Vaccination.ipynb

Vaccination.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>Pyramid 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 \n\nfrom sklearn.preprocessing import MinMaxScaler\nimport seaborn as sns\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: ad3ffea3caa8ee28fb0d84159eb14dadc58dd97a\n\npandas    : 1.1.3\njson      : 2.0.9\nnumpy     : 1.19.2\nwatermark : 2.1.0\nmatplotlib: 3.3.2\nseaborn   : 0.11.0\n\n",
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "# Code"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data = [[\"Country\", \"Vaccination\", \"Deaths\"],\n[\"IE\", 93, 15],\n[\"PT\", 92, 10],\n[\"MT\", 92, 0],\n[\"DK\", 89, 10],\n[\"BE\", 87, 29],\n[\"ES\", 84, 4],\n[\"FI\", 83, 7],\n[\"SE\", 83, 3],\n[\"IT\", 82, 9],\n[\"FR\", 81, 7],\n[\"DE\", 81, 10],\n[\"NL\", 81, 17],\n[\"CY\", 80, 5],\n[\"LU\", 76, 13],\n[\"AT\", 74, 35],\n[\"LT\", 72, 69],\n[\"GR\", 72, 75],\n[\"CZ\", 69, 69],\n[\"HU\", 68, 135],\n[\"EE\", 67, 113],\n[\"LV\", 66, 266],\n[\"SI\", 64, 56],\n[\"PL\", 62, 49],\n[\"HR\", 55, 168],\n[\"SK\", 54, 29],\n[\"RO\", 43, 267],\n[\"BG\", 29, 325]]",
      "execution_count": 3,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data = pd.DataFrame.from_records(data[1:], columns=data[0])\ndata['y'] = np.arange(0, 27)",
      "execution_count": 4,
      "outputs": []
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "text/plain": "   Country  Vaccination  Deaths   y\n0       IE           93      15   0\n1       PT           92      10   1\n2       MT           92       0   2\n3       DK           89      10   3\n4       BE           87      29   4\n5       ES           84       4   5\n6       FI           83       7   6\n7       SE           83       3   7\n8       IT           82       9   8\n9       FR           81       7   9\n10      DE           81      10  10\n11      NL           81      17  11\n12      CY           80       5  12\n13      LU           76      13  13\n14      AT           74      35  14\n15      LT           72      69  15\n16      GR           72      75  16\n17      CZ           69      69  17\n18      HU           68     135  18\n19      EE           67     113  19\n20      LV           66     266  20\n21      SI           64      56  21\n22      PL           62      49  22\n23      HR           55     168  23\n24      SK           54      29  24\n25      RO           43     267  25\n26      BG           29     325  26",
            "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>Country</th>\n      <th>Vaccination</th>\n      <th>Deaths</th>\n      <th>y</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>IE</td>\n      <td>93</td>\n      <td>15</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>PT</td>\n      <td>92</td>\n      <td>10</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>MT</td>\n      <td>92</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>DK</td>\n      <td>89</td>\n      <td>10</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>BE</td>\n      <td>87</td>\n      <td>29</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>ES</td>\n      <td>84</td>\n      <td>4</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>FI</td>\n      <td>83</td>\n      <td>7</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>SE</td>\n      <td>83</td>\n      <td>3</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>IT</td>\n      <td>82</td>\n      <td>9</td>\n      <td>8</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>FR</td>\n      <td>81</td>\n      <td>7</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>DE</td>\n      <td>81</td>\n      <td>10</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>NL</td>\n      <td>81</td>\n      <td>17</td>\n      <td>11</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>CY</td>\n      <td>80</td>\n      <td>5</td>\n      <td>12</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>LU</td>\n      <td>76</td>\n      <td>13</td>\n      <td>13</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>AT</td>\n      <td>74</td>\n      <td>35</td>\n      <td>14</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>LT</td>\n      <td>72</td>\n      <td>69</td>\n      <td>15</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>GR</td>\n      <td>72</td>\n      <td>75</td>\n      <td>16</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>CZ</td>\n      <td>69</td>\n      <td>69</td>\n      <td>17</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>HU</td>\n      <td>68</td>\n      <td>135</td>\n      <td>18</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>EE</td>\n      <td>67</td>\n      <td>113</td>\n      <td>19</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>LV</td>\n      <td>66</td>\n      <td>266</td>\n      <td>20</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>SI</td>\n      <td>64</td>\n      <td>56</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>PL</td>\n      <td>62</td>\n      <td>49</td>\n      <td>22</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>HR</td>\n      <td>55</td>\n      <td>168</td>\n      <td>23</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>SK</td>\n      <td>54</td>\n      <td>29</td>\n      <td>24</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>RO</td>\n      <td>43</td>\n      <td>267</td>\n      <td>25</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>BG</td>\n      <td>29</td>\n      <td>325</td>\n      <td>26</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "We use seaborn's barplot axis function to generate the basic structure of the figure"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, (ax, ax2) = plt.subplots(1, 2, figsize=(10, 15))\n\nsns.barplot(x=data['Vaccination'], y=data['Country'], ax=ax)\nsns.barplot(x=data['Deaths'], y=data['Country'], ax=ax2)\nax.invert_xaxis()",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x1080 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Use sklearn's MinMaxScaler to scale Vaccination and Death values to be between 0 and 1. We'll use these scaled values to color the bars"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "data['Color_Vaccination'] = MinMaxScaler().fit_transform(data[['Vaccination']])\ndata['Color_Deaths'] = MinMaxScaler().fit_transform(data[['Deaths']])",
      "execution_count": 7,
      "outputs": []
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Now we color the bars proportionally to their respective values. We invert the scale for Deaths to make larger values red instead of blue"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, (ax, ax2) = plt.subplots(1, 2, figsize=(10, 15))\n\nsns.barplot(x=data['Vaccination'], y=data['Country'], ax=ax, \n            palette=plt.cm.RdYlBu(data['Color_Vaccination']))\nsns.barplot(x=data['Deaths'], y=data['Country'], ax=ax2, \n            palette=plt.cm.RdYlBu_r(data['Color_Deaths']))\nax.invert_xaxis()",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x1080 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Remove the box decorations, leaving us with just the raw bars"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, (ax, ax2) = plt.subplots(1, 2, figsize=(10, 15))\n\nsns.barplot(x=data['Vaccination'], y=data['Country'], ax=ax, \n            palette=plt.cm.RdYlBu(data['Color_Vaccination']))\nsns.barplot(x=data['Deaths'], y=data['Country'], ax=ax2, \n            palette=plt.cm.RdYlBu_r(data['Color_Deaths']))\nax.invert_xaxis()\n\n# Remove box with axes, tickmarks, etc\nax.axis('off')\nax2.axis('off')",
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 9,
          "data": {
            "text/plain": "(0.0, 341.25, 26.5, -0.5)"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x1080 with 2 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "Next we add the country codes along the y axis"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, (ax, ax2) = plt.subplots(1, 2, figsize=(10, 15))\n\nsns.barplot(x=data['Vaccination'], y=data['Country'], ax=ax, \n            palette=plt.cm.RdYlBu(data['Color_Vaccination']))\nsns.barplot(x=data['Deaths'], y=data['Country'], ax=ax2, \n            palette=plt.cm.RdYlBu_r(data['Color_Deaths']))\nax.invert_xaxis()\n\n# Remove box with axes, tickmarks, etc\nax.axis('off')\nax2.axis('off')\n\n# Add y tick labels in between the two subplots\ndata.apply(lambda x: ax.text(x=-9, y=x.y, \n                                           s=x.Country, fontsize=16, \n                                           va='center', ha='center'), axis=1);",
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x1080 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "And the values for each bar"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, (ax, ax2) = plt.subplots(1, 2, figsize=(10, 15))\n\nsns.barplot(x=data['Vaccination'], y=data['Country'], ax=ax, \n            palette=plt.cm.RdYlBu(data['Color_Vaccination']))\nsns.barplot(x=data['Deaths'], y=data['Country'], ax=ax2, \n            palette=plt.cm.RdYlBu_r(data['Color_Deaths']))\nax.invert_xaxis()\n\n# Remove box with axes, tickmarks, etc\nax.axis('off')\nax2.axis('off')\n\n# Add y tick labels in between the two subplots\ndata.apply(lambda x: ax.text(x=-9, y=x.y, \n                                           s=x.Country, fontsize=16, \n                                           va='center', ha='center'), axis=1)\n\n# Add x values next to every bar\ndata.apply(lambda x: ax.text(x=x.Vaccination+10, y=x.y, \n                                           s=x.Vaccination, fontsize=16, \n                                           va='center', ha='center'), axis=1)\ndata.apply(lambda x: ax2.text(x=x.Deaths+15, y=x.y, \n                                            s=x.Deaths, fontsize=16, \n                                            va='center', ha='left'), axis=1);",
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x1080 with 2 Axes>",
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "And finally, we add the titles and subtitles to each subplot"
    },
    {
      "metadata": {
        "trusted": true
      },
      "cell_type": "code",
      "source": "fig, (ax, ax2) = plt.subplots(1, 2, figsize=(10, 15))\n\nsns.barplot(x=data['Vaccination'], y=data['Country'], ax=ax, \n            palette=plt.cm.RdYlBu(data['Color_Vaccination']))\nsns.barplot(x=data['Deaths'], y=data['Country'], ax=ax2, \n            palette=plt.cm.RdYlBu_r(data['Color_Deaths']))\nax.invert_xaxis()\n\n# Remove box with axes, tickmarks, etc\nax.axis('off')\nax2.axis('off')\n\n# Add y tick labels in between the two subplots\ndata.apply(lambda x: ax.text(x=-9, y=x.y, \n                                           s=x.Country, fontsize=16, \n                                           va='center', ha='center'), axis=1)\n\n# Add x values next to every bar\ndata.apply(lambda x: ax.text(x=x.Vaccination+10, y=x.y, \n                                           s=x.Vaccination, fontsize=16, \n                                           va='center', ha='center'), axis=1)\ndata.apply(lambda x: ax2.text(x=x.Deaths+15, y=x.y, \n                                            s=x.Deaths, fontsize=16, \n                                            va='center', ha='left'), axis=1)\n\n# Add title and subtitle to each subplot\n(xmin, xmax) = ax.get_xlim()\nax.text(x=(xmin-xmax)/2, y=-1.6, s='Vaccination'.upper(), \n        fontsize=20, va='center', ha='center')\nax.text(x=(xmin-xmax)/2, y=-1.0, s='% adult population fully vaccinated', \n        fontsize=12, va='center', ha='center')\n\n(xmin, xmax) = ax2.get_xlim()\nax2.text(x=(xmax-xmin)/2, y=-1.6, s='Deaths'.upper(), \n         fontsize=20, va='center', ha='center')\nax2.text(x=(xmax-xmin)/2, y=-1.0, s='per 1 million population, 14-day period', \n         fontsize=12, va='center', ha='center');",
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 720x1080 with 2 Axes>",
            "image/png": 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\n"
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    },
    {
      "metadata": {},
      "cell_type": "markdown",
      "source": "<div style=\"width: 100%; overflow: hidden;\">\n     <img src=\"https://raw.githubusercontent.com/DataForScience/Networks/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": {
      "name": "python3",
      "display_name": "Python 3",
      "language": "python"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.5",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "varInspector": {
      "window_display": false,
      "cols": {
        "lenName": 16,
        "lenType": 16,
        "lenVar": 40
      },
      "kernels_config": {
        "python": {
          "library": "var_list.py",
          "delete_cmd_prefix": "del ",
          "delete_cmd_postfix": "",
          "varRefreshCmd": "print(var_dic_list())"
        },
        "r": {
          "library": "var_list.r",
          "delete_cmd_prefix": "rm(",
          "delete_cmd_postfix": ") ",
          "varRefreshCmd": "cat(var_dic_list()) "
        }
      },
      "types_to_exclude": [
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        "builtin_function_or_method",
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        "_Feature"
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    },
    "gist": {
      "id": "",
      "data": {
        "description": "Vaccination.ipynb",
        "public": true
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 4
}