module4-sequence-your-narrative/LS_DS_124_Sequence_your_narrative.ipynb

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      "name": "LS_DS_124_Sequence_your_narrative.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/ryanleeallred/DS-Unit-1-Sprint-2-Data-Wrangling-and-Storytelling/blob/master/module4-sequence-your-narrative/LS_DS_124_Sequence_your_narrative.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "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)"
      ]
    },
    {
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      "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"
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      "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": []
    }
  ]
}