Nightingale.ipynb

Nightingale.ipynb

{
  "cells": [
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<div style=\"width: 100%; overflow: hidden;\">\n    <div style=\"width: 150px; float: left;\"> <img src=\"https://raw.githubusercontent.com/DataForScience/Graphs4Sci/master/data/D4Sci_logo_ball.png\" alt=\"Data For Science, Inc\" align=\"left\" border=\"0\" width=150px> </div>\n    <div style=\"float: left; margin-left: 10px;\"><h1>Visualization for Science</h1>\n<h1>Nightingale Plot</h1>\n        <a href=\"http://www.data4sci.com/\">www.data4sci.com</a><br/>\n            @bgoncalves, @data4sci</p></div>\n</div>"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "import pandas as pd\nimport numpy as np\n\nimport matplotlib\nimport matplotlib.pyplot as plt \nfrom matplotlib.patches import Wedge, Patch\nfrom matplotlib.collections import PatchCollection\nimport matplotlib.font_manager as font_manager\n\nimport watermark\n\n%load_ext watermark\n%matplotlib inline",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "We start by print out the versions of the libraries we're using for future reference"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "%watermark -n -v -m -g -iv",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": "Python implementation: CPython\nPython version       : 3.8.5\nIPython version      : 7.19.0\n\nCompiler    : Clang 10.0.0 \nOS          : Darwin\nRelease     : 20.6.0\nMachine     : x86_64\nProcessor   : i386\nCPU cores   : 16\nArchitecture: 64bit\n\nGit hash: 9fa3dbd9d128a84e679d23456a0b79a628fff382\n\nwatermark : 2.1.0\njson      : 2.0.9\nmatplotlib: 3.3.2\npandas    : 1.1.3\nnumpy     : 1.19.2\n\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Load the dataset"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data = pd.read_excel('https://github.com/DataForScience/Viz4Sci/raw/master/data/Nightingale.xlsx')",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data.head()",
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "    Month  Year      Cause of Death  Monthly Death Total  \\\n0   April  1854  Infectious Disease                    1   \n1     May  1854  Infectious Disease                   12   \n2    June  1854  Infectious Disease                   11   \n3    July  1854  Infectious Disease                  359   \n4  August  1854  Infectious Disease                  828   \n\n   Annual Mortality Rate (per 1000 soldiers)  \n0                                        1.4  \n1                                        6.2  \n2                                        4.7  \n3                                      150.0  \n4                                      328.5  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Month</th>\n      <th>Year</th>\n      <th>Cause of Death</th>\n      <th>Monthly Death Total</th>\n      <th>Annual Mortality Rate (per 1000 soldiers)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>April</td>\n      <td>1854</td>\n      <td>Infectious Disease</td>\n      <td>1</td>\n      <td>1.4</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>May</td>\n      <td>1854</td>\n      <td>Infectious Disease</td>\n      <td>12</td>\n      <td>6.2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>June</td>\n      <td>1854</td>\n      <td>Infectious Disease</td>\n      <td>11</td>\n      <td>4.7</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>July</td>\n      <td>1854</td>\n      <td>Infectious Disease</td>\n      <td>359</td>\n      <td>150.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>August</td>\n      <td>1854</td>\n      <td>Infectious Disease</td>\n      <td>828</td>\n      <td>328.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Combine month and year into a single column"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data['date'] = data[['Month', 'Year']].apply(lambda x:x['Month'] + ' ' + str(x['Year']), axis=1)",
      "execution_count": 5,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Pivot data into a table with each cause of death as a column. We're keeping only the mortality rate."
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "infectious = pd.pivot_table(data, index='date', columns='Cause of Death', \n                            values='Annual Mortality Rate (per 1000 soldiers)', aggfunc='sum')",
      "execution_count": 6,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "infectious",
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 7,
          "data": {
            "text/plain": "Cause of Death  All Other Causes  Infectious Disease  Wounds and Injuries\ndate                                                                     \nApril 1854                   7.0                 1.4                  0.0\nApril 1855                  21.2               177.5                 17.9\nAugust 1854                 11.9               328.5                  0.4\nAugust 1855                  6.7               129.9                 44.1\nDecember 1854               48.0               631.5                 41.7\nDecember 1855                7.8                25.3                  5.0\nFebruary 1855              140.1               822.8                 16.3\nFebruary 1856                5.2                 6.6                  0.0\nJanuary 1855               120.0              1022.8                 30.7\nJanuary 1856                13.0                11.4                  0.5\nJuly 1854                    9.6               150.0                  0.0\nJuly 1855                    9.3               107.5                 37.7\nJune 1854                    2.5                 4.7                  0.0\nJune 1855                    9.6               247.6                 64.5\nMarch 1855                  68.6               480.3                 12.8\nMarch 1856                   9.1                 3.9                  0.0\nMay 1854                     4.6                 6.2                  0.0\nMay 1855                    12.5               171.8                 16.6\nNovember 1854               42.8               340.6                115.8\nNovember 1855               10.1                56.4                 10.5\nOctober 1854                50.1               197.0                 51.7\nOctober 1855                 4.6                32.8                 13.6\nSeptember 1854              27.7               312.2                 32.1\nSeptember 1855               5.0                47.5                 69.4",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th>Cause of Death</th>\n      <th>All Other Causes</th>\n      <th>Infectious Disease</th>\n      <th>Wounds and Injuries</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>April 1854</th>\n      <td>7.0</td>\n      <td>1.4</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>April 1855</th>\n      <td>21.2</td>\n      <td>177.5</td>\n      <td>17.9</td>\n    </tr>\n    <tr>\n      <th>August 1854</th>\n      <td>11.9</td>\n      <td>328.5</td>\n      <td>0.4</td>\n    </tr>\n    <tr>\n      <th>August 1855</th>\n      <td>6.7</td>\n      <td>129.9</td>\n      <td>44.1</td>\n    </tr>\n    <tr>\n      <th>December 1854</th>\n      <td>48.0</td>\n      <td>631.5</td>\n      <td>41.7</td>\n    </tr>\n    <tr>\n      <th>December 1855</th>\n      <td>7.8</td>\n      <td>25.3</td>\n      <td>5.0</td>\n    </tr>\n    <tr>\n      <th>February 1855</th>\n      <td>140.1</td>\n      <td>822.8</td>\n      <td>16.3</td>\n    </tr>\n    <tr>\n      <th>February 1856</th>\n      <td>5.2</td>\n      <td>6.6</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>January 1855</th>\n      <td>120.0</td>\n      <td>1022.8</td>\n      <td>30.7</td>\n    </tr>\n    <tr>\n      <th>January 1856</th>\n      <td>13.0</td>\n      <td>11.4</td>\n      <td>0.5</td>\n    </tr>\n    <tr>\n      <th>July 1854</th>\n      <td>9.6</td>\n      <td>150.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>July 1855</th>\n      <td>9.3</td>\n      <td>107.5</td>\n      <td>37.7</td>\n    </tr>\n    <tr>\n      <th>June 1854</th>\n      <td>2.5</td>\n      <td>4.7</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>June 1855</th>\n      <td>9.6</td>\n      <td>247.6</td>\n      <td>64.5</td>\n    </tr>\n    <tr>\n      <th>March 1855</th>\n      <td>68.6</td>\n      <td>480.3</td>\n      <td>12.8</td>\n    </tr>\n    <tr>\n      <th>March 1856</th>\n      <td>9.1</td>\n      <td>3.9</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>May 1854</th>\n      <td>4.6</td>\n      <td>6.2</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>May 1855</th>\n      <td>12.5</td>\n      <td>171.8</td>\n      <td>16.6</td>\n    </tr>\n    <tr>\n      <th>November 1854</th>\n      <td>42.8</td>\n      <td>340.6</td>\n      <td>115.8</td>\n    </tr>\n    <tr>\n      <th>November 1855</th>\n      <td>10.1</td>\n      <td>56.4</td>\n      <td>10.5</td>\n    </tr>\n    <tr>\n      <th>October 1854</th>\n      <td>50.1</td>\n      <td>197.0</td>\n      <td>51.7</td>\n    </tr>\n    <tr>\n      <th>October 1855</th>\n      <td>4.6</td>\n      <td>32.8</td>\n      <td>13.6</td>\n    </tr>\n    <tr>\n      <th>September 1854</th>\n      <td>27.7</td>\n      <td>312.2</td>\n      <td>32.1</td>\n    </tr>\n    <tr>\n      <th>September 1855</th>\n      <td>5.0</td>\n      <td>47.5</td>\n      <td>69.4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Put the columns in the right order"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "infectious = infectious[['Wounds and Injuries', 'All Other Causes', 'Infectious Disease', ]]",
      "execution_count": 8,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Normalize values"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "infectious = (infectious**2).cumsum(axis=1)\ninfectious /= infectious.max().max()\ninfectious = np.sqrt(infectious)",
      "execution_count": 9,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Subset and order the rows"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "order = [\n 'April 1854',   'May 1854',        'June 1854', \n 'July 1854',    'August 1854',     'September 1854', \n 'October 1854', 'November 1854', 'December 1854',\n 'January 1855', 'February 1855', 'March 1855',]\n\ninfectious = infectious.loc[order].reset_index()\ninfectious = infectious[::-1]",
      "execution_count": 10,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": false
      },
      "cell_type": "markdown",
      "source": "Color list"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "colors =['#51a7f9',  'black', '#f9517b', ]",
      "execution_count": 11,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Generate the figure"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, ax = plt.subplots(1, figsize=(10, 10))\nax.set_aspect(1.)\n\npatches = []\ncolor = []\ntotal = 90 # First wedge starts off at 90'\nangle = 30 # Each wedge covers 30'\n\nfor i in range(infectious.shape[0])[::1]:\n    for j, col in enumerate(['Infectious Disease', \n                             'All Other Causes', \n                             'Wounds and Injuries', ]):\n        value = infectious[col].iloc[i]\n        patches.append(Wedge((0, 0), np.sqrt(value), \n                                     total,\n                                     total+angle\n                                     ))\n        color.append(colors[j])\n    \n    length = np.max([np.sqrt(infectious.iloc[i, 1:4].max())+0.02, 0.4])\n    x = length * np.cos((total+angle/2)*np.pi/180)\n    y = length * np.sin((total+angle/2)*np.pi/180)\n    \n    label = infectious['date'].iloc[i]\n        \n    if label[:3] not in ['Mar', 'Jan', 'Apr']:\n        label = label.split()[0]\n    elif label[:3] == 'Apr':\n        label = '\\n'.join(label.split())\n    \n    ax.text(x, y, label.upper(), rotation=(total+angle/2-90), \n            ha='center', va='center', fontsize=12)\n    \n    total += angle\n\np = PatchCollection(patches, color=color, alpha=0.5)\nax.add_collection(p)\nax.set_xlim(-1.2, 1.2)\nax.set_ylim(-1.2, 1.2)\n\npatches = []\nlegend = ['deaths from preventable diseases', \n          'deaths from wounds', \n          'deaths from all other sources']\n\n# you'll need to change the font location\nfont = font_manager.FontProperties(fname='Lucida Calligraphy Italic.ttf',\n                               weight='normal',\n                               style='italic', size=16)\n\nfor i, leg in enumerate(legend):\n    patches.append(Patch(color=colors[i], label=leg, alpha=0.5))\n\nax.legend(handles=patches, loc=(0.5, 0.7), \n          fancybox=False, frameon=False, prop=font)\n\nax.axis('off')\nfig.tight_layout()",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x720 with 1 Axes>",
            "image/png": 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\n"
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    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Which compares nicely with the original"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<img src='https://pbs.twimg.com/media/BqzygKNCUAEHaNf?format=png&name=small'>"
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<div style=\"width: 100%; overflow: hidden;\">\n     <img src=\"https://raw.githubusercontent.com/DataForScience/Graphs4Sci/master/data/D4Sci_logo_full.png\" alt=\"Data For Science, Inc\" align=\"center\" border=\"0\" width=300px> \n</div>"
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  ],
  "metadata": {
    "kernelspec": {
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      "display_name": "Python 3",
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    },
    "language_info": {
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      "nbconvert_exporter": "python",
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    "toc": {
      "base_numbering": 1,
      "nav_menu": {},
      "number_sections": true,
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      "skip_h1_title": true,
      "title_cell": "Table of Contents",
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      "toc_position": {},
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    "varInspector": {
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      "cols": {
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      "kernels_config": {
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          "delete_cmd_postfix": "",
          "varRefreshCmd": "print(var_dic_list())"
        },
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          "varRefreshCmd": "cat(var_dic_list()) "
        }
      },
      "types_to_exclude": [
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