oztalha/Lockdown Likelihood per Polity.ipynb

## Lockdown Likelihood per Polity.ipynb
{
  "cells": [
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-03T13:05:35.725275Z",
          "end_time": "2020-04-03T13:05:36.435954Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "%matplotlib inline",
      "execution_count": 1,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-03T13:05:51.187984Z",
          "end_time": "2020-04-03T13:05:53.105074Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# plotting\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom matplotlib import cm\nplt.style.use('ggplot')\nimport seaborn as sns\nmpl.rcParams['savefig.dpi'] = 150\nmpl.rcParams['figure.dpi'] = 150\nmpl.rcParams['figure.figsize'] = 12, 5\nmpl.rcParams['axes.formatter.useoffset']=False\nmpl.rcParams['figure.autolayout']=True",
      "execution_count": 2,
      "outputs": []
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-03T13:08:22.817529Z",
          "end_time": "2020-04-03T13:08:22.839656Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "# grouped bar-graph: polity score above/below median\n# x-axis case rate (binned): 0%-.01%, .01%-.02%, .02%-.03%\n# y-axis: rate of countries locked-down (in the bin).\nimport pandas as pd\nfrom io import StringIO\ndf = '''\\\npolity_group,case_rate,countries_locked,not_locked\nbelow,.00%-.01%,5,10\nabove,.00%-.01%,8,7\nbelow,.01%-.02%,5,6\nabove,.01%-.02%,8,3\nbelow,.02%-.03%,5,1\nabove,.02%-.03%,6,0\n'''\ndf = pd.read_csv(StringIO(df))\ndf",
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 5,
          "data": {
            "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>polity_group</th>\n      <th>case_rate</th>\n      <th>countries_locked</th>\n      <th>not_locked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>below</td>\n      <td>.00%-.01%</td>\n      <td>5</td>\n      <td>10</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>above</td>\n      <td>.00%-.01%</td>\n      <td>8</td>\n      <td>7</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>below</td>\n      <td>.01%-.02%</td>\n      <td>5</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>above</td>\n      <td>.01%-.02%</td>\n      <td>8</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>below</td>\n      <td>.02%-.03%</td>\n      <td>5</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>above</td>\n      <td>.02%-.03%</td>\n      <td>6</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "  polity_group  case_rate  countries_locked  not_locked\n0        below  .00%-.01%                 5          10\n1        above  .00%-.01%                 8           7\n2        below  .01%-.02%                 5           6\n3        above  .01%-.02%                 8           3\n4        below  .02%-.03%                 5           1\n5        above  .02%-.03%                 6           0"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-03T13:08:33.055923Z",
          "end_time": "2020-04-03T13:08:33.084092Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "def a(x):\n    return x['countries_locked'] / (x['countries_locked'] + x['not_locked'])\n\ndf2 = df.set_index('polity_group').groupby('case_rate').apply(a)\ndf2",
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 6,
          "data": {
            "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>polity_group</th>\n      <th>below</th>\n      <th>above</th>\n    </tr>\n    <tr>\n      <th>case_rate</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>.00%-.01%</th>\n      <td>0.333333</td>\n      <td>0.533333</td>\n    </tr>\n    <tr>\n      <th>.01%-.02%</th>\n      <td>0.454545</td>\n      <td>0.727273</td>\n    </tr>\n    <tr>\n      <th>.02%-.03%</th>\n      <td>0.833333</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
            "text/plain": "polity_group     below     above\ncase_rate                       \n.00%-.01%     0.333333  0.533333\n.01%-.02%     0.454545  0.727273\n.02%-.03%     0.833333  1.000000"
          },
          "metadata": {}
        }
      ]
    },
    {
      "metadata": {
        "ExecuteTime": {
          "start_time": "2020-04-03T13:09:43.325383Z",
          "end_time": "2020-04-03T13:09:44.079280Z"
        },
        "trusted": true
      },
      "cell_type": "code",
      "source": "ax = df2.plot(kind='bar',rot=0,\n              title='Lockdown Likelihood per Polity Group')\nax.set(ylabel='Rate of lockdown',\n       xlabel='Rate of covid-19 cases');",
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": "<Figure size 1800x750 with 1 Axes>"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3",
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    "toc": {
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      "number_sections": true,
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      "title_cell": "Table of Contents",
      "title_sidebar": "Contents",
      "toc_cell": false,
      "toc_position": {},
      "toc_section_display": true,
      "toc_window_display": false
    },
    "language_info": {
      "mimetype": "text/x-python",
      "nbconvert_exporter": "python",
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    },
    "gist": {
      "id": "be7b60d17fc434c09417fc5f73e396cc",
      "data": {
        "description": "Lockdown Likelihood per Polity",
        "public": true
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    },
    "_draft": {
      "nbviewer_url": "https://gist.github.com/be7b60d17fc434c09417fc5f73e396cc"
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}