", + "0 DEERFIELD ILLINOIS USA 600154620.0 \n", + "1 NaN NaN NaN NaN \n", + "2 NaN NaN NaN NaN \n", + "3 PHILADELPHIA PENNSYLVANIA USA 191402309.0 \n", + "4 WOONSOCKET RHODE ISLAND USA 28956195.0 \n", + "\n", + " DOMESTIC_ULTIMATE_BUSINESS_NAME \\\n", + "0 WALGREENS BOOTS ALLIANCE, INC. \n", + "1 NaN \n", + "2 NaN \n", + "3 GET WELL PHARMACY \n", + "4 CVS HEALTH CORPORATION \n", + "\n", + " DOMESTIC_ULTIMATE_ADDRESS \\\n", + "0 108 WILMOT RD, DEERFIELD, ILLINOIS, 600155145 \n", + "1 NaN \n", + "2 NaN \n", + "3 4511 N 5TH ST, PHILADELPHIA, PENNSYLVANIA, 191... \n", + "4 1 CVS DR, WOONSOCKET, RHODE ISLAND, 028956195 \n", + "\n", + " DOMESTIC_ULTIMATE_STREET_ADDRESS DOMESTIC_ULTIMATE_AREANAME3 \\\n", + "0 108 WILMOT RD DEERFIELD \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 4511 N 5TH ST PHILADELPHIA \n", + "4 1 CVS DR WOONSOCKET \n", + "\n", + " DOMESTIC_ULTIMATE_AREANAME1 DOMESTIC_ULTIMATE_POSTCODE \\\n", + "0 ILLINOIS 600155145.0 \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 PENNSYLVANIA 191402309.0 \n", + "4 RHODE ISLAND 28956195.0 \n", + "\n", + " GLOBAL_ULTIMATE_INDICATOR GLOBAL_ULTIMATE_BUSINESS_NAME \\\n", + "0 N WALGREENS BOOTS ALLIANCE, INC. \n", + "1 N NaN \n", + "2 N NaN \n", + "3 N GET WELL PHARMACY \n", + "4 N CVS HEALTH CORPORATION \n", + "\n", + " GLOBAL_ULTIMATE_ADDRESS \\\n", + "0 108 WILMOT RD, DEERFIELD, ILLINOIS, 600155145,... \n", + "1 NaN \n", + "2 NaN \n", + "3 4511 N 5TH ST, PHILADELPHIA, PENNSYLVANIA, 191... \n", + "4 1 CVS DR, WOONSOCKET, RHODE ISLAND, 028956195,... \n", + "\n", + " GLOBAL_ULTIMATE_STREET_ADDRESS GLOBAL_ULTIMATE_AREANAME3 \\\n", + "0 108 WILMOT RD DEERFIELD \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 4511 N 5TH ST PHILADELPHIA \n", + "4 1 CVS DR WOONSOCKET \n", + "\n", + " GLOBAL_ULTIMATE_AREANAME1 GLOBAL_ULTIMATE_COUNTRY GLOBAL_ULTIMATE_POSTCODE \\\n", + "0 ILLINOIS USA 600155145.0 \n", + "1 NaN NaN NaN \n", + "2 NaN NaN NaN \n", + "3 PENNSYLVANIA USA 191402309.0 \n", + "4 RHODE ISLAND USA 28956195.0 \n", + "\n", + " FAMILY_MEMBERS HIERARCHY_CODE TICKER_SYMBOL EXCHANGE_NAME \\\n", + "0 9551.0 3.0 NaN NaN \n", + "1 0.0 0.0 NaN NaN \n", + "2 0.0 0.0 NaN NaN \n", + "3 2.0 1.0 NaN NaN \n", + "4 13290.0 2.0 NaN NaN \n", + "\n", + " geom \\\n", + "0 POINT (-75.18660 39.91830) \n", + "1 POINT (-75.16018 39.97048) \n", + "2 POINT (-75.06704 39.93570) \n", + "3 POINT (-75.14860 39.97018) \n", + "4 POINT (-75.09665 39.90663) \n", + "\n", + " iso_5car POPCY \\\n", + "0 MULTIPOLYGON (((-75.19996 39.93393, -75.19953 ... 15037.340234 \n", + "1 MULTIPOLYGON (((-75.17592 39.96826, -75.17550 ... 54623.530980 \n", + "2 MULTIPOLYGON (((-75.10743 39.94440, -75.10717 ... 11454.933682 \n", + "3 MULTIPOLYGON (((-75.17043 39.97375, -75.16983 ... 47722.338755 \n", + "4 MULTIPOLYGON (((-75.12031 39.90440, -75.11971 ... 13244.490216 \n", + "\n", + " AGECY8084 AGECYGT85 AGECY6569 AGECY7579 AGECY7074 AGECY6064 \\\n", + "0 293.522613 310.290651 783.930533 454.807673 599.859593 922.264170 \n", + "1 813.192570 983.703376 2391.863557 1246.655119 1799.527805 2877.782715 \n", + "2 194.094570 219.712464 510.703182 295.695152 426.563198 636.486082 \n", + "3 754.767397 894.095294 2154.851189 1133.520312 1662.060943 2561.433928 \n", + "4 153.927121 179.820079 533.225761 236.796307 409.091195 686.861691 \n", + "\n", + " HINCYMED65 HINCYMED75 INCCYMEDHH \n", + "0 50981.227273 38971.636364 55397.409091 \n", + "1 46537.368421 37788.385965 49895.807018 \n", + "2 41940.346154 34227.653846 50875.269231 \n", + "3 45865.367347 36400.428571 47234.571429 \n", + "4 48903.484848 40511.363636 53548.757576 " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ph_pharmacies_enriched.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "id": "Ywu6fFj7FyXE" + }, + "outputs": [], + "source": [ + "ph_pharmacies = ph_pharmacies_enriched.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "id": "SZwLsCxWUlUA" + }, + "outputs": [], + "source": [ + "ph_pharmacies['pop_60plus'] = ph_pharmacies[['AGECY8084', 'AGECYGT85', 'AGECY6569', 'AGECY7579', 'AGECY7074', 'AGECY6064']].sum(1)\n", + "ph_pharmacies.drop(columns=['AGECY8084', 'AGECYGT85', 'AGECY6569', 'AGECY7579', 'AGECY7074', 'AGECY6064'], inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Points of Interest\n", + "\n", + "We will use [Pitney Bowes' Consumer Points of Interest](https://carto.com/spatial-data-catalog/browser/dataset/pb_consumer_po_62cddc04/) premium dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wXrgsacjvZ8M" + }, + "source": [ + "##### Variable selection\n", + "\n", + "We are interested in knowing how many of the following POIs can be found in each isochrone:\n", + " - Beauty shops and beauty salons\n", + " - Gyms and other sports centers\n", + "\n", + "These POI's will be considered as an indicator of personal care awareness in a specific area." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The hierarchy classification variable `SUB_CLASS` variable allows us to identify beaty shops and salons (`BEAUTY SHOPS/BEAUTY SALON`) and gyms (`MEMBERSHIP SPORTS AND RECREATION CLUBS/CLUB AND ASSOCIATION - UNSPECIFIED`)." + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 692 + }, + "id": "bZdJKVsaDssS", + "outputId": "8daadc49-49e8-4109-bd28-00bec4e6cbf6" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['LABOR ORGANIZATIONS', 'MEMBERSHIP ORGANIZATIONS, NEC',\n", + " 'FLATS, APARTMENT COMPLEX', 'HOTEL, MOTEL - UNSPECIFIED',\n", + " 'MEDIA FACILITY', 'COLLEGES AND UNIVERSITIES',\n", + " 'VOCATIONAL SCHOOLS, NEC/VOCATIONAL TRAINING', 'LIBRARIES',\n", + " 'SCHOOL', 'GENERAL MEDICAL AND SURGICAL HOSPITALS',\n", + " 'OFFICES AND CLINICS OF DENTISTS/ DENTIST',\n", + " 'CIVIC AND SOCIAL ASSOCIATIONS',\n", + " 'OFFICES AND CLINICS OF MEDICAL DOCTORS',\n", + " 'RELIGIOUS ORGANIZATIONS',\n", + " 'PARK AND RECREATION AREA - UNSPECIFIED',\n", + " 'SKILLED NURSING CARE FACILITIES',\n", + " 'NURSING AND PERSONAL CARE, NEC', 'RETIREMENT COMMUNITY',\n", + " 'SPECIALTY OUTPATIENT CLINICS, NEC',\n", + " 'PASSENGER CAR RENTAL/RENT-A-CAR FACILITY',\n", + " 'GENERAL AUTOMOTIVE REPAIR SHOPS/GENERAL CAR REPAIR AND SERVICING',\n", + " 'LEGAL SERVICES', 'TOP AND BODY REPAIR AND PAINT SHOPS/BODYSHOPS',\n", + " 'CARWASHES', 'HEALTH AND ALLIED SERVICES, NEC',\n", + " 'SCHOOLS AND EDUCATIONAL SERVICES/SCHOOL - UNSPECIFIED',\n", + " 'LAUNDRY AND GARMENT SERVICES, NEC',\n", + " 'MISCELLANEOUS PERSONAL SERVICES',\n", + " 'PHOTOGRAPHIC STUDIOS, PORTRAIT', 'PRIMARY SCHOOL',\n", + " 'INDIVIDUAL AND FAMILY SERVICES', 'RESIDENTIAL CARE',\n", + " 'CHILD DAY CARE SERVICES/CHILD CARE CENTER',\n", + " 'BARBER SHOPS/HAIRDRESSERS AND BARBERS',\n", + " 'BEAUTY SHOPS/BEAUTY SALON', 'FUNERAL SERVICE AND CREMATORIES',\n", + " 'OFFICES OF HEALTH PRACTITIONER',\n", + " 'ENTERTAINERS AND ENTERTAINMENT GROUPS',\n", + " 'AUTO EXHAUST SYSTEM REPAIR SHOPS',\n", + " 'THEATRICAL PRODUCERS AND SERVICES',\n", + " 'PHYSICAL FITNESS FACILITIES/FITNESS CLUB AND CENTER',\n", + " 'AMUSEMENT AND RECREATION, NEC/AMUSEMENT PLACE',\n", + " 'AUTOMOBILE PARKING/OPEN PARKING AREA',\n", + " 'MUSEUMS AND ART GALLERIES', 'ELEMENTARY AND SECONDARY SCHOOLS',\n", + " 'MOTION PICTURE THEATERS, EXCEPT DRIVE-IN',\n", + " 'HOME HEALTH CARE SERVICES',\n", + " 'OFFICES AND CLINICS OF OSTEOPATHIC PHYSICIANS',\n", + " 'SOCIAL SERVICES, NEC', 'DANCE STUDIOS, SCHOOLS, AND HALLS',\n", + " 'ENTERTAINMENT'], dtype=object)" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sample.loc[sample['TRADE_DIVISION'] == 'DIVISION I. - SERVICES', 'SUB_CLASS'].unique()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### Isochrone enrichment" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "OpjHz3iGway_" + }, + "source": [ + "In order to count only Beauty Shops/Salons and Gyms, we will apply a filter to the enrichment. All filters are applied with an AND-like relationship. This means we need to run two independent enrichment calls, one for the beauty shops/salons and another one for the gyms." + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "id": "SsSAQXI1Dsp6" + }, + "outputs": [], + "source": [ + "ph_pharmacies_enriched = enrichment.enrich_polygons(\n", + " ph_pharmacies,\n", + " variables=['SUB_CLASS_10243439'],\n", + " aggregation='COUNT',\n", + " geom_col='iso_5car',\n", + " filters={Variable.get('SUB_CLASS_10243439').id : \"= 'BEAUTY SHOPS/BEAUTY SALON'\"}\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "id": "muDXaFakDsmm" + }, + "outputs": [], + "source": [ + "ph_pharmacies = ph_pharmacies_enriched.rename(columns={'SUB_CLASS_y':'n_beauty_pois'})" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "id": "djUSIiXuDsj5" + }, + "outputs": [], + "source": [ + "ph_pharmacies_enriched = enrichment.enrich_polygons(\n", + " ph_pharmacies,\n", + " variables=['SUB_CLASS_10243439'],\n", + " aggregation='COUNT',\n", + " geom_col='iso_5car',\n", + " filters={Variable.get('SUB_CLASS_10243439').id : \"= 'MEMBERSHIP SPORTS AND RECREATION CLUBS/CLUB AND ASSOCIATION - UNSPECIFIED'\"}\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "id": "JJYbu4k1Dsf_" + }, + "outputs": [], + "source": [ + "ph_pharmacies = ph_pharmacies_enriched.rename(columns={'SUB_CLASS':'n_gym_pois'})" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "id": "D5x3UTeBMgBz", + "outputId": "07be18f1-d92c-4901-ada4-2c0c14c97c5b" + }, + "outputs": [], + "source": [ + "ph_pharmacies['n_pois_personal_care'] = ph_pharmacies['n_beauty_pois'] + ph_pharmacies['n_gym_pois']\n", + "ph_pharmacies.drop(columns=['n_beauty_pois', 'n_gym_pois'], inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Consumer spending\n", + "\n", + "For consumer spending, we will use AGS premium data. In particular, we will work with the dataset `ags_consumer_sp_dbabddfb` which contains the [latest version of yearly consumer data](https://carto.com/spatial-data-catalog/browser/dataset/ags_consumer_sp_dbabddfb/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9CTgbC4rwiMk" + }, + "source": [ + "##### Variable selection\n", + "\n", + "We are interested in spending in:\n", + " - Personal care services\n", + " - Personal care products\n", + " - Health care services" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "id": "e37Yqug9Mf7f" + }, + "outputs": [], + "source": [ + "dataset = Dataset.get('ags_consumer_sp_dbabddfb')" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "5EtgN_ElO1GH", + "outputId": "c01502a5-2f01-4d7b-8b36-8e66398a413d" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                  slugnamedescriptiondb_typeagg_methodcolumn_namevariable_group_iddataset_idid
                                                                  0BLOCKGROUP_9c78947bBLOCKGROUPGeographic IdentifierSTRINGNoneBLOCKGROUPNonecarto-do.ags.demographics_consumerspending_usa...carto-do.ags.demographics_consumerspending_usa...
                                                                  1HHDCY_11b160d2Number of householdsHouseholds (2019A)INTEGERSUMHHDCYNonecarto-do.ags.demographics_consumerspending_usa...carto-do.ags.demographics_consumerspending_usa...
                                                                  2XCYAP1_ef233ec0XCYAP1Men's Apparel (Ave Hhd Exp)FLOATAVGXCYAP1carto-do.ags.demographics_consumerspending_usa...carto-do.ags.demographics_consumerspending_usa...carto-do.ags.demographics_consumerspending_usa...
                                                                  3XCYAP2_762a6f7aXCYAP2Boys Apparel (Ave Hhd Exp)FLOATAVGXCYAP2carto-do.ags.demographics_consumerspending_usa...carto-do.ags.demographics_consumerspending_usa...carto-do.ags.demographics_consumerspending_usa...
                                                                  4XCYAP3_12d5fecXCYAP3Women's Apparel (Ave Hhd Exp)FLOATAVGXCYAP3carto-do.ags.demographics_consumerspending_usa...carto-do.ags.demographics_consumerspending_usa...carto-do.ags.demographics_consumerspending_usa...
                                                                  \n", + "
                                                                  " + ], + "text/plain": [ + " slug name description \\\n", + "0 BLOCKGROUP_9c78947b BLOCKGROUP Geographic Identifier \n", + "1 HHDCY_11b160d2 Number of households Households (2019A) \n", + "2 XCYAP1_ef233ec0 XCYAP1 Men's Apparel (Ave Hhd Exp) \n", + "3 XCYAP2_762a6f7a XCYAP2 Boys Apparel (Ave Hhd Exp) \n", + "4 XCYAP3_12d5fec XCYAP3 Women's Apparel (Ave Hhd Exp) \n", + "\n", + " db_type agg_method column_name \\\n", + "0 STRING None BLOCKGROUP \n", + "1 INTEGER SUM HHDCY \n", + "2 FLOAT AVG XCYAP1 \n", + "3 FLOAT AVG XCYAP2 \n", + "4 FLOAT AVG XCYAP3 \n", + "\n", + " variable_group_id \\\n", + "0 None \n", + "1 None \n", + "2 carto-do.ags.demographics_consumerspending_usa... \n", + "3 carto-do.ags.demographics_consumerspending_usa... \n", + "4 carto-do.ags.demographics_consumerspending_usa... \n", + "\n", + " dataset_id \\\n", + "0 carto-do.ags.demographics_consumerspending_usa... \n", + "1 carto-do.ags.demographics_consumerspending_usa... \n", + "2 carto-do.ags.demographics_consumerspending_usa... \n", + "3 carto-do.ags.demographics_consumerspending_usa... \n", + "4 carto-do.ags.demographics_consumerspending_usa... \n", + "\n", + " id \n", + "0 carto-do.ags.demographics_consumerspending_usa... \n", + "1 carto-do.ags.demographics_consumerspending_usa... \n", + "2 carto-do.ags.demographics_consumerspending_usa... \n", + "3 carto-do.ags.demographics_consumerspending_usa... \n", + "4 carto-do.ags.demographics_consumerspending_usa... " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.variables.to_dataframe().head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The variables we're interested in are:\n", + " - `XCYHC2` Health care services expenditure\n", + " - `XCYPC3` Personal care services expenditure\n", + " - `XCYPC4` Personal care products expenditure" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 176 + }, + "id": "4hnIrl9gO1CZ", + "outputId": "000283d5-423d-427c-9269-c726c5288323" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'slug': 'XCYHC2_18141567',\n", + " 'name': 'XCYHC2',\n", + " 'description': 'Health Care Services (Ave Hhd Exp)',\n", + " 'db_type': 'FLOAT',\n", + " 'agg_method': 'AVG',\n", + " 'column_name': 'XCYHC2',\n", + " 'variable_group_id': 'carto-do.ags.demographics_consumerspending_usa_blockgroup_2015_yearly_2020.consumer_spending_by_product_category',\n", + " 'dataset_id': 'carto-do.ags.demographics_consumerspending_usa_blockgroup_2015_yearly_2020',\n", + " 'id': 'carto-do.ags.demographics_consumerspending_usa_blockgroup_2015_yearly_2020.XCYHC2'}" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Variable.get('XCYHC2_18141567').to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "id": "vtbXm1ayO0_g" + }, + "outputs": [], + "source": [ + "ph_pharmacies_enriched = enrichment.enrich_polygons(\n", + " ph_pharmacies,\n", + " variables=['XCYPC3_7d26d739', 'XCYPC4_e342429a', 'XCYHC2_18141567'],\n", + " geom_col='iso_5car'\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We rename the new columns to give them a more descriptive name." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "id": "cw68th5dQa3r" + }, + "outputs": [], + "source": [ + "ph_pharmacies = ph_pharmacies_enriched.rename(columns={'XCYHC2':'health_care_services_exp',\n", + " 'XCYPC3':'personal_care_services_exp',\n", + " 'XCYPC4':'personal_care_products_exp'})" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 227 + }, + "id": "bdOlUKo4TPQs", + "outputId": "e684af95-31b0-467e-b216-f6b1d8b09553" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                  PB_IDgeoiddo_dateNAMEBRANDNAMETRADE_NAMEFRANCHISE_NAMEISO3AREANAME4AREANAME3AREANAME2AREANAME1STABBPOSTCODEFORMATTEDADDRESSMAINADDRESSLINEADDRESSLASTLINELONGITUDELATITUDEGEORESULTCONFIDENCE_CODECOUNTRY_ACCESS_CODETEL_NUMFAXNUMEMAILHTTPOPEN_24HBUSINESS_LINESIC1SIC2SIC8SIC8_DESCRIPTIONALT_INDUSTRY_CODEMICODETRADE_DIVISIONGROUPCLASSSUB_CLASS_xEMPLOYEE_HEREEMPLOYEE_COUNTYEAR_STARTSALES_VOLUME_LOCALSALES_VOLUME_US_DOLLARSCURRENCY_CODEAGENT_CODELEGAL_STATUS_CODESTATUS_CODESUBSIDIARY_INDICATORPARENT_BUSINESS_NAMEPARENT_ADDRESSPARENT_STREET_ADDRESSPARENT_AREANAME3PARENT_AREANAME1PARENT_COUNTRYPARENT_POSTCODEDOMESTIC_ULTIMATE_BUSINESS_NAMEDOMESTIC_ULTIMATE_ADDRESSDOMESTIC_ULTIMATE_STREET_ADDRESSDOMESTIC_ULTIMATE_AREANAME3DOMESTIC_ULTIMATE_AREANAME1DOMESTIC_ULTIMATE_POSTCODEGLOBAL_ULTIMATE_INDICATORGLOBAL_ULTIMATE_BUSINESS_NAMEGLOBAL_ULTIMATE_ADDRESSGLOBAL_ULTIMATE_STREET_ADDRESSGLOBAL_ULTIMATE_AREANAME3GLOBAL_ULTIMATE_AREANAME1GLOBAL_ULTIMATE_COUNTRYGLOBAL_ULTIMATE_POSTCODEFAMILY_MEMBERSHIERARCHY_CODETICKER_SYMBOLEXCHANGE_NAMEgeomiso_5carPOPCYHINCYMED65HINCYMED75INCCYMEDHHpop_60plusn_pois_personal_carehealth_care_services_exppersonal_care_services_exppersonal_care_products_exp
                                                                  011146893271114689327#-75.186601#39.9183012020-08-01WALGREEN EASTERN CO., INC.WALGREENSWALGREENSNaNUSANaNPHILADELPHIAPHILADELPHIAPENNSYLVANIAPA19145-41222310 W OREGON AVE, PHILADELPHIA, PA, 19145-41222310 W OREGON AVEPHILADELPHIA, PA, 19145-4122-75.18660139.918301S8HPNTSCZAHIGH1.0(215) 468-2481NaNNaNWWW.WALLGREENSBOOTSALLIANCE.COMNaNDRUG STORES AND PROPRIETARY STORES5912NaN59129901DRUG STORES446110.010010700DIVISION G. - RETAIL TRADEMISCELLANEOUS RETAILDRUG STORES AND PROPRIETARY STORESDRUG STORES AND PROPRIETARY STORES/PHARMACY40.00.00.00.00.0NaNG0.02.00.0WALGREEN EASTERN CO., INC.200 WILMOT RD, DEERFIELD, ILLINOIS, 600154620,...200 WILMOT RDDEERFIELDILLINOISUSA600154620.0WALGREENS BOOTS ALLIANCE, INC.108 WILMOT RD, DEERFIELD, ILLINOIS, 600155145108 WILMOT RDDEERFIELDILLINOIS600155145.0NWALGREENS BOOTS ALLIANCE, INC.108 WILMOT RD, DEERFIELD, ILLINOIS, 600155145,...108 WILMOT RDDEERFIELDILLINOISUSA600155145.09551.03.0NaNNaNPOINT (-75.18660 39.91830)MULTIPOLYGON (((-75.19996 39.93393, -75.19953 ...15037.34023450981.22727338971.63636455397.4090913364.675232541.0925.483182349.088636302.686364
                                                                  111149424691114942469#-75.160179#39.9704782020-08-01TANG PHARMACY IVNaNTANG PHARMACY IVNaNUSANaNPHILADELPHIAPHILADELPHIAPENNSYLVANIAPA19130-1604900 N BROAD ST, PHILADELPHIA, PA, 19130-1604900 N BROAD STPHILADELPHIA, PA, 19130-1604-75.16017939.970478S8HPNTSCZAHIGH1.0(215) 235-1400NaNNaNNaNNaNDRUG STORES AND PROPRIETARY STORES5912NaN59120000DRUG STORES AND PROPRIETARY STORES446110.010230030DIVISION G. - RETAIL TRADEMISCELLANEOUS RETAILDRUG STORES AND PROPRIETARY STORESDRUG STORES AND PROPRIETARY STORES/PHARMACY4.04.02012.0610000.0610000.020.0G3.00.00.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNNaNNaNNaNNaNNaNNaNNaN0.00.0NaNNaNPOINT (-75.16018 39.97048)MULTIPOLYGON (((-75.17592 39.96826, -75.17550 ...54623.53098046537.36842137788.38596549895.80701810112.7251431442.0833.376667321.834211279.067719
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                                                                  " + ], + "text/plain": [ + " PB_ID geoid do_date \\\n", + "0 1114689327 1114689327#-75.186601#39.918301 2020-08-01 \n", + "1 1114942469 1114942469#-75.160179#39.970478 2020-08-01 \n", + "\n", + " NAME BRANDNAME TRADE_NAME FRANCHISE_NAME \\\n", + "0 WALGREEN EASTERN CO., INC. WALGREENS WALGREENS NaN \n", + "1 TANG PHARMACY IV NaN TANG PHARMACY IV NaN \n", + "\n", + " ISO3 AREANAME4 AREANAME3 AREANAME2 AREANAME1 STABB POSTCODE \\\n", + "0 USA NaN PHILADELPHIA PHILADELPHIA PENNSYLVANIA PA 19145-4122 \n", + "1 USA NaN PHILADELPHIA PHILADELPHIA PENNSYLVANIA PA 19130-1604 \n", + "\n", + " FORMATTEDADDRESS MAINADDRESSLINE \\\n", + "0 2310 W OREGON AVE, PHILADELPHIA, PA, 19145-4122 2310 W OREGON AVE \n", + "1 900 N BROAD ST, PHILADELPHIA, PA, 19130-1604 900 N BROAD ST \n", + "\n", + " ADDRESSLASTLINE LONGITUDE LATITUDE GEORESULT \\\n", + "0 PHILADELPHIA, PA, 19145-4122 -75.186601 39.918301 S8HPNTSCZA \n", + "1 PHILADELPHIA, PA, 19130-1604 -75.160179 39.970478 S8HPNTSCZA \n", + "\n", + " CONFIDENCE_CODE COUNTRY_ACCESS_CODE TEL_NUM FAXNUM EMAIL \\\n", + "0 HIGH 1.0 (215) 468-2481 NaN NaN \n", + "1 HIGH 1.0 (215) 235-1400 NaN NaN \n", + "\n", + " HTTP OPEN_24H \\\n", + "0 WWW.WALLGREENSBOOTSALLIANCE.COM NaN \n", + "1 NaN NaN \n", + "\n", + " BUSINESS_LINE SIC1 SIC2 SIC8 \\\n", + "0 DRUG STORES AND PROPRIETARY STORES 5912 NaN 59129901 \n", + "1 DRUG STORES AND PROPRIETARY STORES 5912 NaN 59120000 \n", + "\n", + " SIC8_DESCRIPTION ALT_INDUSTRY_CODE MICODE \\\n", + "0 DRUG STORES 446110.0 10010700 \n", + "1 DRUG STORES AND PROPRIETARY STORES 446110.0 10230030 \n", + "\n", + " TRADE_DIVISION GROUP \\\n", + "0 DIVISION G. - RETAIL TRADE MISCELLANEOUS RETAIL \n", + "1 DIVISION G. - RETAIL TRADE MISCELLANEOUS RETAIL \n", + "\n", + " CLASS \\\n", + "0 DRUG STORES AND PROPRIETARY STORES \n", + "1 DRUG STORES AND PROPRIETARY STORES \n", + "\n", + " SUB_CLASS_x EMPLOYEE_HERE EMPLOYEE_COUNT \\\n", + "0 DRUG STORES AND PROPRIETARY STORES/PHARMACY 40.0 0.0 \n", + "1 DRUG STORES AND PROPRIETARY STORES/PHARMACY 4.0 4.0 \n", + "\n", + " YEAR_START SALES_VOLUME_LOCAL SALES_VOLUME_US_DOLLARS CURRENCY_CODE \\\n", + "0 0.0 0.0 0.0 NaN \n", + "1 2012.0 610000.0 610000.0 20.0 \n", + "\n", + " AGENT_CODE LEGAL_STATUS_CODE STATUS_CODE SUBSIDIARY_INDICATOR \\\n", + "0 G 0.0 2.0 0.0 \n", + "1 G 3.0 0.0 0.0 \n", + "\n", + " PARENT_BUSINESS_NAME \\\n", + "0 WALGREEN EASTERN CO., INC. \n", + "1 NaN \n", + "\n", + " PARENT_ADDRESS PARENT_STREET_ADDRESS \\\n", + "0 200 WILMOT RD, DEERFIELD, ILLINOIS, 600154620,... 200 WILMOT RD \n", + "1 NaN NaN \n", + "\n", + " PARENT_AREANAME3 PARENT_AREANAME1 PARENT_COUNTRY PARENT_POSTCODE \\\n", + "0 DEERFIELD ILLINOIS USA 600154620.0 \n", + "1 NaN NaN NaN NaN \n", + "\n", + " DOMESTIC_ULTIMATE_BUSINESS_NAME \\\n", + "0 WALGREENS BOOTS ALLIANCE, INC. \n", + "1 NaN \n", + "\n", + " DOMESTIC_ULTIMATE_ADDRESS \\\n", + "0 108 WILMOT RD, DEERFIELD, ILLINOIS, 600155145 \n", + "1 NaN \n", + "\n", + " DOMESTIC_ULTIMATE_STREET_ADDRESS DOMESTIC_ULTIMATE_AREANAME3 \\\n", + "0 108 WILMOT RD DEERFIELD \n", + "1 NaN NaN \n", + "\n", + " DOMESTIC_ULTIMATE_AREANAME1 DOMESTIC_ULTIMATE_POSTCODE \\\n", + "0 ILLINOIS 600155145.0 \n", + "1 NaN NaN \n", + "\n", + " GLOBAL_ULTIMATE_INDICATOR GLOBAL_ULTIMATE_BUSINESS_NAME \\\n", + "0 N WALGREENS BOOTS ALLIANCE, INC. \n", + "1 N NaN \n", + "\n", + " GLOBAL_ULTIMATE_ADDRESS \\\n", + "0 108 WILMOT RD, DEERFIELD, ILLINOIS, 600155145,... \n", + "1 NaN \n", + "\n", + " GLOBAL_ULTIMATE_STREET_ADDRESS GLOBAL_ULTIMATE_AREANAME3 \\\n", + "0 108 WILMOT RD DEERFIELD \n", + "1 NaN NaN \n", + "\n", + " GLOBAL_ULTIMATE_AREANAME1 GLOBAL_ULTIMATE_COUNTRY GLOBAL_ULTIMATE_POSTCODE \\\n", + "0 ILLINOIS USA 600155145.0 \n", + "1 NaN NaN NaN \n", + "\n", + " FAMILY_MEMBERS HIERARCHY_CODE TICKER_SYMBOL EXCHANGE_NAME \\\n", + "0 9551.0 3.0 NaN NaN \n", + "1 0.0 0.0 NaN NaN \n", + "\n", + " geom \\\n", + "0 POINT (-75.18660 39.91830) \n", + "1 POINT (-75.16018 39.97048) \n", + "\n", + " iso_5car POPCY \\\n", + "0 MULTIPOLYGON (((-75.19996 39.93393, -75.19953 ... 15037.340234 \n", + "1 MULTIPOLYGON (((-75.17592 39.96826, -75.17550 ... 54623.530980 \n", + "\n", + " HINCYMED65 HINCYMED75 INCCYMEDHH pop_60plus \\\n", + "0 50981.227273 38971.636364 55397.409091 3364.675232 \n", + "1 46537.368421 37788.385965 49895.807018 10112.725143 \n", + "\n", + " n_pois_personal_care health_care_services_exp personal_care_services_exp \\\n", + "0 541.0 925.483182 349.088636 \n", + "1 1442.0 833.376667 321.834211 \n", + "\n", + " personal_care_products_exp \n", + "0 302.686364 \n", + "1 279.067719 " + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ph_pharmacies.head(2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NSrbnR9qxGiT" + }, + "source": [ + "\n", + "### 4. Dashboard\n", + "\n", + "Finally, with all the data gathered, we will build the dashboard and publish it so we can share it with our client/manager/colleague for them to explore it.\n", + "\n", + "This dashboard allows you to select a range of desired expenditure in care products, people aged 60+, household income, and so forth. Selecting the desired ranges will filter out pharmacies, so that in the end you can identify the target pharmacies for your marketing campaign." + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "id": "SCT0p3tbQq8b" + }, + "outputs": [], + "source": [ + "cmap = Map(Layer(ph_pharmacies, \n", + " geom_col='geom', \n", + " style=color_category_style('SIC8_DESCRIPTION', size=4, opacity=0.85, palette='safe', stroke_width=0.15), \n", + " widgets=[formula_widget(\n", + " 'PB_ID', \n", + " operation='COUNT', \n", + " title='Total number of pharmacies', \n", + " description='Keep track of the total amount of pharmacies that meet the ranges selected on the widgets below'),\n", + " histogram_widget(\n", + " 'pop_60plus',\n", + " title='Population 60+',\n", + " description='Select a range of values to filter',\n", + " buckets=15\n", + " ),\n", + " histogram_widget(\n", + " 'HINCYMED65',\n", + " title='Household income 65-74',\n", + " buckets=15\n", + " ),\n", + " histogram_widget(\n", + " 'HINCYMED75',\n", + " title='Household income 75+',\n", + " buckets=15\n", + " ),\n", + " histogram_widget(\n", + " 'n_pois_personal_care',\n", + " title='Number of personal care POIs',\n", + " buckets=15\n", + " ),\n", + " histogram_widget(\n", + " 'personal_care_products_exp',\n", + " title='Expenditure in personal care products ($)',\n", + " buckets=15\n", + " )], \n", + " legends=color_category_legend(\n", + " title='Pharmacies',\n", + " description='Type of store'), \n", + " popup_hover=[popup_element('NAME', title='Name')]\n", + " ), \n", + " viewport={'zoom': 11}\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 674 + }, + "id": "eYvmWDnsXoQK", + "outputId": "81fcf9bf-75bd-4c10-a67e-0139a3389474" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + " \n", + "\n", + "\n", + " \n", + "
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                                                                    \n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\">\n", + "\n", + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cmap" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "7A4fyiG1xWVp" + }, + "source": [ + "#### Publish dashboard" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "id": "hu6lMFmvxWnM" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'id': '6765be0f-02da-4bec-b80c-63ec9bcd5624',\n", + " 'url': 'https://cartoframes-org.carto.com/u/cartoframes/kuviz/6765be0f-02da-4bec-b80c-63ec9bcd5624',\n", + " 'name': 'ph_pharmacies_dashboard',\n", + " 'privacy': 'password'}" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "cmap.publish('ph_pharmacies_dashboard', password='MY_PASS', if_exists='replace')" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "[CARTO] Building a dashboard.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/use_cases/combining_two_datasets.ipynb b/docs/examples/use_cases/combining_two_datasets.ipynb new file mode 100644 index 000000000..d59c73e62 --- /dev/null +++ b/docs/examples/use_cases/combining_two_datasets.ipynb @@ -0,0 +1,3455 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "IkfOIflwHsJN" + }, + "source": [ + "## Combining two datasets. Using geosocial media data to understand retail performance\n", + "\n", + "Geosocial data is location-based social media data that can be interpreted and analyzed as part of any location-oriented business decision. \n", + "\n", + "In this notebook, [Spatial.ai geosocial data](https://carto.com/spatial-data-catalog/browser/?provider=spatial_ai) is combined with [Mastercard financial data](https://carto.com/spatial-data-catalog/browser/?provider=mastercard) to quantify how much of retail performance can be explained by geosocial behavior. We compare the cities of Chicago and Los Angeles to show how retail performance is driven by different social segments depending on the city.\n", + "\n", + "**Note** this use case leverages premium datasets from [CARTO Data Observatory](https://carto.com/spatial-data-catalog/).\n", + "\n", + "The notebook is organized in the following sections:\n", + "1. [Download data from the Data Observatory](#section1)\n", + "2. [Analyzing geosocial drivers](#section2)\n", + " - [Identify where top performers are](#section21)\n", + " - [Characterization of top performers](#section22)\n", + " - [Correlation analysis](#section23)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3dhzR472HsJO" + }, + "source": [ + "### 0. Setup\n", + "\n", + "#### 0.1. Import packages" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 52 + }, + "id": "UhohOUSPHsJO", + "outputId": "e4d9b65d-ec5d-49da-ee4a-b43404c8561b" + }, + "outputs": [], + "source": [ + "import geopandas as gpd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "\n", + "from cartoframes.auth import set_default_credentials\n", + "from cartoframes.data.observatory import *\n", + "from cartoframes.viz import *\n", + "from shapely import wkt\n", + "\n", + "pd.set_option('display.max_columns', None)\n", + "sns.set_style('whitegrid')\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 0.2. Set CARTO default credentials\n", + "\n", + "In order to be able to use the Data Observatory via CARTOframes, you need to set your CARTO account credentials first.\n", + "\n", + "Please, visit the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/) for further detail." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note about credentials**\n", + "\n", + "For security reasons, we recommend storing your credentials in an external file to prevent publishing them by accident when sharing your notebooks. You can get more information in the section _Setting your credentials_ of the [Authentication guide](https://carto.com/developers/cartoframes/guides/Authentication/)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "UYz9tHunHsJY" + }, + "source": [ + "\n", + "### 1. Download data from the Data Observatory\n", + "\n", + "In this section, we'll download the two datasets we're interested in and combine them into a single dataframe.\n", + "\n", + "For more information on how to access Data Observatory datasets using CARTOframes visit the [Guides](https://carto.com/developers/cartoframes/guides/) or take a look at the Access Premium Data template." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MxuN2Vs-zscL" + }, + "source": [ + "#### Data loading functions" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following function loads the census block group ids for the city of interest. This is used afterwards to filter the data downloaded from the Data Observatory." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "noaWSMg6SQwB" + }, + "outputs": [], + "source": [ + "LA_CBG_PATH = 'https://libs.cartocdn.com/cartoframes/samples/la_cbg.csv'\n", + "LA_BB = '-118.673619,33.553967,-117.997960,34.360425'\n", + "CHICACO_CBG_PATH = 'https://libs.cartocdn.com/cartoframes/samples/chicago_cbg.csv'\n", + "CHICAGO_BB = '-88.638285,41.434892,-87.487468,42.502873'\n", + "\n", + "def read_cbg(city):\n", + " cbg_list = pd.DataFrame()\n", + " bbox = ''\n", + " if city == 'la':\n", + " cbg_list = pd.read_csv(LA_CBG_PATH, dtype={'geoid':str})['geoid'].tolist()\n", + " bbox = LA_BB\n", + " elif city == 'chicago':\n", + " cbg_list = pd.read_csv(CHICACO_CBG_PATH, dtype={'geoid':str})['geoid'].tolist()\n", + " bbox = CHICAGO_BB\n", + " return cbg_list, bbox" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following function downloads the latest [Spatial.ai Geosocial Segments](https://carto.com/spatial-data-catalog/browser/dataset/spa_geosocial_s_d5dc42ae/) data for the specified bounding box." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "DOuH4oA1ToXo" + }, + "outputs": [], + "source": [ + "def download_social(bbox, cbg_list):\n", + " dataset = Dataset.get('spa_geosocial_s_d5dc42ae')\n", + " sql_query = f\"SELECT * FROM $dataset$ WHERE CAST(do_date AS date) >= (SELECT MAX(CAST(do_date AS date)) FROM $dataset$) AND ST_IntersectsBox(geom, {bbox})\"\n", + " social = dataset.to_dataframe(sql_query=sql_query)\n", + " social = social[social['geoid'].isin(cbg_list)]\n", + " social.drop(columns=['do_label', 'do_area', 'do_perimeter', 'do_num_vertices'], inplace=True)\n", + " return social" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following function downloads the latest [Mastercard Geographic Insights](https://carto.com/spatial-data-catalog/browser/dataset/mc_geographic__7980c5c3/) data for the specified bounding box.\n", + "\n", + "Note the data is also filtered by geo_type. There are three types of geo_types depending if the indices shown represent a comparison with regards to country (c), province (p) or metropolitan area (m)." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "aS0Pb2C-UgzH" + }, + "outputs": [], + "source": [ + "def download_mastercard(bbox, cbg_list):\n", + " dataset = Dataset.get('mc_geographic__7980c5c3')\n", + " sql_query = f\"SELECT * FROM $dataset$ WHERE geo_type = 'm' AND CAST(do_date AS date) >= (SELECT MAX(CAST(do_date AS date)) FROM $dataset$) AND ST_IntersectsBox(geom, {bbox})\"\n", + " mrli = dataset.to_dataframe(sql_query=sql_query)\n", + " mrli = mrli[mrli['geoid'].isin(cbg_list)]\n", + " mrli.drop(columns=['do_label', 'do_area', 'do_perimeter', 'do_num_vertices'], inplace=True)\n", + "\n", + " mrli = mrli.sort_values(['geoid', 'industry', 'segment', 'geo_type', 'do_date']).reset_index(drop=True)\n", + " return mrli" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The following function loads the three datasets and merges them into a single one.\n", + "\n", + "**Note** we can merge the dataframes because both datasets are defined at the census block group level, so the geoid's are common to both of them." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "0W7bJ9iEU1mx" + }, + "outputs": [], + "source": [ + "def download_data(city):\n", + " cbg_list, bbox = read_cbg(city)\n", + " social = download_social(bbox, cbg_list)\n", + " social.drop(columns='geom', inplace=True)\n", + " mrli = download_mastercard(bbox, cbg_list)\n", + " mrli = mrli[(mrli['segment'] == 'o')]\n", + " mrli = mrli.merge(social, on='geoid')\n", + " mrli = gpd.GeoDataFrame(mrli, crs='epsg:4326')\n", + " return mrli" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8eQ05DR1z1vL" + }, + "source": [ + "### Download data into dataframes\n", + "\n", + "Here we'll load the data for Chicago and LA." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 328 + }, + "id": "VywMyAySHsJb", + "outputId": "cf3d5c82-6548-4c6a-d070-e821530e9606" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                    21703101010022020-11-0117appom0.00.00.00.00.00.0POLYGON ((-87.67710 42.02255, -87.67355 42.019...CountyCook County170313993.074.2368.1467.1729.3820.3844.7752.0847.3936.7148.0966.7181.1919.8449.9752.4068.9518.5720.6527.6313.7732.4317.9218.8757.5762.7839.2262.8322.2151.9963.9455.2050.4671.4867.8236.4756.3165.6146.3147.2546.7444.1234.3134.5851.4367.7422.8669.6869.5794.1525.4732.5154.0779.4345.2833.0548.5770.8632.1331.704.8230.2469.1618.1762.6144.3144.2929.1962.6167.7637.6062.8346.7959.1951.3752.8248.1755.5738.7461.8650.9456.2423.4159.0456.9149.9043.0561.8742.3733.2255.6538.142.552020-10-01
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                                                                    " + ], + "text/plain": [ + " geoid do_date_x region_id industry segment geo_type txn_amt \\\n", + "0 170310101002 2020-11-01 17 app o m 0.0 \n", + "1 170310101002 2020-11-01 17 app o m 0.0 \n", + "2 170310101002 2020-11-01 17 app o m 0.0 \n", + "\n", + " txn_cnt acct_cnt avg_ticket avg_freq avg_spend_amt \\\n", + "0 0.0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " geom comparison_level \\\n", + "0 POLYGON ((-87.67710 42.02255, -87.67355 42.019... National \n", + "1 POLYGON ((-87.67710 42.02255, -87.67355 42.019... DMA \n", + "2 POLYGON ((-87.67710 42.02255, -87.67355 42.019... County \n", + "\n", + " comparison_geography_name comparison_geography_ID \\\n", + "0 USA USA \n", + "1 Chicago (IL) 602 \n", + "2 Cook County 17031 \n", + "\n", + " comparison_geography_count EA01_bookish EA02_engine_enthusiasts \\\n", + "0 217739.0 83.62 43.28 \n", + "1 6735.0 79.19 64.75 \n", + "2 3993.0 74.23 68.14 \n", + "\n", + " EA03_green_thumb EA04_natural_beauty EA05_wanderlust EA06_handcrafted \\\n", + "0 68.41 14.97 6.35 25.00 \n", + "1 71.43 29.65 21.09 42.97 \n", + "2 67.17 29.38 20.38 44.77 \n", + "\n", + " EA07_animal_advocates EA08_dog_lovers EA09_smoke_culture \\\n", + "0 32.48 28.97 29.19 \n", + "1 50.86 45.40 35.06 \n", + "2 52.08 47.39 36.71 \n", + "\n", + " EB01_daily_grind EB02_nerd_culture EB03_lgbtq_culture \\\n", + "0 52.57 67.76 93.59 \n", + "1 52.34 64.25 87.77 \n", + "2 48.09 66.71 81.19 \n", + "\n", + " EB04_wealth_signaling EB05_hipster EB06_student_life EB07_farm_culture \\\n", + "0 16.93 56.80 48.33 33.18 \n", + "1 24.20 57.88 52.57 60.02 \n", + "2 19.84 49.97 52.40 68.95 \n", + "\n", + " EC01_love_and_romance EC02_happily_ever_after EC03_dating_life \\\n", + "0 14.03 8.83 27.18 \n", + "1 20.35 20.87 29.03 \n", + "2 18.57 20.65 27.63 \n", + "\n", + " EC04_connected_motherhood EC05_family_time EC06_my_crew EC07_girl_squad \\\n", + "0 5.68 30.27 9.68 21.23 \n", + "1 14.91 34.34 19.88 19.74 \n", + "2 13.77 32.43 17.92 18.87 \n", + "\n", + " EC08_networking ED01_sweet_treats ED02_coffee_connoisseur \\\n", + "0 77.17 71.39 42.15 \n", + "1 66.99 67.17 42.67 \n", + "2 57.57 62.78 39.22 \n", + "\n", + " ED03_trendy_eats ED04_whiskey_business ED05_asian_food_and_culture \\\n", + "0 71.69 13.01 61.73 \n", + "1 68.96 22.51 57.91 \n", + "2 62.83 22.21 51.99 \n", + "\n", + " ED06_ingredient_attentive ED07_fueling_for_fitness ED08_wine_lovers \\\n", + "0 56.89 51.81 38.39 \n", + "1 64.65 57.94 51.36 \n", + "2 63.94 55.20 50.46 \n", + "\n", + " ED09_hops_and_brews EE01_film_lovers EE02_competitive_nature \\\n", + "0 68.34 78.92 29.93 \n", + "1 70.87 75.45 37.59 \n", + "2 71.48 67.82 36.47 \n", + "\n", + " EE03_live_experiences EE04_late_night_leisure EE05_party_life \\\n", + "0 67.00 69.64 64.63 \n", + "1 60.06 67.76 59.11 \n", + "2 56.31 65.61 46.31 \n", + "\n", + " EE06_live_and_local_music EE07_lighthearted_fun EF01_deep_emotions \\\n", + "0 46.62 23.23 44.27 \n", + "1 50.06 44.04 49.97 \n", + "2 47.25 46.74 44.12 \n", + "\n", + " EF02_heartfelt_sharing EF03_awestruck EF04_happy_place EF05_gratitude \\\n", + "0 49.23 18.97 49.69 67.29 \n", + "1 42.51 33.33 52.34 69.80 \n", + "2 34.31 34.58 51.43 67.74 \n", + "\n", + " EF06_memory_lane EG01_dance_devotion EG02_artistic_appreciation \\\n", + "0 20.29 79.46 79.36 \n", + "1 26.48 74.55 76.61 \n", + "2 22.86 69.68 69.57 \n", + "\n", + " EG03_pieces_of_history EG04_sites_to_see EG05_hip_hop_culture \\\n", + "0 98.01 11.81 53.29 \n", + "1 95.61 24.87 42.35 \n", + "2 94.15 25.47 32.51 \n", + "\n", + " EH01_mindfulness_and_spirituality EH02_activism EH03_politically_engaged \\\n", + "0 53.82 86.23 45.19 \n", + "1 59.96 86.19 51.25 \n", + "2 54.07 79.43 45.28 \n", + "\n", + " EH04_praise_and_worship EH05_conservation EH06_civic_attentiveness \\\n", + "0 23.09 40.48 81.35 \n", + "1 34.39 52.47 77.81 \n", + "2 33.05 48.57 70.86 \n", + "\n", + " EI01_trend_trackers EI02_all_about_hair EI03_mens_style \\\n", + "0 28.62 34.72 6.44 \n", + "1 32.89 36.26 6.89 \n", + "2 32.13 31.70 4.82 \n", + "\n", + " EI04_fitness_fashion EI05_body_art EI06_smart_chic \\\n", + "0 26.99 69.13 12.31 \n", + "1 31.87 71.56 20.08 \n", + "2 30.24 69.16 18.17 \n", + "\n", + " EJ01_home_and_leisure EJ02_past_reflections EJ03_humanitarian \\\n", + "0 62.56 40.60 45.24 \n", + "1 65.11 50.15 45.91 \n", + "2 62.61 44.31 44.29 \n", + "\n", + " EJ04_deal_seekers EK01_organized_sports EK02_fitness_obsession \\\n", + "0 23.93 52.53 53.74 \n", + "1 30.44 58.59 63.08 \n", + "2 29.19 62.61 67.76 \n", + "\n", + " EK03_outdoor_adventures EK04_yoga_advocates EK05_functional_fitness \\\n", + "0 12.33 56.89 50.25 \n", + "1 32.02 64.68 47.21 \n", + "2 37.60 62.83 46.79 \n", + "\n", + " index_high_end_affinity index_mid_range_consumer_affinity \\\n", + "0 54.33 41.08 \n", + "1 61.84 51.14 \n", + "2 59.19 51.37 \n", + "\n", + " index_discount_affinity index_foodie_affinity \\\n", + "0 60.49 42.64 \n", + "1 58.85 49.54 \n", + "2 52.82 48.17 \n", + "\n", + " index_breakfast_and_brunch_affinity index_fast_casual_affinity \\\n", + "0 59.48 32.71 \n", + "1 59.94 38.50 \n", + "2 55.57 38.74 \n", + "\n", + " index_coffee_affinity index_fast_food_affinity \\\n", + "0 67.48 36.41 \n", + "1 67.08 49.82 \n", + "2 61.86 50.94 \n", + "\n", + " index_organic_and_local_affinity index_fashion_affinity \\\n", + "0 54.37 21.82 \n", + "1 60.42 25.60 \n", + "2 56.24 23.41 \n", + "\n", + " index_health_conscious_affinity index_entertainment_affinity \\\n", + "0 53.04 59.01 \n", + "1 58.30 58.63 \n", + "2 59.04 56.91 \n", + "\n", + " index_late_night_affinity index_personal_care_affinity \\\n", + "0 54.09 42.52 \n", + "1 53.51 46.46 \n", + "2 49.90 43.05 \n", + "\n", + " index_younger_affinity index_older_affinity index_family_affinity \\\n", + "0 68.76 22.14 20.38 \n", + "1 65.93 39.58 32.71 \n", + "2 61.87 42.37 33.22 \n", + "\n", + " index_politically_liberal_affinity \\\n", + "0 64.57 \n", + "1 62.90 \n", + "2 55.65 \n", + "\n", + " index_politically_conservative_affinity VOLUME_INDEX do_date_y \n", + "0 24.70 2.55 2020-10-01 \n", + "1 36.44 2.55 2020-10-01 \n", + "2 38.14 2.55 2020-10-01 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chicago = download_data('chicago')\n", + "chicago.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 328 + }, + "id": "NL1vy1K0HsJh", + "outputId": "e6d94d0f-0cf7-49da-946b-5456535ebcb6" + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                    geoiddo_date_xregion_idindustrysegmentgeo_typetxn_amttxn_cntacct_cntavg_ticketavg_freqavg_spend_amtgeomcomparison_levelcomparison_geography_namecomparison_geography_IDcomparison_geography_countEA01_bookishEA02_engine_enthusiastsEA03_green_thumbEA04_natural_beautyEA05_wanderlustEA06_handcraftedEA07_animal_advocatesEA08_dog_loversEA09_smoke_cultureEB01_daily_grindEB02_nerd_cultureEB03_lgbtq_cultureEB04_wealth_signalingEB05_hipsterEB06_student_lifeEB07_farm_cultureEC01_love_and_romanceEC02_happily_ever_afterEC03_dating_lifeEC04_connected_motherhoodEC05_family_timeEC06_my_crewEC07_girl_squadEC08_networkingED01_sweet_treatsED02_coffee_connoisseurED03_trendy_eatsED04_whiskey_businessED05_asian_food_and_cultureED06_ingredient_attentiveED07_fueling_for_fitnessED08_wine_loversED09_hops_and_brewsEE01_film_loversEE02_competitive_natureEE03_live_experiencesEE04_late_night_leisureEE05_party_lifeEE06_live_and_local_musicEE07_lighthearted_funEF01_deep_emotionsEF02_heartfelt_sharingEF03_awestruckEF04_happy_placeEF05_gratitudeEF06_memory_laneEG01_dance_devotionEG02_artistic_appreciationEG03_pieces_of_historyEG04_sites_to_seeEG05_hip_hop_cultureEH01_mindfulness_and_spiritualityEH02_activismEH03_politically_engagedEH04_praise_and_worshipEH05_conservationEH06_civic_attentivenessEI01_trend_trackersEI02_all_about_hairEI03_mens_styleEI04_fitness_fashionEI05_body_artEI06_smart_chicEJ01_home_and_leisureEJ02_past_reflectionsEJ03_humanitarianEJ04_deal_seekersEK01_organized_sportsEK02_fitness_obsessionEK03_outdoor_adventuresEK04_yoga_advocatesEK05_functional_fitnessindex_high_end_affinityindex_mid_range_consumer_affinityindex_discount_affinityindex_foodie_affinityindex_breakfast_and_brunch_affinityindex_fast_casual_affinityindex_coffee_affinityindex_fast_food_affinityindex_organic_and_local_affinityindex_fashion_affinityindex_health_conscious_affinityindex_entertainment_affinityindex_late_night_affinityindex_personal_care_affinityindex_younger_affinityindex_older_affinityindex_family_affinityindex_politically_liberal_affinityindex_politically_conservative_affinityVOLUME_INDEXdo_date_y
                                                                    0603710111022020-11-016autom0.00.00.00.00.00.0POLYGON ((-118.29945 34.25598, -118.29104 34.2...NationalUSAUSA217739.099.1193.3998.6381.0186.0581.8992.4479.6565.6480.1496.6595.1457.9182.1796.7971.5897.0470.1578.7592.9298.6398.4463.3765.6290.2494.6392.9784.7391.3656.7173.2989.6092.8199.0661.6698.0796.9886.2397.1398.7198.5596.2198.8380.6590.8292.3999.4996.5289.7195.9695.5396.2988.5978.2998.6977.7483.3452.0286.2758.5968.6795.6167.5192.9598.5999.4477.2186.5775.9444.9788.2764.1589.7985.6486.3276.9193.5879.2891.1581.0989.0366.6177.6494.2687.3078.5895.6576.6292.0593.6275.810.732020-10-01
                                                                    1603710111022020-11-016autom0.00.00.00.00.00.0POLYGON ((-118.29945 34.25598, -118.29104 34.2...DMALos Angeles (CA)80310816.099.5288.6798.9187.1288.6383.4591.4083.6856.5281.8297.6688.4456.3475.8997.4991.5197.0580.6380.3494.3798.8598.6075.5070.7588.7692.9089.0090.9989.5565.5373.9790.4896.0594.9063.9296.0997.3384.3196.2699.2296.0992.0498.9984.7581.4991.3298.7292.8790.3596.5093.8391.2982.8888.6698.6080.7978.1773.7184.7254.1970.0092.8264.8292.8798.7199.5167.6789.3777.3751.5386.5161.5588.0886.6282.0780.7991.7084.6688.4284.9086.0069.4977.7695.1489.8976.0895.5483.4396.3989.1783.320.732020-10-01
                                                                    2603710111022020-11-016autom0.00.00.00.00.00.0POLYGON ((-118.29945 34.25598, -118.29104 34.2...CountyLos Angeles County60376425.099.4290.0198.6288.7089.8884.8591.1683.5756.7279.9797.7583.0252.2870.0897.6594.5797.0083.3178.0195.4399.1298.8576.1066.6087.4292.6287.4891.1388.7062.5373.8989.2396.3092.4963.1194.6897.1578.8295.6199.4894.5088.6399.0883.8879.3490.5898.2089.6787.8196.7191.2388.7175.0385.8898.9377.6273.2974.8884.9850.3569.0393.4262.7291.1398.1599.4667.3991.2379.4755.7284.3861.7585.7986.9879.0480.0490.4885.0985.9785.3283.5468.3978.1494.4788.8074.4294.3784.9897.3885.1184.680.732020-10-01
                                                                    \n", + "
                                                                    " + ], + "text/plain": [ + " geoid do_date_x region_id industry segment geo_type txn_amt \\\n", + "0 60371011102 2020-11-01 6 aut o m 0.0 \n", + "1 60371011102 2020-11-01 6 aut o m 0.0 \n", + "2 60371011102 2020-11-01 6 aut o m 0.0 \n", + "\n", + " txn_cnt acct_cnt avg_ticket avg_freq avg_spend_amt \\\n", + "0 0.0 0.0 0.0 0.0 0.0 \n", + "1 0.0 0.0 0.0 0.0 0.0 \n", + "2 0.0 0.0 0.0 0.0 0.0 \n", + "\n", + " geom comparison_level \\\n", + "0 POLYGON ((-118.29945 34.25598, -118.29104 34.2... National \n", + "1 POLYGON ((-118.29945 34.25598, -118.29104 34.2... DMA \n", + "2 POLYGON ((-118.29945 34.25598, -118.29104 34.2... County \n", + "\n", + " comparison_geography_name comparison_geography_ID \\\n", + "0 USA USA \n", + "1 Los Angeles (CA) 803 \n", + "2 Los Angeles County 6037 \n", + "\n", + " comparison_geography_count EA01_bookish EA02_engine_enthusiasts \\\n", + "0 217739.0 99.11 93.39 \n", + "1 10816.0 99.52 88.67 \n", + "2 6425.0 99.42 90.01 \n", + "\n", + " EA03_green_thumb EA04_natural_beauty EA05_wanderlust EA06_handcrafted \\\n", + "0 98.63 81.01 86.05 81.89 \n", + "1 98.91 87.12 88.63 83.45 \n", + "2 98.62 88.70 89.88 84.85 \n", + "\n", + " EA07_animal_advocates EA08_dog_lovers EA09_smoke_culture \\\n", + "0 92.44 79.65 65.64 \n", + "1 91.40 83.68 56.52 \n", + "2 91.16 83.57 56.72 \n", + "\n", + " EB01_daily_grind EB02_nerd_culture EB03_lgbtq_culture \\\n", + "0 80.14 96.65 95.14 \n", + "1 81.82 97.66 88.44 \n", + "2 79.97 97.75 83.02 \n", + "\n", + " EB04_wealth_signaling EB05_hipster EB06_student_life EB07_farm_culture \\\n", + "0 57.91 82.17 96.79 71.58 \n", + "1 56.34 75.89 97.49 91.51 \n", + "2 52.28 70.08 97.65 94.57 \n", + "\n", + " EC01_love_and_romance EC02_happily_ever_after EC03_dating_life \\\n", + "0 97.04 70.15 78.75 \n", + "1 97.05 80.63 80.34 \n", + "2 97.00 83.31 78.01 \n", + "\n", + " EC04_connected_motherhood EC05_family_time EC06_my_crew EC07_girl_squad \\\n", + "0 92.92 98.63 98.44 63.37 \n", + "1 94.37 98.85 98.60 75.50 \n", + "2 95.43 99.12 98.85 76.10 \n", + "\n", + " EC08_networking ED01_sweet_treats ED02_coffee_connoisseur \\\n", + "0 65.62 90.24 94.63 \n", + "1 70.75 88.76 92.90 \n", + "2 66.60 87.42 92.62 \n", + "\n", + " ED03_trendy_eats ED04_whiskey_business ED05_asian_food_and_culture \\\n", + "0 92.97 84.73 91.36 \n", + "1 89.00 90.99 89.55 \n", + "2 87.48 91.13 88.70 \n", + "\n", + " ED06_ingredient_attentive ED07_fueling_for_fitness ED08_wine_lovers \\\n", + "0 56.71 73.29 89.60 \n", + "1 65.53 73.97 90.48 \n", + "2 62.53 73.89 89.23 \n", + "\n", + " ED09_hops_and_brews EE01_film_lovers EE02_competitive_nature \\\n", + "0 92.81 99.06 61.66 \n", + "1 96.05 94.90 63.92 \n", + "2 96.30 92.49 63.11 \n", + "\n", + " EE03_live_experiences EE04_late_night_leisure EE05_party_life \\\n", + "0 98.07 96.98 86.23 \n", + "1 96.09 97.33 84.31 \n", + "2 94.68 97.15 78.82 \n", + "\n", + " EE06_live_and_local_music EE07_lighthearted_fun EF01_deep_emotions \\\n", + "0 97.13 98.71 98.55 \n", + "1 96.26 99.22 96.09 \n", + "2 95.61 99.48 94.50 \n", + "\n", + " EF02_heartfelt_sharing EF03_awestruck EF04_happy_place EF05_gratitude \\\n", + "0 96.21 98.83 80.65 90.82 \n", + "1 92.04 98.99 84.75 81.49 \n", + "2 88.63 99.08 83.88 79.34 \n", + "\n", + " EF06_memory_lane EG01_dance_devotion EG02_artistic_appreciation \\\n", + "0 92.39 99.49 96.52 \n", + "1 91.32 98.72 92.87 \n", + "2 90.58 98.20 89.67 \n", + "\n", + " EG03_pieces_of_history EG04_sites_to_see EG05_hip_hop_culture \\\n", + "0 89.71 95.96 95.53 \n", + "1 90.35 96.50 93.83 \n", + "2 87.81 96.71 91.23 \n", + "\n", + " EH01_mindfulness_and_spirituality EH02_activism EH03_politically_engaged \\\n", + "0 96.29 88.59 78.29 \n", + "1 91.29 82.88 88.66 \n", + "2 88.71 75.03 85.88 \n", + "\n", + " EH04_praise_and_worship EH05_conservation EH06_civic_attentiveness \\\n", + "0 98.69 77.74 83.34 \n", + "1 98.60 80.79 78.17 \n", + "2 98.93 77.62 73.29 \n", + "\n", + " EI01_trend_trackers EI02_all_about_hair EI03_mens_style \\\n", + "0 52.02 86.27 58.59 \n", + "1 73.71 84.72 54.19 \n", + "2 74.88 84.98 50.35 \n", + "\n", + " EI04_fitness_fashion EI05_body_art EI06_smart_chic \\\n", + "0 68.67 95.61 67.51 \n", + "1 70.00 92.82 64.82 \n", + "2 69.03 93.42 62.72 \n", + "\n", + " EJ01_home_and_leisure EJ02_past_reflections EJ03_humanitarian \\\n", + "0 92.95 98.59 99.44 \n", + "1 92.87 98.71 99.51 \n", + "2 91.13 98.15 99.46 \n", + "\n", + " EJ04_deal_seekers EK01_organized_sports EK02_fitness_obsession \\\n", + "0 77.21 86.57 75.94 \n", + "1 67.67 89.37 77.37 \n", + "2 67.39 91.23 79.47 \n", + "\n", + " EK03_outdoor_adventures EK04_yoga_advocates EK05_functional_fitness \\\n", + "0 44.97 88.27 64.15 \n", + "1 51.53 86.51 61.55 \n", + "2 55.72 84.38 61.75 \n", + "\n", + " index_high_end_affinity index_mid_range_consumer_affinity \\\n", + "0 89.79 85.64 \n", + "1 88.08 86.62 \n", + "2 85.79 86.98 \n", + "\n", + " index_discount_affinity index_foodie_affinity \\\n", + "0 86.32 76.91 \n", + "1 82.07 80.79 \n", + "2 79.04 80.04 \n", + "\n", + " index_breakfast_and_brunch_affinity index_fast_casual_affinity \\\n", + "0 93.58 79.28 \n", + "1 91.70 84.66 \n", + "2 90.48 85.09 \n", + "\n", + " index_coffee_affinity index_fast_food_affinity \\\n", + "0 91.15 81.09 \n", + "1 88.42 84.90 \n", + "2 85.97 85.32 \n", + "\n", + " index_organic_and_local_affinity index_fashion_affinity \\\n", + "0 89.03 66.61 \n", + "1 86.00 69.49 \n", + "2 83.54 68.39 \n", + "\n", + " index_health_conscious_affinity index_entertainment_affinity \\\n", + "0 77.64 94.26 \n", + "1 77.76 95.14 \n", + "2 78.14 94.47 \n", + "\n", + " index_late_night_affinity index_personal_care_affinity \\\n", + "0 87.30 78.58 \n", + "1 89.89 76.08 \n", + "2 88.80 74.42 \n", + "\n", + " index_younger_affinity index_older_affinity index_family_affinity \\\n", + "0 95.65 76.62 92.05 \n", + "1 95.54 83.43 96.39 \n", + "2 94.37 84.98 97.38 \n", + "\n", + " index_politically_liberal_affinity \\\n", + "0 93.62 \n", + "1 89.17 \n", + "2 85.11 \n", + "\n", + " index_politically_conservative_affinity VOLUME_INDEX do_date_y \n", + "0 75.81 0.73 2020-10-01 \n", + "1 83.32 0.73 2020-10-01 \n", + "2 84.68 0.73 2020-10-01 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "la = download_data('la')\n", + "la.head(3)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Z8-bhK-lHsJj" + }, + "source": [ + "\n", + "### 2. Analysis\n", + "\n", + "Once we have the data ready for analysis, we will start by selecting a target variable out of the Mastercard financial variables. This is the variable we are interested in explaining. We'll select the variable `txn_amt` which is the total transaction amount by census block group. In addition, we can select an industry. Mastercard provides data for 5 different industries: eating places, groceries, apparel, automotive fuel, and accomodation. They also provide the industry retail which is the sum of all the latter. For this use case, we'll select eating places, `eap`." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "id": "inWqXwQkHsJk" + }, + "outputs": [], + "source": [ + "target_var = 'txn_amt' # Total transaction amount\n", + "industry = 'eap' # Eating places" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "ch_ret = chicago[(chicago['industry'] == industry) & (chicago['comparison_level'] == 'DMA')]\n", + "la_ret = la[(la['industry'] == industry) & (la['comparison_level'] == 'DMA')]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nnZ6HzJ9HsJm" + }, + "source": [ + "\n", + "#### 2.1 Top performers. Location visualization.\n", + "\n", + "Here we identify the top 5% performers and visualize them. \n", + "\n", + "An interesting insight here is how in Chicago top performers tend to concentrate in the downtown area, while in LA they are more spread throughout the city." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "id": "kMgpD-ppHsJn" + }, + "outputs": [], + "source": [ + "def classify_finance(value, thres_95):\n", + " if value >= thres_95:\n", + " return 'Top 5%'\n", + " else:\n", + " return 'Rest'" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 230 + }, + "id": "kza5n_vtHsJr", + "outputId": "ef5593b3-2b88-4c4c-c238-027401093b91" + }, + "outputs": [], + "source": [ + "ch_ret.loc[:, f'{target_var}_class'] = list(map(classify_finance, ch_ret[target_var], [ch_ret[target_var].quantile(0.95)]*ch_ret.shape[0]))\n", + "la_ret.loc[:, f'{target_var}_class'] = list(map(classify_finance, la_ret[target_var], [la_ret[target_var].quantile(0.95)]*la_ret.shape[0]))" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 462 + }, + "id": "LGlbGpkuHsJ1", + "outputId": "7e666c83-47bc-4894-dab6-e0eee74fce90" + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " CARTOframes\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + "
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These segments are related to food and drinks, and lgbtq culture.\n", + " - Regarding the social segments which are different from one city to the other, while in Chicago Asian food and culture, whiskey business, and sweet treats are on the top 7 segments, in LA it is film lovers, fitness fashion, and heartfelt sharing. This shows two very different cities in terms of social behavior patterns." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "gJL2Xd3AHsJ8" + }, + "outputs": [], + "source": [ + "social_segment_columns = ch_ret.columns[17:-22]\n", + "\n", + "top_ch_ret = ch_ret[ch_ret[f'{target_var}_class'] == 'Top 5%']\n", + "top_ch_ret = top_ch_ret[social_segment_columns].describe().transpose()\n", + "top_ch_ret.sort_values(['50%'], ascending=False, inplace=True)\n", + "\n", + "top_la_ret = la_ret[la_ret[f'{target_var}_class'] == 'Top 5%']\n", + "top_la_ret = top_la_ret[social_segment_columns].describe().transpose()\n", + "top_la_ret.sort_values(['50%'], ascending=False, inplace=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We'll use the following function to make sure social segments have same color in both cities for an easier comparison.\n", + "\n", + "Note we'll use CARTO's bold palette colors. [Here](https://carto.com/carto-colors/) you can explore all our palettes." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "ucswIkkkHsJ-" + }, + "outputs": [], + "source": [ + "def palette(segments, color_dict):\n", + " return [color_dict[segment] for segment in segments]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
                                                                      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "unique_segments = np.unique(top_ch_ret.head(7).index.tolist() + top_la_ret.head(7).index.tolist())\n", + "palette_c=['#7F3C8D','#11A579','#3969AC','#F2B701','#E73F74','#80BA5A','#E68310','#008695','#CF1C90','#f97b72', '#4b4b8f', '#A5AA99']\n", + "color_dict = dict(zip(unique_segments, palette_c))\n", + "\n", + "def plot_top_segments(df, ax, color_dict):\n", + " sns.barplot(x='50%', y='social_segment', data=df.head(7).reset_index().rename(columns={'index':'social_segment'}), \n", + " alpha=0.96, ax=ax, palette=palette(df.head(7).index.tolist(), color_dict))\n", + " ax.set_title('Chicago', fontsize=18, fontweight='bold')\n", + " ax.set_xlabel('')\n", + " ax.set_ylabel('')\n", + " ax.tick_params(labelsize=15)\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(18, 6))\n", + "plot_top_segments(top_ch_ret, axs[0], color_dict)\n", + "plot_top_segments(top_la_ret, axs[1], color_dict)\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "93S22LzIHsKC" + }, + "source": [ + "##### Characterization based on indices\n", + "\n", + "Main insights:\n", + "- Both cities share 4 out of the 7 most impactful social indices: Breakfast+brunch, coffe, foodie, and high end affinity.\n", + "- In Chicago top eating places performers are located where late night, fashion, and entertainment affinity are high, whereas in LA they are located where politically liberal, personal care, and organic+local affinity are high." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "YvuCW5knHsKC" + }, + "outputs": [], + "source": [ + "social_indices_columns = ch_ret.columns[-22:-3]\n", + "\n", + "top_ch_ret_ix = ch_ret[ch_ret[f'{target_var}_class'] == 'Top 5%']\n", + "top_ch_ret_ix = top_ch_ret_ix[social_indices_columns].describe().transpose()\n", + "top_ch_ret_ix.sort_values(['50%'], ascending=False, inplace=True)\n", + "\n", + "top_la_ret_ix = la_ret[la_ret[f'{target_var}_class'] == 'Top 5%']\n", + "top_la_ret_ix = top_la_ret_ix[social_indices_columns].describe().transpose()\n", + "top_la_ret_ix.sort_values(['50%'], ascending=False, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
                                                                      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "unique_segments = np.unique(top_ch_ret_ix.head(7).index.tolist() + top_la_ret_ix.head(7).index.tolist())\n", + "palette_c=['#7F3C8D','#11A579','#3969AC','#F2B701','#E73F74','#80BA5A','#E68310','#008695','#CF1C90','#f97b72']\n", + "color_dict = dict(zip(unique_segments, palette_c))\n", + "\n", + "def plot_top_indices(df, ax, color_dict):\n", + " sns.barplot(x='50%', y='index', data=df.head(7).reset_index(), \n", + " alpha=0.96, ax=ax, palette=palette(df.head(7).index.tolist(), color_dict))\n", + " ax.set_title('Chicago', fontsize=18, fontweight='bold')\n", + " ax.set_xlabel('')\n", + " ax.set_ylabel('')\n", + " ax.tick_params(labelsize=11)\n", + "\n", + "fig, axs = plt.subplots(1, 2, figsize=(18, 6))\n", + "plot_top_indices(top_ch_ret_ix, axs[0], color_dict)\n", + "plot_top_indices(top_la_ret_ix, axs[1], color_dict)\n", + "fig.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cbWC9pjlHsKH" + }, + "source": [ + "\n", + "#### 2.3 Correlation analysis\n", + "\n", + "In this section we'll carry out a deeper analysis on how geosocial data can help explain retail performance. We'll compare the most important features for Chicago and LA." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iIDt1LRuHsKI" + }, + "source": [ + "##### Social segments\n", + "\n", + "Here we calculate the correlation coefficient between every social segment and the total transaction amount for restaurants. This allows us to identify which segments have a strongest impact and the differences between both cities.\n", + "\n", + "This analysis provides very interesting insights. We can see how the social segment _\"sites to see\"_ impacts positively in Chicago, while it barely impacts in LA." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "sdxUnCQqHsKI" + }, + "outputs": [], + "source": [ + "def calculate_corrcoef(target_var, df, columns):\n", + " corr_coefs = []\n", + " for ssegment in columns:\n", + " corr_aux = df[~df[ssegment].isnull()]\n", + " corr_coefs.append(np.corrcoef(corr_aux[target_var], corr_aux[ssegment])[0][1])\n", + "\n", + " corr_df_ch = pd.DataFrame(data={'social_segment':columns, 'corr_coef':corr_coefs})\n", + " corr_df_ch['corr_coef_abs'] = np.abs(corr_df_ch['corr_coef'])\n", + " corr_df_ch.sort_values('corr_coef_abs', ascending=False, inplace=True)\n", + " return corr_df_ch" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "id": "3tO7Eh94HsKK" + }, + "outputs": [], + "source": [ + "corr_df_ch = calculate_corrcoef(target_var, ch_ret, social_segment_columns)\n", + "corr_df_la = calculate_corrcoef(target_var, la_ret, social_segment_columns)\n", + "\n", + "corr_df_ch['city'] = 'Chicago'\n", + "corr_df_la['city'] = 'LA'\n", + "corr_df = pd.concat([corr_df_ch, corr_df_la], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
                                                                      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(18, 6))\n", + "sns.barplot(x='social_segment', y='corr_coef', hue='city', data=corr_df, alpha=0.99)\n", + "plt.xticks(rotation=90)\n", + "plt.xlabel('')\n", + "plt.title(f\"Correlation strength of social segments with target var '{target_var}' - industry '{industry}'\", \n", + " fontsize=15, fontweight='light', pad=15)\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "fWG2fLiFHsKb" + }, + "source": [ + "##### Indices\n", + "\n", + "Now, we'll calculate the correlation coefficient between every social index and the metric total transaction amount for restaurants. This allows us to identify which indices have a strongest impact and the differences between both cities.\n", + "\n", + "It is interesting to see the different correlation coefficients for older affinity and discount affinity in Chicago and LA." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "id": "l2UKdUaoHsKc" + }, + "outputs": [], + "source": [ + "corr_df_ch_ix = calculate_corrcoef(target_var, ch_ret, social_indices_columns)\n", + "corr_df_la_ix = calculate_corrcoef(target_var, la_ret, social_indices_columns)\n", + "\n", + "corr_df_ch_ix['city'] = 'Chicago'\n", + "corr_df_la_ix['city'] = 'LA'\n", + "corr_df_ix = pd.concat([corr_df_ch_ix, corr_df_la_ix], ignore_index=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 441 + }, + "id": "t-R3_TU1HsKj", + "outputId": "d729c804-7d62-493a-fe69-0bedaa8c9478" + }, + "outputs": [ + { + "data": { + "image/png": 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                                                                      " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "sns.barplot(y='social_segment', x='corr_coef', hue='city', data=corr_df_ix, alpha=0.95)\n", + "plt.xticks(rotation=90)\n", + "plt.ylabel('')\n", + "plt.yticks(fontsize=12)\n", + "plt.title(f\"Correlation strength of indices with target var '{target_var}' - industry '{industry}'\", \n", + " fontsize=14, fontweight='light', pad=15)\n", + "plt.tight_layout()" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "name": "[CARTO] Combining two datasets.ipynb", + "provenance": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/use_cases/geocoding_london_stations.ipynb b/docs/examples/use_cases/geocoding_london_stations.ipynb new file mode 100644 index 000000000..5228a6a7b --- /dev/null +++ b/docs/examples/use_cases/geocoding_london_stations.ipynb @@ -0,0 +1,1785 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Geocode London Stations\n", + "\n", + "Source: https://data.london.gov.uk/dataset/london-underground-performance-reports" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas\n", + "from cartoframes.auth import set_default_credentials\n", + "from cartoframes.data.services import Geocoding\n", + "\n", + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                      nlcStationBoroughNoteWeekdaySaturdaySundayWeekday.1Saturday.1Sunday.1million
                                                                      0500.0Acton TownEaling9531.06716.04744.09382.06617.04785.06.040516
                                                                      1502.0AldgateCity of London15080.04397.03261.016023.05909.04230.08.846940
                                                                      2503.0Aldgate EastTower Hamlets22327.016166.013323.021071.013893.011347.013.998292
                                                                      3505.0AlpertonBrent4495.03279.02345.05081.03392.02445.03.052230
                                                                      4506.0AmershamChiltern3848.01876.01232.04025.01797.01121.02.321692
                                                                      \n", + "
                                                                      " + ], + "text/plain": [ + " nlc Station Borough Note Weekday Saturday Sunday \\\n", + "0 500.0 Acton Town Ealing 9531.0 6716.0 4744.0 \n", + "1 502.0 Aldgate City of London 15080.0 4397.0 3261.0 \n", + "2 503.0 Aldgate East Tower Hamlets 22327.0 16166.0 13323.0 \n", + "3 505.0 Alperton Brent 4495.0 3279.0 2345.0 \n", + "4 506.0 Amersham Chiltern 3848.0 1876.0 1232.0 \n", + "\n", + " Weekday.1 Saturday.1 Sunday.1 million \n", + "0 9382.0 6617.0 4785.0 6.040516 \n", + "1 16023.0 5909.0 4230.0 8.846940 \n", + "2 21071.0 13893.0 11347.0 13.998292 \n", + "3 5081.0 3392.0 2445.0 3.052230 \n", + "4 4025.0 1797.0 1121.0 2.321692 " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "file_path = 'https://libs.cartocdn.com/cartoframes/samples/london_stations.xls'\n", + "df = pandas.read_excel(file_path, header=6, sheet_name=1)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                      nlcStationBoroughNoteweekday_entrysaturday_entrysunday_entryweekday_exitsaturday_exitsunday_exitmillion
                                                                      0500.0Acton TownEaling9531.06716.04744.09382.06617.04785.06.040516
                                                                      1502.0AldgateCity of London15080.04397.03261.016023.05909.04230.08.846940
                                                                      2503.0Aldgate EastTower Hamlets22327.016166.013323.021071.013893.011347.013.998292
                                                                      3505.0AlpertonBrent4495.03279.02345.05081.03392.02445.03.052230
                                                                      4506.0AmershamChiltern3848.01876.01232.04025.01797.01121.02.321692
                                                                      \n", + "
                                                                      " + ], + "text/plain": [ + " nlc Station Borough Note weekday_entry saturday_entry \\\n", + "0 500.0 Acton Town Ealing 9531.0 6716.0 \n", + "1 502.0 Aldgate City of London 15080.0 4397.0 \n", + "2 503.0 Aldgate East Tower Hamlets 22327.0 16166.0 \n", + "3 505.0 Alperton Brent 4495.0 3279.0 \n", + "4 506.0 Amersham Chiltern 3848.0 1876.0 \n", + "\n", + " sunday_entry weekday_exit saturday_exit sunday_exit million \n", + "0 4744.0 9382.0 6617.0 4785.0 6.040516 \n", + "1 3261.0 16023.0 5909.0 4230.0 8.846940 \n", + "2 13323.0 21071.0 13893.0 11347.0 13.998292 \n", + "3 2345.0 5081.0 3392.0 2445.0 3.052230 \n", + "4 1232.0 4025.0 1797.0 1121.0 2.321692 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = df.rename(columns={\n", + " \"Saturday.1\": \"saturday_exit\",\n", + " \"Sunday.1\": \"sunday_exit\",\n", + " \"Weekday.1\": \"weekday_exit\",\n", + " \"Saturday\": \"saturday_entry\",\n", + " \"Sunday\": \"sunday_entry\",\n", + " \"Weekday\": \"weekday_entry\"\n", + "})\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success! Data geocoded correctly\n" + ] + } + ], + "source": [ + "gc = Geocoding()\n", + "\n", + "london_stations_gdf, london_stations_metadata = gc.geocode(\n", + " df,\n", + " street='Borough',\n", + " city={'value': 'London'},\n", + " country={'value': 'United Kingdom'}\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                      the_geomnlcstationboroughnoteweekday_entrysaturday_entrysunday_entryweekday_exitsaturday_exitsunday_exitmilliongc_status_relcarto_geocode_hash
                                                                      0POINT (-0.30421 51.51335)500.0Acton TownEaling9531.06716.04744.09382.06617.04785.06.0405160.55985f9d318280297b6108bc30e92d92e1
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                                                                      2POINT (-0.03228 51.51740)503.0Aldgate EastTower Hamlets22327.016166.013323.021071.013893.011347.013.9982920.75c1ba0efb09cc8e5caf79092547a7a3e7
                                                                      3POINT (-0.24564 51.55492)505.0AlpertonBrent4495.03279.02345.05081.03392.02445.03.0522300.55c9f07eb29a060527ca81a613cca80007
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                                                                      " + ], + "text/plain": [ + " the_geom nlc station borough note \\\n", + "0 POINT (-0.30421 51.51335) 500.0 Acton Town Ealing \n", + "1 POINT (-0.08895 51.51333) 502.0 Aldgate City of London \n", + "2 POINT (-0.03228 51.51740) 503.0 Aldgate East Tower Hamlets \n", + "3 POINT (-0.24564 51.55492) 505.0 Alperton Brent \n", + "4 POINT (-0.15566 51.52048) 506.0 Amersham Chiltern \n", + "\n", + " weekday_entry saturday_entry sunday_entry weekday_exit saturday_exit \\\n", + "0 9531.0 6716.0 4744.0 9382.0 6617.0 \n", + "1 15080.0 4397.0 3261.0 16023.0 5909.0 \n", + "2 22327.0 16166.0 13323.0 21071.0 13893.0 \n", + "3 4495.0 3279.0 2345.0 5081.0 3392.0 \n", + "4 3848.0 1876.0 1232.0 4025.0 1797.0 \n", + "\n", + " sunday_exit million gc_status_rel carto_geocode_hash \n", + "0 4785.0 6.040516 0.55 985f9d318280297b6108bc30e92d92e1 \n", + "1 4230.0 8.846940 0.88 7abfcd5c0283e877a275f0d059836a09 \n", + "2 11347.0 13.998292 0.75 c1ba0efb09cc8e5caf79092547a7a3e7 \n", + "3 2445.0 3.052230 0.55 c9f07eb29a060527ca81a613cca80007 \n", + "4 1121.0 2.321692 0.54 518e37ccd63a56df5d5758fb8e52a5dd " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "london_stations_gdf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " None\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                                                                          - Quickstart + Installation -

                                                                          Complete a real project that walks through the library step by step

                                                                          +

                                                                          Install the library and set up your environment

                                                                          -
                                                                          - Installation + Authentication -

                                                                          Install the library and configure your environment

                                                                          +

                                                                          Login into CARTO to unlock the power of spatial analysis

                                                                          - Authentication + Quickstart -

                                                                          Login into CARTO to unlock the power of spatial analysis (data management, data enrichment, location data services, etc)

                                                                          +

                                                                          Complete a real project that walks through the library step by step

                                                                          -
                                                                          - Visualization + Data Management -

                                                                          Explore and visualize your spatial data with interactive maps

                                                                          +

                                                                          Work with data both locally and with a CARTO account

                                                                          +
                                                                          - Data Discovery + Data Visualization -

                                                                          Discover the spatial data you need by searching the data catalog

                                                                          +

                                                                          Explore and visualize your spatial data with interactive maps

                                                                          -
                                                                          - Data Enrichment + Data Services -

                                                                          Enrich your data with data from the Data Observatory

                                                                          +

                                                                          Geocode and discover areas of influence with Isolines

                                                                          - Location Data Services + Data Observatory -

                                                                          Geocode and discover areas of influence with Isolines

                                                                          +

                                                                          Discover and enrich your data with the Data Observatory

                                                                          diff --git a/docs/guides/01-Installation.ipynb b/docs/guides/01-Installation.ipynb new file mode 100644 index 000000000..162315339 --- /dev/null +++ b/docs/guides/01-Installation.ipynb @@ -0,0 +1,131 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Installation\n", + "\n", + "Welcome! This guide contains the information you need to install CARTOframes." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### System requirements\n", + "\n", + "#### Operating system\n", + "\n", + "Windows, macOS, GNU/Linux\n", + "\n", + "#### Python interpreter\n", + "\n", + "Python 3.5+ (previous versions are not supported)\n", + "\n", + "You can check your version by running:\n", + "\n", + "```\n", + "python --version\n", + "```\n", + "\n", + "If you don't have Python installed, please install the latest version from [python.org](https://python.org/) or refer to the [Installing Python guide](https://docs.python-guide.org/starting/installation/).\n", + "\n", + "#### Python environments\n", + "\n", + "Although installing a Python environment is not strictly required, it's highly recommended to have cleaner and more organized installations. You can check the tutorial on [Creating Virtual Environments](https://packaging.python.org/tutorials/installing-packages/#creating-virtual-environments) or [Managing Conda Environments](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "heading_collapsed": true + }, + "source": [ + "### Install it with `pip`\n", + "\n", + "The latest stable version of CARTOframes may be installed or upgraded via [pip](https://pypi.org/project/pip/) (Python Package Manager) by running one of the following commands:\n", + "\n", + "Install the package:\n", + "\n", + "```\n", + "pip install cartoframes\n", + "```\n", + "\n", + "Upgrade the package to the latest version:\n", + "\n", + "```\n", + "pip install --upgrade cartoframes\n", + "```\n", + "\n", + "Install a specific version:\n", + "\n", + "```\n", + "pip install cartoframes==1.x.y\n", + "```\n", + "\n", + "If pip does not exist, you have to install it manually. See https://pip.pypa.io/en/stable/installing/.\n", + "\n", + "#### Optional Dependencies\n", + "\n", + "[RTree](https://github.com/Toblerity/rtree) is a spatial index package to improve performance and is required for overlay operations. It provides an interface to [libspatialindex](https://github.com/libspatialindex/libspatialindex).\n", + "\n", + "\n", + "#### Windows users\n", + "\n", + "CARTOframes needs that [Shapely](https://github.com/Toblerity/Shapely) and [Fiona](https://github.com/Toblerity/Fiona) libraries are already installed in your system. Execute these commands to install CARTOframes correctly:\n", + "```bash\n", + "pip install shapely\n", + "conda install -c conda-forge fiona\n", + "pip install cartoframes\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Use Jupyter Notebooks\n", + "\n", + "CARTOframes is created to run in [Jupyter Notebooks](https://jupyter.org/), which allows rendering powerful HTML visualizations. If you are not familiar with Jupyter Notebooks, we recommend reading this [introduction](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html).\n", + "\n", + "To install CARTOframes through a Jupyter Notebook, run this command:\n", + "\n", + "```\n", + "!pip install cartoframes\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### About metrics\n", + "\n", + "We collect data on the usage of CARTOframes to improve the library. Usage data includes information like \"map_created,\" which is the event triggered when a map is created. The collected usage data is anonymous and it does not contain the data processed, just the information of the event, and it's handled in accordance with our Privacy Notice .\n", + "You may choose to opt out by calling `cartoframes.utils.setup_metrics(False)`." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/guides/02-Authentication.ipynb b/docs/guides/02-Authentication.ipynb similarity index 56% rename from guides/02-Authentication.ipynb rename to docs/guides/02-Authentication.ipynb index 22d17683b..41a60d556 100644 --- a/guides/02-Authentication.ipynb +++ b/docs/guides/02-Authentication.ipynb @@ -6,21 +6,19 @@ "source": [ "## Authentication\n", "\n", - "### Introduction\n", - "\n", "This guide focuses on the basics of authentication in CARTOframes.\n", "\n", - "> To visualize your local data with CARTOframes, you don't need to be authenticated.\n", + "Authentication is needed to store your data tables and map visualizations in your CARTO account, to use [Data Services](/developers/cartoframes/guides/Data-Services/) (geocoding, isolines) or [Data Observatory](/developers/cartoframes/guides/Data-Observatory/) (download, enrichment). If you don't already have an account, you can [create one here](https://carto.com/signup/).\n", "\n", - "Authentication is needed to set up your credentials to save your data and visualizations to CARTO, to use [Location Data Services](/developers/cartoframes/guides/Location-Data-Services/), or the [Data Observatory](developers/cartoframes/guides/Data-discovery/). Since a CARTO account is required for these cases, if you don't already have an account, you can [create one here](https://carto.com/signup/)." + "> To visualize your local data with CARTOframes, you don't need to authenticate." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Get your Master API Key\n", - "Once you have created an account, you need to get your **Master** API Key. The API keys page can be accessed from your [CARTO Dashboard](https://carto.com/help/tutorials/your-dashboard-overview/). Once there, click on your avatar to open the dashboard menu. The API keys link will be shown.\n", + "### Getting your API Key\n", + "Once you have created an account, you need to get your **Master API Key**. The API keys page can be accessed from your [CARTO Dashboard](https://carto.com/help/tutorials/your-dashboard-overview/). Once there, click on your avatar to open the dashboard menu. The API keys link will be shown.\n", "\n", "![API Keys link - CARTO Dashboard](img/credentials/dashboard.png)\n", "\n", @@ -35,7 +33,7 @@ "source": [ "### Setting your credentials\n", "\n", - "The [Credentials](/developers/cartoframes/reference/#heading-Auth) class is used to load your credentials in CARTOframes. This should be passed to every method that insteracts with your CARTO account. You can create multiple instances of credentials to manage different CARTO accounts.\n", + "[Credentials](/developers/cartoframes/reference/#heading-Auth) class is used to load your credentials in CARTOframes. This should be passed to every method that interacts with your CARTO account. You can create multiple instances of credentials to manage different CARTO accounts.\n", "\n", "There are different ways to set them but we encourage you to use the one that reads the credentials from a JSON file:\n", "\n", @@ -70,8 +68,34 @@ "### Credential parameters\n", "\n", "- `username`: your CARTO account username\n", - "- `api_key`: API Key of user's CARTO account. If the data to be accessed is **public**, it can be set to `default_public`.\n", - "- `base_url`: only needed for on-premise or custom installations. Typically in the form of `https://username.carto.com/` for user `username`. On-premises installation (and others) have a different URL pattern." + "- `api_key`: your CARTO account API Key. If the data to be accessed is **public**, it can be set to `default_public`\n", + "- `base_url`: only needed for on-premise or custom installations. Typically in the form of `https://username.carto.com/` for user `username`. On-premises installation (and others) may have a different URL pattern." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Google cloud credentials\n", + "\n", + "CARTO [Data Observatory](/developers/cartoframes/guides/Data-Observatory/) is built on top of Google BigQuery, so every CARTO Enterprise organization has an associated Google Cloud account to run Data Observatory operations.\n", + "\n", + "In case you have an Enterprise account and want to perform data operations directly with the Python BigQuery client, you can obtain the credentials of the associated Google Cloud account and create a Google Credentials instance." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import Credentials\n", + "from google.oauth2.credentials import Credentials as GoogleCredentials\n", + "\n", + "creds = Credentials.from_file('creds.json')\n", + "\n", + "gcloud_project, gcloud_token = creds.get_gcloud_credentials()\n", + "gcloud_credentials = GoogleCredentials(gcloud_token)" ] } ], diff --git a/guides/03-Quickstart.ipynb b/docs/guides/03-Quickstart.ipynb similarity index 81% rename from guides/03-Quickstart.ipynb rename to docs/guides/03-Quickstart.ipynb index dced5aff0..e36776c19 100644 --- a/guides/03-Quickstart.ipynb +++ b/docs/guides/03-Quickstart.ipynb @@ -6,8 +6,6 @@ "source": [ "## Quickstart\n", "\n", - "### Introduction\n", - "\n", "Hi! Glad to see you made it to the Quickstart guide! In this guide you are introduced to how CARTOframes can be used by data scientists in spatial analysis workflows. Using simulated Starbucks revenue data, this guide walks through some common steps a data scientist takes to answer the following question: which stores are performing better than others?\n", "\n", "Before you get started, we encourage you to have CARTOframes and Python 3 installed so you can get a feel for the library by using it:" @@ -21,14 +19,14 @@ }, "outputs": [], "source": [ - "pip install cartoframes" + "# pip install cartoframes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "For additional ways to install CARTOframes, check out the [Installation Guide](/developers/cartoframes/guides/Installation)." + "For additional ways to install CARTOframes, check out the [Installation guide](/developers/cartoframes/guides/Installation)." ] }, { @@ -41,9 +39,9 @@ "\n", "To begin, let's outline a workflow:\n", "\n", - "- Get and explore your company's data\n", + "- Get and explore your own data\n", "- Create areas of influence for your stores\n", - "- Enrich your data with demographic data\n", + "- Enrich your data\n", "- And finally, share the results of your analysis with your team\n", "\n", "Let's get started!" @@ -53,7 +51,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Get and explore your company's data\n", + "### Get and explore your own data\n", "\n", "We are going to use a dataset that contains information about the location of Starbucks and each store's annual revenue.\n", "\n", @@ -158,7 +156,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "To display your stores as points on a map, you first have to convert the `address` column into geometries. This process is called [geocoding](https://carto.com/help/working-with-data/geocoding-types/) and CARTO provides a straightforward way to do it (you can learn more about it in the [Location Data Services guide](/developers/cartoframes/guides/Location-Data-Services)).\n", + "To display your stores as points on a map, you first have to convert the `address` column into geometries. This process is called [geocoding](https://carto.com/help/working-with-data/geocoding-types/) and CARTO provides a straightforward way to do it (you can learn more about it in the [Data Services guide](/developers/cartoframes/guides/Data-Services)).\n", "\n", "In order to geocode, you have to set your CARTO credentials. If you aren't sure about your API key, check the [Authentication guide](/developers/cartoframes/guides/Authentication/) to learn how to get it. In case you want to see the geocoded result, without being logged in, [you can get it here](http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_geocoded.csv).\n", "\n", @@ -315,7 +313,7 @@ "source": [ "Done! Now that the stores are geocoded, you will notice a new column named `geometry` has been added. This column stores the geographic location of each store and it's used to plot each location on the map.\n", "\n", - "You can quickly visualize your geocoded dataframe using the Map and Layer classes. Check out the [Visualization guide](/developers/cartoframes/guides/Visualization) to learn more about the visualization capabilities inside of CARTOframes." + "You can quickly visualize your geocoded dataframe using the Map and Layer classes. Check out the [Data Visualization guide](/developers/cartoframes/guides/Data-Visualization) to learn more about the visualization capabilities inside of CARTOframes." ] }, { @@ -1031,14 +1029,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -1112,7 +1110,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -1127,12 +1125,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                                                          ${popupHTML}
                                                                          `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                                                          ${popupHTML}
                                                                          `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1491,7 +1493,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1537,7 +1539,7 @@ " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": "H4sIAHBZBF8C/7XSTYvDIBAG4P/iOQ06fozTa6HnvZceSusuQhuDdQ8l5L/X2CyYu3vM8JqHd5iJpdfo2J4d3SX9RncI97u7Jh8G1rHvz+zJ9qeJ+VtO8TzdPsiDMYbRxeSX4DR37MeFh0vxlb/+wl/BDylHryHEmx8uqfx0h7Injcp0ivcGBYfzPHcrJdpTRgAslEYkU1HQnrKEtrQSpFRFycaU6jmgLpKEzf5Ue4lzKqU0SRAVpdvvj8CI0spaRRVlmlMWufgsUEuhKwrbL1BKIderAKwo+w8HqHS5dUObUtReUhZhPUBYqPP8BlYpQ3lVBAAA", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAHBZBF8C/7XSTYvDIBAG4P/iOQ06fozTa6HnvZceSusuQhuDdQ8l5L/X2CyYu3vM8JqHd5iJpdfo2J4d3SX9RncI97u7Jh8G1rHvz+zJ9qeJ+VtO8TzdPsiDMYbRxeSX4DR37MeFh0vxlb/+wl/BDylHryHEmx8uqfx0h7Injcp0ivcGBYfzPHcrJdpTRgAslEYkU1HQnrKEtrQSpFRFycaU6jmgLpKEzf5Ue4lzKqU0SRAVpdvvj8CI0spaRRVlmlMWufgsUEuhKwrbL1BKIderAKwo+w8HqHS5dUObUtReUhZhPUBYqPP8BlYpQ3lVBAAA", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\n", "widgets": []}];\n", + " const layers = [{"credentials": null, "data": "H4sIAGoVyl8C/7XSTYvDIBAG4P/iOQ06fozTa6HnvZceSusuQhuDdQ8l5L/X2CyYu3vM8JqHd5iJpdfo2J4d3SX9RncI97u7Jh8G1rHvz+zJ9qeJ+VtO8TzdPsiDMYbRxeSX4DR37MeFh0vxlb/+wl/BDylHryHEmx8uqfx0h7Injcp0ivcGBYfzPHcrJdpTRgAslEYkU1HQnrKEtrQSpFRFycaU6jmgLpKEzf5Ue4lzKqU0SRAVpdvvj8CI0spaRRVlmlMWufgsUEuhKwrbL1BKIderAKwo+w8HqHS5dUObUtReUhZhPUBYqPP8BlYpQ3lVBAAA", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAGoVyl8C/7XSTYvDIBAG4P/iOQ06fozTa6HnvZceSusuQhuDdQ8l5L/X2CyYu3vM8JqHd5iJpdfo2J4d3SX9RncI97u7Jh8G1rHvz+zJ9qeJ+VtO8TzdPsiDMYbRxeSX4DR37MeFh0vxlb/+wl/BDylHryHEmx8uqfx0h7Injcp0ivcGBYfzPHcrJdpTRgAslEYkU1HQnrKEtrQSpFRFycaU6jmgLpKEzf5Ue4lzKqU0SRAVpdvvj8CI0spaRRVlmlMWufgsUEuhKwrbL1BKIderAKwo+w8HqHS5dUObUtReUhZhPUBYqPP8BlYpQ3lVBAAA", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -1561,7 +1563,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -2326,14 +2328,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -2407,7 +2409,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -2422,12 +2424,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                                                          ${popupHTML}
                                                                          `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                                                          ${popupHTML}
                                                                          `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -2786,7 +2792,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -2832,7 +2838,7 @@ " const has_legends = 'true' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": "H4sIAHBZBF8C/5XTTWvDMAwG4P/ic2asD0t2r4Wddx89lNYbgS4pWTooJf99TppDetRVyHnQK+Xhxvu1uJ17L8fxNpR9f7mU09j2nWvc17P263afD9eea1eo1dcHtXAd+msZxnZufLih/JXuVjuAEAIHr4pT475L/1PG4T63rF/46NturO9PfT+c2+44LtKbks9RWZr6VhQCHqapWX2w+CgppOAZktkXQJz9qJpl46PJ5wREXnI2+ylrWuaHzLzxyeJDihqwZig2n31AjQtP+BI/m3hOjFrjBzMfQl7Gj7ne0MaPNp+DiBeJ5vgzCizzp8R544vFD/X8E9UszetPGuCZfySIG18Nfsa6ffVEaF8/EdB6fqgbPxl8laiZPAvYr5/j8vdJfpk+W3TOCOIJkp3npLheP87+YfoHPW7k4CYFAAA=", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "vabf72c", "title": "Annual Revenue ($)"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "width", "title": "Annual Revenue ($)", "type": "size-continuous", "variable": "size_value"}], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAHBZBF8C/5XTTWvDMAwG4P/ic2asD0t2r4Wddx89lNYbgS4pWTooJf99TppDetRVyHnQK+Xhxvu1uJ17L8fxNpR9f7mU09j2nWvc17P263afD9eea1eo1dcHtXAd+msZxnZufLih/JXuVjuAEAIHr4pT475L/1PG4T63rF/46NturO9PfT+c2+44LtKbks9RWZr6VhQCHqapWX2w+CgppOAZktkXQJz9qJpl46PJ5wREXnI2+ylrWuaHzLzxyeJDihqwZig2n31AjQtP+BI/m3hOjFrjBzMfQl7Gj7ne0MaPNp+DiBeJ5vgzCizzp8R544vFD/X8E9UszetPGuCZfySIG18Nfsa6ffVEaF8/EdB6fqgbPxl8laiZPAvYr5/j8vdJfpk+W3TOCOIJkp3npLheP87+YfoHPW7k4CYFAAA=", "title": "Annual Revenue ($)", "type": "GeoJSON", "viz": "@vabf72c: prop(\\u0027revenue\\u0027)\\n@size_value: ramp(linear(prop(\\u0027revenue\\u0027), globalMIN(prop(\\u0027revenue\\u0027)), globalMAX(prop(\\u0027revenue\\u0027))), [10, 40])\\ncolor: opacity(#FFB927, 0.8)\\nfilter: 1\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(sqrt(prop(\\u0027revenue\\u0027)), sqrt(globalMIN(prop(\\u0027revenue\\u0027))), sqrt(globalMAX(prop(\\u0027revenue\\u0027)))), [10, 40])\\n", "widgets": []}];\n", + " const layers = [{"credentials": null, "data": "H4sIAGoVyl8C/52TvW7DMAyE30WzK0gUxZ+sBTp3LzIEiVoYSO3AdQoEgd+9suPBGVmNBOnvfDze3Xi7FLdzb+UwXofy2p/P5Ti2feca9/mo/bjdx921p9oVavV5oBYuQ38pw9jOjXc3lN/SXWtHTBADBs8MU+O+Sv9dxuE2t6xfeO/bbqzzx74fTm13GBfSCyevmZGaOkscA+ynqVn50cIHkiDBYxQznyLAzM/MShs+mPgoMSVPqma+KMvy/1ERN/xk4UfJHKB6SDY++gCcF3yCJ/vRhEdB4Gp/0OWJWUUIuriQtUZpIyPbZGAg8kTZvAUFiosNIqgbPln4oV6BpGqphuVFqwzhEB/byCnmjQw2yFCoWWCfEtjDkFJMaxiBN3wx8Jkya/JIaxj+cRKYl5MkfTJBLSJQIZJPUcxLUBSG9SRg5u+nP/xeZSY7BQAA", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "vabf72c", "title": "Annual Revenue ($)"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "width", "title": "Annual Revenue ($)", "type": "size-continuous", "variable": "size_value"}], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAGoVyl8C/52TvW7DMAyE30WzK0gUxZ+sBTp3LzIEiVoYSO3AdQoEgd+9suPBGVmNBOnvfDze3Xi7FLdzb+UwXofy2p/P5Ti2feca9/mo/bjdx921p9oVavV5oBYuQ38pw9jOjXc3lN/SXWtHTBADBs8MU+O+Sv9dxuE2t6xfeO/bbqzzx74fTm13GBfSCyevmZGaOkscA+ynqVn50cIHkiDBYxQznyLAzM/MShs+mPgoMSVPqma+KMvy/1ERN/xk4UfJHKB6SDY++gCcF3yCJ/vRhEdB4Gp/0OWJWUUIuriQtUZpIyPbZGAg8kTZvAUFiosNIqgbPln4oV6BpGqphuVFqwzhEB/byCnmjQw2yFCoWWCfEtjDkFJMaxiBN3wx8Jkya/JIaxj+cRKYl5MkfTJBLSJQIZJPUcxLUBSG9SRg5u+nP/xeZSY7BQAA", "title": "Annual Revenue ($)", "type": "GeoJSON", "viz": "@vabf72c: prop(\\u0027revenue\\u0027)\\n@size_value: ramp(linear(prop(\\u0027revenue\\u0027), globalMIN(prop(\\u0027revenue\\u0027)), globalMAX(prop(\\u0027revenue\\u0027))), [10, 40])\\ncolor: opacity(#FFB927, 0.8)\\nfilter: 1\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(sqrt(prop(\\u0027revenue\\u0027)), sqrt(globalMIN(prop(\\u0027revenue\\u0027))), sqrt(globalMAX(prop(\\u0027revenue\\u0027)))), [10, 40])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -2856,7 +2862,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -2939,7 +2945,7 @@ " 1\n", " 1\n", " 900\n", - " MULTIPOLYGON (((-73.97017 40.57732, -73.96957 ...\n", + " MULTIPOLYGON (((-73.97035 40.57749, -73.97009 ...\n", " \n", " \n", " 2\n", @@ -2966,7 +2972,7 @@ "text/plain": [ " source_id data_range the_geom\n", "0 0 900 MULTIPOLYGON (((-73.96743 40.67345, -73.96683 ...\n", - "1 1 900 MULTIPOLYGON (((-73.97017 40.57732, -73.96957 ...\n", + "1 1 900 MULTIPOLYGON (((-73.97035 40.57749, -73.97009 ...\n", "2 2 900 MULTIPOLYGON (((-73.99781 40.62418, -73.99755 ...\n", "3 3 900 MULTIPOLYGON (((-74.03678 40.63431, -74.03618 ...\n", "4 4 900 MULTIPOLYGON (((-74.00451 40.59723, -74.00391 ..." @@ -3728,14 +3734,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -3809,7 +3815,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -3824,12 +3830,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                                                          ${popupHTML}
                                                                          `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                                                          ${popupHTML}
                                                                          `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -4188,7 +4198,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -4234,7 +4244,7 @@ " const has_legends = 'true' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": "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", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": 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"title": null, "type": "GeoJSON", "viz": "color: hex(\\"#826DBA\\")\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": []}, {"credentials": null, "data": "H4sIAHpZBF8C/5XTTWvDMAwG4P/ic2asD0t2r4Wddx89lNYbgS4pWTooJf99TppDetRVyHnQK+Xhxvu1uJ17L8fxNpR9f7mU09j2nWvc17P263afD9eea1eo1dcHtXAd+msZxnZufLih/JXuVjuAEAIHr4pT475L/1PG4T63rF/46NturO9PfT+c2+44LtKbks9RWZr6VhQCHqapWX2w+CgppOAZktkXQJz9qJpl46PJ5wREXnI2+ylrWuaHzLzxyeJDihqwZig2n31AjQtP+BI/m3hOjFrjBzMfQl7Gj7ne0MaPNp+DiBeJ5vgzCizzp8R544vFD/X8E9UszetPGuCZfySIG18Nfsa6ffVEaF8/EdB6fqgbPxl8laiZPAvYr5/j8vdJfpk+W3TOCOIJkp3npLheP87+YfoHPW7k4CYFAAA=", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "vabf72c", "title": "Annual Revenue ($)"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "width", "title": "Annual Revenue ($)", "type": "size-continuous", "variable": "size_value"}], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAHpZBF8C/5XTTWvDMAwG4P/ic2asD0t2r4Wddx89lNYbgS4pWTooJf99TppDetRVyHnQK+Xhxvu1uJ17L8fxNpR9f7mU09j2nWvc17P263afD9eea1eo1dcHtXAd+msZxnZufLih/JXuVjuAEAIHr4pT475L/1PG4T63rF/46NturO9PfT+c2+44LtKbks9RWZr6VhQCHqapWX2w+CgppOAZktkXQJz9qJpl46PJ5wREXnI2+ylrWuaHzLzxyeJDihqwZig2n31AjQtP+BI/m3hOjFrjBzMfQl7Gj7ne0MaPNp+DiBeJ5vgzCizzp8R544vFD/X8E9UszetPGuCZfySIG18Nfsa6ffVEaF8/EdB6fqgbPxl8laiZPAvYr5/j8vdJfpk+W3TOCOIJkp3npLheP87+YfoHPW7k4CYFAAA=", "title": "Annual Revenue ($)", "type": "GeoJSON", "viz": "@vabf72c: prop(\\u0027revenue\\u0027)\\n@size_value: ramp(linear(prop(\\u0027revenue\\u0027), globalMIN(prop(\\u0027revenue\\u0027)), globalMAX(prop(\\u0027revenue\\u0027))), [10, 40])\\ncolor: opacity(#FFB927, 0.8)\\nfilter: 1\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(sqrt(prop(\\u0027revenue\\u0027)), sqrt(globalMIN(prop(\\u0027revenue\\u0027))), sqrt(globalMAX(prop(\\u0027revenue\\u0027)))), [10, 40])\\n", "widgets": []}];\n", + " const layers = [{"credentials": null, "data": 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"title": null, "type": "GeoJSON", "viz": "color: hex(\\"#826DBA\\")\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": []}, {"credentials": null, "data": "H4sIAHMVyl8C/52TvW7DMAyE30WzK0gUxZ+sBTp3LzIEiVoYSO3AdQoEgd+9suPBGVmNBOnvfDze3Xi7FLdzb+UwXofy2p/P5Ti2feca9/mo/bjdx921p9oVavV5oBYuQ38pw9jOjXc3lN/SXWtHTBADBs8MU+O+Sv9dxuE2t6xfeO/bbqzzx74fTm13GBfSCyevmZGaOkscA+ynqVn50cIHkiDBYxQznyLAzM/MShs+mPgoMSVPqma+KMvy/1ERN/xk4UfJHKB6SDY++gCcF3yCJ/vRhEdB4Gp/0OWJWUUIuriQtUZpIyPbZGAg8kTZvAUFiosNIqgbPln4oV6BpGqphuVFqwzhEB/byCnmjQw2yFCoWWCfEtjDkFJMaxiBN3wx8Jkya/JIaxj+cRKYl5MkfTJBLSJQIZJPUcxLUBSG9SRg5u+nP/xeZSY7BQAA", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "vabf72c", "title": "Annual Revenue ($)"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "width", "title": "Annual Revenue ($)", "type": "size-continuous", "variable": "size_value"}], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAHMVyl8C/52TvW7DMAyE30WzK0gUxZ+sBTp3LzIEiVoYSO3AdQoEgd+9suPBGVmNBOnvfDze3Xi7FLdzb+UwXofy2p/P5Ti2feca9/mo/bjdx921p9oVavV5oBYuQ38pw9jOjXc3lN/SXWtHTBADBs8MU+O+Sv9dxuE2t6xfeO/bbqzzx74fTm13GBfSCyevmZGaOkscA+ynqVn50cIHkiDBYxQznyLAzM/MShs+mPgoMSVPqma+KMvy/1ERN/xk4UfJHKB6SDY++gCcF3yCJ/vRhEdB4Gp/0OWJWUUIuriQtUZpIyPbZGAg8kTZvAUFiosNIqgbPln4oV6BpGqphuVFqwzhEB/byCnmjQw2yFCoWWCfEtjDkFJMaxiBN3wx8Jkya/JIaxj+cRKYl5MkfTJBLSJQIZJPUcxLUBSG9SRg5u+nP/xeZSY7BQAA", "title": "Annual Revenue ($)", "type": "GeoJSON", "viz": "@vabf72c: prop(\\u0027revenue\\u0027)\\n@size_value: ramp(linear(prop(\\u0027revenue\\u0027), globalMIN(prop(\\u0027revenue\\u0027)), globalMAX(prop(\\u0027revenue\\u0027))), [10, 40])\\ncolor: opacity(#FFB927, 0.8)\\nfilter: 1\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(sqrt(prop(\\u0027revenue\\u0027)), sqrt(globalMIN(prop(\\u0027revenue\\u0027))), sqrt(globalMAX(prop(\\u0027revenue\\u0027)))), [10, 40])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -4258,7 +4268,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 8, @@ -4279,7 +4289,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "There they are! To learn more about creating isochrones and isodistances check out the [Location Data Services guide](/developers/cartoframes/guides/Location-Data-Services).\n", + "There they are! To learn more about creating isochrones and isodistances check out the [Data Services guide](/developers/cartoframes/guides/Data-Services).\n", "\n", "> Note: You will see how to publish a map in the last section. If you already want to publish this map, you can do it by calling `stores_map.publish('starbucks_isochrones', password=None)`." ] @@ -4288,7 +4298,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Enrich your data with demographic data\n", + "### Enrich your data\n", "\n", "Now that you have the area of influence calculated for each store, let's take a look at how to augment the result with population information to help better understand a store's average revenue per person.\n", "\n", @@ -4303,6 +4313,14 @@ "execution_count": 9, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "You can find more entities with the Global country filter. To apply that filter run:\n", + "\tCatalog().country('glo')\n" + ] + }, { "data": { "text/html": [ @@ -4347,114 +4365,114 @@ " \n", " \n", " \n", - " 2\n", - " acs_sociodemogr_ecbce31e\n", - " Sociodemographics - United States of America (...\n", - " The American Community Survey (ACS) is an ongo...\n", + " 4\n", + " wp_population_704f6b75\n", + " Population Mosaics - United States of America ...\n", + " Mosaiced 1km resolution global datasets. The m...\n", " demographics\n", " usa\n", - " sociodemographics\n", - " usa_acs\n", - " County - United States of America\n", - " Shoreline clipped TIGER/Line boundaries. 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"2 carto-do-public-data.usa_acs.demographics_soci... \n", - "3 carto-do-public-data.usa_acs.demographics_soci... \n", - "4 carto-do-public-data.usa_acs.demographics_soci... \n", - "6 carto-do-public-data.usa_acs.demographics_soci... \n", - "11 carto-do-public-data.usa_acs.demographics_soci... \n", + "4 carto-do-public-data.worldpop.demographics_pop... \n", + "5 carto-do-public-data.worldpop.demographics_pop... \n", + "6 carto-do-public-data.worldpop.demographics_pop... \n", + "7 carto-do-public-data.worldpop.demographics_pop... \n", + "8 carto-do-public-data.worldpop.demographics_pop... \n", ".. ... \n", - "365 carto-do-public-data.usa_acs.demographics_soci... \n", - "366 carto-do-public-data.usa_acs.demographics_soci... \n", - "367 carto-do-public-data.usa_acs.demographics_soci... \n", - "368 carto-do-public-data.usa_acs.demographics_soci... \n", - "369 carto-do-public-data.usa_acs.demographics_soci... \n", + "530 carto-do-public-data.usa_bls.demographics_labo... \n", + "531 carto-do-public-data.usa_bls.demographics_labo... \n", + "532 carto-do-public-data.usa_bls.demographics_labo... \n", + "533 carto-do-public-data.usa_bls.demographics_labo... \n", + "534 carto-do-public-data.usa_acs.demographics_soci... \n", "\n", - "[321 rows x 19 columns]" + "[440 rows x 19 columns]" ] }, "execution_count": 9, @@ -4892,14 +4910,14 @@ " 0\n", " 900\n", " MULTIPOLYGON (((-73.96743 40.67345, -73.96683 ...\n", - " 39029.654005\n", + " 39565.324924\n", " \n", " \n", " 1\n", " 1\n", " 900\n", - " MULTIPOLYGON (((-73.97017 40.57732, -73.96957 ...\n", - " 32309.490606\n", + " MULTIPOLYGON (((-73.97035 40.57749, -73.97009 ...\n", + " 32622.336873\n", " \n", " \n", " 2\n", @@ -4920,7 +4938,7 @@ " 4\n", " 900\n", " MULTIPOLYGON (((-74.00451 40.59723, -74.00391 ...\n", - " 6155.559780\n", + " 6086.711014\n", " \n", " \n", "\n", @@ -4929,17 +4947,17 @@ "text/plain": [ " source_id data_range the_geom \\\n", "0 0 900 MULTIPOLYGON (((-73.96743 40.67345, -73.96683 ... \n", - "1 1 900 MULTIPOLYGON (((-73.97017 40.57732, -73.96957 ... \n", + "1 1 900 MULTIPOLYGON (((-73.97035 40.57749, -73.97009 ... \n", "2 2 900 MULTIPOLYGON (((-73.99781 40.62418, -73.99755 ... \n", "3 3 900 MULTIPOLYGON (((-74.03678 40.63431, -74.03618 ... \n", "4 4 900 MULTIPOLYGON (((-74.00451 40.59723, -74.00391 ... \n", "\n", " total_pop \n", - "0 39029.654005 \n", - "1 32309.490606 \n", + "0 39565.324924 \n", + "1 32622.336873 \n", "2 29685.123333 \n", "3 28988.433349 \n", - "4 6155.559780 " + "4 6086.711014 " ] }, "execution_count": 11, @@ -5723,14 +5741,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -5804,7 +5822,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -5819,12 +5837,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                                                          ${popupHTML}
                                                                          `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                                                          ${popupHTML}
                                                                          `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -6183,7 +6205,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -6229,7 +6251,7 @@ " const has_legends = 'true' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": "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", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "v18db55", "title": "Total Population"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "color", "title": "Total Population", "type": "color-continuous", "variable": null}], "map_index": 0, "options": {"dateColumns": []}, "source": 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", "title": "Total Population", "type": "GeoJSON", "viz": "@v18db55: prop(\\u0027total_pop\\u0027)\\ncolor: opacity(ramp(linear(prop(\\u0027total_pop\\u0027), globalMIN(prop(\\u0027total_pop\\u0027)), globalMAX(prop(\\u0027total_pop\\u0027))), bluyl), 0.9)\\nfilter: 1\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": []}, {"credentials": null, "data": "H4sIAJJZBF8C/5XTTWvDMAwG4P/ic2asD0t2r4Wddx89lNYbgS4pWTooJf99TppDetRVyHnQK+Xhxvu1uJ17L8fxNpR9f7mU09j2nWvc17P263afD9eea1eo1dcHtXAd+msZxnZufLih/JXuVjuAEAIHr4pT475L/1PG4T63rF/46NturO9PfT+c2+44LtKbks9RWZr6VhQCHqapWX2w+CgppOAZktkXQJz9qJpl46PJ5wREXnI2+ylrWuaHzLzxyeJDihqwZig2n31AjQtP+BI/m3hOjFrjBzMfQl7Gj7ne0MaPNp+DiBeJ5vgzCizzp8R544vFD/X8E9UszetPGuCZfySIG18Nfsa6ffVEaF8/EdB6fqgbPxl8laiZPAvYr5/j8vdJfpk+W3TOCOIJkp3npLheP87+YfoHPW7k4CYFAAA=", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "vabf72c", "title": "Annual 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[10, 40])\\ncolor: opacity(#FFB927, 0.8)\\nfilter: 1\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(sqrt(prop(\\u0027revenue\\u0027)), sqrt(globalMIN(prop(\\u0027revenue\\u0027))), sqrt(globalMAX(prop(\\u0027revenue\\u0027)))), [10, 40])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -6253,7 +6275,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 12, @@ -6276,7 +6298,7 @@ "source": [ "At this stage, we could say that the store on the right performs better than others because its area of influence is the one with the lowest population but the store is not the one with lowest revenue. This insight will help us to focus on them in further analyses.\n", "\n", - "To learn more about discovering the data you want, check out the [data discovery guide](/developers/cartoframes/guides/Data-discovery). To learn more about enriching your data check out the [data enrichment guide](/developers/cartoframes/guides/Data-enrichment/)." + "To learn more about discovering and enriching data you want, check out the [Data Observatory guide](/developers/cartoframes/guides/Data-Observatory)." ] }, { @@ -7095,14 +7117,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -7176,7 +7198,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -7191,12 +7213,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                                                          ${popupHTML}
                                                                          `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                                                          ${popupHTML}
                                                                          `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -7555,7 +7581,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -7601,7 +7627,7 @@ " const has_legends = 'true' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": "H4sIAJJZBF8C/6VcyY5dtxH9l14rDc4sehsguwDZB0Zg2IohQHELSnthGPr3HN5HsoZbWnRa2kg8r1gcamSR98+n1z++fHz64elvH396/f3rx7++fP788efXTy+/PX14+vej7b9PP/zzz6dPv+BXAa2aAA1fvr58+fj19dP84Z9Pry+vP33+15eXL08/5BHSeG61hFBLbb19+/D068eX/3x8/frH9dNHT3///fPrp3+8fP7j14vrzy8vX3/59NtPrxdj/PlLz8+j9ZIofSjhufVcSus/flhAo9SLC+SYNtAi5QPU0UZ8AKUVCZRaNkBxjAOUkX2KUkelBeRQuT1Q2O0xZybItbkEsl1xwCIe1mPE5rGoNbTmUbSQBEXOo6wlbHHkdIBEfSSPItVch8c8hRSGt1KY7OahgYD/Ja+rUPrmoZjXAWw4w60DG549gNDTBjI2jYES22rvgZigj7BXCkvAv+8pluIBrY2xgB5qPPtXJ7IAbIsEcmrRBWLdPDqoXUAzL4MSedObYk/eguQwSvWAhKlXb9FTCd2TKgkoia4p5ZYcNZvT2DzSyLEzEPIeVerMAa193Jox6Zx2cyXuv4xY2trtiH3sLlCx9wwUGksOYoo1ekCgLrqiAXVcQA+BKbBsMbtAoM081CSYw+aUvgDQFoeizdkWh0fD3jXi4bbmAhWrsPYC3CpPUKxtI1gOF4BEZJZCSPdakmmpSADU0vCAmMJm3jtFHlWEOiWPB8uUBUZK5HXFAg1paM0FKo3MUoX1WfrXau4kAHS8AYxcaFMaPgWkaul4m9bX03HsLUbvWAULwCItCow3uBQtNEnBPKZ5Gi6ghsu2qk0PnB3r1jJlsVZ9KqpHQQEquYBcBI9jWG37scSmp9FpA7k1dkGQ9LR6yjmS5x4MEGPbFIqFAuSghAtqUxuFb2rQNUcUJIVadNi5sgG1TQ0/StXZceFKtdq0BANSHSWQgNJZ4a61lgsHr+2CiBUscOIXbXum7OwgQhnXBmHd1jgVHiylEPrNeDeoWFk/z6nzzztVP2i7hXn48+3bhxV/xrfFnwlW4bmM0ELFEmDZ3hGAdixCuZYKapJjY3kbayq39vDYcLQX7BivCVHPD4ACVtMFGhwSA/Bj/QGMFEq7rzqA3IU6IXLwKbCqaY2WqMV03/AbsONry0NQDBqCYovINGxZygi3Y02G0xGFkSTrVtcsoIc1xnsEXyk1GL17BA8AnZW7BljgBN4ABgkeAsg5lXLXGQDQWfIAqKvsakfkANro/a6wAKiJUR2LdAO2nTRdsWEFkCsPl02xGdWx3WYabOwtcNyD7alnam57KH5PsJh9A4hm4j32tcDxvWY3OPY1GyuBWDLLLft3I241I9+ojuBycAEAMc64BzAWiDSWrGvd4FjIaJOkUPoneGirIEZlgTMPmNnIiqYAWLN2j7eMrZKj6ggamgdAr2P2uoJC0D0Oqx07QPcwDO2psvkU+2eBnQYBCFT6PRKCQUaSUu7RFoAKrWFrWGPx2o+lgqserOCUe1oGuoRaume5M1IU1vwBt+NRIAWg4QwVGpM3wVzyzMDMEte6wqmKnpAoLAoEVCwGM63Ys1N71+EP83A81t3FKeeb3uR8MRKqzzEhVURXMeb3OF8Yt/hYlpYKJPpsyUCW+hgygJo4w8VksHMLwDYwRZ1uxQWQEVavqwLXkTzmsCR5A0GcK0Czu0+REZEOjwc6OACJ0wB477ZC7ITYVHQFS9k2kMXBwkB67VME5J3dGRVsBzTEA5DqbUB1RdDZXBzmhPitkQfAYUfyumoVguLMnBC1ZW+jCIruUxTYgwXUOakDZKz8AuBaKTJQ+ncoMvIAbx6C4rvMkVV9ByjiEEaOCj4tusw1kDrSlgUgsB4MzBO5B4CAqLhAxBoyc/hQWlIC98jnOZQi1n0BMFLDAwKcR+PhxlwXj1CC6EoCoTkdxdFj84Hc3Y4iTRVyVipC46h4FDBlgkLwaNPiHQAsc1tAEVmVpKhD5GFCFCLcB2dVYmOxtEN0VaZn8YCK+DZ5XbGsY7+jYM7aESFf0dMnQ4E4cClzhJPiMwehzLDS4lxDqL+lwL71xSMlzlkJIta6M8GZQubsAnCe2elKmDHNXBg+PVwEJHEMD2Cra7piO22Yl5g2cw2wk4gZElMdtxJzEEcF86zAp0CGkaM3XEFhmFMmT0okcyUMkqIiaWKgIWSMjlALv6nVQFIoxZE8XMZKY2U3+Bdn/sKTa+VHkpPakkJIHnkAYurGh9gzFVo8UuydDcy0enFRpCiqE1CMuPZC208JKFM8T6V7dsz9PVRRQVR+WxCFidDzNM8FEUIEq/83iCrPITck3Y/ZY0fCivIvAMazeUCdp3QLKH1UBnIKj0nmWoMAECvVDWCj0wHQc/LaobR9tVNdecFsjwipHwufYSEkgCB8AT3sRHcCsCS1uEBEWO10laZD3Txq5lkkmLFMHoDcaFN0SEpkAHFCXQACehcgZMOdgVI3D5jNxAASQ1oAYiaeR0Js9YjbLQV+uAEECGKCcBKheQBEaq3IQM7ALBDqrI4GBZ4E3Kz/87iyFTtUJqA6JEFbyQoAJHXRA/RI03TgC6hNdAUrsU4cza7GfWCc8beTR6BELaVdQNaynBAi1UWAWKtUh0BpBaxQ2e1xnzjM9lD3aSeWvawU7QIybQ5IJYoPpMIaiWAurhJ1xrqkA8QR6QCZmMc8vV0n0jlQLz4AbTvtRGUdFqd5XsjtZazz6Jn2jO4BsFetMAfqu6c+vZ8zPazkTn8vYOxjZEPBmweNKvm+Fab5SI3p5whmaiM1R/BT67U4imJ+X2BMN4PUeDESIrlt/vXUKlL45FGwkiJY38cNF9DzpqiIA3kSHSlUssnJBfTmUyDEC7c8R5snkRldJjAWn4KNpkjktJkVWaQ2zCJNvkw50gKXgm28SP0MhWEOgYrOzCVzNUFJgfRXULDn0XuesYXZE072YUoIkU6E/XulMFD0nWooDZPOU2qk+D2UIIjfp7G9cAyl+YA0BtJvK/MhPT2yO9HVrBhtoETRVc87l8ngztYOZrpuJ5JbEq6+77hdm0fsdfDDj1vAokKp8qZQagbAz3Uek4cCIQrjHZFUQEr/EBH0l3gVETqHRynftsM97naoQGcgrPL7zM1yOKsLS/g4LUE74onTHFt/XCKAVyWwYwCbkD0ASrI6GqUJBmGfGiJvQj53wtexaxji91d7i3fOFxAQ8y4AfqDdQ2cLnPDcdnVyAzFrlUzIVVJ5D9YVeWHzKHjBDQsEiT17QOvR5T3jyu7xRjhRfQCpUvS6Kq2WeE/4bkBN60B2IAIRK3KyNMvjZFE3CgFoHjzxPOuv3nDzNNCcNYexKkcDEUV3EnAAIKkOBba+cH0YHYe2ATjecU/ALUUprWweCGEEBUTAZV7n9R6PgidoePB+WIDXCpa6JG8/YIlH8iggn4JCbC0GJRZRACkhm/d4pJLEBIWiGQCqvQFsO+/ggAsvC0CyXT0tN8AxC9yTNiPMWpsd/Xthp1ioLoOX2wGOGE6gxrBN5FHMR/vI3WryZZzHupBmjLA12sqZ1DfeLKi1PWeoHlK9iFCC3uNNMMGVCcJ9iNwnhFbWlTFICJXqATGmTrdlvy6bxei1k8jTpgNaByLYpxDjbb9v7VtAAMTK9WcWKQscIbRd8SkNflGKY6oQekQSXR3VAFCzYz/RjhSnOOdio4hbEGx2bPu2U2ifR0ds2XpZp5SzICuA2OMBqrDDiGAX4zqPH/m4c0U6GLPwY3w6qtpFYUZ3M/Pdutau1CbOeKFr/b5CovqilxThf6re7ojzZdgnai7A0nodVRNtHpC37p1hE/aW6+pUEV3SAtI+ZjEU1JOkODwIxpur5ApARDk8Hp3EjTNxFm+B2OoG2iheFWneER3NAyqFJuoWlFYoTBXOMTulAyqytCXbm5BMyaIUURnh4pIhKBAQcjhwpUG3syCAlygnINc87bNY7ZQTKM9qlFNOoBwqV9WP7Jvmoyq6H6FamrMoiVrgFFEp1SIA1mtISaXoGAKKbR9d6AKAoTjxB3ggSC/3+ANAFoHlCT8sgQA0C64xwKyJUr8CogpSj50jROOi5MuW0VDwMbxhLgA9XDa+ZnElQOSV5s3G8im8EjVx1q5kXLYr9Zpav5wRITtl9eKcZO7pSCJZgaoXC2hHSPN27z1Pkjpv2o/10K5ZWCiVusHYIWoQAO3r6gQRbiJW6i1sS1vEmXKIdSXxJl64BRIqxGlvvL9Rn8G1VERN88ZI/f+vb8CM9nWZFK4jn4SP5hODZdUMUGbxcQGI5jIDrSzzVbFjgmD+z2mHV/E7Qri5zlosgEBm9TRLPgwkhCnNBfo6zJz3/bvXjtD4ePVpUWmzxs+rANK6vjsXnXgSEZHS6qnNmvwBYFEPgHVJDLSx2xE1e+1wNef5keqI4H0Ls95X92eacpRbtWN20WEwOgw7t0fyfj8vMK+TPixKFAA0ukdnqLN45FP03leQq+cwXwostUDaht09ANK7pWAWmIdYXlcSUMwbhrgBeBJiAJHjYt5hOKs3XPzEn0ef9WoGSt485lXx6k2wJQouUJBCJAbmzccF5MGCI4cLZ8qiJicoFUyuCAREtKd1yaxNF+auYBhV/B5STk47HPQ6666zsJ4ZmNfaHAZizrqdl1V3JARKcRaiCWt6DnjmddyxjpTrPItnZZkWeAORT51ovgT6DgXrneIxNz5voBNPo6d5AuwCbFV0VxJQzI+5KYN9pGoGZb+bM8QtsbDKFwj88AAY1ji8nhqsQfVG1OYLMmePep+Bgmd7e23bg2jjC2fbXcOP+YXk+RzrpJTz7G9znrCO9XkGhJNfLKm+43ygwK2vh5DiqtYEwthZoLjcZQC+znABdO7t8QWyC0iDbnfR5hk8IqZzSU1SZETC58rZuUtxAWHsK4AamBLhdoU1yrcbcrrGIK5LXpUB2jmaKRBBGkJ2KcK5U6drTbCMu/Snq1MI6LtHIKo3ugY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"", "footer": "", "has_bridge": true, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Annual Revenue ($)", "type": "histogram", "value": "revenue", "variable_name": "vabf72c"}]}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -7625,7 +7651,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -7666,8 +7692,8 @@ { "data": { "text/plain": [ - "{'id': '607ba79e-5728-4f25-b44a-f01f64ab7bb0',\n", - " 'url': 'https://team.carto.com/u/arroyo-carto/kuviz/607ba79e-5728-4f25-b44a-f01f64ab7bb0',\n", + "{'id': '94e546ac-9aab-4857-a9b2-46874299c2ed',\n", + " 'url': 'https://cartoframes-org.carto.com/u/cartoframes/kuviz/94e546ac-9aab-4857-a9b2-46874299c2ed',\n", " 'name': 'starbucks_analysis',\n", " 'privacy': 'public'}" ] @@ -7723,9 +7749,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Conclusion\n", + "### Congratulations!\n", "\n", - "Congratulations! You have finished this guide and have a sense about how CARTOframes can speed up your workflow. To continue learning, you can check out a specific [Guide](/developers/cartoframes/guides), the [Reference](/developers/cartoframes/reference) to know everything about a class or a method or check the [Examples](/developers/cartoframes/examples) to see CARTOframes in action." + "You have finished this guide and have a sense about how CARTOframes can speed up your workflow. To continue learning, you can check out a specific [Guide](/developers/cartoframes/guides), the [Reference](/developers/cartoframes/reference) to know everything about a class or a method or check the [Examples](/developers/cartoframes/examples) to see CARTOframes in action." ] } ], diff --git a/docs/guides/04-Data-Management.ipynb b/docs/guides/04-Data-Management.ipynb new file mode 100644 index 000000000..23bfb0acf --- /dev/null +++ b/docs/guides/04-Data-Management.ipynb @@ -0,0 +1,1009 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Management\n", + "\n", + "This guide shows how to load different data files into DataFrames and how to interact with the CARTO platform to upload DataFrames into tables and download tables or SQL queries into DataFrames.\n", + "\n", + "CARTOframes is built on top of [Pandas](https://pandas.pydata.org/) and [GeoPandas](https://geopandas.org/). Therefore, it's compatible with all the data formats supported in those projects like GeoJSON, Shapefile, CSV, etc.\n", + "\n", + "There are two main concepts we should know before continuing with the guide:\n", + "- A [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) is a two-dimensional data structure for generic data. It can be thought of as a table with rows and columns. It's composed of [Series](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html) objects, which are one-dimensional data structures.\n", + "- A [GeoDataFrame](https://geopandas.org/data_structures.html#geodataframe) is a DataFrame with an extra geometry column. This geometry column is a [GeoSeries](https://geopandas.org/data_structures.html#geoseries) object.\n", + "\n", + "Every time we manage geographic data, a GeoDataFrame should be used. In case a DataFrame with an encoded geometry column is used (WKB, WKT, etc.) every method contains a `geom_col` param to provide the name of that column and decode the geometry internally.\n", + "\n", + "You can check also the [Data Management examples](/developers/cartoframes/examples/#example-data-management)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read a GeoJSON file\n", + "\n", + "It shows how to load geographic data from a GeoJSON file using [GeoPandas](https://geopandas.org/). To read pure JSON files check this [example](/developers/cartoframes/examples/#example-read-a-json-file)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                          cartodb_idfield_1nameaddressrevenuegeometry
                                                                          010Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381321040.772POINT (-73.95901 40.67109)
                                                                          121607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351268080.418POINT (-73.96122 40.57796)
                                                                          23265th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041248133.699POINT (-73.98976 40.61912)
                                                                          343Bay Ridge Pkwy & 3rd Ave7419 3rd Avenue,Brooklyn, NY 112091185702.676POINT (-74.02744 40.63152)
                                                                          454Caesar's Bay Shopping Center8973 Bay Parkway,Brooklyn, NY 112141148427.411POINT (-74.00098 40.59321)
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                                                                          " + ], + "text/plain": [ + " cartodb_id field_1 name \\\n", + "0 1 0 Franklin Ave & Eastern Pkwy \n", + "1 2 1 607 Brighton Beach Ave \n", + "2 3 2 65th St & 18th Ave \n", + "3 4 3 Bay Ridge Pkwy & 3rd Ave \n", + "4 5 4 Caesar's Bay Shopping Center \n", + "\n", + " address revenue \\\n", + "0 341 Eastern Pkwy,Brooklyn, NY 11238 1321040.772 \n", + "1 607 Brighton Beach Avenue,Brooklyn, NY 11235 1268080.418 \n", + "2 6423 18th Avenue,Brooklyn, NY 11204 1248133.699 \n", + "3 7419 3rd Avenue,Brooklyn, NY 11209 1185702.676 \n", + "4 8973 Bay Parkway,Brooklyn, NY 11214 1148427.411 \n", + "\n", + " geometry \n", + "0 POINT (-73.95901 40.67109) \n", + "1 POINT (-73.96122 40.57796) \n", + "2 POINT (-73.98976 40.61912) \n", + "3 POINT (-74.02744 40.63152) \n", + "4 POINT (-74.00098 40.59321) " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from geopandas import read_file\n", + "\n", + "gdf = read_file('https://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn_geocoded.geojson')\n", + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read a Shapefile\n", + "\n", + "Shapefile is a complex format, compared to CSV or GeoJSON. To learn more about this format check [GeoPandas documentation](https://geopandas.org/io.html#reading-spatial-data)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                          cartodb_idfield_1nameaddressrevenuegeometry
                                                                          010Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381321040.772POINT (-73.95901 40.67109)
                                                                          121607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351268080.418POINT (-73.96122 40.57796)
                                                                          23265th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041248133.699POINT (-73.98976 40.61912)
                                                                          343Bay Ridge Pkwy & 3rd Ave7419 3rd Avenue,Brooklyn, NY 112091185702.676POINT (-74.02744 40.63152)
                                                                          454Caesar's Bay Shopping Center8973 Bay Parkway,Brooklyn, NY 112141148427.411POINT (-74.00098 40.59321)
                                                                          \n", + "
                                                                          " + ], + "text/plain": [ + " cartodb_id field_1 name \\\n", + "0 1 0 Franklin Ave & Eastern Pkwy \n", + "1 2 1 607 Brighton Beach Ave \n", + "2 3 2 65th St & 18th Ave \n", + "3 4 3 Bay Ridge Pkwy & 3rd Ave \n", + "4 5 4 Caesar's Bay Shopping Center \n", + "\n", + " address revenue \\\n", + "0 341 Eastern Pkwy,Brooklyn, NY 11238 1321040.772 \n", + "1 607 Brighton Beach Avenue,Brooklyn, NY 11235 1268080.418 \n", + "2 6423 18th Avenue,Brooklyn, NY 11204 1248133.699 \n", + "3 7419 3rd Avenue,Brooklyn, NY 11209 1185702.676 \n", + "4 8973 Bay Parkway,Brooklyn, NY 11214 1148427.411 \n", + "\n", + " geometry \n", + "0 POINT (-73.95901 40.67109) \n", + "1 POINT (-73.96122 40.57796) \n", + "2 POINT (-73.98976 40.61912) \n", + "3 POINT (-74.02744 40.63152) \n", + "4 POINT (-74.00098 40.59321) " + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from geopandas import read_file\n", + "\n", + "gdf = read_file('https://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn_geocoded.zip')\n", + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read a CSV file" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Compute geometry from longitude and latitude**" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                          incident_datetimeincident_dateincident_timeincident_yearincident_day_of_weekreport_datetimerow_idincident_idincident_numbercad_number...:@computed_region_qgnn_b9vv:@computed_region_26cr_cadq:@computed_region_ajp5_b2md:@computed_region_nqbw_i6c3:@computed_region_2dwj_jsy4:@computed_region_h4ep_8xdi:@computed_region_y6ts_4iup:@computed_region_jg9y_a9du:@computed_region_6pnf_4xz7geometry
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                                                                          " + ], + "text/plain": [ + " incident_datetime incident_date incident_time \\\n", + "0 2020-08-16T03:13:00.000 2020-08-16T00:00:00.000 03:13 \n", + "1 2020-08-16T03:38:00.000 2020-08-16T00:00:00.000 03:38 \n", + "2 2020-08-16T13:40:00.000 2020-08-16T00:00:00.000 13:40 \n", + "3 2020-08-16T16:18:00.000 2020-08-16T00:00:00.000 16:18 \n", + "4 2020-08-12T22:00:00.000 2020-08-12T00:00:00.000 22:00 \n", + "\n", + " incident_year incident_day_of_week report_datetime row_id \\\n", + "0 2020 Sunday 2020-08-16T03:14:00.000 95319604083 \n", + "1 2020 Sunday 2020-08-16T04:56:00.000 95326228100 \n", + "2 2020 Sunday 2020-08-16T13:56:00.000 95336264020 \n", + "3 2020 Sunday 2020-08-16T16:18:00.000 95335012010 \n", + "4 2020 Wednesday 2020-08-15T08:30:00.000 95300674000 \n", + "\n", + " incident_id incident_number cad_number ... :@computed_region_qgnn_b9vv \\\n", + "0 953196 200491669 202290313.0 ... 2.0 \n", + "1 953262 200491738 202290404.0 ... 3.0 \n", + "2 953362 200492463 202291631.0 ... 1.0 \n", + "3 953350 200492792 202292091.0 ... 10.0 \n", + "4 953006 200489880 202280827.0 ... 4.0 \n", + "\n", + " :@computed_region_26cr_cadq :@computed_region_ajp5_b2md \\\n", + "0 9 26.0 \n", + "1 2 20.0 \n", + "2 10 8.0 \n", + "3 7 35.0 \n", + "4 11 39.0 \n", + "\n", + " :@computed_region_nqbw_i6c3 :@computed_region_2dwj_jsy4 \\\n", + "0 NaN NaN \n", + "1 3.0 NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " :@computed_region_h4ep_8xdi :@computed_region_y6ts_4iup \\\n", + "0 NaN NaN \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "\n", + " :@computed_region_jg9y_a9du :@computed_region_6pnf_4xz7 \\\n", + "0 NaN 2.0 \n", + "1 NaN 2.0 \n", + "2 NaN 1.0 \n", + "3 NaN 1.0 \n", + "4 NaN 2.0 \n", + "\n", + " geometry \n", + "0 POINT (-122.39773 37.75483) \n", + "1 POINT (-122.42204 37.76654) \n", + "2 POINT (-122.40371 37.78404) \n", + "3 POINT (-122.50742 37.75100) \n", + "4 POINT (-122.43214 37.78050) \n", + "\n", + "[5 rows x 37 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pandas import read_csv\n", + "from geopandas import GeoDataFrame, points_from_xy\n", + "\n", + "df = read_csv('https://libs.cartocdn.com/cartoframes/samples/sf_incidents.csv')\n", + "\n", + "gdf = GeoDataFrame(df, geometry=points_from_xy(df['longitude'], df['latitude']))\n", + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Compute geometry from WKT/WKB**" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                          40101000020E6100000CEFC6A0E108052C080D4264EEE4B...54Caesar's Bay Shopping Center8973 Bay Parkway,Brooklyn, NY 112141148427.411EPOINT (-74.00098 40.59321)
                                                                          \n", + "
                                                                          " + ], + "text/plain": [ + " the_geom cartodb_id field_1 \\\n", + "0 0101000020E61000005EA27A6B607D52C01956F146E655... 1 0 \n", + "1 0101000020E6100000B610E4A0847D52C0B532E197FA49... 2 1 \n", + "2 0101000020E6100000E5B8533A587F52C05726FC523F4F... 3 2 \n", + "3 0101000020E61000008BA6B393C18152C08D62B9A5D550... 4 3 \n", + "4 0101000020E6100000CEFC6A0E108052C080D4264EEE4B... 5 4 \n", + "\n", + " name address \\\n", + "0 Franklin Ave & Eastern Pkwy 341 Eastern Pkwy,Brooklyn, NY 11238 \n", + "1 607 Brighton Beach Ave 607 Brighton Beach Avenue,Brooklyn, NY 11235 \n", + "2 65th St & 18th Ave 6423 18th Avenue,Brooklyn, NY 11204 \n", + "3 Bay Ridge Pkwy & 3rd Ave 7419 3rd Avenue,Brooklyn, NY 11209 \n", + "4 Caesar's Bay Shopping Center 8973 Bay Parkway,Brooklyn, NY 11214 \n", + "\n", + " revenue id_store geometry \n", + "0 1321040.772 A POINT (-73.95901 40.67109) \n", + "1 1268080.418 B POINT (-73.96122 40.57796) \n", + "2 1248133.699 C POINT (-73.98976 40.61912) \n", + "3 1185702.676 D POINT (-74.02744 40.63152) \n", + "4 1148427.411 E POINT (-74.00098 40.59321) " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pandas import read_csv\n", + "from geopandas import GeoDataFrame\n", + "from cartoframes.utils import decode_geometry\n", + "\n", + "df = read_csv('https://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn_geocoded.csv')\n", + "\n", + "gdf = GeoDataFrame(df, geometry=decode_geometry(df['the_geom']))\n", + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read data from a CARTO Table\n", + "\n", + "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to perform this action._" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('cartoframes')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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                                                                          cartodb_idthe_geomfield_1nameaddressrevenue
                                                                          01POINT (-73.95901 40.67109)0Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381321040.772
                                                                          12POINT (-73.96122 40.57796)1607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351268080.418
                                                                          23POINT (-73.98976 40.61912)265th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041248133.699
                                                                          34POINT (-74.02744 40.63152)3Bay Ridge Pkwy & 3rd Ave7419 3rd Avenue,Brooklyn, NY 112091185702.676
                                                                          45POINT (-74.00098 40.59321)4Caesar's Bay Shopping Center8973 Bay Parkway,Brooklyn, NY 112141148427.411
                                                                          \n", + "
                                                                          " + ], + "text/plain": [ + " cartodb_id the_geom field_1 \\\n", + "0 1 POINT (-73.95901 40.67109) 0 \n", + "1 2 POINT (-73.96122 40.57796) 1 \n", + "2 3 POINT (-73.98976 40.61912) 2 \n", + "3 4 POINT (-74.02744 40.63152) 3 \n", + "4 5 POINT (-74.00098 40.59321) 4 \n", + "\n", + " name address \\\n", + "0 Franklin Ave & Eastern Pkwy 341 Eastern Pkwy,Brooklyn, NY 11238 \n", + "1 607 Brighton Beach Ave 607 Brighton Beach Avenue,Brooklyn, NY 11235 \n", + "2 65th St & 18th Ave 6423 18th Avenue,Brooklyn, NY 11204 \n", + "3 Bay Ridge Pkwy & 3rd Ave 7419 3rd Avenue,Brooklyn, NY 11209 \n", + "4 Caesar's Bay Shopping Center 8973 Bay Parkway,Brooklyn, NY 11214 \n", + "\n", + " revenue \n", + "0 1321040.772 \n", + "1 1268080.418 \n", + "2 1248133.699 \n", + "3 1185702.676 \n", + "4 1148427.411 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes import read_carto\n", + "\n", + "gdf = read_carto('starbucks_brooklyn')\n", + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read data from a CARTO SQL Query\n", + "\n", + "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to perform this action._" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('cartoframes')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
                                                                          \n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
                                                                          cartodb_idthe_geomfield_1nameaddressrevenue
                                                                          01POINT (-73.95901 40.67109)0Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381321040.772
                                                                          12POINT (-73.96122 40.57796)1607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351268080.418
                                                                          23POINT (-73.98976 40.61912)265th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041248133.699
                                                                          \n", + "
                                                                          " + ], + "text/plain": [ + " cartodb_id the_geom field_1 \\\n", + "0 1 POINT (-73.95901 40.67109) 0 \n", + "1 2 POINT (-73.96122 40.57796) 1 \n", + "2 3 POINT (-73.98976 40.61912) 2 \n", + "\n", + " name address \\\n", + "0 Franklin Ave & Eastern Pkwy 341 Eastern Pkwy,Brooklyn, NY 11238 \n", + "1 607 Brighton Beach Ave 607 Brighton Beach Avenue,Brooklyn, NY 11235 \n", + "2 65th St & 18th Ave 6423 18th Avenue,Brooklyn, NY 11204 \n", + "\n", + " revenue \n", + "0 1321040.772 \n", + "1 1268080.418 \n", + "2 1248133.699 " + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes import read_carto\n", + "\n", + "gdf = read_carto(\"SELECT * FROM starbucks_brooklyn WHERE revenue > 1200000\")\n", + "gdf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Upload data to CARTO\n", + "\n", + "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to perform this action._" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Success! Data uploaded to table \"starbucks_brooklyn_filtered\" correctly\n" + ] + }, + { + "data": { + "text/plain": [ + "'starbucks_brooklyn_filtered'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes import to_carto\n", + "\n", + "to_carto(gdf, 'starbucks_brooklyn_filtered', if_exists='replace')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/guides/05-Visualization.ipynb b/docs/guides/05-Data-Visualization.ipynb similarity index 64% rename from guides/05-Visualization.ipynb rename to docs/guides/05-Data-Visualization.ipynb index 52e9fbd3a..8cc9340a8 100644 --- a/guides/05-Visualization.ipynb +++ b/docs/guides/05-Data-Visualization.ipynb @@ -4,49 +4,33 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Visualization\n", + "## Data Visualization\n", "\n", - "### Introduction\n", + "As a data scientist, you likely perform data exploration on nearly every project. Exploratory data analysis can entail many things, from finding relevant data and cleaning it to running analysis and building models. The ability to visually analyze and interact with data is key during the exploratory process and the final presentation of insights.\n", "\n", - "As a data scientist, you likely work through a data exploration processes on nearly every project. Exploratory data analysis can entail many things from finding relevant data and cleaning it to running analysis and building models. The ability to visually analyze and interact with data is key during the exploratory process and the final presentation of insights.\n", + "With that in mind, this guide introduces the basic building blocks for creating web-based, dynamic, and interactive map visualizations inside a Jupyter Notebook with CARTOframes. A Map can display one or several Layers. Each Layer can render local data or remote data from a CARTO account with a specific style and UI elements like legends, widgets or popups.\n", "\n", - "With that in mind, this guide introduces the basic building blocks for creating web-based, dynamic, and interactive map visualizations inside of a Jupyter Notebook with CARTOframes.\n", - "\n", - "In this guide you are introduced to the Map and Layer classes, how to explore data with Widgets and Popups, how to use visualization styles to quickly symbolize thematic attributes, and options for creating maps to share your findings.\n", - "\n", - "### Data\n", - "\n", - "This guide uses two datasets: a point dataset of simulated Starbucks locations in Brooklyn, New York and 15 minute walk time polygons (isochrones) around each store augmented with demographic variables from CARTO's [Data Observatory](developers/cartoframes/guides/Data-discovery/). To follow along, you can get the [point dataset here](http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_geocoded.csv) and the [polygon dataset here](http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_iso_enriched.csv).\n", - "\n", - "As a first step, load both datasets as [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/getting_started/dsintro.html#dataframe) into the notebook:" + "In this guide you will be introduced to the Map and Layer classes. You will learn how to explore data with Widgets and Popups, how to use visualization styles to quickly symbolize thematic attributes and how to share your findings by publishing your visualizations. You can check also the [Data Visualization examples](/developers/cartoframes/examples/#example-data-visualization)." ] }, { - "cell_type": "code", - "execution_count": 1, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "from pandas import read_csv\n", - "from geopandas import GeoDataFrame\n", - "from cartoframes.utils import decode_geometry\n", - "\n", - "# store point locations\n", - "stores_df = read_csv('http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_geocoded.csv')\n", - "stores_gdf = GeoDataFrame(stores_df, geometry=decode_geometry(stores_df['the_geom']))\n", + "### Visualize data from a file\n", "\n", - "# 15 minute walk time polygons\n", - "iso_df = read_csv('http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_iso_enriched.csv')\n", - "iso_gdf = GeoDataFrame(iso_df, geometry=decode_geometry(iso_df['the_geom']))" + "CARTOframes allows creating maps directly with data from GeoJSON or CSV files (with geometric data)." ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 1, "metadata": {}, + "outputs": [], "source": [ - "### Add Layers to a Map\n", + "from geopandas import read_file\n", "\n", - "Next, import the Map and Layer classes from the [Viz namespace](/developers/cartoframes/reference/#heading-Viz) to visualize the two datasets. The resulting map draws each dataset with default symbology on top of CARTO's Positron basemap with the zoom and center set to the extent of both datasets:" + "points_gdf = read_file('http://libs.cartocdn.com/cartoframes/samples/starbucks_brooklyn_geocoded.geojson')" ] }, { @@ -762,14 +746,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -843,7 +827,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -858,12 +842,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                                                          ${popupHTML}
                                                                          `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                                                          ${popupHTML}
                                                                          `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -1222,7 +1210,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -1263,12 +1251,12 @@ " .addEventListener('ready', () => {\n", " const basecolor = '';\n", " const basemap = 'Positron';\n", - " const bounds = [[-74.04169, 40.57381], [-73.85993, 40.70181]];\n", + " const bounds = [[-74.03313, 40.57796], [-73.87015, 40.6915]];\n", " const camera = null;\n", " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": "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", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": 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"title": null, "type": "GeoJSON", "viz": "color: hex(\\"#826DBA\\")\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": []}, {"credentials": null, "data": "H4sIAJdZBF8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAJdZBF8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\n", "widgets": []}];\n", + " const layers = [{"credentials": null, "data": "H4sIAKMVyl8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAKMVyl8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -1292,7 +1280,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -1301,276 +1289,34 @@ } ], "source": [ - "from cartoframes.viz import Map, Layer\n", + "from cartoframes.viz import Layer\n", "\n", - "Map([\n", - " Layer(iso_gdf),\n", - " Layer(stores_gdf)\n", - "])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "> To learn more about basemap options, visit the Map Configuration [Examples](/developers/cartoframes/examples/) section of the CARTOframes Developer Center" + "Layer(points_gdf)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Explore Attributes\n", - "\n", - "Before going further, take a look at the attributes in each dataset to get a sense of the information available to visualize and interact with.\n", - "\n", - "First, explore the store location attributes. In this dataset you will use the fields:\n", - "* `id_store` that is a unique identifier for each of the locations\n", - "* `revenue` which provides the information about how much a particular store earned in the year 2018" + "If the source, instead of a GeoJSON, is a CSV file with a column containing the geometry encoded in WKB/WKT, you can pass the name of the column as `geom_col` directly to the Layer and the geometry is automatically decoded." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                          the_geomcartodb_idfield_1nameaddressrevenueid_storegeometry
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                                                                          \n", "
                                                                          \n", "
                                                                          \n", @@ -2400,14 +2026,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -2481,7 +2107,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -2496,12 +2122,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                                                          ${popupHTML}
                                                                          `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                                                          ${popupHTML}
                                                                          `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -2860,7 +2490,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -2906,7 +2536,7 @@ " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": 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"H4sIAKQVyl8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -2930,50 +2560,20 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from cartoframes.viz import formula_widget, category_widget\n", + "from cartoframes.viz import Map\n", "\n", "Map([\n", - " Layer(\n", - " iso_gdf,\n", - " widgets=[\n", - " formula_widget(\n", - " 'popcy',\n", - " 'sum',\n", - " title='Total Population Served'\n", - " ),\n", - " formula_widget(\n", - " 'inccymedhh',\n", - " 'sum',\n", - " title='Median Income ($)'\n", - " ),\n", - " formula_widget(\n", - " 'lbfcyempl',\n", - " 'sum',\n", - " title='Employed Population',\n", - " ),\n", - " formula_widget(\n", - " 'educybach',\n", - " 'sum',\n", - " title='Number of People with Bachelor Degree',\n", - " ),\n", - " category_widget(\n", - " 'id_store',\n", - " title='Store ID'\n", - " )\n", - " ]\n", - " ),\n", - " Layer(\n", - " stores_gdf\n", - " )\n", + " Layer(polygons_df, geom_col='the_geom'),\n", + " Layer(points_gdf)\n", "])" ] }, @@ -2981,16 +2581,22 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "At this point, take a few minutes to explore the map to see how the Widget values update. For example, select a Store ID from the Category Widget to summarize the demographics for a particular store. Alternatively, zoom and pan the map to get summary statistics for the features in the current map view.\n" + "### Visualize data from CARTO\n", + "\n", + "For big datasets visualization (>30MB), we recommend rendering layers directly from a CARTO account. This, in addition to improving the performance, allows you to apply SQL queries to the tables stored in CARTO.\n", + "\n", + "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to perform this action._" ] }, { - "cell_type": "markdown", + "cell_type": "code", + "execution_count": 5, "metadata": {}, + "outputs": [], "source": [ - "#### Add Popups\n", + "from cartoframes.auth import set_default_credentials\n", "\n", - "In order to aid this map-based exploration, import the [Popup](developers/cartoframes/examples/#example-popup-on-hover) class and use the hover option on the `iso_gdf` Layer to be able to quickly hover over stores and get their ID:" + "set_default_credentials('cartoframes')" ] }, { @@ -3191,116 +2797,3866 @@ " Static map image\n", " \n", " \n", - " \n", - "\n", - "
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                                                                                ${popupHTML}
                                                                                `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                                                                ${popupHTML}
                                                                                `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -4286,7 +7675,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -4327,12 +7716,12 @@ " .addEventListener('ready', () => {\n", " const basecolor = '';\n", " const basemap = 'Positron';\n", - " const bounds = [[-74.04169, 40.57381], [-73.85993, 40.70181]];\n", + " const bounds = [[-6.365712, 49.883409], [1.771169, 55.805486]];\n", " const camera = null;\n", - " const has_legends = 'False' === 'true';\n", + " const has_legends = 'true' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": 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"encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "vd11807", "title": "Store ID"}, "event": "hover"}], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": "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", "title": null, "type": "GeoJSON", "viz": "@v2a0130: viewportSum(prop(\\u0027inccymedhh\\u0027))\\n@v339732: viewportSum(prop(\\u0027lbfcyempl\\u0027))\\n@v77c3db: viewportSum(prop(\\u0027educybach\\u0027))\\n@vb11b3e: viewportSum(prop(\\u0027popcy\\u0027))\\n@vd11807: prop(\\u0027id_store\\u0027)\\ncolor: hex(\\"#826DBA\\")\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": [{"description": "", "footer": "", "has_bridge": false, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Total Population Served", "type": "formula", "value": "viewportSum(prop(\\u0027popcy\\u0027))", "variable_name": "vb11b3e"}, {"description": "", "footer": "", "has_bridge": false, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Median Income ($)", "type": "formula", "value": "viewportSum(prop(\\u0027inccymedhh\\u0027))", "variable_name": "v2a0130"}, {"description": "", "footer": "", "has_bridge": false, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Employed Population", "type": "formula", "value": "viewportSum(prop(\\u0027lbfcyempl\\u0027))", "variable_name": "v339732"}, {"description": "", "footer": "", "has_bridge": false, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Number of People with Bachelor Degree", "type": "formula", "value": "viewportSum(prop(\\u0027educybach\\u0027))", "variable_name": "v77c3db"}, {"description": "", "footer": "", "has_bridge": true, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Store ID", "type": "category", "value": "id_store", "variable_name": "vd11807"}]}, {"credentials": null, "data": "H4sIAJhZBF8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAJhZBF8C/7XSPW8DIQwG4P/CfD1h82GcNVLn7lWGKKEVUnqcKB2i0/33clwqkZ2OWIZHNu8i8n324iBe/Tn/JH+Mt5u/5BAnMYiPvfYtDu+LCNfSJUv1+UIpzCnOPuWwNS7rID59/PI53cvpr/kthimX1kuM6Rqmc66PvpAa2bCEQcvREkg+revwoKA/ZQFxowwR24bC/pRjsnUqYMCGUp0pPUokrSulwLSU7k9Jya4ukBVCQ5n+C2S0eyyc020sbHfKkQRTKVM22FDUf4FKgXrEAqmh3D8kUJsadstPQ3F/STu9f5VCuQ11Wn8BOR2ZplYEAAA=", "title": null, "type": "GeoJSON", "viz": "color: hex(\\"#EE4D5A\\")\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(zoom(),0,18),[2,10])\\n", "widgets": []}];\n", + " const layers = [{"credentials": {"api_key": "default_public", "base_url": "https://cartoframes.carto.com", "username": "cartoframes"}, "data": "SELECT * FROM \\"cartoframes\\".\\"eng_wales_pop\\"", "encode_data": true, "has_legend_list": true, "interactivity": [], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "color", "title": "pop_sq_km", "type": "color-bins", "variable": null}], "map_index": 0, "options": {}, "source": "SELECT * FROM \\"cartoframes\\".\\"eng_wales_pop\\"", "title": null, "type": "Query", "viz": "@v18f1e3: prop(\\u0027pop_sq_km\\u0027)\\ncolor: opacity(ramp(globalQuantiles(prop(\\u0027pop_sq_km\\u0027), 5), mint), 0.8)\\nfilter: 1\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": [{"description": "", "footer": "", "has_bridge": true, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "pop_sq_km", "type": "histogram", "value": "pop_sq_km", "variable_name": "v18f1e3"}]}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -4356,77 +7745,44 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from cartoframes.viz import popup_element\n", + "from cartoframes.viz import Layer, color_bins_style\n", "\n", - "Map([\n", - " Layer(\n", - " iso_gdf,\n", - " widgets=[\n", - " formula_widget(\n", - " 'popcy',\n", - " 'sum',\n", - " title='Total Population Served'\n", - " ),\n", - " formula_widget(\n", - " 'inccymedhh',\n", - " 'sum',\n", - " title='Median Income ($)'\n", - " ),\n", - " formula_widget(\n", - " 'lbfcyempl',\n", - " 'sum',\n", - " title='Employed Population',\n", - " ),\n", - " formula_widget(\n", - " 'educybach',\n", - " 'sum',\n", - " title='Number of People with Bachelor Degree',\n", - " ),\n", - " category_widget(\n", - " 'id_store',\n", - " title='Store ID'\n", - " )\n", - " ],\n", - " popup_hover=[\n", - " popup_element('id_store', 'Store ID')\n", - " ]\n", - " ),\n", - " Layer(\n", - " stores_gdf\n", - " )\n", - "])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, as you explore the map and summarize demographics, it is much easier to relate the summarized values to a unique store ID." + "Layer(\n", + " 'eng_wales_pop',\n", + " color_bins_style('pop_sq_km', palette='mint', bins=5, opacity=0.8),\n", + " default_widget=True,\n", + " default_popup_hover=False\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Symbolize Store Points\n", + "### Legends\n", + "\n", + "Legends are aligned with style helpers. Each style helper contains what we call the \"default legend\" based on the style:\n", + "- Basic\n", + "- Color: bins, category, continuous\n", + "- Size: bins, category, continuous\n", "\n", - "At this point, you have some really useful information available on the map but only coming from the isochrone Layer. Sizing the store points by the attribute `revenue` will provide a way to visually locate which stores are performing better than others. A quick way to visualize numeric or categorical attributes during the data exploration process is to take advantage of [visualization styles](/developers/cartoframes/reference/#heading-Helpers).\n", + "Depending on the geometry of the visualization (points, lines, polygons), the legend will render the corresponding symbology. Legends are also customizable, you can edit the title or add a description or a footer.\n", "\n", - "To size the store points proportionate to their revenue, you'll use the [`size_continuous_style`](/developers/cartoframes/examples/#example-size-continuous-style):" + "When having multiple layers in a map, legends will stack according to the order of the layers." ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -4622,126 +7978,6 @@ " Static map image\n", " \n", " \n", - " \n", - "\n", - "\n", - " \n", "
                                                                                \n", "
                                                                                \n", "
                                                                                \n", @@ -4761,18 +7997,18 @@ " \n", " \n", " \n", + " heading="Annual Revenue"\n", + " description="Revenue in dollars ($)">\n", " \n", " \n", + " Source: Starbucks\n", + " \n", " \n", " \n", " \n", "
                                                                                \n", " \n", " \n", - " \n", - " \n", " \n", "\n", " \n", @@ -5288,14 +8524,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -5369,7 +8605,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -5384,12 +8620,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                                                                ${popupHTML}
                                                                                `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                                                                ${popupHTML}
                                                                                `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -5748,7 +8988,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -5789,12 +9029,12 @@ " .addEventListener('ready', () => {\n", " const basecolor = '';\n", " const basemap = 'Positron';\n", - " const bounds = [[-74.04169, 40.57381], [-73.85993, 40.70181]];\n", + " const bounds = [[-74.03313, 40.57796], [-73.87015, 40.6915]];\n", " const camera = null;\n", " const has_legends = 'true' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": 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"title": null, "type": "GeoJSON", "viz": "@v2a0130: viewportSum(prop(\\u0027inccymedhh\\u0027))\\n@v339732: viewportSum(prop(\\u0027lbfcyempl\\u0027))\\n@v77c3db: viewportSum(prop(\\u0027educybach\\u0027))\\n@vb11b3e: viewportSum(prop(\\u0027popcy\\u0027))\\n@vd11807: prop(\\u0027id_store\\u0027)\\ncolor: hex(\\"#826DBA\\")\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": [{"description": "", "footer": "", "has_bridge": false, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Total Population Served", "type": "formula", "value": "viewportSum(prop(\\u0027popcy\\u0027))", "variable_name": "vb11b3e"}, {"description": "", "footer": "", "has_bridge": false, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Median Income ($)", "type": "formula", "value": "viewportSum(prop(\\u0027inccymedhh\\u0027))", "variable_name": "v2a0130"}, {"description": "", "footer": "", "has_bridge": false, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Employed Population", "type": "formula", "value": "viewportSum(prop(\\u0027lbfcyempl\\u0027))", "variable_name": "v339732"}, {"description": "", "footer": "", "has_bridge": false, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Number of People with Bachelor Degree", "type": "formula", "value": "viewportSum(prop(\\u0027educybach\\u0027))", "variable_name": "v77c3db"}, {"description": "", "footer": "", "has_bridge": true, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Store ID", "type": "category", "value": "id_store", "variable_name": "vd11807"}]}, {"credentials": null, "data": "H4sIAJhZBF8C/52TsW7DMAxE/0WzK5CURIpZC3TuXmQIErUwkNqB6xQIAv97ZceDM7IaCdLvfDze3Xi7FLdzb+UwXofy2p/P5Ti2feca9/mo/bjdx921p9oFtfo8UAuXob+UYWznxrsbym/prrUDAyFE8CI0Ne6r9N9lHG5zy/qF977txjp/7Pvh1HaHcSG9SPCaFLCpsywIup+mZuWjhU+cIYOPmM18RqKZn0SUN3wy8WPGEDyrmvlZhZf/R0Xa8IOFjzkJUPWQbfzogSTGhR8wbfnRxI85klT/QZeXzTIANC9r0JqljYxkkxGB2TMn8xqU+BHDnOM2hmzhQz2DHKqnCstDq4wsgGmRkeo6NjLEIEOphkF8CGRPQwgY1jSSbPjZwBdOosFHXsPwj5uIablJ1icT1CIiKiH7gNm8BK1hfmQhEMwm7Kc/O1OfSTwFAAA=", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "vabf72c", "title": "revenue"}, "event": "hover"}], "legends": [{"ascending": false, "description": "", "dynamic": true, "footer": "", "format": null, "prop": "width", "title": "revenue", "type": "size-continuous", "variable": "size_value"}], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAJhZBF8C/52TsW7DMAxE/0WzK5CURIpZC3TuXmQIErUwkNqB6xQIAv97ZceDM7IaCdLvfDze3Xi7FLdzb+UwXofy2p/P5Ti2feca9/mo/bjdx921p9oFtfo8UAuXob+UYWznxrsbym/prrUDAyFE8CI0Ne6r9N9lHG5zy/qF977txjp/7Pvh1HaHcSG9SPCaFLCpsywIup+mZuWjhU+cIYOPmM18RqKZn0SUN3wy8WPGEDyrmvlZhZf/R0Xa8IOFjzkJUPWQbfzogSTGhR8wbfnRxI85klT/QZeXzTIANC9r0JqljYxkkxGB2TMn8xqU+BHDnOM2hmzhQz2DHKqnCstDq4wsgGmRkeo6NjLEIEOphkF8CGRPQwgY1jSSbPjZwBdOosFHXsPwj5uIablJ1icT1CIiKiH7gNm8BK1hfmQhEMwm7Kc/O1OfSTwFAAA=", "title": null, "type": "GeoJSON", "viz": "@vabf72c: prop(\\u0027revenue\\u0027)\\n@size_value: ramp(linear(prop(\\u0027revenue\\u0027), globalMIN(prop(\\u0027revenue\\u0027)), globalMAX(prop(\\u0027revenue\\u0027))), [2, 40])\\ncolor: opacity(#FFB927, 0.8)\\nfilter: 1\\nstrokeColor: opacity(#222,ramp(linear(zoom(),0,18),[0,0.6]))\\nstrokeWidth: ramp(linear(zoom(),0,18),[0,1])\\nwidth: ramp(linear(sqrt(prop(\\u0027revenue\\u0027)), sqrt(globalMIN(prop(\\u0027revenue\\u0027))), sqrt(globalMAX(prop(\\u0027revenue\\u0027)))), [2, 40])\\n", "widgets": []}];\n", + " const layers = [{"credentials": null, "data": "H4sIAKkVyl8C/5XTwWrDMAwG4HfxOTOSbEvWroOddx89lNYbgS4pWTooJe8+J+3BPeoq5HxI+nNz8/Vc3Kt7L/v5MpW38XQqh7kfB9e5r3vt171+3lx/rF1Qq88PauE8jecyzf3aeHNT+SvDpXZgIIQIXoSWzn2X8afM03VteXzhY+yHub4/jON07If9vEkvErwmBezqWxYE3S1L9/DR4hNnyOAjZrPPSLT6SUS58cnkx4wheFY1+1mFt/lRkRo/WHzMSYDqDtnmRw8kMW5+wNT60eTHHEnq/tHsA2je9q81RI2fbH4EZs+czPtX4nv+co5t/tjiQ81/DnWZ5vtnAUybn+oBGl8MvlI9v/gQyH7/EDA88kfS+NngCyfR4COjPf4xbb8f69P0atGjErIPmO18ze39+oFgnX63/AOe9pnjJwUAAA==", "encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "vabf72c", "title": "revenue"}, "event": "hover"}], "legends": [{"ascending": false, "description": "Revenue in dollars ($)", "dynamic": true, "footer": "Source: \\u003ca href=\\"https://www.starbucks.com/\\"\\u003eStarbucks\\u003c/a\\u003e", "format": null, "prop": "width", "title": "Annual Revenue", "type": "size-continuous", "variable": "size_value"}], "map_index": 0, "options": {"dateColumns": []}, "source": "H4sIAKkVyl8C/5XTwWrDMAwG4HfxOTOSbEvWroOddx89lNYbgS4pWTooJe8+J+3BPeoq5HxI+nNz8/Vc3Kt7L/v5MpW38XQqh7kfB9e5r3vt171+3lx/rF1Qq88PauE8jecyzf3aeHNT+SvDpXZgIIQIXoSWzn2X8afM03VteXzhY+yHub4/jON07If9vEkvErwmBezqWxYE3S1L9/DR4hNnyOAjZrPPSLT6SUS58cnkx4wheFY1+1mFt/lRkRo/WHzMSYDqDtnmRw8kMW5+wNT60eTHHEnq/tHsA2je9q81RI2fbH4EZs+czPtX4nv+co5t/tjiQ81/DnWZ5vtnAUybn+oBGl8MvlI9v/gQyH7/EDA88kfS+NngCyfR4COjPf4xbb8f69P0atGjErIPmO18ze39+oFgnX63/AOe9pnjJwUAAA==", "title": null, "type": "GeoJSON", "viz": "@vabf72c: prop(\\u0027revenue\\u0027)\\n@size_value: ramp(linear(prop(\\u0027revenue\\u0027), globalMIN(prop(\\u0027revenue\\u0027)), globalMAX(prop(\\u0027revenue\\u0027))), [10, 50])\\ncolor: opacity(#FFB927, 0)\\nfilter: 1\\nstrokeColor: turquoise\\nstrokeWidth: 2\\nwidth: ramp(linear(sqrt(prop(\\u0027revenue\\u0027)), sqrt(globalMIN(prop(\\u0027revenue\\u0027))), sqrt(globalMAX(prop(\\u0027revenue\\u0027)))), [10, 50])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -5818,77 +9058,47 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 7, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from cartoframes.viz import size_continuous_style\n", + "from cartoframes.viz import size_continuous_style, size_continuous_legend # or just default_legend\n", "\n", - "Map([\n", - " Layer(\n", - " iso_gdf,\n", - " widgets=[\n", - " formula_widget(\n", - " 'popcy',\n", - " 'sum',\n", - " title='Total Population Served'\n", - " ),\n", - " formula_widget(\n", - " 'inccymedhh',\n", - " 'sum',\n", - " title='Median Income ($)'\n", - " ),\n", - " formula_widget(\n", - " 'lbfcyempl',\n", - " 'sum',\n", - " title='Employed Population',\n", - " ),\n", - " formula_widget(\n", - " 'educybach',\n", - " 'sum',\n", - " title='Number of People with Bachelor Degree',\n", - " ),\n", - " category_widget(\n", - " 'id_store',\n", - " title='Store ID'\n", - " )\n", - " ],\n", - " popup_hover=[\n", - " popup_element('id_store', title='Store ID')\n", - " ]\n", - " ),\n", - " Layer(\n", - " stores_gdf,\n", - " size_continuous_style('revenue')\n", + "Layer(\n", + " points_gdf,\n", + " size_continuous_style('revenue', size_range=[10,50], stroke_color='turquoise', stroke_width=2, opacity=0),\n", + " legends=size_continuous_legend(\n", + " title='Annual Revenue',\n", + " description='Revenue in dollars ($)',\n", + " footer='Source: Starbucks'\n", " )\n", - "])" + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Now you have a proportional symbol map where points are sized by revenue. You will also notice that an appropriate legend has been added to the map and when you hover over the points, you will see each store's revenue value.\n", - "\n", - "Next, let's take a look at how to modify some of the defaults.\n", - "\n", - "Every Visualization Layer has a set of parameters available to customize the defaults to better suit a given map. A quick way to see which parameters are available for customization in the `size_continuous_style`, is to run `help(size_continuous_style)` in a notebook cell.\n", + "### Interactive widgets\n", "\n", - "Let's make a few adjustments to make it easier to distinguish and locate the highest and lowest performing stores:\n", + "There are several widgets:\n", + "- Formula\n", + "- Histogram\n", + "- Category\n", + "- Time-series\n", + "- Animation\n", "\n", - "* The continuous point size reads between a minimum and maximum range of symbol sizes. Since the smallest revenue value on this map is hard to see, set `size_range=[10,50]`\n", - "* By default both the Legend and Popup titles are set to the attribute being visualized. To give them more descriptive titles, set `title=Annual Revenue ($)`\n", - "* In order to see and interact with the distribution of revenue values, you can also add a Histogram Widget (turned off by default) by setting `default_widget=True`" + "Widgets interactivity is bidirectional: filtering the widgets affects the visualization and the other widgets, and when the viewport of the visualization changes, the widgets show the information filtered by viewport (unless the flag is_global is set)." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -6064,86 +9274,43 @@ " margin-bottom: 16px;\n", " }\n", "\n", - " \n", - "\n", - "\n", - "\n", - " Static map image\n", - " \n", - " \n", - " \n", - "\n", - "
                                                                                \n", "                                                                                 \n", @@ -6768,14 +9896,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -6849,7 +9977,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -6864,12 +9992,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                                                                ${popupHTML}
                                                                                `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                                                                ${popupHTML}
                                                                                `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -7228,7 +10360,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -7271,10 +10403,10 @@ " const basemap = 'Positron';\n", " const bounds = [[-74.04169, 40.57381], [-73.85993, 40.70181]];\n", " const camera = null;\n", - " const has_legends = 'true' === 'true';\n", + " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": 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"title": null, "type": "GeoJSON", "viz": "@v464b95: prop(\\u0027educybach\\u0027)\\n@v794f1f: globalSum(prop(\\u0027inccymedhh\\u0027))\\n@vb11b3e: viewportSum(prop(\\u0027popcy\\u0027))\\n@vd11807: prop(\\u0027id_store\\u0027)\\ncolor: hex(\\"#826DBA\\")\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": [{"description": "", "footer": "", "has_bridge": false, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Population", "type": "formula", "value": "viewportSum(prop(\\u0027popcy\\u0027))", "variable_name": "vb11b3e"}, {"description": "", "footer": "", "has_bridge": false, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Median Income Average", "type": "formula", "value": "globalSum(prop(\\u0027inccymedhh\\u0027))", "variable_name": "v794f1f"}, {"description": "", "footer": "", "has_bridge": true, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Education", "type": "histogram", "value": "educybach", "variable_name": "v464b95"}, {"description": "", "footer": "", "has_bridge": true, "options": {"autoplay": true, "buckets": 20, "format": null, "readOnly": false, "weight": 1}, "prop": "", "title": "Store ID", "type": "category", "value": "id_store", "variable_name": "vd11807"}]}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -7298,100 +10430,41 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 8, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from cartoframes.viz import size_continuous_style\n", - "\n", - "Map([\n", - " Layer(\n", - " iso_gdf,\n", - " widgets=[\n", - " formula_widget(\n", - " 'popcy',\n", - " 'sum',\n", - " title='Total Population Served'\n", - " ),\n", - " formula_widget(\n", - " 'inccymedhh',\n", - " 'sum',\n", - " title='Median Income ($)'\n", - " ),\n", - " formula_widget(\n", - " 'lbfcyempl',\n", - " 'sum',\n", - " title='Employed Population',\n", - " ),\n", - " formula_widget(\n", - " 'educybach',\n", - " 'sum',\n", - " title='Number of People with Bachelor Degree',\n", - " ),\n", - " category_widget(\n", - " 'id_store',\n", - " title='Store ID'\n", - " )\n", - " ],\n", - " popup_hover=[\n", - " popup_element('id_store', 'Store ID')\n", - " ]\n", - " ),\n", - " Layer(\n", - " stores_gdf,\n", - " size_continuous_style(\n", - " 'revenue',\n", - " size_range=[10,50]\n", - " ),\n", - " title='Annual Revenue ($)',\n", - " default_widget=True\n", - " )\n", - "])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And now you have a map to visually and interactively explore the relationship between revenue and demographic variables for each store:" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Insights\n", - "\n", - "The map above provides a way to explore the data both visually and interactively in different ways:\n", + "from cartoframes.viz import formula_widget, category_widget, histogram_widget\n", "\n", - "* you can almost instantaneously locate higher and lower performing stores based on the symbol sizes\n", - "* you can zoom in on any store to summarize demographic characteristics\n", - "* you can quickly find out the store ID by hovering on it\n", - "* you can select a range of revenues from the Histogram Widget and have the map update to only display those stores\n", - "* you can use the Store ID Category Widget to isolate a particular store and summarize values\n", - "\n", - "Use the map to see if you can find the highest and lowest performing stores and summarize the demographic characteristics of each one!" + "Layer(\n", + " polygons_gdf,\n", + " widgets=[\n", + " formula_widget('popcy', 'sum', 'Population'),\n", + " formula_widget('inccymedhh', 'sum', 'Median Income Average', is_global=True),\n", + " histogram_widget('educybach', 'Education'),\n", + " category_widget('id_store', 'Store ID')\n", + " ]\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Present Insights\n", - "\n", - "Now that you have gained insight into the relationship between revenue and demographics, let's say that the most influential factor of how well a store performed was median income and you want to create a map to show that particular relationship.\n", + "### Click and hover popups\n", + "Popups are a nice tool to explore your data in a map. CARTOframes provides full customization to both click and hover popups. Using the helper `popup_element` you can display any property from your data.\n", "\n", - "To show this, the map below uses another Visualization Layer, this time the [`color_bins_style`](/developers/cartoframes/examples/#example-color-bins-style) to color each isochrone according to the range of median household income it falls within. Additionally, the `size_continuous_style` used in the previous map has been further customized to account for the new thematic median income style, and the store points have been added again as a third Layer to show their location and ID on hover. The map also has a custom [viewport](https://carto.com/developers/cartoframes/examples/#example-set-custom-viewport) set to center it on the highest performing (A) and lowest performing (J) stores that have similar median income values." + "When there are multiple layers in a map, popups will stack according to the order of the layers." ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -7592,56 +10665,6 @@ "
                                                                                \n", " \n", " \n", - "
                                                                                \n", - "
                                                                                \n", - " \n", - "\n", - "
                                                                                \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
                                                                                \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
                                                                                \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
                                                                                \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " Source: US Census Bureau\n", - " \n", - " \n", - " \n", - " \n", - "
                                                                                \n", - " \n", - " \n", - "                                                                                 \n", - "
                                                                                \n", - "
                                                                                \n", - "
                                                                                \n", - " \n", " \n", " \n", " \n", @@ -8152,14 +11175,14 @@ " const lat = center.lat.toFixed(6);\n", " const lng = center.lng.toFixed(6);\n", " const zoom = map.getZoom().toFixed(2);\n", - " \n", + "\n", " mapInfo$.innerText = `viewport={'zoom': ${zoom}, 'lat': ${lat}, 'lng': ${lng}}`;\n", " }\n", "\n", " updateMapInfo();\n", "\n", " map.on('zoom', updateMapInfo);\n", - " map.on('move', updateMapInfo); \n", + " map.on('move', updateMapInfo);\n", " }\n", "\n", " function getBasecolorSettings(basecolor) {\n", @@ -8233,7 +11256,7 @@ " }\n", " // Track layers to add only one feature per layer\n", " layerIDs.push(feature.layerId);\n", - " \n", + "\n", " for (const item of attrs) {\n", " const variable = feature.variables[item.name];\n", " if (variable) {\n", @@ -8248,12 +11271,16 @@ " }\n", " }\n", "\n", - " popup\n", - " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", - " .setHTML(`
                                                                                ${popupHTML}
                                                                                `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                                                                ${popupHTML}
                                                                                `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -8612,7 +11639,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -8654,11 +11681,11 @@ " const basecolor = '';\n", " const basemap = 'Positron';\n", " const bounds = [[-74.04169, 40.57381], [-73.85993, 40.70181]];\n", - " const camera = {"bearing": null, "center": [-73.93471, 40.644417], "pitch": null, "zoom": 12};\n", - " const has_legends = 'true' === 'true';\n", + " const camera = null;\n", + " const has_legends = 'False' === 'true';\n", " const is_static = 'None' === 'true';\n", " const layer_selector = 'False' === 'true';\n", - " const layers = [{"credentials": null, "data": 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"encode_data": true, "has_legend_list": true, "interactivity": [{"attrs": {"format": null, "name": "v0ab1cc", "title": "Employed Population"}, "event": "click"}, {"attrs": {"format": null, "name": "ve0ae0e", "title": "Median Income"}, "event": "click"}, {"attrs": {"format": null, "name": "vd11807", "title": "Store ID"}, "event": "click"}, {"attrs": {"format": null, "name": "v9b6a6d", "title": "Population"}, "event": "click"}, {"attrs": {"format": null, "name": "v9b6a6d", "title": "Population"}, "event": "hover"}], "legends": [], "map_index": 0, "options": {"dateColumns": []}, "source": 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"title": null, "type": "GeoJSON", "viz": "@v0ab1cc: prop(\\u0027lbfcyempl\\u0027)\\n@v9b6a6d: prop(\\u0027popcy\\u0027)\\n@vd11807: prop(\\u0027id_store\\u0027)\\n@ve0ae0e: prop(\\u0027inccymedhh\\u0027)\\ncolor: hex(\\"#826DBA\\")\\nstrokeColor: opacity(#2c2c2c,ramp(linear(zoom(),2,18),[0.2,0.6]))\\nstrokeWidth: ramp(linear(zoom(),2,18),[0.5,1])\\n", "widgets": []}];\n", " const mapboxtoken = '';\n", " const show_info = 'None' === 'true';\n", "\n", @@ -8682,64 +11709,43 @@ "" ], "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "from cartoframes.viz import Map, Layer, color_bins_style, size_continuous_style, default_legend\n", + "from cartoframes.viz import popup_element\n", "\n", - "Map([\n", - " Layer(\n", - " iso_gdf,\n", - " style=color_bins_style(\n", - " 'inccymedhh',\n", - " bins=7,\n", - " palette='pinkyl',\n", - " opacity=0.8,\n", - " stroke_width=0\n", - " ),\n", - " legends=default_legend(title='Median Household Income ($)', footer='Source: US Census Bureau')\n", - " ),\n", - " Layer(\n", - " stores_gdf,\n", - " style=size_continuous_style(\n", - " 'revenue',\n", - " size_range=[10,50],\n", - " range_max=1000000,\n", - " opacity=0,\n", - " stroke_color='turquoise',\n", - " stroke_width=2\n", - " ),\n", - " legends=default_legend(title='Legend Title')\n", - " ),\n", - " Layer(\n", - " stores_gdf,\n", - " popup_hover=[\n", - " popup_element('id_store', title='Store ID')\n", - " ]\n", - " )\n", - "], viewport={'zoom': 12, 'lat': 40.644417, 'lng': -73.934710})" + "Layer(\n", + " polygons_gdf,\n", + " popup_hover=[\n", + " popup_element('popcy', 'Population')\n", + " ],\n", + " popup_click=[\n", + " popup_element('popcy', 'Population'),\n", + " popup_element('id_store', 'Store ID'),\n", + " popup_element('inccymedhh', 'Median Income'),\n", + " popup_element('lbfcyempl', 'Employed Population'),\n", + " ]\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "#### Compare Variables with a Layout\n", - "\n", - "If you want to compare store revenue with multiple demographic variables, you can create a [Layout](https://carto.com/developers/cartoframes/examples/#example-custom-layout) with multiple maps.\n", + "### Grid layout\n", "\n", - "In the example below, one map symbolizes annual revenue and the other three maps symbolize three demographic variables that use the same color palette where yellow is low and red is high. Each map has a title to label which attribute is being mapped." + "Using the Layout class we can show different maps side-to-side, for example, to compare variables for the same visualization. These grid maps can also be rendered as static by setting the `is_static` parameter." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -8750,7 +11756,7 @@ " style=\"\n", " border: 1px solid #cfcfcf;\n", " width: 100%;\n", - " height: 800px;;\n", + " height: 400px;;\n", " \"\n", " srcDoc=\"\n", " \n", @@ -9031,52 +12037,6 @@ " \n", " \n", " \n", - "
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                                                                                ${popupHTML}
                                                                                `);\n", + " if (popupHTML) {\n", + " popup\n", + " .setLngLat([event.coordinates.lng, event.coordinates.lat])\n", + " .setHTML(`
                                                                                ${popupHTML}
                                                                                `);\n", "\n", - " if (!popup.isOpen()) {\n", - " popup.addTo(map);\n", + " if (!popup.isOpen()) {\n", + " popup.addTo(map);\n", + " }\n", + " } else {\n", + " popup.remove();\n", " }\n", " } else {\n", " popup.remove();\n", @@ -10044,7 +13008,7 @@ " });\n", "\n", " const layerSelector = new AsBridge.VL.Layers(layerSelector$, carto, layersInfo, mapLayers);\n", - " \n", + "\n", " layerSelector.build();\n", " }\n", "\n", @@ -10080,7 +13044,7 @@ "}());\n", "\n", "\n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + "\n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n", + "\n", + "\n", + " Static map image\n", + " \n", + " \n", + "
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                                                                                  00300MULTIPOLYGON (((-73.95936 40.67087, -73.95910 ...
                                                                                  10900MULTIPOLYGON (((-73.96743 40.67345, -73.96683 ...
                                                                                  201800MULTIPOLYGON (((-73.97584 40.67499, -73.97558 ...
                                                                                  31300MULTIPOLYGON (((-73.96417 40.57749, -73.96391 ...
                                                                                  41900MULTIPOLYGON (((-73.97035 40.57749, -73.97009 ...
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                                                                                    00100MULTIPOLYGON (((-73.95782 40.67139, -73.95721 ...
                                                                                    10500MULTIPOLYGON (((-73.96262 40.67276, -73.96219 ...
                                                                                    201000MULTIPOLYGON (((-73.96880 40.67345, -73.96820 ...
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It offers thousands of datasets from around the globe.\n", "\n", - "The Data Observatory is a spatial data repository that enables data scientists to augment their data and broaden their analysis. It offers a wide range of datasets from around the globe.\n", - "\n", - "This guide is intended for those who want to start augmenting their own data using CARTOframes and wish to explore CARTO's public Data Observatory catalog to find datasets that best fit their use cases and analyses.\n", - "\n", - "**Note: The catalog is public and you don't need a CARTO account to search for available datasets**" + "This guide is intended for those who want to start augmenting their data using CARTOframes and wish to explore CARTO's public Data Observatory catalog to find datasets that best fit their use cases and analyses.\n", + "You can check also the [Data Observatory examples](/developers/cartoframes/examples/#example-data-observatory)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Find demographic data for the US\n", - "\n", - "In this guide we walk through the Data Observatory catalog looking for demographics data in the US.\n", - "\n", - "The catalog is comprised of thousands of curated spatial datasets, so when searching for data the easiest way to find what you are looking for is to make use of a faceted search. A faceted (or hierarchical) search allows you to narrow down search results by applying multiple filters based on faceted classification of catalog datasets.\n", + "### Data Discovery\n", "\n", - "Datasets are organized in three main hierarchies:\n", + "The catalog is comprised of thousands of curated spatial datasets, so when searching for data the easiest way to find what you are looking for is to make use of a faceted search. A faceted (or hierarchical) search allows you to narrow down search results by applying multiple filters based on the faceted classification of catalog datasets.\n", "\n", - "- Country\n", - "- Category\n", - "- Geography (or spatial resolution)\n", + "Datasets are organized in these main hierarchies: country, category, provider and geography (or spatial resolution).\n", "\n", - "For our analysis we are looking for demographic datasets in the US with a spatial resolution at the block group level.\n", - "\n", - "We can start by discovering which available geographies (or spatial resolutions) we have for demographic data in the US, by filtering the `catalog` by `country` and `category` and listing the available `geographies`.\n", - "\n", - "Let's start exploring the available categories of data for the US:" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 1, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.data.observatory import Catalog\n", - "Catalog().country('usa').categories" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the case of the US, the Data Observatory provides six different categories of datasets. Let's discover the available spatial resolutions for the demographics category (which at a first sight will contain the population data we need)." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.data.observatory import Catalog\n", - "geographies = Catalog().country('usa').category('demographics').geographies\n", - "geographies" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's filter the geographies by those that contain information at the level of blockgroup. For that purpose we are converting the geographies to a pandas `DataFrame` and search for the string `blockgroup` in the `id` of the geographies:" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                      slugnamedescriptioncountry_idprovider_idgeom_typegeom_coverageupdate_frequencyis_public_datalangversionprovider_nameid
                                                                                      0mbi_blockgroups_535aed6dBlockgroups - United States of AmericaMBI Digital Boundaries for USA at Blockgroups ...usambiMULTIPOLYGONNoneyearlyFalseeng2020Michael Bauer Internationalcarto-do.mbi.geography_usa_blockgroups_2020
                                                                                      4cdb_blockgroup_7753dd51Census Block Group - United States of AmericaShoreline clipped TIGER/Line boundaries. More ...usacartoMULTIPOLYGONNoneNoneTrueeng2015CARTOcarto-do-public-data.carto.geography_usa_block...
                                                                                      \n", - "
                                                                                      " - ], - "text/plain": [ - " slug name \\\n", - "0 mbi_blockgroups_535aed6d Blockgroups - United States of America \n", - "4 cdb_blockgroup_7753dd51 Census Block Group - United States of America \n", - "\n", - " description country_id provider_id \\\n", - "0 MBI Digital Boundaries for USA at Blockgroups ... usa mbi \n", - "4 Shoreline clipped TIGER/Line boundaries. More ... usa carto \n", - "\n", - " geom_type geom_coverage update_frequency is_public_data lang version \\\n", - "0 MULTIPOLYGON None yearly False eng 2020 \n", - "4 MULTIPOLYGON None None True eng 2015 \n", - "\n", - " provider_name \\\n", - "0 Michael Bauer International \n", - "4 CARTO \n", - "\n", - " id \n", - "0 carto-do.mbi.geography_usa_blockgroups_2020 \n", - "4 carto-do-public-data.carto.geography_usa_block... " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = geographies.to_dataframe()\n", - "df[df['id'].str.contains('blockgroup', case=False, na=False)]" + "> The catalog is public and you don't need a CARTO account to search for available datasets. You can access our web catalog here: [https://carto.com/spatial-data-catalog](https://carto.com/spatial-data-catalog)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "We have three available datasets, from three different providers: Michael Bauer International, Open Data and AGS. For this example, we are going to look for demographic datasets for the MBI blockgroups geography `mbi_blockgroups_535aed6d`:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "datasets = Catalog().country('usa').category('demographics').geography('mbi_blockgroups_535aed6d').datasets\n", - "datasets" + "The catalog can also be explored directly from CARTOframes. For more information check the [discovery example](/developers/cartoframes/examples/#example-discover-a-dataset)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's continue with the data discovery. We have 6 datasets in the US with demographics information at the level of MBI blockgroups:" + "#### Dataset and variables metadata\n", + "\n", + "The Data Observatory catalog is not only a repository of curated spatial datasets, it also contains valuable information that helps better understand the underlying data for every dataset so you can make an informed decision on what data best fits your problem.\n", + "\n", + "Some of the augmented metadata you can find for each dataset in the catalog is:\n", + "\n", + "- `head` and `tail` methods to get a glimpse of the actual data. This helps you to understand the available columns, data types, etc., to start modelling your problem right away.\n", + "- `geom_coverage` to visualize on a map the geographical coverage of the data in the `Dataset`.\n", + "- `counts`, `fields_by_type` and a full `describe` method with stats of the actual values in the dataset, such as: average, stdev, quantiles, min, max, median for each of the variables of the dataset.\n", + "\n", + "You don't need a subscription to a dataset to be able to query the augmented metadata, it's publicly available for anyone exploring the Data Observatory catalog.\n", + "\n", + "Let's review some of that information, starting by getting a glimpse of the ten first or last rows of the actual data of the dataset:" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -298,2046 +77,505 @@ " \n", " \n", " \n", - " slug\n", - " name\n", - " description\n", - " category_id\n", - " country_id\n", - " data_source_id\n", - " provider_id\n", - " geography_name\n", - " geography_description\n", - " temporal_aggregation\n", - " time_coverage\n", - " update_frequency\n", - " is_public_data\n", - " lang\n", - " version\n", - " category_name\n", - " provider_name\n", - " geography_id\n", - " id\n", + " DWLCY\n", + " DWLPY\n", + " HHDCY\n", + " HHDPY\n", + " POPCY\n", + " POPPY\n", + " geoid\n", + " VPHCY1\n", + " do_date\n", + " AGECYMED\n", + " ...\n", + " MARCYDIVOR\n", + " MARCYNEVER\n", + " MARCYWIDOW\n", + " RCHCYAMNHS\n", + " RCHCYASNHS\n", + " RCHCYBLNHS\n", + " RCHCYHANHS\n", + " RCHCYMUNHS\n", + " RCHCYOTNHS\n", + " RCHCYWHNHS\n", " \n", " \n", " \n", " \n", " 0\n", - " mbi_consumer_sp_fdc16f97\n", - " Consumer Spending - United States of America (...\n", - " MBI Consumer Spending by product groups quanti...\n", - " demographics\n", - " usa\n", - " consumer_spending\n", - " mbi\n", - " Blockgroups - United States of America\n", - " MBI Digital Boundaries for USA at Blockgroups ...\n", - " yearly\n", - " [2019-01-01, 2020-01-01)\n", - " yearly\n", - " False\n", - " eng\n", - " 2020\n", - " Demographics\n", - " Michael Bauer International\n", - " carto-do.mbi.geography_usa_blockgroups_2020\n", - " carto-do.mbi.demographics_consumerspending_usa...\n", + " 1057\n", + " 1112\n", + " 932\n", + " 986\n", + " 1500\n", + " 1648\n", + " 040130405071\n", + " 442\n", + " 2020-01-01 00:00:00+00:00\n", + " 77.40\n", + " ...\n", + " 149\n", + " 4\n", + " 228\n", + " 0\n", + " 11\n", + " 20\n", + " 0\n", + " 25\n", + " 0\n", + " 1317\n", " \n", " \n", " 1\n", - " mbi_households__ec03bf40\n", - " Households By Type - United States of America ...\n", - " Distribution of the households in an area by t...\n", - " demographics\n", - " usa\n", - " households_by_type\n", - " mbi\n", - " Blockgroups - United States of America\n", - " MBI Digital Boundaries for USA at Blockgroups ...\n", - " yearly\n", - " [2019-01-01, 2020-01-01)\n", - " yearly\n", - " False\n", - " eng\n", - " 2020\n", - " Demographics\n", - " Michael Bauer International\n", - " carto-do.mbi.geography_usa_blockgroups_2020\n", - " carto-do.mbi.demographics_householdsbytype_usa...\n", + " 1964\n", + " 2069\n", + " 1774\n", + " 1877\n", + " 2595\n", + " 2868\n", + " 040130405072\n", + " 1049\n", + " 2020-01-01 00:00:00+00:00\n", + " 76.88\n", + " ...\n", + " 414\n", + " 160\n", + " 699\n", + " 0\n", + " 74\n", + " 68\n", + " 7\n", + " 55\n", + " 0\n", + " 2167\n", " \n", " \n", " 2\n", - " mbi_sociodemogr_1c54ac66\n", - " Sociodemographics - United States of America (...\n", - " MBI Sociodemographics includes:\\n- Population\\...\n", - " demographics\n", - " usa\n", - " sociodemographics\n", - " mbi\n", - " Blockgroups - United States of America\n", - " MBI Digital Boundaries for USA at Blockgroups ...\n", - " yearly\n", - " [2019-01-01, 2020-01-01)\n", - " yearly\n", - " False\n", - " eng\n", - " 2020\n", - " Demographics\n", - " Michael Bauer International\n", - " carto-do.mbi.geography_usa_blockgroups_2020\n", - " carto-do.mbi.demographics_sociodemographics_us...\n", + " 1049\n", + " 1101\n", + " 897\n", + " 933\n", + " 1585\n", + " 1716\n", + " 040130610182\n", + " 460\n", + " 2020-01-01 00:00:00+00:00\n", + " 69.88\n", + " ...\n", + " 31\n", + " 217\n", + " 246\n", + " 2\n", + " 55\n", + " 43\n", + " 9\n", + " 26\n", + " 0\n", + " 1313\n", " \n", " \n", " 3\n", - " mbi_purchasing__faaee3c9\n", - " Purchasing Power - United States of America (B...\n", - " Purchasing Power describes the disposable inco...\n", - " demographics\n", - " usa\n", - " purchasing_power\n", - " mbi\n", - " Blockgroups - United States of America\n", - " MBI Digital Boundaries for USA at Blockgroups ...\n", - " yearly\n", - " [2019-01-01, 2020-01-01)\n", - " yearly\n", - " False\n", - " eng\n", - " 2020\n", - " Demographics\n", - " Michael Bauer International\n", - " carto-do.mbi.geography_usa_blockgroups_2020\n", - " carto-do.mbi.demographics_purchasingpower_usa_...\n", + " 1084\n", + " 1137\n", + " 910\n", + " 940\n", + " 1503\n", + " 1616\n", + " 040138175002\n", + " 392\n", + " 2020-01-01 00:00:00+00:00\n", + " 71.44\n", + " ...\n", + " 191\n", + " 79\n", + " 268\n", + " 8\n", + " 24\n", + " 38\n", + " 0\n", + " 8\n", + " 0\n", + " 1290\n", " \n", " \n", " 4\n", - " mbi_population_9d1b276f\n", - " Population - United States of America (Blockgr...\n", - " Population figures are shown as projected aver...\n", - " demographics\n", - " usa\n", - " population\n", - " mbi\n", - " Blockgroups - United States of America\n", - " MBI Digital Boundaries for USA at Blockgroups ...\n", - " yearly\n", - " [2019-01-01, 2020-01-01)\n", - " yearly\n", - " False\n", - " eng\n", - " 2020\n", - " Demographics\n", - " Michael Bauer International\n", - " carto-do.mbi.geography_usa_blockgroups_2020\n", - " carto-do.mbi.demographics_population_usa_block...\n", - " \n", - " \n", + " 682\n", + " 706\n", + " 574\n", + " 591\n", + " 980\n", + " 1039\n", + " 040190043241\n", + " 244\n", + " 2020-01-01 00:00:00+00:00\n", + " 72.38\n", + " ...\n", + " 30\n", + " 44\n", + " 195\n", + " 3\n", + " 9\n", + " 0\n", + " 0\n", + " 0\n", + " 0\n", + " 902\n", + " \n", + " \n", " 5\n", - " mbi_retail_spen_6a1acff4\n", - " Retail Spending - United States of America (Bl...\n", - " Retail Spending relates to the proportion of P...\n", - " demographics\n", - " usa\n", - " retail_spending\n", - " mbi\n", - " Blockgroups - United States of America\n", - " MBI Digital Boundaries for USA at Blockgroups ...\n", - " yearly\n", - " [2019-01-01, 2020-01-01)\n", - " yearly\n", - " False\n", - " eng\n", - " 2020\n", - " Demographics\n", - " Michael Bauer International\n", - " carto-do.mbi.geography_usa_blockgroups_2020\n", - " carto-do.mbi.demographics_retailspending_usa_b...\n", + " 880\n", + " 910\n", + " 840\n", + " 869\n", + " 1249\n", + " 1284\n", + " 060133511032\n", + " 539\n", + " 2020-01-01 00:00:00+00:00\n", + " 76.75\n", + " ...\n", + " 160\n", + " 40\n", + " 319\n", + " 0\n", + " 136\n", + " 19\n", + " 2\n", + " 12\n", + " 5\n", + " 1024\n", " \n", " \n", " 6\n", - " mbi_consumer_pr_c1d4e20e\n", - " Consumer Profiles - United States of America (...\n", - " The MB International Consumer Styles describe ...\n", - " demographics\n", - " usa\n", - " consumer_profiles\n", - " mbi\n", - " Blockgroups - United States of America\n", - " MBI Digital Boundaries for USA at Blockgroups ...\n", - " yearly\n", - " [2019-01-01, 2020-01-01)\n", - " yearly\n", - " False\n", - " eng\n", - " 2020\n", - " Demographics\n", - " Michael Bauer International\n", - " carto-do.mbi.geography_usa_blockgroups_2020\n", - " carto-do.mbi.demographics_consumerprofiles_usa...\n", + " 1467\n", + " 1534\n", + " 1314\n", + " 1467\n", + " 1658\n", + " 1800\n", + " 060590995101\n", + " 831\n", + " 2020-01-01 00:00:00+00:00\n", + " 74.58\n", + " ...\n", + " 423\n", + " 136\n", + " 496\n", + " 3\n", + " 226\n", + " 10\n", + " 1\n", + " 16\n", + " 0\n", + " 1269\n", " \n", " \n", " 7\n", - " mbi_households__60466314\n", - " Households By Income Quintiles - United States...\n", - " On the national level the number of households...\n", - " demographics\n", - " usa\n", - " households_by_income_quintiles\n", - " mbi\n", - " Blockgroups - United States of America\n", - " MBI Digital Boundaries for USA at Blockgroups ...\n", - " yearly\n", - " [2019-01-01, 2020-01-01)\n", - " yearly\n", - " False\n", - " eng\n", - " 2020\n", - " Demographics\n", - " Michael Bauer International\n", - " carto-do.mbi.geography_usa_blockgroups_2020\n", - " carto-do.mbi.demographics_householdsbyincomequ...\n", + " 704\n", + " 753\n", + " 693\n", + " 730\n", + " 1078\n", + " 1176\n", + " 060610210391\n", + " 338\n", + " 2020-01-01 00:00:00+00:00\n", + " 73.86\n", + " ...\n", + " 117\n", + " 63\n", + " 215\n", + " 5\n", + " 33\n", + " 7\n", + " 0\n", + " 9\n", + " 0\n", + " 986\n", " \n", " \n", " 8\n", - " mbi_education_8903fc2c\n", - " Education - United States of America (Blockgro...\n", - " Distribution of the population in an area by t...\n", - " demographics\n", - " usa\n", - " education\n", - " mbi\n", - " Blockgroups - United States of America\n", - " MBI Digital Boundaries for USA at Blockgroups ...\n", - " yearly\n", - " [2019-01-01, 2020-01-01)\n", - " yearly\n", - " False\n", - " eng\n", - " 2020\n", - " Demographics\n", - " Michael Bauer International\n", - " carto-do.mbi.geography_usa_blockgroups_2020\n", - " carto-do.mbi.demographics_education_usa_blockg...\n", + " 1582\n", + " 1691\n", + " 1553\n", + " 1650\n", + " 2540\n", + " 2795\n", + " 060610236001\n", + " 818\n", + " 2020-01-01 00:00:00+00:00\n", + " 68.80\n", + " ...\n", + " 406\n", + " 45\n", + " 301\n", + " 5\n", + " 168\n", + " 26\n", + " 3\n", + " 19\n", + " 0\n", + " 2183\n", + " \n", + " \n", + " 9\n", + " 1186\n", + " 1268\n", + " 1163\n", + " 1234\n", + " 1980\n", + " 2176\n", + " 060610236002\n", + " 415\n", + " 2020-01-01 00:00:00+00:00\n", + " 68.59\n", + " ...\n", + " 253\n", + " 60\n", + " 223\n", + " 5\n", + " 97\n", + " 22\n", + " 1\n", + " 10\n", + " 0\n", + " 1750\n", " \n", " \n", "\n", + "

                                                                                      10 rows × 110 columns

                                                                                      \n", "" ], "text/plain": [ - " slug \\\n", - "0 mbi_consumer_sp_fdc16f97 \n", - "1 mbi_households__ec03bf40 \n", - "2 mbi_sociodemogr_1c54ac66 \n", - "3 mbi_purchasing__faaee3c9 \n", - "4 mbi_population_9d1b276f \n", - "5 mbi_retail_spen_6a1acff4 \n", - "6 mbi_consumer_pr_c1d4e20e \n", - "7 mbi_households__60466314 \n", - "8 mbi_education_8903fc2c \n", - "\n", - " name \\\n", - "0 Consumer Spending - United States of America (... \n", - "1 Households By Type - United States of America ... \n", - "2 Sociodemographics - United States of America (... \n", - "3 Purchasing Power - United States of America (B... \n", - "4 Population - United States of America (Blockgr... \n", - "5 Retail Spending - United States of America (Bl... \n", - "6 Consumer Profiles - United States of America (... \n", - "7 Households By Income Quintiles - United States... \n", - "8 Education - United States of America (Blockgro... \n", - "\n", - " description category_id country_id \\\n", - "0 MBI Consumer Spending by product groups quanti... demographics usa \n", - "1 Distribution of the households in an area by t... demographics usa \n", - "2 MBI Sociodemographics includes:\\n- Population\\... demographics usa \n", - "3 Purchasing Power describes the disposable inco... demographics usa \n", - "4 Population figures are shown as projected aver... demographics usa \n", - "5 Retail Spending relates to the proportion of P... demographics usa \n", - "6 The MB International Consumer Styles describe ... demographics usa \n", - "7 On the national level the number of households... demographics usa \n", - "8 Distribution of the population in an area by t... demographics usa \n", - "\n", - " data_source_id provider_id \\\n", - "0 consumer_spending mbi \n", - "1 households_by_type mbi \n", - "2 sociodemographics mbi \n", - "3 purchasing_power mbi \n", - "4 population mbi \n", - "5 retail_spending mbi \n", - "6 consumer_profiles mbi \n", - "7 households_by_income_quintiles mbi \n", - "8 education mbi \n", - "\n", - " geography_name \\\n", - "0 Blockgroups - United States of America \n", - "1 Blockgroups - United States of America \n", - "2 Blockgroups - United States of America \n", - "3 Blockgroups - United States of America \n", - "4 Blockgroups - United States of America \n", - "5 Blockgroups - United States of America \n", - "6 Blockgroups - United States of America \n", - "7 Blockgroups - United States of America \n", - "8 Blockgroups - United States of America \n", - "\n", - " geography_description temporal_aggregation \\\n", - "0 MBI Digital Boundaries for USA at Blockgroups ... yearly \n", - "1 MBI Digital Boundaries for USA at Blockgroups ... yearly \n", - "2 MBI Digital Boundaries for USA at Blockgroups ... yearly \n", - "3 MBI Digital Boundaries for USA at Blockgroups ... yearly \n", - "4 MBI Digital Boundaries for USA at Blockgroups ... yearly \n", - "5 MBI Digital Boundaries for USA at Blockgroups ... yearly \n", - "6 MBI Digital Boundaries for USA at Blockgroups ... yearly \n", - "7 MBI Digital Boundaries for USA at Blockgroups ... yearly \n", - "8 MBI Digital Boundaries for USA at Blockgroups ... yearly \n", - "\n", - " time_coverage update_frequency is_public_data lang version \\\n", - "0 [2019-01-01, 2020-01-01) yearly False eng 2020 \n", - "1 [2019-01-01, 2020-01-01) yearly False eng 2020 \n", - "2 [2019-01-01, 2020-01-01) yearly False eng 2020 \n", - "3 [2019-01-01, 2020-01-01) yearly False eng 2020 \n", - "4 [2019-01-01, 2020-01-01) yearly False eng 2020 \n", - "5 [2019-01-01, 2020-01-01) yearly False eng 2020 \n", - "6 [2019-01-01, 2020-01-01) yearly False eng 2020 \n", - "7 [2019-01-01, 2020-01-01) yearly False eng 2020 \n", - "8 [2019-01-01, 2020-01-01) yearly False eng 2020 \n", - "\n", - " category_name provider_name \\\n", - "0 Demographics Michael Bauer International \n", - "1 Demographics Michael Bauer International \n", - "2 Demographics Michael Bauer International \n", - "3 Demographics Michael Bauer International \n", - "4 Demographics Michael Bauer International \n", - "5 Demographics Michael Bauer International \n", - "6 Demographics Michael Bauer International \n", - "7 Demographics Michael Bauer International \n", - "8 Demographics Michael Bauer International \n", - "\n", - " geography_id \\\n", - "0 carto-do.mbi.geography_usa_blockgroups_2020 \n", - "1 carto-do.mbi.geography_usa_blockgroups_2020 \n", - "2 carto-do.mbi.geography_usa_blockgroups_2020 \n", - "3 carto-do.mbi.geography_usa_blockgroups_2020 \n", - "4 carto-do.mbi.geography_usa_blockgroups_2020 \n", - "5 carto-do.mbi.geography_usa_blockgroups_2020 \n", - "6 carto-do.mbi.geography_usa_blockgroups_2020 \n", - "7 carto-do.mbi.geography_usa_blockgroups_2020 \n", - "8 carto-do.mbi.geography_usa_blockgroups_2020 \n", - "\n", - " id \n", - "0 carto-do.mbi.demographics_consumerspending_usa... \n", - "1 carto-do.mbi.demographics_householdsbytype_usa... \n", - "2 carto-do.mbi.demographics_sociodemographics_us... \n", - "3 carto-do.mbi.demographics_purchasingpower_usa_... \n", - "4 carto-do.mbi.demographics_population_usa_block... \n", - "5 carto-do.mbi.demographics_retailspending_usa_b... \n", - "6 carto-do.mbi.demographics_consumerprofiles_usa... \n", - "7 carto-do.mbi.demographics_householdsbyincomequ... \n", - "8 carto-do.mbi.demographics_education_usa_blockg... " + " DWLCY DWLPY HHDCY HHDPY POPCY POPPY geoid VPHCY1 \\\n", + "0 1057 1112 932 986 1500 1648 040130405071 442 \n", + "1 1964 2069 1774 1877 2595 2868 040130405072 1049 \n", + "2 1049 1101 897 933 1585 1716 040130610182 460 \n", + "3 1084 1137 910 940 1503 1616 040138175002 392 \n", + "4 682 706 574 591 980 1039 040190043241 244 \n", + "5 880 910 840 869 1249 1284 060133511032 539 \n", + "6 1467 1534 1314 1467 1658 1800 060590995101 831 \n", + "7 704 753 693 730 1078 1176 060610210391 338 \n", + "8 1582 1691 1553 1650 2540 2795 060610236001 818 \n", + "9 1186 1268 1163 1234 1980 2176 060610236002 415 \n", + "\n", + " do_date AGECYMED ... MARCYDIVOR MARCYNEVER \\\n", + "0 2020-01-01 00:00:00+00:00 77.40 ... 149 4 \n", + "1 2020-01-01 00:00:00+00:00 76.88 ... 414 160 \n", + "2 2020-01-01 00:00:00+00:00 69.88 ... 31 217 \n", + "3 2020-01-01 00:00:00+00:00 71.44 ... 191 79 \n", + "4 2020-01-01 00:00:00+00:00 72.38 ... 30 44 \n", + "5 2020-01-01 00:00:00+00:00 76.75 ... 160 40 \n", + "6 2020-01-01 00:00:00+00:00 74.58 ... 423 136 \n", + "7 2020-01-01 00:00:00+00:00 73.86 ... 117 63 \n", + "8 2020-01-01 00:00:00+00:00 68.80 ... 406 45 \n", + "9 2020-01-01 00:00:00+00:00 68.59 ... 253 60 \n", + "\n", + " MARCYWIDOW RCHCYAMNHS RCHCYASNHS RCHCYBLNHS RCHCYHANHS RCHCYMUNHS \\\n", + "0 228 0 11 20 0 25 \n", + "1 699 0 74 68 7 55 \n", + "2 246 2 55 43 9 26 \n", + "3 268 8 24 38 0 8 \n", + "4 195 3 9 0 0 0 \n", + "5 319 0 136 19 2 12 \n", + "6 496 3 226 10 1 16 \n", + "7 215 5 33 7 0 9 \n", + "8 301 5 168 26 3 19 \n", + "9 223 5 97 22 1 10 \n", + "\n", + " RCHCYOTNHS RCHCYWHNHS \n", + "0 0 1317 \n", + "1 0 2167 \n", + "2 0 1313 \n", + "3 0 1290 \n", + "4 0 902 \n", + "5 5 1024 \n", + "6 0 1269 \n", + "7 0 986 \n", + "8 0 2183 \n", + "9 0 1750 \n", + "\n", + "[10 rows x 110 columns]" ] }, - "execution_count": 5, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "datasets.to_dataframe()" + "from cartoframes.data.observatory import Dataset\n", + "\n", + "dataset = Dataset.get('ags_sociodemogr_a7e14220')\n", + "dataset.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "They comprise different information: consumer spending, retail potential, consumer profiles, etc.\n", - "\n", - "At a first sight, it looks the dataset with `data_source_id: sociodemographic` might contain the population information we are looking for. Let's try to understand a little bit better what data this dataset contains by looking at its variables:" + "Alternatively, you can get the last ten ones with `dataset.tail()`" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "An overview of the coverage of the dataset" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[ #'Geographic Identifier',\n", - " #'Population (2019A)',\n", - " #'Population in Group Quarters (2019A)',\n", - " #'Institutional Group Quarters Population (2019A)',\n", - " #'Population age 0-4 (2019A)',\n", - " #'Population age 5-9 (2019A)',\n", - " #'Population age 10-14 (2019A)',\n", - " #'Population age 15-19 (2019A)',\n", - " #'Population age 20-24 (2019A)',\n", - " #'Population age 25-29 (2019A)',\n", - " #'Population age 30-34 (2019A)',\n", - " #'Population age 35-39 (2019A)',\n", - " #'Population age 40-44 (2019A)',\n", - " #'Population age 45-49 (2019A)',\n", - " #'Population age 50-54 (2019A)',\n", - " #'Population age 55-59 (2019A)',\n", - " #'Population age 60-64 (2019A)',\n", - " #'Population age 65-69 (2019A)',\n", - " #'Population age 70-74 (2019A)',\n", - " #'Population age 75-79 (2019A)',\n", - " #'Population age 80-84 (2019A)',\n", - " #'Population age 85+ (2019A)',\n", - " #'Median Age (2019A)',\n", - " #'Population male (2019A)',\n", - " #'Population female (2019A)',\n", - " #'Non Hispanic White (2019A)',\n", - " #'Non Hispanic Black (2019A)',\n", - " #'Non Hispanic American Indian (2019A)',\n", - " #'Non Hispanic Asian (2019A)',\n", - " #'Non Hispanic Hawaiian/Pacific Islander (2019A)',\n", - " #'Non Hispanic Other Race (2019A)',\n", - " #'Non Hispanic Multiple Race (2019A)',\n", - " #'Population Hispanic (2019A)',\n", - " #'Never Married (2019A)',\n", - " #'Now Married (2019A)',\n", - " #'Separated (2019A)',\n", - " #'Widowed (2019A)',\n", - " #'Divorced (2019A)',\n", - " #'Population Age 15+ (2019A)',\n", - " #'Pop 25+ less than 9th grade (2019A)',\n", - " #'Pop 25+ 9th-12th grade no diploma (2019A)',\n", - " #'Pop 25+ HS graduate (2019A)',\n", - " #'Pop 25+ college no diploma (2019A)',\n", - " #'Pop 25+ Associate degree (2019A)',\n", - " #'Pop 25+ Bachelor's degree (2019A)',\n", - " #'Pop 25+ graduate or prof school degree (2019A)',\n", - " #'Population Age 25+ (2019A)',\n", - " #'Households (2019A)',\n", - " #'Family Households (2019A)',\n", - " #'Families married couple w children (2019A)',\n", - " #'Families male no wife w children (2019A)',\n", - " #'Families female no husband children (2019A)',\n", - " #'Average Household Size (2019A)',\n", - " #'Median Age of Householder (2019A)',\n", - " #'Households: No Vehicle Available (2019A)',\n", - " #'Households: One Vehicle Available (2019A)',\n", - " #'Households: Two or More Vehicles Available (2019A)',\n", - " #'Per capita income (2019A)',\n", - " #'Average household Income (2019A)',\n", - " #'Median household income (2019A)',\n", - " #'Median family income (2019A)',\n", - " #'Household Income < $10000 (2019A)',\n", - " #'Household Income $10000-$14999 (2019A)',\n", - " #'Household Income $15000-$19999 (2019A)',\n", - " #'Household Income $20000-$24999 (2019A)',\n", - " #'Household Income $25000-$29999 (2019A)',\n", - " #'Household Income $30000-$34999 (2019A)',\n", - " #'Household Income $35000-$39999 (2019A)',\n", - " #'Household Income $40000-$44999 (2019A)',\n", - " #'Household Income $45000-$49999 (2019A)',\n", - " #'Household Income $50000-$59999 (2019A)',\n", - " #'Household Income $60000-$74999 (2019A)',\n", - " #'Household Income $75000-$99999 (2019A)',\n", - " #'Household Income $100000-$124999 (2019A)',\n", - " #'Household Income $125000-$149999 (2019A)',\n", - " #'Household Income $150000-$199999 (2019A)',\n", - " #'Household Income > $200000 (2019A)',\n", - " #'Median Household Income: Age < 25 (2019A)',\n", - " #'Median Household Income: Age 25-34 (2019A)',\n", - " #'Median Household Income: Age 35-44 (2019A)',\n", - " #'Median Household Income: Age 45-54 (2019A)',\n", - " #'Median Household Income: Age 55-64 (2019A)',\n", - " #'Median Household Income: Age 65-74 (2019A)',\n", - " #'Median Household Income: Age 75+ (2019A)',\n", - " #'Population Age 16+ (2019A)',\n", - " #'Pop 16+ in Armed Forces (2019A)',\n", - " #'Pop 16+ civilian employed (2019A)',\n", - " #'Pop 16+ civilian unemployed (2019A)',\n", - " #'Pop 16+ not in labor force (2019A)',\n", - " #'Unemployment Rate (2019A)',\n", - " #'Population In Labor Force (2019A)',\n", - " #'SPANISH SPEAKING HOUSEHOLDS',\n", - " #'LINGUISTICALLY ISOLATED HOUSEHOLDS (NON-ENGLISH SP...',\n", - " #'Median Value of Owner Occupied Housing Units',\n", - " #'Median Cash Rent',\n", - " #'UNITS IN STRUCTURE: 1 DETACHED',\n", - " #'UNITS IN STRUCTURE: 20 OR MORE',\n", - " #'Housing units (2019A)',\n", - " #'Housing units vacant (2019A)',\n", - " #'Occupied units renter (2019A)',\n", - " #'Occupied units owner (2019A)',\n", - " #'Population (2024A)',\n", - " #'Households (2024A)',\n", - " #'Housing units (2024A)',\n", - " #'Median Age (2024A)',\n", - " #'Per capita income (2024A)',\n", - " #'Average household Income (2024A)',\n", - " #'Median household income (2024A)']" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.data.observatory import Dataset\n", - "dataset = Dataset.get('ags_sociodemogr_a7e14220')\n", - "variables = dataset.variables\n", - "variables" - ] - }, - { - "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ - "
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                                                                                      slugnamedescriptiondb_typeagg_methodcolumn_namevariable_group_iddataset_idid
                                                                                      0BLOCKGROUP_30e525a6BLOCKGROUPGeographic IdentifierSTRINGNoneBLOCKGROUPNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      1POPCY_4534fac4Total PopulationPopulation (2019A)INTEGERSUMPOPCYNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      2POPCYGRP_3033ef2ePOPCYGRPPopulation in Group Quarters (2019A)INTEGERSUMPOPCYGRPNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      3POPCYGRPI_1e42899POPCYGRPIInstitutional Group Quarters Population (2019A)INTEGERSUMPOPCYGRPINonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      4AGECY0004_aaae373aAGECY0004Population age 0-4 (2019A)INTEGERSUMAGECY0004carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      ..............................
                                                                                      103DWLPY_312aaf70Number of household unitsHousing units (2024A)INTEGERSUMDWLPYNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      104AGEPYMED_d5583bbbAGEPYMEDMedian Age (2024A)FLOATAVGAGEPYMEDcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      105INCPYPCAP_f9c107faINCPYPCAPPer capita income (2024A)FLOATAVGINCPYPCAPcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      106INCPYAVEHH_48c1d530INCPYAVEHHAverage household Income (2024A)FLOATAVGINCPYAVEHHcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      107INCPYMEDHH_ce5faa77INCPYMEDHHMedian household income (2024A)FLOATAVGINCPYMEDHHcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
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                                                                                      108 rows × 9 columns

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                                                                                      " - ], - "text/plain": [ - " slug name \\\n", - "0 BLOCKGROUP_30e525a6 BLOCKGROUP \n", - "1 POPCY_4534fac4 Total Population \n", - "2 POPCYGRP_3033ef2e POPCYGRP \n", - "3 POPCYGRPI_1e42899 POPCYGRPI \n", - "4 AGECY0004_aaae373a AGECY0004 \n", - ".. ... ... \n", - "103 DWLPY_312aaf70 Number of household units \n", - "104 AGEPYMED_d5583bbb AGEPYMED \n", - "105 INCPYPCAP_f9c107fa INCPYPCAP \n", - "106 INCPYAVEHH_48c1d530 INCPYAVEHH \n", - "107 INCPYMEDHH_ce5faa77 INCPYMEDHH \n", + " \n", "\n", - " description db_type agg_method \\\n", - "0 Geographic Identifier STRING None \n", - "1 Population (2019A) INTEGER SUM \n", - "2 Population in Group Quarters (2019A) INTEGER SUM \n", - "3 Institutional Group Quarters Population (2019A) INTEGER SUM \n", - "4 Population age 0-4 (2019A) INTEGER SUM \n", - ".. ... ... ... \n", - "103 Housing units (2024A) INTEGER SUM \n", - "104 Median Age (2024A) FLOAT AVG \n", - "105 Per capita income (2024A) FLOAT AVG \n", - "106 Average household Income (2024A) FLOAT AVG \n", - "107 Median household income (2024A) FLOAT AVG \n", + " \n", "\n", - " column_name variable_group_id \\\n", - "0 BLOCKGROUP None \n", - "1 POPCY None \n", - "2 POPCYGRP None \n", - "3 POPCYGRPI None \n", - "4 AGECY0004 carto-do.ags.demographics_sociodemographics_us... \n", - ".. ... ... \n", - "103 DWLPY None \n", - "104 AGEPYMED carto-do.ags.demographics_sociodemographics_us... \n", - "105 INCPYPCAP carto-do.ags.demographics_sociodemographics_us... \n", - "106 INCPYAVEHH carto-do.ags.demographics_sociodemographics_us... \n", - "107 INCPYMEDHH carto-do.ags.demographics_sociodemographics_us... \n", + " \n", + " \n", + " \n", + " \n", + " \n", "\n", - " dataset_id \\\n", - "0 carto-do.ags.demographics_sociodemographics_us... \n", - "1 carto-do.ags.demographics_sociodemographics_us... \n", - "2 carto-do.ags.demographics_sociodemographics_us... \n", - "3 carto-do.ags.demographics_sociodemographics_us... \n", - "4 carto-do.ags.demographics_sociodemographics_us... \n", - 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                                                                                      slugnamedescriptiondb_typeagg_methodcolumn_namevariable_group_iddataset_idid
                                                                                      1POPCY_4534fac4Total PopulationPopulation (2019A)INTEGERSUMPOPCYNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      2POPCYGRP_3033ef2ePOPCYGRPPopulation in Group Quarters (2019A)INTEGERSUMPOPCYGRPNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      3POPCYGRPI_1e42899POPCYGRPIInstitutional Group Quarters Population (2019A)INTEGERSUMPOPCYGRPINonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      4AGECY0004_aaae373aAGECY0004Population age 0-4 (2019A)INTEGERSUMAGECY0004carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      5AGECY0509_d2d4896cAGECY0509Population age 5-9 (2019A)INTEGERSUMAGECY0509carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      6AGECY1014_b09611eAGECY1014Population age 10-14 (2019A)INTEGERSUMAGECY1014carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      7AGECY1519_7373df48AGECY1519Population age 15-19 (2019A)INTEGERSUMAGECY1519carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      8AGECY2024_32919d33AGECY2024Population age 20-24 (2019A)INTEGERSUMAGECY2024carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      9AGECY2529_4aeb2365AGECY2529Population age 25-29 (2019A)INTEGERSUMAGECY2529carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      10AGECY3034_9336cb17AGECY3034Population age 30-34 (2019A)INTEGERSUMAGECY3034carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      11AGECY3539_eb4c7541AGECY3539Population age 35-39 (2019A)INTEGERSUMAGECY3539carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      12AGECY4044_41a06569AGECY4044Population age 40-44 (2019A)INTEGERSUMAGECY4044carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      13AGECY4549_39dadb3fAGECY4549Population age 45-49 (2019A)INTEGERSUMAGECY4549carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      14AGECY5054_e007334dAGECY5054Population age 50-54 (2019A)INTEGERSUMAGECY5054carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      15AGECY5559_987d8d1bAGECY5559Population age 55-59 (2019A)INTEGERSUMAGECY5559carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      16AGECY6064_d99fcf60AGECY6064Population age 60-64 (2019A)INTEGERSUMAGECY6064carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      17AGECY6569_a1e57136AGECY6569Population age 65-69 (2019A)INTEGERSUMAGECY6569carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      18AGECY7074_78389944AGECY7074Population age 70-74 (2019A)INTEGERSUMAGECY7074carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      19AGECY7579_422712AGECY7579Population age 75-79 (2019A)INTEGERSUMAGECY7579carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      20AGECY8084_a7c395ddAGECY8084Population age 80-84 (2019A)INTEGERSUMAGECY8084carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      21AGECYGT85_ac46767dAGECYGT85Population age 85+ (2019A)INTEGERSUMAGECYGT85carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      23SEXCYMAL_8ee6ade5SEXCYMALPopulation male (2019A)INTEGERSUMSEXCYMALcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      24SEXCYFEM_91d8b796SEXCYFEMPopulation female (2019A)INTEGERSUMSEXCYFEMcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      32HISCYHISP_e62d7c2eHISCYHISPPopulation Hispanic (2019A)INTEGERSUMHISCYHISPcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      38AGECYGT15_7d84cd34AGECYGT15Population Age 15+ (2019A)INTEGERSUMAGECYGT15carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      39EDUCYLTGR9_ed0362faEDUCYLTGR9Pop 25+ less than 9th grade (2019A)INTEGERSUMEDUCYLTGR9carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      40EDUCYSHSCH_7a88e398EDUCYSHSCHPop 25+ 9th-12th grade no diploma (2019A)INTEGERSUMEDUCYSHSCHcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      41EDUCYHSCH_a7a81733EDUCYHSCHPop 25+ HS graduate (2019A)INTEGERSUMEDUCYHSCHcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      42EDUCYSCOLL_3840e65bEDUCYSCOLLPop 25+ college no diploma (2019A)INTEGERSUMEDUCYSCOLLcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      43EDUCYASSOC_dcd76160EDUCYASSOCPop 25+ Associate degree (2019A)INTEGERSUMEDUCYASSOCcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      44EDUCYBACH_d7b78049EDUCYBACHPop 25+ Bachelor's degree (2019A)INTEGERSUMEDUCYBACHcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      45EDUCYGRAD_c58943fbEDUCYGRADPop 25+ graduate or prof school degree (2019A)INTEGERSUMEDUCYGRADcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      46AGECYGT25_56a99ef7AGECYGT25Population Age 25+ (2019A)INTEGERSUMAGECYGT25carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      84LBFCYPOP16_75363c6fLBFCYPOP16Population Age 16+ (2019A)INTEGERSUMLBFCYPOP16carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      85LBFCYARM_c8f45b67LBFCYARMPop 16+ in Armed Forces (2019A)INTEGERSUMLBFCYARMcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      86LBFCYEMPL_1902fd90LBFCYEMPLPop 16+ civilian employed (2019A)INTEGERSUMLBFCYEMPLcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      87LBFCYUNEM_befc2d4LBFCYUNEMPop 16+ civilian unemployed (2019A)INTEGERSUMLBFCYUNEMcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      88LBFCYNLF_803bfa0dLBFCYNLFPop 16+ not in labor force (2019A)INTEGERSUMLBFCYNLFcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      90LBFCYLBF_1d3c03edLBFCYLBFPopulation In Labor Force (2019A)INTEGERSUMLBFCYLBFcarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      101POPPY_24dbbb56Total populationPopulation (2024A)INTEGERSUMPOPPYNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      \n", - "" - ], - "text/plain": [ - " slug name \\\n", - "1 POPCY_4534fac4 Total Population \n", - "2 POPCYGRP_3033ef2e POPCYGRP \n", - "3 POPCYGRPI_1e42899 POPCYGRPI \n", - "4 AGECY0004_aaae373a AGECY0004 \n", - "5 AGECY0509_d2d4896c AGECY0509 \n", - "6 AGECY1014_b09611e AGECY1014 \n", - "7 AGECY1519_7373df48 AGECY1519 \n", - "8 AGECY2024_32919d33 AGECY2024 \n", - "9 AGECY2529_4aeb2365 AGECY2529 \n", - "10 AGECY3034_9336cb17 AGECY3034 \n", - "11 AGECY3539_eb4c7541 AGECY3539 \n", - "12 AGECY4044_41a06569 AGECY4044 \n", - "13 AGECY4549_39dadb3f AGECY4549 \n", - "14 AGECY5054_e007334d AGECY5054 \n", - "15 AGECY5559_987d8d1b AGECY5559 \n", - "16 AGECY6064_d99fcf60 AGECY6064 \n", - "17 AGECY6569_a1e57136 AGECY6569 \n", - "18 AGECY7074_78389944 AGECY7074 \n", - "19 AGECY7579_422712 AGECY7579 \n", - "20 AGECY8084_a7c395dd AGECY8084 \n", - "21 AGECYGT85_ac46767d AGECYGT85 \n", - "23 SEXCYMAL_8ee6ade5 SEXCYMAL \n", - "24 SEXCYFEM_91d8b796 SEXCYFEM \n", - "32 HISCYHISP_e62d7c2e HISCYHISP \n", - "38 AGECYGT15_7d84cd34 AGECYGT15 \n", - "39 EDUCYLTGR9_ed0362fa EDUCYLTGR9 \n", - "40 EDUCYSHSCH_7a88e398 EDUCYSHSCH \n", - "41 EDUCYHSCH_a7a81733 EDUCYHSCH \n", - "42 EDUCYSCOLL_3840e65b EDUCYSCOLL \n", - "43 EDUCYASSOC_dcd76160 EDUCYASSOC \n", - "44 EDUCYBACH_d7b78049 EDUCYBACH \n", - "45 EDUCYGRAD_c58943fb EDUCYGRAD \n", - "46 AGECYGT25_56a99ef7 AGECYGT25 \n", - "84 LBFCYPOP16_75363c6f LBFCYPOP16 \n", - "85 LBFCYARM_c8f45b67 LBFCYARM \n", - "86 LBFCYEMPL_1902fd90 LBFCYEMPL \n", - "87 LBFCYUNEM_befc2d4 LBFCYUNEM \n", - "88 LBFCYNLF_803bfa0d LBFCYNLF \n", - "90 LBFCYLBF_1d3c03ed LBFCYLBF \n", - "101 POPPY_24dbbb56 Total population \n", - "\n", - " description db_type agg_method \\\n", - "1 Population (2019A) INTEGER SUM \n", - "2 Population in Group Quarters (2019A) INTEGER SUM \n", - "3 Institutional Group Quarters Population (2019A) INTEGER SUM \n", - "4 Population age 0-4 (2019A) INTEGER SUM \n", - "5 Population age 5-9 (2019A) INTEGER SUM \n", - "6 Population age 10-14 (2019A) INTEGER SUM \n", - "7 Population age 15-19 (2019A) INTEGER SUM \n", - "8 Population age 20-24 (2019A) INTEGER SUM \n", - "9 Population age 25-29 (2019A) INTEGER SUM \n", - "10 Population age 30-34 (2019A) INTEGER SUM \n", - "11 Population age 35-39 (2019A) INTEGER SUM \n", - "12 Population age 40-44 (2019A) INTEGER SUM \n", - "13 Population age 45-49 (2019A) INTEGER SUM \n", - "14 Population age 50-54 (2019A) INTEGER SUM \n", - "15 Population age 55-59 (2019A) INTEGER SUM \n", - "16 Population age 60-64 (2019A) INTEGER SUM \n", - "17 Population age 65-69 (2019A) INTEGER SUM \n", - "18 Population age 70-74 (2019A) INTEGER SUM \n", - "19 Population age 75-79 (2019A) INTEGER SUM \n", - "20 Population age 80-84 (2019A) INTEGER SUM \n", - "21 Population age 85+ (2019A) INTEGER SUM \n", - "23 Population male (2019A) INTEGER SUM \n", - "24 Population female (2019A) INTEGER SUM \n", - "32 Population Hispanic (2019A) INTEGER SUM \n", - "38 Population Age 15+ (2019A) INTEGER SUM \n", - "39 Pop 25+ less than 9th grade (2019A) INTEGER SUM \n", - "40 Pop 25+ 9th-12th grade no diploma (2019A) INTEGER SUM \n", - "41 Pop 25+ HS graduate (2019A) INTEGER SUM \n", - "42 Pop 25+ college no diploma (2019A) INTEGER SUM \n", - "43 Pop 25+ Associate degree (2019A) INTEGER SUM \n", - "44 Pop 25+ Bachelor's degree (2019A) INTEGER SUM \n", - "45 Pop 25+ graduate or prof school degree (2019A) INTEGER SUM \n", - "46 Population Age 25+ (2019A) INTEGER SUM \n", - "84 Population Age 16+ (2019A) INTEGER SUM \n", - "85 Pop 16+ in Armed Forces (2019A) INTEGER SUM \n", - "86 Pop 16+ civilian employed (2019A) INTEGER SUM \n", - "87 Pop 16+ civilian unemployed (2019A) INTEGER SUM \n", - "88 Pop 16+ not in labor force (2019A) INTEGER SUM \n", - "90 Population In Labor Force (2019A) INTEGER SUM \n", - "101 Population (2024A) INTEGER SUM \n", - "\n", - " column_name variable_group_id \\\n", - "1 POPCY None \n", - "2 POPCYGRP None \n", - "3 POPCYGRPI None \n", - "4 AGECY0004 carto-do.ags.demographics_sociodemographics_us... \n", - "5 AGECY0509 carto-do.ags.demographics_sociodemographics_us... \n", - "6 AGECY1014 carto-do.ags.demographics_sociodemographics_us... \n", - "7 AGECY1519 carto-do.ags.demographics_sociodemographics_us... \n", - "8 AGECY2024 carto-do.ags.demographics_sociodemographics_us... \n", - "9 AGECY2529 carto-do.ags.demographics_sociodemographics_us... \n", - "10 AGECY3034 carto-do.ags.demographics_sociodemographics_us... \n", - "11 AGECY3539 carto-do.ags.demographics_sociodemographics_us... \n", - "12 AGECY4044 carto-do.ags.demographics_sociodemographics_us... \n", - "13 AGECY4549 carto-do.ags.demographics_sociodemographics_us... \n", - "14 AGECY5054 carto-do.ags.demographics_sociodemographics_us... \n", - "15 AGECY5559 carto-do.ags.demographics_sociodemographics_us... \n", - "16 AGECY6064 carto-do.ags.demographics_sociodemographics_us... \n", - "17 AGECY6569 carto-do.ags.demographics_sociodemographics_us... \n", - "18 AGECY7074 carto-do.ags.demographics_sociodemographics_us... \n", - "19 AGECY7579 carto-do.ags.demographics_sociodemographics_us... \n", - "20 AGECY8084 carto-do.ags.demographics_sociodemographics_us... \n", - "21 AGECYGT85 carto-do.ags.demographics_sociodemographics_us... \n", - "23 SEXCYMAL carto-do.ags.demographics_sociodemographics_us... \n", - "24 SEXCYFEM carto-do.ags.demographics_sociodemographics_us... \n", - "32 HISCYHISP carto-do.ags.demographics_sociodemographics_us... \n", - "38 AGECYGT15 carto-do.ags.demographics_sociodemographics_us... \n", - "39 EDUCYLTGR9 carto-do.ags.demographics_sociodemographics_us... \n", - "40 EDUCYSHSCH carto-do.ags.demographics_sociodemographics_us... \n", - "41 EDUCYHSCH carto-do.ags.demographics_sociodemographics_us... \n", - "42 EDUCYSCOLL carto-do.ags.demographics_sociodemographics_us... \n", - "43 EDUCYASSOC carto-do.ags.demographics_sociodemographics_us... \n", - "44 EDUCYBACH carto-do.ags.demographics_sociodemographics_us... \n", - "45 EDUCYGRAD carto-do.ags.demographics_sociodemographics_us... \n", - "46 AGECYGT25 carto-do.ags.demographics_sociodemographics_us... \n", - "84 LBFCYPOP16 carto-do.ags.demographics_sociodemographics_us... \n", - "85 LBFCYARM carto-do.ags.demographics_sociodemographics_us... \n", - "86 LBFCYEMPL carto-do.ags.demographics_sociodemographics_us... \n", - "87 LBFCYUNEM carto-do.ags.demographics_sociodemographics_us... \n", - "88 LBFCYNLF carto-do.ags.demographics_sociodemographics_us... \n", - "90 LBFCYLBF carto-do.ags.demographics_sociodemographics_us... \n", - "101 POPPY None \n", - "\n", - " dataset_id \\\n", - "1 carto-do.ags.demographics_sociodemographics_us... \n", - "2 carto-do.ags.demographics_sociodemographics_us... \n", - "3 carto-do.ags.demographics_sociodemographics_us... \n", - "4 carto-do.ags.demographics_sociodemographics_us... \n", - "5 carto-do.ags.demographics_sociodemographics_us... \n", - "6 carto-do.ags.demographics_sociodemographics_us... \n", - "7 carto-do.ags.demographics_sociodemographics_us... \n", - "8 carto-do.ags.demographics_sociodemographics_us... \n", - "9 carto-do.ags.demographics_sociodemographics_us... \n", - "10 carto-do.ags.demographics_sociodemographics_us... \n", - 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"90 carto-do.ags.demographics_sociodemographics_us... \n", - "101 carto-do.ags.demographics_sociodemographics_us... " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vdf[vdf['description'].str.contains('pop', case=False, na=False)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset and variables metadata\n", - "\n", - "The Data Observatory catalog is not only a repository of curated spatial datasets, it also contains valuable information that helps on understanding better the underlying data for every dataset, so you can take an informed decision on what data best fits your problem.\n", - "\n", - "Some of the augmented metadata you can find for each dataset in the catalog is:\n", - "\n", - "- `head` and `tail` methods to get a glimpse of the actual data. This helps you to understand the available columns, data types, etc. To start modelling your problem right away.\n", - "- `geom_coverage` to visualize on a map the geographical coverage of the data in the `Dataset`.\n", - "- `counts`, `fields_by_type` and a full `describe` method with stats of the actual values in the dataset, such as: average, stdev, quantiles, min, max, median for each of the variables of the dataset.\n", - "\n", - "You don't need a subscription to a dataset to be able to query the augmented metadata, it's just publicly available for anyone exploring the Data Observatory catalog.\n", - "\n", - "Let's overview some of that information, starting by getting a glimpse of the ten first or last rows of the actual data of the dataset:" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.data.observatory import Dataset\n", - "dataset = Dataset.get('ags_sociodemogr_a7e14220')" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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MARCYDIVOR MARCYNEVER \\\n", - "0 2020-01-01 00:00:00+00:00 77.40 ... 149 4 \n", - "1 2020-01-01 00:00:00+00:00 76.88 ... 414 160 \n", - "2 2020-01-01 00:00:00+00:00 69.88 ... 31 217 \n", - "3 2020-01-01 00:00:00+00:00 71.44 ... 191 79 \n", - "4 2020-01-01 00:00:00+00:00 72.38 ... 30 44 \n", - "5 2020-01-01 00:00:00+00:00 76.75 ... 160 40 \n", - "6 2020-01-01 00:00:00+00:00 74.58 ... 423 136 \n", - "7 2020-01-01 00:00:00+00:00 73.86 ... 117 63 \n", - "8 2020-01-01 00:00:00+00:00 68.80 ... 406 45 \n", - "9 2020-01-01 00:00:00+00:00 68.59 ... 253 60 \n", - "\n", - " MARCYWIDOW RCHCYAMNHS RCHCYASNHS RCHCYBLNHS RCHCYHANHS RCHCYMUNHS \\\n", - "0 228 0 11 20 0 25 \n", - "1 699 0 74 68 7 55 \n", - "2 246 2 55 43 9 26 \n", - "3 268 8 24 38 0 8 \n", - "4 195 3 9 0 0 0 \n", - "5 319 0 136 19 2 12 \n", - "6 496 3 226 10 1 16 \n", - "7 215 5 33 7 0 9 \n", - "8 301 5 168 26 3 19 \n", - "9 223 5 97 22 1 10 \n", - "\n", - " RCHCYOTNHS RCHCYWHNHS \n", - "0 0 1317 \n", - "1 0 2167 \n", - "2 0 1313 \n", - "3 0 1290 \n", - "4 0 902 \n", - "5 5 1024 \n", - "6 0 1269 \n", - "7 0 986 \n", - "8 0 2183 \n", - "9 0 1750 \n", - "\n", - "[10 rows x 110 columns]" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Alternatively, you can get the last ten ones with `dataset.tail()`" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "An overview of the coverage of the dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " None\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", " \n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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                                                                                      avg1.520470e+033.725678e+011.798816e+019.047040e+019.341024e+019.581574e+019.726450e+011.000845e+021.083624e+021.044326e+02...4.957671e+012.116296e+023.835577e+021.568307e+036.075594e+026.718555e+023.989449e+014.292896e+041.073098e+057.933328e+04
                                                                                      max6.710000e+041.975200e+041.205300e+045.393000e+035.294000e+035.195000e+037.606000e+031.480400e+045.767000e+035.616000e+03...6.547000e+031.005700e+042.367600e+047.584500e+042.811500e+043.264000e+048.750000e+013.824975e+061.112720e+073.500000e+05
                                                                                      min0.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+00...0.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+000.000000e+00
                                                                                      sum3.302187e+088.091501e+063.906704e+061.964854e+072.028702e+072.080945e+072.112410e+072.173655e+072.353436e+072.268087e+07...1.076717e+074.596213e+078.330182e+073.406080e+081.319510e+081.459149e+088.664364e+069.323398e+092.330576e+101.722976e+10
                                                                                      range6.710000e+041.975200e+041.205300e+045.393000e+035.294000e+035.195000e+037.606000e+031.480400e+045.767000e+035.616000e+03...6.547000e+031.005700e+042.367600e+047.584500e+042.811500e+043.264000e+048.750000e+013.824975e+061.112720e+073.500000e+05
                                                                                      stdev1.063417e+032.428693e+021.582057e+028.044792e+018.338915e+018.351758e+011.117828e+021.244125e+029.642758e+019.358961e+01...9.849786e+012.353261e+023.163312e+021.141981e+034.138545e+024.467692e+027.567177e+003.178870e+047.835131e+044.262018e+04
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                                                                                      10 rows × 107 columns

                                                                                      \n", + "" + ], + "text/plain": [ + " POPCY POPCYGRP POPCYGRPI AGECY0004 \\\n", + "avg 1.520470e+03 3.725678e+01 1.798816e+01 9.047040e+01 \n", + "max 6.710000e+04 1.975200e+04 1.205300e+04 5.393000e+03 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 3.302187e+08 8.091501e+06 3.906704e+06 1.964854e+07 \n", + "range 6.710000e+04 1.975200e+04 1.205300e+04 5.393000e+03 \n", + "stdev 1.063417e+03 2.428693e+02 1.582057e+02 8.044792e+01 \n", + "q1 8.500000e+02 0.000000e+00 0.000000e+00 4.400000e+01 \n", + "q3 1.454000e+03 0.000000e+00 0.000000e+00 8.300000e+01 \n", + "median 1.125000e+03 0.000000e+00 0.000000e+00 6.200000e+01 \n", + "interquartile_range 6.040000e+02 0.000000e+00 0.000000e+00 3.900000e+01 \n", "\n", - " return new mapboxgl.Map({\n", - " container,\n", - " style,\n", - " zoom: 9,\n", - " dragRotate: false,\n", - " attributionControl: false\n", - " });\n", - " }\n", + " AGECY0509 AGECY1014 AGECY1519 AGECY2024 \\\n", + "avg 9.341024e+01 9.581574e+01 9.726450e+01 1.000845e+02 \n", + "max 5.294000e+03 5.195000e+03 7.606000e+03 1.480400e+04 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 2.028702e+07 2.080945e+07 2.112410e+07 2.173655e+07 \n", + "range 5.294000e+03 5.195000e+03 7.606000e+03 1.480400e+04 \n", + "stdev 8.338915e+01 8.351758e+01 1.117828e+02 1.244125e+02 \n", + "q1 4.400000e+01 4.600000e+01 4.500000e+01 4.300000e+01 \n", + "q3 8.600000e+01 8.900000e+01 8.700000e+01 8.600000e+01 \n", + "median 6.300000e+01 6.500000e+01 6.400000e+01 6.200000e+01 \n", + "interquartile_range 4.200000e+01 4.300000e+01 4.200000e+01 4.300000e+01 \n", "\n", - " function init(settings) {\n", - " setReady(settings);\n", - " }\n", + " AGECY2529 AGECY3034 ... DWLCYVACNT \\\n", + "avg 1.083624e+02 1.044326e+02 ... 4.957671e+01 \n", + "max 5.767000e+03 5.616000e+03 ... 6.547000e+03 \n", + "min 0.000000e+00 0.000000e+00 ... 0.000000e+00 \n", + "sum 2.353436e+07 2.268087e+07 ... 1.076717e+07 \n", + "range 5.767000e+03 5.616000e+03 ... 6.547000e+03 \n", + "stdev 9.642758e+01 9.358961e+01 ... 9.849786e+01 \n", + "q1 5.000000e+01 4.900000e+01 ... 1.100000e+01 \n", + "q3 9.800000e+01 9.500000e+01 ... 3.400000e+01 \n", + "median 7.100000e+01 6.900000e+01 ... 2.000000e+01 \n", + "interquartile_range 4.800000e+01 4.600000e+01 ... 2.300000e+01 \n", "\n", - " return init;\n", + " DWLCYRENT DWLCYOWNED POPPY HHDPY \\\n", + "avg 2.116296e+02 3.835577e+02 1.568307e+03 6.075594e+02 \n", + "max 1.005700e+04 2.367600e+04 7.584500e+04 2.811500e+04 \n", + "min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", + "sum 4.596213e+07 8.330182e+07 3.406080e+08 1.319510e+08 \n", + "range 1.005700e+04 2.367600e+04 7.584500e+04 2.811500e+04 \n", + "stdev 2.353261e+02 3.163312e+02 1.141981e+03 4.138545e+02 \n", + "q1 6.000000e+01 1.820000e+02 8.670000e+02 3.440000e+02 \n", + "q3 1.780000e+02 3.750000e+02 1.485000e+03 5.810000e+02 \n", + "median 1.080000e+02 2.740000e+02 1.143000e+03 4.520000e+02 \n", + "interquartile_range 1.180000e+02 1.930000e+02 6.180000e+02 2.370000e+02 \n", "\n", - "}());\n", - "\n", - "\n", - "\n", - "\">\n", + " INCPYMEDHH \n", + "avg 7.933328e+04 \n", + "max 3.500000e+05 \n", + "min 0.000000e+00 \n", + "sum 1.722976e+10 \n", + "range 3.500000e+05 \n", + "stdev 4.262018e+04 \n", + "q1 4.701800e+04 \n", + "q3 7.908300e+04 \n", + "median 6.212200e+04 \n", + "interquartile_range 3.206500e+04 \n", "\n", - "" - ], + "[10 rows x 107 columns]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Every `Dataset` instance in the catalog contains other useful metadata:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'slug': 'ags_sociodemogr_a7e14220',\n", + " 'name': 'Sociodemographics - United States of America (Census Block Group)',\n", + " 'description': 'Census and ACS sociodemographic data estimated for the current year and data projected to five years. Projected fields are general aggregates (total population, total households, median age, avg income etc.)',\n", + " 'category_id': 'demographics',\n", + " 'country_id': 'usa',\n", + " 'data_source_id': 'sociodemographics',\n", + " 'provider_id': 'ags',\n", + " 'geography_name': 'Census Block Group - United States of America',\n", + " 'geography_description': None,\n", + " 'temporal_aggregation': 'yearly',\n", + " 'time_coverage': None,\n", + " 'update_frequency': 'yearly',\n", + " 'is_public_data': False,\n", + " 'lang': 'eng',\n", + " 'version': '2020',\n", + " 'category_name': 'Demographics',\n", + " 'provider_name': 'Applied Geographic Solutions',\n", + " 'geography_id': 'carto-do.ags.geography_usa_blockgroup_2015',\n", + " 'id': 'carto-do.ags.demographics_sociodemographics_usa_blockgroup_2015_yearly_2020'}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.to_dict()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When exploring datasets in the Data Observatory catalog it's very important that you understand clearly what variables are available to enrich your own data.\n", + "\n", + "For each `Variable` in each dataset, the Data Observatory provides (as it does for datasets) a set of methods and attributes to understand their underlaying data.\n", + "\n", + "Some of them are:\n", + "\n", + "- `head` and `tail` methods to get a glimpse of the actual data and start modelling your problem right away.\n", + "- `counts`, `quantiles` and a full `describe` method with stats of the actual values in the dataset, such as: average, stdev, quantiles, min, max, median for each of the variables of the dataset.\n", + "- an `histogram` plot with the distribution of the values on each variable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's review some of that augmented metadata for the variables in the AGS population dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + " #'Population (2019A)'" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cartoframes.data.observatory import Variable\n", + "\n", + "variable = Variable.get('POPCY_4534fac4')\n", + "variable" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'slug': 'POPCY_4534fac4',\n", + " 'name': 'Total Population',\n", + " 'description': 'Population (2019A)',\n", + " 'db_type': 'INTEGER',\n", + " 'agg_method': 'SUM',\n", + " 'column_name': 'POPCY',\n", + " 'variable_group_id': None,\n", + " 'dataset_id': 'carto-do.ags.demographics_sociodemographics_usa_blockgroup_2015_yearly_2020',\n", + " 'id': 'carto-do.ags.demographics_sociodemographics_usa_blockgroup_2015_yearly_2020.POPCY'}" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variable.to_dict()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There's also some utility methods to understand the underlying data for each variable:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0 1500\n", + "1 2595\n", + "2 1585\n", + "3 1503\n", + "4 980\n", + "5 1249\n", + "6 1658\n", + "7 1078\n", + "8 2540\n", + "9 1980\n", + "dtype: int64" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variable.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "all 217182.000000\n", + "null 0.000000\n", + "zero 299.000000\n", + "extreme 9073.000000\n", + "distinct 6756.000000\n", + "outliers 26998.000000\n", + "null_percent 0.000000\n", + "zero_percent 0.137673\n", + "extreme_percent 0.041776\n", + "distinct_percent 3.110755\n", + "outliers_percent 0.124310\n", + "dtype: float64" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variable.counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { "text/plain": [ - "" + "q1 850\n", + "q3 1454\n", + "median 1125\n", + "interquartile_range 604\n", + "dtype: int64" ] }, "execution_count": 11, @@ -3438,566 +2325,805 @@ } ], "source": [ - "dataset.geom_coverage()" + "variable.quantiles()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "
                                                                                      " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "variable.histogram()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "avg 1.520470e+03\n", + "max 6.710000e+04\n", + "min 0.000000e+00\n", + "sum 3.302187e+08\n", + "range 6.710000e+04\n", + "stdev 1.063417e+03\n", + "q1 8.500000e+02\n", + "q3 1.454000e+03\n", + "median 1.125000e+03\n", + "interquartile_range 6.040000e+02\n", + "dtype: float64" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "variable.describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Subscribe to a Dataset in the catalog\n", + "\n", + "Once you have explored the catalog and have identified a dataset with the variables you need for your analysis and in the right spatial resolution, you can check `is_public_data` to know whether the dataset is freely accessible or you first need to purchase a license. Subscriptions are available for CARTO's Enterprise plan users.\n", + "\n", + "Subscriptions to datasets allow you to use them from CARTOframes to enrich your own data or to download them. See the enrichment guides for more information.\n", + "\n", + "Let's see the dataset and geography in our previous example:" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "dataset = Dataset.get('ags_sociodemogr_a7e14220')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dataset.is_public_data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `dataset` is not public data, that means you need a subscription to be able to use them to enrich your own data.\n", + "\n", + "> To subscribe to data in the Data Observatory catalog you need a CARTO account with access to Data Observatory" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.subscribe()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Some stats about the dataset:" + "**Licenses to data in the Data Observatory grant you the right to use the data for the period of one year. Every dataset or geography you want to use to enrich your own data, as long as they are not public data, require a valid license.**\n", + "\n", + "You can check the actual status of your subscriptions directly from the catalog." ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "rows 217182.0\n", - "cells 23890020.0\n", - "null_cells 0.0\n", - "null_cells_percent 0.0\n", - "dtype: float64" + "Datasets: [, ]\n", + "Geographies: []" ] }, - "execution_count": 12, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dataset.counts()" + "from cartoframes.data.observatory import Catalog\n", + "\n", + "Catalog().subscriptions()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Access\n", + "\n", + "Now that we have explored some basic information about the Dataset, we will proceed to download a sample of the Dataset into a dataframe so we can operate with it in Python. \n", + "\n", + "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to perform this action._" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 19, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "float 5\n", - "string 2\n", - "integer 102\n", - "timestamp 1\n", - "dtype: int64" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "dataset.fields_by_type()" + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('creds.json')" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.data.observatory import Dataset\n", + "\n", + "dataset = Dataset.get('ags_sociodemogr_a7e14220')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# Filter by SQL query\n", + "query = \"SELECT * FROM $dataset$ LIMIT 50\"\n", + "\n", + "dataset_df = dataset.to_dataframe(sql_query=query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note about SQL filters**\n", + "\n", + "Our SQL filtering queries allow for any PostgreSQL and PostGIS operation, so you can filter the rows (by a WHERE condition) or the columns (using the SELECT). Some common examples are filtering the Dataset by bounding box or filtering by column value: \n", + "\n", + "```\n", + "SELECT * FROM $dataset$ WHERE ST_IntersectsBox(geom, -74.044467,40.706128,-73.891345,40.837690)\n", + "```\n", + "\n", + "```\n", + "SELECT total_pop, geom FROM $dataset$\n", + "```\n", + "\n", + "A good tool to get the bounding box details for a specific area is [bboxfinder.com](http://bboxfinder.com/#0.000000,0.000000,0.000000,0.000000)." + ] + }, + { + "cell_type": "code", + "execution_count": 22, "metadata": {}, "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                      sum3.302187e+088.091501e+063.906704e+061.964854e+072.028702e+072.080945e+072.112410e+072.173655e+072.353436e+072.268087e+07...1.076717e+074.596213e+078.330182e+073.406080e+081.319510e+081.459149e+088.664364e+069.323398e+092.330576e+101.722976e+10
                                                                                      range6.710000e+041.975200e+041.205300e+045.393000e+035.294000e+035.195000e+037.606000e+031.480400e+045.767000e+035.616000e+03...6.547000e+031.005700e+042.367600e+047.584500e+042.811500e+043.264000e+048.750000e+013.824975e+061.112720e+073.500000e+05
                                                                                      \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
                                                                                      BLOCKGROUPPOPCYPOPCYGRPPOPCYGRPIAGECY0004AGECY0509AGECY1014AGECY1519AGECY2024AGECY2529...POPPYHHDPYDWLPYAGEPYMEDINCPYPCAPINCPYAVEHHINCPYMEDHHgeoiddo_dategeom
                                                                                      stdev1.063417e+032.428693e+021.582057e+028.044792e+018.338915e+018.351758e+011.117828e+021.244125e+029.642758e+019.358961e+0105303300740131109001614926106256...9.849786e+012.353261e+023.163312e+021.141981e+034.138545e+024.467692e+027.567177e+003.178870e+047.835131e+044.262018e+04113681188733.831339731876621322665303300740132020-01-01 00:00:00+00:00POLYGON ((-122.32814 47.62490, -122.32812 47.6...
                                                                                      q18.500000e+020.000000e+000.000000e+004.400000e+014.400000e+014.600000e+014.500000e+014.300000e+015.000000e+014.900000e+0114013422625215449595191912191624...1.100000e+016.000000e+011.820000e+028.670000e+023.440000e+023.840000e+023.407000e+012.168000e+045.536100e+044.701800e+0417531208149274.27315214524348999401342262522020-01-01 00:00:00+00:00POLYGON ((-111.70519 33.41560, -111.70522 33.4...
                                                                                      q31.454000e+030.000000e+000.000000e+008.300000e+018.600000e+018.900000e+018.700000e+018.600000e+019.800000e+019.500000e+0121305101100611531414031323324916...3.400000e+011.780000e+023.750000e+021.485000e+035.810000e+026.480000e+024.101000e+014.058200e+041.012720e+057.908300e+04152383289969.371015211855391315211305101100612020-01-01 00:00:00+00:00POLYGON ((-81.06554 31.94715, -81.06484 31.946...
                                                                                      median1.125000e+030.000000e+000.000000e+006.200000e+016.300000e+016.500000e+016.400000e+016.200000e+017.100000e+016.900000e+0132501735410041015140024101152123167...2.000000e+011.080000e+022.740000e+021.143000e+034.520000e+025.040000e+023.771000e+013.056300e+047.671300e+046.212200e+04105254460441.691489622863751795442501735410042020-01-01 00:00:00+00:00POLYGON ((-71.12449 42.37597, -71.12478 42.375...
                                                                                      interquartile_range6.040000e+020.000000e+000.000000e+003.900000e+014.200000e+014.300000e+014.200000e+014.300000e+014.800000e+014.600000e+01425025000401421280078522534138273...2.300000e+011.180000e+021.930000e+026.180000e+022.370000e+022.640000e+026.940000e+001.890200e+044.591100e+043.206500e+0422231264129152.883624063736174382502500040142020-01-01 00:00:00+00:00POLYGON ((-71.15473 42.34121, -71.15446 42.341...
                                                                                      \n", - "

                                                                                      10 rows × 107 columns

                                                                                      \n", + "

                                                                                      5 rows × 111 columns

                                                                                      \n", "
                                                                                      " ], "text/plain": [ - " POPCY POPCYGRP POPCYGRPI AGECY0004 \\\n", - "avg 1.520470e+03 3.725678e+01 1.798816e+01 9.047040e+01 \n", - "max 6.710000e+04 1.975200e+04 1.205300e+04 5.393000e+03 \n", - "min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", - "sum 3.302187e+08 8.091501e+06 3.906704e+06 1.964854e+07 \n", - "range 6.710000e+04 1.975200e+04 1.205300e+04 5.393000e+03 \n", - "stdev 1.063417e+03 2.428693e+02 1.582057e+02 8.044792e+01 \n", - "q1 8.500000e+02 0.000000e+00 0.000000e+00 4.400000e+01 \n", - "q3 1.454000e+03 0.000000e+00 0.000000e+00 8.300000e+01 \n", - "median 1.125000e+03 0.000000e+00 0.000000e+00 6.200000e+01 \n", - "interquartile_range 6.040000e+02 0.000000e+00 0.000000e+00 3.900000e+01 \n", - "\n", - " AGECY0509 AGECY1014 AGECY1519 AGECY2024 \\\n", - "avg 9.341024e+01 9.581574e+01 9.726450e+01 1.000845e+02 \n", - "max 5.294000e+03 5.195000e+03 7.606000e+03 1.480400e+04 \n", - "min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", - "sum 2.028702e+07 2.080945e+07 2.112410e+07 2.173655e+07 \n", - "range 5.294000e+03 5.195000e+03 7.606000e+03 1.480400e+04 \n", - "stdev 8.338915e+01 8.351758e+01 1.117828e+02 1.244125e+02 \n", - "q1 4.400000e+01 4.600000e+01 4.500000e+01 4.300000e+01 \n", - "q3 8.600000e+01 8.900000e+01 8.700000e+01 8.600000e+01 \n", - "median 6.300000e+01 6.500000e+01 6.400000e+01 6.200000e+01 \n", - "interquartile_range 4.200000e+01 4.300000e+01 4.200000e+01 4.300000e+01 \n", - "\n", - " AGECY2529 AGECY3034 ... DWLCYVACNT \\\n", - "avg 1.083624e+02 1.044326e+02 ... 4.957671e+01 \n", - "max 5.767000e+03 5.616000e+03 ... 6.547000e+03 \n", - "min 0.000000e+00 0.000000e+00 ... 0.000000e+00 \n", - "sum 2.353436e+07 2.268087e+07 ... 1.076717e+07 \n", - "range 5.767000e+03 5.616000e+03 ... 6.547000e+03 \n", - "stdev 9.642758e+01 9.358961e+01 ... 9.849786e+01 \n", - "q1 5.000000e+01 4.900000e+01 ... 1.100000e+01 \n", - "q3 9.800000e+01 9.500000e+01 ... 3.400000e+01 \n", - "median 7.100000e+01 6.900000e+01 ... 2.000000e+01 \n", - "interquartile_range 4.800000e+01 4.600000e+01 ... 2.300000e+01 \n", - "\n", - " DWLCYRENT DWLCYOWNED POPPY HHDPY \\\n", - "avg 2.116296e+02 3.835577e+02 1.568307e+03 6.075594e+02 \n", - "max 1.005700e+04 2.367600e+04 7.584500e+04 2.811500e+04 \n", - "min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", - "sum 4.596213e+07 8.330182e+07 3.406080e+08 1.319510e+08 \n", - "range 1.005700e+04 2.367600e+04 7.584500e+04 2.811500e+04 \n", - "stdev 2.353261e+02 3.163312e+02 1.141981e+03 4.138545e+02 \n", - "q1 6.000000e+01 1.820000e+02 8.670000e+02 3.440000e+02 \n", - "q3 1.780000e+02 3.750000e+02 1.485000e+03 5.810000e+02 \n", - "median 1.080000e+02 2.740000e+02 1.143000e+03 4.520000e+02 \n", - "interquartile_range 1.180000e+02 1.930000e+02 6.180000e+02 2.370000e+02 \n", - "\n", - " DWLPY AGEPYMED INCPYPCAP INCPYAVEHH \\\n", - "avg 6.718555e+02 3.989449e+01 4.292896e+04 1.073098e+05 \n", - "max 3.264000e+04 8.750000e+01 3.824975e+06 1.112720e+07 \n", - "min 0.000000e+00 0.000000e+00 0.000000e+00 0.000000e+00 \n", - "sum 1.459149e+08 8.664364e+06 9.323398e+09 2.330576e+10 \n", - "range 3.264000e+04 8.750000e+01 3.824975e+06 1.112720e+07 \n", - "stdev 4.467692e+02 7.567177e+00 3.178870e+04 7.835131e+04 \n", - "q1 3.840000e+02 3.407000e+01 2.168000e+04 5.536100e+04 \n", - "q3 6.480000e+02 4.101000e+01 4.058200e+04 1.012720e+05 \n", - "median 5.040000e+02 3.771000e+01 3.056300e+04 7.671300e+04 \n", - "interquartile_range 2.640000e+02 6.940000e+00 1.890200e+04 4.591100e+04 \n", - "\n", - " INCPYMEDHH \n", - "avg 7.933328e+04 \n", - "max 3.500000e+05 \n", - "min 0.000000e+00 \n", - "sum 1.722976e+10 \n", - "range 3.500000e+05 \n", - "stdev 4.262018e+04 \n", - "q1 4.701800e+04 \n", - "q3 7.908300e+04 \n", - "median 6.212200e+04 \n", - "interquartile_range 3.206500e+04 \n", - "\n", - "[10 rows x 107 columns]" + " BLOCKGROUP POPCY POPCYGRP POPCYGRPI AGECY0004 AGECY0509 AGECY1014 \\\n", + "0 530330074013 1109 0 0 16 14 9 \n", + "1 40134226252 1544 95 95 19 19 12 \n", + "2 130510110061 1531 41 40 31 32 33 \n", + "3 250173541004 1015 140 0 24 10 11 \n", + "4 250250004014 2128 0 0 78 52 25 \n", + "\n", + " AGECY1519 AGECY2024 AGECY2529 ... POPPY HHDPY DWLPY AGEPYMED \\\n", + "0 26 106 256 ... 1136 811 887 33.83 \n", + "1 19 16 24 ... 1753 1208 1492 74.27 \n", + "2 24 9 16 ... 1523 832 899 69.37 \n", + "3 52 123 167 ... 1052 544 604 41.69 \n", + "4 34 138 273 ... 2223 1264 1291 52.88 \n", + "\n", + " INCPYPCAP INCPYAVEHH INCPYMEDHH geoid do_date \\\n", + "0 133973 187662 132266 530330074013 2020-01-01 00:00:00+00:00 \n", + "1 31521 45243 48999 40134226252 2020-01-01 00:00:00+00:00 \n", + "2 101521 185539 131521 130510110061 2020-01-01 00:00:00+00:00 \n", + "3 148962 286375 179544 250173541004 2020-01-01 00:00:00+00:00 \n", + "4 36240 63736 17438 250250004014 2020-01-01 00:00:00+00:00 \n", + "\n", + " geom \n", + "0 POLYGON ((-122.32814 47.62490, -122.32812 47.6... \n", + "1 POLYGON ((-111.70519 33.41560, -111.70522 33.4... \n", + "2 POLYGON ((-81.06554 31.94715, -81.06484 31.946... \n", + "3 POLYGON ((-71.12449 42.37597, -71.12478 42.375... \n", + "4 POLYGON ((-71.15473 42.34121, -71.15446 42.341... \n", + "\n", + "[5 rows x 111 columns]" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# First rows of the Dataset sample\n", + "dataset_df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also download the dataset directly to a CSV file" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data saved: my_dataset.csv\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "To load it as a DataFrame you can do:\n", + "\n", + " df = pandas.read_csv('my_dataset.csv')\n", + "\n" + ] + } + ], + "source": [ + "query = \"SELECT * FROM $dataset$ LIMIT 50\"\n", + "\n", + "dataset_df = dataset.to_csv('my_dataset.csv', sql_query=query)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data Enrichment\n", + "\n", + "We define enrichment as the process of augmenting your data with new variables by means of a spatial join between your data and a `Dataset` aggregated at a given spatial resolution in the CARTO Data Observatory, or in other words:\n", + "\n", + "\"*Enrichment is the process of adding variables to a geometry, which we call the target, (point, line, polygon…) from a spatial (polygon) dataset, which we call the source*\"\n", + "\n", + "We recommend you check out the [CARTOframes quickstart](/developers/cartoframes/guides/Quickstart/) since this guide uses some of the generated DataFrames to learn about exploring the Data Observatory catalog to find variables of interest for your analyses.\n", + "\n", + "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to perform this action._" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [], + "source": [ + "from cartoframes.auth import set_default_credentials\n", + "\n", + "set_default_credentials('creds.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[ #'Geographic Identifier',\n", + " #'Population (2019A)',\n", + " #'Population in Group Quarters (2019A)',\n", + " #'Institutional Group Quarters Population (2019A)',\n", + " #'Population age 0-4 (2019A)',\n", + " #'Population age 5-9 (2019A)',\n", + " #'Population age 10-14 (2019A)',\n", + " #'Population age 15-19 (2019A)',\n", + " #'Population age 20-24 (2019A)',\n", + " #'Population age 25-29 (2019A)',\n", + " #'Population age 30-34 (2019A)',\n", + " #'Population age 35-39 (2019A)',\n", + " #'Population age 40-44 (2019A)',\n", + " #'Population age 45-49 (2019A)',\n", + " #'Population age 50-54 (2019A)',\n", + " #'Population age 55-59 (2019A)',\n", + " #'Population age 60-64 (2019A)',\n", + " #'Population age 65-69 (2019A)',\n", + " #'Population age 70-74 (2019A)',\n", + " #'Population age 75-79 (2019A)',\n", + " #'Population age 80-84 (2019A)',\n", + " #'Population age 85+ (2019A)',\n", + " #'Median Age (2019A)',\n", + " #'Population male (2019A)',\n", + " #'Population female (2019A)',\n", + " #'Non Hispanic White (2019A)',\n", + " #'Non Hispanic Black (2019A)',\n", + " #'Non Hispanic American Indian (2019A)',\n", + " #'Non Hispanic Asian (2019A)',\n", + " #'Non Hispanic Hawaiian/Pacific Islander (2019A)',\n", + " #'Non Hispanic Other Race (2019A)',\n", + " #'Non Hispanic Multiple Race (2019A)',\n", + " #'Population Hispanic (2019A)',\n", + " #'Never Married (2019A)',\n", + " #'Now Married (2019A)',\n", + " #'Separated (2019A)',\n", + " #'Widowed (2019A)',\n", + " #'Divorced (2019A)',\n", + " #'Population Age 15+ (2019A)',\n", + " #'Pop 25+ less than 9th grade (2019A)',\n", + " #'Pop 25+ 9th-12th grade no diploma (2019A)',\n", + " #'Pop 25+ HS graduate (2019A)',\n", + " #'Pop 25+ college no diploma (2019A)',\n", + " #'Pop 25+ Associate degree (2019A)',\n", + " #'Pop 25+ Bachelor's degree (2019A)',\n", + " #'Pop 25+ graduate or prof school degree (2019A)',\n", + " #'Population Age 25+ (2019A)',\n", + " #'Households (2019A)',\n", + " #'Family Households (2019A)',\n", + " #'Families married couple w children (2019A)',\n", + " #'Families male no wife w children (2019A)',\n", + " #'Families female no husband children (2019A)',\n", + " #'Average Household Size (2019A)',\n", + " #'Median Age of Householder (2019A)',\n", + " #'Households: No Vehicle Available (2019A)',\n", + " #'Households: One Vehicle Available (2019A)',\n", + " #'Households: Two or More Vehicles Available (2019A)',\n", + " #'Per capita income (2019A)',\n", + " #'Average household Income (2019A)',\n", + " #'Median household income (2019A)',\n", + " #'Median family income (2019A)',\n", + " #'Household Income < $10000 (2019A)',\n", + " #'Household Income $10000-$14999 (2019A)',\n", + " #'Household Income $15000-$19999 (2019A)',\n", + " #'Household Income $20000-$24999 (2019A)',\n", + " #'Household Income $25000-$29999 (2019A)',\n", + " #'Household Income $30000-$34999 (2019A)',\n", + " #'Household Income $35000-$39999 (2019A)',\n", + " #'Household Income $40000-$44999 (2019A)',\n", + " #'Household Income $45000-$49999 (2019A)',\n", + " #'Household Income $50000-$59999 (2019A)',\n", + " #'Household Income $60000-$74999 (2019A)',\n", + " #'Household Income $75000-$99999 (2019A)',\n", + " #'Household Income $100000-$124999 (2019A)',\n", + " #'Household Income $125000-$149999 (2019A)',\n", + " #'Household Income $150000-$199999 (2019A)',\n", + " #'Household Income > $200000 (2019A)',\n", + " #'Median Household Income: Age < 25 (2019A)',\n", + " #'Median Household Income: Age 25-34 (2019A)',\n", + " #'Median Household Income: Age 35-44 (2019A)',\n", + " #'Median Household Income: Age 45-54 (2019A)',\n", + " #'Median Household Income: Age 55-64 (2019A)',\n", + " #'Median Household Income: Age 65-74 (2019A)',\n", + " #'Median Household Income: Age 75+ (2019A)',\n", + " #'Population Age 16+ (2019A)',\n", + " #'Pop 16+ in Armed Forces (2019A)',\n", + " #'Pop 16+ civilian employed (2019A)',\n", + " #'Pop 16+ civilian unemployed (2019A)',\n", + " #'Pop 16+ not in labor force (2019A)',\n", + " #'Unemployment Rate (2019A)',\n", + " #'Population In Labor Force (2019A)',\n", + " #'SPANISH SPEAKING HOUSEHOLDS',\n", + " #'LINGUISTICALLY ISOLATED HOUSEHOLDS (NON-ENGLISH SP...',\n", + " #'Median Value of Owner Occupied Housing Units',\n", + " #'Median Cash Rent',\n", + " #'UNITS IN STRUCTURE: 1 DETACHED',\n", + " #'UNITS IN STRUCTURE: 20 OR MORE',\n", + " #'Housing units (2019A)',\n", + " #'Housing units vacant (2019A)',\n", + " #'Occupied units renter (2019A)',\n", + " #'Occupied units owner (2019A)',\n", + " #'Population (2024A)',\n", + " #'Households (2024A)',\n", + " #'Housing units (2024A)',\n", + " #'Median Age (2024A)',\n", + " #'Per capita income (2024A)',\n", + " #'Average household Income (2024A)',\n", + " #'Median household income (2024A)',\n", + " #'First day of time period',\n", + " #'Geographical Identifier']" ] }, - "execution_count": 14, + "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dataset.describe()" + "from cartoframes.data.observatory import Dataset\n", + "\n", + "dataset = Dataset.get('ags_sociodemogr_a7e14220')\n", + "variables = dataset.variables\n", + "variables" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Every `Dataset` instance in the catalog contains other useful metadata:\n", + "The `ags_sociodemogr_f510a947` dataset contains socio-demographic variables aggregated to the Census block group level. \n", "\n", - "- slug: A short ID\n", - "- name and description: Free text attributes\n", - "- country\n", - "- geography: Every dataset is related to a Geography instance\n", - "- category\n", - "- provider\n", - "- data source\n", - "- lang\n", - "- temporal aggregation\n", - "- time coverage\n", - "- update frequency\n", - "- version\n", - "- is_public_data: whether you need a license to use the dataset for enrichment purposes or not" + "Let's try and find a variable for total population:" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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                                                                                      slugnamedescriptiondb_typeagg_methodcolumn_namevariable_group_iddataset_idid
                                                                                      1POPCY_4534fac4Total PopulationPopulation (2019A)INTEGERSUMPOPCYNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      2POPCYGRP_3033ef2ePOPCYGRPPopulation in Group Quarters (2019A)INTEGERSUMPOPCYGRPNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      3POPCYGRPI_1e42899POPCYGRPIInstitutional Group Quarters Population (2019A)INTEGERSUMPOPCYGRPINonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      84LBFCYPOP16_75363c6fLBFCYPOP16Population Age 16+ (2019A)INTEGERSUMLBFCYPOP16carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      101POPPY_24dbbb56Total populationPopulation (2024A)INTEGERSUMPOPPYNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                      \n", + "
                                                                                      " + ], "text/plain": [ - "{'slug': 'ags_sociodemogr_a7e14220',\n", - " 'name': 'Sociodemographics - United States of America (Census Block Group)',\n", - " 'description': 'Census and ACS sociodemographic data estimated for the current year and data projected to five years. Projected fields are general aggregates (total population, total households, median age, avg income etc.)',\n", - " 'category_id': 'demographics',\n", - " 'country_id': 'usa',\n", - " 'data_source_id': 'sociodemographics',\n", - " 'provider_id': 'ags',\n", - " 'geography_name': 'Census Block Group - United States of America',\n", - " 'geography_description': None,\n", - " 'temporal_aggregation': 'yearly',\n", - " 'time_coverage': None,\n", - " 'update_frequency': 'yearly',\n", - " 'is_public_data': False,\n", - " 'lang': 'eng',\n", - " 'version': '2020',\n", - " 'category_name': 'Demographics',\n", - " 'provider_name': 'Applied Geographic Solutions',\n", - " 'geography_id': 'carto-do.ags.geography_usa_blockgroup_2015',\n", - " 'id': 'carto-do.ags.demographics_sociodemographics_usa_blockgroup_2015_yearly_2020'}" + " slug name \\\n", + "1 POPCY_4534fac4 Total Population \n", + "2 POPCYGRP_3033ef2e POPCYGRP \n", + "3 POPCYGRPI_1e42899 POPCYGRPI \n", + "84 LBFCYPOP16_75363c6f LBFCYPOP16 \n", + "101 POPPY_24dbbb56 Total population \n", + "\n", + " description db_type agg_method \\\n", + "1 Population (2019A) INTEGER SUM \n", + "2 Population in Group Quarters (2019A) INTEGER SUM \n", + "3 Institutional Group Quarters Population (2019A) INTEGER SUM \n", + "84 Population Age 16+ (2019A) INTEGER SUM \n", + "101 Population (2024A) INTEGER SUM \n", + "\n", + " column_name variable_group_id \\\n", + "1 POPCY None \n", + "2 POPCYGRP None \n", + "3 POPCYGRPI None \n", + "84 LBFCYPOP16 carto-do.ags.demographics_sociodemographics_us... \n", + "101 POPPY None \n", + "\n", + " dataset_id \\\n", + "1 carto-do.ags.demographics_sociodemographics_us... \n", + "2 carto-do.ags.demographics_sociodemographics_us... \n", + "3 carto-do.ags.demographics_sociodemographics_us... \n", + "84 carto-do.ags.demographics_sociodemographics_us... \n", + "101 carto-do.ags.demographics_sociodemographics_us... \n", + "\n", + " id \n", + "1 carto-do.ags.demographics_sociodemographics_us... \n", + "2 carto-do.ags.demographics_sociodemographics_us... \n", + "3 carto-do.ags.demographics_sociodemographics_us... \n", + "84 carto-do.ags.demographics_sociodemographics_us... \n", + "101 carto-do.ags.demographics_sociodemographics_us... " ] }, - "execution_count": 15, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dataset.to_dict()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "There's also some intersting metadata, for each variable in the dataset:\n", - "\n", - "- id\n", - "- slug: A short ID\n", - "- name and description\n", - "- column_name: Actual column name in the table that contains the data\n", - "- db_type: SQL type in the database\n", - "- dataset_id\n", - "- agg_method: Aggregation method used\n", - "- temporal aggregation and time coverage" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Variables are the most important asset in the catalog and when exploring datasets in the Data Observatory catalog it's very important that you understand clearly what variables are available to enrich your own data.\n", - "\n", - "For each `Variable` in each dataset, the Data Observatory provides (as it does with datasets) a set of methods and attributes to understand their underlaying data.\n", - "\n", - "Some of them are:\n", - "\n", - "- `head` and `tail` methods to get a glimpse of the actual data and start modelling your problem right away.\n", - "- `counts`, `quantiles` and a full `describe` method with stats of the actual values in the dataset, such as: average, stdev, quantiles, min, max, median for each of the variables of the dataset.\n", - "- an `histogram` plot with the distribution of the values on each variable." + "vdf = variables.to_dataframe()\n", + "vdf[vdf['name'].str.contains('pop', case=False, na=False)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Let's overview some of that augmented metadata for the variables in the AGS population dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " #'Population (2019A)'" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.data.observatory import Variable\n", - "variable = Variable.get('POPCY_4534fac4')\n", - "variable" + "We can store the variable instance we need by searching the Catalog by its `slug`, in this case `POPCY_4534fac4`:" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -4014,12 +3140,13 @@ " 'id': 'carto-do.ags.demographics_sociodemographics_usa_blockgroup_2015_yearly_2020.POPCY'}" ] }, - "execution_count": 17, + "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "variable = Variable.get('POPCY_4534fac4')\n", "variable.to_dict()" ] }, @@ -4027,334 +3154,558 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "There's also some utility methods ot understand the underlying data for each variable:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0 1500\n", - "1 2595\n", - "2 1585\n", - "3 1503\n", - "4 980\n", - "5 1249\n", - "6 1658\n", - "7 1078\n", - "8 2540\n", - "9 1980\n", - "dtype: int64" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "variable.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "all 217182.000000\n", - "null 0.000000\n", - "zero 299.000000\n", - "extreme 9073.000000\n", - "distinct 6756.000000\n", - "outliers 26998.000000\n", - "null_percent 0.000000\n", - "zero_percent 0.137673\n", - "extreme_percent 0.041776\n", - "distinct_percent 3.110755\n", - "outliers_percent 0.124310\n", - "dtype: float64" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "variable.counts()" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "q1 850\n", - "q3 1454\n", - "median 1125\n", - "interquartile_range 604\n", - "dtype: int64" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "variable.quantiles()" + "The `POPCY` variable contains the `SUM` of the population for blockgroup for the year 2019. Let's enrich our stores DataFrame with that variable." ] }, { - "cell_type": "code", - "execution_count": 21, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "
                                                                                      " - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "variable.histogram()" + "#### Enrich points\n", + "\n", + "Let's start by loading the geocoded Starbucks stores:" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 28, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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                                                                                      cartodb_idfield_1nameaddressrevenuegeometry
                                                                                      010Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381321040.772POINT (-73.95901 40.67109)
                                                                                      121607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351268080.418POINT (-73.96122 40.57796)
                                                                                      23265th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041248133.699POINT (-73.98976 40.61912)
                                                                                      343Bay Ridge Pkwy & 3rd Ave7419 3rd Avenue,Brooklyn, NY 112091185702.676POINT (-74.02744 40.63152)
                                                                                      454Caesar's Bay Shopping Center8973 Bay Parkway,Brooklyn, NY 112141148427.411POINT (-74.00098 40.59321)
                                                                                      \n", + "
                                                                                      " + ], "text/plain": [ - "avg 1.520470e+03\n", - "max 6.710000e+04\n", - "min 0.000000e+00\n", - "sum 3.302187e+08\n", - "range 6.710000e+04\n", - "stdev 1.063417e+03\n", - "q1 8.500000e+02\n", - "q3 1.454000e+03\n", - "median 1.125000e+03\n", - "interquartile_range 6.040000e+02\n", - "dtype: float64" + " cartodb_id field_1 name \\\n", + "0 1 0 Franklin Ave & Eastern Pkwy \n", + "1 2 1 607 Brighton Beach Ave \n", + "2 3 2 65th St & 18th Ave \n", + "3 4 3 Bay Ridge Pkwy & 3rd Ave \n", + "4 5 4 Caesar's Bay Shopping Center \n", + "\n", + " address revenue \\\n", + "0 341 Eastern Pkwy,Brooklyn, NY 11238 1321040.772 \n", + "1 607 Brighton Beach Avenue,Brooklyn, NY 11235 1268080.418 \n", + "2 6423 18th Avenue,Brooklyn, NY 11204 1248133.699 \n", + "3 7419 3rd Avenue,Brooklyn, NY 11209 1185702.676 \n", + "4 8973 Bay Parkway,Brooklyn, NY 11214 1148427.411 \n", + "\n", + " geometry \n", + "0 POINT (-73.95901 40.67109) \n", + "1 POINT (-73.96122 40.57796) \n", + "2 POINT (-73.98976 40.61912) \n", + "3 POINT (-74.02744 40.63152) \n", + "4 POINT (-74.00098 40.59321) " ] }, - "execution_count": 22, + "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "variable.describe()" + "from geopandas import read_file\n", + "\n", + "stores_gdf = read_file('http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_geocoded.geojson')\n", + "stores_gdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Subscribe to a Dataset in the catalog\n", - "\n", - "Once you have explored the catalog and have detected a dataset with the variables you need for your analysis and the right spatial resolution, you have to look at the `is_public_data` to know if you can just use it from CARTOframes or you first need to subscribe for a license.\n", - "\n", - "Subscriptions to datasets allow you to use them from CARTOframes to enrich your own data or to download them. See the enrichment guides for more information about this.\n", - "\n", - "Let's see the dataset and geography in our previous example:" + "Alternatively, you can load data in any geospatial format supported by GeoPandas or CARTO." ] }, { - "cell_type": "code", - "execution_count": 23, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "dataset = Dataset.get('ags_sociodemogr_a7e14220')" + "As we can see, for each store we have its name, address, the total revenue by year and a `geometry` column indicating the location of the store. This is important because for the enrichment service to work, we need a DataFrame with a geometry column encoded as a [shapely](https://pypi.org/project/Shapely/) object.\n", + "\n", + "We can now create a new `Enrichment` instance, and since the `stores_gdf` dataset represents store locations (points), we can use the `enrich_points` function passing as arguments the stores DataFrame and a list of `Variables` (that we have a valid subscription from the Data Observatory catalog for).\n", + "\n", + "In this case we are only enriching one variable (the total population), but we could enrich a list of them." ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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                                                                                      cartodb_idfield_1nameaddressrevenuegeometryPOPCYdo_area
                                                                                      010Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381321040.772POINT (-73.95901 40.67109)260859840.196748
                                                                                      121607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351268080.418POINT (-73.96122 40.57796)179260150.636995
                                                                                      23265th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041248133.699POINT (-73.98976 40.61912)73338950.618837
                                                                                      343Bay Ridge Pkwy & 3rd Ave7419 3rd Avenue,Brooklyn, NY 112091185702.676POINT (-74.02744 40.63152)115557353.293114
                                                                                      454Caesar's Bay Shopping Center8973 Bay Parkway,Brooklyn, NY 112141148427.411POINT (-74.00098 40.59321)2266188379.242640
                                                                                      \n", + "
                                                                                      " + ], "text/plain": [ - "False" + " cartodb_id field_1 name \\\n", + "0 1 0 Franklin Ave & Eastern Pkwy \n", + "1 2 1 607 Brighton Beach Ave \n", + "2 3 2 65th St & 18th Ave \n", + "3 4 3 Bay Ridge Pkwy & 3rd Ave \n", + "4 5 4 Caesar's Bay Shopping Center \n", + "\n", + " address revenue \\\n", + "0 341 Eastern Pkwy,Brooklyn, NY 11238 1321040.772 \n", + "1 607 Brighton Beach Avenue,Brooklyn, NY 11235 1268080.418 \n", + "2 6423 18th Avenue,Brooklyn, NY 11204 1248133.699 \n", + "3 7419 3rd Avenue,Brooklyn, NY 11209 1185702.676 \n", + "4 8973 Bay Parkway,Brooklyn, NY 11214 1148427.411 \n", + "\n", + " geometry POPCY do_area \n", + "0 POINT (-73.95901 40.67109) 2608 59840.196748 \n", + "1 POINT (-73.96122 40.57796) 1792 60150.636995 \n", + "2 POINT (-73.98976 40.61912) 733 38950.618837 \n", + "3 POINT (-74.02744 40.63152) 1155 57353.293114 \n", + "4 POINT (-74.00098 40.59321) 2266 188379.242640 " ] }, - "execution_count": 24, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "dataset.is_public_data" + "from cartoframes.data.observatory import Enrichment\n", + "\n", + "enriched_stores_gdf = Enrichment().enrich_points(stores_gdf, [variable])\n", + "enriched_stores_gdf.head()" ] }, { - "cell_type": "code", - "execution_count": 25, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "from cartoframes.data.observatory import Geography\n", - "geography = Geography.get(dataset.geography)" + "Once the enrichment finishes, we can see there is a new column in our DataFrame called `POPCY` with population projected for the year 2019, from the US Census block group which contains each one of our Starbucks stores." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Enrich polygons\n", + "\n", + "Next, let's do a second enrichment, but this time using a DataFrame with areas of influence calculated using the [CARTOframes isochrones](/developers/cartoframes/reference/#heading-Isolines) service to obtain the polygon around each store that covers the area within an 8, 17 and 25 minute walk." ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 30, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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                                                                                      data_rangelower_data_rangerange_labelgeometry
                                                                                      050008 min.MULTIPOLYGON (((-73.95959 40.67571, -73.95971 ...
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                                                                                      21500100025 min.POLYGON ((-73.95986 40.68815, -73.95711 40.688...
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                                                                                      4100050017 min.MULTIPOLYGON (((-73.96684 40.57483, -73.96830 ...
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                                                                                      " + ], "text/plain": [ - "False" + " data_range lower_data_range range_label \\\n", + "0 500 0 8 min. \n", + "1 1000 500 17 min. \n", + "2 1500 1000 25 min. \n", + "3 500 0 8 min. \n", + "4 1000 500 17 min. \n", + "\n", + " geometry \n", + "0 MULTIPOLYGON (((-73.95959 40.67571, -73.95971 ... \n", + "1 POLYGON ((-73.95988 40.68110, -73.95863 40.681... \n", + "2 POLYGON ((-73.95986 40.68815, -73.95711 40.688... \n", + "3 MULTIPOLYGON (((-73.96185 40.58321, -73.96231 ... \n", + "4 MULTIPOLYGON (((-73.96684 40.57483, -73.96830 ... " ] }, - "execution_count": 26, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "geography.is_public_data" + "aoi_gdf = read_file('http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_isolines.geojson')\n", + "aoi_gdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Both `dataset` and `geography` are not public data, that means you need a subscription to be able to use them to enrich your own data.\n", - "\n", - "**To subscribe to data in the Data Observatory catalog you need a CARTO account with access to Data Observatory**" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.auth import set_default_credentials\n", + "In this case we have a DataFrame which, for each index in the `stores_gdf`, contains a polygon of the areas of influence around each store at 8, 17 and 25 minute walking intervals. Again the `geometry` is encoded as a `shapely` object.\n", "\n", - "set_default_credentials('creds.json')" + "In this case, the `Enrichment` service provides an `enrich_polygons` function, which in its basic version, works in the same way as the `enrich_points` function. It just needs a DataFrame with polygon geometries and a list of variables to enrich:" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 31, "metadata": {}, "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "2fd201472cf240ad9e0aabe3f3ceb86f", - "version_major": 2, - "version_minor": 0 - }, + "text/html": [ + "
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                                                                                      \\n The dataset carto-do.ags.demographic…" + " data_range lower_data_range range_label \\\n", + "0 500 0 8 min. \n", + "1 1000 500 17 min. \n", + "2 1500 1000 25 min. \n", + "3 500 0 8 min. \n", + "4 1000 500 17 min. \n", + "\n", + " geometry POPCY \n", + "0 MULTIPOLYGON (((-73.95959 40.67571, -73.95971 ... 21893.521552 \n", + "1 POLYGON ((-73.95988 40.68110, -73.95863 40.681... 60463.830128 \n", + "2 POLYGON ((-73.95986 40.68815, -73.95711 40.688... 111036.739845 \n", + "3 MULTIPOLYGON (((-73.96185 40.58321, -73.96231 ... 23118.113571 \n", + "4 MULTIPOLYGON (((-73.96684 40.57483, -73.96830 ... 29212.999892 " ] }, + "execution_count": 31, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" } ], "source": [ - "dataset.subscribe()" + "from cartoframes.data.observatory import Enrichment\n", + "\n", + "enriched_aoi_gdf = Enrichment().enrich_polygons(aoi_gdf, [variable])\n", + "enriched_aoi_gdf.head()" ] }, { - "cell_type": "code", - "execution_count": 29, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "24b77d25b541441b8b32d370ce9992d8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HTML(value='\\n

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                                                                                      \\n The geography carto-do.ags.geography…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], "source": [ - "geography.subscribe()" + "We now have a new column in our areas of influence DataFrame, `SUM_POPCY`, which represents the `SUM` of the total population in the Census block groups that instersect with each polygon in our DataFrame." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "**Licenses to data in the Data Observatory grant you the right to use the data subscribed for the period of one year. Every dataset or geography you want to use to enrich your own data, as long as they are not public data, require a valid license.**\n", - "\n", - "You can check the actual status of your subscriptions directly from the catalog." + "#### How enrichment works" ] }, { - "cell_type": "code", - "execution_count": 30, + "cell_type": "markdown", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Datasets: [, , , , ]\n", - "Geographies: [, ]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], "source": [ - "Catalog().subscriptions()" + "Let's take a deeper look into what happens under the hood when you execute a polygon enrichment.\n", + "\n", + "Imagine we have polygons representing municipalities, in blue, each of which have a population attribute, and we want to find out the population inside the green circle.\n", + "\n", + "\"Enrichment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Conclusion\n", - "\n", - "In this guide you've seen how to explore the Data Observatory catalog to identify variables of datasets that you can use to enrich your own data.\n", + "We don’t know how the population is distributed inside these municipalities. They are probably concentrated in cities somewhere, but, since we don’t know where they are, our best guess is to assume that the population is evenly distributed in the municipality (i.e. every point inside the municipality has the same population density).\n", "\n", - "You've learned how to:\n", + "Population is an extensive property (it grows with area), so we can subset it (a region inside the municipality will always have a smaller population than the whole municipality), and also aggregate it by summing.\n", "\n", - "- Explore the catalog using nested hierarchical filters.\n", - "- Describe the three main entities in the catalog: `Geography`, `Dataset` and their `Variables`.\n", - "- Taken a look at the data and stats taken from the actual repository, to make a more informed decision on which variables to choose.\n", - "- How to subscribe to the chosen dataset to get a license that grants the right to enrich your own data.\n", - "\n", - "We also recommend checking out the resources below to learn more about the Data Observatory catalog:\n", + "In this case, we’d calculate the population inside each part of the circle that intersects with a municipality." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Default aggregation methods**\n", "\n", - "- The CARTOframes [enrichment guide](/developers/cartoframes/guides/Data-enrichment/)\n", - "- [Our public website](https://carto.com/platform/location-data-streams/)\n", - "- Your user dashboard: Under the data section\n", - "- The CARTOframes catalog [API reference](/developers/cartoframes/reference/#heading-Data-Observatory)\n" + "In the Data Observatory, we suggest a default aggregation method for certain fields. However, some fields don’t have a clear best method, and some just can’t be aggregated. In these cases, we leave the `agg_method` field blank and let the user choose the method that best fits their needs." ] } ], @@ -4375,6 +3726,102 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "state": { + "2bbf01a7a8cc4f4fb67f5a46318443a7": { + "model_module": "@jupyter-widgets/base", + "model_module_version": "1.2.0", + "model_name": "LayoutModel", + "state": { + "_model_module": "@jupyter-widgets/base", + "_model_module_version": "1.2.0", + "_model_name": "LayoutModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "LayoutView", + "align_content": null, + "align_items": null, + "align_self": null, + "border": null, + "bottom": null, + "display": null, + "flex": null, + "flex_flow": null, + "grid_area": null, + "grid_auto_columns": null, + "grid_auto_flow": null, + "grid_auto_rows": null, + "grid_column": null, + "grid_gap": null, + "grid_row": null, + "grid_template_areas": null, + "grid_template_columns": null, + "grid_template_rows": null, + "height": null, + "justify_content": null, + "justify_items": null, + "left": null, + "margin": null, + "max_height": null, + "max_width": null, + "min_height": null, + "min_width": null, + "object_fit": null, + "object_position": null, + "order": null, + "overflow": null, + "overflow_x": null, + "overflow_y": null, + "padding": null, + "right": null, + "top": null, + "visibility": null, + "width": null + } + }, + "9445287e884c4af0a7ee2f3fcf43851f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "DescriptionStyleModel", + "state": { + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + }, + "de036e4308ec4cdba6a093b2236bd42f": { + "model_module": "@jupyter-widgets/controls", + "model_module_version": "1.5.0", + "model_name": "HTMLModel", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HTMLModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HTMLView", + "description": "", + "description_tooltip": null, + "layout": "IPY_MODEL_2bbf01a7a8cc4f4fb67f5a46318443a7", + "placeholder": "​", + "style": "IPY_MODEL_9445287e884c4af0a7ee2f3fcf43851f", + "value": "\n

                                                                                      Subscription already purchased

                                                                                      \n The dataset carto-do.ags.demographics_sociodemographics_usa_blockgroup_2015_yearly_2020 has already been purchased.\n " + } + } + }, + "version_major": 2, + "version_minor": 0 + } } }, "nbformat": 4, diff --git a/guides/creds.sample.json b/docs/guides/creds.sample.json similarity index 100% rename from guides/creds.sample.json rename to docs/guides/creds.sample.json diff --git a/docs/guides/guides.json b/docs/guides/guides.json new file mode 100644 index 000000000..8d3d406ff --- /dev/null +++ b/docs/guides/guides.json @@ -0,0 +1,55 @@ +{ + "main": { + "title": "Introduction", + "file": "introduction.md" + }, + "categories": [ + { + "title": "Guides", + "samples": [ + { + "title": "Installation", + "desc": "Install the library and set up your environment", + "file": "installation", + "path": "" + }, + { + "title": "Authentication", + "desc": "Login into CARTO to unlock the power of spatial analysis", + "file": "authentication", + "path": "" + }, + { + "title": "Quickstart", + "desc": "Complete a real project that walks through the library step by step", + "file": "quickstart", + "path": "" + }, + { + "title": "Data Management", + "desc": "Work with data both locally and with a CARTO account", + "file": "data_management", + "path": "" + }, + { + "title": "Data Visualization", + "desc": "Explore and visualize your spatial data with interactive maps", + "file": "data_visualization", + "path": "" + }, + { + "title": "Data Services", + "desc": "Geocode and discover areas of influence with Isolines", + "file": "data_services", + "path": "" + }, + { + "title": "Data Observatory", + "desc": "Discover and enrich your data with the Data Observatory", + "file": "data_observatory", + "path": "" + } + ] + } + ] +} diff --git a/docs/developer-center/img/guides/credentials/api-keys.png b/docs/guides/img/credentials/api-keys.png similarity index 100% rename from docs/developer-center/img/guides/credentials/api-keys.png rename to docs/guides/img/credentials/api-keys.png diff --git a/docs/developer-center/img/guides/credentials/dashboard.png b/docs/guides/img/credentials/dashboard.png similarity index 100% rename from docs/developer-center/img/guides/credentials/dashboard.png rename to docs/guides/img/credentials/dashboard.png diff --git a/docs/developer-center/img/guides/enrichment/enrichment_01.png b/docs/guides/img/enrichment/enrichment_01.png similarity index 100% rename from docs/developer-center/img/guides/enrichment/enrichment_01.png rename to docs/guides/img/enrichment/enrichment_01.png diff --git a/docs/developer-center/img/guides/explore_data_observatory_catalog_files/sub_dat.png b/docs/guides/img/subscriptions/sub_dat.png similarity index 100% rename from docs/developer-center/img/guides/explore_data_observatory_catalog_files/sub_dat.png rename to docs/guides/img/subscriptions/sub_dat.png diff --git a/docs/developer-center/img/guides/explore_data_observatory_catalog_files/sub_geo.png b/docs/guides/img/subscriptions/sub_geo.png similarity index 100% rename from docs/developer-center/img/guides/explore_data_observatory_catalog_files/sub_geo.png rename to docs/guides/img/subscriptions/sub_geo.png diff --git a/docs/includes/auth.rst b/docs/reference/auth.rst similarity index 90% rename from docs/includes/auth.rst rename to docs/reference/auth.rst index 7e1af9e4d..32cae03a1 100644 --- a/docs/includes/auth.rst +++ b/docs/reference/auth.rst @@ -2,6 +2,7 @@ Auth ---- .. automodule:: cartoframes.auth + :noindex: :members: :member-order: bysource :no-undoc-members: diff --git a/docs/includes/data_clients.rst b/docs/reference/data_clients.rst similarity index 91% rename from docs/includes/data_clients.rst rename to docs/reference/data_clients.rst index e60a25dd5..007890a8c 100644 --- a/docs/includes/data_clients.rst +++ b/docs/reference/data_clients.rst @@ -2,6 +2,7 @@ Data Clients ------------ .. automodule:: cartoframes.data.clients + :noindex: :members: :member-order: bysource :undoc-members: diff --git a/docs/includes/data_observatory.rst b/docs/reference/data_observatory.rst similarity index 97% rename from docs/includes/data_observatory.rst rename to docs/reference/data_observatory.rst index 0cfbeb111..18d48e845 100644 --- a/docs/includes/data_observatory.rst +++ b/docs/reference/data_observatory.rst @@ -9,6 +9,7 @@ users in a private beta. We're still open to more beta testers, so if you're int `please get in touch. `__ .. automodule:: cartoframes.data.observatory + :noindex: :members: :member-order: bysource :exclude-members: get_datasets_spatial_filtered diff --git a/docs/includes/data_services.rst b/docs/reference/data_services.rst similarity index 91% rename from docs/includes/data_services.rst rename to docs/reference/data_services.rst index 52937e1a6..0499d5a75 100644 --- a/docs/includes/data_services.rst +++ b/docs/reference/data_services.rst @@ -2,6 +2,7 @@ Data Services ------------- .. automodule:: cartoframes.data.services + :noindex: :members: :member-order: bysource :undoc-members: diff --git a/docs/includes/exceptions.rst b/docs/reference/exceptions.rst similarity index 91% rename from docs/includes/exceptions.rst rename to docs/reference/exceptions.rst index 5ca709498..c77124dcc 100644 --- a/docs/includes/exceptions.rst +++ b/docs/reference/exceptions.rst @@ -2,6 +2,7 @@ Exceptions ---------- .. automodule:: cartoframes.exceptions + :noindex: :members: :member-order: bysource :undoc-members: diff --git a/docs/includes/introduction.rst b/docs/reference/introduction.rst similarity index 100% rename from docs/includes/introduction.rst rename to docs/reference/introduction.rst diff --git a/docs/includes/io_functions.rst b/docs/reference/io_functions.rst similarity index 90% rename from docs/includes/io_functions.rst rename to docs/reference/io_functions.rst index c2113feb3..a195bd790 100644 --- a/docs/includes/io_functions.rst +++ b/docs/reference/io_functions.rst @@ -2,6 +2,7 @@ I/O functions ------------- .. automodule:: cartoframes + :noindex: :members: :member-order: bysource :undoc-members: diff --git a/docs/includes/utils.rst b/docs/reference/utils.rst similarity index 90% rename from docs/includes/utils.rst rename to docs/reference/utils.rst index c007c9bd1..483e1fe36 100644 --- a/docs/includes/utils.rst +++ b/docs/reference/utils.rst @@ -2,6 +2,7 @@ Utils ----- .. automodule:: cartoframes.utils + :noindex: :members: :member-order: bysource :undoc-members: diff --git a/docs/includes/viz.rst b/docs/reference/viz.rst similarity index 89% rename from docs/includes/viz.rst rename to docs/reference/viz.rst index 55e650f6e..a6fd04bc9 100644 --- a/docs/includes/viz.rst +++ b/docs/reference/viz.rst @@ -7,6 +7,7 @@ Map ^^^ .. autoclass:: cartoframes.viz.Map + :noindex: :members: :member-order: bysource :show-inheritance: @@ -15,6 +16,7 @@ Layer ^^^^^ .. autoclass:: cartoframes.viz.Layer + :noindex: :members: :member-order: bysource :show-inheritance: @@ -23,6 +25,7 @@ Source ^^^^^^ .. autoclass:: cartoframes.viz.Source + :noindex: :members: :member-order: bysource :show-inheritance: @@ -31,6 +34,7 @@ Layout ^^^^^^ .. autoclass:: cartoframes.viz.Layout + :noindex: :members: :member-order: bysource :show-inheritance: @@ -85,3 +89,11 @@ Popups :noindex: :members: popup_element, default_popup_element + +Publications +^^^^^^^^^^^^ + +.. automodule:: cartoframes.viz + :noindex: + :members: all_publications, + delete_publication \ No newline at end of file diff --git a/docs/developer-center/support/01-support-options.md b/docs/support/01-Support-options.md similarity index 100% rename from docs/developer-center/support/01-support-options.md rename to docs/support/01-Support-options.md diff --git a/docs/developer-center/support/02-contribute.md b/docs/support/02-Contribute.md similarity index 100% rename from docs/developer-center/support/02-contribute.md rename to docs/support/02-Contribute.md diff --git a/examples/_debug/.gitkeep b/examples/_debug/.gitkeep deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/_debug/enrichment_big_polygons.ipynb b/examples/_debug/enrichment_big_polygons.ipynb deleted file mode 100644 index 1ee13ac3d..000000000 --- a/examples/_debug/enrichment_big_polygons.ipynb +++ /dev/null @@ -1,154 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.utils import set_log_level\n", - "set_log_level('debug')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import geopandas as gpd\n", - "from cartoframes.auth import set_default_credentials\n", - "\n", - "set_default_credentials()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas\n", - "\n", - "df = pandas.read_csv('../files/ags.geography_usa_blockgroup_2015.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df = df[['geoid','geom']]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.data.observatory import Dataset, Variable\n", - "\n", - "dataset = Dataset.get('carto-do-public-data.usa_acs.demographics_sociodemographics_usa_blockgroup_2015_5yrs_20132017')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "dvs = dataset.variables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "for variable in dvs:\n", - " print(variable.agg_method, variable.db_type, variable.slug)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "variables = []\n", - "for i in range(10):\n", - " variables.append(dvs[i])\n", - " \n", - "variables" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.data.observatory import Enrichment\n", - "\n", - "enrichment = Enrichment()\n", - "\n", - "enriched_dataset_cdf = enrichment.enrich_polygons(\n", - " df,\n", - " geom_col='geom',\n", - " variables=variables\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "enriched_dataset_cdf.head(10)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/_debug/testing_polygons_features.ipynb b/examples/_debug/testing_polygons_features.ipynb deleted file mode 100644 index 12ad562a8..000000000 --- a/examples/_debug/testing_polygons_features.ipynb +++ /dev/null @@ -1,1865 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Enrich Polygons from a Dataset\n", - "\n", - "This example illustrates how to enrich polygons that are in a dataset with variables from CARTO's Data Observatory.\n", - "\n", - "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to reproduce this example._" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.utils import set_log_level\n", - "set_log_level('debug')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.auth import set_default_credentials\n", - "\n", - "set_default_credentials()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                      2351013102902102902POLYGON ((-77.09520 38.84499, -77.09442 38.844...
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                                                                                      " - ], - "text/plain": [ - " OBJECTID FULLTRACTID TRACTID \\\n", - "0 1 51013102901 102901 \n", - "1 2 51013103000 103000 \n", - "2 3 51013102902 102902 \n", - "\n", - " geometry \n", - "0 POLYGON ((-77.09099 38.84516, -77.08957 38.844... \n", - "1 POLYGON ((-77.08558 38.82992, -77.08625 38.828... \n", - "2 POLYGON ((-77.09520 38.84499, -77.09442 38.844... " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from geopandas import read_file\n", - "\n", - "census_track = 'http://libs.cartocdn.com/cartoframes/files/census_track.geojson'\n", - "census_track_gdf = read_file(census_track)\n", - "census_track_gdf.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "census_track_gdf = census_track_gdf.head(1)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.data.observatory import Catalog\n", - "\n", - "Catalog().country('usa').category('demographics').geographies" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                      idslugnamedescriptioncountry_idgeography_idgeography_namegeography_descriptioncategory_idcategory_nameprovider_idprovider_namedata_source_idlangtemporal_aggregationtime_coverageupdate_frequencyversionis_public_data
                                                                                      0carto-do-public-data.usa_acs.demographics_soci...acs_sociodemogr_d4b2cf035-yr ACS data at Census Tracts level (2006 - 2...The American Community Survey (ACS) is an ongo...usacarto-do-public-data.usa_carto.geography_usa_c...NoneNonedemographicsNoneNoneNoneNoneeng5yrs[2006-01-01, 2011-01-01)None20062010True
                                                                                      1carto-do-public-data.usa_acs.demographics_soci...acs_sociodemogr_9ed5d6255-yr ACS data at Census Tracts level (2007 - 2...The American Community Survey (ACS) is an ongo...usacarto-do-public-data.usa_carto.geography_usa_c...NoneNonedemographicsNoneNoneNoneNoneeng5yrs[2007-01-01, 2012-01-01)None20072011True
                                                                                      2carto-do-public-data.usa_acs.demographics_soci...acs_sociodemogr_858c104e5-yr ACS data at Census Tracts level (2008 - 2...The American Community Survey (ACS) is an ongo...usacarto-do-public-data.usa_carto.geography_usa_c...NoneNonedemographicsNoneNoneNoneNoneeng5yrs[2008-01-01, 2013-01-01)None20082012True
                                                                                      3carto-do-public-data.usa_acs.demographics_soci...acs_sociodemogr_cfeb09685-yr ACS data at Census Tracts level (2009 - 2...The American Community Survey (ACS) is an ongo...usacarto-do-public-data.usa_carto.geography_usa_c...NoneNonedemographicsNoneNoneNoneNoneeng5yrs[2009-01-01, 2014-01-01)None20092013True
                                                                                      4carto-do-public-data.usa_acs.demographics_soci...acs_sociodemogr_97c32d1f5-yr ACS data at Census Tracts level (2010 - 2...The American Community Survey (ACS) is an ongo...usacarto-do-public-data.usa_carto.geography_usa_c...NoneNonedemographicsNoneNoneNoneNoneeng5yrs[2010-01-01, 2015-01-01)None20102014True
                                                                                      5carto-do-public-data.usa_acs.demographics_soci...acs_sociodemogr_dda434395-yr ACS data at Census Tracts level (2011 - 2...The American Community Survey (ACS) is an ongo...usacarto-do-public-data.usa_carto.geography_usa_c...NoneNonedemographicsNoneNoneNoneNoneeng5yrs[2011-01-01, 2016-01-01)None20112015True
                                                                                      6carto-do-public-data.usa_acs.demographics_soci...acs_sociodemogr_30d1f535-yr ACS data at Census Tracts level (2012 - 2...The American Community Survey (ACS) is an ongo...usacarto-do-public-data.usa_carto.geography_usa_c...NoneNonedemographicsNoneNoneNoneNoneeng5yrs[2012-01-01, 2017-01-01)None20122016True
                                                                                      7carto-do-public-data.usa_acs.demographics_soci...acs_sociodemogr_496a06755-yr ACS data at Census Tracts level (2013 - 2...The American Community Survey (ACS) is an ongo...usacarto-do-public-data.usa_carto.geography_usa_c...NoneNonedemographicsNoneNoneNoneNoneeng5yrs[2013-01-01, 2018-01-01)None20132017True
                                                                                      \n", - "
                                                                                      " - ], - "text/plain": [ - " id \\\n", - "0 carto-do-public-data.usa_acs.demographics_soci... \n", - "1 carto-do-public-data.usa_acs.demographics_soci... \n", - "2 carto-do-public-data.usa_acs.demographics_soci... \n", - "3 carto-do-public-data.usa_acs.demographics_soci... \n", - "4 carto-do-public-data.usa_acs.demographics_soci... \n", - "5 carto-do-public-data.usa_acs.demographics_soci... \n", - "6 carto-do-public-data.usa_acs.demographics_soci... \n", - "7 carto-do-public-data.usa_acs.demographics_soci... \n", - "\n", - " slug \\\n", - "0 acs_sociodemogr_d4b2cf03 \n", - "1 acs_sociodemogr_9ed5d625 \n", - "2 acs_sociodemogr_858c104e \n", - "3 acs_sociodemogr_cfeb0968 \n", - "4 acs_sociodemogr_97c32d1f \n", - "5 acs_sociodemogr_dda43439 \n", - "6 acs_sociodemogr_30d1f53 \n", - "7 acs_sociodemogr_496a0675 \n", - "\n", - " name \\\n", - "0 5-yr ACS data at Census Tracts level (2006 - 2... \n", - "1 5-yr ACS data at Census Tracts level (2007 - 2... \n", - "2 5-yr ACS data at Census Tracts level (2008 - 2... \n", - "3 5-yr ACS data at Census Tracts level (2009 - 2... \n", - "4 5-yr ACS data at Census Tracts level (2010 - 2... \n", - "5 5-yr ACS data at Census Tracts level (2011 - 2... \n", - "6 5-yr ACS data at Census Tracts level (2012 - 2... \n", - "7 5-yr ACS data at Census Tracts level (2013 - 2... \n", - "\n", - " description country_id \\\n", - "0 The American Community Survey (ACS) is an ongo... usa \n", - "1 The American Community Survey (ACS) is an ongo... usa \n", - "2 The American Community Survey (ACS) is an ongo... usa \n", - "3 The American Community Survey (ACS) is an ongo... usa \n", - "4 The American Community Survey (ACS) is an ongo... usa \n", - "5 The American Community Survey (ACS) is an ongo... usa \n", - "6 The American Community Survey (ACS) is an ongo... usa \n", - "7 The American Community Survey (ACS) is an ongo... usa \n", - "\n", - " geography_id geography_name \\\n", - "0 carto-do-public-data.usa_carto.geography_usa_c... None \n", - "1 carto-do-public-data.usa_carto.geography_usa_c... None \n", - "2 carto-do-public-data.usa_carto.geography_usa_c... None \n", - "3 carto-do-public-data.usa_carto.geography_usa_c... None \n", - "4 carto-do-public-data.usa_carto.geography_usa_c... None \n", - "5 carto-do-public-data.usa_carto.geography_usa_c... None \n", - "6 carto-do-public-data.usa_carto.geography_usa_c... None \n", - "7 carto-do-public-data.usa_carto.geography_usa_c... None \n", - "\n", - " geography_description category_id category_name provider_id provider_name \\\n", - "0 None demographics None None None \n", - "1 None demographics None None None \n", - "2 None demographics None None None \n", - "3 None demographics None None None \n", - "4 None demographics None None None \n", - "5 None demographics None None None \n", - "6 None demographics None None None \n", - "7 None demographics None None None \n", - "\n", - " data_source_id lang temporal_aggregation time_coverage \\\n", - "0 None eng 5yrs [2006-01-01, 2011-01-01) \n", - "1 None eng 5yrs [2007-01-01, 2012-01-01) \n", - "2 None eng 5yrs [2008-01-01, 2013-01-01) \n", - "3 None eng 5yrs [2009-01-01, 2014-01-01) \n", - "4 None eng 5yrs [2010-01-01, 2015-01-01) \n", - "5 None eng 5yrs [2011-01-01, 2016-01-01) \n", - "6 None eng 5yrs [2012-01-01, 2017-01-01) \n", - "7 None eng 5yrs [2013-01-01, 2018-01-01) \n", - "\n", - " update_frequency version is_public_data \n", - "0 None 20062010 True \n", - "1 None 20072011 True \n", - "2 None 20082012 True \n", - "3 None 20092013 True \n", - "4 None 20102014 True \n", - "5 None 20112015 True \n", - "6 None 20122016 True \n", - "7 None 20132017 True " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "datasets = Catalog().country('usa').category('demographics').geography('usct_censustract_bc698c5a').datasets\n", - "datasets.to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.data.observatory import Dataset\n", - "\n", - "dataset = Dataset.get('acs_sociodemogr_d4b2cf03')\n", - "variables_df = dataset.variables" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "AVG FLOAT median_age_1b299936\n", - "None STRING geoidsl_52dffc46\n", - "None STRING geoidsc_c260e1d7\n", - "SUM FLOAT owner_occupied__a242d69b\n", - "SUM FLOAT bachelors_degre_c97d7ec4\n", - "SUM FLOAT bachelors_degre_619dbbbf\n", - "SUM FLOAT children_ccc14aa2\n", - "SUM FLOAT children_in_sin_96bc4ba1\n", - "SUM FLOAT employed_inform_cc19ebad\n", - "SUM FLOAT employed_manufa_4e5264bc\n", - "SUM FLOAT employed_other__a5acf594\n", - "SUM FLOAT employed_public_80ba451f\n", - "SUM FLOAT employed_retail_3cfcad1f\n", - "SUM FLOAT employed_scienc_10f6c661\n", - "SUM FLOAT employed_transp_66aedbfd\n", - "SUM FLOAT employed_wholes_dfec4891\n", - "SUM FLOAT female_female_h_d11712ea\n", - "SUM FLOAT four_more_cars_297e8a8a\n", - "AVG FLOAT gini_index_e8c30f9a\n", - "SUM FLOAT graduate_profes_ce2840a8\n", - "SUM FLOAT three_cars_32e541e7\n", - "SUM FLOAT pop_25_64_ce8c2ef0\n", - "SUM FLOAT pop_determined__486c4212\n", - "SUM FLOAT population_1_ye_dcac6ed6\n", - "SUM FLOAT population_3_ye_ca9ce3ea\n", - "AVG FLOAT poverty_4c2c9ac5\n", - "SUM FLOAT sales_office_em_abb972b6\n", - "SUM FLOAT some_college_an_23a0fb89\n", - "SUM FLOAT pop_15_and_over_1b25d822\n", - "SUM FLOAT nonfamily_house_2c17f15c\n", - "SUM FLOAT family_househol_f2823597\n", - "AVG FLOAT median_year_str_b19af9b2\n", - "SUM FLOAT rent_under_10_p_211ac215\n", - "SUM FLOAT total_pop_a33e39b6\n", - "SUM FLOAT two_or_more_rac_1ffad07d\n", - "SUM FLOAT not_hispanic_po_e7e18ed1\n", - "SUM FLOAT commuters_by_pu_424732d2\n", - "SUM FLOAT households_284a100f\n", - "AVG FLOAT median_income_57be5af4\n", - "AVG FLOAT income_per_capi_b4d33d67\n", - "SUM FLOAT housing_units_8326b884\n", - "SUM FLOAT vacant_housing__693f6a2e\n", - "SUM FLOAT one_parent_fami_1dd05196\n", - "SUM FLOAT father_one_pare_25ca6df1\n", - "SUM FLOAT aggregate_trave_17822663\n", - "SUM FLOAT income_less_100_765da10b\n", - "SUM FLOAT income_10000_14_95a8a848\n", - "SUM FLOAT income_15000_19_81ebbbd1\n", - "SUM FLOAT income_20000_24_4beab499\n", - "SUM FLOAT income_150000_1_ead90f3e\n", - "SUM FLOAT income_200000_o_70cb9ea7\n", - "SUM FLOAT renter_occupied_c1345262\n", - "AVG FLOAT owner_occupied__a9e65f75\n", - "AVG FLOAT owner_occupied__1cd53499\n", - "AVG FLOAT owner_occupied__6fbe04\n", - "SUM FLOAT married_househo_ba272390\n", - "SUM FLOAT occupied_housin_1c3f3f66\n", - "SUM FLOAT housing_units_r_3ee939f2\n", - "SUM FLOAT dwellings_1_uni_58e5d43\n", - "SUM FLOAT dwellings_1_uni_8cc7c45d\n", - "SUM FLOAT dwellings_2_uni_c5cf037c\n", - "SUM FLOAT dwellings_3_to__ff82fdc3\n", - "SUM FLOAT dwellings_5_to__716839e3\n", - "SUM FLOAT housing_built_1_7de983ff\n", - "SUM FLOAT rent_burden_not_da5f7289\n", - "SUM FLOAT rent_over_50_pe_ad21c098\n", - "SUM FLOAT rent_40_to_50_p_d4c51557\n", - "SUM FLOAT rent_35_to_40_p_f5793897\n", - "SUM FLOAT rent_30_to_35_p_6eb5d82a\n", - "SUM FLOAT rent_25_to_30_p_b9cdb27f\n", - "SUM FLOAT rent_20_to_25_p_2f1f2285\n", - "SUM FLOAT rent_15_to_20_p_7fc76db3\n", - "SUM FLOAT rent_10_to_15_p_ede02d74\n", - "SUM FLOAT male_pop_354090a6\n", - "SUM FLOAT female_pop_bcc7b2e6\n", - "SUM FLOAT white_pop_c874003d\n", - "SUM FLOAT black_pop_3865cbdc\n", - "SUM FLOAT asian_pop_12b4b482\n", - "SUM FLOAT hispanic_pop_234f387e\n", - "SUM FLOAT amerindian_pop_828802fd\n", - "SUM FLOAT other_race_pop_36c463b6\n", - "SUM FLOAT vacant_housing__85c50752\n", - "SUM FLOAT vacant_housing__62f68a9b\n", - "AVG FLOAT median_rent_e817137c\n", - "AVG FLOAT percent_income__d1ee2cb7\n", - "SUM FLOAT million_dollar__ece23260\n", - "SUM FLOAT mortgaged_housi_3a77552a\n", - "SUM FLOAT families_with_y_1e479228\n", - "SUM FLOAT two_parent_fami_6d4ac4e8\n", - "SUM FLOAT two_parents_in__bb6a383b\n", - "SUM FLOAT two_parents_fat_ead57f01\n", - "SUM FLOAT two_parents_mot_738c0aab\n", - "SUM FLOAT two_parents_not_792221c\n", - "SUM FLOAT father_in_labor_196dbc7c\n", - "SUM FLOAT commute_10_14_m_f54c364d\n", - "SUM FLOAT commute_15_19_m_34560dc\n", - "SUM FLOAT commute_20_24_m_2f9397d3\n", - "SUM FLOAT commute_25_29_m_d99ac142\n", - "SUM FLOAT commute_30_34_m_6626f759\n", - "SUM FLOAT commute_45_59_m_11238f8b\n", - "SUM FLOAT income_25000_29_5fa9a700\n", - "SUM FLOAT income_30000_34_b70442e9\n", - "SUM FLOAT income_35000_39_a3475170\n", - "SUM FLOAT income_40000_44_2c1f8b7a\n", - "SUM FLOAT income_45000_49_385c98e3\n", - "SUM FLOAT income_50000_59_229ba5d7\n", - "SUM FLOAT income_60000_74_33d3486b\n", - "SUM FLOAT income_75000_99_592e3a53\n", - "SUM FLOAT income_100000_1_f7c091e2\n", - "SUM FLOAT income_125000_1_17868e42\n", - "SUM FLOAT dwellings_10_to_78e88389\n", - "SUM FLOAT dwellings_20_to_4c69a836\n", - "SUM FLOAT dwellings_50_or_abd034b2\n", - "SUM FLOAT mobile_homes_20947ee9\n", - "SUM FLOAT housing_built_2_874b0fbe\n", - "SUM FLOAT housing_built_2_1215578e\n", - "SUM FLOAT male_under_5_2e8459e4\n", - "SUM FLOAT male_5_to_9_7552c2ff\n", - "SUM FLOAT male_10_to_14_7fe0e4e7\n", - "SUM FLOAT male_15_to_17_b4d19afa\n", - "SUM FLOAT male_18_to_19_32bed63d\n", - "SUM FLOAT male_20_c3c94d9c\n", - "SUM FLOAT male_21_b4ce7d0a\n", - "SUM FLOAT male_22_to_24_4ddda1ee\n", - "SUM FLOAT male_25_to_29_f6cbe3dd\n", - "SUM FLOAT male_30_to_34_ff38118\n", - "SUM FLOAT male_35_to_39_237ad202\n", - "SUM FLOAT male_40_to_44_4a771ec6\n", - "SUM FLOAT male_45_to_49_66fe4ddc\n", - "SUM FLOAT male_50_to_54_9fc62f19\n", - "SUM FLOAT male_55_to_59_b34f7c03\n", - "SUM FLOAT male_60_61_e6858d1b\n", - "SUM FLOAT male_62_64_3ce6b11f\n", - "SUM FLOAT male_65_to_66_865235b2\n", - "SUM FLOAT male_67_to_69_8172390a\n", - "SUM FLOAT male_70_to_74_efd54ae6\n", - "SUM FLOAT male_75_to_79_c35c19fc\n", - "SUM FLOAT male_80_to_84_b16d4485\n", - "SUM FLOAT male_85_and_ove_64f22713\n", - "SUM FLOAT female_under_5_6effa65f\n", - "SUM FLOAT female_5_to_9_d1a61f15\n", - "SUM FLOAT female_10_to_14_2313f51c\n", - "SUM FLOAT female_15_to_17_e8228b01\n", - "SUM FLOAT female_18_to_19_6e4dc7c6\n", - "SUM FLOAT female_20_97c0ed05\n", - "SUM FLOAT female_21_e0c7dd93\n", - "SUM FLOAT female_22_to_24_112eb015\n", - "SUM FLOAT female_25_to_29_aa38f226\n", - "SUM FLOAT female_30_to_34_530090e3\n", - "SUM FLOAT female_35_to_39_7f89c3f9\n", - "SUM FLOAT female_40_to_44_16840f3d\n", - "SUM FLOAT female_45_to_49_3a0d5c27\n", - "SUM FLOAT female_50_to_54_c3353ee2\n", - "SUM FLOAT female_55_to_59_efbc6df8\n", - "SUM FLOAT female_60_to_61_16fd9e4d\n", - "SUM FLOAT female_62_to_64_f1087beb\n", - "SUM FLOAT female_65_to_66_daa12449\n", - "SUM FLOAT female_67_to_69_dd8128f1\n", - "SUM FLOAT female_70_to_74_b3265b1d\n", - "SUM FLOAT female_75_to_79_9faf0807\n", - "SUM FLOAT female_80_to_84_ed9e557e\n", - "SUM FLOAT female_85_and_o_aa0e3bc8\n", - "SUM FLOAT white_including_13a12d2a\n", - "SUM FLOAT black_including_60df12f\n", - "SUM FLOAT amerindian_incl_2cc3ddb8\n", - "SUM FLOAT households_reti_5455ca9\n", - "SUM FLOAT asian_including_5f6cf2a1\n", - "SUM FLOAT commute_5_9_min_bfa74847\n", - "SUM FLOAT commute_35_39_m_902fa1c8\n", - "SUM FLOAT commute_40_44_m_415dd2ae\n", - "SUM FLOAT commute_60_89_m_3e967b39\n", - "SUM FLOAT commute_90_more_cbc109e\n", - "SUM FLOAT asian_male_45_5_7e082193\n", - "SUM FLOAT asian_male_55_6_68455be0\n", - "SUM FLOAT black_male_45_5_8c8c15de\n", - "SUM FLOAT black_male_55_6_9ac16fad\n", - "SUM FLOAT hispanic_male_4_c3d75b15\n", - "SUM FLOAT hispanic_male_5_d59a2166\n", - "SUM FLOAT white_male_45_5_8992fe9f\n", - "SUM FLOAT white_male_55_6_9fdf84ec\n", - "SUM FLOAT commuters_by_bu_f462d018\n", - "SUM FLOAT commuters_by_ca_120481e3\n", - "SUM FLOAT commuters_by_ca_59febe6f\n", - "SUM FLOAT commuters_by_su_4321535d\n", - "SUM FLOAT commuters_drove_acf77a1\n", - "SUM FLOAT different_house_168a8dda\n", - "SUM FLOAT different_house_307db01c\n", - "SUM FLOAT employed_agricu_f4fac119\n", - "SUM FLOAT employed_arts_e_7f42bf01\n", - "SUM FLOAT employed_constr_a28f52\n", - "SUM FLOAT employed_educat_958fdcfe\n", - "SUM FLOAT employed_financ_832aff0\n", - "SUM FLOAT group_quarters_f24e7e81\n", - "SUM FLOAT high_school_inc_801ba545\n", - "SUM FLOAT households_publ_d903e7c5\n", - "SUM FLOAT in_grades_1_to__793491e9\n", - "SUM FLOAT in_grades_5_to__eb139fd4\n", - "SUM FLOAT in_grades_9_to__8de26a62\n", - "SUM FLOAT in_school_5d68bd33\n", - "SUM FLOAT in_undergrad_co_11ce5095\n", - "SUM FLOAT less_than_high__18fc6722\n", - "SUM FLOAT male_45_64_asso_be1663b3\n", - "SUM FLOAT male_45_64_bach_6b3eabae\n", - "SUM FLOAT male_45_64_grad_fbd67eb0\n", - "SUM FLOAT male_45_64_less_50cc49a5\n", - "SUM FLOAT male_45_64_grad_d3f1aec0\n", - "SUM FLOAT male_45_64_high_6fa71b7c\n", - "SUM FLOAT male_45_64_some_dc1e1dff\n", - "SUM FLOAT male_45_to_64_2a7953e3\n", - "SUM FLOAT male_male_house_aa5c9704\n", - "SUM FLOAT management_busi_6f59f766\n", - "SUM FLOAT no_car_2207f034\n", - "SUM FLOAT no_cars_3a983c4e\n", - "SUM FLOAT not_us_citizen__46c0b5c4\n", - "SUM FLOAT occupation_mana_4f052cbd\n", - "SUM FLOAT occupation_natu_35dc85be\n", - "SUM FLOAT occupation_prod_4c913f7f\n", - "SUM FLOAT occupation_sale_e7afd291\n", - "SUM FLOAT occupation_serv_a3e87fbe\n", - "SUM FLOAT one_car_13b3a60b\n", - "SUM FLOAT two_cars_fec37223\n", - "SUM FLOAT walked_to_work_3e52e21c\n", - "SUM FLOAT worked_at_home_5ff2f52f\n", - "SUM FLOAT workers_16_and__f5bce7ef\n", - "SUM FLOAT hispanic_any_ra_319301\n", - "SUM FLOAT pop_5_years_ove_2d330bc0\n", - "SUM FLOAT speak_only_engl_73f2e1ff\n", - "SUM FLOAT speak_spanish_a_8aee2c1f\n", - "SUM FLOAT speak_spanish_a_ce691296\n", - "SUM FLOAT pop_never_marri_651528ed\n", - "SUM FLOAT pop_now_married_ee923870\n", - "SUM FLOAT pop_separated_9f2ec8b9\n", - "SUM FLOAT pop_widowed_a4b162d6\n", - "SUM FLOAT pop_divorced_61aba4d6\n", - "None STRING geoid_c6cf8662\n", - "None STRING do_date_a1b1496e\n" - ] - } - ], - "source": [ - "for variable in variables_df:\n", - " print(variable.agg_method, variable.db_type, variable.slug)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.data.observatory import Variable\n", - "\n", - "v1 = Variable.get('no_car_2207f034') # SUM, FLOAT\n", - "v2 = Variable.get('poverty_4c2c9ac5') # AVG, FLOAT\n", - "v3 = Variable.get('geoidsl_52dffc46') # None, STRING\n", - "\n", - "variables = [v1, v2, v3]" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2020-03-13 13:18:04,568 - DEBUG - _prepare_data in 0.01 s\n", - "2020-03-13 13:18:09,230 - DEBUG - _upload_data in 4.66 s\n", - "2020-03-13 13:18:17,120 - DEBUG - _execute_enrichment in 7.89 s\n", - "2020-03-13 13:18:17,126 - DEBUG - _enrich in 12.57 s\n" - ] - } - ], - "source": [ - "from cartoframes.data.observatory import Enrichment\n", - "\n", - "enrichment = Enrichment()\n", - "\n", - "enriched_dataset_gdf = enrichment.enrich_polygons(\n", - " census_track_gdf,\n", - " variables=variables,\n", - " aggregation=None\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                      OBJECTIDFULLTRACTIDTRACTIDgeometrygeoidslpovertyno_carintersected_areado_areauser_areado_geoid
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                                                                                      4151013102901102901POLYGON ((-77.09099 38.84516, -77.08957 38.844...51510200107341.0217.0161.3818236.377265e+05681621.69313851510200107
                                                                                      5151013102901102901POLYGON ((-77.09099 38.84516, -77.08957 38.844...51013102702247.061.00.3900702.834087e+05681621.69313851013102702
                                                                                      6151013102901102901POLYGON ((-77.09099 38.84516, -77.08957 38.844...51013103100489.0102.0267.8238041.908856e+06681621.69313851013103100
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                                                                                      " - ], - "text/plain": [ - " OBJECTID FULLTRACTID TRACTID \\\n", - "0 1 51013102901 102901 \n", - "1 1 51013102901 102901 \n", - "2 1 51013102901 102901 \n", - "3 1 51013102901 102901 \n", - "4 1 51013102901 102901 \n", - "5 1 51013102901 102901 \n", - "6 1 51013102901 102901 \n", - "\n", - " geometry geoidsl poverty \\\n", - "0 POLYGON ((-77.09099 38.84516, -77.08957 38.844... 51013102802 55.0 \n", - "1 POLYGON ((-77.09099 38.84516, -77.08957 38.844... 51013102901 68.0 \n", - "2 POLYGON ((-77.09099 38.84516, -77.08957 38.844... 51510201000 100.0 \n", - "3 POLYGON ((-77.09099 38.84516, -77.08957 38.844... 51013102902 256.0 \n", - "4 POLYGON ((-77.09099 38.84516, -77.08957 38.844... 51510200107 341.0 \n", - "5 POLYGON ((-77.09099 38.84516, -77.08957 38.844... 51013102702 247.0 \n", - "6 POLYGON ((-77.09099 38.84516, -77.08957 38.844... 51013103100 489.0 \n", - "\n", - " no_car intersected_area do_area user_area do_geoid \n", - "0 0.0 35.033737 7.313817e+05 681621.693138 51013102802 \n", - "1 0.0 677561.538702 6.961294e+05 681621.693138 51013102901 \n", - "2 14.0 23.771227 7.811361e+05 681621.693138 51510201000 \n", - "3 124.0 3571.753693 7.531163e+05 681621.693138 51013102902 \n", - "4 217.0 161.381823 6.377265e+05 681621.693138 51510200107 \n", - "5 61.0 0.390070 2.834087e+05 681621.693138 51013102702 \n", - "6 102.0 267.823804 1.908856e+06 681621.693138 51013103100 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "enriched_dataset_gdf.head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2020-03-13 13:23:45,199 - DEBUG - _prepare_data in 0.01 s\n", - "2020-03-13 13:23:48,893 - DEBUG - _upload_data in 3.69 s\n", - "2020-03-13 13:23:55,352 - DEBUG - _execute_enrichment in 6.46 s\n", - "2020-03-13 13:23:55,360 - DEBUG - _enrich in 10.17 s\n" - ] - }, - { - "data": { - "text/html": [ - "
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                                                                                      OBJECTIDFULLTRACTIDTRACTIDgeometrygeoidslpovertyno_carintersected_areado_areauser_areado_geoid
                                                                                      0151013102901102901POLYGON ((-77.09099 38.84516, -77.08957 38.844...51013102902256.0124.03571.753693753116.255091681621.69313851013102902
                                                                                      \n", - "
                                                                                      " - ], - "text/plain": [ - " OBJECTID FULLTRACTID TRACTID \\\n", - "0 1 51013102901 102901 \n", - "\n", - " geometry geoidsl poverty \\\n", - "0 POLYGON ((-77.09099 38.84516, -77.08957 38.844... 51013102902 256.0 \n", - "\n", - " no_car intersected_area do_area user_area do_geoid \n", - "0 124.0 3571.753693 753116.255091 681621.693138 51013102902 " - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.data.observatory import Enrichment\n", - "\n", - "enrichment = Enrichment()\n", - "\n", - "enriched_dataset_gdf = enrichment.enrich_polygons(\n", - " census_track_gdf,\n", - " variables=variables,\n", - " aggregation=None,\n", - " filters={v1.id: \"> 100\", v2.id: \"< 300\"}\n", - ")\n", - "\n", - "enriched_dataset_gdf.head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2020-03-13 13:18:17,187 - DEBUG - _prepare_data in 0.0 s\n", - "2020-03-13 13:18:22,133 - DEBUG - _upload_data in 4.94 s\n", - "2020-03-13 13:18:29,719 - DEBUG - _execute_enrichment in 7.59 s\n", - "2020-03-13 13:18:29,769 - DEBUG - _enrich in 12.58 s\n" - ] - } - ], - "source": [ - "from cartoframes.data.observatory import Enrichment\n", - "\n", - "enrichment = Enrichment()\n", - "\n", - "enriched_dataset_gdf = enrichment.enrich_polygons(\n", - " census_track_gdf,\n", - " variables=variables\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                      OBJECTIDFULLTRACTIDTRACTIDgeometrypovertyno_car
                                                                                      0151013102901102901POLYGON ((-77.09099 38.84516, -77.08957 38.844...222.2857140.657821
                                                                                      \n", - "
                                                                                      " - ], - "text/plain": [ - " OBJECTID FULLTRACTID TRACTID \\\n", - "0 1 51013102901 102901 \n", - "\n", - " geometry poverty no_car \n", - "0 POLYGON ((-77.09099 38.84516, -77.08957 38.844... 222.285714 0.657821 " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "enriched_dataset_gdf.head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2020-03-13 13:18:29,843 - DEBUG - _prepare_data in 0.01 s\n", - "2020-03-13 13:18:33,361 - DEBUG - _upload_data in 3.52 s\n", - "2020-03-13 13:18:41,083 - DEBUG - _execute_enrichment in 7.72 s\n", - "2020-03-13 13:18:41,109 - DEBUG - _enrich in 11.28 s\n" - ] - } - ], - "source": [ - "from cartoframes.data.observatory import Enrichment\n", - "\n", - "enrichment = Enrichment()\n", - "\n", - "enriched_dataset_gdf = enrichment.enrich_polygons(\n", - " census_track_gdf,\n", - " variables=variables,\n", - " aggregation='ARRAY_AGG'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                      OBJECTIDFULLTRACTIDTRACTIDgeometrygeoidslpovertyno_car
                                                                                      0151013102901102901POLYGON ((-77.09099 38.84516, -77.08957 38.844...['51013102902', '51013103100', '51510200107', ...[256.0, 489.0, 341.0, 247.0, 55.0, 68.0, 100.0][124.0, 102.0, 217.0, 61.0, 0.0, 0.0, 14.0]
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                                                                                      " - ], - "text/plain": [ - " OBJECTID FULLTRACTID TRACTID \\\n", - "0 1 51013102901 102901 \n", - "\n", - " geometry \\\n", - "0 POLYGON ((-77.09099 38.84516, -77.08957 38.844... \n", - "\n", - " geoidsl \\\n", - "0 ['51013102902', '51013103100', '51510200107', ... \n", - "\n", - " poverty \\\n", - "0 [256.0, 489.0, 341.0, 247.0, 55.0, 68.0, 100.0] \n", - "\n", - " no_car \n", - "0 [124.0, 102.0, 217.0, 61.0, 0.0, 0.0, 14.0] " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "enriched_dataset_gdf.head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2020-03-13 13:18:41,154 - DEBUG - _prepare_data in 0.0 s\n", - "2020-03-13 13:18:44,561 - DEBUG - _upload_data in 3.41 s\n", - "2020-03-13 13:18:50,709 - DEBUG - _execute_enrichment in 6.15 s\n", - "2020-03-13 13:18:50,739 - DEBUG - _enrich in 9.59 s\n" - ] - }, - { - "data": { - "text/html": [ - "
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                                                                                      OBJECTIDFULLTRACTIDTRACTIDgeometrypovertysum_no_caravg_no_car
                                                                                      0151013102901102901POLYGON ((-77.09099 38.84516, -77.08957 38.844...222.2857140.65782174.0
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                                                                                      " - ], - "text/plain": [ - " OBJECTID FULLTRACTID TRACTID \\\n", - "0 1 51013102901 102901 \n", - "\n", - " geometry poverty sum_no_car \\\n", - "0 POLYGON ((-77.09099 38.84516, -77.08957 38.844... 222.285714 0.657821 \n", - "\n", - " avg_no_car \n", - "0 74.0 " - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "enrichment = Enrichment()\n", - "\n", - "enriched_dataset_gdf = enrichment.enrich_polygons(\n", - " census_track_gdf,\n", - " variables=variables,\n", - " aggregation={v1.id: ['SUM', 'AVG'], v2.id:'AVG'}\n", - ")\n", - "\n", - "enriched_dataset_gdf.head(20)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2020-03-13 13:18:50,770 - DEBUG - _prepare_data in 0.0 s\n", - "2020-03-13 13:18:54,184 - DEBUG - _upload_data in 3.41 s\n", - "2020-03-13 13:19:00,228 - DEBUG - _execute_enrichment in 6.04 s\n", - "2020-03-13 13:19:00,237 - DEBUG - _enrich in 9.47 s\n" - ] - }, - { - "data": { - "text/html": [ - "
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                                                                                      " - ], - "text/plain": [ - " OBJECTID FULLTRACTID TRACTID \\\n", - "0 1 51013102901 102901 \n", - "\n", - " geometry poverty sum_no_car \\\n", - "0 POLYGON ((-77.09099 38.84516, -77.08957 38.844... 273.0 0.602908 \n", - "\n", - " avg_no_car \n", - "0 75.25 " - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "enrichment = Enrichment()\n", - "\n", - "enriched_dataset_gdf = enrichment.enrich_polygons(\n", - " census_track_gdf,\n", - " variables=variables,\n", - " aggregation={v1.id: ['SUM', 'AVG'], v2.id:'AVG'},\n", - " filters={v1.id: [\"> 0\", \"< 200\"]}\n", - ")\n", - "\n", - "enriched_dataset_gdf.head(20)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/data_management/load_data_different_crs.ipynb b/examples/data_management/load_data_different_crs.ipynb deleted file mode 100644 index 6380a4fef..000000000 --- a/examples/data_management/load_data_different_crs.ipynb +++ /dev/null @@ -1,1337 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Load data with a different CRS\n", - "\n", - "This example illustrates how to load data from a GeoPandas dataset with a CRS different than `EPSG:4326`, the one that CARTOframes needs to visualize layers, and the one CARTO needs to upload data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.viz import Layer\n", - "from geopandas import datasets, read_file" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "gdf = read_file(datasets.get_path('nybb'))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'epsg:2263'" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "str(gdf.crs)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "gdf.to_crs(epsg=4326, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " None\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " Static map image\n", - " \n", - " \n", - "
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                                                                                        " - ], - "text/plain": [ - " cartodb_id the_geom fuel4 country_abbrev country \\\n", - "0 2161 POINT (-40.57080 -20.39530) BRA Brazil \n", - "1 2502 POINT (-53.00000 -19.00000) BRA Brazil \n", - "2 2989 POINT (-46.50000 -22.60000) BRA Brazil \n", - "3 1116 POINT (-48.72300 -1.53740) BRA Brazil \n", - "4 1386 POINT (-44.06640 -19.54880) BRA Brazil \n", - "\n", - " name gppd_idnr capacity_mw latitude longitude ... owner \\\n", - "0 Jucu BRA0001272 4.840 -20.3953 -40.5708 ... \n", - "1 Paraíso I BRA0027587 21.600 -19.0000 -53.0000 ... \n", - "2 Socorro BRA0002762 1.000 -22.6000 -46.5000 ... \n", - "3 Alunorte BRA0028916 103.854 -1.5374 -48.7230 ... \n", - "4 COSIMAT BRA0032959 2.000 -19.5488 -44.0664 ... \n", - "\n", - " source \\\n", - "0 Agência Nacional de Energia Elétrica (Brazil) \n", - "1 Agência Nacional de Energia Elétrica (Brazil) \n", - "2 Agência Nacional de Energia Elétrica (Brazil) \n", - "3 Agência Nacional de Energia Elétrica (Brazil) \n", - "4 Agência Nacional de Energia Elétrica (Brazil) \n", - "\n", - " url \\\n", - "0 http://www2.aneel.gov.br/aplicacoes/capacidade... \n", - "1 http://www2.aneel.gov.br/aplicacoes/capacidade... \n", - "2 http://www2.aneel.gov.br/aplicacoes/capacidade... \n", - "3 http://www2.aneel.gov.br/aplicacoes/capacidade... \n", - "4 http://www2.aneel.gov.br/aplicacoes/capacidade... \n", - "\n", - " geolocation_source year_of_capacity_data \\\n", - "0 Agência Nacional de Energia Elétrica (Brazil) 2017 \n", - "1 Agência Nacional de Energia Elétrica (Brazil) 2017 \n", - "2 Agência Nacional de Energia Elétrica (Brazil) 2017 \n", - "3 Agência Nacional de Energia Elétrica (Brazil) 2017 \n", - "4 Agência Nacional de Energia Elétrica (Brazil) 2017 \n", - "\n", - " generation_gwh_2013 generation_gwh_2014 generation_gwh_2015 \\\n", - "0 None None \n", - "1 None None \n", - "2 None None \n", - "3 None None \n", - "4 None None \n", - "\n", - " generation_gwh_2016 estimated_generation_gwh \n", - "0 None 18.435905 \n", - "1 None 82.275939 \n", - "2 None 3.809071 \n", - "3 None 994.854387 \n", - "4 None 8.554761 \n", - "\n", - "[5 rows x 24 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes import read_carto\n", - "\n", - "gdf = read_carto(\"SELECT * FROM global_power_plants WHERE country IN ('Brazil')\")\n", - "gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " None\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " Static map image\n", - " \n", - " \n", - "
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                                                                                          objectidcartodb_identity_idlatitudelongitudeaudit_statlegal_radiillegal_raradius_umdradius_for...carbon_r_3peat_for_2peat_for_3primary_10primary_11mill_nameparent_comrspo_certidate_updatgeometry
                                                                                          0159ID1822-1.585833103.205556ASA 100.3217640.2257591099...12240290.0045080.135391Muara Bulian MillPT Inti Indosawit Suburyes14-AugPOINT (103.20556 -1.58583)
                                                                                          12153ID18470.077043102.030838Renewal Certification00.4459600.2588551979...203875230.0173040.321418Pabrik Kelapa Sawit Batang Kulim POMPT Musim Masyes14-AugPOINT (102.03084 0.07704)
                                                                                          23103ID17201.660222100.590611Initial Certification00.4985310.2485201432...151804760.0008110.193365Kayangan and Kencana POMPT Salim Ivomas Pratama Tbkyes14-AugPOINT (100.59061 1.66022)
                                                                                          34216ID1945-2.894444112.543611ASA 100.6628630.186332226...26959660.1248820.189169PT Sarana Titian Permata POMWilmar International Ltdyes14-AugPOINT (112.54361 -2.89444)
                                                                                          45156ID15533.59333398.947222Initial Certification00.5336680.028972382...412000.0002160.009296Adolina POMPT Perkebunan Nusantara IV (PERSERO)yes14-AugPOINT (98.94722 3.59333)
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                                                                                          5 rows × 73 columns

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                                                                                          " - ], - "text/plain": [ - " objectid cartodb_id entity_id latitude longitude \\\n", - "0 1 59 ID1822 -1.585833 103.205556 \n", - "1 2 153 ID1847 0.077043 102.030838 \n", - "2 3 103 ID1720 1.660222 100.590611 \n", - "3 4 216 ID1945 -2.894444 112.543611 \n", - "4 5 156 ID1553 3.593333 98.947222 \n", - "\n", - " audit_stat legal_radi illegal_ra radius_umd radius_for ... \\\n", - "0 ASA 1 0 0.321764 0.225759 1099 ... \n", - "1 Renewal Certification 0 0.445960 0.258855 1979 ... \n", - "2 Initial Certification 0 0.498531 0.248520 1432 ... \n", - "3 ASA 1 0 0.662863 0.186332 226 ... \n", - "4 Initial Certification 0 0.533668 0.028972 382 ... \n", - "\n", - " carbon_r_3 peat_for_2 peat_for_3 primary_10 primary_11 \\\n", - "0 1224 0 29 0.004508 0.135391 \n", - "1 2038 7 523 0.017304 0.321418 \n", - "2 1518 0 476 0.000811 0.193365 \n", - "3 269 59 66 0.124882 0.189169 \n", - "4 412 0 0 0.000216 0.009296 \n", - "\n", - " mill_name parent_com \\\n", - "0 Muara Bulian Mill PT Inti Indosawit Subur \n", - "1 Pabrik Kelapa Sawit Batang Kulim POM PT Musim Mas \n", - "2 Kayangan and Kencana POM PT Salim Ivomas Pratama Tbk \n", - "3 PT Sarana Titian Permata POM Wilmar International Ltd \n", - "4 Adolina POM PT Perkebunan Nusantara IV (PERSERO) \n", - "\n", - " rspo_certi date_updat geometry \n", - "0 yes 14-Aug POINT (103.20556 -1.58583) \n", - "1 yes 14-Aug POINT (102.03084 0.07704) \n", - "2 yes 14-Aug POINT (100.59061 1.66022) \n", - "3 yes 14-Aug POINT (112.54361 -2.89444) \n", - "4 yes 14-Aug POINT (98.94722 3.59333) \n", - "\n", - "[5 rows x 73 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from geopandas import read_file\n", - "\n", - "gdf = read_file('http://libs.cartocdn.com/cartoframes/files/sustainable_palm_oil_production_mills.geojson')\n", - "gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Success! Data uploaded to table \"sustainable_palm_oil_production_mills\" correctly\n" - ] - } - ], - "source": [ - "from cartoframes import to_carto\n", - "\n", - "to_carto(gdf, 'sustainable_palm_oil_production_mills', if_exists='replace')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/data_observatory/download_dataset.ipynb b/examples/data_observatory/download_dataset.ipynb deleted file mode 100644 index 27621376c..000000000 --- a/examples/data_observatory/download_dataset.ipynb +++ /dev/null @@ -1,8255 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Download a Dataset\n", - "\n", - "This example illustrates how to download a dataset from CARTO's Data Observatory. As datasets can be really big, we will show you how to apply [standard SQL filters](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax) and [spatial SQL filters](https://cloud.google.com/bigquery/docs/reference/standard-sql/geography_functions) to download them. \n", - "\n", - "_Note: You'll need [CARTO Account](https://carto.com/signup) credentials to reproduce this example._" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.auth import set_default_credentials\n", - "\n", - "set_default_credentials('creds.json')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As datasets may have a lot of columns, let's force pandas to display all the columns" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas\n", - "\n", - "pandas.set_option('display.max_columns', None)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Choose a dataset\n", - "Learn how to discover a dataset through the catalog by checking the [`discovery example`](/developers/cartoframes/examples/#example-discover-a-dataset).\n", - "\n", - "You can also choose a dataset from your subscriptions. Call `Catalog().subscriptions()` to list your active subscriptions." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'slug': 'acs_sociodemogr_8c2655e0',\n", - " 'name': 'Sociodemographics - United States of America (County, 2017, 5yrs)',\n", - " 'description': 'The American Community Survey (ACS) is an ongoing survey that provides vital information on a yearly basis about the USA and its people. This dataset contains only a subset of the variables that have been deemed most relevant. More info: https://www.census.gov/programs-surveys/acs/about.html',\n", - " 'category_id': 'demographics',\n", - " 'country_id': 'usa',\n", - " 'data_source_id': 'sociodemographics',\n", - " 'provider_id': 'usa_acs',\n", - " 'geography_name': 'County - United States of America',\n", - " 'geography_description': 'Shoreline clipped TIGER/Line boundaries. More info: https://carto.com/blog/tiger-shoreline-clip/',\n", - " 'temporal_aggregation': '5yrs',\n", - " 'time_coverage': '[2013-01-01, 2018-01-01)',\n", - " 'update_frequency': 'yearly',\n", - " 'is_public_data': True,\n", - " 'lang': 'eng',\n", - " 'version': '20132017',\n", - " 'category_name': 'Demographics',\n", - " 'provider_name': 'American Community Survey',\n", - " 'geography_id': 'carto-do-public-data.carto.geography_usa_county_2015',\n", - " 'id': 'carto-do-public-data.usa_acs.demographics_sociodemographics_usa_county_2015_5yrs_20132017'}" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.data.observatory import Dataset\n", - "\n", - "dataset = Dataset.get('acs_sociodemogr_8c2655e0')\n", - "dataset.to_dict()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call `head()` to check a sample of the dataset and see the available columns." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                          03000743.02013201732.020.065.0463.0448.01151.02896.0887.00.04.00.00.01017.02936.05755.05432.02859.00.44522405.0105.0196.046.3622.0132.0550.04762.0None0.02621.0152.0112.0377.0None207.02743.0196.0117.075.054.0135.0131.0160.0163.0184.0908.0210.0213.063.0171.0191.0129.066.055295.0None84.0146.0368.044.0293.00.0112.0135.0205.0124.0117.0123.090.091.0113.0165.0153.0218.0250.0196.0116.0137.074.0114.0173.0158.052.0NoneNone0.00.0866.00.00.0359.00.0253.0252.041.05603.0None369.0463.0268.02287.015.01552.0152.0336.096.032268.04210.0None866.0337.0132.0225.0159.0162.0214.0247.033.0271.0150.042.0135.096.0101.088.084.0210.0166.076.0305.0241.0267.01348.02029.020.02733.01366.00.05.0461.02492.02733.0203.053.0551.0267.031.079.0170.082.0186.00.0853.047.0338.01852.0370.0180.0102.0261.024.0683.094.022.052.015.045.05.032.06.0404.0None83.041.049.0215.0287.02405.0247.01122.0404.05553.00.04.041.00.042.00.067.05533.01564.02119.00.02208.00.061.087.0955.05634.0None16.0158.01987.0147.0335.0337.0625.092.013.026.02020.023.660655.084.097.0385.05711.011.034.0291.0231.00.0873.0380.0None0.032.01212.0179.0955.0141.0471.0141.064.0271.094.0195000.090.0310.00.0119800.0301300.0460.0491.090.0186.0640.018.00.0163.00.0
                                                                                          119137106.02013201717.042.0291.01298.01653.02349.04931.01826.00.00.042.0103.02094.05037.010239.09640.05308.00.42704614.0149.0256.044.5774.0385.0482.08243.0None11.04838.0371.0259.0178.0None310.05231.0313.0238.0115.0182.0217.0239.0252.0323.0301.01449.0366.0377.0148.0162.0193.0146.0137.043674.0None40.0335.0425.0210.0227.00.0349.0194.0233.0302.0207.0127.0119.0220.0233.0262.0345.0320.0373.0452.0152.0200.0126.0168.0273.0233.0177.0NoneNone0.00.0867.00.00.01023.021.0569.0512.0114.09868.0None628.0782.0926.04560.0177.03080.0371.0530.0282.025005.07143.0None867.0568.0487.0485.0205.0236.039.082.043.0292.0205.0229.0419.0298.0345.0218.0343.0211.0226.0222.0433.0515.0485.02221.03045.0136.05198.02447.039.00.0824.04793.05187.0319.0114.01886.0453.0153.0176.0317.0121.093.00.01534.0246.0617.03813.0309.0641.086.0716.0111.0935.0180.0193.0164.0144.051.077.0168.075.01081.0None188.0291.0101.0870.0680.04614.0264.01552.01081.09869.00.00.049.040.0120.03.0185.09756.02785.04266.077.04318.00.027.032.01470.010138.0None5.0178.01952.0756.0662.0281.0807.0151.033.00.03131.026.386680.064.0181.01483.010014.0142.067.0122.0633.00.0856.01172.0None21.0550.02577.0831.01470.0170.0839.0173.0327.0335.0301.081800.0245.0949.029.049500.0126300.0349.0514.0248.0213.0664.012.025.064.011.0
                                                                                          22010110.0201320178.00.036.0181.0181.0407.0864.0350.00.01.01.08.0332.0822.01702.01539.0838.00.4473828.010.027.041.5133.026.0345.01379.0None0.0885.0113.074.046.0None74.0991.046.054.022.045.059.026.076.049.056.0226.072.061.019.022.025.040.039.051765.0None39.048.0128.08.069.00.032.032.027.047.038.035.013.040.029.048.053.044.045.063.019.045.022.014.040.042.038.0NoneNone0.00.0206.00.00.0178.00.064.079.08.01589.0None121.096.0135.0817.00.0484.0113.067.062.029768.01163.0None206.098.093.064.038.047.015.015.00.09.035.032.042.050.067.067.035.032.035.012.079.0109.0139.0401.0462.012.0917.0302.06.02.0165.0844.0917.043.08.0410.042.010.048.045.011.018.00.0344.016.0163.0732.0112.073.025.0106.07.0207.027.016.018.013.019.08.00.010.0185.0None3.033.00.06.082.0828.061.0306.0185.01623.00.01.00.00.013.00.052.01649.0361.0774.022.0890.00.015.03.0264.01659.0None0.048.01957.043.0137.072.0107.022.03.00.0649.022.9NaN39.095.0179.01690.028.00.028.076.00.0211.0196.0None0.061.0448.061.0264.030.084.034.020.0117.051.076400.055.073.013.044300.0124400.0253.0198.065.024.0551.00.013.048.04.0
                                                                                          348307133.0201320171.093.0220.0883.01296.01935.04171.01232.00.0161.039.014.01623.04096.08145.05067.03974.00.44953143.0240.0134.043.5510.0370.0471.06496.0None0.03658.02571.0327.0393.0None263.04302.0212.0220.033.0111.0284.0236.0272.0152.0206.01054.0310.0164.0106.0111.0296.094.0139.042367.0None1.0154.0298.0154.0213.0202.0122.0104.061.0258.0131.066.021.0340.082.0271.0321.0285.0188.0273.0121.0217.0147.0117.0228.0156.0134.0NoneNone0.00.0671.018.00.0867.02.0483.0334.060.05574.0None327.0391.0217.03389.0229.02164.02571.0453.0228.023398.05832.0None671.0535.0412.0410.023.0202.071.0117.046.075.052.0148.0265.0288.0127.0156.0203.0207.0137.0124.0259.0329.0385.01592.02716.0207.03780.01597.0164.0147.0770.03450.03780.0133.081.01482.0424.017.070.0176.0114.069.00.0979.0227.01159.02857.0171.0383.076.0500.0201.01092.064.0100.059.044.046.025.024.019.0809.0None143.0110.016.0214.0458.03143.0203.0889.0809.07863.00.0161.06.018.039.00.0137.06969.01862.03281.023.03498.00.078.017.0850.08140.0None39.093.01969.0396.0589.0261.0679.07.075.035.02398.032.053750.01.0551.0745.07955.031.057.0192.01229.00.0556.0647.0None2.0634.01809.0600.0850.0133.0829.0231.0206.0383.0363.082000.0401.0510.0165.041600.0165300.0602.0719.0214.0265.0721.00.0165.0169.00.0
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                                                                                          529155308.02013201785.0189.0934.02639.04859.04597.08210.02043.00.04639.0197.0153.04301.08474.017344.011948.09134.00.48626875.0225.0238.037.7871.0566.0344.013251.0None0.06326.0421.0614.0742.0None496.08178.0747.0421.0226.0282.0479.0431.0449.0422.0526.02156.0567.0600.0227.0234.0239.0164.0153.032468.0None18.0671.0388.0202.0483.00.0756.0153.0109.0689.0393.0194.0209.0573.0521.0629.0452.0564.0593.0715.0199.0291.0159.0271.0397.0291.0254.0NoneNone0.00.0815.0239.0230.01273.023.0877.01260.0126.016923.0None824.0828.0660.06067.0517.04342.0421.01082.0956.018883.011212.0None815.0792.0998.0913.0366.0587.078.0167.089.0181.087.0223.0689.0583.0512.0504.0377.0385.0382.0336.0544.0392.0555.02758.06169.097.07082.03602.019.019.01441.06176.07082.0159.072.01904.0594.026.0492.0347.096.0143.06.02533.0641.01303.05259.0194.0741.074.0705.0408.01354.0263.0285.0371.0367.0188.0167.0267.0155.01164.0None316.0513.049.01328.0866.06875.0337.01652.01164.016514.00.04717.090.016.0110.05.0540.012113.04279.05853.054.06098.091.0206.025.01560.017033.0None20.0211.01968.02120.01330.0532.01138.058.0251.00.03631.028.8105750.018.0853.03244.017042.0313.089.0313.02867.00.01066.01780.0None23.01577.02719.02237.01560.0282.01594.0164.0813.0517.0304.073300.0299.01565.089.037000.0120900.0405.0596.0216.0380.0568.02.069.0299.00.0
                                                                                          62115928.02013201769.089.0286.01464.03190.02498.06744.01695.00.0575.083.064.02290.06830.012175.011250.05431.00.44224316.0213.0261.039.0645.0326.0307.09909.0None0.03032.0226.0323.01911.0None342.05240.0432.0217.0125.0481.0485.0720.0476.0592.0430.01631.0433.0294.0123.0142.0247.0166.061.029239.0None9.0295.0226.01441.0225.00.0552.034.08.0334.0229.0101.0118.0303.0308.0384.0316.0401.0419.0466.074.0255.0158.0176.0203.0158.0150.0NoneNone0.00.0373.0159.017.0195.00.0554.0575.039.011949.0None657.0726.0728.02791.036.02940.0226.0481.0782.014914.08547.0None373.0117.0575.0297.0111.0235.0220.0384.0164.0358.0325.094.0288.0476.0430.0203.0138.0224.0365.0182.0271.0373.0356.02311.06325.034.03584.02353.033.017.0428.02799.03584.0447.0122.0267.0460.030.0292.0120.050.058.00.01376.0267.0924.02292.0197.0520.00.0360.0347.01107.0109.022.083.098.0107.049.035.071.0894.0None107.053.0105.045.0736.04316.0218.0958.0894.011860.00.0606.028.018.088.00.0361.011397.03313.02752.037.02976.054.0143.041.0875.012072.0None55.019.01985.0473.0787.0327.0987.0117.0153.00.03114.031.898955.09.0141.01202.010690.063.038.0164.02339.00.0553.0775.0None0.074.02195.01218.0875.0162.01449.085.0184.0603.0183.065500.0165.0319.024.030000.0117600.0392.0516.0275.0226.0483.078.024.0211.039.0
                                                                                          7310050.0201320170.08.00.035.046.0132.0204.046.00.00.01.01.0149.0172.0421.0411.0217.00.4498177.09.03.043.354.027.0388.0298.0None0.0177.00.03.022.0None21.0261.020.04.02.013.08.01.06.018.017.050.08.013.02.05.08.010.017.041250.0None0.08.042.00.015.01.01.013.023.035.014.00.00.06.07.05.013.014.011.025.03.05.09.03.010.015.010.0NoneNone0.00.060.00.00.016.00.032.048.02.0421.0None24.025.033.0146.00.096.00.018.06.021799.0264.0None60.09.03.016.04.012.03.017.014.015.03.01.021.025.013.04.09.08.012.010.014.022.020.087.0120.02.0178.073.00.00.017.0169.0178.06.03.064.024.01.017.07.01.02.00.081.08.084.0109.015.013.03.026.00.034.013.03.01.03.02.05.00.00.020.0None9.00.00.013.021.0177.07.025.020.0415.00.00.00.00.03.00.030.0411.079.0133.010.0229.00.03.01.083.0421.0None0.06.01959.03.027.019.023.015.00.00.0112.025.83040.00.031.065.0421.02.00.03.013.00.059.037.0None0.01.093.06.083.01.054.03.02.025.06.0101500.09.021.02.057000.0162500.067.036.013.06.0658.00.02.012.00.0
                                                                                          801067205.020132017124.074.0427.01953.02310.03581.08208.02346.00.04762.033.0120.03517.08535.017110.011757.08902.00.45296727.0176.0317.043.51373.0459.0398.013972.0None10.06949.0435.0468.02311.0None438.09055.0594.0307.0171.0396.0369.0350.0658.0418.0528.02232.0536.0675.0252.0324.0452.0293.0121.045569.0None5.0452.0628.0289.0835.00.0496.048.0176.0511.0352.0162.0326.0415.0465.0527.0598.0547.0617.0586.0222.0531.0166.0404.0567.0353.0222.0NoneNone0.00.01087.0291.0314.0605.00.0750.0892.0146.016675.0None757.0839.0925.06689.0291.04545.0435.0844.0552.023983.012123.0None1087.0694.0858.01025.0749.01067.0154.0395.0241.0445.0301.0288.0504.0566.0332.0278.0457.0308.0323.0412.0616.0694.0614.03625.06517.0169.07455.03398.013.00.01319.06865.07445.0431.0130.01025.0513.074.0595.0379.0287.0257.010.02182.0185.02328.05900.0386.0769.0148.0828.0237.01843.0149.096.0162.091.035.0109.075.051.01347.0None151.0163.00.0787.0918.06727.0540.02932.01347.016615.00.04762.052.012.0221.00.0243.012144.04105.06413.041.06183.02.0218.0113.02279.016964.0None102.0153.01983.0761.01033.0987.01480.0175.096.00.05531.026.0171195.05.0343.01196.016806.0222.0164.0196.02348.00.01485.01673.0None0.0378.03596.01129.02279.0279.0983.0495.0394.0639.0231.0112600.0788.01238.089.062700.0189100.0353.0766.0386.0697.0603.08.080.0245.00.0
                                                                                          9410214.02013201719.016.025.0176.0188.0426.0962.0330.00.00.00.014.0396.0893.01910.01643.0948.00.4153805.028.061.047.0191.079.0537.01529.0None0.0751.0154.051.0146.0None51.01070.051.044.022.013.018.049.041.032.051.0283.086.057.047.029.077.048.029.039831.0None99.054.083.015.064.00.061.064.055.064.032.01.026.044.058.049.049.069.031.065.061.044.022.032.062.044.022.0NoneNone0.00.0200.00.00.0191.00.0102.0101.014.01756.0None114.0130.077.0684.015.0512.0154.096.052.024178.01373.0None200.0101.081.024.018.034.00.020.020.041.05.054.050.020.073.056.077.077.020.052.050.077.090.0420.0717.030.0812.0426.011.00.0113.0739.0812.024.019.0341.037.014.026.080.020.02.00.0293.026.0265.0575.074.057.09.090.014.0297.032.046.08.034.026.015.013.022.0156.0None0.06.06.022.0111.0805.085.0257.0156.01870.00.00.00.021.020.00.068.01783.0493.0612.07.0863.06.012.015.0233.01903.0None15.040.01957.0109.0128.064.0169.05.013.00.0515.025.411965.099.073.0290.01906.022.038.062.0152.00.0194.0121.0None0.0108.0464.099.0233.011.0114.089.049.079.017.0110900.096.0126.021.083300.0162900.096.0123.010.072.0814.00.021.067.02.0
                                                                                          \n", - "
                                                                                          " - ], - "text/plain": [ - " geoid no_car do_date male_20 male_21 no_cars one_car poverty \\\n", - "0 30007 43.0 20132017 32.0 20.0 65.0 463.0 448.0 \n", - "1 19137 106.0 20132017 17.0 42.0 291.0 1298.0 1653.0 \n", - "2 20101 10.0 20132017 8.0 0.0 36.0 181.0 181.0 \n", - "3 48307 133.0 20132017 1.0 93.0 220.0 883.0 1296.0 \n", - "4 46123 66.0 20132017 8.0 21.0 118.0 544.0 1045.0 \n", - "5 29155 308.0 20132017 85.0 189.0 934.0 2639.0 4859.0 \n", - "6 21159 28.0 20132017 69.0 89.0 286.0 1464.0 3190.0 \n", - "7 31005 0.0 20132017 0.0 8.0 0.0 35.0 46.0 \n", - "8 01067 205.0 20132017 124.0 74.0 427.0 1953.0 2310.0 \n", - "9 41021 4.0 20132017 19.0 16.0 25.0 176.0 188.0 \n", - "\n", - " children male_pop two_cars asian_pop black_pop female_20 female_21 \\\n", - "0 1151.0 2896.0 887.0 0.0 4.0 0.0 0.0 \n", - "1 2349.0 4931.0 1826.0 0.0 0.0 42.0 103.0 \n", - "2 407.0 864.0 350.0 0.0 1.0 1.0 8.0 \n", - "3 1935.0 4171.0 1232.0 0.0 161.0 39.0 14.0 \n", - "4 1237.0 2781.0 649.0 0.0 0.0 9.0 59.0 \n", - "5 4597.0 8210.0 2043.0 0.0 4639.0 197.0 153.0 \n", - "6 2498.0 6744.0 1695.0 0.0 575.0 83.0 64.0 \n", - "7 132.0 204.0 46.0 0.0 0.0 1.0 1.0 \n", - "8 3581.0 8208.0 2346.0 0.0 4762.0 33.0 120.0 \n", - "9 426.0 962.0 330.0 0.0 0.0 0.0 14.0 \n", - "\n", - " in_school pop_25_64 total_pop white_pop female_pop gini_index \\\n", - "0 1017.0 2936.0 5755.0 5432.0 2859.0 0.4452 \n", - "1 2094.0 5037.0 10239.0 9640.0 5308.0 0.4270 \n", - "2 332.0 822.0 1702.0 1539.0 838.0 0.4473 \n", - "3 1623.0 4096.0 8145.0 5067.0 3974.0 0.4495 \n", - "4 1121.0 2688.0 5480.0 4500.0 2699.0 0.4633 \n", - "5 4301.0 8474.0 17344.0 11948.0 9134.0 0.4862 \n", - "6 2290.0 6830.0 12175.0 11250.0 5431.0 0.4422 \n", - "7 149.0 172.0 421.0 411.0 217.0 0.4498 \n", - "8 3517.0 8535.0 17110.0 11757.0 8902.0 0.4529 \n", - "9 396.0 893.0 1910.0 1643.0 948.0 0.4153 \n", - "\n", - " households male_60_61 male_62_64 median_age three_cars male_5_to_9 \\\n", - "0 2405.0 105.0 196.0 46.3 622.0 132.0 \n", - "1 4614.0 149.0 256.0 44.5 774.0 385.0 \n", - "2 828.0 10.0 27.0 41.5 133.0 26.0 \n", - "3 3143.0 240.0 134.0 43.5 510.0 370.0 \n", - "4 2424.0 133.0 141.0 46.5 639.0 135.0 \n", - "5 6875.0 225.0 238.0 37.7 871.0 566.0 \n", - "6 4316.0 213.0 261.0 39.0 645.0 326.0 \n", - "7 177.0 9.0 3.0 43.3 54.0 27.0 \n", - "8 6727.0 176.0 317.0 43.5 1373.0 459.0 \n", - "9 805.0 28.0 61.0 47.0 191.0 79.0 \n", - "\n", - " median_rent pop_16_over pop_widowed armed_forces employed_pop \\\n", - "0 550.0 4762.0 None 0.0 2621.0 \n", - "1 482.0 8243.0 None 11.0 4838.0 \n", - "2 345.0 1379.0 None 0.0 885.0 \n", - "3 471.0 6496.0 None 0.0 3658.0 \n", - "4 341.0 4362.0 None 0.0 2859.0 \n", - "5 344.0 13251.0 None 0.0 6326.0 \n", - "6 307.0 9909.0 None 0.0 3032.0 \n", - "7 388.0 298.0 None 0.0 177.0 \n", - "8 398.0 13972.0 None 10.0 6949.0 \n", - "9 537.0 1529.0 None 0.0 751.0 \n", - "\n", - " hispanic_pop male_under_5 mobile_homes pop_divorced female_5_to_9 \\\n", - "0 152.0 112.0 377.0 None 207.0 \n", - "1 371.0 259.0 178.0 None 310.0 \n", - "2 113.0 74.0 46.0 None 74.0 \n", - "3 2571.0 327.0 393.0 None 263.0 \n", - "4 18.0 140.0 496.0 None 125.0 \n", - "5 421.0 614.0 742.0 None 496.0 \n", - "6 226.0 323.0 1911.0 None 342.0 \n", - "7 0.0 3.0 22.0 None 21.0 \n", - "8 435.0 468.0 2311.0 None 438.0 \n", - "9 154.0 51.0 146.0 None 51.0 \n", - "\n", - " housing_units male_10_to_14 male_15_to_17 male_18_to_19 male_22_to_24 \\\n", - "0 2743.0 196.0 117.0 75.0 54.0 \n", - "1 5231.0 313.0 238.0 115.0 182.0 \n", - "2 991.0 46.0 54.0 22.0 45.0 \n", - "3 4302.0 212.0 220.0 33.0 111.0 \n", - "4 3085.0 222.0 141.0 50.0 127.0 \n", - "5 8178.0 747.0 421.0 226.0 282.0 \n", - "6 5240.0 432.0 217.0 125.0 481.0 \n", - "7 261.0 20.0 4.0 2.0 13.0 \n", - "8 9055.0 594.0 307.0 171.0 396.0 \n", - "9 1070.0 51.0 44.0 22.0 13.0 \n", - "\n", - " male_25_to_29 male_30_to_34 male_35_to_39 male_40_to_44 male_45_to_49 \\\n", - "0 135.0 131.0 160.0 163.0 184.0 \n", - "1 217.0 239.0 252.0 323.0 301.0 \n", - "2 59.0 26.0 76.0 49.0 56.0 \n", - "3 284.0 236.0 272.0 152.0 206.0 \n", - "4 129.0 143.0 155.0 116.0 164.0 \n", - "5 479.0 431.0 449.0 422.0 526.0 \n", - "6 485.0 720.0 476.0 592.0 430.0 \n", - "7 8.0 1.0 6.0 18.0 17.0 \n", - "8 369.0 350.0 658.0 418.0 528.0 \n", - "9 18.0 49.0 41.0 32.0 51.0 \n", - "\n", - " male_45_to_64 male_50_to_54 male_55_to_59 male_65_to_66 male_67_to_69 \\\n", - "0 908.0 210.0 213.0 63.0 171.0 \n", - "1 1449.0 366.0 377.0 148.0 162.0 \n", - "2 226.0 72.0 61.0 19.0 22.0 \n", - "3 1054.0 310.0 164.0 106.0 111.0 \n", - "4 881.0 238.0 205.0 34.0 116.0 \n", - "5 2156.0 567.0 600.0 227.0 234.0 \n", - "6 1631.0 433.0 294.0 123.0 142.0 \n", - "7 50.0 8.0 13.0 2.0 5.0 \n", - "8 2232.0 536.0 675.0 252.0 324.0 \n", - "9 283.0 86.0 57.0 47.0 29.0 \n", - "\n", - " male_70_to_74 male_75_to_79 male_80_to_84 median_income pop_separated \\\n", - "0 191.0 129.0 66.0 55295.0 None \n", - "1 193.0 146.0 137.0 43674.0 None \n", - "2 25.0 40.0 39.0 51765.0 None \n", - "3 296.0 94.0 139.0 42367.0 None \n", - "4 112.0 143.0 42.0 48409.0 None \n", - "5 239.0 164.0 153.0 32468.0 None \n", - "6 247.0 166.0 61.0 29239.0 None \n", - "7 8.0 10.0 17.0 41250.0 None \n", - "8 452.0 293.0 121.0 45569.0 None \n", - "9 77.0 48.0 29.0 39831.0 None \n", - "\n", - " amerindian_pop female_under_5 four_more_cars group_quarters \\\n", - "0 84.0 146.0 368.0 44.0 \n", - "1 40.0 335.0 425.0 210.0 \n", - "2 39.0 48.0 128.0 8.0 \n", - "3 1.0 154.0 298.0 154.0 \n", - "4 729.0 172.0 474.0 159.0 \n", - "5 18.0 671.0 388.0 202.0 \n", - "6 9.0 295.0 226.0 1441.0 \n", - "7 0.0 8.0 42.0 0.0 \n", - "8 5.0 452.0 628.0 289.0 \n", - "9 99.0 54.0 83.0 15.0 \n", - "\n", - " masters_degree other_race_pop unemployed_pop walked_to_work \\\n", - "0 293.0 0.0 112.0 135.0 \n", - "1 227.0 0.0 349.0 194.0 \n", - "2 69.0 0.0 32.0 32.0 \n", - "3 213.0 202.0 122.0 104.0 \n", - "4 112.0 0.0 52.0 132.0 \n", - "5 483.0 0.0 756.0 153.0 \n", - "6 225.0 0.0 552.0 34.0 \n", - "7 15.0 1.0 1.0 13.0 \n", - "8 835.0 0.0 496.0 48.0 \n", - "9 64.0 0.0 61.0 64.0 \n", - "\n", - " worked_at_home female_10_to_14 female_15_to_17 female_18_to_19 \\\n", - "0 205.0 124.0 117.0 123.0 \n", - "1 233.0 302.0 207.0 127.0 \n", - "2 27.0 47.0 38.0 35.0 \n", - "3 61.0 258.0 131.0 66.0 \n", - "4 223.0 203.0 99.0 46.0 \n", - "5 109.0 689.0 393.0 194.0 \n", - "6 8.0 334.0 229.0 101.0 \n", - "7 23.0 35.0 14.0 0.0 \n", - "8 176.0 511.0 352.0 162.0 \n", - "9 55.0 64.0 32.0 1.0 \n", - "\n", - " female_22_to_24 female_25_to_29 female_30_to_34 female_35_to_39 \\\n", - "0 90.0 91.0 113.0 165.0 \n", - "1 119.0 220.0 233.0 262.0 \n", - "2 13.0 40.0 29.0 48.0 \n", - "3 21.0 340.0 82.0 271.0 \n", - "4 65.0 122.0 123.0 133.0 \n", - "5 209.0 573.0 521.0 629.0 \n", - "6 118.0 303.0 308.0 384.0 \n", - "7 0.0 6.0 7.0 5.0 \n", - "8 326.0 415.0 465.0 527.0 \n", - "9 26.0 44.0 58.0 49.0 \n", - "\n", - " female_40_to_44 female_45_to_49 female_50_to_54 female_55_to_59 \\\n", - "0 153.0 218.0 250.0 196.0 \n", - "1 345.0 320.0 373.0 452.0 \n", - "2 53.0 44.0 45.0 63.0 \n", - "3 321.0 285.0 188.0 273.0 \n", - "4 97.0 173.0 242.0 190.0 \n", - "5 452.0 564.0 593.0 715.0 \n", - "6 316.0 401.0 419.0 466.0 \n", - "7 13.0 14.0 11.0 25.0 \n", - "8 598.0 547.0 617.0 586.0 \n", - "9 49.0 69.0 31.0 65.0 \n", - "\n", - " female_60_to_61 female_62_to_64 female_65_to_66 female_67_to_69 \\\n", - "0 116.0 137.0 74.0 114.0 \n", - "1 152.0 200.0 126.0 168.0 \n", - "2 19.0 45.0 22.0 14.0 \n", - "3 121.0 217.0 147.0 117.0 \n", - "4 108.0 76.0 87.0 84.0 \n", - "5 199.0 291.0 159.0 271.0 \n", - "6 74.0 255.0 158.0 176.0 \n", - "7 3.0 5.0 9.0 3.0 \n", - "8 222.0 531.0 166.0 404.0 \n", - "9 61.0 44.0 22.0 32.0 \n", - "\n", - " female_70_to_74 female_75_to_79 female_80_to_84 pop_15_and_over \\\n", - "0 173.0 158.0 52.0 None \n", - "1 273.0 233.0 177.0 None \n", - "2 40.0 42.0 38.0 None \n", - "3 228.0 156.0 134.0 None \n", - "4 114.0 174.0 55.0 None \n", - "5 397.0 291.0 254.0 None \n", - "6 203.0 158.0 150.0 None \n", - "7 10.0 15.0 10.0 None \n", - "8 567.0 353.0 222.0 None \n", - "9 62.0 44.0 22.0 None \n", - "\n", - " pop_now_married asian_male_45_54 asian_male_55_64 bachelors_degree \\\n", - "0 None 0.0 0.0 866.0 \n", - "1 None 0.0 0.0 867.0 \n", - "2 None 0.0 0.0 206.0 \n", - "3 None 0.0 0.0 671.0 \n", - "4 None 0.0 0.0 643.0 \n", - "5 None 0.0 0.0 815.0 \n", - "6 None 0.0 0.0 373.0 \n", - "7 None 0.0 0.0 60.0 \n", - "8 None 0.0 0.0 1087.0 \n", - "9 None 0.0 0.0 200.0 \n", - "\n", - " black_male_45_54 black_male_55_64 commute_5_9_mins commuters_by_bus \\\n", - "0 0.0 0.0 359.0 0.0 \n", - "1 0.0 0.0 1023.0 21.0 \n", - "2 0.0 0.0 178.0 0.0 \n", - "3 18.0 0.0 867.0 2.0 \n", - "4 0.0 0.0 878.0 37.0 \n", - "5 239.0 230.0 1273.0 23.0 \n", - "6 159.0 17.0 195.0 0.0 \n", - "7 0.0 0.0 16.0 0.0 \n", - "8 291.0 314.0 605.0 0.0 \n", - "9 0.0 0.0 191.0 0.0 \n", - "\n", - " in_grades_1_to_4 in_grades_5_to_8 male_85_and_over not_hispanic_pop \\\n", - "0 253.0 252.0 41.0 5603.0 \n", - "1 569.0 512.0 114.0 9868.0 \n", - "2 64.0 79.0 8.0 1589.0 \n", - "3 483.0 334.0 60.0 5574.0 \n", - "4 272.0 269.0 66.0 5462.0 \n", - "5 877.0 1260.0 126.0 16923.0 \n", - "6 554.0 575.0 39.0 11949.0 \n", - "7 32.0 48.0 2.0 421.0 \n", - "8 750.0 892.0 146.0 16675.0 \n", - "9 102.0 101.0 14.0 1756.0 \n", - "\n", - " pop_5_years_over white_male_45_54 white_male_55_64 associates_degree \\\n", - "0 None 369.0 463.0 268.0 \n", - "1 None 628.0 782.0 926.0 \n", - "2 None 121.0 96.0 135.0 \n", - "3 None 327.0 391.0 217.0 \n", - "4 None 310.0 392.0 319.0 \n", - "5 None 824.0 828.0 660.0 \n", - "6 None 657.0 726.0 728.0 \n", - "7 None 24.0 25.0 33.0 \n", - "8 None 757.0 839.0 925.0 \n", - "9 None 114.0 130.0 77.0 \n", - "\n", - " commuters_16_over dwellings_2_units family_households hispanic_any_race \\\n", - "0 2287.0 15.0 1552.0 152.0 \n", - "1 4560.0 177.0 3080.0 371.0 \n", - "2 817.0 0.0 484.0 113.0 \n", - "3 3389.0 229.0 2164.0 2571.0 \n", - "4 2592.0 24.0 1473.0 18.0 \n", - "5 6067.0 517.0 4342.0 421.0 \n", - "6 2791.0 36.0 2940.0 226.0 \n", - "7 146.0 0.0 96.0 0.0 \n", - "8 6689.0 291.0 4545.0 435.0 \n", - "9 684.0 15.0 512.0 154.0 \n", - "\n", - " in_grades_9_to_12 income_less_10000 income_per_capita pop_25_years_over \\\n", - "0 336.0 96.0 32268.0 4210.0 \n", - "1 530.0 282.0 25005.0 7143.0 \n", - "2 67.0 62.0 29768.0 1163.0 \n", - "3 453.0 228.0 23398.0 5832.0 \n", - "4 273.0 239.0 27613.0 3858.0 \n", - "5 1082.0 956.0 18883.0 11212.0 \n", - "6 481.0 782.0 14914.0 8547.0 \n", - "7 18.0 6.0 21799.0 264.0 \n", - "8 844.0 552.0 23983.0 12123.0 \n", - "9 96.0 52.0 24178.0 1373.0 \n", - "\n", - " pop_never_married bachelors_degree_2 commute_10_14_mins \\\n", - "0 None 866.0 337.0 \n", - "1 None 867.0 568.0 \n", - "2 None 206.0 98.0 \n", - "3 None 671.0 535.0 \n", - "4 None 643.0 347.0 \n", - "5 None 815.0 792.0 \n", - "6 None 373.0 117.0 \n", - "7 None 60.0 9.0 \n", - "8 None 1087.0 694.0 \n", - "9 None 200.0 101.0 \n", - "\n", - " commute_15_19_mins commute_20_24_mins commute_25_29_mins \\\n", - "0 132.0 225.0 159.0 \n", - "1 487.0 485.0 205.0 \n", - "2 93.0 64.0 38.0 \n", - "3 412.0 410.0 23.0 \n", - "4 158.0 167.0 120.0 \n", - "5 998.0 913.0 366.0 \n", - "6 575.0 297.0 111.0 \n", - "7 3.0 16.0 4.0 \n", - "8 858.0 1025.0 749.0 \n", - "9 81.0 24.0 18.0 \n", - "\n", - " commute_30_34_mins commute_35_39_mins commute_35_44_mins \\\n", - "0 162.0 214.0 247.0 \n", - "1 236.0 39.0 82.0 \n", - "2 47.0 15.0 15.0 \n", - "3 202.0 71.0 117.0 \n", - "4 92.0 17.0 56.0 \n", - "5 587.0 78.0 167.0 \n", - "6 235.0 220.0 384.0 \n", - "7 12.0 3.0 17.0 \n", - "8 1067.0 154.0 395.0 \n", - "9 34.0 0.0 20.0 \n", - "\n", - " commute_40_44_mins commute_45_59_mins commute_60_89_mins \\\n", - "0 33.0 271.0 150.0 \n", - "1 43.0 292.0 205.0 \n", - "2 0.0 9.0 35.0 \n", - "3 46.0 75.0 52.0 \n", - "4 39.0 55.0 143.0 \n", - "5 89.0 181.0 87.0 \n", - "6 164.0 358.0 325.0 \n", - "7 14.0 15.0 3.0 \n", - "8 241.0 445.0 301.0 \n", - "9 20.0 41.0 5.0 \n", - "\n", - " female_85_and_over income_10000_14999 income_15000_19999 \\\n", - "0 42.0 135.0 96.0 \n", - "1 229.0 419.0 298.0 \n", - "2 32.0 42.0 50.0 \n", - "3 148.0 265.0 288.0 \n", - "4 143.0 174.0 110.0 \n", - "5 223.0 689.0 583.0 \n", - "6 94.0 288.0 476.0 \n", - "7 1.0 21.0 25.0 \n", - "8 288.0 504.0 566.0 \n", - "9 54.0 50.0 20.0 \n", - "\n", - " income_20000_24999 income_25000_29999 income_30000_34999 \\\n", - "0 101.0 88.0 84.0 \n", - "1 345.0 218.0 343.0 \n", - "2 67.0 67.0 35.0 \n", - "3 127.0 156.0 203.0 \n", - "4 132.0 111.0 119.0 \n", - "5 512.0 504.0 377.0 \n", - "6 430.0 203.0 138.0 \n", - "7 13.0 4.0 9.0 \n", - "8 332.0 278.0 457.0 \n", - "9 73.0 56.0 77.0 \n", - "\n", - " income_35000_39999 income_40000_44999 income_45000_49999 \\\n", - "0 210.0 166.0 76.0 \n", - "1 211.0 226.0 222.0 \n", - "2 32.0 35.0 12.0 \n", - "3 207.0 137.0 124.0 \n", - "4 168.0 91.0 124.0 \n", - "5 385.0 382.0 336.0 \n", - "6 224.0 365.0 182.0 \n", - "7 8.0 12.0 10.0 \n", - "8 308.0 323.0 412.0 \n", - "9 77.0 20.0 52.0 \n", - "\n", - " income_50000_59999 income_60000_74999 income_75000_99999 \\\n", - "0 305.0 241.0 267.0 \n", - "1 433.0 515.0 485.0 \n", - "2 79.0 109.0 139.0 \n", - "3 259.0 329.0 385.0 \n", - "4 227.0 270.0 279.0 \n", - "5 544.0 392.0 555.0 \n", - "6 271.0 373.0 356.0 \n", - "7 14.0 22.0 20.0 \n", - "8 616.0 694.0 614.0 \n", - "9 50.0 77.0 90.0 \n", - "\n", - " married_households not_in_labor_force not_us_citizen_pop \\\n", - "0 1348.0 2029.0 20.0 \n", - "1 2221.0 3045.0 136.0 \n", - "2 401.0 462.0 12.0 \n", - "3 1592.0 2716.0 207.0 \n", - "4 1217.0 1451.0 27.0 \n", - "5 2758.0 6169.0 97.0 \n", - "6 2311.0 6325.0 34.0 \n", - "7 87.0 120.0 2.0 \n", - "8 3625.0 6517.0 169.0 \n", - "9 420.0 717.0 30.0 \n", - "\n", - " pop_in_labor_force high_school_diploma hispanic_male_45_54 \\\n", - "0 2733.0 1366.0 0.0 \n", - "1 5198.0 2447.0 39.0 \n", - "2 917.0 302.0 6.0 \n", - "3 3780.0 1597.0 164.0 \n", - "4 2911.0 1367.0 0.0 \n", - "5 7082.0 3602.0 19.0 \n", - "6 3584.0 2353.0 33.0 \n", - "7 178.0 73.0 0.0 \n", - "8 7455.0 3398.0 13.0 \n", - "9 812.0 426.0 11.0 \n", - "\n", - " hispanic_male_55_64 occupation_services workers_16_and_over \\\n", - "0 5.0 461.0 2492.0 \n", - "1 0.0 824.0 4793.0 \n", - "2 2.0 165.0 844.0 \n", - "3 147.0 770.0 3450.0 \n", - "4 0.0 393.0 2815.0 \n", - "5 19.0 1441.0 6176.0 \n", - "6 17.0 428.0 2799.0 \n", - "7 0.0 17.0 169.0 \n", - "8 0.0 1319.0 6865.0 \n", - "9 0.0 113.0 739.0 \n", - "\n", - " civilian_labor_force commute_60_more_mins commute_90_more_mins \\\n", - "0 2733.0 203.0 53.0 \n", - "1 5187.0 319.0 114.0 \n", - "2 917.0 43.0 8.0 \n", - "3 3780.0 133.0 81.0 \n", - "4 2911.0 175.0 32.0 \n", - "5 7082.0 159.0 72.0 \n", - "6 3584.0 447.0 122.0 \n", - "7 178.0 6.0 3.0 \n", - "8 7445.0 431.0 130.0 \n", - "9 812.0 24.0 19.0 \n", - "\n", - " commute_less_10_mins commuters_by_carpool employed_information \\\n", - "0 551.0 267.0 31.0 \n", - "1 1886.0 453.0 153.0 \n", - "2 410.0 42.0 10.0 \n", - "3 1482.0 424.0 17.0 \n", - "4 1422.0 247.0 30.0 \n", - "5 1904.0 594.0 26.0 \n", - "6 267.0 460.0 30.0 \n", - "7 64.0 24.0 1.0 \n", - "8 1025.0 513.0 74.0 \n", - "9 341.0 37.0 14.0 \n", - "\n", - " in_undergrad_college income_100000_124999 income_125000_149999 \\\n", - "0 79.0 170.0 82.0 \n", - "1 176.0 317.0 121.0 \n", - "2 48.0 45.0 11.0 \n", - "3 70.0 176.0 114.0 \n", - "4 102.0 226.0 37.0 \n", - "5 492.0 347.0 96.0 \n", - "6 292.0 120.0 50.0 \n", - "7 17.0 7.0 1.0 \n", - "8 595.0 379.0 287.0 \n", - "9 26.0 80.0 20.0 \n", - "\n", - " income_150000_199999 male_male_households nonfamily_households \\\n", - "0 186.0 0.0 853.0 \n", - "1 93.0 0.0 1534.0 \n", - "2 18.0 0.0 344.0 \n", - "3 69.0 0.0 979.0 \n", - "4 32.0 0.0 951.0 \n", - "5 143.0 6.0 2533.0 \n", - "6 58.0 0.0 1376.0 \n", - "7 2.0 0.0 81.0 \n", - "8 257.0 10.0 2182.0 \n", - "9 2.0 0.0 293.0 \n", - "\n", - " rent_over_50_percent vacant_housing_units commuters_drove_alone \\\n", - "0 47.0 338.0 1852.0 \n", - "1 246.0 617.0 3813.0 \n", - "2 16.0 163.0 732.0 \n", - "3 227.0 1159.0 2857.0 \n", - "4 128.0 661.0 2163.0 \n", - "5 641.0 1303.0 5259.0 \n", - "6 267.0 924.0 2292.0 \n", - "7 8.0 84.0 109.0 \n", - "8 185.0 2328.0 5900.0 \n", - "9 26.0 265.0 575.0 \n", - "\n", - " employed_construction employed_retail_trade income_200000_or_more \\\n", - "0 370.0 180.0 102.0 \n", - "1 309.0 641.0 86.0 \n", - "2 112.0 73.0 25.0 \n", - "3 171.0 383.0 76.0 \n", - "4 271.0 363.0 85.0 \n", - "5 194.0 741.0 74.0 \n", - "6 197.0 520.0 0.0 \n", - "7 15.0 13.0 3.0 \n", - "8 386.0 769.0 148.0 \n", - "9 74.0 57.0 9.0 \n", - "\n", - " less_one_year_college male_45_64_grade_9_12 one_year_more_college \\\n", - "0 261.0 24.0 683.0 \n", - "1 716.0 111.0 935.0 \n", - "2 106.0 7.0 207.0 \n", - "3 500.0 201.0 1092.0 \n", - "4 196.0 31.0 571.0 \n", - "5 705.0 408.0 1354.0 \n", - "6 360.0 347.0 1107.0 \n", - "7 26.0 0.0 34.0 \n", - "8 828.0 237.0 1843.0 \n", - "9 90.0 14.0 297.0 \n", - "\n", - " rent_10_to_15_percent rent_15_to_20_percent rent_20_to_25_percent \\\n", - "0 94.0 22.0 52.0 \n", - "1 180.0 193.0 164.0 \n", - "2 27.0 16.0 18.0 \n", - "3 64.0 100.0 59.0 \n", - "4 87.0 63.0 58.0 \n", - "5 263.0 285.0 371.0 \n", - "6 109.0 22.0 83.0 \n", - "7 13.0 3.0 1.0 \n", - "8 149.0 96.0 162.0 \n", - "9 32.0 46.0 8.0 \n", - "\n", - " rent_25_to_30_percent rent_30_to_35_percent rent_35_to_40_percent \\\n", - "0 15.0 45.0 5.0 \n", - "1 144.0 51.0 77.0 \n", - "2 13.0 19.0 8.0 \n", - "3 44.0 46.0 25.0 \n", - "4 79.0 2.0 37.0 \n", - "5 367.0 188.0 167.0 \n", - "6 98.0 107.0 49.0 \n", - "7 3.0 2.0 5.0 \n", - "8 91.0 35.0 109.0 \n", - "9 34.0 26.0 15.0 \n", - "\n", - " rent_40_to_50_percent rent_under_10_percent sales_office_employed \\\n", - "0 32.0 6.0 404.0 \n", - "1 168.0 75.0 1081.0 \n", - "2 0.0 10.0 185.0 \n", - "3 24.0 19.0 809.0 \n", - "4 104.0 64.0 484.0 \n", - "5 267.0 155.0 1164.0 \n", - "6 35.0 71.0 894.0 \n", - "7 0.0 0.0 20.0 \n", - "8 75.0 51.0 1347.0 \n", - "9 13.0 22.0 156.0 \n", - "\n", - " speak_spanish_at_home two_or_more_races_pop dwellings_3_to_4_units \\\n", - "0 None 83.0 41.0 \n", - "1 None 188.0 291.0 \n", - "2 None 3.0 33.0 \n", - "3 None 143.0 110.0 \n", - "4 None 233.0 108.0 \n", - "5 None 316.0 513.0 \n", - "6 None 107.0 53.0 \n", - "7 None 9.0 0.0 \n", - "8 None 151.0 163.0 \n", - "9 None 0.0 6.0 \n", - "\n", - " dwellings_5_to_9_units employed_manufacturing male_45_64_high_school \\\n", - "0 49.0 215.0 287.0 \n", - "1 101.0 870.0 680.0 \n", - "2 0.0 6.0 82.0 \n", - "3 16.0 214.0 458.0 \n", - "4 70.0 41.0 387.0 \n", - "5 49.0 1328.0 866.0 \n", - "6 105.0 45.0 736.0 \n", - "7 0.0 13.0 21.0 \n", - "8 0.0 787.0 918.0 \n", - "9 6.0 22.0 111.0 \n", - "\n", - " occupied_housing_units male_45_64_some_college mortgaged_housing_units \\\n", - "0 2405.0 247.0 1122.0 \n", - "1 4614.0 264.0 1552.0 \n", - "2 828.0 61.0 306.0 \n", - "3 3143.0 203.0 889.0 \n", - "4 2424.0 140.0 663.0 \n", - "5 6875.0 337.0 1652.0 \n", - "6 4316.0 218.0 958.0 \n", - "7 177.0 7.0 25.0 \n", - "8 6727.0 540.0 2932.0 \n", - "9 805.0 85.0 257.0 \n", - "\n", - " occupation_sales_office population_3_years_over asian_including_hispanic \\\n", - "0 404.0 5553.0 0.0 \n", - "1 1081.0 9869.0 0.0 \n", - "2 185.0 1623.0 0.0 \n", - "3 809.0 7863.0 0.0 \n", - "4 484.0 5334.0 0.0 \n", - "5 1164.0 16514.0 0.0 \n", - "6 894.0 11860.0 0.0 \n", - "7 20.0 415.0 0.0 \n", - "8 1347.0 16615.0 0.0 \n", - "9 156.0 1870.0 0.0 \n", - "\n", - " black_including_hispanic dwellings_10_to_19_units \\\n", - "0 4.0 41.0 \n", - "1 0.0 49.0 \n", - "2 1.0 0.0 \n", - "3 161.0 6.0 \n", - "4 0.0 38.0 \n", - "5 4717.0 90.0 \n", - "6 606.0 28.0 \n", - "7 0.0 0.0 \n", - "8 4762.0 52.0 \n", - "9 0.0 0.0 \n", - "\n", - " dwellings_20_to_49_units employed_wholesale_trade \\\n", - "0 0.0 42.0 \n", - "1 40.0 120.0 \n", - "2 0.0 13.0 \n", - "3 18.0 39.0 \n", - "4 52.0 75.0 \n", - "5 16.0 110.0 \n", - "6 18.0 88.0 \n", - "7 0.0 3.0 \n", - "8 12.0 221.0 \n", - "9 21.0 20.0 \n", - "\n", - " female_female_households rent_burden_not_computed \\\n", - "0 0.0 67.0 \n", - "1 3.0 185.0 \n", - "2 0.0 52.0 \n", - "3 0.0 137.0 \n", - "4 0.0 125.0 \n", - "5 5.0 540.0 \n", - "6 0.0 361.0 \n", - "7 0.0 30.0 \n", - "8 0.0 243.0 \n", - "9 0.0 68.0 \n", - "\n", - " white_including_hispanic high_school_including_ged \\\n", - "0 5533.0 1564.0 \n", - "1 9756.0 2785.0 \n", - "2 1649.0 361.0 \n", - "3 6969.0 1862.0 \n", - "4 4500.0 1511.0 \n", - "5 12113.0 4279.0 \n", - "6 11397.0 3313.0 \n", - "7 411.0 79.0 \n", - "8 12144.0 4105.0 \n", - "9 1783.0 493.0 \n", - "\n", - " commuters_by_car_truck_van dwellings_1_units_attached \\\n", - "0 2119.0 0.0 \n", - "1 4266.0 77.0 \n", - "2 774.0 22.0 \n", - "3 3281.0 23.0 \n", - "4 2410.0 16.0 \n", - "5 5853.0 54.0 \n", - "6 2752.0 37.0 \n", - "7 133.0 10.0 \n", - "8 6413.0 41.0 \n", - "9 612.0 7.0 \n", - "\n", - " dwellings_1_units_detached dwellings_50_or_more_units \\\n", - "0 2208.0 0.0 \n", - "1 4318.0 0.0 \n", - "2 890.0 0.0 \n", - "3 3498.0 0.0 \n", - "4 2281.0 0.0 \n", - "5 6098.0 91.0 \n", - "6 2976.0 54.0 \n", - "7 229.0 0.0 \n", - "8 6183.0 2.0 \n", - "9 863.0 6.0 \n", - "\n", - " housing_built_2000_to_2004 male_45_64_graduate_degree \\\n", - "0 61.0 87.0 \n", - "1 27.0 32.0 \n", - "2 15.0 3.0 \n", - "3 78.0 17.0 \n", - "4 43.0 32.0 \n", - "5 206.0 25.0 \n", - "6 143.0 41.0 \n", - "7 3.0 1.0 \n", - "8 218.0 113.0 \n", - "9 12.0 15.0 \n", - "\n", - " occupation_management_arts population_1_year_and_over \\\n", - "0 955.0 5634.0 \n", - "1 1470.0 10138.0 \n", - "2 264.0 1659.0 \n", - "3 850.0 8140.0 \n", - "4 1217.0 5425.0 \n", - "5 1560.0 17033.0 \n", - "6 875.0 12072.0 \n", - "7 83.0 421.0 \n", - "8 2279.0 16964.0 \n", - "9 233.0 1903.0 \n", - "\n", - " speak_only_english_at_home housing_built_2005_or_later \\\n", - "0 None 16.0 \n", - "1 None 5.0 \n", - "2 None 0.0 \n", - "3 None 39.0 \n", - "4 None 19.0 \n", - "5 None 20.0 \n", - "6 None 55.0 \n", - "7 None 0.0 \n", - "8 None 102.0 \n", - "9 None 15.0 \n", - "\n", - " male_45_64_bachelors_degree median_year_structure_built \\\n", - "0 158.0 1987.0 \n", - "1 178.0 1952.0 \n", - "2 48.0 1957.0 \n", - "3 93.0 1969.0 \n", - "4 152.0 1966.0 \n", - "5 211.0 1968.0 \n", - "6 19.0 1985.0 \n", - "7 6.0 1959.0 \n", - "8 153.0 1983.0 \n", - "9 40.0 1957.0 \n", - "\n", - " children_in_single_female_hh families_with_young_children \\\n", - "0 147.0 335.0 \n", - "1 756.0 662.0 \n", - "2 43.0 137.0 \n", - "3 396.0 589.0 \n", - "4 220.0 338.0 \n", - "5 2120.0 1330.0 \n", - "6 473.0 787.0 \n", - "7 3.0 27.0 \n", - "8 761.0 1033.0 \n", - "9 109.0 128.0 \n", - "\n", - " graduate_professional_degree households_retirement_income \\\n", - "0 337.0 625.0 \n", - "1 281.0 807.0 \n", - "2 72.0 107.0 \n", - "3 261.0 679.0 \n", - "4 210.0 261.0 \n", - "5 532.0 1138.0 \n", - "6 327.0 987.0 \n", - "7 19.0 23.0 \n", - "8 987.0 1480.0 \n", - "9 64.0 169.0 \n", - "\n", - " male_45_64_associates_degree male_45_64_less_than_9_grade \\\n", - "0 92.0 13.0 \n", - "1 151.0 33.0 \n", - "2 22.0 3.0 \n", - "3 7.0 75.0 \n", - "4 83.0 56.0 \n", - "5 58.0 251.0 \n", - "6 117.0 153.0 \n", - "7 15.0 0.0 \n", - "8 175.0 96.0 \n", - "9 5.0 13.0 \n", - "\n", - " million_dollar_housing_units owner_occupied_housing_units \\\n", - "0 26.0 2020.0 \n", - "1 0.0 3131.0 \n", - "2 0.0 649.0 \n", - "3 35.0 2398.0 \n", - "4 16.0 1677.0 \n", - "5 0.0 3631.0 \n", - "6 0.0 3114.0 \n", - "7 0.0 112.0 \n", - "8 0.0 5531.0 \n", - "9 0.0 515.0 \n", - "\n", - " percent_income_spent_on_rent aggregate_travel_time_to_work \\\n", - "0 23.6 60655.0 \n", - "1 26.3 86680.0 \n", - "2 22.9 NaN \n", - "3 32.0 53750.0 \n", - "4 27.5 40040.0 \n", - "5 28.8 105750.0 \n", - "6 31.8 98955.0 \n", - "7 25.8 3040.0 \n", - "8 26.0 171195.0 \n", - "9 25.4 11965.0 \n", - "\n", - " amerindian_including_hispanic housing_built_1939_or_earlier \\\n", - "0 84.0 97.0 \n", - "1 64.0 181.0 \n", - "2 39.0 95.0 \n", - "3 1.0 551.0 \n", - "4 747.0 255.0 \n", - "5 18.0 853.0 \n", - "6 9.0 141.0 \n", - "7 0.0 31.0 \n", - "8 5.0 343.0 \n", - "9 99.0 73.0 \n", - "\n", - " housing_units_renter_occupied pop_determined_poverty_status \\\n", - "0 385.0 5711.0 \n", - "1 1483.0 10014.0 \n", - "2 179.0 1690.0 \n", - "3 745.0 7955.0 \n", - "4 747.0 5294.0 \n", - "5 3244.0 17042.0 \n", - "6 1202.0 10690.0 \n", - "7 65.0 421.0 \n", - "8 1196.0 16806.0 \n", - "9 290.0 1906.0 \n", - "\n", - " vacant_housing_units_for_rent vacant_housing_units_for_sale \\\n", - "0 11.0 34.0 \n", - "1 142.0 67.0 \n", - "2 28.0 0.0 \n", - "3 31.0 57.0 \n", - "4 95.0 39.0 \n", - "5 313.0 89.0 \n", - "6 63.0 38.0 \n", - "7 2.0 0.0 \n", - "8 222.0 164.0 \n", - "9 22.0 38.0 \n", - "\n", - " employed_public_administration less_than_high_school_graduate \\\n", - "0 291.0 231.0 \n", - "1 122.0 633.0 \n", - "2 28.0 76.0 \n", - "3 192.0 1229.0 \n", - "4 118.0 408.0 \n", - "5 313.0 2867.0 \n", - "6 164.0 2339.0 \n", - "7 3.0 13.0 \n", - "8 196.0 2348.0 \n", - "9 62.0 152.0 \n", - "\n", - " commuters_by_subway_or_elevated bachelors_degree_or_higher_25_64 \\\n", - "0 0.0 873.0 \n", - "1 0.0 856.0 \n", - "2 0.0 211.0 \n", - "3 0.0 556.0 \n", - "4 0.0 580.0 \n", - "5 0.0 1066.0 \n", - "6 0.0 553.0 \n", - "7 0.0 59.0 \n", - "8 0.0 1485.0 \n", - "9 0.0 194.0 \n", - "\n", - " employed_education_health_social speak_spanish_at_home_low_english \\\n", - "0 380.0 None \n", - "1 1172.0 None \n", - "2 196.0 None \n", - "3 647.0 None \n", - "4 674.0 None \n", - "5 1780.0 None \n", - "6 775.0 None \n", - "7 37.0 None \n", - "8 1673.0 None \n", - "9 121.0 None \n", - "\n", - " commuters_by_public_transportation different_house_year_ago_same_city \\\n", - "0 0.0 32.0 \n", - "1 21.0 550.0 \n", - "2 0.0 61.0 \n", - "3 2.0 634.0 \n", - "4 37.0 182.0 \n", - "5 23.0 1577.0 \n", - "6 0.0 74.0 \n", - "7 0.0 1.0 \n", - "8 0.0 378.0 \n", - "9 0.0 108.0 \n", - "\n", - " some_college_and_associates_degree households_public_asst_or_food_stamps \\\n", - "0 1212.0 179.0 \n", - "1 2577.0 831.0 \n", - "2 448.0 61.0 \n", - "3 1809.0 600.0 \n", - "4 1086.0 340.0 \n", - "5 2719.0 2237.0 \n", - "6 2195.0 1218.0 \n", - "7 93.0 6.0 \n", - "8 3596.0 1129.0 \n", - "9 464.0 99.0 \n", - "\n", - " management_business_sci_arts_employed \\\n", - "0 955.0 \n", - "1 1470.0 \n", - "2 264.0 \n", - "3 850.0 \n", - "4 1217.0 \n", - "5 1560.0 \n", - "6 875.0 \n", - "7 83.0 \n", - "8 2279.0 \n", - "9 233.0 \n", - "\n", - " employed_finance_insurance_real_estate \\\n", - "0 141.0 \n", - "1 170.0 \n", - "2 30.0 \n", - "3 133.0 \n", - "4 184.0 \n", - "5 282.0 \n", - "6 162.0 \n", - "7 1.0 \n", - "8 279.0 \n", - "9 11.0 \n", - "\n", - " different_house_year_ago_different_city \\\n", - "0 471.0 \n", - "1 839.0 \n", - "2 84.0 \n", - "3 829.0 \n", - "4 444.0 \n", - "5 1594.0 \n", - "6 1449.0 \n", - "7 54.0 \n", - "8 983.0 \n", - "9 114.0 \n", - "\n", - " employed_science_management_admin_waste \\\n", - "0 141.0 \n", - "1 173.0 \n", - "2 34.0 \n", - "3 231.0 \n", - "4 92.0 \n", - "5 164.0 \n", - "6 85.0 \n", - "7 3.0 \n", - "8 495.0 \n", - "9 89.0 \n", - "\n", - " one_parent_families_with_young_children \\\n", - "0 64.0 \n", - "1 327.0 \n", - "2 20.0 \n", - "3 206.0 \n", - "4 85.0 \n", - "5 813.0 \n", - "6 184.0 \n", - "7 2.0 \n", - "8 394.0 \n", - "9 49.0 \n", - "\n", - " two_parent_families_with_young_children \\\n", - "0 271.0 \n", - "1 335.0 \n", - "2 117.0 \n", - "3 383.0 \n", - "4 253.0 \n", - "5 517.0 \n", - "6 603.0 \n", - "7 25.0 \n", - "8 639.0 \n", - "9 79.0 \n", - "\n", - " employed_other_services_not_public_admin \\\n", - "0 94.0 \n", - "1 301.0 \n", - "2 51.0 \n", - "3 363.0 \n", - "4 139.0 \n", - "5 304.0 \n", - "6 183.0 \n", - "7 6.0 \n", - "8 231.0 \n", - "9 17.0 \n", - "\n", - " owner_occupied_housing_units_median_value \\\n", - "0 195000.0 \n", - "1 81800.0 \n", - "2 76400.0 \n", - "3 82000.0 \n", - "4 85800.0 \n", - "5 73300.0 \n", - "6 65500.0 \n", - "7 101500.0 \n", - "8 112600.0 \n", - "9 110900.0 \n", - "\n", - " employed_transportation_warehousing_utilities \\\n", - "0 90.0 \n", - "1 245.0 \n", - "2 55.0 \n", - "3 401.0 \n", - "4 52.0 \n", - "5 299.0 \n", - "6 165.0 \n", - "7 9.0 \n", - "8 788.0 \n", - "9 96.0 \n", - "\n", - " occupation_production_transportation_material \\\n", - "0 310.0 \n", - "1 949.0 \n", - "2 73.0 \n", - "3 510.0 \n", - "4 306.0 \n", - "5 1565.0 \n", - "6 319.0 \n", - "7 21.0 \n", - "8 1238.0 \n", - "9 126.0 \n", - "\n", - " father_one_parent_families_with_young_children \\\n", - "0 0.0 \n", - "1 29.0 \n", - "2 13.0 \n", - "3 165.0 \n", - "4 26.0 \n", - "5 89.0 \n", - "6 24.0 \n", - "7 2.0 \n", - "8 89.0 \n", - "9 21.0 \n", - "\n", - " owner_occupied_housing_units_lower_value_quartile \\\n", - "0 119800.0 \n", - "1 49500.0 \n", - "2 44300.0 \n", - "3 41600.0 \n", - "4 51600.0 \n", - "5 37000.0 \n", - "6 30000.0 \n", - "7 57000.0 \n", - "8 62700.0 \n", - "9 83300.0 \n", - "\n", - " owner_occupied_housing_units_upper_value_quartile \\\n", - "0 301300.0 \n", - "1 126300.0 \n", - "2 124400.0 \n", - "3 165300.0 \n", - "4 156600.0 \n", - "5 120900.0 \n", - "6 117600.0 \n", - "7 162500.0 \n", - "8 189100.0 \n", - "9 162900.0 \n", - "\n", - " employed_agriculture_forestry_fishing_hunting_mining \\\n", - "0 460.0 \n", - "1 349.0 \n", - "2 253.0 \n", - "3 602.0 \n", - "4 706.0 \n", - "5 405.0 \n", - "6 392.0 \n", - "7 67.0 \n", - "8 353.0 \n", - "9 96.0 \n", - "\n", - " occupation_natural_resources_construction_maintenance \\\n", - "0 491.0 \n", - "1 514.0 \n", - "2 198.0 \n", - "3 719.0 \n", - "4 459.0 \n", - "5 596.0 \n", - "6 516.0 \n", - "7 36.0 \n", - "8 766.0 \n", - "9 123.0 \n", - "\n", - " two_parents_in_labor_force_families_with_young_children \\\n", - "0 90.0 \n", - "1 248.0 \n", - "2 65.0 \n", - "3 214.0 \n", - "4 215.0 \n", - "5 216.0 \n", - "6 275.0 \n", - "7 13.0 \n", - "8 386.0 \n", - "9 10.0 \n", - "\n", - " employed_arts_entertainment_recreation_accommodation_food \\\n", - "0 186.0 \n", - "1 213.0 \n", - "2 24.0 \n", - "3 265.0 \n", - "4 114.0 \n", - "5 380.0 \n", - "6 226.0 \n", - "7 6.0 \n", - "8 697.0 \n", - "9 72.0 \n", - "\n", - " renter_occupied_housing_units_paying_cash_median_gross_rent \\\n", - "0 640.0 \n", - "1 664.0 \n", - "2 551.0 \n", - "3 721.0 \n", - "4 562.0 \n", - "5 568.0 \n", - "6 483.0 \n", - "7 658.0 \n", - "8 603.0 \n", - "9 814.0 \n", - "\n", - " two_parents_not_in_labor_force_families_with_young_children \\\n", - "0 18.0 \n", - "1 12.0 \n", - "2 0.0 \n", - "3 0.0 \n", - "4 0.0 \n", - "5 2.0 \n", - "6 78.0 \n", - "7 0.0 \n", - "8 8.0 \n", - "9 0.0 \n", - "\n", - " father_in_labor_force_one_parent_families_with_young_children \\\n", - "0 0.0 \n", - "1 25.0 \n", - "2 13.0 \n", - "3 165.0 \n", - "4 14.0 \n", - "5 69.0 \n", - "6 24.0 \n", - "7 2.0 \n", - "8 80.0 \n", - "9 21.0 \n", - "\n", - " two_parents_father_in_labor_force_families_with_young_children \\\n", - "0 163.0 \n", - "1 64.0 \n", - "2 48.0 \n", - "3 169.0 \n", - "4 32.0 \n", - "5 299.0 \n", - "6 211.0 \n", - "7 12.0 \n", - "8 245.0 \n", - "9 67.0 \n", - "\n", - " two_parents_mother_in_labor_force_families_with_young_children \n", - "0 0.0 \n", - "1 11.0 \n", - "2 4.0 \n", - "3 0.0 \n", - "4 6.0 \n", - "5 0.0 \n", - "6 39.0 \n", - "7 0.0 \n", - "8 0.0 \n", - "9 2.0 " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Apply a standard filter when downloading the dataset" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's say we are interested in the `children` variable to know the number of children per county, we can download just that variable and the geometry and visualise the result in a map." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "dataset_df = dataset.to_dataframe(sql_query=\"select children, geom from $dataset$\")" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " None\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " Static map image\n", - " \n", - " \n", - "
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                                                                                            0180252013-01-0110598.04007.05355.05243.043.7283.0369.0309.0235.0142.011.041.0205.0282.0301.0284.0341.0355.0417.0438.0163.0259.0243.0171.0194.0145.0100.067.0278.0353.0326.0192.094.054.042.0191.0256.0276.0307.0328.0318.0415.0462.0160.0176.0131.0192.0264.0134.0182.0112.010214.010469.010216.0NaNNaN8559.07473.05538.0NaNNaNNaNNaNNaN6.021.00.0154.063.00.0146.010309.021.014.087.0154.010444.00.07.02.00.09.07.0757.0834.040067.019424.0360.0286.0314.0362.0179.0316.0183.0189.0189.0404.0524.0395.0160.076.060.010.0819.010412.01851.00.39755543.0562.051400.086700.0146100.04007.0673.01536.067.00.04190.019.030.030.060.014.066.00.01094.02.068.0210.01985.02069.01297.02710.0625.00.02.02345.0484.0348.030.1164.0116.072.026.041.075.077.050.038.014.03334.07.01764.0704.098.0615.0492.0287.0196.00.09.0123.054.048.0309.0475.0362.0374.0401.0216.0660.0143.0140.0283.0670.0647.0530.0117.04088.044.0107.047.0179.01004.01405.0957.0462.0133665.013.013.03987.0310.00.03677.0102.0502.0486.02782.0434.0226.0861.01354.03543.0486.0532.0293.01797.0114.0102.057.0110.0241.0832.0176.01632.04241.0191.04439.04120.04195.07.04432.0127.0391.0275.0873.0139.079.01071.0116.0204.0448.0226.0264.028.0912.0493.01246.0712.0878.0912.0712.0626.0492.0617.02321.0306.0NaNNaNNaNCrawford7.998498e+08150379.20538POLYGON((-86.679511 38.263086, -86.570136 38.2...
                                                                                            110912013-01-0119743.07975.09235.010508.042.1514.0601.0660.0421.0236.0133.0117.0307.0635.0487.0529.0506.0569.0628.0728.0212.0358.0242.0234.0475.0372.0215.056.0681.0503.0755.0399.0366.0177.082.0234.0600.0561.0566.0661.0667.0717.0579.0396.0470.0266.0361.0459.0439.0347.0222.08909.019541.019169.0NaNNaN15822.013557.09869.0NaNNaNNaNNaNNaN78.010689.00.088.011.09.037.08954.010689.00.011.088.019655.00.00.0583.0650.00.00.0605.0648.032255.022996.01429.0656.0701.0580.0451.0468.0258.0335.0310.0412.0713.0663.0321.0272.0301.0105.01763.019502.05000.00.530310307.0552.040700.088100.0172700.07975.02286.02332.098.086.06806.064.0109.0183.0494.0105.043.09.02472.083.0128.0715.01979.03102.03358.04617.01971.00.01.04534.01909.0349.029.9706.0349.0195.067.0176.0189.099.0212.0133.0160.05689.05.02383.0985.0734.01398.0572.0268.0168.056.080.0826.0122.0122.01043.01643.0698.0695.0822.0397.0749.0143.0144.0287.0377.0690.0433.0257.06358.081.0184.0297.0922.03037.02720.0783.0513.0161900.00.00.06126.0371.00.05755.0233.01084.01315.04881.0800.0611.01930.02279.05375.01315.01665.0774.03814.0277.0366.0126.086.0351.0993.0296.02495.06645.0975.07620.08202.06542.00.07620.0249.0371.0266.01606.0278.0107.01144.0376.0295.0916.0340.0607.090.01970.0760.01386.01361.01168.01970.01361.0918.01229.01185.04829.0811.0NaNNaNNaNMarengo2.550527e+09252631.60668POLYGON((-87.967893 32.298249, -87.975667 32.3...
                                                                                            250032013-01-0120771.08182.010126.010645.041.8536.0865.0713.0478.0269.080.079.0577.0429.0560.0599.0513.0622.0692.0807.0306.0260.0438.0289.0350.0346.0164.0154.0578.0834.0482.0333.0209.082.051.0439.0611.0511.0767.0634.0759.0813.0755.0235.0415.0284.0303.0626.0447.0223.0254.014126.020622.020198.0NaNNaN16422.014166.010288.0NaNNaNNaNNaNNaN416.05309.00.01083.049.031.0173.014939.05309.00.049.01083.019688.00.00.0341.0352.023.028.0950.0993.036407.020703.0857.0784.0637.0498.0503.0637.0560.0369.0283.0527.0646.0872.0542.0178.0164.0125.01722.020552.04368.00.465810152.0608.038900.068200.0115200.08182.02128.01970.0376.0136.07052.056.0229.012.0190.034.082.041.02446.066.0208.0480.01976.04163.02622.05560.01651.00.00.04819.01436.0393.034.2357.0534.0216.0118.0109.0247.0169.0235.057.086.06054.06.02373.01188.0494.01186.0823.0351.0450.022.00.0363.022.022.01807.02364.01463.01102.0722.0341.0524.018.0144.0162.0400.0578.0348.0230.07656.0178.0126.0248.0542.02795.02854.01490.0501.0156480.019.019.07226.0607.00.06619.0193.0704.01333.05003.0786.0462.02485.02409.05883.01333.01483.0566.03975.0135.0219.052.077.0391.01158.0655.02687.07905.0870.08775.07647.07782.00.08775.0640.0287.0722.02045.0176.047.01407.0244.0418.0951.0329.0539.0100.02176.01086.01820.01494.01329.02176.01494.01344.01124.01022.05037.0801.0NaNNaNNaNAshley2.432888e+09212505.74837POLYGON((-91.561673 33.391561, -91.60694 33.39...
                                                                                            \n", - "
                                                                                            " - ], - "text/plain": [ - " geoid do_date total_pop households male_pop female_pop median_age \\\n", - "0 18025 2013-01-01 10598.0 4007.0 5355.0 5243.0 43.7 \n", - "1 1091 2013-01-01 19743.0 7975.0 9235.0 10508.0 42.1 \n", - "2 5003 2013-01-01 20771.0 8182.0 10126.0 10645.0 41.8 \n", - "\n", - " male_under_5 male_5_to_9 male_10_to_14 male_15_to_17 male_18_to_19 \\\n", - "0 283.0 369.0 309.0 235.0 142.0 \n", - "1 514.0 601.0 660.0 421.0 236.0 \n", - "2 536.0 865.0 713.0 478.0 269.0 \n", - "\n", - " male_20 male_21 male_22_to_24 male_25_to_29 male_30_to_34 \\\n", - "0 11.0 41.0 205.0 282.0 301.0 \n", - "1 133.0 117.0 307.0 635.0 487.0 \n", - "2 80.0 79.0 577.0 429.0 560.0 \n", - "\n", - " male_35_to_39 male_40_to_44 male_45_to_49 male_50_to_54 male_55_to_59 \\\n", - "0 284.0 341.0 355.0 417.0 438.0 \n", - "1 529.0 506.0 569.0 628.0 728.0 \n", - "2 599.0 513.0 622.0 692.0 807.0 \n", - "\n", - " male_60_to_61 male_62_to_64 male_65_to_66 male_67_to_69 male_70_to_74 \\\n", - "0 163.0 259.0 243.0 171.0 194.0 \n", - "1 212.0 358.0 242.0 234.0 475.0 \n", - "2 306.0 260.0 438.0 289.0 350.0 \n", - "\n", - " male_75_to_79 male_80_to_84 male_85_and_over female_under_5 \\\n", - "0 145.0 100.0 67.0 278.0 \n", - "1 372.0 215.0 56.0 681.0 \n", - "2 346.0 164.0 154.0 578.0 \n", - "\n", - " female_5_to_9 female_10_to_14 female_15_to_17 female_18_to_19 \\\n", - "0 353.0 326.0 192.0 94.0 \n", - "1 503.0 755.0 399.0 366.0 \n", - "2 834.0 482.0 333.0 209.0 \n", - "\n", - " female_20 female_21 female_22_to_24 female_25_to_29 female_30_to_34 \\\n", - "0 54.0 42.0 191.0 256.0 276.0 \n", - "1 177.0 82.0 234.0 600.0 561.0 \n", - "2 82.0 51.0 439.0 611.0 511.0 \n", - "\n", - " female_35_to_39 female_40_to_44 female_45_to_49 female_50_to_54 \\\n", - "0 307.0 328.0 318.0 415.0 \n", - "1 566.0 661.0 667.0 717.0 \n", - "2 767.0 634.0 759.0 813.0 \n", - "\n", - " female_55_to_59 female_60_to_61 female_62_to_64 female_65_to_66 \\\n", - "0 462.0 160.0 176.0 131.0 \n", - "1 579.0 396.0 470.0 266.0 \n", - "2 755.0 235.0 415.0 284.0 \n", - "\n", - " female_67_to_69 female_70_to_74 female_75_to_79 female_80_to_84 \\\n", - "0 192.0 264.0 134.0 182.0 \n", - "1 361.0 459.0 439.0 347.0 \n", - "2 303.0 626.0 447.0 223.0 \n", - "\n", - " female_85_and_over white_pop population_1_year_and_over \\\n", - "0 112.0 10214.0 10469.0 \n", - "1 222.0 8909.0 19541.0 \n", - "2 254.0 14126.0 20622.0 \n", - "\n", - " population_3_years_over pop_5_years_over pop_15_and_over pop_16_over \\\n", - "0 10216.0 NaN NaN 8559.0 \n", - "1 19169.0 NaN NaN 15822.0 \n", - "2 20198.0 NaN NaN 16422.0 \n", - "\n", - " pop_25_years_over pop_25_64 pop_never_married pop_now_married \\\n", - "0 7473.0 5538.0 NaN NaN \n", - "1 13557.0 9869.0 NaN NaN \n", - "2 14166.0 10288.0 NaN NaN \n", - "\n", - " pop_separated pop_widowed pop_divorced not_us_citizen_pop black_pop \\\n", - "0 NaN NaN NaN 6.0 21.0 \n", - "1 NaN NaN NaN 78.0 10689.0 \n", - "2 NaN NaN NaN 416.0 5309.0 \n", - "\n", - " asian_pop hispanic_pop amerindian_pop other_race_pop \\\n", - "0 0.0 154.0 63.0 0.0 \n", - "1 0.0 88.0 11.0 9.0 \n", - "2 0.0 1083.0 49.0 31.0 \n", - "\n", - " two_or_more_races_pop white_including_hispanic black_including_hispanic \\\n", - "0 146.0 10309.0 21.0 \n", - "1 37.0 8954.0 10689.0 \n", - "2 173.0 14939.0 5309.0 \n", - "\n", - " asian_including_hispanic amerindian_including_hispanic hispanic_any_race \\\n", - "0 14.0 87.0 154.0 \n", - "1 0.0 11.0 88.0 \n", - "2 0.0 49.0 1083.0 \n", - "\n", - " not_hispanic_pop asian_male_45_54 asian_male_55_64 black_male_45_54 \\\n", - "0 10444.0 0.0 7.0 2.0 \n", - "1 19655.0 0.0 0.0 583.0 \n", - "2 19688.0 0.0 0.0 341.0 \n", - "\n", - " black_male_55_64 hispanic_male_45_54 hispanic_male_55_64 \\\n", - "0 0.0 9.0 7.0 \n", - "1 650.0 0.0 0.0 \n", - "2 352.0 23.0 28.0 \n", - "\n", - " white_male_45_54 white_male_55_64 median_income income_per_capita \\\n", - "0 757.0 834.0 40067.0 19424.0 \n", - "1 605.0 648.0 32255.0 22996.0 \n", - "2 950.0 993.0 36407.0 20703.0 \n", - "\n", - " income_less_10000 income_10000_14999 income_15000_19999 \\\n", - "0 360.0 286.0 314.0 \n", - "1 1429.0 656.0 701.0 \n", - "2 857.0 784.0 637.0 \n", - "\n", - " income_20000_24999 income_25000_29999 income_30000_34999 \\\n", - "0 362.0 179.0 316.0 \n", - "1 580.0 451.0 468.0 \n", - "2 498.0 503.0 637.0 \n", - "\n", - " income_35000_39999 income_40000_44999 income_45000_49999 \\\n", - "0 183.0 189.0 189.0 \n", - "1 258.0 335.0 310.0 \n", - "2 560.0 369.0 283.0 \n", - "\n", - " income_50000_59999 income_60000_74999 income_75000_99999 \\\n", - "0 404.0 524.0 395.0 \n", - "1 412.0 713.0 663.0 \n", - "2 527.0 646.0 872.0 \n", - "\n", - " income_100000_124999 income_125000_149999 income_150000_199999 \\\n", - "0 160.0 76.0 60.0 \n", - "1 321.0 272.0 301.0 \n", - "2 542.0 178.0 164.0 \n", - "\n", - " income_200000_or_more households_retirement_income \\\n", - "0 10.0 819.0 \n", - "1 105.0 1763.0 \n", - "2 125.0 1722.0 \n", - "\n", - " pop_determined_poverty_status poverty gini_index housing_units \\\n", - "0 10412.0 1851.0 0.3975 5543.0 \n", - "1 19502.0 5000.0 0.5303 10307.0 \n", - "2 20552.0 4368.0 0.4658 10152.0 \n", - "\n", - " renter_occupied_housing_units_paying_cash_median_gross_rent \\\n", - "0 562.0 \n", - "1 552.0 \n", - "2 608.0 \n", - "\n", - " owner_occupied_housing_units_lower_value_quartile \\\n", - "0 51400.0 \n", - "1 40700.0 \n", - "2 38900.0 \n", - "\n", - " owner_occupied_housing_units_median_value \\\n", - "0 86700.0 \n", - "1 88100.0 \n", - "2 68200.0 \n", - "\n", - " owner_occupied_housing_units_upper_value_quartile occupied_housing_units \\\n", - "0 146100.0 4007.0 \n", - "1 172700.0 7975.0 \n", - "2 115200.0 8182.0 \n", - "\n", - " housing_units_renter_occupied vacant_housing_units \\\n", - "0 673.0 1536.0 \n", - "1 2286.0 2332.0 \n", - "2 2128.0 1970.0 \n", - "\n", - " vacant_housing_units_for_rent vacant_housing_units_for_sale \\\n", - "0 67.0 0.0 \n", - "1 98.0 86.0 \n", - "2 376.0 136.0 \n", - "\n", - " dwellings_1_units_detached dwellings_1_units_attached dwellings_2_units \\\n", - "0 4190.0 19.0 30.0 \n", - "1 6806.0 64.0 109.0 \n", - "2 7052.0 56.0 229.0 \n", - "\n", - " dwellings_3_to_4_units dwellings_5_to_9_units dwellings_10_to_19_units \\\n", - "0 30.0 60.0 14.0 \n", - "1 183.0 494.0 105.0 \n", - "2 12.0 190.0 34.0 \n", - "\n", - " dwellings_20_to_49_units dwellings_50_or_more_units mobile_homes \\\n", - "0 66.0 0.0 1094.0 \n", - "1 43.0 9.0 2472.0 \n", - "2 82.0 41.0 2446.0 \n", - "\n", - " housing_built_2005_or_later housing_built_2000_to_2004 \\\n", - "0 2.0 68.0 \n", - "1 83.0 128.0 \n", - "2 66.0 208.0 \n", - "\n", - " housing_built_1939_or_earlier median_year_structure_built \\\n", - "0 210.0 1985.0 \n", - "1 715.0 1979.0 \n", - "2 480.0 1976.0 \n", - "\n", - " married_households nonfamily_households family_households \\\n", - "0 2069.0 1297.0 2710.0 \n", - "1 3102.0 3358.0 4617.0 \n", - "2 4163.0 2622.0 5560.0 \n", - "\n", - " households_public_asst_or_food_stamps male_male_households \\\n", - "0 625.0 0.0 \n", - "1 1971.0 0.0 \n", - "2 1651.0 0.0 \n", - "\n", - " female_female_households children children_in_single_female_hh \\\n", - "0 2.0 2345.0 484.0 \n", - "1 1.0 4534.0 1909.0 \n", - "2 0.0 4819.0 1436.0 \n", - "\n", - " median_rent percent_income_spent_on_rent rent_burden_not_computed \\\n", - "0 348.0 30.1 164.0 \n", - "1 349.0 29.9 706.0 \n", - "2 393.0 34.2 357.0 \n", - "\n", - " rent_over_50_percent rent_40_to_50_percent rent_35_to_40_percent \\\n", - "0 116.0 72.0 26.0 \n", - "1 349.0 195.0 67.0 \n", - "2 534.0 216.0 118.0 \n", - "\n", - " rent_30_to_35_percent rent_25_to_30_percent rent_20_to_25_percent \\\n", - "0 41.0 75.0 77.0 \n", - "1 176.0 189.0 99.0 \n", - "2 109.0 247.0 169.0 \n", - "\n", - " rent_15_to_20_percent rent_10_to_15_percent rent_under_10_percent \\\n", - "0 50.0 38.0 14.0 \n", - "1 212.0 133.0 160.0 \n", - "2 235.0 57.0 86.0 \n", - "\n", - " owner_occupied_housing_units million_dollar_housing_units \\\n", - "0 3334.0 7.0 \n", - "1 5689.0 5.0 \n", - "2 6054.0 6.0 \n", - "\n", - " mortgaged_housing_units different_house_year_ago_different_city \\\n", - "0 1764.0 704.0 \n", - "1 2383.0 985.0 \n", - "2 2373.0 1188.0 \n", - "\n", - " different_house_year_ago_same_city families_with_young_children \\\n", - "0 98.0 615.0 \n", - "1 734.0 1398.0 \n", - "2 494.0 1186.0 \n", - "\n", - " two_parent_families_with_young_children \\\n", - "0 492.0 \n", - "1 572.0 \n", - "2 823.0 \n", - "\n", - " two_parents_in_labor_force_families_with_young_children \\\n", - "0 287.0 \n", - "1 268.0 \n", - "2 351.0 \n", - "\n", - " two_parents_father_in_labor_force_families_with_young_children \\\n", - "0 196.0 \n", - "1 168.0 \n", - "2 450.0 \n", - "\n", - " two_parents_mother_in_labor_force_families_with_young_children \\\n", - "0 0.0 \n", - "1 56.0 \n", - "2 22.0 \n", - "\n", - " two_parents_not_in_labor_force_families_with_young_children \\\n", - "0 9.0 \n", - "1 80.0 \n", - "2 0.0 \n", - "\n", - " one_parent_families_with_young_children \\\n", - "0 123.0 \n", - "1 826.0 \n", - "2 363.0 \n", - "\n", - " father_one_parent_families_with_young_children \\\n", - "0 54.0 \n", - "1 122.0 \n", - "2 22.0 \n", - "\n", - " father_in_labor_force_one_parent_families_with_young_children \\\n", - "0 48.0 \n", - "1 122.0 \n", - "2 22.0 \n", - "\n", - " commute_5_9_mins commute_less_10_mins commute_10_14_mins \\\n", - "0 309.0 475.0 362.0 \n", - "1 1043.0 1643.0 698.0 \n", - "2 1807.0 2364.0 1463.0 \n", - "\n", - " commute_15_19_mins commute_20_24_mins commute_25_29_mins \\\n", - "0 374.0 401.0 216.0 \n", - "1 695.0 822.0 397.0 \n", - "2 1102.0 722.0 341.0 \n", - "\n", - " commute_30_34_mins commute_35_39_mins commute_40_44_mins \\\n", - "0 660.0 143.0 140.0 \n", - "1 749.0 143.0 144.0 \n", - "2 524.0 18.0 144.0 \n", - "\n", - " commute_35_44_mins commute_45_59_mins commute_60_more_mins \\\n", - "0 283.0 670.0 647.0 \n", - "1 287.0 377.0 690.0 \n", - "2 162.0 400.0 578.0 \n", - "\n", - " commute_60_89_mins commute_90_more_mins commuters_16_over \\\n", - "0 530.0 117.0 4088.0 \n", - "1 433.0 257.0 6358.0 \n", - "2 348.0 230.0 7656.0 \n", - "\n", - " walked_to_work worked_at_home no_car no_cars one_car two_cars \\\n", - "0 44.0 107.0 47.0 179.0 1004.0 1405.0 \n", - "1 81.0 184.0 297.0 922.0 3037.0 2720.0 \n", - "2 178.0 126.0 248.0 542.0 2795.0 2854.0 \n", - "\n", - " three_cars four_more_cars aggregate_travel_time_to_work \\\n", - "0 957.0 462.0 133665.0 \n", - "1 783.0 513.0 161900.0 \n", - "2 1490.0 501.0 156480.0 \n", - "\n", - " commuters_by_public_transportation commuters_by_bus \\\n", - "0 13.0 13.0 \n", - "1 0.0 0.0 \n", - "2 19.0 19.0 \n", - "\n", - " commuters_by_car_truck_van commuters_by_carpool \\\n", - "0 3987.0 310.0 \n", - "1 6126.0 371.0 \n", - "2 7226.0 607.0 \n", - "\n", - " commuters_by_subway_or_elevated commuters_drove_alone group_quarters \\\n", - "0 0.0 3677.0 102.0 \n", - "1 0.0 5755.0 233.0 \n", - "2 0.0 6619.0 193.0 \n", - "\n", - " associates_degree bachelors_degree high_school_diploma \\\n", - "0 502.0 486.0 2782.0 \n", - "1 1084.0 1315.0 4881.0 \n", - "2 704.0 1333.0 5003.0 \n", - "\n", - " less_one_year_college masters_degree one_year_more_college \\\n", - "0 434.0 226.0 861.0 \n", - "1 800.0 611.0 1930.0 \n", - "2 786.0 462.0 2485.0 \n", - "\n", - " less_than_high_school_graduate high_school_including_ged \\\n", - "0 1354.0 3543.0 \n", - "1 2279.0 5375.0 \n", - "2 2409.0 5883.0 \n", - "\n", - " bachelors_degree_2 bachelors_degree_or_higher_25_64 \\\n", - "0 486.0 532.0 \n", - "1 1315.0 1665.0 \n", - "2 1333.0 1483.0 \n", - "\n", - " graduate_professional_degree some_college_and_associates_degree \\\n", - "0 293.0 1797.0 \n", - "1 774.0 3814.0 \n", - "2 566.0 3975.0 \n", - "\n", - " male_45_64_associates_degree male_45_64_bachelors_degree \\\n", - "0 114.0 102.0 \n", - "1 277.0 366.0 \n", - "2 135.0 219.0 \n", - "\n", - " male_45_64_graduate_degree male_45_64_less_than_9_grade \\\n", - "0 57.0 110.0 \n", - "1 126.0 86.0 \n", - "2 52.0 77.0 \n", - "\n", - " male_45_64_grade_9_12 male_45_64_high_school male_45_64_some_college \\\n", - "0 241.0 832.0 176.0 \n", - "1 351.0 993.0 296.0 \n", - "2 391.0 1158.0 655.0 \n", - "\n", - " male_45_to_64 employed_pop unemployed_pop pop_in_labor_force \\\n", - "0 1632.0 4241.0 191.0 4439.0 \n", - "1 2495.0 6645.0 975.0 7620.0 \n", - "2 2687.0 7905.0 870.0 8775.0 \n", - "\n", - " not_in_labor_force workers_16_and_over armed_forces \\\n", - "0 4120.0 4195.0 7.0 \n", - "1 8202.0 6542.0 0.0 \n", - "2 7647.0 7782.0 0.0 \n", - "\n", - " civilian_labor_force employed_agriculture_forestry_fishing_hunting_mining \\\n", - "0 4432.0 127.0 \n", - "1 7620.0 249.0 \n", - "2 8775.0 640.0 \n", - "\n", - " employed_arts_entertainment_recreation_accommodation_food \\\n", - "0 391.0 \n", - "1 371.0 \n", - "2 287.0 \n", - "\n", - " employed_construction employed_education_health_social \\\n", - "0 275.0 873.0 \n", - "1 266.0 1606.0 \n", - "2 722.0 2045.0 \n", - "\n", - " employed_finance_insurance_real_estate employed_information \\\n", - "0 139.0 79.0 \n", - "1 278.0 107.0 \n", - "2 176.0 47.0 \n", - "\n", - " employed_manufacturing employed_other_services_not_public_admin \\\n", - "0 1071.0 116.0 \n", - "1 1144.0 376.0 \n", - "2 1407.0 244.0 \n", - "\n", - " employed_public_administration employed_retail_trade \\\n", - "0 204.0 448.0 \n", - "1 295.0 916.0 \n", - "2 418.0 951.0 \n", - "\n", - " employed_science_management_admin_waste \\\n", - "0 226.0 \n", - "1 340.0 \n", - "2 329.0 \n", - "\n", - " employed_transportation_warehousing_utilities employed_wholesale_trade \\\n", - "0 264.0 28.0 \n", - "1 607.0 90.0 \n", - "2 539.0 100.0 \n", - "\n", - " occupation_management_arts \\\n", - "0 912.0 \n", - "1 1970.0 \n", - "2 2176.0 \n", - "\n", - " occupation_natural_resources_construction_maintenance \\\n", - "0 493.0 \n", - "1 760.0 \n", - "2 1086.0 \n", - "\n", - " occupation_production_transportation_material occupation_sales_office \\\n", - "0 1246.0 712.0 \n", - "1 1386.0 1361.0 \n", - "2 1820.0 1494.0 \n", - "\n", - " occupation_services management_business_sci_arts_employed \\\n", - "0 878.0 912.0 \n", - "1 1168.0 1970.0 \n", - "2 1329.0 2176.0 \n", - "\n", - " sales_office_employed in_grades_1_to_4 in_grades_5_to_8 \\\n", - "0 712.0 626.0 492.0 \n", - "1 1361.0 918.0 1229.0 \n", - "2 1494.0 1344.0 1124.0 \n", - "\n", - " in_grades_9_to_12 in_school in_undergrad_college \\\n", - "0 617.0 2321.0 306.0 \n", - "1 1185.0 4829.0 811.0 \n", - "2 1022.0 5037.0 801.0 \n", - "\n", - " speak_only_english_at_home speak_spanish_at_home \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "\n", - " speak_spanish_at_home_low_english do_label do_area do_perimeter \\\n", - "0 NaN Crawford 7.998498e+08 150379.205 \n", - "1 NaN Marengo 2.550527e+09 252631.606 \n", - "2 NaN Ashley 2.432888e+09 212505.748 \n", - "\n", - " do_num_vertices geom \n", - "0 38 POLYGON((-86.679511 38.263086, -86.570136 38.2... \n", - "1 68 POLYGON((-87.967893 32.298249, -87.975667 32.3... \n", - "2 37 POLYGON((-91.561673 33.391561, -91.60694 33.39... " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sql_query = \"SELECT * FROM $dataset$ WHERE ST_IntersectsBox(geom, -95.031738, 32.082575, -84.616699, 39.537940)\"\n", - "dataset_df = dataset.to_dataframe(sql_query=sql_query)\n", - "dataset_df.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " None\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " Static map image\n", - " \n", - " \n", - "
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                                                                                              Subscription already purchased

                                                                                              \\n The dataset carto-do.ags.demographic…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "dataset.subscribe()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6fbfb4486cc44fd8bd706977af0be749", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "HTML(value='\\n

                                                                                              Subscription already purchased

                                                                                              \\n The geography carto-do-public-data.c…" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "geography.subscribe()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Datasets: [, , , , ]\n", - "Geographies: [, ]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "Catalog().subscriptions()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[ #'Geographic Identifier',\n", - " #'Households (2019A)',\n", - " #'New Car Dealers (Total Exp)',\n", - " #'User Car Dealers (Total Exp)',\n", - " #'Recreational Vehicle Dealers (Total Exp)',\n", - " #'Motorcycle and Boat Dealers (Total Exp)',\n", - " #'Auto Parts and Accessories (Total Exp)',\n", - " #'Tire Dealers (Total Exp)',\n", - " #'Furniture Stores (Total Exp)',\n", - " #'Floor Covering Stores (Total Exp)',\n", - " #'Other Home Furnishing Stores (Total Exp)',\n", - " #'Appliances and Electronics Stores (Total Exp)',\n", - " #'Home Centers (Total Exp)',\n", - " #'Paint and Wallpaper Stores (Total Exp)',\n", - " #'Hardware Stores (Total Exp)',\n", - " #'Other Building Materials Stores (Total Exp)',\n", - " #'Outdoor Power Equipment Stores (Total Exp)',\n", - " #'Nursery and Garden Stores (Total Exp)',\n", - " #'Grocery Stores (Total Exp)',\n", - " #'Convenience Stores (Total Exp)',\n", - " #'Meat Markets (Total Exp)',\n", - " #'Fish and Seafood Markets (Total Exp)',\n", - " #'Fruit and Vegetable Markets (Total Exp)',\n", - " #'Other Specialty Food Markets (Total Exp)',\n", - " #'Liquor Stores (Total Exp)',\n", - " #'Pharmacy and Drug Stores (Total Exp)',\n", - " #'Costmetics and Beauty Stores (Total Exp)',\n", - " #'Optical Goods Stores (Total Exp)',\n", - " #'Other Health and Personal Care Stores (Total Exp)',\n", - " #'Gasoline Stations (Total Exp)',\n", - " #'Mens Clothing Stores (Total Exp)',\n", - " #'Womens Clothing Stores (Total Exp)',\n", - " #'Childrens and Infants Clothing Stores (Total Exp)',\n", - " #'Family Clothing Stores (Total Exp)',\n", - " #'Clothing Accessory Stores (Total Exp)',\n", - " #'Other Apparel Stores (Total Exp)',\n", - " #'Shoe Stores (Total Exp)',\n", - " #'Jewelry Stores (Total Exp)',\n", - " #'Luggage Stores (Total Exp)',\n", - " #'Sporting Goods Stores (Total Exp)',\n", - " #'Hobby, Toy, and Game Stores (Total Exp)',\n", - " #'Sewing and Needlecraft Stores (Total Exp)',\n", - " #'Musical Instrument Stores (Total Exp)',\n", - " #'Book Stores (Total Exp)',\n", - " #'Department Stores (Total Exp)',\n", - " #'Warehouse Superstores (Total Exp)',\n", - " #'Other General Merchandise Stores (Total Exp)',\n", - " #'Florists (Total Exp)',\n", - " #'Office and Stationary Stores (Total Exp)',\n", - " #'Gift and Souvenir Stores (Total Exp)',\n", - " #'Used Merchandise Stores (Total Exp)',\n", - " #'Pet and Pet Supply Stores (Total Exp)',\n", - " #'Art Dealers (Total Exp)',\n", - " #'Mobile Home Dealers (Total Exp)',\n", - " #'Other Miscellaneous Retail Stores (Total Exp)',\n", - " #'Mail Order and Catalog Stores (Total Exp)',\n", - " #'Vending Machines (Total Exp)',\n", - " #'Fuel Dealers (Total Exp)',\n", - " #'Other Direct Selling Establishments (Total Exp)',\n", - " #'Retail at Hotels and Other Travel Accomodations (T...',\n", - " #'Retail at RV Parks (Total Exp)',\n", - " #'Retail at Rooming and Boarding Houses (Total Exp)',\n", - " #'Full Service Restaurants (Total Exp)',\n", - " #'Limited Service Restaurants (Total Exp)',\n", - " #'Special Food Services and Catering (Total Exp)',\n", - " #'Drinking Places (Total Exp)',\n", - " #'Retail Gap: New Car Dealers (44111)',\n", - " #'Retail Gap: User Car Dealers (44112)',\n", - " #'Retail Gap: Recreational Vehicle Dealers (44121)',\n", - " #'Retail Gap: Motorcycle and Boat Dealers (44122)',\n", - " #'Retail Gap: Auto Parts and Accessories (44131)',\n", - " #'Retail Gap: Tire Dealers (44132)',\n", - " #'Retail Gap: Furniture Stores (44211)',\n", - " #'Retail Gap: Floor Covering Stores (44221)',\n", - " #'Retail Gap: Other Home Furnishing Stores (44229)',\n", - " #'Retail Gap: Appliances and Electronics Stores (443...',\n", - " #'Retail Gap: Home Centers (44411)',\n", - " #'Retail Gap: Paint and Wallpaper Stores (44412)',\n", - " #'Retail Gap: Hardware Stores (44413)',\n", - " #'Retail Gap: Other Building Materials Stores (44419...',\n", - " #'Retail Gap: Outdoor Power Equipment Stores (44421)',\n", - " #'Retail Gap: Nursery and Garden Stores (44422)',\n", - " #'Retail Gap: Grocery Stores (44511)',\n", - " #'Retail Gap: Convenience Stores (44512)',\n", - " #'Retail Gap: Meat Markets (44521)',\n", - " #'Retail Gap: Fish and Seafood Markets (44522)',\n", - " #'Retail Gap: Fruit and Vegetable Markets (44523)',\n", - " #'Retail Gap: Other Specialty Food Markets (44529)',\n", - " #'Retail Gap: Liquor Stores (44531)',\n", - " #'Retail Gap: Pharmacy and Drug Stores (44611)',\n", - " #'Retail Gap: Costmetics and Beauty Stores (44612)',\n", - " #'Retail Gap: Optical Goods Stores (44613)',\n", - " #'Retail Gap: Other Health and Personal Care Stores ...',\n", - " #'Retail Gap: Gasoline Stations (44719)',\n", - " #'Retail Gap: Men's Clothing Stores (44811)',\n", - " #'Retail Gap: Women's Clothing Stores (44812)',\n", - " #'Retail Gap: Childrens' and Infant's Clothing Store...',\n", - " #'Retail Gap: Family Clothing Stores (44814)',\n", - " #'Retail Gap: Clothing Accessory Stores (44815)',\n", - " #'Retail Gap: Other Apparel Stores (44819)',\n", - " #'Retail Gap: Shoe Stores (44821)',\n", - " #'Retail Gap: Jewelry Stores (44831)',\n", - " #'Retail Gap: Luggage Stores (44832)',\n", - " #'Retail Gap: Sporting Goods Stores (45111)',\n", - " #'Retail Gap: Hobby, Toy, and Game Stores (45112)',\n", - " #'Retail Gap: Sewing and Needlecraft Stores (45113)',\n", - " #'Retail Gap: Musical Instrument Stores (45114)',\n", - " #'Retail Gap: Book Stores (45121)',\n", - " #'Retail Gap: Department Stores (45211)',\n", - " #'Retail Gap: Warehouse Superstores (45291)',\n", - " #'Retail Gap: Other General Merchandise Stores (4529...',\n", - " #'Retail Gap: Florists (45311)',\n", - " #'Retail Gap: Office and Stationary Stores (45321)',\n", - " #'Retail Gap: Gift and Souvenir Stores (45322)',\n", - " #'Retail Gap: Used Merchandise Stores (45331)',\n", - " #'Retail Gap: Pet and Pet Supply Stores (45391)',\n", - " #'Retail Gap: Art Dealers (45392)',\n", - " #'Retail Gap: Mobile Home Dealers (45393)',\n", - " #'Retail Gap: Other Miscellaneous Retail Stores (453...',\n", - " #'Retail Gap: Mail Order and Catalog Stores (45411)',\n", - " #'Retail Gap: Vending Machines (45421)',\n", - " #'Retail Gap: Fuel Dealers (45431)',\n", - " #'Retail Gap: Other Direct Selling Establishments (4...',\n", - " #'Retail Gap: Retail at Hotels and Other Travel Acco...',\n", - " #'Retail Gap: Retail at RV Parks (7212)',\n", - " #'Retail Gap: Retail at Rooming and Boarding Houses ...',\n", - " #'Retail Gap: Full Service Restaurants (7221)',\n", - " #'Retail Gap: Limited Service Restaurants (7222)',\n", - " #'Retail Gap: Special Food Services and Catering (72...',\n", - " #'Retail Gap: Drinking Places (7224)']" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.variables" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - " #'Home Centers (Total Exp)'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.data.observatory import Variable\n", - "\n", - "variable = Variable.get('MLTCY44411_acc8382c')\n", - "variable" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                              OBJECTIDFULLTRACTIDTRACTIDgeometry
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                                                                                              OBJECTIDFULLTRACTIDTRACTIDgeometryMLTCY44411
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                                                                                              2351013102902102902POLYGON ((-77.09520 38.84499, -77.09442 38.844...2.671894e+06
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                                                                                              " - ], - "text/plain": [ - " OBJECTID FULLTRACTID TRACTID \\\n", - "0 1 51013102901 102901 \n", - "1 2 51013103000 103000 \n", - "2 3 51013102902 102902 \n", - "\n", - " geometry MLTCY44411 \n", - "0 POLYGON ((-77.09099 38.84516, -77.08957 38.844... 1.862995e+06 \n", - "1 POLYGON ((-77.08558 38.82992, -77.08625 38.828... 2.099457e+06 \n", - "2 POLYGON ((-77.09520 38.84499, -77.09442 38.844... 2.671894e+06 " - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "enriched_dataset_gdf.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " None\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " Static map image\n", - " \n", - " \n", - "
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                                                                                                OBJECTIDFULLTRACTIDTRACTIDgeometry
                                                                                                0151013102901102901POLYGON ((-77.09099 38.84516, -77.08957 38.844...
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                                                                                                " - ], - "text/plain": [ - " OBJECTID FULLTRACTID TRACTID \\\n", - "0 1 51013102901 102901 \n", - "1 2 51013103000 103000 \n", - "2 3 51013102902 102902 \n", - "\n", - " geometry \n", - "0 POLYGON ((-77.09099 38.84516, -77.08957 38.844... \n", - "1 POLYGON ((-77.08558 38.82992, -77.08625 38.828... \n", - "2 POLYGON ((-77.09520 38.84499, -77.09442 38.844... " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from geopandas import read_file\n", - "\n", - "census_track = 'http://libs.cartocdn.com/cartoframes/files/census_track.geojson'\n", - "census_track_gdf = read_file(census_track)\n", - "census_track_gdf.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.data.observatory import Catalog\n", - "\n", - "Catalog().country('usa').category('demographics').geographies" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                slugnamedescriptioncategory_idcountry_iddata_source_idprovider_idgeography_namegeography_descriptiontemporal_aggregationtime_coverageupdate_frequencyis_public_datalangversioncategory_nameprovider_namegeography_idid
                                                                                                0acs_sociodemogr_cfeb0968Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of AmericaShoreline clipped TIGER/Line boundaries. More ...5yrs[2009-01-01, 2014-01-01)yearlyTrueeng20092013DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                1acs_sociodemogr_858c104eSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of AmericaShoreline clipped TIGER/Line boundaries. More ...5yrs[2008-01-01, 2013-01-01)yearlyTrueeng20082012DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                2acs_sociodemogr_d4b2cf03Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of AmericaShoreline clipped TIGER/Line boundaries. More ...5yrs[2006-01-01, 2011-01-01)yearlyTrueeng20062010DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                3acs_sociodemogr_496a0675Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of AmericaShoreline clipped TIGER/Line boundaries. More ...5yrs[2013-01-01, 2018-01-01)yearlyTrueeng20132017DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                4acs_sociodemogr_30d1f53Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of AmericaShoreline clipped TIGER/Line boundaries. More ...5yrs[2012-01-01, 2017-01-01)yearlyTrueeng20122016DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                5acs_sociodemogr_dda43439Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of AmericaShoreline clipped TIGER/Line boundaries. More ...5yrs[2011-01-01, 2016-01-01)yearlyTrueeng20112015DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                6acs_sociodemogr_97c32d1fSociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of AmericaShoreline clipped TIGER/Line boundaries. More ...5yrs[2010-01-01, 2015-01-01)yearlyTrueeng20102014DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                7acs_sociodemogr_9ed5d625Sociodemographics - United States of America (...The American Community Survey (ACS) is an ongo...demographicsusasociodemographicsusa_acsCensus Tract - United States of AmericaShoreline clipped TIGER/Line boundaries. More ...5yrs[2007-01-01, 2012-01-01)yearlyTrueeng20072011DemographicsAmerican Community Surveycarto-do-public-data.carto.geography_usa_censu...carto-do-public-data.usa_acs.demographics_soci...
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More ... 5yrs \n", - "1 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", - "2 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", - "3 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", - "4 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", - "5 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", - "6 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", - "7 Shoreline clipped TIGER/Line boundaries. More ... 5yrs \n", - "\n", - " time_coverage update_frequency is_public_data lang version \\\n", - "0 [2009-01-01, 2014-01-01) yearly True eng 20092013 \n", - "1 [2008-01-01, 2013-01-01) yearly True eng 20082012 \n", - "2 [2006-01-01, 2011-01-01) yearly True eng 20062010 \n", - "3 [2013-01-01, 2018-01-01) yearly True eng 20132017 \n", - "4 [2012-01-01, 2017-01-01) yearly True eng 20122016 \n", - "5 [2011-01-01, 2016-01-01) yearly True eng 20112015 \n", - "6 [2010-01-01, 2015-01-01) yearly True eng 20102014 \n", - "7 [2007-01-01, 2012-01-01) yearly True eng 20072011 \n", - "\n", - " category_name provider_name \\\n", - "0 Demographics American Community Survey \n", - "1 Demographics American Community Survey \n", - "2 Demographics American Community Survey \n", - "3 Demographics American Community Survey \n", - "4 Demographics American Community Survey \n", - "5 Demographics American Community Survey \n", - "6 Demographics American Community Survey \n", - "7 Demographics American Community Survey \n", - "\n", - " geography_id \\\n", - "0 carto-do-public-data.carto.geography_usa_censu... \n", - "1 carto-do-public-data.carto.geography_usa_censu... \n", - "2 carto-do-public-data.carto.geography_usa_censu... \n", - "3 carto-do-public-data.carto.geography_usa_censu... \n", - "4 carto-do-public-data.carto.geography_usa_censu... \n", - "5 carto-do-public-data.carto.geography_usa_censu... \n", - "6 carto-do-public-data.carto.geography_usa_censu... \n", - "7 carto-do-public-data.carto.geography_usa_censu... \n", - "\n", - " id \n", - "0 carto-do-public-data.usa_acs.demographics_soci... \n", - "1 carto-do-public-data.usa_acs.demographics_soci... \n", - "2 carto-do-public-data.usa_acs.demographics_soci... \n", - "3 carto-do-public-data.usa_acs.demographics_soci... \n", - "4 carto-do-public-data.usa_acs.demographics_soci... \n", - "5 carto-do-public-data.usa_acs.demographics_soci... \n", - "6 carto-do-public-data.usa_acs.demographics_soci... \n", - "7 carto-do-public-data.usa_acs.demographics_soci... " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "datasets = Catalog().country('usa').category('demographics').geography('cdb_censustract_af861cba').datasets\n", - "datasets.to_dataframe()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                slugnamedescriptiondb_typeagg_methodcolumn_namevariable_group_iddataset_idid
                                                                                                79median_income_57be5af4Median IncomeMedian Household Income in the past 12 Months....FLOATAVGmedian_incomeNonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                165no_car_2207f034no_carWorkers age 16 and over with no vehicle. All p...FLOATSUMno_carcarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                166no_cars_3a983c4eno_carsCar-free households. The number of households ...FLOATSUMno_carscarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                167one_car_13b3a60bone_carOne car households. The number of households w...FLOATSUMone_carcarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                168two_cars_fec37223two_carsTwo car households. The number of households w...FLOATSUMtwo_carscarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                169three_cars_32e541e7three_carsThree car households. The number of households...FLOATSUMthree_carscarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                170four_more_cars_297e8a8afour_more_carsFour car households. The number of households ...FLOATSUMfour_more_carscarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                174commuters_by_ca_120481e3commuters_by_car_truck_vanCommuters by Car, Truck, or Van. The number of...FLOATSUMcommuters_by_car_truck_vancarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                175commuters_by_ca_59febe6fcommuters_by_carpoolCommuters by Carpool. The number of workers ag...FLOATSUMcommuters_by_carpoolcarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                177commuters_drove_acf77a1commuters_drove_aloneCommuters who drove alone. The number of worke...FLOATSUMcommuters_drove_alonecarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                197employed_educat_958fdcfeemployed_education_health_socialWorkers employed in firms in educational servi...FLOATSUMemployed_education_health_socialcarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                201employed_other__a5acf594employed_other_services_not_public_adminWorkers employed in firms in other services ex...FLOATSUMemployed_other_services_not_public_admincarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
                                                                                                205employed_transp_66aedbfdemployed_transportation_warehousing_utilitiesWorkers employed in firms in transportation, w...FLOATSUMemployed_transportation_warehousing_utilitiescarto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...carto-do-public-data.usa_acs.demographics_soci...
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                                                                                                " - ], - "text/plain": [ - " slug name \\\n", - "79 median_income_57be5af4 Median Income \n", - "165 no_car_2207f034 no_car \n", - "166 no_cars_3a983c4e no_cars \n", - "167 one_car_13b3a60b one_car \n", - "168 two_cars_fec37223 two_cars \n", - "169 three_cars_32e541e7 three_cars \n", - "170 four_more_cars_297e8a8a four_more_cars \n", - "174 commuters_by_ca_120481e3 commuters_by_car_truck_van \n", - "175 commuters_by_ca_59febe6f commuters_by_carpool \n", - "177 commuters_drove_acf77a1 commuters_drove_alone \n", - "197 employed_educat_958fdcfe employed_education_health_social \n", - "201 employed_other__a5acf594 employed_other_services_not_public_admin \n", - "205 employed_transp_66aedbfd employed_transportation_warehousing_utilities \n", - "\n", - " description db_type agg_method \\\n", - "79 Median Household Income in the past 12 Months.... FLOAT AVG \n", - "165 Workers age 16 and over with no vehicle. All p... FLOAT SUM \n", - "166 Car-free households. The number of households ... FLOAT SUM \n", - "167 One car households. The number of households w... FLOAT SUM \n", - "168 Two car households. The number of households w... FLOAT SUM \n", - "169 Three car households. The number of households... FLOAT SUM \n", - "170 Four car households. The number of households ... FLOAT SUM \n", - "174 Commuters by Car, Truck, or Van. The number of... FLOAT SUM \n", - "175 Commuters by Carpool. The number of workers ag... FLOAT SUM \n", - "177 Commuters who drove alone. The number of worke... FLOAT SUM \n", - "197 Workers employed in firms in educational servi... FLOAT SUM \n", - "201 Workers employed in firms in other services ex... FLOAT SUM \n", - "205 Workers employed in firms in transportation, w... FLOAT SUM \n", - "\n", - " column_name \\\n", - "79 median_income \n", - "165 no_car \n", - "166 no_cars \n", - "167 one_car \n", - "168 two_cars \n", - "169 three_cars \n", - "170 four_more_cars \n", - "174 commuters_by_car_truck_van \n", - "175 commuters_by_carpool \n", - "177 commuters_drove_alone \n", - "197 employed_education_health_social \n", - "201 employed_other_services_not_public_admin \n", - "205 employed_transportation_warehousing_utilities \n", - "\n", - " variable_group_id \\\n", - "79 None \n", - "165 carto-do-public-data.usa_acs.demographics_soci... \n", - "166 carto-do-public-data.usa_acs.demographics_soci... \n", - "167 carto-do-public-data.usa_acs.demographics_soci... \n", - "168 carto-do-public-data.usa_acs.demographics_soci... \n", - "169 carto-do-public-data.usa_acs.demographics_soci... \n", - "170 carto-do-public-data.usa_acs.demographics_soci... \n", - "174 carto-do-public-data.usa_acs.demographics_soci... \n", - "175 carto-do-public-data.usa_acs.demographics_soci... \n", - "177 carto-do-public-data.usa_acs.demographics_soci... \n", - "197 carto-do-public-data.usa_acs.demographics_soci... \n", - "201 carto-do-public-data.usa_acs.demographics_soci... \n", - "205 carto-do-public-data.usa_acs.demographics_soci... \n", - "\n", - " dataset_id \\\n", - "79 carto-do-public-data.usa_acs.demographics_soci... \n", - "165 carto-do-public-data.usa_acs.demographics_soci... \n", - "166 carto-do-public-data.usa_acs.demographics_soci... \n", - "167 carto-do-public-data.usa_acs.demographics_soci... \n", - "168 carto-do-public-data.usa_acs.demographics_soci... \n", - "169 carto-do-public-data.usa_acs.demographics_soci... \n", - "170 carto-do-public-data.usa_acs.demographics_soci... \n", - "174 carto-do-public-data.usa_acs.demographics_soci... \n", - "175 carto-do-public-data.usa_acs.demographics_soci... \n", - "177 carto-do-public-data.usa_acs.demographics_soci... \n", - "197 carto-do-public-data.usa_acs.demographics_soci... \n", - "201 carto-do-public-data.usa_acs.demographics_soci... \n", - "205 carto-do-public-data.usa_acs.demographics_soci... \n", - "\n", - " id \n", - "79 carto-do-public-data.usa_acs.demographics_soci... \n", - "165 carto-do-public-data.usa_acs.demographics_soci... \n", - "166 carto-do-public-data.usa_acs.demographics_soci... \n", - "167 carto-do-public-data.usa_acs.demographics_soci... \n", - "168 carto-do-public-data.usa_acs.demographics_soci... \n", - "169 carto-do-public-data.usa_acs.demographics_soci... \n", - "170 carto-do-public-data.usa_acs.demographics_soci... \n", - "174 carto-do-public-data.usa_acs.demographics_soci... \n", - "175 carto-do-public-data.usa_acs.demographics_soci... \n", - "177 carto-do-public-data.usa_acs.demographics_soci... \n", - "197 carto-do-public-data.usa_acs.demographics_soci... \n", - "201 carto-do-public-data.usa_acs.demographics_soci... \n", - "205 carto-do-public-data.usa_acs.demographics_soci... " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.data.observatory import Dataset\n", - "\n", - "dataset = Dataset.get('acs_sociodemogr_d4b2cf03')\n", - "variables_df = dataset.variables.to_dataframe()\n", - "variables_df[variables_df['description'].str.contains('car', case=False, na=False)]" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'slug': 'no_car_2207f034',\n", - " 'name': 'no_car',\n", - " 'description': 'Workers age 16 and over with no vehicle. All people in a geographic area over the age of 16 who do not own a car.',\n", - " 'db_type': 'FLOAT',\n", - " 'agg_method': 'SUM',\n", - " 'column_name': 'no_car',\n", - " 'variable_group_id': 'carto-do-public-data.usa_acs.demographics_sociodemographics_usa_censustract_2015_5yrs_20062010.car_ownership',\n", - " 'dataset_id': 'carto-do-public-data.usa_acs.demographics_sociodemographics_usa_censustract_2015_5yrs_20062010',\n", - " 'id': 'carto-do-public-data.usa_acs.demographics_sociodemographics_usa_censustract_2015_5yrs_20062010.no_car'}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.data.observatory import Variable\n", - "\n", - "variable = Variable.get('no_car_2207f034')\n", - "variable.to_dict()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.data.observatory import Enrichment\n", - "\n", - "enrichment = Enrichment()\n", - "\n", - "enriched_dataset_gdf = enrichment.enrich_polygons(\n", - " census_track_gdf,\n", - " variables=[variable],\n", - " aggregation='SUM'\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                OBJECTIDFULLTRACTIDTRACTIDgeometryno_car
                                                                                                0151013102901102901POLYGON ((-77.09099 38.84516, -77.08957 38.844...0.657821
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                                                                                                " - ], - "text/plain": [ - " OBJECTID FULLTRACTID TRACTID \\\n", - "0 1 51013102901 102901 \n", - "1 2 51013103000 103000 \n", - "2 3 51013102902 102902 \n", - "\n", - " geometry no_car \n", - "0 POLYGON ((-77.09099 38.84516, -77.08957 38.844... 0.657821 \n", - "1 POLYGON ((-77.08558 38.82992, -77.08625 38.828... 87.516848 \n", - "2 POLYGON ((-77.09520 38.84499, -77.09442 38.844... 123.512837 " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "enriched_dataset_gdf.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " None\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " Static map image\n", - " \n", - " \n", - "
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                                                                                                  addresscity
                                                                                                  0Gran Vía 46Madrid
                                                                                                  1Ebro 1Sevilla
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                                                                                                  " - ], - "text/plain": [ - " address city\n", - "0 Gran Vía 46 Madrid\n", - "1 Ebro 1 Sevilla" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas\n", - "\n", - "df = pandas.DataFrame([['Gran Vía 46', 'Madrid'], ['Ebro 1', 'Sevilla']], columns=['address','city'])\n", - "df" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Success! Data geocoded correctly\n" - ] - } - ], - "source": [ - "from cartoframes.data.services import Geocoding\n", - "\n", - "gc = Geocoding()\n", - "gdf, metadata = gc.geocode(df, street='address', city='city', country={'value': 'Spain'})" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                  the_geomaddresscitygc_status_relcarto_geocode_hash
                                                                                                  0POINT (-3.70588 40.42049)Gran Vía 46Madrid0.8495e4f39284efeab8e759aaa547d84567
                                                                                                  1POINT (-5.98312 37.35547)Ebro 1Sevilla0.7066940c4beeb395e1b628587ac772763a
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deleted file mode 100644 index 951b9c3a6..000000000 --- a/examples/files/README.md +++ /dev/null @@ -1,9 +0,0 @@ -## List of available files - -- http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn.csv -- http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_geocoded.csv -- http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_geocoded.geojson -- http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_iso_enriched.csv -- http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_isolines.geojson -- http://libs.cartocdn.com/cartoframes/files/census_track.geojson -- http://libs.cartocdn.com/cartoframes/files/sustainable_palm_oil_production_mills.geojson \ No newline at end of file diff --git a/examples/files/arbres_remarquables_paris.json b/examples/files/arbres_remarquables_paris.json deleted file mode 100644 index 99a09c138..000000000 --- a/examples/files/arbres_remarquables_paris.json +++ /dev/null @@ -1 +0,0 @@ -{"nhits": 182, "parameters": {"dataset": "arbresremarquablesparis", "timezone": "UTC", "rows": 200, "format": "json"}, "records": [{"datasetid": "arbresremarquablesparis", "recordid": "a236960f295288698ca57fdccfd9825547ffb4d0", "fields": {"geom_x_y": [48.8630096006, 2.2417737512], "libellefrancais": "Pin", "objectid": 49884, "idemplacement": "000901001", "arrondissement": "BOIS DE BOULOGNE", "circonferenceencm": 241.0, "hauteurenm": 30.0, "espece": "nigra subsp. laricio", "adresse": "RESERVOIR DE LA GRANDE CASCADE - CARREFOUR DE LONGCHAMP", "domanialite": "Jardin", "pepiniere": "Inconnue", "stadedeveloppement": "M", "remarquable": "1", "idbase": 2002349.0, "genre": "Pinus", "complementadresse": "16-08", "typeemplacement": "Arbre", "dateplantation": "1882-01-01T00:09:21+00:00"}, "geometry": {"type": "Point", "coordinates": [2.2417737512, 48.8630096006]}, "record_timestamp": "2019-10-04T10:00:41.063000+00:00"}, {"datasetid": "arbresremarquablesparis", "recordid": "d7635937c3636051c99963676d3a97dd4d9dd8c9", "fields": 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"typeemplacement": "Arbre", "hauteurenm": 3.0, "espece": "sylvatica", "adresse": "SQUARE DES ARENES DE LUTECE", "circonferenceencm": 85.0, "stadedeveloppement": "M", "pepiniere": "Inconnue", "remarquable": "1", "idbase": 132669.0, "genre": "Fagus", "varieteoucultivar": "''Tortuosa''", "domanialite": "Jardin", "dateplantation": "1905-01-01T00:09:21+00:00"}, "geometry": {"type": "Point", "coordinates": [2.35366916319, 48.8448875264]}, "record_timestamp": "2019-10-04T10:00:41.063000+00:00"}, {"datasetid": "arbresremarquablesparis", "recordid": "b60b80e0f8327afba6d0c513b0b6b85639e516b6", "fields": {"geom_x_y": [48.8787029872, 2.30647417371], "libellefrancais": "Erable", "objectid": 68801, "idemplacement": "00NO0144", "arrondissement": "PARIS 8E ARRDT", "circonferenceencm": 450.0, "hauteurenm": 28.0, "espece": "pseudoplatanus", "adresse": "PARC MONCEAU", "domanialite": "Jardin", "pepiniere": "Inconnue", "stadedeveloppement": "M", "remarquable": "1", "idbase": 305507.0, "genre": "Acer", 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"2019-10-04T10:00:41.063000+00:00"}, {"datasetid": "arbresremarquablesparis", "recordid": "89771215c6ae72720e5b0943c6ebb84f3c62f3f4", "fields": {"geom_x_y": [48.8330206707, 2.34991527056], "libellefrancais": "Ch\u00eane", "objectid": 100153, "idemplacement": "00010268", "arrondissement": "PARIS 13E ARRDT", "circonferenceencm": 130.0, "hauteurenm": 10.0, "espece": "cerris", "adresse": "SQUARE RENE LE GALL", "domanialite": "Jardin", "pepiniere": "Inconnue", "stadedeveloppement": "A", "remarquable": "1", "idbase": 104839.0, "genre": "Quercus", "complementadresse": "13-04", "typeemplacement": "Arbre", "dateplantation": "1700-01-01T00:09:21+00:00"}, "geometry": {"type": "Point", "coordinates": [2.34991527056, 48.8330206707]}, "record_timestamp": "2019-10-04T10:00:41.063000+00:00"}, {"datasetid": "arbresremarquablesparis", "recordid": "82532fe483eb98c8e8093f05df693502817886ae", "fields": {"geom_x_y": [48.8213206283, 2.45536669589], "libellefrancais": "Pin", "objectid": 204461, 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"state": "displayed", "name": "PW0087"}, {"count": 1, "path": "PW0088", "state": "displayed", "name": "PW0088"}, {"count": 1, "path": "PW0089", "state": "displayed", "name": "PW0089"}, {"count": 1, "path": "PW0090", "state": "displayed", "name": "PW0090"}, {"count": 1, "path": "PW0091", "state": "displayed", "name": "PW0091"}, {"count": 1, "path": "PW0092", "state": "displayed", "name": "PW0092"}, {"count": 1, "path": "PW0093", "state": "displayed", "name": "PW0093"}, {"count": 1, "path": "PW0094", "state": "displayed", "name": "PW0094"}], "name": "idpw"}, {"facets": [{"count": 229, "path": "Op\u00e9rationnel", "state": "displayed", "name": "Op\u00e9rationnel"}, {"count": 16, "path": "En \u00e9tude", "state": "displayed", "name": "En \u00e9tude"}, {"count": 4, "path": "Ferm\u00e9 pour travaux", "state": "displayed", "name": "Ferm\u00e9 pour travaux"}, {"count": 3, "path": "En d\u00e9ploiement", "state": "displayed", "name": "En d\u00e9ploiement"}], "name": "etat2"}]} \ No newline at end of file diff --git a/examples/map_configuration/change_default_carto_basemap.ipynb b/examples/map_configuration/change_default_carto_basemap.ipynb deleted file mode 100644 index e07630a3f..000000000 --- a/examples/map_configuration/change_default_carto_basemap.ipynb +++ /dev/null @@ -1,995 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Change default CARTO basemap\n", - "\n", - "By default, CARTOframes uses CARTO's Positron basemap with labels under. The basemap can be customized to use another CARTO style (Voyager, Dark Matter), a Mapbox basemap, or a custom background color; all through the `basemap` parameter.\n", - "\n", - "In the example below, the default is changed from Positron to Voyager." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.auth import set_default_credentials\n", - "from cartoframes.viz import Map, Layer, basemaps\n", - "\n", - "set_default_credentials('cartoframes')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " None\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " Static map image\n", - " \n", - " \n", - "
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                                                                                                                      nlcStationBoroughNoteWeekdaySaturdaySundayWeekday.1Saturday.1Sunday.1million
                                                                                                                      0500.0Acton TownEaling9531.06716.04744.09382.06617.04785.06.040516
                                                                                                                      1502.0AldgateCity of London15080.04397.03261.016023.05909.04230.08.846940
                                                                                                                      2503.0Aldgate EastTower Hamlets22327.016166.013323.021071.013893.011347.013.998292
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                                                                                                                      " - ], - "text/plain": [ - " nlc Station Borough Note Weekday Saturday Sunday \\\n", - "0 500.0 Acton Town Ealing 9531.0 6716.0 4744.0 \n", - "1 502.0 Aldgate City of London 15080.0 4397.0 3261.0 \n", - "2 503.0 Aldgate East Tower Hamlets 22327.0 16166.0 13323.0 \n", - "3 505.0 Alperton Brent 4495.0 3279.0 2345.0 \n", - "4 506.0 Amersham Chiltern 3848.0 1876.0 1232.0 \n", - "\n", - " Weekday.1 Saturday.1 Sunday.1 million \n", - "0 9382.0 6617.0 4785.0 6.040516 \n", - "1 16023.0 5909.0 4230.0 8.846940 \n", - "2 21071.0 13893.0 11347.0 13.998292 \n", - "3 5081.0 3392.0 2445.0 3.052230 \n", - "4 4025.0 1797.0 1121.0 2.321692 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "local_file_path = '../files/london_stations.xls'\n", - "df = pandas.read_excel(local_file_path, header=6, sheet_name=1)\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                      nlcStationBoroughNoteweekday_entrysaturday_entrysunday_entryweekday_exitsaturday_exitsunday_exitmillion
                                                                                                                      0500.0Acton TownEaling9531.06716.04744.09382.06617.04785.06.040516
                                                                                                                      1502.0AldgateCity of London15080.04397.03261.016023.05909.04230.08.846940
                                                                                                                      2503.0Aldgate EastTower Hamlets22327.016166.013323.021071.013893.011347.013.998292
                                                                                                                      3505.0AlpertonBrent4495.03279.02345.05081.03392.02445.03.052230
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                                                                                                                      " - ], - "text/plain": [ - " nlc Station Borough Note weekday_entry saturday_entry \\\n", - "0 500.0 Acton Town Ealing 9531.0 6716.0 \n", - "1 502.0 Aldgate City of London 15080.0 4397.0 \n", - "2 503.0 Aldgate East Tower Hamlets 22327.0 16166.0 \n", - "3 505.0 Alperton Brent 4495.0 3279.0 \n", - "4 506.0 Amersham Chiltern 3848.0 1876.0 \n", - "\n", - " sunday_entry weekday_exit saturday_exit sunday_exit million \n", - "0 4744.0 9382.0 6617.0 4785.0 6.040516 \n", - "1 3261.0 16023.0 5909.0 4230.0 8.846940 \n", - "2 13323.0 21071.0 13893.0 11347.0 13.998292 \n", - "3 2345.0 5081.0 3392.0 2445.0 3.052230 \n", - "4 1232.0 4025.0 1797.0 1121.0 2.321692 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df = df.rename(columns={\n", - " \"Saturday.1\": \"saturday_exit\",\n", - " \"Sunday.1\": \"sunday_exit\",\n", - " \"Weekday.1\": \"weekday_exit\",\n", - " \"Saturday\": \"saturday_entry\",\n", - " \"Sunday\": \"sunday_entry\",\n", - " \"Weekday\": \"weekday_entry\"\n", - "})\n", - "\n", - "df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Success! Data geocoded correctly\n" - ] - } - ], - "source": [ - "gc = Geocoding()\n", - "\n", - "london_stations_gdf, london_stations_metadata = gc.geocode(\n", - " df,\n", - " street='Borough',\n", - " city={'value': 'London'},\n", - " country={'value': 'United Kingdom'}\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                      the_geomnlcstationboroughnoteweekday_entrysaturday_entrysunday_entryweekday_exitsaturday_exitsunday_exitmilliongc_status_relcarto_geocode_hash
                                                                                                                      0POINT (-0.30421 51.51335)500.0Acton TownEaling9531.06716.04744.09382.06617.04785.06.0405160.55985f9d318280297b6108bc30e92d92e1
                                                                                                                      1POINT (-0.08895 51.51333)502.0AldgateCity of London15080.04397.03261.016023.05909.04230.08.8469400.887abfcd5c0283e877a275f0d059836a09
                                                                                                                      2POINT (0.01695 51.55042)503.0Aldgate EastTower Hamlets22327.016166.013323.021071.013893.011347.013.9982920.74c1ba0efb09cc8e5caf79092547a7a3e7
                                                                                                                      3POINT (-0.30262 51.55225)505.0AlpertonBrent4495.03279.02345.05081.03392.02445.03.0522300.55c9f07eb29a060527ca81a613cca80007
                                                                                                                      4POINT (-0.21470 51.56339)506.0AmershamChiltern3848.01876.01232.04025.01797.01121.02.3216920.53518e37ccd63a56df5d5758fb8e52a5dd
                                                                                                                      \n", - "
                                                                                                                      " - ], - "text/plain": [ - " the_geom nlc station borough note \\\n", - "0 POINT (-0.30421 51.51335) 500.0 Acton Town Ealing \n", - "1 POINT (-0.08895 51.51333) 502.0 Aldgate City of London \n", - "2 POINT (0.01695 51.55042) 503.0 Aldgate East Tower Hamlets \n", - "3 POINT (-0.30262 51.55225) 505.0 Alperton Brent \n", - "4 POINT (-0.21470 51.56339) 506.0 Amersham Chiltern \n", - "\n", - " weekday_entry saturday_entry sunday_entry weekday_exit saturday_exit \\\n", - "0 9531.0 6716.0 4744.0 9382.0 6617.0 \n", - "1 15080.0 4397.0 3261.0 16023.0 5909.0 \n", - "2 22327.0 16166.0 13323.0 21071.0 13893.0 \n", - "3 4495.0 3279.0 2345.0 5081.0 3392.0 \n", - "4 3848.0 1876.0 1232.0 4025.0 1797.0 \n", - "\n", - " sunday_exit million gc_status_rel carto_geocode_hash \n", - "0 4785.0 6.040516 0.55 985f9d318280297b6108bc30e92d92e1 \n", - "1 4230.0 8.846940 0.88 7abfcd5c0283e877a275f0d059836a09 \n", - "2 11347.0 13.998292 0.74 c1ba0efb09cc8e5caf79092547a7a3e7 \n", - "3 2445.0 3.052230 0.55 c9f07eb29a060527ca81a613cca80007 \n", - "4 1121.0 2.321692 0.53 518e37ccd63a56df5d5758fb8e52a5dd " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "london_stations_gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " None\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " Static map image\n", - " \n", - " \n", - "
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                                                                                                                        nameaddress
                                                                                                                        0Matadero Madrid. Bicycle hirePaseo de la Chopera , 14
                                                                                                                        1Bikes Rental Point in Juan Carlos I ParkVía Dublín, s/n
                                                                                                                        2Fun&BikesAvenida de Manzanares, 164
                                                                                                                        3Mi Bike RíoCalle Aniceto Marinas, 26
                                                                                                                        4Bravo BikeCalle Juan Álvarez Mendizábal, 19
                                                                                                                        527 BikesCalle del Alcalde Sainz de Baranda, 16
                                                                                                                        6Bike Spain TourPlaza de la Villa, 1
                                                                                                                        7Biobike Pasillo VerdePaseo de Juan Antonio Vallejo - Nájero Botas, 55
                                                                                                                        8Bike Tour Trixi MadridCalle de los Jardines, 12
                                                                                                                        9Otero CiclosCalle de Segovia, 18-20
                                                                                                                        10Rent & RollCalle de Felipe IV, 10
                                                                                                                        11PangeaPaseo de las Yeserías, 15
                                                                                                                        \n", - "
                                                                                                                        " - ], - "text/plain": [ - " name \\\n", - "0 Matadero Madrid. Bicycle hire \n", - "1 Bikes Rental Point in Juan Carlos I Park \n", - "2 Fun&Bikes \n", - "3 Mi Bike Río \n", - "4 Bravo Bike \n", - "5 27 Bikes \n", - "6 Bike Spain Tour \n", - "7 Biobike Pasillo Verde \n", - "8 Bike Tour Trixi Madrid \n", - "9 Otero Ciclos \n", - "10 Rent & Roll \n", - "11 Pangea \n", - "\n", - " address \n", - "0 Paseo de la Chopera , 14 \n", - "1 Vía Dublín, s/n \n", - "2 Avenida de Manzanares, 164 \n", - "3 Calle Aniceto Marinas, 26 \n", - "4 Calle Juan Álvarez Mendizábal, 19 \n", - "5 Calle del Alcalde Sainz de Baranda, 16 \n", - "6 Plaza de la Villa, 1 \n", - "7 Paseo de Juan Antonio Vallejo - Nájero Botas, 55 \n", - "8 Calle de los Jardines, 12 \n", - "9 Calle de Segovia, 18-20 \n", - "10 Calle de Felipe IV, 10 \n", - "11 Paseo de las Yeserías, 15 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from pandas import DataFrame\n", - "\n", - "bike_rental_stores_df = DataFrame([\n", - " ['Matadero Madrid. Bicycle hire', 'Paseo de la Chopera , 14'],\n", - " ['Bikes Rental Point in Juan Carlos I Park', 'Vía Dublín, s/n'],\n", - " ['Fun&Bikes', 'Avenida de Manzanares, 164'],\n", - " ['Mi Bike Río', 'Calle Aniceto Marinas, 26'],\n", - " ['Bravo Bike', 'Calle Juan Álvarez Mendizábal, 19'],\n", - " ['27 Bikes', 'Calle del Alcalde Sainz de Baranda, 16'],\n", - " ['Bike Spain Tour', 'Plaza de la Villa, 1'],\n", - " ['Biobike Pasillo Verde', 'Paseo de Juan Antonio Vallejo - Nájero Botas, 55'],\n", - " ['Bike Tour Trixi Madrid', 'Calle de los Jardines, 12'],\n", - " ['Otero Ciclos ', 'Calle de Segovia, 18-20'],\n", - " ['Rent & Roll', 'Calle de Felipe IV, 10'],\n", - " ['Pangea', 'Paseo de las Yeserías, 15']],\n", - " columns=['name', 'address']\n", - ")\n", - "\n", - "bike_rental_stores_df" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Success! Data geocoded correctly\n" - ] - } - ], - "source": [ - "bike_rental_stores_data, bike_rental_stores_metadata = gc.geocode(\n", - " bike_rental_stores_df,\n", - " street='address',\n", - " city={'value': 'Madrid'},\n", - " country={'value': 'Spain'})" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                        the_geomnameaddressgc_status_relcarto_geocode_hash
                                                                                                                        0POINT (-3.69744 40.39274)Matadero Madrid. Bicycle hirePaseo de la Chopera , 140.9535830402de3843fd3309309469b50efc
                                                                                                                        1POINT (-3.61520 40.46088)Bikes Rental Point in Juan Carlos I ParkVía Dublín, s/n0.7586ecf14d283f407e5d5af49b233afa71
                                                                                                                        2POINT (-3.70846 40.39559)Fun&BikesAvenida de Manzanares, 1640.8930833b87570ed1d90e100f6781665f97
                                                                                                                        3POINT (-3.72274 40.42205)Mi Bike RíoCalle Aniceto Marinas, 260.917945970d13061be0bc7fe64472e723ff
                                                                                                                        4POINT (-3.71509 40.42493)Bravo BikeCalle Juan Álvarez Mendizábal, 190.956f1b2606125e2462f6235df39aac5176
                                                                                                                        5POINT (-3.67672 40.41680)27 BikesCalle del Alcalde Sainz de Baranda, 160.993c45d9e073bfe074be2895af7c84f846
                                                                                                                        6POINT (-3.71020 40.41543)Bike Spain TourPlaza de la Villa, 10.95846343c4eb3f6e295246e4b3e9a601a4
                                                                                                                        7POINT (-3.71480 40.40359)Biobike Pasillo VerdePaseo de Juan Antonio Vallejo - Nájero Botas, 550.996d19e11e1d529bc849a2b53c27c9a785
                                                                                                                        8POINT (-3.70143 40.41888)Bike Tour Trixi MadridCalle de los Jardines, 120.951a83b6a267b2c23d32bb5db2d29c3e92
                                                                                                                        9POINT (-3.71208 40.41402)Otero CiclosCalle de Segovia, 18-200.9270f30859922b437bec9c747962fcca38
                                                                                                                        10POINT (-3.68974 40.41497)Rent & RollCalle de Felipe IV, 100.953fb28606fd7c9eb91cf9aa34320c1945
                                                                                                                        11POINT (-3.71026 40.39951)PangeaPaseo de las Yeserías, 150.95b199bc7ad784ab3d86cef425bcd99385
                                                                                                                        \n", - "
                                                                                                                        " - ], - "text/plain": [ - " the_geom name \\\n", - "0 POINT (-3.69744 40.39274) Matadero Madrid. Bicycle hire \n", - "1 POINT (-3.61520 40.46088) Bikes Rental Point in Juan Carlos I Park \n", - "2 POINT (-3.70846 40.39559) Fun&Bikes \n", - "3 POINT (-3.72274 40.42205) Mi Bike Río \n", - "4 POINT (-3.71509 40.42493) Bravo Bike \n", - "5 POINT (-3.67672 40.41680) 27 Bikes \n", - "6 POINT (-3.71020 40.41543) Bike Spain Tour \n", - "7 POINT (-3.71480 40.40359) Biobike Pasillo Verde \n", - "8 POINT (-3.70143 40.41888) Bike Tour Trixi Madrid \n", - "9 POINT (-3.71208 40.41402) Otero Ciclos \n", - "10 POINT (-3.68974 40.41497) Rent & Roll \n", - "11 POINT (-3.71026 40.39951) Pangea \n", - "\n", - " address gc_status_rel \\\n", - "0 Paseo de la Chopera , 14 0.95 \n", - "1 Vía Dublín, s/n 0.75 \n", - "2 Avenida de Manzanares, 164 0.89 \n", - "3 Calle Aniceto Marinas, 26 0.91 \n", - "4 Calle Juan Álvarez Mendizábal, 19 0.95 \n", - "5 Calle del Alcalde Sainz de Baranda, 16 0.99 \n", - "6 Plaza de la Villa, 1 0.95 \n", - "7 Paseo de Juan Antonio Vallejo - Nájero Botas, 55 0.99 \n", - "8 Calle de los Jardines, 12 0.95 \n", - "9 Calle de Segovia, 18-20 0.92 \n", - "10 Calle de Felipe IV, 10 0.95 \n", - "11 Paseo de las Yeserías, 15 0.95 \n", - "\n", - " carto_geocode_hash \n", - "0 35830402de3843fd3309309469b50efc \n", - "1 86ecf14d283f407e5d5af49b233afa71 \n", - "2 30833b87570ed1d90e100f6781665f97 \n", - "3 7945970d13061be0bc7fe64472e723ff \n", - "4 6f1b2606125e2462f6235df39aac5176 \n", - "5 3c45d9e073bfe074be2895af7c84f846 \n", - "6 846343c4eb3f6e295246e4b3e9a601a4 \n", - "7 6d19e11e1d529bc849a2b53c27c9a785 \n", - "8 1a83b6a267b2c23d32bb5db2d29c3e92 \n", - "9 70f30859922b437bec9c747962fcca38 \n", - "10 3fb28606fd7c9eb91cf9aa34320c1945 \n", - "11 b199bc7ad784ab3d86cef425bcd99385 " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "bike_rental_stores_data" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Available Quota: 77995\n", - "Required Quota: 36\n" - ] - } - ], - "source": [ - "from cartoframes.data.services import Isolines\n", - "\n", - "iso_service = Isolines()\n", - "isodistances = iso_service.isodistances(\n", - " bike_rental_stores_data,\n", - " [100, 200, 300],\n", - " mode='walk',\n", - " dry_run=True,\n", - " exclusive=True)\n", - "\n", - "print('Available Quota: {0}'.format(iso_service.available_quota()))\n", - "print('Required Quota: {0}'.format(isodistances.metadata.get('required_quota')))" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Success! Isolines created correctly\n" - ] - } - ], - "source": [ - "isodistances_data, isodistances_metadata = iso_service.isodistances(\n", - " bike_rental_stores_data,\n", - " [100, 200, 300],\n", - " mode='walk',\n", - " exclusive=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                        source_iddata_rangelower_data_rangethe_geomrange_label
                                                                                                                        011300200POLYGON ((-3.71329 40.40085, -3.71269 40.40085...5 min.
                                                                                                                        110300200MULTIPOLYGON (((-3.69269 40.41595, -3.69226 40...5 min.
                                                                                                                        27300200POLYGON ((-3.71741 40.40497, -3.71698 40.40514...5 min.
                                                                                                                        39300200POLYGON ((-3.71535 40.41458, -3.71492 40.41475...5 min.
                                                                                                                        46300200POLYGON ((-3.71346 40.41544, -3.71338 40.41595...5 min.
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                                                                                                                        " - ], - "text/plain": [ - " source_id data_range lower_data_range \\\n", - "0 11 300 200 \n", - "1 10 300 200 \n", - "2 7 300 200 \n", - "3 9 300 200 \n", - "4 6 300 200 \n", - "\n", - " the_geom range_label \n", - "0 POLYGON ((-3.71329 40.40085, -3.71269 40.40085... 5 min. \n", - "1 MULTIPOLYGON (((-3.69269 40.41595, -3.69226 40... 5 min. \n", - "2 POLYGON ((-3.71741 40.40497, -3.71698 40.40514... 5 min. \n", - "3 POLYGON ((-3.71535 40.41458, -3.71492 40.41475... 5 min. \n", - "4 POLYGON ((-3.71346 40.41544, -3.71338 40.41595... 5 min. " - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "isodistances_data.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# isodistances_data['geometry'] = isodistances_data['__carto_geometry']" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " None\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " Static map image\n", - " \n", - " \n", - "
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                                                                                                                          " - ], - "text/plain": [ - " incident_datetime incident_date incident_time \\\n", - "0 2020-02-03T14:45:00.000 2020-02-03T00:00:00.000 14:45 \n", - "1 2020-02-03T03:45:00.000 2020-02-03T00:00:00.000 03:45 \n", - "2 2020-02-03T10:00:00.000 2020-02-03T00:00:00.000 10:00 \n", - "3 2020-01-19T17:12:00.000 2020-01-19T00:00:00.000 17:12 \n", - "4 2020-01-05T00:00:00.000 2020-01-05T00:00:00.000 00:00 \n", - "\n", - " incident_year incident_day_of_week report_datetime row_id \\\n", - "0 2020 Monday 2020-02-03T17:50:00.000 89881675000 \n", - "1 2020 Monday 2020-02-03T03:45:00.000 89860711012 \n", - "2 2020 Monday 2020-02-03T10:06:00.000 89867264015 \n", - "3 2020 Sunday 2020-02-01T13:01:00.000 89863571000 \n", - "4 2020 Sunday 2020-02-03T16:09:00.000 89877368020 \n", - "\n", - " incident_id incident_number cad_number ... :@computed_region_6qbp_sg9q \\\n", - "0 898816 200085557 200342870.0 ... 41.0 \n", - "1 898607 200083749 200340316.0 ... 53.0 \n", - "2 898672 200084060 200340808.0 ... 19.0 \n", - "3 898635 206024187 NaN ... NaN \n", - "4 898773 200085193 200342341.0 ... 103.0 \n", - "\n", - " :@computed_region_qgnn_b9vv :@computed_region_26cr_cadq \\\n", - "0 10.0 8.0 \n", - "1 3.0 2.0 \n", - "2 5.0 3.0 \n", - "3 NaN NaN \n", - "4 4.0 6.0 \n", - "\n", - " :@computed_region_ajp5_b2md :@computed_region_nqbw_i6c3 \\\n", - "0 16.0 NaN \n", - "1 20.0 3.0 \n", - "2 8.0 NaN \n", - "3 NaN NaN \n", - "4 30.0 NaN \n", - "\n", - " :@computed_region_2dwj_jsy4 :@computed_region_h4ep_8xdi \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 35.0 NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " :@computed_region_y6ts_4iup :@computed_region_jg9y_a9du \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "\n", - " :@computed_region_6pnf_4xz7 \n", - "0 2.0 \n", - "1 2.0 \n", - "2 2.0 \n", - "3 NaN \n", - "4 1.0 \n", - "\n", - "[5 rows x 36 columns]" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "incident_reports_df = pd.read_csv('http://data.sfgov.org/resource/wg3w-h783.csv')\n", - "incident_reports_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['incident_datetime', 'incident_date', 'incident_time', 'incident_year',\n", - " 'incident_day_of_week', 'report_datetime', 'row_id', 'incident_id',\n", - " 'incident_number', 'cad_number', 'report_type_code',\n", - " 'report_type_description', 'filed_online', 'incident_code',\n", - " 'incident_category', 'incident_subcategory', 'incident_description',\n", - " 'resolution', 'intersection', 'cnn', 'police_district',\n", - " 'analysis_neighborhood', 'supervisor_district', 'latitude', 'longitude',\n", - " 'point', ':@computed_region_6qbp_sg9q', ':@computed_region_qgnn_b9vv',\n", - " ':@computed_region_26cr_cadq', ':@computed_region_ajp5_b2md',\n", - " ':@computed_region_nqbw_i6c3', ':@computed_region_2dwj_jsy4',\n", - " ':@computed_region_h4ep_8xdi', ':@computed_region_y6ts_4iup',\n", - " ':@computed_region_jg9y_a9du', ':@computed_region_6pnf_4xz7'],\n", - " dtype='object')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "incident_reports_df.columns" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Some of the `latitude` and `longitude` values are `NaN`, in the next step we get rid of them. After that, we create a dataset from the dataframe and use it in a Layer to visualize the data:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                          " - ], - "text/plain": [ - " incident_datetime incident_date incident_time \\\n", - "0 2020-02-03T14:45:00.000 2020-02-03T00:00:00.000 14:45 \n", - "1 2020-02-03T03:45:00.000 2020-02-03T00:00:00.000 03:45 \n", - "2 2020-02-03T10:00:00.000 2020-02-03T00:00:00.000 10:00 \n", - "4 2020-01-05T00:00:00.000 2020-01-05T00:00:00.000 00:00 \n", - "5 2020-02-03T08:36:00.000 2020-02-03T00:00:00.000 08:36 \n", - "\n", - " incident_year incident_day_of_week report_datetime row_id \\\n", - "0 2020 Monday 2020-02-03T17:50:00.000 89881675000 \n", - "1 2020 Monday 2020-02-03T03:45:00.000 89860711012 \n", - "2 2020 Monday 2020-02-03T10:06:00.000 89867264015 \n", - "4 2020 Sunday 2020-02-03T16:09:00.000 89877368020 \n", - "5 2020 Monday 2020-02-03T08:36:00.000 89876268020 \n", - "\n", - " incident_id incident_number cad_number ... :@computed_region_qgnn_b9vv \\\n", - "0 898816 200085557 200342870.0 ... 10.0 \n", - "1 898607 200083749 200340316.0 ... 3.0 \n", - "2 898672 200084060 200340808.0 ... 5.0 \n", - "4 898773 200085193 200342341.0 ... 4.0 \n", - "5 898762 200083909 200340826.0 ... 6.0 \n", - "\n", - " :@computed_region_26cr_cadq :@computed_region_ajp5_b2md \\\n", - "0 8.0 16.0 \n", - "1 2.0 20.0 \n", - "2 3.0 8.0 \n", - "4 6.0 30.0 \n", - "5 3.0 8.0 \n", - "\n", - " :@computed_region_nqbw_i6c3 :@computed_region_2dwj_jsy4 \\\n", - "0 NaN NaN \n", - "1 3.0 NaN \n", - "2 NaN 35.0 \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "\n", - " :@computed_region_h4ep_8xdi :@computed_region_y6ts_4iup \\\n", - "0 NaN NaN \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "\n", - " :@computed_region_jg9y_a9du :@computed_region_6pnf_4xz7 \\\n", - "0 NaN 2.0 \n", - "1 NaN 2.0 \n", - "2 NaN 2.0 \n", - "4 NaN 1.0 \n", - "5 NaN 1.0 \n", - "\n", - " geometry \n", - "0 POINT (-122.47604 37.72695) \n", - "1 POINT (-122.41517 37.75244) \n", - "2 POINT (-122.40734 37.78456) \n", - "4 POINT (-122.44025 37.78711) \n", - "5 POINT (-122.39951 37.79693) \n", - "\n", - "[5 rows x 37 columns]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "incident_reports_df = incident_reports_df[incident_reports_df.longitude == incident_reports_df.longitude]\n", - "incident_reports_df = incident_reports_df[incident_reports_df.latitude == incident_reports_df.latitude]\n", - "\n", - "incident_reports_gdf = gpd.GeoDataFrame(\n", - " incident_reports_df,\n", - " geometry=gpd.points_from_xy(incident_reports_df.longitude, incident_reports_df.latitude)\n", - ")\n", - "\n", - "incident_reports_gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " None\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " Static map image\n", - " \n", - " \n", - "
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                                                                                                                                    objectidcnncodecnn_1street1street2street3street4detectionsup_dist...percent_popointpoint_addresspoint_citypoint_statepoint_zip:@computed_region_6qbp_sg9q:@computed_region_qgnn_b9vv:@computed_region_26cr_cadq:@computed_region_ajp5_b2md
                                                                                                                                    0240026392000NaN26392000MASONICTURKNaNNaNNaN1,2,5...NaNPOINT (-122.44702658724947 37.778617364507106)NaNNaNNaNNaN97.07.011.018.0
                                                                                                                                    1273720421000NaN20421000GENEVASANTOSNaNNaNNaN10...NaNPOINT (-122.42006985840429 37.70830632733309)NaNNaNNaNNaNNaNNaN9.0NaN
                                                                                                                                    2298124018000NaN2401800016TH STUTAHNaNNaNNaN10...NaNPOINT (-122.40653961377903 37.76583732660274)NaNNaNNaNNaN54.03.09.020.0
                                                                                                                                    329349941000NaN9941000OLYMPIAMIDBLOCK ATDELLBROOK (N)DELLBROOK (S)NaN7...NaNPOINT (-122.45364260943721 37.75120299724448)NaNNaNNaNNaN48.07.08.038.0
                                                                                                                                    4292226932000NaN26932000BEAUMONTGEARYNaNNaNNaN1,2...NaNPOINT (-122.4552675286903 37.781453718876065)NaNNaNNaNNaN12.08.04.018.0
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                                                                                                                                    " - ], - "text/plain": [ - " objectid cnn code cnn_1 street1 street2 street3 \\\n", - "0 2400 26392000 NaN 26392000 MASONIC TURK NaN \n", - "1 2737 20421000 NaN 20421000 GENEVA SANTOS NaN \n", - "2 2981 24018000 NaN 24018000 16TH ST UTAH NaN \n", - "3 2934 9941000 NaN 9941000 OLYMPIA MIDBLOCK AT DELLBROOK (N) \n", - "4 2922 26932000 NaN 26932000 BEAUMONT GEARY NaN \n", - "\n", - " street4 detection sup_dist ... percent_po \\\n", - "0 NaN NaN 1,2,5 ... NaN \n", - "1 NaN NaN 10 ... NaN \n", - "2 NaN NaN 10 ... NaN \n", - "3 DELLBROOK (S) NaN 7 ... NaN \n", - "4 NaN NaN 1,2 ... NaN \n", - "\n", - " point point_address point_city \\\n", - "0 POINT (-122.44702658724947 37.778617364507106) NaN NaN \n", - "1 POINT (-122.42006985840429 37.70830632733309) NaN NaN \n", - "2 POINT (-122.40653961377903 37.76583732660274) NaN NaN \n", - "3 POINT (-122.45364260943721 37.75120299724448) NaN NaN \n", - "4 POINT (-122.4552675286903 37.781453718876065) NaN NaN \n", - "\n", - " point_state point_zip :@computed_region_6qbp_sg9q \\\n", - "0 NaN NaN 97.0 \n", - "1 NaN NaN NaN \n", - "2 NaN NaN 54.0 \n", - "3 NaN NaN 48.0 \n", - "4 NaN NaN 12.0 \n", - "\n", - " :@computed_region_qgnn_b9vv :@computed_region_26cr_cadq \\\n", - "0 7.0 11.0 \n", - "1 NaN 9.0 \n", - "2 3.0 9.0 \n", - "3 7.0 8.0 \n", - "4 8.0 4.0 \n", - "\n", - " :@computed_region_ajp5_b2md \n", - "0 18.0 \n", - "1 NaN \n", - "2 20.0 \n", - "3 38.0 \n", - "4 18.0 \n", - "\n", - "[5 rows x 44 columns]" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "traffic_signals_df = pd.read_csv('http://data.sfgov.org/resource/c8ue-f4py.csv')\n", - "traffic_signals_df.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['objectid', 'cnn', 'code', 'cnn_1', 'street1', 'street2', 'street3',\n", - " 'street4', 'detection', 'sup_dist', 'veh_actuat', 'aps', 'ped_signal',\n", - " 'ped_actuat', 'tbc', 'preempt_pr', 'd_ate2070', 'project_ne',\n", - " 'project_ol', 'upgraded', 'yr_of_cont', 'last_upgra', 'new_signal',\n", - " 'mod_projec', 'full_upgra', 'beacon_fla', 'funding', 'rlcam',\n", - " 'startyear', 'caltrans_r', 'caltrans', 'percent_c', 'sf', 'percent_sf',\n", - " 'percent_po', 'point', 'point_address', 'point_city', 'point_state',\n", - " 'point_zip', ':@computed_region_6qbp_sg9q',\n", - " ':@computed_region_qgnn_b9vv', ':@computed_region_26cr_cadq',\n", - " ':@computed_region_ajp5_b2md'],\n", - " dtype='object')" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "traffic_signals_df.columns" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([nan])" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "traffic_signals_df.code.unique()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Since there is no `latitude` and `longitude` columns, we can use the `point` column to create a [GeoDataFrame](https://geopandas.readthedocs.io/en/latest/gallery/create_geopandas_from_pandas.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "from shapely import wkt\n", - "\n", - "traffic_signals_df['point'] = traffic_signals_df['point'].apply(wkt.loads)\n", - "traffic_signals_df = traffic_signals_df.rename(columns={'point': 'geometry'}).set_geometry('geometry')\n", - "trafic_signals_gdf = gpd.GeoDataFrame(traffic_signals_df, geometry='geometry')" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " None\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " Static map image\n", - " \n", - " \n", - "
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It covers:\n", - "- The **Python version** required\n", - "- How to install it using **pip**\n", - "- How to install it in a **Jupyter Notebook**\n", - "- How to install it in **Windows**\n", - "- How to create a **Virtual Enviroment**\n", - "- Considerations in **Jupyter Lab**\n", - "- Metrics\n", - "\n", - "> We recommend installing CARTOframes in a [Virtual Environment](http://docs.python-guide.org/en/latest/dev/virtualenvs/), since they are very useful when working with Python locally.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Python version required\n", - "\n", - "CARTOframes needs Python 3 to work properly. You can check your version by running:\n", - "\n", - "```\n", - "python --version\n", - "```\n", - "\n", - "You should get some output like Python 3.6.3. If you do not have Python, please install the latest 3.x version from [python.org](https://python.org/) or refer to the [Installing Python](http://docs.python-guide.org/en/latest/starting/installation/) section of the Hitchhiker’s Guide to Python." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "heading_collapsed": true - }, - "source": [ - "### Install it using `pip`\n", - "\n", - "CARTOframes can be installed with [`pip`](https://pypi.org/project/pip/) by simply typing one of the following commands to do a system install:\n", - "\n", - "To install the package:\n", - "\n", - "```bash\n", - "pip install cartoframes\n", - "```\n", - "\n", - "To upgrade the package to the latest version:\n", - "\n", - "```bash\n", - "pip install --upgrade cartoframes\n", - "```\n", - "\n", - "To install a specific version:\n", - "\n", - "```bash\n", - "pip install cartoframes==1.x.y\n", - "```\n", - "\n", - "#### Optional dependency\n", - "\n", - "[RTree](https://github.com/Toblerity/rtree) is a spatial index package to improve performance and required for overlay operations. It provides an interface to [libspatialindex](https://github.com/libspatialindex/libspatialindex)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install it in a Jupyter Notebook\n", - "\n", - "In the CARTOframes Developer Center, all of the examples are created with [Jupyter Notebooks](https://jupyter.org/). If you aren't familiar with Jupyter Notebooks, we recommend reading the [beginner documentation](https://jupyter-notebook-beginner-guide.readthedocs.io/en/latest/what_is_jupyter.html) to get familiar with the environment.\n", - "\n", - "To install CARTOframes through a Jupyter Notebook, run this command:\n", - "\n", - "```bash\n", - "!pip install cartoframes\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Install it in Windows\n", - "\n", - "In order to work properly, CARTOframes needs that [Shapely](https://github.com/Toblerity/Shapely) and [Fiona](https://github.com/Toblerity/Fiona) libraries are already installed in your system. Execute these commands to install CARTOframes correctly:\n", - "\n", - "```bash\n", - "pip install shapely\n", - "conda install -c conda-forge fiona\n", - "pip install cartoframes\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use a Virtual Environment\n", - "\n", - "This section provides the necessary information to create a simple Virtual Environment with virtualenv and conda and also how to install CARTOframes inside of it.\n", - "\n", - "```bash\n", - "$ virtualenv cartoframes_env\n", - "$ source cartoframes_env/bin/activate\n", - "(cartoframes_env) $ pip install cartoframes\n", - "```\n", - "\n", - "When the virtual environment is activated, it is visible in the command line prompt, in this case: `(cartoframes_env)`. It can be deactivated by typing `deactivate` to exit the virtualenv:\n", - "\n", - "```bash\n", - "(cartoframes_env) $ deactivate\n", - "```\n", - "\n", - "#### Conda users\n", - "\n", - "Let's see how to achieve it with conda.\n", - "\n", - "```bash\n", - "$ conda create --name cartoframes_env\n", - "$ conda activate cartoframes_env\n", - "(cartoframes_env) $ pip install shapely\n", - "(cartoframes_env) $ conda install -c conda-forge fiona\n", - "(cartoframes_env) $ pip install cartoframes\n", - "```\n", - "\n", - "When the virtual environment is activated, it is visible in the command line prompt, in this case: `(cartoframes_env)`. It can be deactivated by typing `deactivate` to exit the virtualenv:\n", - "\n", - "```bash\n", - "(cartoframes_env) $ conda deactivate\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Use Jupyter Lab\n", - "\n", - "Jupyter Lab does not install by default some requirements we need in this version of CARTOframes, so it requires to run also:\n", - "\n", - "```bash\n", - "pip install ipywidgets\n", - "jupyter labextension install @jupyter-widgets/jupyterlab-manager\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Metrics\n", - "\n", - "We collect data on your usage of CARTOframes to understand how you use it and use this information to improve the product. Usage data includes information like \"map_created,\" which tells us the date and time you created a map. The usage data we collect is anonymous, does not allow us to see the contents of what you're working on, and is handled in accordance with our Privacy Notice .\n", - "You may choose to opt out by calling `cartoframes.utils.setup_metrics(False)`." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/guides/04-Data-management.ipynb b/guides/04-Data-management.ipynb deleted file mode 100644 index c4eafa673..000000000 --- a/guides/04-Data-management.ipynb +++ /dev/null @@ -1,4412 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data management\n", - "\n", - "### Introduction\n", - "\n", - "CARTOframes is built on top of [Pandas](https://pandas.pydata.org/) and [GeoPandas](https://geopandas.org/). Therefore, it's compatible with all the data formats supported in those projects like CSV, GeoJSON, Shapefile, etc. This guide will show how to load different data files into DataFrames and how to interact with the CARTO platform to upload DataFrames into tables and download tables or SQL queries into DataFrames.\n", - "\n", - "There are two main concepts we should know before continuing with the guide:\n", - "- A [DataFrame](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html) is a two-dimensional data structure for generic data. It can be thought of as a table with rows and columns. It's composed of [Series](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html) objects which are one-dimensional data structures.\n", - "- A [GeoDataFrame](https://geopandas.org/data_structures.html#geodataframe) is a DataFrame with an extra geometry column. This geometry column is a [GeoSeries](https://geopandas.org/data_structures.html#geoseries) object.\n", - "\n", - "Every time we manage Geographic data, a GeoDataFrame should be used. In case a DataFrame with an encoded geometry column is used (WKB, WKT, etc) every method contains a `geom_col` param to provide the name of that column and decode the geometry internally.\n", - "\n", - "For more information, you can see all the examples [here](https://carto.com/developers/cartoframes/examples/#example-data-management).\n", - "\n", - "#### Steps\n", - "\n", - "\n", - "To show how to manage your data with CARTOframes, we will follow the next steps:\n", - "\n", - "- Load San Francisco neighborhoods from a GeoJSON file\n", - "- Load San Francisco incidents from a CSV file\n", - "- Upload data to CARTO\n", - "- Calculate their intersection using a SQL query\n", - "- Download the result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load San Francisco neighborhoods from a GeoJSON file\n", - "\n", - "Let's start by loading and visualizing San Francisco neighborhoods reading an external GeoJSON file." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                                                        incident_datetimeincident_dateincident_timeincident_yearincident_day_of_weekreport_datetimerow_idincident_idincident_numbercad_number...:@computed_region_qgnn_b9vv:@computed_region_26cr_cadq:@computed_region_ajp5_b2md:@computed_region_nqbw_i6c3:@computed_region_2dwj_jsy4:@computed_region_h4ep_8xdi:@computed_region_y6ts_4iup:@computed_region_jg9y_a9du:@computed_region_6pnf_4xz7geometry
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                                                                                                                                                          cartodb_idthe_geomnameincidents
                                                                                                                                                          0828MULTIPOLYGON (((-122.51314 37.77133, -122.5110...Golden Gate Park7
                                                                                                                                                          1934MULTIPOLYGON (((-122.43734 37.76235, -122.4370...Eureka Valley4
                                                                                                                                                          2908MULTIPOLYGON (((-122.39899 37.71534, -122.3988...Little Hollywood2
                                                                                                                                                          3850MULTIPOLYGON (((-122.38452 37.78739, -122.3843...South Beach7
                                                                                                                                                          4894MULTIPOLYGON (((-122.41622 37.70833, -122.4159...Visitacion Valley4
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                                                                                                                                                          " - ], - "text/plain": [ - " cartodb_id the_geom \\\n", - "0 828 MULTIPOLYGON (((-122.51314 37.77133, -122.5110... \n", - "1 934 MULTIPOLYGON (((-122.43734 37.76235, -122.4370... \n", - "2 908 MULTIPOLYGON (((-122.39899 37.71534, -122.3988... \n", - "3 850 MULTIPOLYGON (((-122.38452 37.78739, -122.3843... \n", - "4 894 MULTIPOLYGON (((-122.41622 37.70833, -122.4159... \n", - "\n", - " name incidents \n", - "0 Golden Gate Park 7 \n", - "1 Eureka Valley 4 \n", - "2 Little Hollywood 2 \n", - "3 South Beach 7 \n", - "4 Visitacion Valley 4 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes import read_carto\n", - "\n", - "incidents_neighborhoods_gdf = read_carto('''\n", - " SELECT n.cartodb_id, n.the_geom, n.the_geom_webmercator, n.name, COUNT(*) AS incidents\n", - " FROM sf_incidents i INNER JOIN sf_neighborhoods n ON ST_Intersects(i.the_geom, n.the_geom)\n", - " GROUP BY n.cartodb_id\n", - "''')\n", - "incidents_neighborhoods_gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " None\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " Static map image\n", - " \n", - " \n", - "
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                                                                                                                                                            \n", - "\n", - "\n", - "\n", - "\n", - "\n", - "\">\n", - "\n", - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.viz import color_continuous_style\n", - "\n", - "Layer(incidents_neighborhoods_gdf, style=color_continuous_style('incidents'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conclusion\n", - "\n", - "Congratulations! You have seen how to load data locally, upload it to CARTO, apply a SQL query and download the results. We recommend to upload your data to CARTO when it is too big (> 30MB) to be visualized from a GeoDataFrame or when you want to apply PostGIS queries.\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/guides/07-Data-enrichment.ipynb b/guides/07-Data-enrichment.ipynb deleted file mode 100644 index eb9c13c8a..000000000 --- a/guides/07-Data-enrichment.ipynb +++ /dev/null @@ -1,1020 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data enrichment\n", - "\n", - "### Introduction\n", - "\n", - "We define enrichment as the process of augmenting your data with new variables by means of a spatial join between your data and a `Dataset` aggregated at a given spatial resolution in the CARTO Data Observatory, or in other words:\n", - "\n", - "\"*Enrichment is the process of adding variables to a geometry, which we call the target, (point, line, polygon…) from a spatial (polygon) dataset, which we call the source*\"\n", - "\n", - "We recommend you check out the [CARTOframes quickstart](/developers/cartoframes/guides/Quickstart/) since this guide uses some of the generated DataFrames as well as the [Data Discovery guide](/developers/cartoframes/guides/Data-discovery) to learn about exploring the Data Observatory catalog to find variables of interest for your analyses.\n", - "\n", - "### Choose variables to enrich from the Data Observatory catalog\n", - "\n", - "Let's follow up with the [Data Discovery guide](/developers/cartoframes/guides/Data-discovery), where we subscribed to the AGS demographics dataset and listed the variables available to enrich our own data." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.auth import set_default_credentials\n", - "\n", - "set_default_credentials('creds.json')" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[,\n", - " ,\n", - " ,\n", - " ,\n", - " ]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.data.observatory import Catalog, Dataset, Variable, Geography\n", - "Catalog().subscriptions().datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[ #'Geographic Identifier',\n", - " #'Population (2019A)',\n", - " #'Population in Group Quarters (2019A)',\n", - " #'Institutional Group Quarters Population (2019A)',\n", - " #'Population age 0-4 (2019A)',\n", - " #'Population age 5-9 (2019A)',\n", - " #'Population age 10-14 (2019A)',\n", - " #'Population age 15-19 (2019A)',\n", - " #'Population age 20-24 (2019A)',\n", - " #'Population age 25-29 (2019A)',\n", - " #'Population age 30-34 (2019A)',\n", - " #'Population age 35-39 (2019A)',\n", - " #'Population age 40-44 (2019A)',\n", - " #'Population age 45-49 (2019A)',\n", - " #'Population age 50-54 (2019A)',\n", - " #'Population age 55-59 (2019A)',\n", - " #'Population age 60-64 (2019A)',\n", - " #'Population age 65-69 (2019A)',\n", - " #'Population age 70-74 (2019A)',\n", - " #'Population age 75-79 (2019A)',\n", - " #'Population age 80-84 (2019A)',\n", - " #'Population age 85+ (2019A)',\n", - " #'Median Age (2019A)',\n", - " #'Population male (2019A)',\n", - " #'Population female (2019A)',\n", - " #'Non Hispanic White (2019A)',\n", - " #'Non Hispanic Black (2019A)',\n", - " #'Non Hispanic American Indian (2019A)',\n", - " #'Non Hispanic Asian (2019A)',\n", - " #'Non Hispanic Hawaiian/Pacific Islander (2019A)',\n", - " #'Non Hispanic Other Race (2019A)',\n", - " #'Non Hispanic Multiple Race (2019A)',\n", - " #'Population Hispanic (2019A)',\n", - " #'Never Married (2019A)',\n", - " #'Now Married (2019A)',\n", - " #'Separated (2019A)',\n", - " #'Widowed (2019A)',\n", - " #'Divorced (2019A)',\n", - " #'Population Age 15+ (2019A)',\n", - " #'Pop 25+ less than 9th grade (2019A)',\n", - " #'Pop 25+ 9th-12th grade no diploma (2019A)',\n", - " #'Pop 25+ HS graduate (2019A)',\n", - " #'Pop 25+ college no diploma (2019A)',\n", - " #'Pop 25+ Associate degree (2019A)',\n", - " #'Pop 25+ Bachelors degree (2019A)',\n", - " #'Pop 25+ graduate or prof school degree (2019A)',\n", - " #'Population Age 25+ (2019A)',\n", - " #'Households (2019A)',\n", - " #'Family Households (2019A)',\n", - " #'Families married couple w children (2019A)',\n", - " #'Families male no wife w children (2019A)',\n", - " #'Families female no husband children (2019A)',\n", - " #'Average Household Size (2019A)',\n", - " #'Median Age of Householder (2019A)',\n", - " #'Households: No Vehicle Available (2019A)',\n", - " #'Households: One Vehicle Available (2019A)',\n", - " #'Households: Two or More Vehicles Available (2019A)',\n", - " #'Per capita income (2019A)',\n", - " #'Average household Income (2019A)',\n", - " #'Median household income (2019A)',\n", - " #'Median family income (2019A)',\n", - " #'Household Income < $10000 (2019A)',\n", - " #'Household Income $10000-$14999 (2019A)',\n", - " #'Household Income $15000-$19999 (2019A)',\n", - " #'Household Income $20000-$24999 (2019A)',\n", - " #'Household Income $25000-$29999 (2019A)',\n", - " #'Household Income $30000-$34999 (2019A)',\n", - " #'Household Income $35000-$39999 (2019A)',\n", - " #'Household Income $40000-$44999 (2019A)',\n", - " #'Household Income $45000-$49999 (2019A)',\n", - " #'Household Income $50000-$59999 (2019A)',\n", - " #'Household Income $60000-$74999 (2019A)',\n", - " #'Household Income $75000-$99999 (2019A)',\n", - " #'Household Income $100000-$124999 (2019A)',\n", - " #'Household Income $125000-$149999 (2019A)',\n", - " #'Household Income $150000-$199999 (2019A)',\n", - " #'Household Income > $200000 (2019A)',\n", - " #'Median Household Income: Age < 25 (2019A)',\n", - " #'Median Household Income: Age 25-34 (2019A)',\n", - " #'Median Household Income: Age 35-44 (2019A)',\n", - " #'Median Household Income: Age 45-54 (2019A)',\n", - " #'Median Household Income: Age 55-64 (2019A)',\n", - " #'Median Household Income: Age 65-74 (2019A)',\n", - " #'Median Household Income: Age 75+ (2019A)',\n", - " #'Population Age 16+ (2019A)',\n", - " #'Pop 16+ in Armed Forces (2019A)',\n", - " #'Pop 16+ civilian employed (2019A)',\n", - " #'Pop 16+ civilian unemployed (2019A)',\n", - " #'Pop 16+ not in labor force (2019A)',\n", - " #'Unemployment Rate (2019A)',\n", - " #'Population In Labor Force (2019A)',\n", - " #'SPANISH SPEAKING HOUSEHOLDS',\n", - " #'LINGUISTICALLY ISOLATED HOUSEHOLDS (NON-ENGLISH SP...',\n", - " #'Median Value of Owner Occupied Housing Units',\n", - " #'Median Cash Rent',\n", - " #'UNITS IN STRUCTURE: 1 DETACHED',\n", - " #'UNITS IN STRUCTURE: 20 OR MORE',\n", - " #'Housing units (2019A)',\n", - " #'Housing units vacant (2019A)',\n", - " #'Occupied units renter (2019A)',\n", - " #'Occupied units owner (2019A)',\n", - " #'Population (2024A)',\n", - " #'Households (2024A)',\n", - " #'Housing units (2024A)',\n", - " #'Median Age (2024A)',\n", - " #'Per capita income (2024A)',\n", - " #'Average household Income (2024A)',\n", - " #'Median household income (2024A)']" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset = Dataset.get('ags_sociodemogr_f510a947')\n", - "variables = dataset.variables\n", - "variables" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we saw in the Data Discovery guide, the `ags_sociodemogr_f510a947` dataset contains socio-demographic variables aggregated to the Census block group level. \n", - "\n", - "Let's try and find a variable for total population:" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                                                            slugnamedescriptiondb_typeagg_methodcolumn_namevariable_group_iddataset_idid
                                                                                                                                                            1POPCY_5e23b8f4Total PopulationPopulation (2019A)INTEGERSUMPOPCYNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                                                                                            2POPCYGRP_55c4a2e5POPCYGRPPopulation in Group Quarters (2019A)INTEGERSUMPOPCYGRPNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                                                                                            3POPCYGRPI_d37c4ecPOPCYGRPIInstitutional Group Quarters Population (2019A)INTEGERSUMPOPCYGRPINonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                                                                                            84LBFCYPOP16_55556a30LBFCYPOP16Population Age 16+ (2019A)INTEGERSUMLBFCYPOP16carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
                                                                                                                                                            101POPPY_3fccf966Total populationPopulation (2024A)FLOATSUMPOPPYNonecarto-do.ags.demographics_sociodemographics_us...carto-do.ags.demographics_sociodemographics_us...
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                                                                                                                                                            " - ], - "text/plain": [ - " slug name \\\n", - "1 POPCY_5e23b8f4 Total Population \n", - "2 POPCYGRP_55c4a2e5 POPCYGRP \n", - "3 POPCYGRPI_d37c4ec POPCYGRPI \n", - "84 LBFCYPOP16_55556a30 LBFCYPOP16 \n", - "101 POPPY_3fccf966 Total population \n", - "\n", - " description db_type agg_method \\\n", - "1 Population (2019A) INTEGER SUM \n", - "2 Population in Group Quarters (2019A) INTEGER SUM \n", - "3 Institutional Group Quarters Population (2019A) INTEGER SUM \n", - "84 Population Age 16+ (2019A) INTEGER SUM \n", - "101 Population (2024A) FLOAT SUM \n", - "\n", - " column_name variable_group_id \\\n", - "1 POPCY None \n", - "2 POPCYGRP None \n", - "3 POPCYGRPI None \n", - "84 LBFCYPOP16 carto-do.ags.demographics_sociodemographics_us... \n", - "101 POPPY None \n", - "\n", - " dataset_id \\\n", - "1 carto-do.ags.demographics_sociodemographics_us... \n", - "2 carto-do.ags.demographics_sociodemographics_us... \n", - "3 carto-do.ags.demographics_sociodemographics_us... \n", - "84 carto-do.ags.demographics_sociodemographics_us... \n", - "101 carto-do.ags.demographics_sociodemographics_us... \n", - "\n", - " id \n", - "1 carto-do.ags.demographics_sociodemographics_us... \n", - "2 carto-do.ags.demographics_sociodemographics_us... \n", - "3 carto-do.ags.demographics_sociodemographics_us... \n", - "84 carto-do.ags.demographics_sociodemographics_us... \n", - "101 carto-do.ags.demographics_sociodemographics_us... " - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "vdf = variables.to_dataframe()\n", - "vdf[vdf['name'].str.contains('pop', case=False, na=False)]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can store the variable instance we need by searching the Catalog by its `slug`, in this case `POPCY_5e23b8f4`:" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'slug': 'POPCY_5e23b8f4',\n", - " 'name': 'Total Population',\n", - " 'description': 'Population (2019A)',\n", - " 'db_type': 'INTEGER',\n", - " 'agg_method': 'SUM',\n", - " 'column_name': 'POPCY',\n", - " 'variable_group_id': None,\n", - " 'dataset_id': 'carto-do.ags.demographics_sociodemographics_usa_blockgroup_2015_yearly_2019',\n", - " 'id': 'carto-do.ags.demographics_sociodemographics_usa_blockgroup_2015_yearly_2019.POPCY'}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "variable = Variable.get('POPCY_5e23b8f4')\n", - "variable.to_dict()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `POPCY` variable contains the `SUM` of the population for blockgroup for the year 2019. Let's enrich our stores DataFrame with that variable." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Enrich a points DataFrame\n", - "\n", - "In the [CARTOframes Quickstart](/developers/cartoframes/guides/Quickstart/) you learned how to load your own data (in this case Starbucks stores) and geocode the addresses to coordinates for further analysis.\n", - "\n", - "Let's start by loading those geocoded Starbucks stores:" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                                                            cartodb_idfield_1nameaddressrevenuegeometry
                                                                                                                                                            010Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381321040.772POINT (-73.95901 40.67109)
                                                                                                                                                            121607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351268080.418POINT (-73.96122 40.57796)
                                                                                                                                                            23265th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041248133.699POINT (-73.98976 40.61912)
                                                                                                                                                            343Bay Ridge Pkwy & 3rd Ave7419 3rd Avenue,Brooklyn, NY 112091185702.676POINT (-74.02744 40.63152)
                                                                                                                                                            454Caesar's Bay Shopping Center8973 Bay Parkway,Brooklyn, NY 112141148427.411POINT (-74.00098 40.59321)
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                                                                                                                                                            " - ], - "text/plain": [ - " cartodb_id field_1 name \\\n", - "0 1 0 Franklin Ave & Eastern Pkwy \n", - "1 2 1 607 Brighton Beach Ave \n", - "2 3 2 65th St & 18th Ave \n", - "3 4 3 Bay Ridge Pkwy & 3rd Ave \n", - "4 5 4 Caesar's Bay Shopping Center \n", - "\n", - " address revenue \\\n", - "0 341 Eastern Pkwy,Brooklyn, NY 11238 1321040.772 \n", - "1 607 Brighton Beach Avenue,Brooklyn, NY 11235 1268080.418 \n", - "2 6423 18th Avenue,Brooklyn, NY 11204 1248133.699 \n", - "3 7419 3rd Avenue,Brooklyn, NY 11209 1185702.676 \n", - "4 8973 Bay Parkway,Brooklyn, NY 11214 1148427.411 \n", - "\n", - " geometry \n", - "0 POINT (-73.95901 40.67109) \n", - "1 POINT (-73.96122 40.57796) \n", - "2 POINT (-73.98976 40.61912) \n", - "3 POINT (-74.02744 40.63152) \n", - "4 POINT (-74.00098 40.59321) " - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from geopandas import read_file\n", - "stores_gdf = read_file('http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_geocoded.geojson')\n", - "stores_gdf.head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note: Alternatively, you can load data in any geospatial format supported by GeoPandas or CARTO.**" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we can see, for each store we have its name, address, the total revenue by year and a `geometry` column indicating the location of the store. This is important because for the enrichment service to work, we need a DataFrame with a geometry column encoded as a [shapely](https://pypi.org/project/Shapely/) object.\n", - "\n", - "We can now create a new `Enrichment` instance, and since the `stores_gdf` dataset represents store locations (points), we can use the `enrich_points` function passing as arguments, the stores DataFrame and a list of `Variables` (that we have a valid subscription from the Data Observatory catalog for).\n", - "\n", - "In this case we are only enriching one variable (the total population), but we could enrich a list of them." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                                                            cartodb_idfield_1nameaddressrevenuegeometryPOPCYdo_area
                                                                                                                                                            010Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381321040.772POINT (-73.95901 40.67109)221559840.196748
                                                                                                                                                            121607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351268080.418POINT (-73.96122 40.57796)183160150.637177
                                                                                                                                                            23265th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041248133.699POINT (-73.98976 40.61912)74538950.618828
                                                                                                                                                            343Bay Ridge Pkwy & 3rd Ave7419 3rd Avenue,Brooklyn, NY 112091185702.676POINT (-74.02744 40.63152)117457353.293114
                                                                                                                                                            454Caesar's Bay Shopping Center8973 Bay Parkway,Brooklyn, NY 112141148427.411POINT (-74.00098 40.59321)2289188379.242624
                                                                                                                                                            \n", - "
                                                                                                                                                            " - ], - "text/plain": [ - " cartodb_id field_1 name \\\n", - "0 1 0 Franklin Ave & Eastern Pkwy \n", - "1 2 1 607 Brighton Beach Ave \n", - "2 3 2 65th St & 18th Ave \n", - "3 4 3 Bay Ridge Pkwy & 3rd Ave \n", - "4 5 4 Caesar's Bay Shopping Center \n", - "\n", - " address revenue \\\n", - "0 341 Eastern Pkwy,Brooklyn, NY 11238 1321040.772 \n", - "1 607 Brighton Beach Avenue,Brooklyn, NY 11235 1268080.418 \n", - "2 6423 18th Avenue,Brooklyn, NY 11204 1248133.699 \n", - "3 7419 3rd Avenue,Brooklyn, NY 11209 1185702.676 \n", - "4 8973 Bay Parkway,Brooklyn, NY 11214 1148427.411 \n", - "\n", - " geometry POPCY do_area \n", - "0 POINT (-73.95901 40.67109) 2215 59840.196748 \n", - "1 POINT (-73.96122 40.57796) 1831 60150.637177 \n", - "2 POINT (-73.98976 40.61912) 745 38950.618828 \n", - "3 POINT (-74.02744 40.63152) 1174 57353.293114 \n", - "4 POINT (-74.00098 40.59321) 2289 188379.242624 " - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.data.observatory import Enrichment\n", - "enriched_stores_gdf = Enrichment().enrich_points(stores_gdf, [variable])\n", - "enriched_stores_gdf.head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once the enrichment finishes, there is a new column in our DataFrame called `POPCY` with population projected for the year 2019, from the US Census block group which contains each one of our Starbucks stores." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All this information, is available in the `ags_sociodemogr_e92b1637` metadata. Let's take a look:" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'slug': 'ags_sociodemogr_f510a947',\n", - " 'name': 'Sociodemographics - United States of America (Census Block Group)',\n", - " 'description': 'Census and ACS sociodemographic data estimated for the current year and data projected to five years. Projected fields are general aggregates (total population, total households, median age, avg income etc.)',\n", - " 'category_id': 'demographics',\n", - " 'country_id': 'usa',\n", - " 'data_source_id': 'sociodemographics',\n", - " 'provider_id': 'ags',\n", - " 'geography_name': 'Census Block Group - United States of America',\n", - " 'geography_description': 'Shoreline clipped TIGER/Line boundaries. More info: https://carto.com/blog/tiger-shoreline-clip/',\n", - " 'temporal_aggregation': 'yearly',\n", - " 'time_coverage': '[2019-01-01, 2020-01-01)',\n", - " 'update_frequency': 'yearly',\n", - " 'is_public_data': False,\n", - " 'lang': 'eng',\n", - " 'version': '2019',\n", - " 'category_name': 'Demographics',\n", - " 'provider_name': 'Applied Geographic Solutions',\n", - " 'geography_id': 'carto-do-public-data.carto.geography_usa_blockgroup_2015',\n", - " 'id': 'carto-do.ags.demographics_sociodemographics_usa_blockgroup_2015_yearly_2019'}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dataset.to_dict()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Enrich a polygon DataFrame\n", - "\n", - "Next, let's do a second enrichment, but this time using a DataFrame with areas of influence calculated using the [CARTOframes isochrones](/developers/cartoframes/reference/#heading-Isolines) service to obtain the polygon around each store that covers the area within an 8, 17 and 25 minute walk." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                                                            data_rangelower_data_rangerange_labelgeometry
                                                                                                                                                            050008 min.MULTIPOLYGON (((-73.95959 40.67571, -73.95971 ...
                                                                                                                                                            1100050017 min.POLYGON ((-73.95988 40.68110, -73.95863 40.681...
                                                                                                                                                            21500100025 min.POLYGON ((-73.95986 40.68815, -73.95711 40.688...
                                                                                                                                                            350008 min.MULTIPOLYGON (((-73.96185 40.58321, -73.96231 ...
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                                                                                                                                                            " - ], - "text/plain": [ - " data_range lower_data_range range_label \\\n", - "0 500 0 8 min. \n", - "1 1000 500 17 min. \n", - "2 1500 1000 25 min. \n", - "3 500 0 8 min. \n", - "4 1000 500 17 min. \n", - "\n", - " geometry \n", - "0 MULTIPOLYGON (((-73.95959 40.67571, -73.95971 ... \n", - "1 POLYGON ((-73.95988 40.68110, -73.95863 40.681... \n", - "2 POLYGON ((-73.95986 40.68815, -73.95711 40.688... \n", - "3 MULTIPOLYGON (((-73.96185 40.58321, -73.96231 ... \n", - "4 MULTIPOLYGON (((-73.96684 40.57483, -73.96830 ... " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "aoi_gdf = read_file('http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn_isolines.geojson')\n", - "aoi_gdf.head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this case we have a DataFrame which, for each index in the `stores_gdf`, contains a polygon of the areas of influence around each store at 8, 17 and 25 minute walking intervals. Again the `geometry` is encoded as a `shapely` object.\n", - "\n", - "In this case, the `Enrichment` service provides an `enrich_polygons` function, which in its basic version, works in the same way as the `enrich_points` function. It just needs a DataFrame with polygon geometries and a list of variables to enrich:" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                                                            data_rangelower_data_rangerange_labelgeometryPOPCY
                                                                                                                                                            050008 min.MULTIPOLYGON (((-73.95959 40.67571, -73.95971 ...21112.458335
                                                                                                                                                            1100050017 min.POLYGON ((-73.95988 40.68110, -73.95863 40.681...60157.083956
                                                                                                                                                            21500100025 min.POLYGON ((-73.95986 40.68815, -73.95711 40.688...110657.471670
                                                                                                                                                            350008 min.MULTIPOLYGON (((-73.96185 40.58321, -73.96231 ...23505.104589
                                                                                                                                                            4100050017 min.MULTIPOLYGON (((-73.96684 40.57483, -73.96830 ...29781.046917
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                                                                                                                                                            " - ], - "text/plain": [ - " data_range lower_data_range range_label \\\n", - "0 500 0 8 min. \n", - "1 1000 500 17 min. \n", - "2 1500 1000 25 min. \n", - "3 500 0 8 min. \n", - "4 1000 500 17 min. \n", - "\n", - " geometry POPCY \n", - "0 MULTIPOLYGON (((-73.95959 40.67571, -73.95971 ... 21112.458335 \n", - "1 POLYGON ((-73.95988 40.68110, -73.95863 40.681... 60157.083956 \n", - "2 POLYGON ((-73.95986 40.68815, -73.95711 40.688... 110657.471670 \n", - "3 MULTIPOLYGON (((-73.96185 40.58321, -73.96231 ... 23505.104589 \n", - "4 MULTIPOLYGON (((-73.96684 40.57483, -73.96830 ... 29781.046917 " - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cartoframes.data.observatory import Enrichment\n", - "enriched_aoi_gdf = Enrichment().enrich_polygons(aoi_gdf, [variable])\n", - "enriched_aoi_gdf.head(5)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We now have a new column in our areas of influence DataFrame, `SUM_POPCY` which represents the `SUM` of total population in the Census block groups that instersect with each polygon in our DataFrame." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### How enrichment works" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a deeper look into what happens under the hood when you execute a polygon enrichment.\n", - "\n", - "Imagine we have polygons representing municipalities, in blue, each of which have a population attribute, and we want to find out the population inside the green circle.\n", - "\n", - "\"Enrichment" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We don’t know how the population is distributed inside these municipalities. They are probably concentrated in cities somewhere, but, since we don’t know where they are, our best guess is to assume that the population is evenly distributed in the municipality (i.e. every point inside the municipality has the same population density).\n", - "\n", - "Population is an extensive property (it grows with area), so we can subset it (a region inside the municipality will always have a smaller population than the whole municipality), and also aggregate it by summing.\n", - "\n", - "In this case, we’d calculate the population inside each part of the circle that intersects with a municipality." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Default aggregation methods**\n", - "\n", - "In the Data Observatory, we suggest a default aggregation method for certain fields. However, some fields don’t have a clear best method, and some just can’t be aggregated. In these cases, we leave the `agg_method` field blank and let the user choose the method that best fits their needs." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conclusion\n", - "\n", - "In this guide you've seen how to use CARTOframes in conjunction with the Data Observatory to enrich a Starbucks dataset with a new population variable for the use case of revenue prediction analysis by:\n", - "\n", - "- Choosing the total population variable from the Data Observatory catalog\n", - "- Calculating the sum of total population for each store\n", - "- Calculating the sum of total population around the walking areas of influence around each store\n", - "\n", - "In addition, you were introduced to some more advanced concepts and further explanation of how the enrichment itself works." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/guides/08-Location-Data-Services.ipynb b/guides/08-Location-Data-Services.ipynb deleted file mode 100644 index f19fa7099..000000000 --- a/guides/08-Location-Data-Services.ipynb +++ /dev/null @@ -1,7681 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Location Data Services" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Introduction\n", - "\n", - "CARTOframes provides the functionality to use the [CARTO Data Services API](https://carto.com/developers/data-services-api/). This API consists of a set of location-based functions that can be applied to your data to perform geospatial analyses without leaving the context of your notebook.\n", - "\n", - "For instance, you can **geocode** a pandas DataFrame with addresses on the fly, and then perform a trade areas analysis by computing **isodistances** or **isochrones** programmatically.\n", - "\n", - "Given a set of ten simulated Starbucks store addresses, this guide walks through the use case of finding good location candidates to open an additional store.\n", - "\n", - "> Based on your account plan, some of these location data services are subject to different [quota limitations](https://carto.com/developers/data-services-api/support/quota-information/)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Data\n", - "\n", - "This guide uses the same dataset of simulated Starbucks locations that has been used in the other guides and can be downloaded [here](http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn.csv)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Authentication\n", - "\n", - "Using Location Data Services requires to be authenticated. For more information about how to authenticate, please read the [Login to CARTO Platform guide](/developers/cartoframes/guides/Login-to-CARTO-Platform/)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.auth import Credentials, set_default_credentials\n", - "\n", - "set_default_credentials('creds.json')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Geocoding\n", - "\n", - "To get started, let's read in and explore the Starbucks location data we have. With the Starbucks store data in a DataFrame, we can see that there are two columns that can be used in the **geocoding** service: `name` and `address`. There's also a third column that reflects the annual revenue of the store." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                                                            nameaddressrevenue
                                                                                                                                                            0Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381.321041e+06
                                                                                                                                                            1607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351.268080e+06
                                                                                                                                                            265th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041.248134e+06
                                                                                                                                                            3Bay Ridge Pkwy & 3rd Ave7419 3rd Avenue,Brooklyn, NY 112091.185703e+06
                                                                                                                                                            4Caesar's Bay Shopping Center8973 Bay Parkway,Brooklyn, NY 112141.148427e+06
                                                                                                                                                            5Court St & Dean St167 Court Street,Brooklyn, NY 112011.144067e+06
                                                                                                                                                            6Target Gateway T-1401519 Gateway Dr,Brooklyn, NY 112391.021083e+06
                                                                                                                                                            73rd Ave & 92nd St9202 Third Avenue,Brooklyn, NY 112099.257073e+05
                                                                                                                                                            8Lam Group @ Sheraton Brooklyn228 Duffield st,Brooklyn, NY 112017.657935e+05
                                                                                                                                                            933-42 Hillel Place33-42 Hillel Place,Brooklyn, NY 112107.492163e+05
                                                                                                                                                            \n", - "
                                                                                                                                                            " - ], - "text/plain": [ - " name \\\n", - "0 Franklin Ave & Eastern Pkwy \n", - "1 607 Brighton Beach Ave \n", - "2 65th St & 18th Ave \n", - "3 Bay Ridge Pkwy & 3rd Ave \n", - "4 Caesar's Bay Shopping Center \n", - "5 Court St & Dean St \n", - "6 Target Gateway T-1401 \n", - "7 3rd Ave & 92nd St \n", - "8 Lam Group @ Sheraton Brooklyn \n", - "9 33-42 Hillel Place \n", - "\n", - " address revenue \n", - "0 341 Eastern Pkwy,Brooklyn, NY 11238 1.321041e+06 \n", - "1 607 Brighton Beach Avenue,Brooklyn, NY 11235 1.268080e+06 \n", - "2 6423 18th Avenue,Brooklyn, NY 11204 1.248134e+06 \n", - "3 7419 3rd Avenue,Brooklyn, NY 11209 1.185703e+06 \n", - "4 8973 Bay Parkway,Brooklyn, NY 11214 1.148427e+06 \n", - "5 167 Court Street,Brooklyn, NY 11201 1.144067e+06 \n", - "6 519 Gateway Dr,Brooklyn, NY 11239 1.021083e+06 \n", - "7 9202 Third Avenue,Brooklyn, NY 11209 9.257073e+05 \n", - "8 228 Duffield st,Brooklyn, NY 11201 7.657935e+05 \n", - "9 33-42 Hillel Place,Brooklyn, NY 11210 7.492163e+05 " - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import pandas as pd\n", - "\n", - "df = pd.read_csv('http://libs.cartocdn.com/cartoframes/files/starbucks_brooklyn.csv')\n", - "df" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Quota consumption\n", - "\n", - "Each time you run Location Data Services, you consume quota. For this reason, we provide the ability to check in advance the **amount of credits** an operation will consume by using the `dry_run` parameter when running the service function.\n", - "\n", - "It is also possible to check the available quota by running the `available_quota` function." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "from cartoframes.data.services import Geocoding\n", - "\n", - "geo_service = Geocoding()\n", - "\n", - "_, geo_dry_metadata = geo_service.geocode(\n", - " df,\n", - " street='address',\n", - " city={'value': 'New York'},\n", - " country={'value': 'USA'},\n", - " dry_run=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'total_rows': 10,\n", - " 'required_quota': 10,\n", - " 'previously_geocoded': 0,\n", - " 'previously_failed': 0,\n", - " 'records_with_geometry': 0}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_dry_metadata" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "4977588" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_service.available_quota()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Success! Data geocoded correctly\n" - ] - } - ], - "source": [ - "geo_gdf, geo_metadata = geo_service.geocode(\n", - " df,\n", - " street='address',\n", - " city={'value': 'New York'},\n", - " country={'value': 'USA'}\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "If the input data file should ever change, cached results will only be applied to unmodified\n", - "records, and new geocoding will be performed only on _new or changed records_.\n", - "\n", - "In order to use cached results, we have to save the results to a CARTO table using the `table_name` and `cached=True` parameters." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Success! Data geocoded correctly\n" - ] - } - ], - "source": [ - "geo_gdf_cached, geo_metadata_cached = geo_service.geocode(\n", - " df,\n", - " street='address',\n", - " city={'value': 'New York'},\n", - " country={'value': 'USA'},\n", - " table_name='starbucks_cache',\n", - " cached=True\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's compare `geo_dry_metadata` and `geo_metadata` to see the differences between the information returned with and without the `dry_run` option. As we can see, this information reflects that all the locations have been geocoded successfully and that it has consumed 10 credits of quota." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'total_rows': 10,\n", - " 'required_quota': 10,\n", - " 'previously_geocoded': 0,\n", - " 'previously_failed': 0,\n", - " 'records_with_geometry': 0,\n", - " 'final_records_with_geometry': 10,\n", - " 'geocoded_increment': 10,\n", - " 'successfully_geocoded': 10,\n", - " 'failed_geocodings': 0}" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_metadata" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The resulting data is a `GeoDataFrame` that contains three new columns:\n", - "\n", - "* `geometry`: The resulting geometry\n", - "* `gc_status_rel`: The percentage of accuracy of each location\n", - "* `carto_geocode_hash`: Geocode information" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                                                            the_geomnameaddressrevenuegc_status_relcarto_geocode_hash
                                                                                                                                                            0POINT (-73.95746 40.67102)Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381321040.7720.97c834a8e289e5bce280775a9bf1f833f1
                                                                                                                                                            1POINT (-73.96122 40.57796)607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351268080.4180.997d39a3fff93efd9034da88aa9ad2da79
                                                                                                                                                            2POINT (-73.98978 40.61944)65th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041248133.6990.981a2312049ddea753ba42bf77f5ccf718
                                                                                                                                                            3POINT (-74.02750 40.63202)Bay Ridge Pkwy & 3rd Ave7419 3rd Avenue,Brooklyn, NY 112091185702.6760.98827ab4dcc2d49d5fd830749597976d4a
                                                                                                                                                            4POINT (-74.00098 40.59321)Caesar's Bay Shopping Center8973 Bay Parkway,Brooklyn, NY 112141148427.4110.98119a38c7b51195cd4153fc81605a8495
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                                                                                                                                                            " - ], - "text/plain": [ - " the_geom name \\\n", - "0 POINT (-73.95746 40.67102) Franklin Ave & Eastern Pkwy \n", - "1 POINT (-73.96122 40.57796) 607 Brighton Beach Ave \n", - "2 POINT (-73.98978 40.61944) 65th St & 18th Ave \n", - "3 POINT (-74.02750 40.63202) Bay Ridge Pkwy & 3rd Ave \n", - "4 POINT (-74.00098 40.59321) Caesar's Bay Shopping Center \n", - "\n", - " address revenue gc_status_rel \\\n", - "0 341 Eastern Pkwy,Brooklyn, NY 11238 1321040.772 0.97 \n", - "1 607 Brighton Beach Avenue,Brooklyn, NY 11235 1268080.418 0.99 \n", - "2 6423 18th Avenue,Brooklyn, NY 11204 1248133.699 0.98 \n", - "3 7419 3rd Avenue,Brooklyn, NY 11209 1185702.676 0.98 \n", - "4 8973 Bay Parkway,Brooklyn, NY 11214 1148427.411 0.98 \n", - "\n", - " carto_geocode_hash \n", - "0 c834a8e289e5bce280775a9bf1f833f1 \n", - "1 7d39a3fff93efd9034da88aa9ad2da79 \n", - "2 1a2312049ddea753ba42bf77f5ccf718 \n", - "3 827ab4dcc2d49d5fd830749597976d4a \n", - "4 119a38c7b51195cd4153fc81605a8495 " - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_gdf.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In addition, to prevent geocoding records that have been **previously geocoded**, and thus spend quota **unnecessarily**, you should always preserve the ``the_geom`` and ``carto_geocode_hash`` columns generated by the geocoding process.\n", - "\n", - "This will happen **automatically** in these cases:\n", - "\n", - "1. Your input is a **table** from CARTO processed in place (without a ``table_name`` parameter)\n", - "2. If you save your results to a CARTO table using the ``table_name`` parameter, and only use the resulting table for any further geocoding.\n", - "\n", - "If you try to geocode this DataFrame now, that contains both ``the_geom`` and the ``carto_geocode_hash``, you will see that the required quota is 0 because it has already been geocoded." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "_, repeat_geo_metadata = geo_service.geocode(\n", - " geo_gdf,\n", - " street='address',\n", - " city={'value': 'New York'},\n", - " country={'value': 'USA'},\n", - " dry_run=True\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "repeat_geo_metadata.get('required_quota')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Precision\n", - "\n", - "The `address` column is more complete than the `name` column, and therefore, the resulting coordinates calculated by the service will be more accurate. If we check this, the accuracy values using the `name` column (`0.95, 0.93, 0.96, 0.83, 0.78, 0.9`) are lower than the ones we get by using the `address` column for geocoding (`0.97, 0.99, 0.98`)." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Success! Data geocoded correctly\n" - ] - } - ], - "source": [ - "geo_name_gdf, geo_name_metadata = geo_service.geocode(\n", - " df,\n", - " street='name',\n", - " city={'value': 'New York'},\n", - " country={'value': 'USA'}\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                                                            the_geomnameaddressrevenuegc_status_relcarto_geocode_hash
                                                                                                                                                            0POINT (-73.95795 40.67071)Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381321040.7720.930be7693fc688eca36e1077656dcb00a5
                                                                                                                                                            1POINT (-73.96122 40.57796)607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351268080.4180.96084a5c4d42ccf3c3c8e69426619f270e
                                                                                                                                                            2POINT (-73.99018 40.61914)65th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041248133.6990.931d9a17c20c11d0454aff10548a328c47
                                                                                                                                                            3POINT (-74.02778 40.63146)Bay Ridge Pkwy & 3rd Ave7419 3rd Avenue,Brooklyn, NY 112091185702.6760.96d531df27fc02336dc722cb4e7028b244
                                                                                                                                                            4POINT (-75.29322 43.07849)Caesar's Bay Shopping Center8973 Bay Parkway,Brooklyn, NY 112141148427.4110.859d8c13b5b4a93591f427d3ce0b5b4ead
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                                                                                                                                                            " - ], - "text/plain": [ - " the_geom name \\\n", - "0 POINT (-73.95795 40.67071) Franklin Ave & Eastern Pkwy \n", - "1 POINT (-73.96122 40.57796) 607 Brighton Beach Ave \n", - "2 POINT (-73.99018 40.61914) 65th St & 18th Ave \n", - "3 POINT (-74.02778 40.63146) Bay Ridge Pkwy & 3rd Ave \n", - "4 POINT (-75.29322 43.07849) Caesar's Bay Shopping Center \n", - "\n", - " address revenue gc_status_rel \\\n", - "0 341 Eastern Pkwy,Brooklyn, NY 11238 1321040.772 0.93 \n", - "1 607 Brighton Beach Avenue,Brooklyn, NY 11235 1268080.418 0.96 \n", - "2 6423 18th Avenue,Brooklyn, NY 11204 1248133.699 0.93 \n", - "3 7419 3rd Avenue,Brooklyn, NY 11209 1185702.676 0.96 \n", - "4 8973 Bay Parkway,Brooklyn, NY 11214 1148427.411 0.85 \n", - "\n", - " carto_geocode_hash \n", - "0 0be7693fc688eca36e1077656dcb00a5 \n", - "1 084a5c4d42ccf3c3c8e69426619f270e \n", - "2 1d9a17c20c11d0454aff10548a328c47 \n", - "3 d531df27fc02336dc722cb4e7028b244 \n", - "4 9d8c13b5b4a93591f427d3ce0b5b4ead " - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_name_gdf.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([0.93, 0.96, 0.85, 0.83, 0.74, 0.87])" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_name_gdf.gc_status_rel.unique()" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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                                                                                                                                                            the_geomnameaddressrevenuegc_status_relcarto_geocode_hash
                                                                                                                                                            0POINT (-73.95746 40.67102)Franklin Ave & Eastern Pkwy341 Eastern Pkwy,Brooklyn, NY 112381321040.7720.97c834a8e289e5bce280775a9bf1f833f1
                                                                                                                                                            1POINT (-73.96122 40.57796)607 Brighton Beach Ave607 Brighton Beach Avenue,Brooklyn, NY 112351268080.4180.997d39a3fff93efd9034da88aa9ad2da79
                                                                                                                                                            2POINT (-73.98978 40.61944)65th St & 18th Ave6423 18th Avenue,Brooklyn, NY 112041248133.6990.981a2312049ddea753ba42bf77f5ccf718
                                                                                                                                                            3POINT (-74.02750 40.63202)Bay Ridge Pkwy & 3rd Ave7419 3rd Avenue,Brooklyn, NY 112091185702.6760.98827ab4dcc2d49d5fd830749597976d4a
                                                                                                                                                            4POINT (-74.00098 40.59321)Caesar's Bay Shopping Center8973 Bay Parkway,Brooklyn, NY 112141148427.4110.98119a38c7b51195cd4153fc81605a8495
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                                                                                                                                                            " - ], - "text/plain": [ - " the_geom name \\\n", - "0 POINT (-73.95746 40.67102) Franklin Ave & Eastern Pkwy \n", - "1 POINT (-73.96122 40.57796) 607 Brighton Beach Ave \n", - "2 POINT (-73.98978 40.61944) 65th St & 18th Ave \n", - "3 POINT (-74.02750 40.63202) Bay Ridge Pkwy & 3rd Ave \n", - "4 POINT (-74.00098 40.59321) Caesar's Bay Shopping Center \n", - "\n", - " address revenue gc_status_rel \\\n", - "0 341 Eastern Pkwy,Brooklyn, NY 11238 1321040.772 0.97 \n", - "1 607 Brighton Beach Avenue,Brooklyn, NY 11235 1268080.418 0.99 \n", - "2 6423 18th Avenue,Brooklyn, NY 11204 1248133.699 0.98 \n", - "3 7419 3rd Avenue,Brooklyn, NY 11209 1185702.676 0.98 \n", - "4 8973 Bay Parkway,Brooklyn, NY 11214 1148427.411 0.98 \n", - "\n", - " carto_geocode_hash \n", - "0 c834a8e289e5bce280775a9bf1f833f1 \n", - "1 7d39a3fff93efd9034da88aa9ad2da79 \n", - "2 1a2312049ddea753ba42bf77f5ccf718 \n", - "3 827ab4dcc2d49d5fd830749597976d4a \n", - "4 119a38c7b51195cd4153fc81605a8495 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "geo_gdf.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Visualize the results\n", - "\n", - "Finally, we can visualize the precision of the geocoded results using a CARTOframes [visualization layer](/developers/cartoframes/examples/#example-color-bins-layer)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - "\n", - "\n", - " None\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - "\n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "\n", - "\n", - "\n", - " Static map image\n", - " \n", - " \n", - "
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                                                                                                                                                              091800MULTIPOLYGON (((-73.96485 40.63379, -73.96460 ...
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                                                                                                                                                                091000MULTIPOLYGON (((-73.95867 40.63311, -73.95842 ...
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popup_element('gc_status_rel', 'Precision'),\n", - " popup_element('revenue', 'Revenue')\n", - " ],\n", - " title='Revenue $',\n", - " ),\n", - " Layer(new_store_gdf)\n", - "])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Conclusion\n", - "\n", - "In this example you've seen how to use Location Data Services to perform a trade area analysis using CARTOframes built-in functionality without leaving the notebook.\n", - "\n", - "Using the results, we've calculated a possible new location for a store, and used the isoline areas to help in the decision making process.\n", - "\n", - "Take into account that finding optimal spots for new stores is not an easy task and requires more analysis, but this is a great first step!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.7" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/guides/img/credentials/api-keys.png b/guides/img/credentials/api-keys.png deleted file mode 100644 index 5334355e6..000000000 Binary files a/guides/img/credentials/api-keys.png and /dev/null differ diff --git a/guides/img/credentials/dashboard.png b/guides/img/credentials/dashboard.png deleted file mode 100644 index 693499128..000000000 Binary files a/guides/img/credentials/dashboard.png and /dev/null differ diff --git a/guides/img/enrichment/enrichment_01.png b/guides/img/enrichment/enrichment_01.png deleted file mode 100644 index 00760724b..000000000 Binary files a/guides/img/enrichment/enrichment_01.png and /dev/null differ diff --git a/guides/img/explore_data_observatory_catalog_files/explore_data_observatory_catalog_37_0.png 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a/guides/img/quickstart/explore_polygon_layer.png and /dev/null differ diff --git a/guides/img/quickstart/share_output.png b/guides/img/quickstart/share_output.png deleted file mode 100644 index 409e2c948..000000000 Binary files a/guides/img/quickstart/share_output.png and /dev/null differ diff --git a/guides/img/visualize/layout.png b/guides/img/visualize/layout.png deleted file mode 100644 index b84f40317..000000000 Binary files a/guides/img/visualize/layout.png and /dev/null differ diff --git a/package.json b/package.json index 56d7e7bef..8513e710f 100644 --- a/package.json +++ b/package.json @@ -1,6 +1,6 @@ { "name": "cartoframes", - "version": "1.0.4", + "version": "1.1.0", "description": "*********** CARTOframes ***********", "main": "./cartoframes/assets/src/index.js", "directories": { diff --git a/rollup.config.js b/rollup.config.js index 3a166ee9a..cbf972dc7 100644 --- a/rollup.config.js +++ b/rollup.config.js @@ -18,4 +18,4 @@ module.exports = { 'cartoframes/assets/src/bundle.js' ] } -}; \ No newline at end of file +}; diff --git a/setup.py b/setup.py index 1b274d6d8..df3ec0209 100644 --- a/setup.py +++ b/setup.py @@ -25,16 +25,11 @@ def get_version(): REQUIRES = [ 'appdirs>=1.4.3,<2.0', - 'carto>=1.11.1,<2.0', + 'carto>=1.11.2,<2.0', 'jinja2>=2.10.1,<3.0', 'pandas>=0.25.0', 'geopandas>=0.6.0,<1.0', - 'tqdm>=4.32.1,<5.0', 'unidecode>=1.1.0,<2.0', - 'google-cloud-storage==1.23.0', - 'google-cloud-bigquery==1.22.0', - 'google-cloud-bigquery-storage==0.7.0', - 'fastavro==0.22.7', 'semantic_version>=2.8.0,<3' ] diff --git a/tests/README.md b/tests/README.md index 83d4ec788..623160ff9 100644 --- a/tests/README.md +++ b/tests/README.md @@ -18,6 +18,74 @@ And for an specific Python version tox -e py37 ``` +## Executing a single test + +Create a virtual environment + +``` +virtualenv -p python3 venv +source venv/bin/activate +``` + +Install the required dependencies + +``` +pip install -r requirements.txt +pip install pytest +pip install pytest-mock +``` + +Execute a single test + +``` +pytest tests/unit/io/test_carto.py::test_read_carto +``` + +## Executing Jupyter Notebooks tests + +These tests execute all the Jupyter Notebooks contained in the `docs/examples` and `docs/guides` directories and overwrite the notebook with the executed versions (if the `OVERWRITE` parameter is set to `true`). + +Create a virtual environment + +``` +virtualenv -p python3 venv +source venv/bin/activate +``` + +Install the required dependencies + +``` +pip install -r requirements.txt +pip install -r tests/notebooks/requirements.txt +``` + +Set your credentials + +Create the file tests/notebooks/creds.json with the following structure: + +``` +{ + "username": "your_username", + "api_key": "your_api_key" +} +``` + +Execute the tests + +``` +pytest [-s] tests/notebooks +``` + +Environment variables: + - `SCOPE`: (default `all`): Scope of the tests: all, guides, examples (Example: `SCOPE=guides`) + - `OVERWRITE` (default `true`): Overwrites the notebooks with the result of the execution (Example: `OVERWRITE=false`) + - `TIMEOUT` (default `600`): Notebook timeout for each cell (Example: `TIMEOUT=100`) + - `KERNEL` (default `python3`): Kernel used to execute the notebooks (Example: `KERNEL=python3`) + +Notes: + - You can select the notebooks to be executed by overriding the value of the `EXECUTE_NOTEBOOKS` variable in `test_notebooks.py` (if not set, all the notebooks in the `docs/examples` and `docs/guides` directories will be executed) + - You can also select the notebooks to be avoided by overriding the value of the `AVOID_NOTEBOOKS` variable in `test_notebooks.py` + ## File structure ``` @@ -136,4 +204,3 @@ pip install pre-commit ``` pre-commit install ``` - diff --git a/tests/e2e/data/client/test_bigquery_client.py b/tests/e2e/data/client/test_bigquery_client.py deleted file mode 100644 index 4b1b308e4..000000000 --- a/tests/e2e/data/client/test_bigquery_client.py +++ /dev/null @@ -1,102 +0,0 @@ -import os -import json -import pytest -import unittest - -from google.auth.exceptions import RefreshError -from google.cloud import bigquery - -from cartoframes.auth import Credentials -from cartoframes.exceptions import DOError -from cartoframes.data.clients.bigquery_client import BigQueryClient - - -_WORKING_PROJECT = 'carto-do-customers' - - -class RefreshTokenChecker(object): - def __init__(self, response, raise_after=1): - self.number_of_calls = 0 - self.response = response - self.raise_after = raise_after - - def query_raiser(self, query, **kwargs): - self.number_of_calls += 1 - if self.number_of_calls < self.raise_after: - return self.response - else: - raise RefreshError() - - -class ResponseMock(list): - def __init__(self, data, **kwargs): - super(ResponseMock, self).__init__(data, **kwargs) - self.total_rows = len(data) - - -class QueryJobMock(object): - def __init__(self, response): - self.response = response - - def result(self): - return ResponseMock(self.response) - - -class TestBigQueryClient(unittest.TestCase): - def setUp(self): - if (os.environ.get('APIKEY') is None or os.environ.get('USERNAME') is None): - creds = json.loads(open('tests/e2e/secret.json').read()) - self.apikey = creds['APIKEY'] - self.username = creds['USERNAME'] - else: - self.apikey = os.environ['APIKEY'] - self.username = os.environ['USERNAME'] - - self.credentials = Credentials(self.username, self.apikey) - self.file_path = '/tmp/test_download.csv' - - def tearDown(self): - if os.path.isfile(self.file_path): - os.remove(self.file_path) - - def test_instantiation(self): - bq_client = BigQueryClient(self.credentials) - assert isinstance(bq_client, BigQueryClient) - - def test_refresh_token_raises_cartoexception(self): - refresh_token_checker = RefreshTokenChecker('', 0) - original_query_method = bigquery.Client.query - bigquery.Client.query = refresh_token_checker.query_raiser - - bq_client = BigQueryClient(self.credentials) - with pytest.raises(DOError): - bq_client.query('select * from') - - bigquery.Client.query = original_query_method - - def test_refresh_token(self): - expected_response = 'ok' - refresh_token_checker = RefreshTokenChecker(expected_response, 2) - original_query_method = bigquery.Client.query - bigquery.Client.query = refresh_token_checker.query_raiser - - bq_client = BigQueryClient(self.credentials) - response = bq_client.query('select * from') - assert response == expected_response - - bigquery.Client.query = original_query_method - - def test_download_using_if_exists(self): - project = _WORKING_PROJECT - dataset = 'fake_dataset' - table = 'fake_table' - file_path = self.file_path - - bq_client = BigQueryClient(self.credentials) - - query = 'SELECT * FROM `{}.{}.{}`'.format(project, dataset, table) - job = bq_client.query(query) - - with open(file_path, 'w'): - with self.assertRaises(OSError): - bq_client.download_to_file(job, file_path, fail_if_exists=True, progress_bar=False) diff --git a/tests/e2e/data/client/test_data_obs_client.py b/tests/e2e/data/client/test_data_obs_client.py index fa21dd204..ebe50806b 100644 --- a/tests/e2e/data/client/test_data_obs_client.py +++ b/tests/e2e/data/client/test_data_obs_client.py @@ -22,6 +22,56 @@ warnings.filterwarnings('ignore') +class TestDataObsClientDeprecation(unittest.TestCase, _UserUrlLoader): + def setUp(self): + self.credentials = Credentials(None, None, self.user_url().format(username=None)) + + def test_class_deprecation(self): + with warnings.catch_warnings(record=True) as w: + _ = DataObsClient(self.credentials) + assert issubclass(w[-1].category, DeprecationWarning) + assert 'deprecated' in str(w[-1].message) + + def test_boundaries_deprecation(self): + with warnings.catch_warnings(record=True) as w: + do = DataObsClient(self.credentials) + + with warnings.catch_warnings(record=True) as w: + try: + do.boundaries() + except Exception: + pass + + assert issubclass(w[-1].category, DeprecationWarning) + assert 'deprecated' in str(w[-1].message) + + def test_discovery_deprecation(self): + with warnings.catch_warnings(record=True) as w: + do = DataObsClient(self.credentials) + + with warnings.catch_warnings(record=True) as w: + try: + do.discovery() + except Exception: + pass + + assert issubclass(w[-1].category, DeprecationWarning) + assert 'deprecated' in str(w[-1].message) + + def test_augment_deprecation(self): + with warnings.catch_warnings(record=True) as w: + do = DataObsClient(self.credentials) + + with warnings.catch_warnings(record=True) as w: + try: + do.augment() + except Exception: + pass + + assert issubclass(w[-1].category, DeprecationWarning) + assert 'deprecated' in str(w[-1].message) + + @pytest.mark.skip() class TestDataObsClient(unittest.TestCase, _UserUrlLoader): """Tests for cartoframes.client.DataObsClient""" diff --git a/tests/e2e/data/observatory/catalog/test_download.py b/tests/e2e/data/observatory/catalog/test_download.py index f40f7d2b9..b376b0ba8 100644 --- a/tests/e2e/data/observatory/catalog/test_download.py +++ b/tests/e2e/data/observatory/catalog/test_download.py @@ -2,7 +2,9 @@ import json import pandas import pytest + from pathlib import Path +from geopandas import GeoDataFrame from cartoframes.auth import Credentials from cartoframes.data.observatory import Dataset, Geography @@ -72,6 +74,9 @@ def test_dataset_to_csv_private(self): def test_dataset_to_dataframe_public(self): df = public_dataset.to_dataframe(self.credentials, limit=PUBLIC_LIMIT) + assert isinstance(df, GeoDataFrame) + assert df.geom.type == 'Polygon' + df.to_csv(self.tmp_file, index=False) df = pandas.read_csv(self.tmp_file) @@ -81,6 +86,9 @@ def test_dataset_to_dataframe_public(self): def test_dataset_to_dataframe_private(self): df = private_dataset.to_dataframe(self.credentials, limit=PRIVATE_LIMIT) + assert isinstance(df, GeoDataFrame) + assert df.geom.type == 'Polygon' + df.to_csv(self.tmp_file, index=False) df = pandas.read_csv(self.tmp_file) @@ -111,6 +119,9 @@ def test_geography_to_csv_private(self): @pytest.mark.skip() # TODO implement equals check using a tolerance def test_geography_to_dataframe_public(self): df = public_geography.to_dataframe(self.credentials, limit=PUBLIC_LIMIT, order_by='geoid') + assert isinstance(df, GeoDataFrame) + assert df.geom.type == 'Polygon' + df.to_csv(self.tmp_file, index=False) df = pandas.read_csv(self.tmp_file) @@ -121,6 +132,9 @@ def test_geography_to_dataframe_public(self): @pytest.mark.skip() # TODO implement equals check using a tolerance def test_geography_to_dataframe_private(self): df = private_geography.to_dataframe(self.credentials, limit=PRIVATE_LIMIT, order_by='geoid') + assert isinstance(df, GeoDataFrame) + assert df.geom.type == 'Polygon' + df.to_csv(self.tmp_file, index=False) df = pandas.read_csv(self.tmp_file) @@ -165,6 +179,9 @@ def test_geography_to_csv_public_with_sql_query(self): def test_dataset_to_dataframe_public_with_sql_query(self): sql_query = 'select * from {dataset} order by geoid limit 2' df = public_dataset.to_dataframe(self.credentials, sql_query=sql_query) + assert isinstance(df, GeoDataFrame) + assert df.geom.type == 'Polygon' + df.to_csv(self.tmp_file, index=False) df = pandas.read_csv(self.tmp_file) @@ -177,6 +194,9 @@ def test_dataset_to_dataframe_public_with_sql_query_and_add_geom(self): sql_query = 'select * from {dataset} order by geoid limit 2' add_geom = True df = public_dataset.to_dataframe(self.credentials, sql_query=sql_query, add_geom=add_geom) + assert isinstance(df, GeoDataFrame) + assert df.geom.type == 'Polygon' + df.to_csv(self.tmp_file, index=False) df = pandas.read_csv(self.tmp_file) @@ -188,6 +208,9 @@ def test_dataset_to_dataframe_public_with_sql_query_and_add_geom(self): def test_geography_to_dataframe_public_with_sql_query(self): sql_query = 'select * from {geography} order by geoid limit 2' df = public_geography.to_dataframe(self.credentials, sql_query=sql_query) + assert isinstance(df, GeoDataFrame) + assert df.geom.type == 'Polygon' + df.to_csv(self.tmp_file, index=False) df = pandas.read_csv(self.tmp_file) diff --git a/tests/notebooks/creds.sample.json b/tests/notebooks/creds.sample.json new file mode 100644 index 000000000..989c3ca88 --- /dev/null +++ b/tests/notebooks/creds.sample.json @@ -0,0 +1,4 @@ +{ + "username": "your_username", + "api_key": "your_api_key" +} \ No newline at end of file diff --git a/tests/notebooks/requirements.txt b/tests/notebooks/requirements.txt new file mode 100644 index 000000000..14c1f7884 --- /dev/null +++ b/tests/notebooks/requirements.txt @@ -0,0 +1,9 @@ +notebook==6.1.5 +pytest==6.1.1 +pytest-mock==3.3.1 +nbconvert==6.0.7 +ipykernel==5.3.4 +ipywidgets==7.5.1 +matplotlib==3.3.2 +xlrd==1.2.0 +seaborn==0.11.0 diff --git a/tests/notebooks/test_notebooks.py b/tests/notebooks/test_notebooks.py new file mode 100644 index 000000000..24d008659 --- /dev/null +++ b/tests/notebooks/test_notebooks.py @@ -0,0 +1,94 @@ +import os +import time +import glob +import pytest +import logging +import nbformat +import subprocess +from nbconvert.preprocessors import ExecutePreprocessor + + +logging.basicConfig(level=logging.INFO) + +EXECUTE_NOTEBOOKS = [] +AVOID_NOTEBOOKS = [ + 'docs/examples/use_cases/building_a_dashboard.ipynb', + 'docs/examples/use_cases/combining_two_datasets.ipynb', + 'docs/examples/templates/do_access_premium_data.ipynb', + 'docs/examples/templates/do_data_enrichment.ipynb', + 'docs/examples/templates/do_dataset_notebook_template.ipynb', + 'docs/examples/templates/do_geography_notebook_template.ipynb', + 'docs/examples/data_management/change_carto_table_privacy.ipynb', + 'docs/examples/data_visualization/publish_and_share/publish_visualization_gdf.ipynb', + 'docs/examples/data_visualization/publish_and_share/publish_visualization_layout.ipynb', + 'docs/examples/data_visualization/publish_and_share/publish_visualization_private_table.ipynb', + 'docs/examples/data_visualization/publish_and_share/publish_visualization_public_table.ipynb', +] + +OVERWRITE = os.environ.get('OVERWRITE', 'true').lower() == 'true' +TIMEOUT = int(os.environ.get('TIMEOUT', 600)) +KERNEL = os.environ.get('KERNEL', 'python3').lower() +SCOPE = os.environ.get('SCOPE', 'all').lower() + +with open('tests/notebooks/creds.json', 'r') as creds_file: + CREDS_FILE = creds_file.read() + + +def find_notebooks(): + notebooks = [] + + if EXECUTE_NOTEBOOKS: + notebooks = list(set(EXECUTE_NOTEBOOKS) - set(AVOID_NOTEBOOKS)) + else: + if SCOPE in ['all', 'guides']: + notebooks += glob.glob('docs/guides/**/*.ipynb', recursive=True) + if SCOPE in ['all', 'examples']: + notebooks += glob.glob('docs/examples/**/*.ipynb', recursive=True) + notebooks = list(set(notebooks) - set(AVOID_NOTEBOOKS)) + + notebooks.sort() + return notebooks + + +class TestNotebooks: + def teardown(self): + time.sleep(0.1) + + def custom_setup(self, path): + with open('{}/creds.json'.format(path), 'w') as creds_file: + creds_file.write(CREDS_FILE) + + def custom_teardown(self, path): + os.remove('{}/creds.json'.format(path)) + + @pytest.mark.parametrize('notebook_filename', find_notebooks()) + def test_docs(self, notebook_filename): + try: + path = os.path.dirname(notebook_filename) + + self.custom_setup(path) + self.execute_notebook(notebook_filename, path) + finally: + self.custom_teardown(path) + + def execute_notebook(self, notebook_filename, path): + with open(notebook_filename) as f: + logging.info('\nExecuting notebook: %s', notebook_filename) + + nb = nbformat.read(f, as_version=4) + ep = ExecutePreprocessor(timeout=TIMEOUT, kernel_name=KERNEL, allow_errors=OVERWRITE, + store_widget_state=OVERWRITE) + ep.preprocess(nb, {'metadata': {'path': path}}) + + if OVERWRITE: + logging.info('Overwriting notebook: %s', notebook_filename) + with open(notebook_filename, 'w') as fwrite: + nbformat.write(nb, fwrite) + + logging.info('Trusting notebook: %s', notebook_filename) + p_jupyter = subprocess.Popen('jupyter trust {}'.format(notebook_filename), shell=True, + stdout=subprocess.PIPE, stderr=subprocess.PIPE) + _, stderr_jupyter = p_jupyter.communicate() + + if len(stderr_jupyter) > 0: + raise RuntimeError('Error trusting the notebook ({}): {}'.format(notebook_filename, stderr_jupyter)) diff --git a/tests/unit/auth/test_credentials.py b/tests/unit/auth/test_credentials.py index 06b7901ae..ab25b4dc9 100644 --- a/tests/unit/auth/test_credentials.py +++ b/tests/unit/auth/test_credentials.py @@ -107,19 +107,26 @@ def test_get_api_key_auth_client(self): credentials.get_api_key_auth_client() assert credentials._api_key_auth_client is not None - def test_get_do_credentials(self, mocker): + def test_do_credentials(self, mocker): access_token = '1234' + project = 'project' + instant_licensing = True - class Token: + class DOCredentials: def __init__(self): self.access_token = access_token + self.bq_project = project + self.instant_licensing = instant_licensing - mocker.patch('carto.do_token.DoTokenManager.get', return_value=Token()) + mocker.patch('carto.do_token.DoTokenManager.get', return_value=DOCredentials()) credentials = Credentials(self.username, self.api_key) - do_credentials = credentials.get_do_credentials() + project_from_do, access_token_from_do = credentials.get_gcloud_credentials() + instant_licensing_from_do = credentials.is_instant_licensing_active() - assert do_credentials.access_token == access_token + assert access_token_from_do == access_token + assert project_from_do == project + assert instant_licensing_from_do == instant_licensing class TestCredentialsFromFile: diff --git a/tests/unit/data/client/test_bigquery_client.py b/tests/unit/data/client/test_bigquery_client.py deleted file mode 100644 index b27783179..000000000 --- a/tests/unit/data/client/test_bigquery_client.py +++ /dev/null @@ -1,80 +0,0 @@ -import os -import csv -import pandas as pd - -from unittest.mock import Mock, patch - -from cartoframes.auth import Credentials -from cartoframes.data.clients.bigquery_client import BigQueryClient - - -class ResponseMock(list): - def __init__(self, data, **kwargs): - super(ResponseMock, self).__init__(data, **kwargs) - self.total_rows = len(data) - - -class QueryJobMock(object): - def __init__(self, response): - self.response = response - - def result(self): - return ResponseMock(self.response) - - -class TestBigQueryClient(object): - def setup_method(self): - self.original_init_clients = BigQueryClient._init_clients - BigQueryClient._init_clients = Mock(return_value=(True, True, True)) - self.username = 'username' - self.apikey = 'apikey' - self.credentials = Credentials(self.username, self.apikey) - self.file_path = '/tmp/test_download.csv' - - def teardown_method(self): - self.credentials = None - BigQueryClient._init_clients = self.original_init_clients - - if os.path.isfile(self.file_path): - os.remove(self.file_path) - - @patch.object(BigQueryClient, 'get_table_column_names') - @patch.object(BigQueryClient, '_download_by_bq_storage_api') - def test_download_to_file_full(self, download_mock, column_names_mock): - data = [{'0': 'word', '1': 'word word'}] - columns = ['column1', 'column2'] - - column_names_mock.return_value = Mock(return_value=columns) - download_mock.return_value = data - - file_path = self.file_path - - bq_client = BigQueryClient(self.credentials) - job = QueryJobMock(data) - bq_client.download_to_file(job, file_path, column_names=columns, progress_bar=False) - - rows = [] - with open(file_path) as csvfile: - csvreader = csv.reader(csvfile) - rows.append(next(csvreader)) - rows.append(next(csvreader)) - - assert rows[0] == columns - assert rows[1] == list(data[0].values()) - - @patch.object(BigQueryClient, 'get_table_column_names') - @patch.object(BigQueryClient, '_download_by_bq_storage_api') - def test_download_to_dataframe_full(self, download_mock, column_names_mock): - data = [{'column1': 'word', 'column2': 'word word'}] - columns = ['column1', 'column2'] - - column_names_mock.return_value = Mock(return_value=columns) - download_mock.return_value = data - - expected_df = pd.DataFrame(data, columns=columns) - - bq_client = BigQueryClient(self.credentials) - job = QueryJobMock(data) - df = bq_client.download_to_dataframe(job) - - assert df.equals(expected_df) diff --git a/tests/unit/data/observatory/catalog/mocks.py b/tests/unit/data/observatory/catalog/mocks.py deleted file mode 100644 index e561086d4..000000000 --- a/tests/unit/data/observatory/catalog/mocks.py +++ /dev/null @@ -1,18 +0,0 @@ -class BigQueryClientMock(object): - def __init__(self, exception=None): - self.exception = exception - - self.bq_public_project = 'public_data_project' - self.bq_project = 'user_data_project' - self.bq_dataset = 'username' - self._gcs_bucket = 'bucket_name' - - def query(self, _1): - return True - - def download_to_file(self, _1, _2, column_names=None): - if isinstance(self.exception, Exception): - raise self.exception - - def get_table_column_names(self, _1, _2, _3): - return True diff --git a/tests/unit/data/observatory/catalog/test_dataset.py b/tests/unit/data/observatory/catalog/test_dataset.py index 38faf38ff..9d3b519e6 100644 --- a/tests/unit/data/observatory/catalog/test_dataset.py +++ b/tests/unit/data/observatory/catalog/test_dataset.py @@ -1,3 +1,5 @@ +import os + import pytest import pandas as pd @@ -292,27 +294,28 @@ def test_datasets_are_exported_as_dataframe(self): assert isinstance(sliced_dataset, pd.Series) assert sliced_dataset.equals(expected_dataset_df) - @patch.object(DatasetRepository, 'get_all') + @patch('cartoframes.data.observatory.catalog.subscriptions.get_subscription_ids') @patch.object(DatasetRepository, 'get_by_id') @patch.object(DODataset, 'download_stream') - def test_dataset_download(self, download_stream_mock, get_by_id_mock, get_all_mock): + def test_dataset_download(self, mock_download_stream, mock_get_by_id, mock_subscription_ids): # Given - get_by_id_mock.return_value = test_dataset1 + mock_get_by_id.return_value = test_dataset1 dataset = Dataset.get(test_dataset1.id) - get_all_mock.return_value = [dataset] - download_stream_mock.return_value = [] + mock_download_stream.return_value = [] + mock_subscription_ids.return_value = [test_dataset1.id] credentials = Credentials('fake_user', '1234') # Then dataset.to_csv('fake_path', credentials) + os.remove('fake_path') - @patch.object(DatasetRepository, 'get_all') + @patch('cartoframes.data.observatory.catalog.subscriptions.get_subscription_ids') @patch.object(DatasetRepository, 'get_by_id') - def test_dataset_not_subscribed_download_fails(self, get_by_id_mock, get_all_mock): - # mock dataset - get_by_id_mock.return_value = test_dataset2 # is private + def test_dataset_not_subscribed_download_not_subscribed(self, mock_get_by_id, mock_subscription_ids): + # Given + mock_get_by_id.return_value = test_dataset2 # is private dataset = Dataset.get(test_dataset2.id) - get_all_mock.return_value = [] + mock_subscription_ids.return_value = [] credentials = Credentials('fake_user', '1234') # When @@ -324,31 +327,28 @@ def test_dataset_not_subscribed_download_fails(self, get_by_id_mock, get_all_moc 'You are not subscribed to this Dataset yet. ' 'Please, use the subscribe method first.') - @patch.object(DatasetRepository, 'get_all') @patch.object(DatasetRepository, 'get_by_id') @patch.object(DODataset, 'download_stream') - def test_dataset_download_not_subscribed_but_public(self, download_stream_mock, get_by_id_mock, get_all_mock): + def test_dataset_download_not_subscribed_but_public(self, mock_download_stream, mock_get_by_id): # Given - get_by_id_mock.return_value = test_dataset1 # is public + mock_get_by_id.return_value = test_dataset1 # is public dataset = Dataset.get(test_dataset1.id) - get_all_mock.return_value = [] - download_stream_mock.return_value = [] + mock_download_stream.return_value = [] credentials = Credentials('fake_user', '1234') dataset.to_csv('fake_path', credentials) + os.remove('fake_path') - @patch.object(DatasetRepository, 'get_all') @patch.object(DatasetRepository, 'get_by_id') @patch.object(DODataset, 'download_stream') - def test_dataset_download_without_do_enabled(self, download_stream_mock, get_by_id_mock, get_all_mock): + def test_dataset_download_without_do_enabled(self, mock_download_stream, mock_get_by_id): # Given - get_by_id_mock.return_value = test_dataset1 + mock_get_by_id.return_value = test_dataset1 dataset = Dataset.get(test_dataset1.id) - get_all_mock.return_value = [] def raise_exception(limit=None, order_by=None, sql_query=None, add_geom=None, is_geography=None): raise ServerErrorException(['The user does not have Data Observatory enabled']) - download_stream_mock.side_effect = raise_exception + mock_download_stream.side_effect = raise_exception credentials = Credentials('fake_user', '1234') # When @@ -402,10 +402,10 @@ def test_dataset_subscribe_existing(self, mock_display_message, mock_display_for @patch('cartoframes.data.observatory.catalog.subscriptions.get_subscription_ids') @patch('cartoframes.data.observatory.catalog.utils.display_subscription_form') @patch('cartoframes.auth.defaults.get_default_credentials') - def test_dataset_subscribe_default_credentials(self, mocked_credentials, mock_display_form, mock_subscription_ids): + def test_dataset_subscribe_default_credentials(self, mock_credentials, mock_display_form, mock_subscription_ids): # Given expected_credentials = Credentials('fake_user', '1234') - mocked_credentials.return_value = expected_credentials + mock_credentials.return_value = expected_credentials dataset = Dataset(db_dataset1) # When @@ -476,10 +476,10 @@ def test_dataset_subscription_info(self, mock_fetch): @patch('cartoframes.data.observatory.catalog.subscription_info.fetch_subscription_info') @patch('cartoframes.auth.defaults.get_default_credentials') - def test_dataset_subscription_info_default_credentials(self, mocked_credentials, mock_fetch): + def test_dataset_subscription_info_default_credentials(self, mock_credentials, mock_fetch): # Given expected_credentials = Credentials('fake_user', '1234') - mocked_credentials.return_value = expected_credentials + mock_credentials.return_value = expected_credentials dataset = Dataset(db_dataset1) # When diff --git a/tests/unit/data/observatory/catalog/test_geography.py b/tests/unit/data/observatory/catalog/test_geography.py index 918dc120a..18447283c 100644 --- a/tests/unit/data/observatory/catalog/test_geography.py +++ b/tests/unit/data/observatory/catalog/test_geography.py @@ -1,3 +1,5 @@ +import os + import pytest import pandas as pd @@ -218,30 +220,28 @@ def test_geographies_are_exported_as_dataframe(self): assert isinstance(sliced_geography, pd.Series) assert sliced_geography.equals(expected_geography_df) - @patch.object(GeographyRepository, 'get_all') + @patch('cartoframes.data.observatory.catalog.subscriptions.get_subscription_ids') @patch.object(GeographyRepository, 'get_by_id') @patch.object(DODataset, 'download_stream') - def test_geography_download(self, download_stream_mock, get_by_id_mock, get_all_mock): + def test_geography_download(self, mock_download_stream, mock_get_by_id, mock_subscription_ids): # Given - get_by_id_mock.return_value = test_geography1 + mock_get_by_id.return_value = test_geography1 geography = Geography.get(test_geography1.id) - get_all_mock.return_value = [geography] - download_stream_mock.return_value = [] + mock_download_stream.return_value = [] + mock_subscription_ids.return_value = [test_geography1.id] credentials = Credentials('fake_user', '1234') # Then geography.to_csv('fake_path', credentials) + os.remove('fake_path') - @patch.object(GeographyRepository, 'get_all') + @patch('cartoframes.data.observatory.catalog.subscriptions.get_subscription_ids') @patch.object(GeographyRepository, 'get_by_id') - @patch.object(DODataset, 'download_stream') - def test_geography_download_not_subscribed(self, download_stream_mock, get_by_id_mock, get_all_mock): + def test_geography_download_not_subscribed(self, mock_get_by_id, mock_subscription_ids): # Given - get_by_id_mock.return_value = test_geography2 # is private - get_by_id_mock.return_value = test_geography2 + mock_get_by_id.return_value = test_geography2 # is private geography = Geography.get(test_geography2.id) - get_all_mock.return_value = [] - download_stream_mock.return_value = [] + mock_subscription_ids.return_value = [] credentials = Credentials('fake_user', '1234') with pytest.raises(Exception) as e: @@ -252,31 +252,28 @@ def test_geography_download_not_subscribed(self, download_stream_mock, get_by_id 'You are not subscribed to this Geography yet. ' 'Please, use the subscribe method first.') - @patch.object(GeographyRepository, 'get_all') @patch.object(GeographyRepository, 'get_by_id') @patch.object(DODataset, 'download_stream') - def test_geography_download_not_subscribed_but_public(self, download_stream_mock, get_by_id_mock, get_all_mock): + def test_geography_download_not_subscribed_but_public(self, mock_download_stream, mock_get_by_id): # Given - get_by_id_mock.return_value = test_geography1 # is public + mock_get_by_id.return_value = test_geography1 # is public geography = Geography.get(test_geography1.id) - get_all_mock.return_value = [] - download_stream_mock.return_value = [] + mock_download_stream.return_value = [] credentials = Credentials('fake_user', '1234') geography.to_csv('fake_path', credentials) + os.remove('fake_path') - @patch.object(GeographyRepository, 'get_all') @patch.object(GeographyRepository, 'get_by_id') @patch.object(DODataset, 'download_stream') - def test_geography_download_without_do_enabled(self, download_stream_mock, get_by_id_mock, get_all_mock): + def test_geography_download_without_do_enabled(self, mock_download_stream, mock_get_by_id): # Given - get_by_id_mock.return_value = test_geography1 + mock_get_by_id.return_value = test_geography1 geography = Geography.get(test_geography1.id) - get_all_mock.return_value = [] def raise_exception(limit=None, order_by=None, sql_query=None, add_geom=None, is_geography=None): raise ServerErrorException(['The user does not have Data Observatory enabled']) - download_stream_mock.side_effect = raise_exception + mock_download_stream.side_effect = raise_exception credentials = Credentials('fake_user', '1234') # When diff --git a/tests/unit/utils/managers/__init__.py b/tests/unit/io/managers/__init__.py similarity index 100% rename from tests/unit/utils/managers/__init__.py rename to tests/unit/io/managers/__init__.py diff --git a/tests/unit/utils/managers/test_context_manager.py b/tests/unit/io/managers/test_context_manager.py similarity index 82% rename from tests/unit/utils/managers/test_context_manager.py rename to tests/unit/io/managers/test_context_manager.py index 2c9fb6626..a8fdb2262 100644 --- a/tests/unit/utils/managers/test_context_manager.py +++ b/tests/unit/io/managers/test_context_manager.py @@ -4,11 +4,12 @@ from carto.datasets import DatasetManager from carto.sql import SQLClient, BatchSQLClient, CopySQLClient +from carto.exceptions import CartoRateLimitException from pandas import DataFrame from geopandas import GeoDataFrame from cartoframes.auth import Credentials -from cartoframes.io.managers.context_manager import ContextManager +from cartoframes.io.managers.context_manager import ContextManager, DEFAULT_RETRY_TIMES, retry_copy from cartoframes.utils.columns import ColumnInfo @@ -41,10 +42,27 @@ def test_execute_long_running_query(self, mocker): # Then mock.assert_called_once_with('query') + def test_copy_to(self, mocker): + # Given + query = '__query__' + columns = [ColumnInfo('A', 'a', 'bigint', False)] + mocker.patch.object(ContextManager, 'compute_query', return_value=query) + mocker.patch.object(ContextManager, '_get_query_columns_info', return_value=columns) + mock = mocker.patch.object(ContextManager, '_copy_to') + + # When + cm = ContextManager(self.credentials) + cm.copy_to(query) + + # Then + mock.assert_called_once_with('SELECT "A" FROM (__query__) _q', columns, 3) + def test_copy_from(self, mocker): # Given mocker.patch('cartoframes.io.managers.context_manager._create_auth_client') mocker.patch.object(ContextManager, 'has_table', return_value=False) + mocker.patch.object(ContextManager, 'get_schema', return_value='schema') + mock_create_table = mocker.patch.object(ContextManager, 'execute_long_running_query') mock = mocker.patch.object(ContextManager, '_copy_from') df = DataFrame({'A': [1]}) columns = [ColumnInfo('A', 'a', 'bigint', False)] @@ -54,7 +72,10 @@ def test_copy_from(self, mocker): cm.copy_from(df, 'TABLE NAME') # Then - mock.assert_called_once_with(df, 'table_name', columns) + mock_create_table.assert_called_once_with(''' + BEGIN; CREATE TABLE table_name ("a" bigint); SELECT CDB_CartodbfyTable(\'schema\', \'table_name\'); COMMIT; + '''.strip()) + mock.assert_called_once_with(df, 'table_name', columns, DEFAULT_RETRY_TIMES) def test_copy_from_exists_fail(self, mocker): # Given @@ -122,7 +143,7 @@ def test_internal_copy_from(self, mocker): # Then assert mock.call_args[0][0] == ''' - COPY table_name(a,b) FROM stdin WITH (FORMAT csv, DELIMITER '|', NULL '__null'); + COPY table_name("a","b") FROM stdin WITH (FORMAT csv, DELIMITER '|', NULL '__null'); '''.strip() assert list(mock.call_args[0][1]) == [ b'1|0101000020E610000000000000000000000000000000000000\n', @@ -239,3 +260,21 @@ def test_list_tables_empty(self, mocker): # Then assert DataFrame(columns=['tables']).equals(tables) + + def test_retry_copy_decorator(self): + @retry_copy + def test_function(retry_times): + class ResponseMock: + def __init__(self): + self.text = 'My text' + self.headers = { + 'Carto-Rate-Limit-Limit': 1, + 'Carto-Rate-Limit-Remaining': 1, + 'Retry-After': 1, + 'Carto-Rate-Limit-Reset': 1 + } + response_mock = ResponseMock() + raise CartoRateLimitException(response_mock) + + with pytest.raises(CartoRateLimitException): + test_function(retry_times=0) diff --git a/tests/unit/io/test_carto.py b/tests/unit/io/test_carto.py index 338dc4c66..4acb40458 100644 --- a/tests/unit/io/test_carto.py +++ b/tests/unit/io/test_carto.py @@ -1,9 +1,14 @@ import pytest +import random + from pandas import Index from geopandas import GeoDataFrame from shapely.geometry import Point +from shapely.geometry.base import BaseGeometry +from shapely import wkt +from carto.exceptions import CartoException from cartoframes.auth import Credentials from cartoframes.io.managers.context_manager import ContextManager from cartoframes.io.carto import read_carto, to_carto, copy_table, create_table_from_query @@ -41,6 +46,58 @@ def test_read_carto(mocker): assert gdf.crs == 'epsg:4326' +def test_read_carto_none_as_null_geom_value(mocker): + # Given + cm_mock = mocker.patch.object(ContextManager, 'copy_to') + cm_mock.return_value = GeoDataFrame({ + 'cartodb_id': [1], + 'the_geom': [ + None + ] + }) + + # When + gdf = read_carto('__source__', CREDENTIALS) + + # Then + expected = GeoDataFrame({ + 'cartodb_id': [1], + 'the_geom': [ + None + ] + }, geometry='the_geom') + + cm_mock.assert_called_once_with('__source__', None, None, 3) + assert expected.equals(gdf) + assert gdf.crs == 'epsg:4326' + + +def test_read_carto_basegeometry_as_null_geom_value(mocker): + # Given + cm_mock = mocker.patch.object(ContextManager, 'copy_to') + cm_mock.return_value = GeoDataFrame({ + 'cartodb_id': [1], + 'the_geom': [ + None + ] + }) + + # When + gdf = read_carto('__source__', CREDENTIALS, null_geom_value=BaseGeometry()) + + # Then + expected = GeoDataFrame({ + 'cartodb_id': [1], + 'the_geom': [ + BaseGeometry() + ] + }, geometry='the_geom') + + cm_mock.assert_called_once_with('__source__', None, None, 3) + assert expected.equals(gdf) + assert gdf.crs == 'epsg:4326' + + def test_read_carto_wrong_source(mocker): # When with pytest.raises(ValueError) as e: @@ -184,7 +241,7 @@ def test_to_carto(mocker): df = GeoDataFrame({'geometry': [Point([0, 0])]}) # When - norm_table_name = to_carto(df, table_name, CREDENTIALS) + norm_table_name = to_carto(df, table_name, CREDENTIALS, skip_quota_warning=True) # Then assert cm_mock.call_args[0][1] == table_name @@ -193,10 +250,141 @@ def test_to_carto(mocker): assert norm_table_name == table_name +def test_to_carto_non_4326(mocker): + # Given + table_name = '__table_name__' + cm_mock = mocker.patch.object(ContextManager, 'copy_from') + cm_mock.return_value = table_name + df = GeoDataFrame({'geometry': [Point([0, 0])]}) + df.crs = 'epsg:4269' + + # When + norm_table_name = to_carto(df, table_name, CREDENTIALS, skip_quota_warning=True) + + # Then + assert cm_mock.call_args[0][1] == table_name + assert cm_mock.call_args[0][2] == 'fail' + assert cm_mock.call_args[0][3] is True + assert norm_table_name == table_name + + +def test_to_carto_quota_warning(mocker): + class NoQuotaCredentials(Credentials): + @property + def me_data(self): + return { + 'user_data': { + 'remaining_byte_quota': 0 + } + } + + # Given + table_name = '__table_name__' + cm_mock = mocker.patch.object(ContextManager, 'copy_from') + cm_mock.return_value = table_name + df = GeoDataFrame({'geometry': [Point([0, 0])]}) + + # Then + with pytest.raises(CartoException): + to_carto(df, table_name, NoQuotaCredentials('fake_user', 'fake_api_key'), skip_quota_warning=False) + + +def test_to_carto_quota_warning_skip(mocker): + class NoQuotaCredentials(Credentials): + @property + def me_data(self): + return { + 'user_data': { + 'remaining_byte_quota': 0 + } + } + + # Given + table_name = '__table_name__' + cm_mock = mocker.patch.object(ContextManager, 'copy_from') + cm_mock.return_value = table_name + df = GeoDataFrame({'geometry': [Point([0, 0])]}) + + # When + norm_table_name = to_carto(df, table_name, NoQuotaCredentials('fake_user', 'fake_api_key'), + skip_quota_warning=True) + + # Then + assert cm_mock.call_args[0][1] == table_name + assert cm_mock.call_args[0][2] == 'fail' + assert cm_mock.call_args[0][3] is True + assert norm_table_name == table_name + + +def test_to_carto_two_geom_columns(mocker): + # Given + table_name = '__table_name__' + cm_mock = mocker.patch.object(ContextManager, 'copy_from') + cm_mock.return_value = table_name + df = GeoDataFrame({'geometry': [Point([0, 0])], + 'the_geom': '010100000000000000000000000000000000000000'}) + + # When + norm_table_name = to_carto(df, table_name, CREDENTIALS, skip_quota_warning=True) + + # Then + assert cm_mock.call_args[0][1] == table_name + assert cm_mock.call_args[0][2] == 'fail' + assert cm_mock.call_args[0][3] is True + assert norm_table_name == table_name + + +def test_to_carto_two_geom_columns_and_geom_col(mocker): + # Given + table_name = '__table_name__' + cm_mock = mocker.patch.object(ContextManager, 'copy_from') + cm_mock.return_value = table_name + df = GeoDataFrame({'geometry': [Point([0, 0])], + 'the_geom': '010100000000000000000000000000000000000000'}) + + # When + norm_table_name = to_carto(df, table_name, CREDENTIALS, geom_col='geometry', skip_quota_warning=True) + + # Then + assert cm_mock.call_args[0][1] == table_name + assert cm_mock.call_args[0][2] == 'fail' + assert cm_mock.call_args[0][3] is True + assert norm_table_name == table_name + + +def test_to_carto_chunks(mocker): + # Given + table_name = '__table_name__' + cm_mock = mocker.patch.object(ContextManager, 'copy_from') + cm_mock.return_value = table_name + + size = 4000 # About 1MB (1150000 bytes) + gdf = GeoDataFrame([ + ['Calle Gran Vía 46', + round(random.uniform(10, 100), 2), + round(random.uniform(100, 1000), 2), + round(random.uniform(1000, 10000), 2), + 'POLYGON((-3.68831 40.42478, -3.68841 40.42478, -3.68841 40.42488, -3.68831 40.42478))'] + for _ in range(size)], + columns=['address', 'value1', 'value2', 'value3', 'polygon'] + ) + gdf.set_geometry(gdf['polygon'].apply(wkt.loads), inplace=True) + + # When + norm_table_name = to_carto(gdf, table_name, CREDENTIALS, max_upload_size=100000, skip_quota_warning=True) + + # Then + assert cm_mock.call_count == 12 # 12 chunks as max_upload_size is 100000 bytes and we are uploading 1150000 bytes + assert cm_mock.call_args[0][1] == table_name + assert cm_mock.call_args[0][2] in ['fail', 'append'] + assert cm_mock.call_args[0][3] is True + assert norm_table_name == table_name + + def test_to_carto_wrong_dataframe(mocker): # When with pytest.raises(ValueError) as e: - to_carto(1234, '') + to_carto(1234, '', skip_quota_warning=True) # Then assert str(e.value) == 'Wrong dataframe. You should provide a valid DataFrame instance.' @@ -208,7 +396,7 @@ def test_to_carto_wrong_table_name(mocker): # When with pytest.raises(ValueError) as e: - to_carto(df, 1234) + to_carto(df, 1234, skip_quota_warning=True) # Then assert str(e.value) == 'Wrong table name. You should provide a valid table name.' @@ -220,7 +408,7 @@ def test_to_carto_wrong_credentials(mocker): # When with pytest.raises(ValueError) as e: - to_carto(df, '__table_name__', 1234) + to_carto(df, '__table_name__', 1234, skip_quota_warning=True) # Then assert str(e.value) == 'Credentials attribute is required. Please pass a `Credentials` ' + \ @@ -233,7 +421,7 @@ def test_to_carto_wrong_if_exists(mocker): # When with pytest.raises(ValueError) as e: - to_carto(df, '__table_name__', if_exists='keep_calm') + to_carto(df, '__table_name__', if_exists='keep_calm', skip_quota_warning=True) # Then assert str(e.value) == 'Wrong option for the `if_exists` param. You should provide: fail, replace, append.' @@ -245,7 +433,7 @@ def test_to_carto_if_exists_replace(mocker): df = GeoDataFrame({'geometry': [Point([0, 0])]}) # When - to_carto(df, '__table_name__', CREDENTIALS, if_exists='replace') + to_carto(df, '__table_name__', CREDENTIALS, if_exists='replace', skip_quota_warning=True) # Then assert cm_mock.call_args[0][2] == 'replace' @@ -257,7 +445,7 @@ def test_to_carto_no_cartodbfy(mocker): df = GeoDataFrame({'geometry': [Point([0, 0])]}) # When - to_carto(df, '__table_name__', CREDENTIALS, cartodbfy=False) + to_carto(df, '__table_name__', CREDENTIALS, cartodbfy=False, skip_quota_warning=True) # Then assert cm_mock.call_args[0][3] is False @@ -269,7 +457,7 @@ def test_to_carto_replace_geometry(mocker): df = GeoDataFrame({'geom': 'POINT(1 1)', 'geometry': [Point([0, 0])]}) # When - to_carto(df, '__table_name__', CREDENTIALS, geom_col='geom', cartodbfy=False) + to_carto(df, '__table_name__', CREDENTIALS, geom_col='geom', cartodbfy=False, skip_quota_warning=True) # Then assert str(cm_mock.call_args[0][0]).strip() == 'the_geom\n0 POINT (1.00000 1.00000)' diff --git a/tests/unit/io/test_crs.py b/tests/unit/io/test_crs.py index 09b61e516..520c43a83 100644 --- a/tests/unit/io/test_crs.py +++ b/tests/unit/io/test_crs.py @@ -11,46 +11,24 @@ CREDENTIALS = Credentials('fake_user', 'fake_api_key') -def test_wrong_crs_layer(): +@pytest.mark.parametrize('crs', ['epsg:4326', 'epsg:2263', 'epsg:3395']) +def test_transform_crs_layer(crs): # Given - gdf = GeoDataFrame({'geometry': [Point([0, 0])]}, crs='epsg:2263') - - # When - with pytest.raises(ValueError) as e: - Layer(gdf) - - # Then - assert str(e.value) == 'No valid geometry CRS "epsg:2263", it must be "epsg:4326".' - - -def test_transform_crs_layer(): - # Given - gdf = GeoDataFrame({'geometry': [Point([0, 0])]}, crs='epsg:4326') + gdf = GeoDataFrame({'geometry': [Point([0, 0])]}, crs=crs) # When Layer(gdf) # No error! -def test_wrong_crs_to_carto(): - # Given - gdf = GeoDataFrame({'geometry': [Point([0, 0])]}, crs='epsg:2263') - - # When - with pytest.raises(ValueError) as e: - to_carto(gdf, 'table_name') - - # Then - assert str(e.value) == 'No valid geometry CRS "epsg:2263", it must be "epsg:4326".' - - -def test_transform_crs_to_carto(mocker): +@pytest.mark.parametrize('crs', ['epsg:4326', 'epsg:2263', 'epsg:3395']) +def test_transform_crs_to_carto(mocker, crs): cm_mock = mocker.patch.object(ContextManager, 'copy_from') # Given - gdf = GeoDataFrame({'geometry': [Point([0, 0])]}, crs='epsg:4326') + gdf = GeoDataFrame({'geometry': [Point([0, 0])]}, crs=crs) # When - to_carto(gdf, 'table_name', CREDENTIALS) + to_carto(gdf, 'table_name', CREDENTIALS, skip_quota_warning=True) # Then - cm_mock.assert_called_once_with(mocker.ANY, 'table_name', 'fail', True) + cm_mock.assert_called_once_with(mocker.ANY, 'table_name', 'fail', True, 3) diff --git a/tests/unit/utils/test_utils.py b/tests/unit/utils/test_utils.py index fc0ba618b..c09426882 100644 --- a/tests/unit/utils/test_utils.py +++ b/tests/unit/utils/test_utils.py @@ -2,6 +2,7 @@ """Unit tests for cartoframes.utils""" import unittest +import warnings from collections import OrderedDict import requests @@ -9,7 +10,7 @@ from cartoframes.utils.utils import (camel_dictionary, cssify, debug_print, dict_items, importify_params, snake_to_camel, dtypes2pg, pg2dtypes, - encode_row, extract_viz_columns, remove_comments) + encode_row, extract_viz_columns, remove_comments, deprecated) class TestUtils(unittest.TestCase): @@ -222,3 +223,27 @@ def test_remove_comments(self): multiline comment */ """ assert remove_comments(viz) == 'color: blue' + + def test_deprecated_decorator_class(self): + @deprecated(message='my message') + class MyClass: + pass + + with warnings.catch_warnings(record=True) as w: + _ = MyClass() + assert issubclass(w[-1].category, DeprecationWarning) + assert 'deprecated' in str(w[-1].message) + assert 'my message' in str(w[-1].message) + assert 'class' in str(w[-1].message) + + def test_deprecated_decorator_function(self): + @deprecated(message='my message') + def my_function(): + pass + + with warnings.catch_warnings(record=True) as w: + my_function() + assert issubclass(w[-1].category, DeprecationWarning) + assert 'deprecated' in str(w[-1].message) + assert 'my message' in str(w[-1].message) + assert 'function' in str(w[-1].message) diff --git a/tests/unit/viz/test_kuviz.py b/tests/unit/viz/test_kuviz.py index 773c6272e..e928c27c1 100644 --- a/tests/unit/viz/test_kuviz.py +++ b/tests/unit/viz/test_kuviz.py @@ -1,9 +1,11 @@ # -*- coding: utf-8 -*- +from collections import namedtuple + import pytest from cartoframes.auth import Credentials -from cartoframes.viz import Map, Layer +from cartoframes.viz import all_publications, delete_publication, Layer, Map from cartoframes.viz.source import Source from cartoframes.viz.kuviz import KuvizPublisher, DEFAULT_PUBLIC, kuviz_to_dict from cartoframes.io.managers.context_manager import ContextManager @@ -163,21 +165,47 @@ def test_kuviz_publisher_update(self, mocker): assert result_update == kuviz_to_dict(kuviz) assert result_publish != kuviz_to_dict(kuviz) - def test_kuviz_publisher_delete(self, mocker): + def test_kuviz_all_publications(self, mocker): setup_mocks(mocker, self.credentials) - kuviz = CartoKuvizMock('fake_kuviz') + html = 'fake_html' + kuviz_name = 'fake_name' + kuviz = CartoKuvizMock(kuviz_name) + + KuvizManagerNamedtuple = namedtuple('KuvizManager', ['all']) + kuviz_manager_namedtuple = KuvizManagerNamedtuple(lambda: [kuviz]) + mocker.patch('cartoframes.viz.kuviz._create_kuviz', return_value=kuviz) + mocker.patch('cartoframes.viz.kuviz._get_kuviz_manager', return_value=kuviz_manager_namedtuple) vmap = Map(Layer('fake_table', credentials=self.credentials)) + kuviz_publisher = KuvizPublisher(None) + kuviz_publisher.set_layers(vmap.layers) + kuviz_publisher.publish(html, kuviz_name, None) + + assert all_publications(credentials=self.credentials)[0]['name'] == kuviz_name + + def test_kuviz_delete_publication(self, mocker): + setup_mocks(mocker, self.credentials) + html = 'fake_html' kuviz_name = 'fake_name' + kuviz = CartoKuvizMock(kuviz_name) + + KuvizManagerNamedtuple = namedtuple('KuvizManager', ['all']) + kuviz_manager_namedtuple = KuvizManagerNamedtuple(lambda: [kuviz]) + + mocker.patch('cartoframes.viz.kuviz._create_kuviz', return_value=kuviz) + mocker.patch('cartoframes.viz.kuviz._get_kuviz_manager', return_value=kuviz_manager_namedtuple) + mock = mocker.patch('tests.unit.mocks.kuviz_mock.CartoKuvizMock.delete') + + vmap = Map(Layer('fake_table', credentials=self.credentials)) kuviz_publisher = KuvizPublisher(None) kuviz_publisher.set_layers(vmap.layers) kuviz_publisher.publish(html, kuviz_name, None) - kuviz_publisher.delete() + delete_publication(kuviz_name, credentials=self.credentials) - assert kuviz_publisher.kuviz is None + mock.assert_called_once_with() diff --git a/tests/unit/viz/test_map.py b/tests/unit/viz/test_map.py index bcd979883..d4ab1823d 100644 --- a/tests/unit/viz/test_map.py +++ b/tests/unit/viz/test_map.py @@ -317,17 +317,6 @@ def test_map_publish_with_password(self, mocker): kuviz_dict = map.publish(name, '1234', credentials=self.credentials) self.assert_kuviz_dict(kuviz_dict, name, 'password') - def test_map_publish_deletion(self, mocker): - setup_mocks(mocker) - - map = Map(Layer(Source('fake_table', credentials=self.credentials))) - - name = 'cf_publish' - map.publish(name, None, credentials=self.credentials) - response = map.delete_publication() - - assert response is True - def test_map_publish_update_name(self, mocker): setup_mocks(mocker) diff --git a/tests/unit/viz/test_source.py b/tests/unit/viz/test_source.py index b164a454b..8aeec39cf 100644 --- a/tests/unit/viz/test_source.py +++ b/tests/unit/viz/test_source.py @@ -7,6 +7,7 @@ from cartoframes.viz.source import Source from cartoframes.io.managers.context_manager import ContextManager + POINT = { "type": "Feature", "geometry": { @@ -77,11 +78,19 @@ "type": "Feature", "geometry": { "type": "GeometryCollection", - "coordinates": None + "geometries": [] }, "properties": {} } +NONE_GEOMETRY = { + "type": "Feature", + "geometry": None, + "properties": { + "prop0": "value0" + } +} + def setup_mocks(mocker): mocker.patch.object(ContextManager, 'compute_query') @@ -158,8 +167,29 @@ def test_different_geometry_types_source(self, features): gdf = gpd.GeoDataFrame.from_features(geojson) source = Source(gdf) + assert len(source.gdf) == len(features) assert source.gdf.equals(gdf) + @pytest.mark.parametrize('features', [ + [POINT, NONE_GEOMETRY], + [MULTIPOINT, NONE_GEOMETRY], + [LINESTRING, NONE_GEOMETRY], + [MULTILINESTRING, NONE_GEOMETRY], + [LINESTRING, MULTILINESTRING, NONE_GEOMETRY], + [POLYGON, NONE_GEOMETRY], + [MULTIPOLYGON, NONE_GEOMETRY], + [POLYGON, MULTIPOLYGON, NONE_GEOMETRY] + ]) + def test_different_geometry_types_source_plus_none(self, features): + geojson = { + "type": "FeatureCollection", + "features": features + } + gdf = gpd.GeoDataFrame.from_features(geojson) + source = Source(gdf) + + assert len(source.gdf) == len(features) - 1 + @pytest.mark.parametrize('features', [ [POINT, LINESTRING], [POINT, POLYGON], @@ -189,3 +219,12 @@ def test_empty_geometries(self): source = Source(gdf) assert len(source.gdf) == 2 + + def test_nan_geometries(self): + df = pd.DataFrame({ + 'geom': ['POINT(0 0)', np.nan, np.nan, 'POINT(0 0)', None] + }) + + source = Source(df, geom_col='geom') + + assert len(source.gdf) == 2 diff --git a/tests/unit/viz/widgets/test_formula_widget.py b/tests/unit/viz/widgets/test_formula_widget.py index bf58db8ae..d9c35d24f 100644 --- a/tests/unit/viz/widgets/test_formula_widget.py +++ b/tests/unit/viz/widgets/test_formula_widget.py @@ -16,13 +16,13 @@ def test_factory(self): def test_count_formula_viewport(self): "should create a formula widget to count viewport features" - widget = widgets.formula_widget('count') + widget = widgets.formula_widget('value', operation='count') widget_info = widget.get_info() assert widget_info.get('value') == 'viewportCount()' def test_count_formula_global(self): "should create a formula widget to count global features" - widget = widgets.formula_widget('count', is_global=True) + widget = widgets.formula_widget('value', operation='count', is_global=True) widget_info = widget.get_info() assert widget_info.get('value') == 'globalCount()' diff --git a/tox.ini b/tox.ini index 1f3ac63c8..05fe93d52 100644 --- a/tox.ini +++ b/tox.ini @@ -1,6 +1,13 @@ [tox] envlist = py35, py36, py37, py38 +[gh-actions] +python = + 3.5: py35 + 3.6: py36 + 3.7: py37 + 3.8: py38 + [testenv] deps = flake8