Untitled.ipynb

{
 "cells": [
  {
   "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>iso_code</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>excess_mortality</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-05</td>\n",
       "      <td>39168.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>33.571</td>\n",
       "      <td>1605.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.571</td>\n",
       "      <td>2635.282</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-06</td>\n",
       "      <td>39189.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>35.000</td>\n",
       "      <td>1606.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.714</td>\n",
       "      <td>2636.695</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-07</td>\n",
       "      <td>39238.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>39.571</td>\n",
       "      <td>1611.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.429</td>\n",
       "      <td>2639.991</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-08</td>\n",
       "      <td>39321.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>46.143</td>\n",
       "      <td>1617.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.571</td>\n",
       "      <td>2645.576</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-09</td>\n",
       "      <td>39432.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>57.286</td>\n",
       "      <td>1622.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.286</td>\n",
       "      <td>2653.044</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-10</td>\n",
       "      <td>39496.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>57.714</td>\n",
       "      <td>1626.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.143</td>\n",
       "      <td>2657.350</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-11</td>\n",
       "      <td>39688.0</td>\n",
       "      <td>192.0</td>\n",
       "      <td>77.714</td>\n",
       "      <td>1629.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.429</td>\n",
       "      <td>2670.268</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-12</td>\n",
       "      <td>39852.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>97.714</td>\n",
       "      <td>1632.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.857</td>\n",
       "      <td>2681.302</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-13</td>\n",
       "      <td>39959.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>110.000</td>\n",
       "      <td>1632.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.714</td>\n",
       "      <td>2688.501</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-14</td>\n",
       "      <td>40077.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>119.857</td>\n",
       "      <td>1635.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.429</td>\n",
       "      <td>2696.441</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-15</td>\n",
       "      <td>40318.0</td>\n",
       "      <td>241.0</td>\n",
       "      <td>142.429</td>\n",
       "      <td>1637.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.857</td>\n",
       "      <td>2712.655</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-16</td>\n",
       "      <td>40556.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>160.571</td>\n",
       "      <td>1640.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.571</td>\n",
       "      <td>2728.668</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-17</td>\n",
       "      <td>40927.0</td>\n",
       "      <td>371.0</td>\n",
       "      <td>204.429</td>\n",
       "      <td>1647.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.000</td>\n",
       "      <td>2753.630</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-18</td>\n",
       "      <td>41335.0</td>\n",
       "      <td>408.0</td>\n",
       "      <td>235.286</td>\n",
       "      <td>1656.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3.857</td>\n",
       "      <td>2781.081</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-19</td>\n",
       "      <td>41628.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>253.714</td>\n",
       "      <td>1666.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>4.857</td>\n",
       "      <td>2800.794</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-20</td>\n",
       "      <td>41779.0</td>\n",
       "      <td>151.0</td>\n",
       "      <td>260.000</td>\n",
       "      <td>1672.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.714</td>\n",
       "      <td>2810.954</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-21</td>\n",
       "      <td>42195.0</td>\n",
       "      <td>416.0</td>\n",
       "      <td>302.571</td>\n",
       "      <td>1685.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>7.143</td>\n",
       "      <td>2838.943</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-22</td>\n",
       "      <td>42714.0</td>\n",
       "      <td>519.0</td>\n",
       "      <td>342.286</td>\n",
       "      <td>1691.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.714</td>\n",
       "      <td>2873.862</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-23</td>\n",
       "      <td>43480.0</td>\n",
       "      <td>766.0</td>\n",
       "      <td>417.714</td>\n",
       "      <td>1692.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.429</td>\n",
       "      <td>2925.400</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2021-06-24</td>\n",
       "      <td>44306.0</td>\n",
       "      <td>826.0</td>\n",
       "      <td>482.714</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>8.857</td>\n",
       "      <td>2980.974</td>\n",
       "      <td>...</td>\n",
       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 59 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          iso_code  location        date  total_cases  new_cases  \\\n",
       "continent                                                          \n",
       "Africa         ZWE  Zimbabwe  2021-06-05      39168.0       24.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-06      39189.0       21.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-07      39238.0       49.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-08      39321.0       83.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-09      39432.0      111.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-10      39496.0       64.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-11      39688.0      192.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-12      39852.0      164.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-13      39959.0      107.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-14      40077.0      118.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-15      40318.0      241.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-16      40556.0      238.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-17      40927.0      371.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-18      41335.0      408.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-19      41628.0      293.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-20      41779.0      151.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-21      42195.0      416.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-22      42714.0      519.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-23      43480.0      766.0   \n",
       "Africa         ZWE  Zimbabwe  2021-06-24      44306.0      826.0   \n",
       "\n",
       "           new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "continent                                                                      \n",
       "Africa                 33.571        1605.0         0.0                1.571   \n",
       "Africa                 35.000        1606.0         1.0                1.714   \n",
       "Africa                 39.571        1611.0         5.0                2.429   \n",
       "Africa                 46.143        1617.0         6.0                2.571   \n",
       "Africa                 57.286        1622.0         5.0                3.286   \n",
       "Africa                 57.714        1626.0         4.0                3.143   \n",
       "Africa                 77.714        1629.0         3.0                3.429   \n",
       "Africa                 97.714        1632.0         3.0                3.857   \n",
       "Africa                110.000        1632.0         0.0                3.714   \n",
       "Africa                119.857        1635.0         3.0                3.429   \n",
       "Africa                142.429        1637.0         2.0                2.857   \n",
       "Africa                160.571        1640.0         3.0                2.571   \n",
       "Africa                204.429        1647.0         7.0                3.000   \n",
       "Africa                235.286        1656.0         9.0                3.857   \n",
       "Africa                253.714        1666.0        10.0                4.857   \n",
       "Africa                260.000        1672.0         6.0                5.714   \n",
       "Africa                302.571        1685.0        13.0                7.143   \n",
       "Africa                342.286        1691.0         6.0                7.714   \n",
       "Africa                417.714        1692.0         1.0                7.429   \n",
       "Africa                482.714        1709.0        17.0                8.857   \n",
       "\n",
       "           total_cases_per_million  ...  extreme_poverty  \\\n",
       "continent                           ...                    \n",
       "Africa                    2635.282  ...             21.4   \n",
       "Africa                    2636.695  ...             21.4   \n",
       "Africa                    2639.991  ...             21.4   \n",
       "Africa                    2645.576  ...             21.4   \n",
       "Africa                    2653.044  ...             21.4   \n",
       "Africa                    2657.350  ...             21.4   \n",
       "Africa                    2670.268  ...             21.4   \n",
       "Africa                    2681.302  ...             21.4   \n",
       "Africa                    2688.501  ...             21.4   \n",
       "Africa                    2696.441  ...             21.4   \n",
       "Africa                    2712.655  ...             21.4   \n",
       "Africa                    2728.668  ...             21.4   \n",
       "Africa                    2753.630  ...             21.4   \n",
       "Africa                    2781.081  ...             21.4   \n",
       "Africa                    2800.794  ...             21.4   \n",
       "Africa                    2810.954  ...             21.4   \n",
       "Africa                    2838.943  ...             21.4   \n",
       "Africa                    2873.862  ...             21.4   \n",
       "Africa                    2925.400  ...             21.4   \n",
       "Africa                    2980.974  ...             21.4   \n",
       "\n",
       "           cardiovasc_death_rate  diabetes_prevalence  female_smokers  \\\n",
       "continent                                                               \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "Africa                   307.846                 1.82             1.6   \n",
       "\n",
       "           male_smokers  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "continent                                                                     \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "Africa             30.7                  36.791                         1.7   \n",
       "\n",
       "           life_expectancy  human_development_index  excess_mortality  \n",
       "continent                                                              \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "Africa               61.49                    0.571               NaN  \n",
       "\n",
       "[20 rows x 59 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "owid = pd.read_csv('./covid-19-data-master/public/data/owid-covid-data.csv', index_col='continent')\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "# owid\n",
    "\n",
    "df = owid.sort_values('date', ascending=True)\n",
    "df['date'] = pd.to_datetime(df['date'], format = '%Y-%m-%d')\n",
    "owid.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>new_cases_per_million</th>\n",
       "      <th>new_vaccinations_smoothed_per_million</th>\n",
       "      <th>icu_patients_per_million</th>\n",
       "      <th>hosp_patients_per_million</th>\n",
       "      <th>median_age</th>\n",
       "      <th>aged_65_older</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>population_density</th>\n",
       "      <th>stringency_index</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.325425</td>\n",
       "      <td>0.937832</td>\n",
       "      <td>0.593062</td>\n",
       "      <td>0.553575</td>\n",
       "      <td>0.401019</td>\n",
       "      <td>0.495652</td>\n",
       "      <td>0.230964</td>\n",
       "      <td>0.291016</td>\n",
       "      <td>0.608211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_vaccinations_smoothed_per_million</th>\n",
       "      <td>0.325425</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.089316</td>\n",
       "      <td>-0.013965</td>\n",
       "      <td>0.827338</td>\n",
       "      <td>0.845999</td>\n",
       "      <td>0.828583</td>\n",
       "      <td>0.795777</td>\n",
       "      <td>0.268794</td>\n",
       "      <td>0.386317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icu_patients_per_million</th>\n",
       "      <td>0.937832</td>\n",
       "      <td>-0.089316</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.669363</td>\n",
       "      <td>0.586777</td>\n",
       "      <td>0.586777</td>\n",
       "      <td>0.586777</td>\n",
       "      <td>-0.586777</td>\n",
       "      <td>0.586777</td>\n",
       "      <td>0.749390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hosp_patients_per_million</th>\n",
       "      <td>0.593062</td>\n",
       "      <td>-0.013965</td>\n",
       "      <td>0.669363</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.119942</td>\n",
       "      <td>-0.119942</td>\n",
       "      <td>-0.119942</td>\n",
       "      <td>0.119942</td>\n",
       "      <td>-0.119942</td>\n",
       "      <td>0.136446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_age</th>\n",
       "      <td>0.553575</td>\n",
       "      <td>0.827338</td>\n",
       "      <td>0.586777</td>\n",
       "      <td>-0.119942</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.938398</td>\n",
       "      <td>0.989812</td>\n",
       "      <td>0.849144</td>\n",
       "      <td>0.400099</td>\n",
       "      <td>0.648584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aged_65_older</th>\n",
       "      <td>0.401019</td>\n",
       "      <td>0.845999</td>\n",
       "      <td>0.586777</td>\n",
       "      <td>-0.119942</td>\n",
       "      <td>0.938398</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.942425</td>\n",
       "      <td>0.955951</td>\n",
       "      <td>0.362176</td>\n",
       "      <td>0.423641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>human_development_index</th>\n",
       "      <td>0.495652</td>\n",
       "      <td>0.828583</td>\n",
       "      <td>0.586777</td>\n",
       "      <td>-0.119942</td>\n",
       "      <td>0.989812</td>\n",
       "      <td>0.942425</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.893641</td>\n",
       "      <td>0.299571</td>\n",
       "      <td>0.583858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <td>0.230964</td>\n",
       "      <td>0.795777</td>\n",
       "      <td>-0.586777</td>\n",
       "      <td>0.119942</td>\n",
       "      <td>0.849144</td>\n",
       "      <td>0.955951</td>\n",
       "      <td>0.893641</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.166375</td>\n",
       "      <td>0.245241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population_density</th>\n",
       "      <td>0.291016</td>\n",
       "      <td>0.268794</td>\n",
       "      <td>0.586777</td>\n",
       "      <td>-0.119942</td>\n",
       "      <td>0.400099</td>\n",
       "      <td>0.362176</td>\n",
       "      <td>0.299571</td>\n",
       "      <td>0.166375</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.701304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stringency_index</th>\n",
       "      <td>0.608211</td>\n",
       "      <td>0.386317</td>\n",
       "      <td>0.749390</td>\n",
       "      <td>0.136446</td>\n",
       "      <td>0.648584</td>\n",
       "      <td>0.423641</td>\n",
       "      <td>0.583858</td>\n",
       "      <td>0.245241</td>\n",
       "      <td>0.701304</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       new_cases_per_million  \\\n",
       "new_cases_per_million                               1.000000   \n",
       "new_vaccinations_smoothed_per_million               0.325425   \n",
       "icu_patients_per_million                            0.937832   \n",
       "hosp_patients_per_million                           0.593062   \n",
       "median_age                                          0.553575   \n",
       "aged_65_older                                       0.401019   \n",
       "human_development_index                             0.495652   \n",
       "gdp_per_capita                                      0.230964   \n",
       "population_density                                  0.291016   \n",
       "stringency_index                                    0.608211   \n",
       "\n",
       "                                       new_vaccinations_smoothed_per_million  \\\n",
       "new_cases_per_million                                               0.325425   \n",
       "new_vaccinations_smoothed_per_million                               1.000000   \n",
       "icu_patients_per_million                                           -0.089316   \n",
       "hosp_patients_per_million                                          -0.013965   \n",
       "median_age                                                          0.827338   \n",
       "aged_65_older                                                       0.845999   \n",
       "human_development_index                                             0.828583   \n",
       "gdp_per_capita                                                      0.795777   \n",
       "population_density                                                  0.268794   \n",
       "stringency_index                                                    0.386317   \n",
       "\n",
       "                                       icu_patients_per_million  \\\n",
       "new_cases_per_million                                  0.937832   \n",
       "new_vaccinations_smoothed_per_million                 -0.089316   \n",
       "icu_patients_per_million                               1.000000   \n",
       "hosp_patients_per_million                              0.669363   \n",
       "median_age                                             0.586777   \n",
       "aged_65_older                                          0.586777   \n",
       "human_development_index                                0.586777   \n",
       "gdp_per_capita                                        -0.586777   \n",
       "population_density                                     0.586777   \n",
       "stringency_index                                       0.749390   \n",
       "\n",
       "                                       hosp_patients_per_million  median_age  \\\n",
       "new_cases_per_million                                   0.593062    0.553575   \n",
       "new_vaccinations_smoothed_per_million                  -0.013965    0.827338   \n",
       "icu_patients_per_million                                0.669363    0.586777   \n",
       "hosp_patients_per_million                               1.000000   -0.119942   \n",
       "median_age                                             -0.119942    1.000000   \n",
       "aged_65_older                                          -0.119942    0.938398   \n",
       "human_development_index                                -0.119942    0.989812   \n",
       "gdp_per_capita                                          0.119942    0.849144   \n",
       "population_density                                     -0.119942    0.400099   \n",
       "stringency_index                                        0.136446    0.648584   \n",
       "\n",
       "                                       aged_65_older  human_development_index  \\\n",
       "new_cases_per_million                       0.401019                 0.495652   \n",
       "new_vaccinations_smoothed_per_million       0.845999                 0.828583   \n",
       "icu_patients_per_million                    0.586777                 0.586777   \n",
       "hosp_patients_per_million                  -0.119942                -0.119942   \n",
       "median_age                                  0.938398                 0.989812   \n",
       "aged_65_older                               1.000000                 0.942425   \n",
       "human_development_index                     0.942425                 1.000000   \n",
       "gdp_per_capita                              0.955951                 0.893641   \n",
       "population_density                          0.362176                 0.299571   \n",
       "stringency_index                            0.423641                 0.583858   \n",
       "\n",
       "                                       gdp_per_capita  population_density  \\\n",
       "new_cases_per_million                        0.230964            0.291016   \n",
       "new_vaccinations_smoothed_per_million        0.795777            0.268794   \n",
       "icu_patients_per_million                    -0.586777            0.586777   \n",
       "hosp_patients_per_million                    0.119942           -0.119942   \n",
       "median_age                                   0.849144            0.400099   \n",
       "aged_65_older                                0.955951            0.362176   \n",
       "human_development_index                      0.893641            0.299571   \n",
       "gdp_per_capita                               1.000000            0.166375   \n",
       "population_density                           0.166375            1.000000   \n",
       "stringency_index                             0.245241            0.701304   \n",
       "\n",
       "                                       stringency_index  \n",
       "new_cases_per_million                          0.608211  \n",
       "new_vaccinations_smoothed_per_million          0.386317  \n",
       "icu_patients_per_million                       0.749390  \n",
       "hosp_patients_per_million                      0.136446  \n",
       "median_age                                     0.648584  \n",
       "aged_65_older                                  0.423641  \n",
       "human_development_index                        0.583858  \n",
       "gdp_per_capita                                 0.245241  \n",
       "population_density                             0.701304  \n",
       "stringency_index                               1.000000  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# df_indexed = pd.read_csv('./covid-19-data-master/public/data/owid-covid-data.csv', index_col='continent')\n",
    "df_indexed = pd.read_csv('./covid-19-data-master/public/data/owid-covid-data.csv')\n",
    "\n",
    "# Select latest available data\n",
    "# df_indexed = df_indexed.loc[df_indexed['date'] == '2021-04-01']\n",
    "df_indexed = df_indexed[df_indexed['date'] > '2021-04-01']\n",
    "\n",
    "# NLD\n",
    "# IND\n",
    "# USA\n",
    "# BRA\n",
    "# NER\n",
    "\n",
    "df_indexed = df_indexed[ (df_indexed.iso_code=='NLD') | (df_indexed.iso_code=='USA') | \\\n",
    "                         (df_indexed.iso_code=='IND') | (df_indexed.iso_code=='BRA') | \\\n",
    "                         (df_indexed.iso_code=='NER') ]\n",
    "# df_indexed = df_indexed + df_indexed[df_indexed.iso_code=='USA']\n",
    "# print(df_indexed)\n",
    "\n",
    "countries = df_indexed['iso_code']\n",
    "# print(countries)\n",
    "# countries = df_indexed[df_indexed.iso_code=='NLD'] + df_indexed[df_indexed.iso_code=='USA']\n",
    "# countries = df_indexed[(df_indexed.iso_code=='NLD') & (df_indexed.iso_code=='IND')]\n",
    "# data_europe = data_indexed.loc['Europe']\n",
    "\n",
    "new_cases_per_million = df_indexed['new_cases_per_million']\n",
    "new_vaccinations_smoothed_per_million = df_indexed['new_vaccinations_smoothed_per_million']\n",
    "icu_patients_per_million = df_indexed['icu_patients_per_million']\n",
    "hosp_patients_per_million = df_indexed['hosp_patients_per_million']\n",
    "median_age = df_indexed['median_age']\n",
    "aged_65_older = df_indexed['aged_65_older']\n",
    "human_development_index = df_indexed['human_development_index']\n",
    "gdp_per_capita = df_indexed['gdp_per_capita']\n",
    "population_density = df_indexed['population_density']\n",
    "stringency_index = df_indexed['stringency_index']\n",
    "\n",
    "df_cases_ = pd.DataFrame({'country':countries,\n",
    "                       'new_cases_per_million': new_cases_per_million,\n",
    "                       'new_vaccinations_smoothed_per_million': new_vaccinations_smoothed_per_million,\n",
    "                          'icu_patients_per_million' : icu_patients_per_million,\n",
    "                        'hosp_patients_per_million' : hosp_patients_per_million,\n",
    "                          'median_age': median_age,\n",
    "                          'aged_65_older': aged_65_older,\n",
    "                          'human_development_index': human_development_index,\n",
    "                          'gdp_per_capita': gdp_per_capita,\n",
    "                          'population_density': population_density,\n",
    "                        'stringency_index': stringency_index,\n",
    "                             })\n",
    "# .dropna()\n",
    "\n",
    "# df_cases_ = df_cases_.groupby('country').sum()\n",
    "# print(df_cases_)\n",
    "\n",
    "df_cases_ = df_cases_.reset_index(drop=True)\n",
    "\n",
    "# df_cases_.set_index([\"country\"], inplace = True,\n",
    "#                     append = True, drop = True)\n",
    "\n",
    "# print(df_cases_.country.unique())\n",
    "\n",
    "df_cases_.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>new_cases_per_million_NLD</th>\n",
       "      <th>new_cases_per_million_IND</th>\n",
       "      <th>new_cases_per_million_USA</th>\n",
       "      <th>new_cases_per_million_BRA</th>\n",
       "      <th>new_cases_per_million_NER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>new_cases_per_million_NLD</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.679305</td>\n",
       "      <td>0.839510</td>\n",
       "      <td>-0.094791</td>\n",
       "      <td>0.298721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_cases_per_million_IND</th>\n",
       "      <td>0.679305</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.376930</td>\n",
       "      <td>-0.099431</td>\n",
       "      <td>0.282457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_cases_per_million_USA</th>\n",
       "      <td>0.839510</td>\n",
       "      <td>0.376930</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.078713</td>\n",
       "      <td>0.125803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_cases_per_million_BRA</th>\n",
       "      <td>-0.094791</td>\n",
       "      <td>-0.099431</td>\n",
       "      <td>0.078713</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.140684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_cases_per_million_NER</th>\n",
       "      <td>0.298721</td>\n",
       "      <td>0.282457</td>\n",
       "      <td>0.125803</td>\n",
       "      <td>-0.140684</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           new_cases_per_million_NLD  \\\n",
       "new_cases_per_million_NLD                   1.000000   \n",
       "new_cases_per_million_IND                   0.679305   \n",
       "new_cases_per_million_USA                   0.839510   \n",
       "new_cases_per_million_BRA                  -0.094791   \n",
       "new_cases_per_million_NER                   0.298721   \n",
       "\n",
       "                           new_cases_per_million_IND  \\\n",
       "new_cases_per_million_NLD                   0.679305   \n",
       "new_cases_per_million_IND                   1.000000   \n",
       "new_cases_per_million_USA                   0.376930   \n",
       "new_cases_per_million_BRA                  -0.099431   \n",
       "new_cases_per_million_NER                   0.282457   \n",
       "\n",
       "                           new_cases_per_million_USA  \\\n",
       "new_cases_per_million_NLD                   0.839510   \n",
       "new_cases_per_million_IND                   0.376930   \n",
       "new_cases_per_million_USA                   1.000000   \n",
       "new_cases_per_million_BRA                   0.078713   \n",
       "new_cases_per_million_NER                   0.125803   \n",
       "\n",
       "                           new_cases_per_million_BRA  \\\n",
       "new_cases_per_million_NLD                  -0.094791   \n",
       "new_cases_per_million_IND                  -0.099431   \n",
       "new_cases_per_million_USA                   0.078713   \n",
       "new_cases_per_million_BRA                   1.000000   \n",
       "new_cases_per_million_NER                  -0.140684   \n",
       "\n",
       "                           new_cases_per_million_NER  \n",
       "new_cases_per_million_NLD                   0.298721  \n",
       "new_cases_per_million_IND                   0.282457  \n",
       "new_cases_per_million_USA                   0.125803  \n",
       "new_cases_per_million_BRA                  -0.140684  \n",
       "new_cases_per_million_NER                   1.000000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "\n",
    "# df_indexed = pd.read_csv('./covid-19-data-master/public/data/owid-covid-data.csv', index_col='continent')\n",
    "df_indexed = pd.read_csv('./covid-19-data-master/public/data/owid-covid-data.csv')\n",
    "\n",
    "# Select latest available data\n",
    "# df_indexed = df_indexed.loc[df_indexed['date'] == '2021-04-01']\n",
    "df_indexed = df_indexed[df_indexed['date'] > '2021-04-01']\n",
    "\n",
    "# NLD\n",
    "# IND\n",
    "# USA\n",
    "# BRA\n",
    "# NER\n",
    "\n",
    "df_indexed = df_indexed[ (df_indexed.iso_code=='NLD') | (df_indexed.iso_code=='USA') | \\\n",
    "                         (df_indexed.iso_code=='IND') | (df_indexed.iso_code=='BRA') | \\\n",
    "                         (df_indexed.iso_code=='NER') ]\n",
    "# df_indexed = df_indexed + df_indexed[df_indexed.iso_code=='USA']\n",
    "# print(df_indexed)\n",
    "\n",
    "countries = df_indexed['iso_code']\n",
    "# print(countries)\n",
    "# countries = df_indexed[df_indexed.iso_code=='NLD'] + df_indexed[df_indexed.iso_code=='USA']\n",
    "# countries = df_indexed[(df_indexed.iso_code=='NLD') & (df_indexed.iso_code=='IND')]\n",
    "# data_europe = data_indexed.loc['Europe']\n",
    "\n",
    "df_indexed_NLD = df_indexed[ (df_indexed.iso_code=='NLD') ]\n",
    "new_cases_per_million_NLD = df_indexed_NLD['new_cases_per_million']\n",
    "\n",
    "df_indexed_IND = df_indexed[ (df_indexed.iso_code=='IND') ]\n",
    "new_cases_per_million_IND = df_indexed_IND['new_cases_per_million']\n",
    "\n",
    "df_indexed_USA = df_indexed[ (df_indexed.iso_code=='USA') ]\n",
    "new_cases_per_million_USA = df_indexed_USA['new_cases_per_million']\n",
    "\n",
    "df_indexed_BRA = df_indexed[ (df_indexed.iso_code=='BRA') ]\n",
    "new_cases_per_million_BRA = df_indexed_BRA['new_cases_per_million']\n",
    "\n",
    "df_indexed_NER = df_indexed[ (df_indexed.iso_code=='NER') ]\n",
    "new_cases_per_million_NER = df_indexed_NER['new_cases_per_million']\n",
    "\n",
    "df_cases_ = pd.DataFrame({\n",
    "                       'new_cases_per_million_NLD': new_cases_per_million_NLD.to_numpy(),\n",
    "                        'new_cases_per_million_IND': new_cases_per_million_IND.to_numpy(),\n",
    "                        'new_cases_per_million_USA': new_cases_per_million_USA.to_numpy(),\n",
    "                        'new_cases_per_million_BRA': new_cases_per_million_BRA.to_numpy(),\n",
    "                        'new_cases_per_million_NER': new_cases_per_million_NER.to_numpy(),\n",
    "                             })\n",
    "\n",
    "df_cases_ = df_cases_.reset_index(drop=True)\n",
    "\n",
    "# df_cases_.set_index([\"country\"], inplace = True,\n",
    "#                     append = True, drop = True)\n",
    "\n",
    "# print(df_cases_.country.unique())\n",
    "# df_cases_\n",
    "df_cases_.corr()"
   ]
  },
  {
   "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>new_vaccinations_NLD</th>\n",
       "      <th>new_vaccinations_IND</th>\n",
       "      <th>new_vaccinations_USA</th>\n",
       "      <th>new_vaccinations_BRA</th>\n",
       "      <th>new_vaccinations_NER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>new_vaccinations_NLD</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.156242</td>\n",
       "      <td>-0.776130</td>\n",
       "      <td>0.402675</td>\n",
       "      <td>0.300270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_vaccinations_IND</th>\n",
       "      <td>0.156242</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.077380</td>\n",
       "      <td>0.329393</td>\n",
       "      <td>-0.210309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_vaccinations_USA</th>\n",
       "      <td>-0.776130</td>\n",
       "      <td>-0.077380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.344941</td>\n",
       "      <td>-0.490342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_vaccinations_BRA</th>\n",
       "      <td>0.402675</td>\n",
       "      <td>0.329393</td>\n",
       "      <td>-0.344941</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.471503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_vaccinations_NER</th>\n",
       "      <td>0.300270</td>\n",
       "      <td>-0.210309</td>\n",
       "      <td>-0.490342</td>\n",
       "      <td>0.471503</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      new_vaccinations_NLD  new_vaccinations_IND  \\\n",
       "new_vaccinations_NLD              1.000000              0.156242   \n",
       "new_vaccinations_IND              0.156242              1.000000   \n",
       "new_vaccinations_USA             -0.776130             -0.077380   \n",
       "new_vaccinations_BRA              0.402675              0.329393   \n",
       "new_vaccinations_NER              0.300270             -0.210309   \n",
       "\n",
       "                      new_vaccinations_USA  new_vaccinations_BRA  \\\n",
       "new_vaccinations_NLD             -0.776130              0.402675   \n",
       "new_vaccinations_IND             -0.077380              0.329393   \n",
       "new_vaccinations_USA              1.000000             -0.344941   \n",
       "new_vaccinations_BRA             -0.344941              1.000000   \n",
       "new_vaccinations_NER             -0.490342              0.471503   \n",
       "\n",
       "                      new_vaccinations_NER  \n",
       "new_vaccinations_NLD              0.300270  \n",
       "new_vaccinations_IND             -0.210309  \n",
       "new_vaccinations_USA             -0.490342  \n",
       "new_vaccinations_BRA              0.471503  \n",
       "new_vaccinations_NER              1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indexed_NLD = df_indexed[ (df_indexed.iso_code=='NLD') ]\n",
    "new_vaccinations_NLD = df_indexed_NLD['new_vaccinations_smoothed_per_million']\n",
    "\n",
    "df_indexed_IND = df_indexed[ (df_indexed.iso_code=='IND') ]\n",
    "new_vaccinations_IND = df_indexed_IND['new_vaccinations_smoothed_per_million']\n",
    "\n",
    "df_indexed_USA = df_indexed[ (df_indexed.iso_code=='USA') ]\n",
    "new_vaccinations_USA = df_indexed_USA['new_vaccinations_smoothed_per_million']\n",
    "\n",
    "df_indexed_BRA = df_indexed[ (df_indexed.iso_code=='BRA') ]\n",
    "new_vaccinations_BRA = df_indexed_BRA['new_vaccinations_smoothed_per_million']\n",
    "\n",
    "df_indexed_NER = df_indexed[ (df_indexed.iso_code=='NER') ]\n",
    "new_vaccinations_NER = df_indexed_NER['new_vaccinations_smoothed_per_million']\n",
    "\n",
    "\n",
    "df_cases_ = pd.DataFrame({\n",
    "                       'new_vaccinations_NLD': new_vaccinations_NLD.to_numpy(),\n",
    "                        'new_vaccinations_IND': new_vaccinations_IND.to_numpy(),\n",
    "                        'new_vaccinations_USA': new_vaccinations_USA.to_numpy(),\n",
    "                        'new_vaccinations_BRA': new_vaccinations_BRA.to_numpy(),\n",
    "                        'new_vaccinations_NER': new_vaccinations_NER.to_numpy(),\n",
    "                             })\n",
    "# .dropna()\n",
    "\n",
    "df_cases_ = df_cases_.reset_index(drop=True)\n",
    "\n",
    "# df_cases_.set_index([\"country\"], inplace = True,\n",
    "#                     append = True, drop = True)\n",
    "\n",
    "# print(df_cases_.country.unique())\n",
    "# df_cases_\n",
    "\n",
    "df_cases_.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>stringency_NLD</th>\n",
       "      <th>stringency_IND</th>\n",
       "      <th>stringency_USA</th>\n",
       "      <th>stringency_BRA</th>\n",
       "      <th>stringency_NER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>stringency_NLD</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.776138</td>\n",
       "      <td>0.653362</td>\n",
       "      <td>0.662962</td>\n",
       "      <td>0.568871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stringency_IND</th>\n",
       "      <td>-0.776138</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.834049</td>\n",
       "      <td>-0.476415</td>\n",
       "      <td>-0.634995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stringency_USA</th>\n",
       "      <td>0.653362</td>\n",
       "      <td>-0.834049</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.152212</td>\n",
       "      <td>0.729066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stringency_BRA</th>\n",
       "      <td>0.662962</td>\n",
       "      <td>-0.476415</td>\n",
       "      <td>0.152212</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.317607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stringency_NER</th>\n",
       "      <td>0.568871</td>\n",
       "      <td>-0.634995</td>\n",
       "      <td>0.729066</td>\n",
       "      <td>0.317607</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                stringency_NLD  stringency_IND  stringency_USA  \\\n",
       "stringency_NLD        1.000000       -0.776138        0.653362   \n",
       "stringency_IND       -0.776138        1.000000       -0.834049   \n",
       "stringency_USA        0.653362       -0.834049        1.000000   \n",
       "stringency_BRA        0.662962       -0.476415        0.152212   \n",
       "stringency_NER        0.568871       -0.634995        0.729066   \n",
       "\n",
       "                stringency_BRA  stringency_NER  \n",
       "stringency_NLD        0.662962        0.568871  \n",
       "stringency_IND       -0.476415       -0.634995  \n",
       "stringency_USA        0.152212        0.729066  \n",
       "stringency_BRA        1.000000        0.317607  \n",
       "stringency_NER        0.317607        1.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df_indexed_NLD = df_indexed[ (df_indexed.iso_code=='NLD') ]\n",
    "stringency_NLD = df_indexed_NLD['stringency_index']\n",
    "\n",
    "df_indexed_IND = df_indexed[ (df_indexed.iso_code=='IND') ]\n",
    "stringency_IND = df_indexed_IND['stringency_index']\n",
    "\n",
    "df_indexed_USA = df_indexed[ (df_indexed.iso_code=='USA') ]\n",
    "stringency_USA = df_indexed_USA['stringency_index']\n",
    "\n",
    "df_indexed_BRA = df_indexed[ (df_indexed.iso_code=='BRA') ]\n",
    "stringency_BRA = df_indexed_BRA['stringency_index']\n",
    "\n",
    "df_indexed_NER = df_indexed[ (df_indexed.iso_code=='NER') ]\n",
    "stringency_NER = df_indexed_NER['stringency_index']\n",
    "\n",
    "\n",
    "df_cases_ = pd.DataFrame({\n",
    "                       'stringency_NLD': stringency_NLD.to_numpy(),\n",
    "                        'stringency_IND': stringency_IND.to_numpy(),\n",
    "                        'stringency_USA': stringency_USA.to_numpy(),\n",
    "                        'stringency_BRA': stringency_BRA.to_numpy(),\n",
    "                        'stringency_NER': stringency_NER.to_numpy(),\n",
    "                             })\n",
    "# .dropna()\n",
    "\n",
    "df_cases_ = df_cases_.reset_index(drop=True)\n",
    "\n",
    "# df_cases_.set_index([\"country\"], inplace = True,\n",
    "#                     append = True, drop = True)\n",
    "\n",
    "# print(df_cases_.country.unique())\n",
    "# df_cases_\n",
    "# .isnull().sum()\n",
    "\n",
    "df_cases_.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}