Update Kaggle.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "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>Country</th>\n",
       "      <th>Tests</th>\n",
       "      <th>Test Pop</th>\n",
       "      <th>Density</th>\n",
       "      <th>Urban Pop</th>\n",
       "      <th>Quarantine</th>\n",
       "      <th>Schools</th>\n",
       "      <th>Restrictions</th>\n",
       "      <th>Hospital Bed</th>\n",
       "      <th>sex0</th>\n",
       "      <th>sex14</th>\n",
       "      <th>sex25</th>\n",
       "      <th>sex54</th>\n",
       "      <th>sex64</th>\n",
       "      <th>sex65plus</th>\n",
       "      <th>Sex Ratio</th>\n",
       "      <th>lung</th>\n",
       "      <th>Female Lung</th>\n",
       "      <th>Male Lung</th>\n",
       "      <th>Median Age</th>\n",
       "      <th>GDP 2018</th>\n",
       "      <th>Crime Index</th>\n",
       "      <th>Population 2020</th>\n",
       "      <th>Smoking 2016</th>\n",
       "      <th>Females 2018</th>\n",
       "      <th>Total Infected</th>\n",
       "      <th>Total Deaths</th>\n",
       "      <th>Total Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.98</td>\n",
       "      <td>11.67</td>\n",
       "      <td>7.02</td>\n",
       "      <td>17.04</td>\n",
       "      <td>32.9</td>\n",
       "      <td>1.510250e+10</td>\n",
       "      <td>40.02</td>\n",
       "      <td>2877.797</td>\n",
       "      <td>28.7</td>\n",
       "      <td>49.063095</td>\n",
       "      <td>259</td>\n",
       "      <td>15</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.03</td>\n",
       "      <td>8.77</td>\n",
       "      <td>5.03</td>\n",
       "      <td>12.81</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.737580e+11</td>\n",
       "      <td>54.41</td>\n",
       "      <td>43851.044</td>\n",
       "      <td>15.6</td>\n",
       "      <td>49.484268</td>\n",
       "      <td>847</td>\n",
       "      <td>58</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>3/20/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.98</td>\n",
       "      <td>29.27</td>\n",
       "      <td>20.16</td>\n",
       "      <td>42.59</td>\n",
       "      <td>31.7</td>\n",
       "      <td>5.198715e+11</td>\n",
       "      <td>62.96</td>\n",
       "      <td>45195.774</td>\n",
       "      <td>21.8</td>\n",
       "      <td>51.237348</td>\n",
       "      <td>1054</td>\n",
       "      <td>28</td>\n",
       "      <td>248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>694.0</td>\n",
       "      <td>4269.802594</td>\n",
       "      <td>104.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.94</td>\n",
       "      <td>23.86</td>\n",
       "      <td>16.17</td>\n",
       "      <td>35.99</td>\n",
       "      <td>35.1</td>\n",
       "      <td>1.243309e+10</td>\n",
       "      <td>20.78</td>\n",
       "      <td>2963.243</td>\n",
       "      <td>24.1</td>\n",
       "      <td>52.956577</td>\n",
       "      <td>571</td>\n",
       "      <td>4</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>31635.0</td>\n",
       "      <td>806.065560</td>\n",
       "      <td>3.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3/23/2020</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.99</td>\n",
       "      <td>18.79</td>\n",
       "      <td>15.90</td>\n",
       "      <td>22.16</td>\n",
       "      <td>38.7</td>\n",
       "      <td>1.433904e+12</td>\n",
       "      <td>42.70</td>\n",
       "      <td>25499.884</td>\n",
       "      <td>14.7</td>\n",
       "      <td>50.199623</td>\n",
       "      <td>4862</td>\n",
       "      <td>20</td>\n",
       "      <td>422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country    Tests     Test Pop  Density  Urban Pop Quarantine Schools  \\\n",
       "0    Albania      NaN          NaN    105.0       63.0        NaN     NaN   \n",
       "1    Algeria      NaN          NaN     18.0       73.0        NaN     NaN   \n",
       "2  Argentina      NaN          NaN     17.0       93.0  3/20/2020     NaN   \n",
       "3    Armenia    694.0  4269.802594    104.0       63.0        NaN     NaN   \n",
       "4  Australia  31635.0   806.065560      3.0       86.0        NaN     NaN   \n",
       "\n",
       "  Restrictions  Hospital Bed  sex0  sex14  sex25  sex54  sex64  sex65plus  \\\n",
       "0          NaN           2.9  1.08   1.11   1.09   0.93   0.95       0.87   \n",
       "1          NaN           1.9  1.05   1.05   1.05   1.03   1.01       0.89   \n",
       "2          NaN           5.0  1.05   1.06   1.05   1.00   0.94       0.71   \n",
       "3          NaN           4.2  1.13   1.14   1.06   0.93   0.84       0.67   \n",
       "4    3/23/2020           3.8  1.06   1.06   1.09   0.99   0.93       0.86   \n",
       "\n",
       "   Sex Ratio   lung  Female Lung  Male Lung  Median Age      GDP 2018  \\\n",
       "0       0.98  11.67         7.02      17.04        32.9  1.510250e+10   \n",
       "1       1.03   8.77         5.03      12.81        28.1  1.737580e+11   \n",
       "2       0.98  29.27        20.16      42.59        31.7  5.198715e+11   \n",
       "3       0.94  23.86        16.17      35.99        35.1  1.243309e+10   \n",
       "4       0.99  18.79        15.90      22.16        38.7  1.433904e+12   \n",
       "\n",
       "   Crime Index  Population 2020  Smoking 2016  Females 2018  Total Infected  \\\n",
       "0        40.02         2877.797          28.7     49.063095             259   \n",
       "1        54.41        43851.044          15.6     49.484268             847   \n",
       "2        62.96        45195.774          21.8     51.237348            1054   \n",
       "3        20.78         2963.243          24.1     52.956577             571   \n",
       "4        42.70        25499.884          14.7     50.199623            4862   \n",
       "\n",
       "   Total Deaths  Total Recovered  \n",
       "0            15               67  \n",
       "1            58               61  \n",
       "2            28              248  \n",
       "3             4               31  \n",
       "4            20              422  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./prod/covid19_by_country.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(['Total Infected', 'Total Deaths', 'Total Recovered'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data cleaning and summing total events by country\n",
    "def preprocess_summation(df, description):\n",
    "    df = df.drop(['Province/State', 'Lat', 'Long'], axis=1).groupby(['Country/Region']).sum().reset_index()\n",
    "    col_name = 'Total' + ' ' + description\n",
    "    df[col_name] = df.apply(lambda r: r[-1], axis=1)\n",
    "    df = df[['Country/Region', col_name]]\n",
    "    df.columns = ['Country', col_name]\n",
    "    df.replace({'Korea, South': 'South Korea', 'Czechia': 'Czech Republic', 'Taiwan*': 'Taiwan', 'US': 'United States'}, inplace=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Tests</th>\n",
       "      <th>Test Pop</th>\n",
       "      <th>Density</th>\n",
       "      <th>Urban Pop</th>\n",
       "      <th>Quarantine</th>\n",
       "      <th>Schools</th>\n",
       "      <th>Restrictions</th>\n",
       "      <th>Hospital Bed</th>\n",
       "      <th>sex0</th>\n",
       "      <th>sex14</th>\n",
       "      <th>sex25</th>\n",
       "      <th>sex54</th>\n",
       "      <th>sex64</th>\n",
       "      <th>sex65plus</th>\n",
       "      <th>Sex Ratio</th>\n",
       "      <th>lung</th>\n",
       "      <th>Female Lung</th>\n",
       "      <th>Male Lung</th>\n",
       "      <th>Median Age</th>\n",
       "      <th>GDP 2018</th>\n",
       "      <th>Crime Index</th>\n",
       "      <th>Population 2020</th>\n",
       "      <th>Smoking 2016</th>\n",
       "      <th>Females 2018</th>\n",
       "      <th>Total Infected</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.98</td>\n",
       "      <td>11.67</td>\n",
       "      <td>7.02</td>\n",
       "      <td>17.04</td>\n",
       "      <td>32.9</td>\n",
       "      <td>1.510250e+10</td>\n",
       "      <td>40.02</td>\n",
       "      <td>2877.797</td>\n",
       "      <td>28.7</td>\n",
       "      <td>49.063095</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.03</td>\n",
       "      <td>8.77</td>\n",
       "      <td>5.03</td>\n",
       "      <td>12.81</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.737580e+11</td>\n",
       "      <td>54.41</td>\n",
       "      <td>43851.044</td>\n",
       "      <td>15.6</td>\n",
       "      <td>49.484268</td>\n",
       "      <td>1251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>3/20/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.98</td>\n",
       "      <td>29.27</td>\n",
       "      <td>20.16</td>\n",
       "      <td>42.59</td>\n",
       "      <td>31.7</td>\n",
       "      <td>5.198715e+11</td>\n",
       "      <td>62.96</td>\n",
       "      <td>45195.774</td>\n",
       "      <td>21.8</td>\n",
       "      <td>51.237348</td>\n",
       "      <td>1451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>694.0</td>\n",
       "      <td>4269.802594</td>\n",
       "      <td>104.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.94</td>\n",
       "      <td>23.86</td>\n",
       "      <td>16.17</td>\n",
       "      <td>35.99</td>\n",
       "      <td>35.1</td>\n",
       "      <td>1.243309e+10</td>\n",
       "      <td>20.78</td>\n",
       "      <td>2963.243</td>\n",
       "      <td>24.1</td>\n",
       "      <td>52.956577</td>\n",
       "      <td>770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>31635.0</td>\n",
       "      <td>806.065560</td>\n",
       "      <td>3.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3/23/2020</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.99</td>\n",
       "      <td>18.79</td>\n",
       "      <td>15.90</td>\n",
       "      <td>22.16</td>\n",
       "      <td>38.7</td>\n",
       "      <td>1.433904e+12</td>\n",
       "      <td>42.70</td>\n",
       "      <td>25499.884</td>\n",
       "      <td>14.7</td>\n",
       "      <td>50.199623</td>\n",
       "      <td>5550</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country    Tests     Test Pop  Density  Urban Pop Quarantine Schools  \\\n",
       "0    Albania      NaN          NaN    105.0       63.0        NaN     NaN   \n",
       "1    Algeria      NaN          NaN     18.0       73.0        NaN     NaN   \n",
       "2  Argentina      NaN          NaN     17.0       93.0  3/20/2020     NaN   \n",
       "3    Armenia    694.0  4269.802594    104.0       63.0        NaN     NaN   \n",
       "4  Australia  31635.0   806.065560      3.0       86.0        NaN     NaN   \n",
       "\n",
       "  Restrictions  Hospital Bed  sex0  sex14  sex25  sex54  sex64  sex65plus  \\\n",
       "0          NaN           2.9  1.08   1.11   1.09   0.93   0.95       0.87   \n",
       "1          NaN           1.9  1.05   1.05   1.05   1.03   1.01       0.89   \n",
       "2          NaN           5.0  1.05   1.06   1.05   1.00   0.94       0.71   \n",
       "3          NaN           4.2  1.13   1.14   1.06   0.93   0.84       0.67   \n",
       "4    3/23/2020           3.8  1.06   1.06   1.09   0.99   0.93       0.86   \n",
       "\n",
       "   Sex Ratio   lung  Female Lung  Male Lung  Median Age      GDP 2018  \\\n",
       "0       0.98  11.67         7.02      17.04        32.9  1.510250e+10   \n",
       "1       1.03   8.77         5.03      12.81        28.1  1.737580e+11   \n",
       "2       0.98  29.27        20.16      42.59        31.7  5.198715e+11   \n",
       "3       0.94  23.86        16.17      35.99        35.1  1.243309e+10   \n",
       "4       0.99  18.79        15.90      22.16        38.7  1.433904e+12   \n",
       "\n",
       "   Crime Index  Population 2020  Smoking 2016  Females 2018  Total Infected  \n",
       "0        40.02         2877.797          28.7     49.063095             333  \n",
       "1        54.41        43851.044          15.6     49.484268            1251  \n",
       "2        62.96        45195.774          21.8     51.237348            1451  \n",
       "3        20.78         2963.243          24.1     52.956577             770  \n",
       "4        42.70        25499.884          14.7     50.199623            5550  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_confirmed = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')\n",
    "total_infections = preprocess_summation(df_confirmed, 'Infected')\n",
    "df = df.merge(total_infections, how='inner', on='Country')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_deaths = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')\n",
    "total_deaths = preprocess_summation(df_deaths, 'Deaths')\n",
    "df = df.merge(total_deaths, how='inner', on='Country')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "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>Country</th>\n",
       "      <th>Tests</th>\n",
       "      <th>Test Pop</th>\n",
       "      <th>Density</th>\n",
       "      <th>Urban Pop</th>\n",
       "      <th>Quarantine</th>\n",
       "      <th>Schools</th>\n",
       "      <th>Restrictions</th>\n",
       "      <th>Hospital Bed</th>\n",
       "      <th>sex0</th>\n",
       "      <th>sex14</th>\n",
       "      <th>sex25</th>\n",
       "      <th>sex54</th>\n",
       "      <th>sex64</th>\n",
       "      <th>sex65plus</th>\n",
       "      <th>Sex Ratio</th>\n",
       "      <th>lung</th>\n",
       "      <th>Female Lung</th>\n",
       "      <th>Male Lung</th>\n",
       "      <th>Median Age</th>\n",
       "      <th>GDP 2018</th>\n",
       "      <th>Crime Index</th>\n",
       "      <th>Population 2020</th>\n",
       "      <th>Smoking 2016</th>\n",
       "      <th>Females 2018</th>\n",
       "      <th>Total Infected</th>\n",
       "      <th>Total Deaths</th>\n",
       "      <th>Total Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Albania</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.08</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.98</td>\n",
       "      <td>11.67</td>\n",
       "      <td>7.02</td>\n",
       "      <td>17.04</td>\n",
       "      <td>32.9</td>\n",
       "      <td>1.510250e+10</td>\n",
       "      <td>40.02</td>\n",
       "      <td>2877.797</td>\n",
       "      <td>28.7</td>\n",
       "      <td>49.063095</td>\n",
       "      <td>333</td>\n",
       "      <td>20</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1.03</td>\n",
       "      <td>8.77</td>\n",
       "      <td>5.03</td>\n",
       "      <td>12.81</td>\n",
       "      <td>28.1</td>\n",
       "      <td>1.737580e+11</td>\n",
       "      <td>54.41</td>\n",
       "      <td>43851.044</td>\n",
       "      <td>15.6</td>\n",
       "      <td>49.484268</td>\n",
       "      <td>1251</td>\n",
       "      <td>130</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>3/20/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.98</td>\n",
       "      <td>29.27</td>\n",
       "      <td>20.16</td>\n",
       "      <td>42.59</td>\n",
       "      <td>31.7</td>\n",
       "      <td>5.198715e+11</td>\n",
       "      <td>62.96</td>\n",
       "      <td>45195.774</td>\n",
       "      <td>21.8</td>\n",
       "      <td>51.237348</td>\n",
       "      <td>1451</td>\n",
       "      <td>43</td>\n",
       "      <td>279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>694.0</td>\n",
       "      <td>4269.802594</td>\n",
       "      <td>104.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.2</td>\n",
       "      <td>1.13</td>\n",
       "      <td>1.14</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.94</td>\n",
       "      <td>23.86</td>\n",
       "      <td>16.17</td>\n",
       "      <td>35.99</td>\n",
       "      <td>35.1</td>\n",
       "      <td>1.243309e+10</td>\n",
       "      <td>20.78</td>\n",
       "      <td>2963.243</td>\n",
       "      <td>24.1</td>\n",
       "      <td>52.956577</td>\n",
       "      <td>770</td>\n",
       "      <td>7</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Australia</td>\n",
       "      <td>31635.0</td>\n",
       "      <td>806.065560</td>\n",
       "      <td>3.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3/23/2020</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.06</td>\n",
       "      <td>1.09</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.99</td>\n",
       "      <td>18.79</td>\n",
       "      <td>15.90</td>\n",
       "      <td>22.16</td>\n",
       "      <td>38.7</td>\n",
       "      <td>1.433904e+12</td>\n",
       "      <td>42.70</td>\n",
       "      <td>25499.884</td>\n",
       "      <td>14.7</td>\n",
       "      <td>50.199623</td>\n",
       "      <td>5550</td>\n",
       "      <td>30</td>\n",
       "      <td>701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country    Tests     Test Pop  Density  Urban Pop Quarantine Schools  \\\n",
       "0    Albania      NaN          NaN    105.0       63.0        NaN     NaN   \n",
       "1    Algeria      NaN          NaN     18.0       73.0        NaN     NaN   \n",
       "2  Argentina      NaN          NaN     17.0       93.0  3/20/2020     NaN   \n",
       "3    Armenia    694.0  4269.802594    104.0       63.0        NaN     NaN   \n",
       "4  Australia  31635.0   806.065560      3.0       86.0        NaN     NaN   \n",
       "\n",
       "  Restrictions  Hospital Bed  sex0  sex14  sex25  sex54  sex64  sex65plus  \\\n",
       "0          NaN           2.9  1.08   1.11   1.09   0.93   0.95       0.87   \n",
       "1          NaN           1.9  1.05   1.05   1.05   1.03   1.01       0.89   \n",
       "2          NaN           5.0  1.05   1.06   1.05   1.00   0.94       0.71   \n",
       "3          NaN           4.2  1.13   1.14   1.06   0.93   0.84       0.67   \n",
       "4    3/23/2020           3.8  1.06   1.06   1.09   0.99   0.93       0.86   \n",
       "\n",
       "   Sex Ratio   lung  Female Lung  Male Lung  Median Age      GDP 2018  \\\n",
       "0       0.98  11.67         7.02      17.04        32.9  1.510250e+10   \n",
       "1       1.03   8.77         5.03      12.81        28.1  1.737580e+11   \n",
       "2       0.98  29.27        20.16      42.59        31.7  5.198715e+11   \n",
       "3       0.94  23.86        16.17      35.99        35.1  1.243309e+10   \n",
       "4       0.99  18.79        15.90      22.16        38.7  1.433904e+12   \n",
       "\n",
       "   Crime Index  Population 2020  Smoking 2016  Females 2018  Total Infected  \\\n",
       "0        40.02         2877.797          28.7     49.063095             333   \n",
       "1        54.41        43851.044          15.6     49.484268            1251   \n",
       "2        62.96        45195.774          21.8     51.237348            1451   \n",
       "3        20.78         2963.243          24.1     52.956577             770   \n",
       "4        42.70        25499.884          14.7     50.199623            5550   \n",
       "\n",
       "   Total Deaths  Total Recovered  \n",
       "0            20               99  \n",
       "1           130               90  \n",
       "2            43              279  \n",
       "3             7               43  \n",
       "4            30              701  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_recovered = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv')\n",
    "total_recover = preprocess_summation(df_recovered, 'Recovered')\n",
    "df = df.merge(total_recover, how='inner', on='Country')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('covid19_by_country.csv', index=False)"
   ]
  }
 ],
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