From 7cb0ac6d87616feed0db6409e3ece504cd6ca10a Mon Sep 17 00:00:00 2001 From: Ryan Allred Date: Thu, 15 Aug 2019 09:29:04 -0600 Subject: [PATCH] Divide Lecture and Assignment Notebooks --- .../LS_DS_124_Sequence_your_narrative.ipynb | 962 +++++++++--------- 1 file changed, 484 insertions(+), 478 deletions(-) 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 bcc54a82..3a8ff2ae 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 @@ -1,479 +1,485 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "JbDHnhet8CWy" - }, - "source": [ - "_Lambda School Data Science_\n", - "\n", - "# Sequence your narrative\n", - "\n", - "Today we will 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", - "- [Income Per Person (GDP Per Capital, Inflation Adjusted) by Geo & Time](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", - "- [Life Expectancy (in Years) by Geo & Time](https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--life_expectancy_years--by--geo--time.csv)\n", - "- [Population Totals, by Geo & Time](https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--population_total--by--geo--time.csv)\n", - "- [Entities](https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--entities--geo--country.csv)\n", - "- [Concepts](https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--concepts.csv)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "zyPYtsY6HtIK" - }, - "source": [ - "Objectives\n", - "- sequence multiple visualizations\n", - "- combine qualitative anecdotes with quantitative aggregates\n", - "\n", - "Links\n", - "- [Hans Rosling’s TED talks](https://www.ted.com/speakers/hans_rosling)\n", - "- [Spiralling global temperatures from 1850-2016](https://twitter.com/ed_hawkins/status/729753441459945474)\n", - "- \"[The Pudding](https://pudding.cool/) explains ideas debated in culture with visual essays.\"\n", - "- [A Data Point Walks Into a Bar](https://lisacharlotterost.github.io/2016/12/27/datapoint-in-bar/): a thoughtful blog post about emotion and empathy in data storytelling" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "SxTJBgRAW3jD" - }, - "source": [ - "## Make a plan\n", - "\n", - "#### How to present the data?\n", - "\n", - "Variables --> Visual Encodings\n", - "- Income --> x\n", - "- Lifespan --> y\n", - "- Region --> color\n", - "- Population --> size\n", - "- Year --> animation frame (alternative: small multiple)\n", - "- Country --> annotation\n", - "\n", - "Qualitative --> Verbal\n", - "- Editorial / contextual explanation --> audio narration (alternative: text)\n", - "\n", - "\n", - "#### How to structure the data?\n", - "\n", - "| Year | Country | Region | Income | Lifespan | Population |\n", - "|------|---------|----------|--------|----------|------------|\n", - "| 1818 | USA | Americas | ### | ## | # |\n", - "| 1918 | USA | Americas | #### | ### | ## |\n", - "| 2018 | USA | Americas | ##### | ### | ### |\n", - "| 1818 | China | Asia | # | # | # |\n", - "| 1918 | China | Asia | ## | ## | ### |\n", - "| 2018 | China | Asia | ### | ### | ##### |\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "3ebEjShbWsIy" - }, - "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." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "4RSxbu7rWr1p" - }, - "outputs": [], - "source": [ - "!pip install --upgrade seaborn" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "5sQ0-7JUWyN4" - }, - "outputs": [], - "source": [ - "import seaborn as sns\n", - "sns.__version__" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "S2dXWRTFTsgd" - }, - "source": [ - "## More imports" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "y-TgL_mA8OkF" - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "import pandas as pd" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "CZGG5prcTxrQ" - }, - "source": [ - "## Load & look at data" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "-uE25LHD8CW0" - }, - "outputs": [], - "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')" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "gg_pJslMY2bq" - }, - "outputs": [], - "source": [ - "lifespan = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--life_expectancy_years--by--geo--time.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "F6knDUevY-xR" - }, - "outputs": [], - "source": [ - "population = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--population_total--by--geo--time.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "hX6abI-iZGLl" - }, - "outputs": [], - "source": [ - "entities = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--entities--geo--country.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "AI-zcaDkZHXm" - }, - "outputs": [], - "source": [ - "concepts = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--concepts.csv')" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "EgFw-g0nZLJy" - }, - "outputs": [], - "source": [ - "income.shape, lifespan.shape, population.shape, entities.shape, concepts.shape" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "I-T62v7FZQu5" - }, - "outputs": [], - "source": [ - "income.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "2zIdtDESZYG5" - }, - "outputs": [], - "source": [ - "lifespan.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "58AXNVMKZj3T" - }, - "outputs": [], - "source": [ - "population.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "0ywWDL2MZqlF" - }, - "outputs": [], - "source": [ - "pd.options.display.max_columns = 500\n", - "entities.head()" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "mk_R0eFZZ0G5" - }, - "outputs": [], - "source": [ - "concepts.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "6HYUytvLT8Kf" - }, - "source": [ - "## Merge data" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "dhALZDsh9n9L" - }, - "source": [ - "https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "A-tnI-hK6yDG" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "4OdEr5IFVdF5" - }, - "source": [ - "## Explore data" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "4IzXea0T64x4" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "hecscpimY6Oz" - }, - "source": [ - "## Plot visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "_o8RmX2M67ai" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "8OFxenCdhocj" - }, - "source": [ - "## Analyze outliers" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "D59bn-7k6-Io" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "DNTMMBkVhrGk" - }, - "source": [ - "## Plot multiple years" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "JkTUmYGF7BQt" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "BB1Ki0v6hxCA" - }, - "source": [ - "## Point out a story" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": {}, - "colab_type": "code", - "id": "eSgZhD3v7HIe" - }, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# ASSIGNMENT\n", - "Replicate the lesson code\n", - "\n", - "# STRETCH OPTIONS\n", - "\n", - "## 1. Animate!\n", - "- [Making animations work in Google Colaboratory](https://medium.com/lambda-school-machine-learning/making-animations-work-in-google-colaboratory-new-home-for-ml-prototyping-c6147186ae75)\n", - "- [How to Create Animated Graphs in Python](https://towardsdatascience.com/how-to-create-animated-graphs-in-python-bb619cc2dec1)\n", - "- [The Ultimate Day of Chicago Bikeshare](https://chrisluedtke.github.io/divvy-data.html) (Lambda School Data Science student)\n", - "\n", - "## 2. Work on anything related to your portfolio site / project" - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "name": "LS_DS_224_Sequence_your_narrative.ipynb", - "provenance": [], - "version": "0.3.2" - }, - "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": "3.7.1" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} + "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" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.1" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "JbDHnhet8CWy" + }, + "source": [ + "_Lambda School Data Science_\n", + "\n", + "# Sequence your narrative\n", + "\n", + "Today we will 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", + "- [Income Per Person (GDP Per Capital, Inflation Adjusted) by Geo & Time](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", + "- [Life Expectancy (in Years) by Geo & Time](https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--life_expectancy_years--by--geo--time.csv)\n", + "- [Population Totals, by Geo & Time](https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--population_total--by--geo--time.csv)\n", + "- [Entities](https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--entities--geo--country.csv)\n", + "- [Concepts](https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--concepts.csv)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "zyPYtsY6HtIK" + }, + "source": [ + "Objectives\n", + "- sequence multiple visualizations\n", + "- combine qualitative anecdotes with quantitative aggregates\n", + "\n", + "Links\n", + "- [Hans Rosling’s TED talks](https://www.ted.com/speakers/hans_rosling)\n", + "- [Spiralling global temperatures from 1850-2016](https://twitter.com/ed_hawkins/status/729753441459945474)\n", + "- \"[The Pudding](https://pudding.cool/) explains ideas debated in culture with visual essays.\"\n", + "- [A Data Point Walks Into a Bar](https://lisacharlotterost.github.io/2016/12/27/datapoint-in-bar/): a thoughtful blog post about emotion and empathy in data storytelling" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "SxTJBgRAW3jD" + }, + "source": [ + "## Make a plan\n", + "\n", + "#### How to present the data?\n", + "\n", + "Variables --> Visual Encodings\n", + "- Income --> x\n", + "- Lifespan --> y\n", + "- Region --> color\n", + "- Population --> size\n", + "- Year --> animation frame (alternative: small multiple)\n", + "- Country --> annotation\n", + "\n", + "Qualitative --> Verbal\n", + "- Editorial / contextual explanation --> audio narration (alternative: text)\n", + "\n", + "\n", + "#### How to structure the data?\n", + "\n", + "| Year | Country | Region | Income | Lifespan | Population |\n", + "|------|---------|----------|--------|----------|------------|\n", + "| 1818 | USA | Americas | ### | ## | # |\n", + "| 1918 | USA | Americas | #### | ### | ## |\n", + "| 2018 | USA | Americas | ##### | ### | ### |\n", + "| 1818 | China | Asia | # | # | # |\n", + "| 1918 | China | Asia | ## | ## | ### |\n", + "| 2018 | China | Asia | ### | ### | ##### |\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3ebEjShbWsIy" + }, + "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." + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "4RSxbu7rWr1p", + "colab": {} + }, + "source": [ + "!pip install --upgrade seaborn" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "5sQ0-7JUWyN4", + "colab": {} + }, + "source": [ + "import seaborn as sns\n", + "sns.__version__" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "S2dXWRTFTsgd" + }, + "source": [ + "## More imports" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "y-TgL_mA8OkF", + "colab": {} + }, + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "CZGG5prcTxrQ" + }, + "source": [ + "## Load & look at data" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "-uE25LHD8CW0", + "colab": {} + }, + "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')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "gg_pJslMY2bq", + "colab": {} + }, + "source": [ + "lifespan = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--life_expectancy_years--by--geo--time.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "F6knDUevY-xR", + "colab": {} + }, + "source": [ + "population = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--datapoints--population_total--by--geo--time.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "hX6abI-iZGLl", + "colab": {} + }, + "source": [ + "entities = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--entities--geo--country.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "AI-zcaDkZHXm", + "colab": {} + }, + "source": [ + "concepts = pd.read_csv('https://raw.githubusercontent.com/open-numbers/ddf--gapminder--systema_globalis/master/ddf--concepts.csv')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "EgFw-g0nZLJy", + "colab": {} + }, + "source": [ + "income.shape, lifespan.shape, population.shape, entities.shape, concepts.shape" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "I-T62v7FZQu5", + "colab": {} + }, + "source": [ + "income.head()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "2zIdtDESZYG5", + "colab": {} + }, + "source": [ + "lifespan.head()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "58AXNVMKZj3T", + "colab": {} + }, + "source": [ + "population.head()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "0ywWDL2MZqlF", + "colab": {} + }, + "source": [ + "pd.options.display.max_columns = 500\n", + "entities.head()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "mk_R0eFZZ0G5", + "colab": {} + }, + "source": [ + "concepts.head()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "6HYUytvLT8Kf" + }, + "source": [ + "## Merge data" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "dhALZDsh9n9L" + }, + "source": [ + "https://github.com/pandas-dev/pandas/blob/master/doc/cheatsheet/Pandas_Cheat_Sheet.pdf" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "A-tnI-hK6yDG", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4OdEr5IFVdF5" + }, + "source": [ + "## Explore data" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "4IzXea0T64x4", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "hecscpimY6Oz" + }, + "source": [ + "## Plot visualization" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "_o8RmX2M67ai", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "8OFxenCdhocj" + }, + "source": [ + "## Analyze outliers" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "D59bn-7k6-Io", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "DNTMMBkVhrGk" + }, + "source": [ + "## Plot multiple years" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "JkTUmYGF7BQt", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "BB1Ki0v6hxCA" + }, + "source": [ + "## Point out a story" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab_type": "code", + "id": "eSgZhD3v7HIe", + "colab": {} + }, + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file