diff --git a/module4-sequence-your-narrative/LS_DS_124_Sequence_your_narrative.ipynb b/module4-sequence-your-narrative/LS_DS_124_Sequence_your_narrative.ipynb
index b3c2b22..f64b5f7 100644
--- a/module4-sequence-your-narrative/LS_DS_124_Sequence_your_narrative.ipynb
+++ b/module4-sequence-your-narrative/LS_DS_124_Sequence_your_narrative.ipynb
@@ -1,55 +1,720 @@
{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "_Lambda School Data Science_\n",
- "\n",
- "# Sequence your narrative\n",
- "\n",
- "Create a sequence of visualizations inspired by [Hans Rosling's 200 Countries, 200 Years, 4 Minutes](https://www.youtube.com/watch?v=jbkSRLYSojo).\n",
- "\n",
- "Using this [data from Gapminder](https://github.com/open-numbers/ddf--gapminder--systema_globalis/):\n",
- "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--income_per_person_gdppercapita_ppp_inflation_adjusted--by--geo--time.csv\n",
- "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--life_expectancy_years--by--geo--time.csv\n",
- "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--entities--geo--country.csv\n",
- "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--concepts.csv\n",
- "\n",
- "### Stretch goals\n",
- "- [ipywidgets](https://github.com/jupyter-widgets/ipywidgets)\n",
- "- [Matplotlib animation](https://matplotlib.org/examples/animation/index.html)\n",
- "- [Connected scatter plots](http://www.thefunctionalart.com/2012/09/in-praise-of-connected-scatter-plots.html)\n",
- "- [Idyll markup language](https://idyll-lang.org/) for \"scrollytelling\""
- ]
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "LS_DS_124_Sequence_your_narrative.ipynb",
+ "version": "0.3.2",
+ "provenance": [],
+ "collapsed_sections": [],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ }
},
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.6.5"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "JbDHnhet8CWy",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "_Lambda School Data Science_\n",
+ "\n",
+ "# Sequence your narrative\n",
+ "\n",
+ "Create a sequence of visualizations inspired by [Hans Rosling's 200 Countries, 200 Years, 4 Minutes](https://www.youtube.com/watch?v=jbkSRLYSojo).\n",
+ "\n",
+ "Using this [data from Gapminder](https://github.com/open-numbers/ddf--gapminder--systema_globalis/):\n",
+ "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--income_per_person_gdppercapita_ppp_inflation_adjusted--by--geo--time.csv\n",
+ "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--life_expectancy_years--by--geo--time.csv\n",
+ "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--population_total--by--geo--time.csv\n",
+ "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--entities--geo--country.csv\n",
+ "- https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--concepts.csv"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "SxTJBgRAW3jD",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "Variables\n",
+ "- Income\n",
+ "- Lifespan\n",
+ "\n",
+ "- Population\n",
+ "- Year\n",
+ "- Country"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "3ebEjShbWsIy",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Upgrade Seaborn\n",
+ "\n",
+ "Make sure you have at least version 0.9.0.\n",
+ "\n",
+ "In Colab, go to **Restart runtime** after you run the `pip` command."
+ ]
+ },
+ {
+ "metadata": {
+ "id": "4RSxbu7rWr1p",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 326
+ },
+ "outputId": "20e44bf7-339c-4506-94bc-4b366ca28039"
+ },
+ "cell_type": "code",
+ "source": [
+ "!pip install --upgrade seaborn"
+ ],
+ "execution_count": 1,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "text": [
+ "Collecting seaborn\n",
+ "\u001b[?25l Downloading https://files.pythonhosted.org/packages/a8/76/220ba4420459d9c4c9c9587c6ce607bf56c25b3d3d2de62056efe482dadc/seaborn-0.9.0-py3-none-any.whl (208kB)\n",
+ "\u001b[K 100% |████████████████████████████████| 215kB 23.3MB/s \n",
+ "\u001b[?25hRequirement already satisfied, skipping upgrade: scipy>=0.14.0 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.1.0)\n",
+ "Requirement already satisfied, skipping upgrade: pandas>=0.15.2 in /usr/local/lib/python3.6/dist-packages (from seaborn) (0.22.0)\n",
+ "Requirement already satisfied, skipping upgrade: numpy>=1.9.3 in /usr/local/lib/python3.6/dist-packages (from seaborn) (1.14.6)\n",
+ "Requirement already satisfied, skipping upgrade: matplotlib>=1.4.3 in /usr/local/lib/python3.6/dist-packages (from seaborn) (2.1.2)\n",
+ "Requirement already satisfied, skipping upgrade: python-dateutil>=2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.15.2->seaborn) (2.5.3)\n",
+ "Requirement already satisfied, skipping upgrade: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas>=0.15.2->seaborn) (2018.7)\n",
+ "Requirement already satisfied, skipping upgrade: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (2.3.0)\n",
+ "Requirement already satisfied, skipping upgrade: six>=1.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (1.11.0)\n",
+ "Requirement already satisfied, skipping upgrade: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.3->seaborn) (0.10.0)\n",
+ "Installing collected packages: seaborn\n",
+ " Found existing installation: seaborn 0.7.1\n",
+ " Uninstalling seaborn-0.7.1:\n",
+ " Successfully uninstalled seaborn-0.7.1\n",
+ "Successfully installed seaborn-0.9.0\n"
+ ],
+ "name": "stdout"
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "5sQ0-7JUWyN4",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "044d9ceb-5803-43b5-d80d-af66b3c621c9"
+ },
+ "cell_type": "code",
+ "source": [
+ "import seaborn as sns\n",
+ "sns.__version__"
+ ],
+ "execution_count": 3,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'0.9.0'"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 3
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "S2dXWRTFTsgd",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## More imports"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "y-TgL_mA8OkF",
+ "colab_type": "code",
+ "colab": {}
+ },
+ "cell_type": "code",
+ "source": [
+ "%matplotlib inline\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd"
+ ],
+ "execution_count": 0,
+ "outputs": []
+ },
+ {
+ "metadata": {
+ "id": "CZGG5prcTxrQ",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Load & look at data"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "-uE25LHD8CW0",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 34
+ },
+ "outputId": "62a6d471-c872-468c-eecd-6fd72ed57f1c"
+ },
+ "cell_type": "code",
+ "source": [
+ "income = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--income_per_person_gdppercapita_ppp_inflation_adjusted--by--geo--time.csv')\n",
+ "lifespan = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--life_expectancy_years--by--geo--time.csv')\n",
+ "population = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--population_total--by--geo--time.csv')\n",
+ "entities = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--entities--geo--country.csv')\n",
+ "\n",
+ "income.shape, lifespan.shape, population.shape, entities.shape"
+ ],
+ "execution_count": 6,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "((44268, 3), (44370, 3), (51939, 3), (273, 33))"
+ ]
+ },
+ "metadata": {
+ "tags": []
+ },
+ "execution_count": 6
+ }
+ ]
+ },
+ {
+ "metadata": {
+ "id": "6HYUytvLT8Kf",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "## Merge data"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "dhALZDsh9n9L",
+ "colab_type": "text"
+ },
+ "cell_type": "markdown",
+ "source": [
+ "https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf"
+ ]
+ },
+ {
+ "metadata": {
+ "id": "z6KBarvARP4B",
+ "colab_type": "code",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 204
+ },
+ "outputId": "d79e43b4-efc6-4142-e36c-d9f18fb693b8"
+ },
+ "cell_type": "code",
+ "source": [
+ "df=pd.merge(income, lifespan)\n",
+ "df = pd.merge(df, population)\n",
+ "\n",
+ "variables = ['country', 'name', 'world_6region']\n",
+ "\n",
+ "df = pd.merge(df, entities[variables], \n",
+ " how='inner', left_on='geo', right_on='country')\n",
+ "\n",
+ "df.drop(columns=['geo', 'country'], inplace=True)\n",
+ "\n",
+ "df.rename(columns={\n",
+ " 'time': 'year', \n",
+ " 'income_per_person_gdppercapita_ppp_inflation_adjusted': 'income', \n",
+ " 'life_expectancy_years': 'lifespan', \n",
+ " 'population_total': 'population', \n",
+ " 'name': 'country', \n",
+ " 'world_6region': 'region'\n",
+ "}, inplace=True)\n",
+ "\n",
+ "df.head()"
+ ],
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " year | \n",
+ " income | \n",
+ " lifespan | \n",
+ " population | \n",
+ " country | \n",
+ " region | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " 1800 | \n",
+ " 833 | \n",
+ " 34.42 | \n",
+ " 19286 | \n",
+ " Aruba | \n",
+ " america | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " 1801 | \n",
+ " 833 | \n",
+ " 34.42 | \n",
+ " 19286 | \n",
+ " Aruba | \n",
+ " america | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " 1802 | \n",
+ " 833 | \n",
+ " 34.42 | \n",
+ " 19286 | \n",
+ " Aruba | \n",
+ " america | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " 1803 | \n",
+ " 833 | \n",
+ " 34.42 | \n",
+ " 19286 | \n",
+ " Aruba | \n",
+ " america | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " 1804 | \n",
+ " 833 | \n",
+ " 34.42 | \n",
+ " 19286 | \n",
+ " Aruba | \n",
+ " america | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " year | \n",
+ " income | \n",
+ " lifespan | \n",
+ " population | \n",
+ " country | \n",
+ " region | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 39399 | \n",
+ " 1818 | \n",
+ " 2258 | \n",
+ " 39.41 | \n",
+ " 9879049 | \n",
+ " United States | \n",
+ " america | \n",
+ "
\n",
+ " \n",
+ " | 39499 | \n",
+ " 1918 | \n",
+ " 9371 | \n",
+ " 47.18 | \n",
+ " 106721812 | \n",
+ " United States | \n",
+ " america | \n",
+ "
\n",
+ " \n",
+ " | 39599 | \n",
+ " 2018 | \n",
+ " 54898 | \n",
+ " 79.14 | \n",
+ " 326766748 | \n",
+ " United States | \n",
+ " america | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " year | \n",
+ " income | \n",
+ " lifespan | \n",
+ " population | \n",
+ " country | \n",
+ " region | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 7120 | \n",
+ " 1818 | \n",
+ " 985 | \n",
+ " 32.00 | \n",
+ " 374161494 | \n",
+ " China | \n",
+ " east_asia_pacific | \n",
+ "
\n",
+ " \n",
+ " | 7220 | \n",
+ " 1918 | \n",
+ " 989 | \n",
+ " 22.13 | \n",
+ " 462444535 | \n",
+ " China | \n",
+ " east_asia_pacific | \n",
+ "
\n",
+ " \n",
+ " | 7320 | \n",
+ " 2018 | \n",
+ " 16018 | \n",
+ " 76.92 | \n",
+ " 1415045928 | \n",
+ " China | \n",
+ " east_asia_pacific | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "