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Eurostatv2 pr #1094

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Eurostatv2 pr #1094

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bdbe359
eurostatv2_pr
shapateriya Oct 17, 2024
c33e6f4
eurostatv2_pr
shapateriya Oct 17, 2024
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89,874 changes: 49,513 additions & 40,361 deletions ...t/regional_statistics_by_nuts/birth_death_migration/EurostatNUTS3_BirthDeathMigration.csv
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15 changes: 11 additions & 4 deletions .../regional_statistics_by_nuts/birth_death_migration/EurostatNUTS3_BirthDeathMigration.tmcf
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Original file line number Diff line number Diff line change
Expand Up @@ -5,45 +5,52 @@ variableMeasured: dcs:Count_Death
observationAbout: C:EurostatNUTS3_BirthDeathMigration->geo
observationDate: C:EurostatNUTS3_BirthDeathMigration->time
value: C:EurostatNUTS3_BirthDeathMigration->Count_Death
measurementMethod: dcs:EurostatRegionalStatistics

Node: E:EurostatNUTS3_BirthDeathMigration->E1
typeOf: dcs:StatVarObservation
variableMeasured: dcs:Count_BirthEvent_AsAFractionOf_Count_Person
variableMeasured: dcs:Count_BirthEvent_AsAFractionOfCount_Person
observationAbout: C:EurostatNUTS3_BirthDeathMigration->geo
observationDate: C:EurostatNUTS3_BirthDeathMigration->time
value: C:EurostatNUTS3_BirthDeathMigration->Count_BirthEvent_AsAFractionOf_Count_Person
value: C:EurostatNUTS3_BirthDeathMigration->Count_BirthEvent_AsAFractionOfCount_Person
measurementMethod: dcs:EurostatRegionalStatistics

Node: E:EurostatNUTS3_BirthDeathMigration->E2
typeOf: dcs:StatVarObservation
variableMeasured: dcs:Count_Death_AsAFractionOf_Count_Person
variableMeasured: dcs:Count_Death_AsAFractionOfCount_Person
observationAbout: C:EurostatNUTS3_BirthDeathMigration->geo
observationDate: C:EurostatNUTS3_BirthDeathMigration->time
value: C:EurostatNUTS3_BirthDeathMigration->Count_Death_AsAFractionOf_Count_Person
value: C:EurostatNUTS3_BirthDeathMigration->Count_Death_AsAFractionOfCount_Person
measurementMethod: dcs:EurostatRegionalStatistics

Node: E:EurostatNUTS3_BirthDeathMigration->E3
typeOf: dcs:StatVarObservation
variableMeasured: dcs:IncrementalCount_Person
observationAbout: C:EurostatNUTS3_BirthDeathMigration->geo
observationDate: C:EurostatNUTS3_BirthDeathMigration->time
value: C:EurostatNUTS3_BirthDeathMigration->IncrementalCount_Person
measurementMethod: dcs:EurostatRegionalStatistics

Node: E:EurostatNUTS3_BirthDeathMigration->E4
typeOf: dcs:StatVarObservation
variableMeasured: dcs:GrowthRate_Count_Person
observationAbout: C:EurostatNUTS3_BirthDeathMigration->geo
observationDate: C:EurostatNUTS3_BirthDeathMigration->time
value: C:EurostatNUTS3_BirthDeathMigration->GrowthRate_Count_Person
measurementMethod: dcs:EurostatRegionalStatistics

Node: E:EurostatNUTS3_BirthDeathMigration->E5
typeOf: dcs:StatVarObservation
variableMeasured: dcs:Count_Person
observationAbout: C:EurostatNUTS3_BirthDeathMigration->geo
observationDate: C:EurostatNUTS3_BirthDeathMigration->time
value: C:EurostatNUTS3_BirthDeathMigration->Count_Person
measurementMethod: dcs:EurostatRegionalStatistics

Node: E:EurostatNUTS3_BirthDeathMigration->E6
typeOf: dcs:StatVarObservation
variableMeasured: dcs:Count_BirthEvent
observationAbout: C:EurostatNUTS3_BirthDeathMigration->geo
observationDate: C:EurostatNUTS3_BirthDeathMigration->time
value: C:EurostatNUTS3_BirthDeathMigration->Count_BirthEvent
measurementMethod: dcs:EurostatRegionalStatistics
38 changes: 24 additions & 14 deletions scripts/eurostat/regional_statistics_by_nuts/birth_death_migration/import_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,13 +13,20 @@
# limitations under the License.

import pandas as pd

from six.moves import urllib
import sys
sys.path.insert(1, '../../../../util')
from alpha2_to_dcid import COUNTRY_MAP
from nuts_codes_names import NUTS1_CODES_NAMES

def download_data(download_link):
"""Downloads raw data from Eurostat website and stores it in instance
data frame.
"""
raw_df = pd.read_table(download_link)
urllib.request.urlretrieve(download_link, "demo_r_gind3.tsv.gz")
raw_df = pd.read_table("demo_r_gind3.tsv.gz")
raw_df = raw_df.rename(columns=({'freq,indic_de,geo\TIME_PERIOD': 'indic_de,geo\\time'}))
raw_df['indic_de,geo\\time'] = raw_df['indic_de,geo\\time'].str.slice(2)
return raw_df


Expand Down Expand Up @@ -73,8 +80,7 @@ def preprocess_data(raw_df):
# '\time' labels the other columns so it is confusing.

# Append extra space for all cells in value column that do not come with a note, so that we can split them without error.
preprocessed_df.value = preprocessed_df.value.str.replace(
"([0-9:])$", lambda m: m.group(0) + ' ')
preprocessed_df.value = preprocessed_df.value.str.replace("([0-9:])$", lambda m: m.group(0) + ' ',regex=True)

first_column_list = preprocessed_df.columns[0].rsplit(sep=",", maxsplit=1)

Expand All @@ -88,15 +94,12 @@ def preprocess_data(raw_df):
assert geo == "geo", "Column header should end with 'geo'."

if statistical_variable:
split_df = preprocessed_df[preprocessed_df.columns[0]].str.rsplit(
",", n=1, expand=True)
split_df = preprocessed_df[preprocessed_df.columns[0]].str.rsplit(",", n=1, expand=True)
preprocessed_df['statistical_variable'] = split_df[0]
preprocessed_df['geo'] = split_df[1]
preprocessed_df.drop(columns=[preprocessed_df.columns[0]], inplace=True)

preprocessed_df = (preprocessed_df.set_index(["geo", "time"]).pivot(
columns="statistical_variable")['value'].reset_index().rename_axis(
None, axis=1))
preprocessed_df = (preprocessed_df.set_index(["geo", "time"]).pivot(columns="statistical_variable")['value'].reset_index().rename_axis(None, axis=1))
# Fill missing 'geo' values with a colon.
preprocessed_df.fillna(': ', inplace=True)

Expand Down Expand Up @@ -126,8 +129,15 @@ def clean_data(preprocessed_df, output_path):

# replace colon with NaN.
clean_df = clean_df.replace(':', '')

clean_df['geo'] = 'dcid:nuts/' + clean_df['geo']
# for ind, geo in enumerate(clean_df['geo']):
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please remove the commented out code

# # Convert geo IDS to geo codes, e.g., "country/SHN" or "nuts/AT342".
# if any(char.isdigit() for char in geo) or ('nuts/' + geo in NUTS1_CODES_NAMES):
# clean_df['geo'][ind] = 'nuts/' + geo
# else:
# clean_df['geo'][ind] = COUNTRY_MAP.get(geo, '~' + geo + '~')
clean_df['geo'] = clean_df['geo'].apply(lambda geo: f'nuts/{geo}' if any(geo.isdigit() for geo in geo) or ('nuts/' + geo in NUTS1_CODES_NAMES) else COUNTRY_MAP.get(geo, f'{geo}'))


# trim the space in the time column i.e. '2020 ' -> '2020'
clean_df['time'] = clean_df['time'].astype('int32')
original_names = [
Expand All @@ -136,8 +146,8 @@ def clean_data(preprocessed_df, output_path):
]
new_names = [
'geo', 'time', 'Count_Death',
'Count_BirthEvent_AsAFractionOf_Count_Person',
'Count_Death_AsAFractionOf_Count_Person', 'IncrementalCount_Person',
'Count_BirthEvent_AsAFractionOfCount_Person',
'Count_Death_AsAFractionOfCount_Person', 'IncrementalCount_Person',
'GrowthRate_Count_Person', 'Count_Person', 'Count_BirthEvent'
]
clean_df = clean_df[original_names]
Expand All @@ -151,7 +161,7 @@ def clean_data(preprocessed_df, output_path):


if __name__ == '__main__':
download_link = "https://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?file=data/demo_r_gind3.tsv.gz"
download_link = "https://ec.europa.eu/eurostat/api/dissemination/sdmx/2.1/data/demo_r_gind3/?format=TSV&compressed=true"
output_path = 'EurostatNUTS3_BirthDeathMigration.csv'
raw_df = download_data(download_link)
preprocessed_df = preprocess_data(raw_df)
Expand Down
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