Cross-country_Corr_Hamid.ipynb

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       "  <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",
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       "      <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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <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",
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       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
       "      <td>1.82</td>\n",
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       "      <td>30.7</td>\n",
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       "      <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",
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       "      <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",
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       "    <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",
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       "      <td>2639.991</td>\n",
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       "      <td>21.4</td>\n",
       "      <td>307.846</td>\n",
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       "    <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",
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       "      <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",
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       "    <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",
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       "      <th>Africa</th>\n",
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       "      <td>2021-06-10</td>\n",
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       "    <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",
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       "      <td>21.4</td>\n",
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       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
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       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>ZWE</td>\n",
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       "      <td>2021-06-12</td>\n",
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       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
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       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
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       "    <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",
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       "      <td>2688.501</td>\n",
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       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
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       "    <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",
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       "    <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",
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       "    <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",
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       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
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       "    <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",
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       "    <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",
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       "      <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",
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       "    <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",
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       "    <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": 1,
     "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": 2,
   "metadata": {},
   "outputs": [
    {
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       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow5_col9 {\n",
       "            background-color:  #f7a889;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col0 {\n",
       "            background-color:  #f49a7b;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col1 {\n",
       "            background-color:  #d1493f;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col2 {\n",
       "            background-color:  #ee8669;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col3 {\n",
       "            background-color:  #ccd9ed;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col4 {\n",
       "            background-color:  #b50927;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col5 {\n",
       "            background-color:  #be242e;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col6 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col7 {\n",
       "            background-color:  #c73635;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col8 {\n",
       "            background-color:  #f6bda2;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col9 {\n",
       "            background-color:  #ef886b;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col0 {\n",
       "            background-color:  #f3c7b1;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col1 {\n",
       "            background-color:  #d75445;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col2 {\n",
       "            background-color:  #7da0f9;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col3 {\n",
       "            background-color:  #ebd3c6;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col4 {\n",
       "            background-color:  #cf453c;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col5 {\n",
       "            background-color:  #bb1b2c;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col6 {\n",
       "            background-color:  #c73635;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col7 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col8 {\n",
       "            background-color:  #efcebd;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col9 {\n",
       "            background-color:  #f4c5ad;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col0 {\n",
       "            background-color:  #f6bea4;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col1 {\n",
       "            background-color:  #f5c1a9;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col2 {\n",
       "            background-color:  #ee8669;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col3 {\n",
       "            background-color:  #ccd9ed;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col4 {\n",
       "            background-color:  #f7ac8e;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col5 {\n",
       "            background-color:  #f7b396;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col6 {\n",
       "            background-color:  #f6bda2;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col7 {\n",
       "            background-color:  #efcebd;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col8 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col9 {\n",
       "            background-color:  #e36c55;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col0 {\n",
       "            background-color:  #ed8366;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col1 {\n",
       "            background-color:  #f7af91;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col2 {\n",
       "            background-color:  #de614d;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col3 {\n",
       "            background-color:  #edd2c3;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col4 {\n",
       "            background-color:  #e9785d;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col5 {\n",
       "            background-color:  #f7a889;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col6 {\n",
       "            background-color:  #ef886b;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col7 {\n",
       "            background-color:  #f4c5ad;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col8 {\n",
       "            background-color:  #e36c55;\n",
       "            color:  #000000;\n",
       "        }    #T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col9 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_a59200f6_d71b_11eb_842c_8d164086f33a\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >new_cases_per_million</th>        <th class=\"col_heading level0 col1\" >new_vaccinations_smoothed_per_million</th>        <th class=\"col_heading level0 col2\" >icu_patients_per_million</th>        <th class=\"col_heading level0 col3\" >hosp_patients_per_million</th>        <th class=\"col_heading level0 col4\" >median_age</th>        <th class=\"col_heading level0 col5\" >aged_65_older</th>        <th class=\"col_heading level0 col6\" >human_development_index</th>        <th class=\"col_heading level0 col7\" >gdp_per_capita</th>        <th class=\"col_heading level0 col8\" >population_density</th>        <th class=\"col_heading level0 col9\" >stringency_index</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_a59200f6_d71b_11eb_842c_8d164086f33alevel0_row0\" class=\"row_heading level0 row0\" >new_cases_per_million</th>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow0_col1\" class=\"data row0 col1\" >0.325425</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow0_col2\" class=\"data row0 col2\" >0.937832</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow0_col3\" class=\"data row0 col3\" >0.593062</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow0_col4\" class=\"data row0 col4\" >0.553575</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow0_col5\" class=\"data row0 col5\" >0.401019</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow0_col6\" class=\"data row0 col6\" >0.495652</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow0_col7\" class=\"data row0 col7\" >0.230964</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow0_col8\" class=\"data row0 col8\" >0.291016</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow0_col9\" class=\"data row0 col9\" >0.608211</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a59200f6_d71b_11eb_842c_8d164086f33alevel0_row1\" class=\"row_heading level0 row1\" >new_vaccinations_smoothed_per_million</th>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow1_col0\" class=\"data row1 col0\" >0.325425</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow1_col2\" class=\"data row1 col2\" >-0.089316</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow1_col3\" class=\"data row1 col3\" >-0.013965</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow1_col4\" class=\"data row1 col4\" >0.827338</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow1_col5\" class=\"data row1 col5\" >0.845999</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow1_col6\" class=\"data row1 col6\" >0.828583</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow1_col7\" class=\"data row1 col7\" >0.795777</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow1_col8\" class=\"data row1 col8\" >0.268794</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow1_col9\" class=\"data row1 col9\" >0.386317</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a59200f6_d71b_11eb_842c_8d164086f33alevel0_row2\" class=\"row_heading level0 row2\" >icu_patients_per_million</th>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow2_col0\" class=\"data row2 col0\" >0.937832</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow2_col1\" class=\"data row2 col1\" >-0.089316</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow2_col3\" class=\"data row2 col3\" >0.669363</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow2_col4\" class=\"data row2 col4\" >0.586777</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow2_col5\" class=\"data row2 col5\" >0.586777</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow2_col6\" class=\"data row2 col6\" >0.586777</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow2_col7\" class=\"data row2 col7\" >-0.586777</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow2_col8\" class=\"data row2 col8\" >0.586777</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow2_col9\" class=\"data row2 col9\" >0.749390</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a59200f6_d71b_11eb_842c_8d164086f33alevel0_row3\" class=\"row_heading level0 row3\" >hosp_patients_per_million</th>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow3_col0\" class=\"data row3 col0\" >0.593062</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow3_col1\" class=\"data row3 col1\" >-0.013965</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow3_col2\" class=\"data row3 col2\" >0.669363</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow3_col4\" class=\"data row3 col4\" >-0.119942</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow3_col5\" class=\"data row3 col5\" >-0.119942</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow3_col6\" class=\"data row3 col6\" >-0.119942</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow3_col7\" class=\"data row3 col7\" >0.119942</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow3_col8\" class=\"data row3 col8\" >-0.119942</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow3_col9\" class=\"data row3 col9\" >0.136446</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a59200f6_d71b_11eb_842c_8d164086f33alevel0_row4\" class=\"row_heading level0 row4\" >median_age</th>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow4_col0\" class=\"data row4 col0\" >0.553575</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow4_col1\" class=\"data row4 col1\" >0.827338</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow4_col2\" class=\"data row4 col2\" >0.586777</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow4_col3\" class=\"data row4 col3\" >-0.119942</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow4_col5\" class=\"data row4 col5\" >0.938398</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow4_col6\" class=\"data row4 col6\" >0.989812</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow4_col7\" class=\"data row4 col7\" >0.849144</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow4_col8\" class=\"data row4 col8\" >0.400099</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow4_col9\" class=\"data row4 col9\" >0.648584</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a59200f6_d71b_11eb_842c_8d164086f33alevel0_row5\" class=\"row_heading level0 row5\" >aged_65_older</th>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow5_col0\" class=\"data row5 col0\" >0.401019</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow5_col1\" class=\"data row5 col1\" >0.845999</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow5_col2\" class=\"data row5 col2\" >0.586777</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow5_col3\" class=\"data row5 col3\" >-0.119942</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow5_col4\" class=\"data row5 col4\" >0.938398</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow5_col6\" class=\"data row5 col6\" >0.942425</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow5_col7\" class=\"data row5 col7\" >0.955951</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow5_col8\" class=\"data row5 col8\" >0.362176</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow5_col9\" class=\"data row5 col9\" >0.423641</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a59200f6_d71b_11eb_842c_8d164086f33alevel0_row6\" class=\"row_heading level0 row6\" >human_development_index</th>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col0\" class=\"data row6 col0\" >0.495652</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col1\" class=\"data row6 col1\" >0.828583</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col2\" class=\"data row6 col2\" >0.586777</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col3\" class=\"data row6 col3\" >-0.119942</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col4\" class=\"data row6 col4\" >0.989812</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col5\" class=\"data row6 col5\" >0.942425</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col7\" class=\"data row6 col7\" >0.893641</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col8\" class=\"data row6 col8\" >0.299571</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow6_col9\" class=\"data row6 col9\" >0.583858</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a59200f6_d71b_11eb_842c_8d164086f33alevel0_row7\" class=\"row_heading level0 row7\" >gdp_per_capita</th>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col0\" class=\"data row7 col0\" >0.230964</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col1\" class=\"data row7 col1\" >0.795777</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col2\" class=\"data row7 col2\" >-0.586777</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col3\" class=\"data row7 col3\" >0.119942</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col4\" class=\"data row7 col4\" >0.849144</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col5\" class=\"data row7 col5\" >0.955951</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col6\" class=\"data row7 col6\" >0.893641</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col8\" class=\"data row7 col8\" >0.166375</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow7_col9\" class=\"data row7 col9\" >0.245241</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a59200f6_d71b_11eb_842c_8d164086f33alevel0_row8\" class=\"row_heading level0 row8\" >population_density</th>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col0\" class=\"data row8 col0\" >0.291016</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col1\" class=\"data row8 col1\" >0.268794</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col2\" class=\"data row8 col2\" >0.586777</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col3\" class=\"data row8 col3\" >-0.119942</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col4\" class=\"data row8 col4\" >0.400099</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col5\" class=\"data row8 col5\" >0.362176</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col6\" class=\"data row8 col6\" >0.299571</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col7\" class=\"data row8 col7\" >0.166375</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow8_col9\" class=\"data row8 col9\" >0.701304</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_a59200f6_d71b_11eb_842c_8d164086f33alevel0_row9\" class=\"row_heading level0 row9\" >stringency_index</th>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col0\" class=\"data row9 col0\" >0.608211</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col1\" class=\"data row9 col1\" >0.386317</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col2\" class=\"data row9 col2\" >0.749390</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col3\" class=\"data row9 col3\" >0.136446</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col4\" class=\"data row9 col4\" >0.648584</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col5\" class=\"data row9 col5\" >0.423641</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col6\" class=\"data row9 col6\" >0.583858</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col7\" class=\"data row9 col7\" >0.245241</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col8\" class=\"data row9 col8\" >0.701304</td>\n",
       "                        <td id=\"T_a59200f6_d71b_11eb_842c_8d164086f33arow9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
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       "<pandas.io.formats.style.Styler at 0x7f25601f33d0>"
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     },
     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "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",
    "corr = df_cases_.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm', vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "        }</style><table id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33a\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >new_cases_per_million_NLD</th>        <th class=\"col_heading level0 col1\" >new_cases_per_million_IND</th>        <th class=\"col_heading level0 col2\" >new_cases_per_million_USA</th>        <th class=\"col_heading level0 col3\" >new_cases_per_million_BRA</th>        <th class=\"col_heading level0 col4\" >new_cases_per_million_NER</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33alevel0_row0\" class=\"row_heading level0 row0\" >new_cases_per_million_NLD</th>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
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       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow0_col4\" class=\"data row0 col4\" >0.298721</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33alevel0_row1\" class=\"row_heading level0 row1\" >new_cases_per_million_IND</th>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow1_col0\" class=\"data row1 col0\" >0.679305</td>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow1_col2\" class=\"data row1 col2\" >0.376930</td>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow1_col3\" class=\"data row1 col3\" >-0.099431</td>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow1_col4\" class=\"data row1 col4\" >0.282457</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33alevel0_row2\" class=\"row_heading level0 row2\" >new_cases_per_million_USA</th>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow2_col0\" class=\"data row2 col0\" >0.839510</td>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow2_col1\" class=\"data row2 col1\" >0.376930</td>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow2_col3\" class=\"data row2 col3\" >0.078713</td>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow2_col4\" class=\"data row2 col4\" >0.125803</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33alevel0_row3\" class=\"row_heading level0 row3\" >new_cases_per_million_BRA</th>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow3_col0\" class=\"data row3 col0\" >-0.094791</td>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow3_col1\" class=\"data row3 col1\" >-0.099431</td>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow3_col2\" class=\"data row3 col2\" >0.078713</td>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
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       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33alevel0_row4\" class=\"row_heading level0 row4\" >new_cases_per_million_NER</th>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow4_col0\" class=\"data row4 col0\" >0.298721</td>\n",
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       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow4_col2\" class=\"data row4 col2\" >0.125803</td>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow4_col3\" class=\"data row4 col3\" >-0.140684</td>\n",
       "                        <td id=\"T_c4359fcc_d71b_11eb_842c_8d164086f33arow4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
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     },
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     "metadata": {},
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    }
   ],
   "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",
    "corr = df_cases_.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm', vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "                <tr>\n",
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       "            <tr>\n",
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       "            <tr>\n",
       "                        <th id=\"T_d32307c2_d71b_11eb_842c_8d164086f33alevel0_row3\" class=\"row_heading level0 row3\" >new_vaccinations_BRA</th>\n",
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       "                        <td id=\"T_d32307c2_d71b_11eb_842c_8d164086f33arow4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
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      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f25601e5d00>"
      ]
     },
     "execution_count": 4,
     "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",
    "corr = df_cases_.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm', vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
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       "            background-color:  #e0654f;\n",
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       "            background-color:  #f7ba9f;\n",
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       "            background-color:  #b40426;\n",
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       "        }</style><table id=\"T_db98605a_d71b_11eb_842c_8d164086f33a\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >stringency_NLD</th>        <th class=\"col_heading level0 col1\" >stringency_IND</th>        <th class=\"col_heading level0 col2\" >stringency_USA</th>        <th class=\"col_heading level0 col3\" >stringency_BRA</th>        <th class=\"col_heading level0 col4\" >stringency_NER</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_db98605a_d71b_11eb_842c_8d164086f33alevel0_row0\" class=\"row_heading level0 row0\" >stringency_NLD</th>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
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       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow0_col3\" class=\"data row0 col3\" >0.662962</td>\n",
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       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_db98605a_d71b_11eb_842c_8d164086f33alevel0_row1\" class=\"row_heading level0 row1\" >stringency_IND</th>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow1_col0\" class=\"data row1 col0\" >-0.776138</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow1_col2\" class=\"data row1 col2\" >-0.834049</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow1_col3\" class=\"data row1 col3\" >-0.476415</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow1_col4\" class=\"data row1 col4\" >-0.634995</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_db98605a_d71b_11eb_842c_8d164086f33alevel0_row2\" class=\"row_heading level0 row2\" >stringency_USA</th>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow2_col0\" class=\"data row2 col0\" >0.653362</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow2_col1\" class=\"data row2 col1\" >-0.834049</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow2_col3\" class=\"data row2 col3\" >0.152212</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow2_col4\" class=\"data row2 col4\" >0.729066</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_db98605a_d71b_11eb_842c_8d164086f33alevel0_row3\" class=\"row_heading level0 row3\" >stringency_BRA</th>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow3_col0\" class=\"data row3 col0\" >0.662962</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow3_col1\" class=\"data row3 col1\" >-0.476415</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow3_col2\" class=\"data row3 col2\" >0.152212</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow3_col4\" class=\"data row3 col4\" >0.317607</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_db98605a_d71b_11eb_842c_8d164086f33alevel0_row4\" class=\"row_heading level0 row4\" >stringency_NER</th>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow4_col0\" class=\"data row4 col0\" >0.568871</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow4_col1\" class=\"data row4 col1\" >-0.634995</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow4_col2\" class=\"data row4 col2\" >0.729066</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow4_col3\" class=\"data row4 col3\" >0.317607</td>\n",
       "                        <td id=\"T_db98605a_d71b_11eb_842c_8d164086f33arow4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f25238d6b50>"
      ]
     },
     "execution_count": 5,
     "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",
    "corr = df_cases_.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm', vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
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       "            color:  #f1f1f1;\n",
       "        }    #T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow0_col1 {\n",
       "            background-color:  #cbd8ee;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f29072;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f39778;\n",
       "            color:  #000000;\n",
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       "            background-color:  #cbd8ee;\n",
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       "            background-color:  #b40426;\n",
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       "            background-color:  #f29072;\n",
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       "            background-color:  #ccd9ed;\n",
       "            color:  #000000;\n",
       "        }    #T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow2_col2 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow2_col3 {\n",
       "            background-color:  #e67259;\n",
       "            color:  #000000;\n",
       "        }    #T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow3_col0 {\n",
       "            background-color:  #f39778;\n",
       "            color:  #000000;\n",
       "        }    #T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow3_col1 {\n",
       "            background-color:  #bcd2f7;\n",
       "            color:  #000000;\n",
       "        }    #T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow3_col2 {\n",
       "            background-color:  #e67259;\n",
       "            color:  #000000;\n",
       "        }    #T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow3_col3 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33a\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >new_deaths_per_million_NLD</th>        <th class=\"col_heading level0 col1\" >new_deaths_per_million_IND</th>        <th class=\"col_heading level0 col2\" >new_deaths_per_million_USA</th>        <th class=\"col_heading level0 col3\" >new_deaths_per_million_BRA</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33alevel0_row0\" class=\"row_heading level0 row0\" >new_deaths_per_million_NLD</th>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow0_col1\" class=\"data row0 col1\" >-0.128170</td>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow0_col2\" class=\"data row0 col2\" >0.541713</td>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow0_col3\" class=\"data row0 col3\" >0.515136</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33alevel0_row1\" class=\"row_heading level0 row1\" >new_deaths_per_million_IND</th>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow1_col0\" class=\"data row1 col0\" >-0.128170</td>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow1_col2\" class=\"data row1 col2\" >-0.117921</td>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow1_col3\" class=\"data row1 col3\" >-0.222284</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33alevel0_row2\" class=\"row_heading level0 row2\" >new_deaths_per_million_USA</th>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow2_col0\" class=\"data row2 col0\" >0.541713</td>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow2_col1\" class=\"data row2 col1\" >-0.117921</td>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow2_col3\" class=\"data row2 col3\" >0.676233</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33alevel0_row3\" class=\"row_heading level0 row3\" >new_deaths_per_million_BRA</th>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow3_col0\" class=\"data row3 col0\" >0.515136</td>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow3_col1\" class=\"data row3 col1\" >-0.222284</td>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow3_col2\" class=\"data row3 col2\" >0.676233</td>\n",
       "                        <td id=\"T_dfdd7b1e_d71b_11eb_842c_8d164086f33arow3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f25238d6610>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indexed_NLD = df_indexed[ (df_indexed.iso_code=='NLD') ]\n",
    "new_deaths_per_million_NLD = df_indexed_NLD['new_deaths_per_million']\n",
    "\n",
    "df_indexed_IND = df_indexed[ (df_indexed.iso_code=='IND') ]\n",
    "new_deaths_per_million_IND = df_indexed_IND['new_deaths_per_million']\n",
    "\n",
    "df_indexed_USA = df_indexed[ (df_indexed.iso_code=='USA') ]\n",
    "new_deaths_per_million_USA = df_indexed_USA['new_deaths_per_million']\n",
    "\n",
    "df_indexed_BRA = df_indexed[ (df_indexed.iso_code=='BRA') ]\n",
    "new_deaths_per_million_BRA = df_indexed_BRA['new_deaths_per_million']\n",
    "\n",
    "df_cases_ = pd.DataFrame({\n",
    "                       'new_deaths_per_million_NLD': new_deaths_per_million_NLD.to_numpy(),\n",
    "                        'new_deaths_per_million_IND': new_deaths_per_million_IND.to_numpy(),\n",
    "                        'new_deaths_per_million_USA': new_deaths_per_million_USA.to_numpy(),\n",
    "    'new_deaths_per_million_BRA': new_deaths_per_million_BRA.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",
    "corr = df_cases_.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm', vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
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       "            background-color:  #bb1b2c;\n",
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       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_09892b8e_d668_11eb_bd2a_8b17a2804afcrow1_col0 {\n",
       "            background-color:  #bb1b2c;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #b70d28;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_09892b8e_d668_11eb_bd2a_8b17a2804afcrow2_col0 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_09892b8e_d668_11eb_bd2a_8b17a2804afcrow2_col1 {\n",
       "            background-color:  #b70d28;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_09892b8e_d668_11eb_bd2a_8b17a2804afcrow2_col2 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_09892b8e_d668_11eb_bd2a_8b17a2804afc\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >total_deaths_per_million_NLD</th>        <th class=\"col_heading level0 col1\" >total_deaths_per_million_IND</th>        <th class=\"col_heading level0 col2\" >total_deaths_per_million_USA</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_09892b8e_d668_11eb_bd2a_8b17a2804afclevel0_row0\" class=\"row_heading level0 row0\" >total_deaths_per_million_NLD</th>\n",
       "                        <td id=\"T_09892b8e_d668_11eb_bd2a_8b17a2804afcrow0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_09892b8e_d668_11eb_bd2a_8b17a2804afcrow0_col1\" class=\"data row0 col1\" >0.954341</td>\n",
       "                        <td id=\"T_09892b8e_d668_11eb_bd2a_8b17a2804afcrow0_col2\" class=\"data row0 col2\" >0.993554</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_09892b8e_d668_11eb_bd2a_8b17a2804afclevel0_row1\" class=\"row_heading level0 row1\" >total_deaths_per_million_IND</th>\n",
       "                        <td id=\"T_09892b8e_d668_11eb_bd2a_8b17a2804afcrow1_col0\" class=\"data row1 col0\" >0.954341</td>\n",
       "                        <td id=\"T_09892b8e_d668_11eb_bd2a_8b17a2804afcrow1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_09892b8e_d668_11eb_bd2a_8b17a2804afcrow1_col2\" class=\"data row1 col2\" >0.977016</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_09892b8e_d668_11eb_bd2a_8b17a2804afclevel0_row2\" class=\"row_heading level0 row2\" >total_deaths_per_million_USA</th>\n",
       "                        <td id=\"T_09892b8e_d668_11eb_bd2a_8b17a2804afcrow2_col0\" class=\"data row2 col0\" >0.993554</td>\n",
       "                        <td id=\"T_09892b8e_d668_11eb_bd2a_8b17a2804afcrow2_col1\" class=\"data row2 col1\" >0.977016</td>\n",
       "                        <td id=\"T_09892b8e_d668_11eb_bd2a_8b17a2804afcrow2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f471da2a0a0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indexed_NLD = df_indexed[ (df_indexed.iso_code=='NLD') ]\n",
    "total_deaths_per_million_NLD = df_indexed_NLD['total_deaths_per_million']\n",
    "\n",
    "df_indexed_IND = df_indexed[ (df_indexed.iso_code=='IND') ]\n",
    "total_deaths_per_million_IND = df_indexed_IND['total_deaths_per_million']\n",
    "\n",
    "df_indexed_USA = df_indexed[ (df_indexed.iso_code=='USA') ]\n",
    "total_deaths_per_million_USA = df_indexed_USA['total_deaths_per_million']\n",
    "\n",
    "df_cases_ = pd.DataFrame({\n",
    "                       'total_deaths_per_million_NLD': total_deaths_per_million_NLD.to_numpy(),\n",
    "                        'total_deaths_per_million_IND': total_deaths_per_million_IND.to_numpy(),\n",
    "                        'total_deaths_per_million_USA': total_deaths_per_million_USA.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",
    "corr = df_cases_.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm', vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    aged_65_older_NLD  aged_65_older_IND  aged_65_older_USA  aged_65_older_BRA\n",
      "0              18.779              5.989             15.413              8.552\n",
      "1              18.779              5.989             15.413              8.552\n",
      "2              18.779              5.989             15.413              8.552\n",
      "3              18.779              5.989             15.413              8.552\n",
      "4              18.779              5.989             15.413              8.552\n",
      "..                ...                ...                ...                ...\n",
      "79             18.779              5.989             15.413              8.552\n",
      "80             18.779              5.989             15.413              8.552\n",
      "81             18.779              5.989             15.413              8.552\n",
      "82             18.779              5.989             15.413              8.552\n",
      "83             18.779              5.989             15.413              8.552\n",
      "\n",
      "[84 rows x 4 columns]\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# aged_65_older\n",
    "\n",
    "df_indexed_NLD = df_indexed[ (df_indexed.iso_code=='NLD') ]\n",
    "aged_65_older_NLD = df_indexed_NLD['aged_65_older'].dropna()\n",
    "\n",
    "df_indexed_IND = df_indexed[ (df_indexed.iso_code=='IND') ]\n",
    "aged_65_older_IND = df_indexed_IND['aged_65_older'].dropna()\n",
    "\n",
    "df_indexed_USA = df_indexed[ (df_indexed.iso_code=='USA') ]\n",
    "aged_65_older_USA = df_indexed_USA['aged_65_older'].dropna()\n",
    "\n",
    "df_indexed_BRA = df_indexed[ (df_indexed.iso_code=='BRA') ]\n",
    "aged_65_older_BRA = df_indexed_BRA['aged_65_older'].dropna()\n",
    "\n",
    "df_cases_ = pd.DataFrame({\n",
    "                       'aged_65_older_NLD': aged_65_older_NLD.to_numpy(),\n",
    "                        'aged_65_older_IND': aged_65_older_IND.to_numpy(),\n",
    "                        'aged_65_older_USA': aged_65_older_USA.to_numpy(),\n",
    "    'aged_65_older_BRA': aged_65_older_BRA.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",
    "print(df_cases_)\n",
    "# corr = df_cases_.corr()\n",
    "# corr.style.background_gradient(cmap='coolwarm', vmin=-1, vmax=1)\n",
    "# \n",
    "nld = aged_65_older_NLD.to_numpy()\n",
    "nld = np.average(nld)\n",
    "ind = aged_65_older_IND.to_numpy()\n",
    "ind = np.average(ind)\n",
    "usa = aged_65_older_USA.to_numpy()\n",
    "usa = np.average(usa)\n",
    "bra = aged_65_older_BRA.to_numpy()\n",
    "bra = np.average(bra)\n",
    "\n",
    "# df_bar = pd.DataFrame({'aged_65_older_NLD': nld,\n",
    "#                         'aged_65_older_IND': ind,\n",
    "#                         'aged_65_older_USA': usa,}, index=0)\n",
    "\n",
    "df_bar = pd.DataFrame({'countries':['NLD', 'IND', 'USA', 'BRA'], 'val':[nld, ind, usa, bra]})\n",
    "ax = df_bar.plot.bar(x='countries', y='val', rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    aged_70_older_NLD  aged_70_older_IND  aged_70_older_USA\n",
      "0              11.881              3.414              9.732\n",
      "1              11.881              3.414              9.732\n",
      "2              11.881              3.414              9.732\n",
      "3              11.881              3.414              9.732\n",
      "4              11.881              3.414              9.732\n",
      "..                ...                ...                ...\n",
      "79             11.881              3.414              9.732\n",
      "80             11.881              3.414              9.732\n",
      "81             11.881              3.414              9.732\n",
      "82             11.881              3.414              9.732\n",
      "83             11.881              3.414              9.732\n",
      "\n",
      "[84 rows x 3 columns]\n"
     ]
    },
    {
     "data": {
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       "                        <th id=\"T_d9e8d4aa_d68b_11eb_9d1b_ab5e46816eb1level0_row0\" class=\"row_heading level0 row0\" >aged_70_older_NLD</th>\n",
       "                        <td id=\"T_d9e8d4aa_d68b_11eb_9d1b_ab5e46816eb1row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_d9e8d4aa_d68b_11eb_9d1b_ab5e46816eb1row0_col1\" class=\"data row0 col1\" >1.000000</td>\n",
       "                        <td id=\"T_d9e8d4aa_d68b_11eb_9d1b_ab5e46816eb1row0_col2\" class=\"data row0 col2\" >1.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d9e8d4aa_d68b_11eb_9d1b_ab5e46816eb1level0_row1\" class=\"row_heading level0 row1\" >aged_70_older_IND</th>\n",
       "                        <td id=\"T_d9e8d4aa_d68b_11eb_9d1b_ab5e46816eb1row1_col0\" class=\"data row1 col0\" >1.000000</td>\n",
       "                        <td id=\"T_d9e8d4aa_d68b_11eb_9d1b_ab5e46816eb1row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_d9e8d4aa_d68b_11eb_9d1b_ab5e46816eb1row1_col2\" class=\"data row1 col2\" >1.000000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_d9e8d4aa_d68b_11eb_9d1b_ab5e46816eb1level0_row2\" class=\"row_heading level0 row2\" >aged_70_older_USA</th>\n",
       "                        <td id=\"T_d9e8d4aa_d68b_11eb_9d1b_ab5e46816eb1row2_col0\" class=\"data row2 col0\" >1.000000</td>\n",
       "                        <td id=\"T_d9e8d4aa_d68b_11eb_9d1b_ab5e46816eb1row2_col1\" class=\"data row2 col1\" >1.000000</td>\n",
       "                        <td id=\"T_d9e8d4aa_d68b_11eb_9d1b_ab5e46816eb1row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f86d26180a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# aged_65_older\n",
    "\n",
    "df_indexed_NLD = df_indexed[ (df_indexed.iso_code=='NLD') ]\n",
    "aged_70_older_NLD = df_indexed_NLD['aged_70_older'].dropna()\n",
    "\n",
    "# print(aged_65_older_NLD)\n",
    "\n",
    "df_indexed_IND = df_indexed[ (df_indexed.iso_code=='IND') ]\n",
    "aged_70_older_IND = df_indexed_IND['aged_70_older'].dropna()\n",
    "\n",
    "# print(aged_65_older_IND)\n",
    "\n",
    "df_indexed_USA = df_indexed[ (df_indexed.iso_code=='USA') ]\n",
    "aged_70_older_USA = df_indexed_USA['aged_70_older'].dropna()\n",
    "\n",
    "# print(aged_65_older_USA)\n",
    "\n",
    "df_cases_ = pd.DataFrame({\n",
    "                       'aged_70_older_NLD': aged_70_older_NLD.to_numpy(),\n",
    "                        'aged_70_older_IND': aged_70_older_IND.to_numpy(),\n",
    "                        'aged_70_older_USA': aged_70_older_USA.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",
    "print(df_cases_)\n",
    "corr = df_cases_.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm', vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row0_col0 {\n",
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       "            color:  #f1f1f1;\n",
       "        }    #T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row0_col1 {\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #b70d28;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #000000;\n",
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       "            background-color:  #000000;\n",
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       "            background-color:  #000000;\n",
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       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #b70d28;\n",
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       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row2_col2 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row2_col3 {\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row3_col0 {\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row3_col1 {\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row3_col2 {\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row3_col3 {\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >icu_patients_per_million_NLD</th>        <th class=\"col_heading level0 col1\" >icu_patients_per_million_IND</th>        <th class=\"col_heading level0 col2\" >icu_patients_per_million_USA</th>        <th class=\"col_heading level0 col3\" >icu_patients_per_million_BRA</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1level0_row0\" class=\"row_heading level0 row0\" >icu_patients_per_million_NLD</th>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row0_col1\" class=\"data row0 col1\" >nan</td>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row0_col2\" class=\"data row0 col2\" >0.979729</td>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1level0_row1\" class=\"row_heading level0 row1\" >icu_patients_per_million_IND</th>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row1_col0\" class=\"data row1 col0\" >nan</td>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row1_col1\" class=\"data row1 col1\" >nan</td>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row1_col2\" class=\"data row1 col2\" >nan</td>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1level0_row2\" class=\"row_heading level0 row2\" >icu_patients_per_million_USA</th>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row2_col0\" class=\"data row2 col0\" >0.979729</td>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row2_col1\" class=\"data row2 col1\" >nan</td>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row2_col3\" class=\"data row2 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1level0_row3\" class=\"row_heading level0 row3\" >icu_patients_per_million_BRA</th>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row3_col0\" class=\"data row3 col0\" >nan</td>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row3_col1\" class=\"data row3 col1\" >nan</td>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row3_col2\" class=\"data row3 col2\" >nan</td>\n",
       "                        <td id=\"T_189ddcc2_d691_11eb_9d1b_ab5e46816eb1row3_col3\" class=\"data row3 col3\" >nan</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f86d2067250>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indexed_NLD = df_indexed[ (df_indexed.iso_code=='NLD') ]\n",
    "icu_patients_per_million_NLD = df_indexed_NLD['icu_patients_per_million']\n",
    "\n",
    "df_indexed_IND = df_indexed[ (df_indexed.iso_code=='IND') ]\n",
    "icu_patients_per_million_IND = df_indexed_IND['icu_patients_per_million']\n",
    "\n",
    "df_indexed_USA = df_indexed[ (df_indexed.iso_code=='USA') ]\n",
    "icu_patients_per_million_USA = df_indexed_USA['icu_patients_per_million']\n",
    "\n",
    "df_indexed_BRA = df_indexed[ (df_indexed.iso_code=='BRA') ]\n",
    "icu_patients_per_million_BRA = df_indexed_BRA['icu_patients_per_million']\n",
    "\n",
    "df_cases_ = pd.DataFrame({\n",
    "                       'icu_patients_per_million_NLD': icu_patients_per_million_NLD.to_numpy(),\n",
    "                        'icu_patients_per_million_IND': icu_patients_per_million_IND.to_numpy(),\n",
    "                        'icu_patients_per_million_USA': icu_patients_per_million_USA.to_numpy(),\n",
    "    'icu_patients_per_million_BRA': icu_patients_per_million_BRA.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",
    "corr = df_cases_.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm', vmin=-1, vmax=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "    #T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row0_col0 {\n",
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       "            color:  #f1f1f1;\n",
       "        }    #T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row0_col1 {\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
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       "            background-color:  #000000;\n",
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       "            background-color:  #000000;\n",
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       "            background-color:  #000000;\n",
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       "            background-color:  #bd1f2d;\n",
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       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row2_col2 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row2_col3 {\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row3_col0 {\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row3_col1 {\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row3_col2 {\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row3_col3 {\n",
       "            background-color:  #000000;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >hosp_patients_per_million_NLD</th>        <th class=\"col_heading level0 col1\" >hosp_patients_per_million_IND</th>        <th class=\"col_heading level0 col2\" >hosp_patients_per_million_USA</th>        <th class=\"col_heading level0 col3\" >hosp_patients_per_million_BRA</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1level0_row0\" class=\"row_heading level0 row0\" >hosp_patients_per_million_NLD</th>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row0_col1\" class=\"data row0 col1\" >nan</td>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row0_col2\" class=\"data row0 col2\" >0.947088</td>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row0_col3\" class=\"data row0 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1level0_row1\" class=\"row_heading level0 row1\" >hosp_patients_per_million_IND</th>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row1_col0\" class=\"data row1 col0\" >nan</td>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row1_col1\" class=\"data row1 col1\" >nan</td>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row1_col2\" class=\"data row1 col2\" >nan</td>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row1_col3\" class=\"data row1 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1level0_row2\" class=\"row_heading level0 row2\" >hosp_patients_per_million_USA</th>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row2_col0\" class=\"data row2 col0\" >0.947088</td>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row2_col1\" class=\"data row2 col1\" >nan</td>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row2_col3\" class=\"data row2 col3\" >nan</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1level0_row3\" class=\"row_heading level0 row3\" >hosp_patients_per_million_BRA</th>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row3_col0\" class=\"data row3 col0\" >nan</td>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row3_col1\" class=\"data row3 col1\" >nan</td>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row3_col2\" class=\"data row3 col2\" >nan</td>\n",
       "                        <td id=\"T_597dd1de_d691_11eb_9d1b_ab5e46816eb1row3_col3\" class=\"data row3 col3\" >nan</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f86cfd27250>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_indexed_NLD = df_indexed[ (df_indexed.iso_code=='NLD') ]\n",
    "hosp_patients_per_million_NLD = df_indexed_NLD['hosp_patients_per_million']\n",
    "\n",
    "df_indexed_IND = df_indexed[ (df_indexed.iso_code=='IND') ]\n",
    "hosp_patients_per_million_IND = df_indexed_IND['hosp_patients_per_million']\n",
    "\n",
    "df_indexed_USA = df_indexed[ (df_indexed.iso_code=='USA') ]\n",
    "hosp_patients_per_million_USA = df_indexed_USA['hosp_patients_per_million']\n",
    "\n",
    "df_indexed_BRA = df_indexed[ (df_indexed.iso_code=='BRA') ]\n",
    "hosp_patients_per_million_BRA = df_indexed_BRA['hosp_patients_per_million']\n",
    "\n",
    "df_cases_ = pd.DataFrame({\n",
    "                       'hosp_patients_per_million_NLD': hosp_patients_per_million_NLD.to_numpy(),\n",
    "                        'hosp_patients_per_million_IND': hosp_patients_per_million_IND.to_numpy(),\n",
    "                        'hosp_patients_per_million_USA': hosp_patients_per_million_USA.to_numpy(),\n",
    "    'hosp_patients_per_million_BRA': hosp_patients_per_million_BRA.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",
    "corr = df_cases_.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm', vmin=-1, vmax=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    human_development_index_NLD  human_development_index_IND  \\\n",
      "0                         0.944                        0.645   \n",
      "1                         0.944                        0.645   \n",
      "2                         0.944                        0.645   \n",
      "3                         0.944                        0.645   \n",
      "4                         0.944                        0.645   \n",
      "..                          ...                          ...   \n",
      "79                        0.944                        0.645   \n",
      "80                        0.944                        0.645   \n",
      "81                        0.944                        0.645   \n",
      "82                        0.944                        0.645   \n",
      "83                        0.944                        0.645   \n",
      "\n",
      "    human_development_index_USA  human_development_index_BRA  \n",
      "0                         0.926                        0.765  \n",
      "1                         0.926                        0.765  \n",
      "2                         0.926                        0.765  \n",
      "3                         0.926                        0.765  \n",
      "4                         0.926                        0.765  \n",
      "..                          ...                          ...  \n",
      "79                        0.926                        0.765  \n",
      "80                        0.926                        0.765  \n",
      "81                        0.926                        0.765  \n",
      "82                        0.926                        0.765  \n",
      "83                        0.926                        0.765  \n",
      "\n",
      "[84 rows x 4 columns]\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# human_development_index - const\n",
    "# gdp_per_capita - const\n",
    "\n",
    "df_indexed_NLD = df_indexed[ (df_indexed.iso_code=='NLD') ]\n",
    "human_development_index_NLD = df_indexed_NLD['human_development_index'].dropna()\n",
    "\n",
    "df_indexed_IND = df_indexed[ (df_indexed.iso_code=='IND') ]\n",
    "human_development_index_IND = df_indexed_IND['human_development_index'].dropna()\n",
    "\n",
    "df_indexed_USA = df_indexed[ (df_indexed.iso_code=='USA') ]\n",
    "human_development_index_USA = df_indexed_USA['human_development_index'].dropna()\n",
    "\n",
    "df_indexed_BRA = df_indexed[ (df_indexed.iso_code=='BRA') ]\n",
    "human_development_index_BRA = df_indexed_BRA['human_development_index'].dropna()\n",
    "\n",
    "df_cases_ = pd.DataFrame({\n",
    "                       'human_development_index_NLD': human_development_index_NLD.to_numpy(),\n",
    "                        'human_development_index_IND': human_development_index_IND.to_numpy(),\n",
    "                        'human_development_index_USA': human_development_index_USA.to_numpy(),\n",
    "    'human_development_index_BRA': human_development_index_BRA.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",
    "print(df_cases_)\n",
    "# corr = df_cases_.corr()\n",
    "# corr.style.background_gradient(cmap='coolwarm', vmin=-1, vmax=1)\n",
    "# \n",
    "nld = human_development_index_NLD.to_numpy()\n",
    "nld = np.average(nld)\n",
    "ind = human_development_index_IND.to_numpy()\n",
    "ind = np.average(ind)\n",
    "usa = human_development_index_USA.to_numpy()\n",
    "usa = np.average(usa)\n",
    "bra = human_development_index_BRA.to_numpy()\n",
    "bra = np.average(bra)\n",
    "\n",
    "# df_bar = pd.DataFrame({'aged_65_older_NLD': nld,\n",
    "#                         'aged_65_older_IND': ind,\n",
    "#                         'aged_65_older_USA': usa,}, index=0)\n",
    "\n",
    "df_bar = pd.DataFrame({'countries':['NLD', 'IND', 'USA', 'BRA'], 'val':[nld, ind, usa, bra]})\n",
    "ax = df_bar.plot.bar(x='countries', y='val', rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
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       "            background-color:  #3b4cc0;\n",
       "            color:  #f1f1f1;\n",
       "        }    #T_302507ac_d692_11eb_9d1b_ab5e46816eb1row4_col4 {\n",
       "            background-color:  #b40426;\n",
       "            color:  #f1f1f1;\n",
       "        }</style><table id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >new_cases_per_million_NLD</th>        <th class=\"col_heading level0 col1\" >new_cases_per_million_IND</th>        <th class=\"col_heading level0 col2\" >new_cases_per_million_USA</th>        <th class=\"col_heading level0 col3\" >new_cases_per_million_BRA</th>        <th class=\"col_heading level0 col4\" >new_cases_per_million_NER</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1level0_row0\" class=\"row_heading level0 row0\" >new_cases_per_million_NLD</th>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row0_col1\" class=\"data row0 col1\" >0.679305</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row0_col2\" class=\"data row0 col2\" >0.839510</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row0_col3\" class=\"data row0 col3\" >-0.094791</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row0_col4\" class=\"data row0 col4\" >0.298721</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1level0_row1\" class=\"row_heading level0 row1\" >new_cases_per_million_IND</th>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row1_col0\" class=\"data row1 col0\" >0.679305</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row1_col2\" class=\"data row1 col2\" >0.376930</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row1_col3\" class=\"data row1 col3\" >-0.099431</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row1_col4\" class=\"data row1 col4\" >0.282457</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1level0_row2\" class=\"row_heading level0 row2\" >new_cases_per_million_USA</th>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row2_col0\" class=\"data row2 col0\" >0.839510</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row2_col1\" class=\"data row2 col1\" >0.376930</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row2_col3\" class=\"data row2 col3\" >0.078713</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row2_col4\" class=\"data row2 col4\" >0.125803</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1level0_row3\" class=\"row_heading level0 row3\" >new_cases_per_million_BRA</th>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row3_col0\" class=\"data row3 col0\" >-0.094791</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row3_col1\" class=\"data row3 col1\" >-0.099431</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row3_col2\" class=\"data row3 col2\" >0.078713</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row3_col4\" class=\"data row3 col4\" >-0.140684</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1level0_row4\" class=\"row_heading level0 row4\" >new_cases_per_million_NER</th>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row4_col0\" class=\"data row4 col0\" >0.298721</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row4_col1\" class=\"data row4 col1\" >0.282457</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row4_col2\" class=\"data row4 col2\" >0.125803</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row4_col3\" class=\"data row4 col3\" >-0.140684</td>\n",
       "                        <td id=\"T_302507ac_d692_11eb_9d1b_ab5e46816eb1row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f86cf295d30>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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",
    "corr = df_cases_.corr()\n",
    "corr.style.background_gradient(cmap='coolwarm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(80.04761904761905,\n",
       " 3.654943097246087e-19,\n",
       " 1,\n",
       " array([[21., 21.],\n",
       "        [21., 21.]]))"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as st\n",
    "\n",
    "# new_cases_per_million_NLD\n",
    "# new_cases_per_million_IND\n",
    "# new_cases_per_million_USA\n",
    "# new_cases_per_million_BRA\n",
    "# new_cases_per_million_NER\n",
    "\n",
    "df_indexed_NLD = df_indexed_NLD.reset_index(drop=True)\n",
    "df_indexed_IND = df_indexed_IND.reset_index(drop=True)\n",
    "\n",
    "new_cases_per_million_NLD = df_indexed_NLD['new_cases_per_million']\n",
    "new_cases_per_million_IND = df_indexed_IND['new_cases_per_million']\n",
    "\n",
    "# df_bis_smoking.set_index([\"country\"], inplace = True,\n",
    "#                     append = True, drop = True)\n",
    "\n",
    "df_indexed_NLD['new_cases_per_million_bin'] = (new_cases_per_million_NLD > new_cases_per_million_NLD.median())\n",
    "df_indexed_IND['new_cases_per_million_bin'] = (new_cases_per_million_IND > new_cases_per_million_IND.median())\n",
    "\n",
    "# print(df_indexed_NLD['new_cases_per_million_bin'])\n",
    "# print(df_indexed_IND['new_cases_per_million_bin'])\n",
    "\n",
    "this = pd.crosstab(df_indexed_NLD['new_cases_per_million_bin'], df_indexed_IND['new_cases_per_million_bin'])\n",
    "\n",
    "# this = pd.crosstab(df_bis_smoking['total_deaths_binarized'], df_bis_smoking['n_smokers_binarized'])\n",
    "st.chi2_contingency(this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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    "name": "ipython",
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