analysis_median_age_per_continent/Median_Age Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import dependencies\n",
    "from path import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas import Series\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primary_key_reaction</th>\n",
       "      <th>date_updated</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>...</th>\n",
       "      <th>primary_key_response</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW2020-03-13</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766.0</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-13</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.56</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW2020-03-19</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766.0</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-19</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.86</td>\n",
       "      <td>29.49</td>\n",
       "      <td>15.015601</td>\n",
       "      <td>34.85</td>\n",
       "      <td>15.016502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW2020-03-20</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766.0</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-20</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.86</td>\n",
       "      <td>29.49</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.85</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABW2020-03-21</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766.0</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-21</td>\n",
       "      <td>44.44</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>60.71</td>\n",
       "      <td>37.18</td>\n",
       "      <td>26.076636</td>\n",
       "      <td>43.94</td>\n",
       "      <td>26.083214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABW2020-03-22</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766.0</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-22</td>\n",
       "      <td>44.44</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.71</td>\n",
       "      <td>37.18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.94</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "<p>5 rows × 30 columns</p>\n",
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      "text/plain": [
       "  primary_key_reaction date_updated iso_code      continent location  \\\n",
       "0        ABW2020-03-13   2020-03-13      ABW  North America    Aruba   \n",
       "1        ABW2020-03-19   2020-03-19      ABW  North America    Aruba   \n",
       "2        ABW2020-03-20   2020-03-20      ABW  North America    Aruba   \n",
       "3        ABW2020-03-21   2020-03-21      ABW  North America    Aruba   \n",
       "4        ABW2020-03-22   2020-03-22      ABW  North America    Aruba   \n",
       "\n",
       "   population  population_density  median_age  new_cases  \\\n",
       "0    106766.0               584.8        41.2        2.0   \n",
       "1    106766.0               584.8        41.2        0.0   \n",
       "2    106766.0               584.8        41.2        2.0   \n",
       "3    106766.0               584.8        41.2        0.0   \n",
       "4    106766.0               584.8        41.2        0.0   \n",
       "\n",
       "   percentage_of_Pop_New_Cases  ...  primary_key_response  \\\n",
       "0                     0.001873  ...         ABW2020-03-13   \n",
       "1                     0.000000  ...         ABW2020-03-19   \n",
       "2                     0.001873  ...         ABW2020-03-20   \n",
       "3                     0.000000  ...         ABW2020-03-21   \n",
       "4                     0.000000  ...         ABW2020-03-22   \n",
       "\n",
       "   StringencyIndex_updated  percentage_change_stringency  \\\n",
       "0                     0.00                      0.000000   \n",
       "1                    33.33                      0.000000   \n",
       "2                    33.33                      0.000000   \n",
       "3                    44.44                     33.333333   \n",
       "4                    44.44                      0.000000   \n",
       "\n",
       "   StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "0                           0.00                             2.56   \n",
       "1                          42.86                            29.49   \n",
       "2                          42.86                            29.49   \n",
       "3                          60.71                            37.18   \n",
       "4                          60.71                            37.18   \n",
       "\n",
       "   percentage_change_GovernmentResponse  ContainmentHealthIndex_updated  \\\n",
       "0                              0.000000                            3.03   \n",
       "1                             15.015601                           34.85   \n",
       "2                              0.000000                           34.85   \n",
       "3                             26.076636                           43.94   \n",
       "4                              0.000000                           43.94   \n",
       "\n",
       "   percentage_change_ContainmentHealth  EconomicSupportIndex_updated  \\\n",
       "0                             0.000000                           0.0   \n",
       "1                            15.016502                           0.0   \n",
       "2                             0.000000                           0.0   \n",
       "3                            26.083214                           0.0   \n",
       "4                             0.000000                           0.0   \n",
       "\n",
       "   percentage_change_EconomicSupport  \n",
       "0                                0.0  \n",
       "1                                0.0  \n",
       "2                                0.0  \n",
       "3                                0.0  \n",
       "4                                0.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import dataset & store into dataframe\n",
    "merged_covid_df = pd.read_csv(\"../Resources/merged_covid_FINAL.csv\")\n",
    "merged_covid_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
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       "      <th>new_deaths_smoothed</th>\n",
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       "      <th>gdp_per_capita</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",
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       "      <td>2020-03-13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.29</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.452</td>\n",
       "      <td>35973.781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.29</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW</td>\n",
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       "      <td>Aruba</td>\n",
       "      <td>2020-03-20</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.452</td>\n",
       "      <td>35973.781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.452</td>\n",
       "      <td>35973.781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.452</td>\n",
       "      <td>35973.781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.29</td>\n",
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       "<p>5 rows × 40 columns</p>\n",
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      ],
      "text/plain": [
       "  iso_code      continent location        date  total_cases  new_cases  \\\n",
       "0      ABW  North America    Aruba  2020-03-13          2.0        2.0   \n",
       "1      ABW  North America    Aruba  2020-03-19          NaN        NaN   \n",
       "2      ABW  North America    Aruba  2020-03-20          4.0        2.0   \n",
       "3      ABW  North America    Aruba  2020-03-21          NaN        NaN   \n",
       "4      ABW  North America    Aruba  2020-03-22          NaN        NaN   \n",
       "\n",
       "   new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  \\\n",
       "0                 NaN           0.0         0.0                  NaN  ...   \n",
       "1               0.286           NaN         NaN                  0.0  ...   \n",
       "2               0.286           0.0         0.0                  0.0  ...   \n",
       "3               0.286           NaN         NaN                  0.0  ...   \n",
       "4               0.286           NaN         NaN                  0.0  ...   \n",
       "\n",
       "   aged_70_older  gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
       "0          7.452       35973.781              NaN                    NaN   \n",
       "1          7.452       35973.781              NaN                    NaN   \n",
       "2          7.452       35973.781              NaN                    NaN   \n",
       "3          7.452       35973.781              NaN                    NaN   \n",
       "4          7.452       35973.781              NaN                    NaN   \n",
       "\n",
       "   diabetes_prevalence  female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0                11.62             NaN           NaN                     NaN   \n",
       "1                11.62             NaN           NaN                     NaN   \n",
       "2                11.62             NaN           NaN                     NaN   \n",
       "3                11.62             NaN           NaN                     NaN   \n",
       "4                11.62             NaN           NaN                     NaN   \n",
       "\n",
       "   hospital_beds_per_thousand  life_expectancy  \n",
       "0                         NaN            76.29  \n",
       "1                         NaN            76.29  \n",
       "2                         NaN            76.29  \n",
       "3                         NaN            76.29  \n",
       "4                         NaN            76.29  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import dataset & store into dataframe\n",
    "owid_df = pd.read_csv(\"../Resources/raw/owid-covid-data(Aug31,2020).csv\")\n",
    "owid_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</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",
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       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>aged_70_older</th>\n",
       "      <th>gdp_per_capita</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>7.452</td>\n",
       "      <td>35973.781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.452</td>\n",
       "      <td>35973.781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.452</td>\n",
       "      <td>35973.781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.452</td>\n",
       "      <td>35973.781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>7.452</td>\n",
       "      <td>35973.781</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11.62</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 40 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso_code      continent location        date  total_cases  new_cases  \\\n",
       "0      ABW  North America    Aruba  2020-03-13          2.0        2.0   \n",
       "1      ABW  North America    Aruba  2020-03-19          NaN        NaN   \n",
       "2      ABW  North America    Aruba  2020-03-20          4.0        2.0   \n",
       "3      ABW  North America    Aruba  2020-03-21          NaN        NaN   \n",
       "4      ABW  North America    Aruba  2020-03-22          NaN        NaN   \n",
       "\n",
       "   new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  \\\n",
       "0                 NaN           0.0         0.0                  NaN  ...   \n",
       "1               0.286           NaN         NaN                  0.0  ...   \n",
       "2               0.286           0.0         0.0                  0.0  ...   \n",
       "3               0.286           NaN         NaN                  0.0  ...   \n",
       "4               0.286           NaN         NaN                  0.0  ...   \n",
       "\n",
       "   aged_70_older  gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
       "0          7.452       35973.781              NaN                    NaN   \n",
       "1          7.452       35973.781              NaN                    NaN   \n",
       "2          7.452       35973.781              NaN                    NaN   \n",
       "3          7.452       35973.781              NaN                    NaN   \n",
       "4          7.452       35973.781              NaN                    NaN   \n",
       "\n",
       "   diabetes_prevalence  female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0                11.62             NaN           NaN                     NaN   \n",
       "1                11.62             NaN           NaN                     NaN   \n",
       "2                11.62             NaN           NaN                     NaN   \n",
       "3                11.62             NaN           NaN                     NaN   \n",
       "4                11.62             NaN           NaN                     NaN   \n",
       "\n",
       "   hospital_beds_per_thousand  life_expectancy  \n",
       "0                         NaN            76.29  \n",
       "1                         NaN            76.29  \n",
       "2                         NaN            76.29  \n",
       "3                         NaN            76.29  \n",
       "4                         NaN            76.29  \n",
       "\n",
       "[5 rows x 40 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "updated_owid_df=owid_df\n",
    "updated_owid_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa           32.671629\n",
       "Asia              5.301100\n",
       "Europe            0.936353\n",
       "North America     5.079318\n",
       "Oceania           0.920652\n",
       "Name: extreme_poverty, dtype: float64"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_owid_df=updated_owid_df.groupby(\"continent\").mean()[\"extreme_poverty\"]\n",
    "covid_owid_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 5760x7200 with 0 Axes>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 5760x7200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plot.\n",
    "covid_owid_df.plot( marker=\"*\", color=\"green\", linewidth=2, label='Extreme Poverty')\n",
    "# Create labels for the x and y axes.\n",
    "plt.xlabel(\"Continent\")\n",
    "plt.ylabel(\"Extreme Poverty Rating\")\n",
    "# Set the y limit between 0 and 45.\n",
    "plt.ylim(0, 45)\n",
    "# Create a title.\n",
    "plt.title(\"Poverty Level Per Continent\")\n",
    "# Add a grid.\n",
    "plt.grid()\n",
    "# Add the legend.\n",
    "plt.legend()\n",
    "plt.figure(figsize=(80, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primary_key_reaction</th>\n",
       "      <th>date_updated</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>...</th>\n",
       "      <th>primary_key_response</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CAN2020-01-01</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>CAN</td>\n",
       "      <td>North America</td>\n",
       "      <td>Canada</td>\n",
       "      <td>37742157.0</td>\n",
       "      <td>4.037</td>\n",
       "      <td>41.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>CAN2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CAN2020-01-02</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>CAN</td>\n",
       "      <td>North America</td>\n",
       "      <td>Canada</td>\n",
       "      <td>37742157.0</td>\n",
       "      <td>4.037</td>\n",
       "      <td>41.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>CAN2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CAN2020-01-03</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>CAN</td>\n",
       "      <td>North America</td>\n",
       "      <td>Canada</td>\n",
       "      <td>37742157.0</td>\n",
       "      <td>4.037</td>\n",
       "      <td>41.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>CAN2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CAN2020-01-04</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>CAN</td>\n",
       "      <td>North America</td>\n",
       "      <td>Canada</td>\n",
       "      <td>37742157.0</td>\n",
       "      <td>4.037</td>\n",
       "      <td>41.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>CAN2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CAN2020-01-05</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>CAN</td>\n",
       "      <td>North America</td>\n",
       "      <td>Canada</td>\n",
       "      <td>37742157.0</td>\n",
       "      <td>4.037</td>\n",
       "      <td>41.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>CAN2020-01-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  primary_key_reaction date_updated iso_code      continent location  \\\n",
       "0        CAN2020-01-01   2020-01-01      CAN  North America   Canada   \n",
       "1        CAN2020-01-02   2020-01-02      CAN  North America   Canada   \n",
       "2        CAN2020-01-03   2020-01-03      CAN  North America   Canada   \n",
       "3        CAN2020-01-04   2020-01-04      CAN  North America   Canada   \n",
       "4        CAN2020-01-05   2020-01-05      CAN  North America   Canada   \n",
       "\n",
       "   population  population_density  median_age  new_cases  \\\n",
       "0  37742157.0               4.037        41.4        0.0   \n",
       "1  37742157.0               4.037        41.4        0.0   \n",
       "2  37742157.0               4.037        41.4        0.0   \n",
       "3  37742157.0               4.037        41.4        0.0   \n",
       "4  37742157.0               4.037        41.4        0.0   \n",
       "\n",
       "   percentage_of_Pop_New_Cases  ...  primary_key_response  \\\n",
       "0                          0.0  ...         CAN2020-01-01   \n",
       "1                          0.0  ...         CAN2020-01-02   \n",
       "2                          0.0  ...         CAN2020-01-03   \n",
       "3                          0.0  ...         CAN2020-01-04   \n",
       "4                          0.0  ...         CAN2020-01-05   \n",
       "\n",
       "   StringencyIndex_updated  percentage_change_stringency  \\\n",
       "0                      0.0                           0.0   \n",
       "1                      0.0                           0.0   \n",
       "2                      0.0                           0.0   \n",
       "3                      0.0                           0.0   \n",
       "4                      0.0                           0.0   \n",
       "\n",
       "   StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "0                            0.0                             3.85   \n",
       "1                            0.0                             3.85   \n",
       "2                            0.0                             3.85   \n",
       "3                            0.0                             3.85   \n",
       "4                            0.0                             3.85   \n",
       "\n",
       "   percentage_change_GovernmentResponse  ContainmentHealthIndex_updated  \\\n",
       "0                                   0.0                            4.55   \n",
       "1                                   0.0                            4.55   \n",
       "2                                   0.0                            4.55   \n",
       "3                                   0.0                            4.55   \n",
       "4                                   0.0                            4.55   \n",
       "\n",
       "   percentage_change_ContainmentHealth  EconomicSupportIndex_updated  \\\n",
       "0                                  0.0                           0.0   \n",
       "1                                  0.0                           0.0   \n",
       "2                                  0.0                           0.0   \n",
       "3                                  0.0                           0.0   \n",
       "4                                  0.0                           0.0   \n",
       "\n",
       "   percentage_change_EconomicSupport  \n",
       "0                                0.0  \n",
       "1                                0.0  \n",
       "2                                0.0  \n",
       "3                                0.0  \n",
       "4                                0.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dataframe for Canada only\n",
    "\n",
    "merged_covid_canada_df = pd.DataFrame(merged_covid_df.loc[merged_covid_df[\"location\"]==\"Canada\"].reset_index())\n",
    "merged_covid_canada_df = merged_covid_canada_df.drop([\"index\"],axis=1)\n",
    "merged_covid_canada_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>primary_key_reaction</th>\n",
       "      <th>date_updated</th>\n",
       "      <th>iso_code</th>\n",
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       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
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       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>NGA2020-01-01</td>\n",
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       "      <td>2020-01-02</td>\n",
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       "      <td>Africa</td>\n",
       "      <td>Nigeria</td>\n",
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       "      <td>209.588</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>NGA2020-01-03</td>\n",
       "      <td>2020-01-03</td>\n",
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       "      <td>Africa</td>\n",
       "      <td>Nigeria</td>\n",
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       "      <td>209.588</td>\n",
       "      <td>18.1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NGA2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NGA2020-01-04</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>206139587.0</td>\n",
       "      <td>209.588</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NGA2020-01-05</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>NGA</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>206139587.0</td>\n",
       "      <td>209.588</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "  primary_key_reaction date_updated iso_code continent location   population  \\\n",
       "0        NGA2020-01-01   2020-01-01      NGA    Africa  Nigeria  206139587.0   \n",
       "1        NGA2020-01-02   2020-01-02      NGA    Africa  Nigeria  206139587.0   \n",
       "2        NGA2020-01-03   2020-01-03      NGA    Africa  Nigeria  206139587.0   \n",
       "3        NGA2020-01-04   2020-01-04      NGA    Africa  Nigeria  206139587.0   \n",
       "4        NGA2020-01-05   2020-01-05      NGA    Africa  Nigeria  206139587.0   \n",
       "\n",
       "   population_density  median_age  new_cases  percentage_of_Pop_New_Cases  \\\n",
       "0             209.588        18.1        0.0                          0.0   \n",
       "1             209.588        18.1        0.0                          0.0   \n",
       "2             209.588        18.1        0.0                          0.0   \n",
       "3             209.588        18.1        0.0                          0.0   \n",
       "4             209.588        18.1        0.0                          0.0   \n",
       "\n",
       "   ...  primary_key_response  StringencyIndex_updated  \\\n",
       "0  ...         NGA2020-01-01                      0.0   \n",
       "1  ...         NGA2020-01-02                      0.0   \n",
       "2  ...         NGA2020-01-03                      0.0   \n",
       "3  ...         NGA2020-01-04                      0.0   \n",
       "4  ...         NGA2020-01-05                      0.0   \n",
       "\n",
       "   percentage_change_stringency  StringencyLegacyIndex_updated  \\\n",
       "0                           0.0                            0.0   \n",
       "1                           0.0                            0.0   \n",
       "2                           0.0                            0.0   \n",
       "3                           0.0                            0.0   \n",
       "4                           0.0                            0.0   \n",
       "\n",
       "   GovernmentResponseIndex_updated  percentage_change_GovernmentResponse  \\\n",
       "0                              0.0                                   0.0   \n",
       "1                              0.0                                   0.0   \n",
       "2                              0.0                                   0.0   \n",
       "3                              0.0                                   0.0   \n",
       "4                              0.0                                   0.0   \n",
       "\n",
       "   ContainmentHealthIndex_updated  percentage_change_ContainmentHealth  \\\n",
       "0                             0.0                                  0.0   \n",
       "1                             0.0                                  0.0   \n",
       "2                             0.0                                  0.0   \n",
       "3                             0.0                                  0.0   \n",
       "4                             0.0                                  0.0   \n",
       "\n",
       "   EconomicSupportIndex_updated  percentage_change_EconomicSupport  \n",
       "0                           0.0                                0.0  \n",
       "1                           0.0                                0.0  \n",
       "2                           0.0                                0.0  \n",
       "3                           0.0                                0.0  \n",
       "4                           0.0                                0.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Dataframe filtered for Nigeria\n",
    "merged_covid_Nigeria_df = pd.DataFrame(merged_covid_df.loc[merged_covid_df[\"location\"]==\"Nigeria\"].reset_index())\n",
    "merged_covid_Nigeria_df = merged_covid_Nigeria_df.drop([\"index\"],axis=1)\n",
    "merged_covid_Nigeria_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>...</th>\n",
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       "      <td>35.608</td>\n",
       "      <td>38.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "      <td>2020-01-02</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>331002647.0</td>\n",
       "      <td>35.608</td>\n",
       "      <td>38.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>USA2020-01-02</td>\n",
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       "      <td>2020-01-03</td>\n",
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       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>331002647.0</td>\n",
       "      <td>35.608</td>\n",
       "      <td>38.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>USA2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2020-01-04</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>331002647.0</td>\n",
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       "      <td>38.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>USA2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>2020-01-05</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>United States</td>\n",
       "      <td>331002647.0</td>\n",
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       "      <td>38.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>USA2020-01-05</td>\n",
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       "      <td>0.0</td>\n",
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      "text/plain": [
       "   index primary_key_reaction date_updated iso_code      continent  \\\n",
       "0  30769        USA2020-01-01   2020-01-01      USA  North America   \n",
       "1  30770        USA2020-01-02   2020-01-02      USA  North America   \n",
       "2  30771        USA2020-01-03   2020-01-03      USA  North America   \n",
       "3  30772        USA2020-01-04   2020-01-04      USA  North America   \n",
       "4  30773        USA2020-01-05   2020-01-05      USA  North America   \n",
       "\n",
       "        location   population  population_density  median_age  new_cases  ...  \\\n",
       "0  United States  331002647.0              35.608        38.3        0.0  ...   \n",
       "1  United States  331002647.0              35.608        38.3        0.0  ...   \n",
       "2  United States  331002647.0              35.608        38.3        0.0  ...   \n",
       "3  United States  331002647.0              35.608        38.3        0.0  ...   \n",
       "4  United States  331002647.0              35.608        38.3        0.0  ...   \n",
       "\n",
       "   primary_key_response  StringencyIndex_updated  \\\n",
       "0         USA2020-01-01                      0.0   \n",
       "1         USA2020-01-02                      0.0   \n",
       "2         USA2020-01-03                      0.0   \n",
       "3         USA2020-01-04                      0.0   \n",
       "4         USA2020-01-05                      0.0   \n",
       "\n",
       "   percentage_change_stringency  StringencyLegacyIndex_updated  \\\n",
       "0                           0.0                            0.0   \n",
       "1                           0.0                            0.0   \n",
       "2                           0.0                            0.0   \n",
       "3                           0.0                            0.0   \n",
       "4                           0.0                            0.0   \n",
       "\n",
       "   GovernmentResponseIndex_updated  percentage_change_GovernmentResponse  \\\n",
       "0                              0.0                                   0.0   \n",
       "1                              0.0                                   0.0   \n",
       "2                              0.0                                   0.0   \n",
       "3                              0.0                                   0.0   \n",
       "4                              0.0                                   0.0   \n",
       "\n",
       "   ContainmentHealthIndex_updated  percentage_change_ContainmentHealth  \\\n",
       "0                             0.0                                  0.0   \n",
       "1                             0.0                                  0.0   \n",
       "2                             0.0                                  0.0   \n",
       "3                             0.0                                  0.0   \n",
       "4                             0.0                                  0.0   \n",
       "\n",
       "   EconomicSupportIndex_updated  percentage_change_EconomicSupport  \n",
       "0                           0.0                                0.0  \n",
       "1                           0.0                                0.0  \n",
       "2                           0.0                                0.0  \n",
       "3                           0.0                                0.0  \n",
       "4                           0.0                                0.0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Dataframe filtered for USA\n",
    "merged_covid_USA_df = pd.DataFrame(merged_covid_df.loc[merged_covid_df[\"location\"]==\"United States\"].reset_index())\n",
    "merged_covid_Nigeria_df = merged_covid_USA_df.drop([\"index\"],axis=1)\n",
    "merged_covid_USA_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median Age')"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# New deaths vs Median Age Canada\n",
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.bar(merged_covid_canada_df.index, merged_covid_canada_df[\"new_deaths\"], color=\"red\", label='new_deaths')\n",
    "ax2.plot(merged_covid_canada_df.index, merged_covid_canada_df[\"median_age\"], color='blue', label='median_age')\n",
    "ax.set_xticklabels(merged_covid_canada_df.index)\n",
    "ax.legend(loc='best')\n",
    "ax.set_ylabel('New Deaths', color=\"red\")\n",
    "ax2.set_ylabel('Median Age', color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median Age')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# New deaths vs Median Age Nigeria\n",
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.plot(merged_covid_Nigeria_df.index, merged_covid_Nigeria_df[\"new_deaths\"], color=\"red\", label='new_deaths')\n",
    "ax2.plot(merged_covid_Nigeria_df.index, merged_covid_Nigeria_df[\"median_age\"], color='blue', label='median_age')\n",
    "ax.set_xticklabels(merged_covid_Nigeria_df.index)\n",
    "ax.legend(loc='best')\n",
    "ax.set_ylabel('New Deaths', color=\"red\")\n",
    "ax2.set_ylabel('Median Age', color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median Age')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total Deaths vs Median Age Canada\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.bar(merged_covid_canada_df.index, merged_covid_canada_df[\"total_deaths_updated\"], color=\"red\", label='Total_deaths')\n",
    "ax2.plot(merged_covid_canada_df.index, merged_covid_canada_df[\"median_age\"], color='blue', label='median_age')\n",
    "ax.set_xticklabels(merged_covid_canada_df.index)\n",
    "ax.legend(loc='best')\n",
    "ax.set_ylabel('Total Deaths', color=\"red\")\n",
    "ax2.set_ylabel('Median Age', color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median Age')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Total Death vs Median Age Nigeria\n",
    "\n",
    "# New Deaths vs Median Age Nigeria\n",
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.bar(merged_covid_Nigeria_df.index, merged_covid_Nigeria_df[\"total_deaths_updated\"], color=\"red\", label='Total deaths')\n",
    "ax2.plot(merged_covid_Nigeria_df.index, merged_covid_Nigeria_df[\"median_age\"], color='blue', label='median_age')\n",
    "ax.set_xticklabels(merged_covid_Nigeria_df.index)\n",
    "ax.legend(loc='best')\n",
    "ax.set_ylabel('Total Deaths', color=\"red\")\n",
    "ax2.set_ylabel('Median Age', color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median Age')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# New Deaths vs Median Age USA\n",
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.plot(merged_covid_USA_df.index, merged_covid_USA_df[\"new_deaths\"], color=\"red\", label='new_deaths')\n",
    "ax2.plot(merged_covid_USA_df.index, merged_covid_USA_df[\"median_age\"], color='blue', label='median_age')\n",
    "ax.set_xticklabels(merged_covid_USA_df.index)\n",
    "ax.legend(loc='best')\n",
    "ax.set_ylabel('New Deaths', color=\"red\")\n",
    "ax2.set_ylabel('Median Age', color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Median Age')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# New Deaths vs Median Age -USA\n",
    "fig, ax = plt.subplots()\n",
    "ax2 = ax.twinx()\n",
    "ax.bar(merged_covid_USA_df.index, merged_covid_USA_df[\"total_deaths_updated\"], color=\"red\", label='total_deaths')\n",
    "ax2.plot(merged_covid_USA_df.index, merged_covid_USA_df[\"median_age\"], color='blue', label='median_age')\n",
    "ax.set_xticklabels(merged_covid_USA_df.index)\n",
    "ax.legend(loc='best')\n",
    "ax.set_ylabel('Total Deaths', color=\"red\")\n",
    "ax2.set_ylabel('Median Age', color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primary_key_reaction</th>\n",
       "      <th>date_updated</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>...</th>\n",
       "      <th>primary_key_response</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ABW2020-03-13</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766.0</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.56</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ABW2020-03-19</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766.0</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-19</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.86</td>\n",
       "      <td>29.49</td>\n",
       "      <td>15.015601</td>\n",
       "      <td>34.85</td>\n",
       "      <td>15.016502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ABW2020-03-20</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766.0</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-20</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.86</td>\n",
       "      <td>29.49</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34.85</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ABW2020-03-21</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766.0</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-21</td>\n",
       "      <td>44.44</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>60.71</td>\n",
       "      <td>37.18</td>\n",
       "      <td>26.076636</td>\n",
       "      <td>43.94</td>\n",
       "      <td>26.083214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ABW2020-03-22</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>Aruba</td>\n",
       "      <td>106766.0</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-22</td>\n",
       "      <td>44.44</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.71</td>\n",
       "      <td>37.18</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.94</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  primary_key_reaction date_updated iso_code      continent location  \\\n",
       "0        ABW2020-03-13   2020-03-13      ABW  North America    Aruba   \n",
       "1        ABW2020-03-19   2020-03-19      ABW  North America    Aruba   \n",
       "2        ABW2020-03-20   2020-03-20      ABW  North America    Aruba   \n",
       "3        ABW2020-03-21   2020-03-21      ABW  North America    Aruba   \n",
       "4        ABW2020-03-22   2020-03-22      ABW  North America    Aruba   \n",
       "\n",
       "   population  population_density  median_age  new_cases  \\\n",
       "0    106766.0               584.8        41.2        2.0   \n",
       "1    106766.0               584.8        41.2        0.0   \n",
       "2    106766.0               584.8        41.2        2.0   \n",
       "3    106766.0               584.8        41.2        0.0   \n",
       "4    106766.0               584.8        41.2        0.0   \n",
       "\n",
       "   percentage_of_Pop_New_Cases  ...  primary_key_response  \\\n",
       "0                     0.001873  ...         ABW2020-03-13   \n",
       "1                     0.000000  ...         ABW2020-03-19   \n",
       "2                     0.001873  ...         ABW2020-03-20   \n",
       "3                     0.000000  ...         ABW2020-03-21   \n",
       "4                     0.000000  ...         ABW2020-03-22   \n",
       "\n",
       "   StringencyIndex_updated  percentage_change_stringency  \\\n",
       "0                     0.00                      0.000000   \n",
       "1                    33.33                      0.000000   \n",
       "2                    33.33                      0.000000   \n",
       "3                    44.44                     33.333333   \n",
       "4                    44.44                      0.000000   \n",
       "\n",
       "   StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "0                           0.00                             2.56   \n",
       "1                          42.86                            29.49   \n",
       "2                          42.86                            29.49   \n",
       "3                          60.71                            37.18   \n",
       "4                          60.71                            37.18   \n",
       "\n",
       "   percentage_change_GovernmentResponse  ContainmentHealthIndex_updated  \\\n",
       "0                              0.000000                            3.03   \n",
       "1                             15.015601                           34.85   \n",
       "2                              0.000000                           34.85   \n",
       "3                             26.076636                           43.94   \n",
       "4                              0.000000                           43.94   \n",
       "\n",
       "   percentage_change_ContainmentHealth  EconomicSupportIndex_updated  \\\n",
       "0                             0.000000                           0.0   \n",
       "1                            15.016502                           0.0   \n",
       "2                             0.000000                           0.0   \n",
       "3                            26.083214                           0.0   \n",
       "4                             0.000000                           0.0   \n",
       "\n",
       "   percentage_change_EconomicSupport  \n",
       "0                                0.0  \n",
       "1                                0.0  \n",
       "2                                0.0  \n",
       "3                                0.0  \n",
       "4                                0.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Dataframe filtered for Continent\n",
    "merged_covid_NA_df = pd.DataFrame(merged_covid_df.loc[merged_covid_df[\"continent\"]==\"North America\"].reset_index())\n",
    "merged_covid_NA_df = merged_covid_NA_df.drop([\"index\"],axis=1)\n",
    "merged_covid_NA_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>primary_key_reaction</th>\n",
       "      <th>date_updated</th>\n",
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       "      <th>continent</th>\n",
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       "      <th>location</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>ABW2020-03-13</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>106766.0</td>\n",
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       "      <td>0.001873</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.56</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>ABW2020-03-19</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>106766.0</td>\n",
       "      <td>584.8</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-19</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.86</td>\n",
       "      <td>29.49</td>\n",
       "      <td>15.015601</td>\n",
       "      <td>34.85</td>\n",
       "      <td>15.016502</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>ABW2020-03-20</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>106766.0</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.001873</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-20</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>42.86</td>\n",
       "      <td>29.49</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>ABW2020-03-21</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>106766.0</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>ABW2020-03-21</td>\n",
       "      <td>44.44</td>\n",
       "      <td>33.333333</td>\n",
       "      <td>60.71</td>\n",
       "      <td>37.18</td>\n",
       "      <td>26.076636</td>\n",
       "      <td>43.94</td>\n",
       "      <td>26.083214</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aruba</th>\n",
       "      <td>ABW2020-03-22</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>ABW</td>\n",
       "      <td>North America</td>\n",
       "      <td>106766.0</td>\n",
       "      <td>584.8</td>\n",
       "      <td>41.2</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>37.18</td>\n",
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       "      <td>43.94</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         primary_key_reaction date_updated iso_code      continent  \\\n",
       "location                                                             \n",
       "Aruba           ABW2020-03-13   2020-03-13      ABW  North America   \n",
       "Aruba           ABW2020-03-19   2020-03-19      ABW  North America   \n",
       "Aruba           ABW2020-03-20   2020-03-20      ABW  North America   \n",
       "Aruba           ABW2020-03-21   2020-03-21      ABW  North America   \n",
       "Aruba           ABW2020-03-22   2020-03-22      ABW  North America   \n",
       "\n",
       "          population  population_density  median_age  new_cases  \\\n",
       "location                                                          \n",
       "Aruba       106766.0               584.8        41.2        2.0   \n",
       "Aruba       106766.0               584.8        41.2        0.0   \n",
       "Aruba       106766.0               584.8        41.2        2.0   \n",
       "Aruba       106766.0               584.8        41.2        0.0   \n",
       "Aruba       106766.0               584.8        41.2        0.0   \n",
       "\n",
       "          percentage_of_Pop_New_Cases  new_deaths  ...  primary_key_response  \\\n",
       "location                                           ...                         \n",
       "Aruba                        0.001873         0.0  ...         ABW2020-03-13   \n",
       "Aruba                        0.000000         0.0  ...         ABW2020-03-19   \n",
       "Aruba                        0.001873         0.0  ...         ABW2020-03-20   \n",
       "Aruba                        0.000000         0.0  ...         ABW2020-03-21   \n",
       "Aruba                        0.000000         0.0  ...         ABW2020-03-22   \n",
       "\n",
       "          StringencyIndex_updated  percentage_change_stringency  \\\n",
       "location                                                          \n",
       "Aruba                        0.00                      0.000000   \n",
       "Aruba                       33.33                      0.000000   \n",
       "Aruba                       33.33                      0.000000   \n",
       "Aruba                       44.44                     33.333333   \n",
       "Aruba                       44.44                      0.000000   \n",
       "\n",
       "          StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "location                                                                   \n",
       "Aruba                              0.00                             2.56   \n",
       "Aruba                             42.86                            29.49   \n",
       "Aruba                             42.86                            29.49   \n",
       "Aruba                             60.71                            37.18   \n",
       "Aruba                             60.71                            37.18   \n",
       "\n",
       "          percentage_change_GovernmentResponse  \\\n",
       "location                                         \n",
       "Aruba                                 0.000000   \n",
       "Aruba                                15.015601   \n",
       "Aruba                                 0.000000   \n",
       "Aruba                                26.076636   \n",
       "Aruba                                 0.000000   \n",
       "\n",
       "          ContainmentHealthIndex_updated  percentage_change_ContainmentHealth  \\\n",
       "location                                                                        \n",
       "Aruba                               3.03                             0.000000   \n",
       "Aruba                              34.85                            15.016502   \n",
       "Aruba                              34.85                             0.000000   \n",
       "Aruba                              43.94                            26.083214   \n",
       "Aruba                              43.94                             0.000000   \n",
       "\n",
       "          EconomicSupportIndex_updated percentage_change_EconomicSupport  \n",
       "location                                                                  \n",
       "Aruba                              0.0                               0.0  \n",
       "Aruba                              0.0                               0.0  \n",
       "Aruba                              0.0                               0.0  \n",
       "Aruba                              0.0                               0.0  \n",
       "Aruba                              0.0                               0.0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "North_America= merged_covid_NA_df.set_index(\"location\")\n",
    "North_America.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# North America"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>primary_key_reaction</th>\n",
       "      <th>date_updated</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>...</th>\n",
       "      <th>primary_key_response</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA2020-01-01</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>331002647.0</td>\n",
       "      <td>35.608</td>\n",
       "      <td>38.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>USA2020-01-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA2020-01-02</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>331002647.0</td>\n",
       "      <td>35.608</td>\n",
       "      <td>38.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>USA2020-01-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA2020-01-03</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>331002647.0</td>\n",
       "      <td>35.608</td>\n",
       "      <td>38.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>USA2020-01-03</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA2020-01-04</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>331002647.0</td>\n",
       "      <td>35.608</td>\n",
       "      <td>38.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>USA2020-01-04</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United States</td>\n",
       "      <td>USA2020-01-05</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>USA</td>\n",
       "      <td>North America</td>\n",
       "      <td>331002647.0</td>\n",
       "      <td>35.608</td>\n",
       "      <td>38.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>USA2020-01-05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        location primary_key_reaction date_updated iso_code      continent  \\\n",
       "0  United States        USA2020-01-01   2020-01-01      USA  North America   \n",
       "1  United States        USA2020-01-02   2020-01-02      USA  North America   \n",
       "2  United States        USA2020-01-03   2020-01-03      USA  North America   \n",
       "3  United States        USA2020-01-04   2020-01-04      USA  North America   \n",
       "4  United States        USA2020-01-05   2020-01-05      USA  North America   \n",
       "\n",
       "    population  population_density  median_age  new_cases  \\\n",
       "0  331002647.0              35.608        38.3        0.0   \n",
       "1  331002647.0              35.608        38.3        0.0   \n",
       "2  331002647.0              35.608        38.3        0.0   \n",
       "3  331002647.0              35.608        38.3        0.0   \n",
       "4  331002647.0              35.608        38.3        0.0   \n",
       "\n",
       "   percentage_of_Pop_New_Cases  ...  primary_key_response  \\\n",
       "0                          0.0  ...         USA2020-01-01   \n",
       "1                          0.0  ...         USA2020-01-02   \n",
       "2                          0.0  ...         USA2020-01-03   \n",
       "3                          0.0  ...         USA2020-01-04   \n",
       "4                          0.0  ...         USA2020-01-05   \n",
       "\n",
       "   StringencyIndex_updated  percentage_change_stringency  \\\n",
       "0                      0.0                           0.0   \n",
       "1                      0.0                           0.0   \n",
       "2                      0.0                           0.0   \n",
       "3                      0.0                           0.0   \n",
       "4                      0.0                           0.0   \n",
       "\n",
       "   StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "0                            0.0                              0.0   \n",
       "1                            0.0                              0.0   \n",
       "2                            0.0                              0.0   \n",
       "3                            0.0                              0.0   \n",
       "4                            0.0                              0.0   \n",
       "\n",
       "   percentage_change_GovernmentResponse  ContainmentHealthIndex_updated  \\\n",
       "0                                   0.0                             0.0   \n",
       "1                                   0.0                             0.0   \n",
       "2                                   0.0                             0.0   \n",
       "3                                   0.0                             0.0   \n",
       "4                                   0.0                             0.0   \n",
       "\n",
       "   percentage_change_ContainmentHealth  EconomicSupportIndex_updated  \\\n",
       "0                                  0.0                           0.0   \n",
       "1                                  0.0                           0.0   \n",
       "2                                  0.0                           0.0   \n",
       "3                                  0.0                           0.0   \n",
       "4                                  0.0                           0.0   \n",
       "\n",
       "   percentage_change_EconomicSupport  \n",
       "0                                0.0  \n",
       "1                                0.0  \n",
       "2                                0.0  \n",
       "3                                0.0  \n",
       "4                                0.0  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NorthAmerica_df= North_America.loc[[\"United States\",\"Canada\", \"Mexico\"]].reset_index()\n",
    "NorthAmerica_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rMV2OPtcjydIkD+wuHwA8Dfg6/a/FlDn6XIuq+ouqOqyqxhjsH66qqj+mx7WYLsMQfkem20/Nei38kt05qKp7k7wK+AywCDi/qrYOIcpDgYsH+ywWA/9cVf/Wx8RJLgTWAIcmuRV4K3Am8LEkLwO+A/zREDKsSbKSwanbbcCfLWQGBv9V+2Jgc/eeCsAb6Xkt7ifHi3pcj2XAh7tP3e4DfKyqLk3yFfpdi+lyfKTn18ZU+n5dTOVve16HKfdTSTYwy7XwmzMkSU3xVKEkqSkWlySpKRaXJKkpFpckqSkWlySpKRaXJKkpFpckqSkWlySpKf8H4uwH+VGrYjMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MultipleLocator\n",
    "# Increase the size of the plot figure.\n",
    "fig, ax = plt.subplots(figsize=(6,3))\n",
    "ax.barh(NorthAmerica_df[\"location\"],NorthAmerica_df[\"median_age\"])\n",
    "\n",
    "ax.set_xticks(np.arange(0, 51, step=5.0))\n",
    "\n",
    "# Create minor ticks at an increment of 1.\n",
    "ax.xaxis.set_minor_locator(MultipleLocator(5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AFRICA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primary_key_reaction</th>\n",
       "      <th>date_updated</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>...</th>\n",
       "      <th>primary_key_response</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AGO2020-03-22</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Angola</td>\n",
       "      <td>32866268.0</td>\n",
       "      <td>23.890</td>\n",
       "      <td>16.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>...</td>\n",
       "      <td>AGO2020-03-22</td>\n",
       "      <td>11.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.29</td>\n",
       "      <td>10.26</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AGO2020-03-23</td>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Angola</td>\n",
       "      <td>32866268.0</td>\n",
       "      <td>23.890</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>AGO2020-03-23</td>\n",
       "      <td>11.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.29</td>\n",
       "      <td>10.26</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>12.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AGO2020-03-24</td>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Angola</td>\n",
       "      <td>32866268.0</td>\n",
       "      <td>23.890</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>AGO2020-03-24</td>\n",
       "      <td>33.33</td>\n",
       "      <td>200.0</td>\n",
       "      <td>46.67</td>\n",
       "      <td>25.64</td>\n",
       "      <td>149.902534</td>\n",
       "      <td>30.30</td>\n",
       "      <td>150.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AGO2020-03-25</td>\n",
       "      <td>2020-03-25</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Angola</td>\n",
       "      <td>32866268.0</td>\n",
       "      <td>23.890</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>AGO2020-03-25</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.67</td>\n",
       "      <td>25.64</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGO2020-03-26</td>\n",
       "      <td>2020-03-26</td>\n",
       "      <td>AGO</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Angola</td>\n",
       "      <td>32866268.0</td>\n",
       "      <td>23.890</td>\n",
       "      <td>16.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>AGO2020-03-26</td>\n",
       "      <td>33.33</td>\n",
       "      <td>0.0</td>\n",
       "      <td>46.67</td>\n",
       "      <td>25.64</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30.30</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8438</th>\n",
       "      <td>ZWE2020-08-27</td>\n",
       "      <td>2020-08-27</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0.000370</td>\n",
       "      <td>...</td>\n",
       "      <td>ZWE2020-08-27</td>\n",
       "      <td>80.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.10</td>\n",
       "      <td>66.03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8439</th>\n",
       "      <td>ZWE2020-08-28</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>...</td>\n",
       "      <td>ZWE2020-08-28</td>\n",
       "      <td>80.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.10</td>\n",
       "      <td>66.03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8440</th>\n",
       "      <td>ZWE2020-08-29</td>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>96.0</td>\n",
       "      <td>0.000646</td>\n",
       "      <td>...</td>\n",
       "      <td>ZWE2020-08-29</td>\n",
       "      <td>80.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.10</td>\n",
       "      <td>66.03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8441</th>\n",
       "      <td>ZWE2020-08-30</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.000121</td>\n",
       "      <td>...</td>\n",
       "      <td>ZWE2020-08-30</td>\n",
       "      <td>80.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.10</td>\n",
       "      <td>66.03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8442</th>\n",
       "      <td>ZWE2020-08-31</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>14862927.0</td>\n",
       "      <td>42.729</td>\n",
       "      <td>19.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>...</td>\n",
       "      <td>ZWE2020-08-31</td>\n",
       "      <td>80.56</td>\n",
       "      <td>0.0</td>\n",
       "      <td>88.10</td>\n",
       "      <td>66.03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>73.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8443 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     primary_key_reaction date_updated iso_code continent  location  \\\n",
       "0           AGO2020-03-22   2020-03-22      AGO    Africa    Angola   \n",
       "1           AGO2020-03-23   2020-03-23      AGO    Africa    Angola   \n",
       "2           AGO2020-03-24   2020-03-24      AGO    Africa    Angola   \n",
       "3           AGO2020-03-25   2020-03-25      AGO    Africa    Angola   \n",
       "4           AGO2020-03-26   2020-03-26      AGO    Africa    Angola   \n",
       "...                   ...          ...      ...       ...       ...   \n",
       "8438        ZWE2020-08-27   2020-08-27      ZWE    Africa  Zimbabwe   \n",
       "8439        ZWE2020-08-28   2020-08-28      ZWE    Africa  Zimbabwe   \n",
       "8440        ZWE2020-08-29   2020-08-29      ZWE    Africa  Zimbabwe   \n",
       "8441        ZWE2020-08-30   2020-08-30      ZWE    Africa  Zimbabwe   \n",
       "8442        ZWE2020-08-31   2020-08-31      ZWE    Africa  Zimbabwe   \n",
       "\n",
       "      population  population_density  median_age  new_cases  \\\n",
       "0     32866268.0              23.890        16.8        2.0   \n",
       "1     32866268.0              23.890        16.8        0.0   \n",
       "2     32866268.0              23.890        16.8        0.0   \n",
       "3     32866268.0              23.890        16.8        0.0   \n",
       "4     32866268.0              23.890        16.8        0.0   \n",
       "...          ...                 ...         ...        ...   \n",
       "8438  14862927.0              42.729        19.6       55.0   \n",
       "8439  14862927.0              42.729        19.6       41.0   \n",
       "8440  14862927.0              42.729        19.6       96.0   \n",
       "8441  14862927.0              42.729        19.6       18.0   \n",
       "8442  14862927.0              42.729        19.6        6.0   \n",
       "\n",
       "      percentage_of_Pop_New_Cases  ...  primary_key_response  \\\n",
       "0                        0.000006  ...         AGO2020-03-22   \n",
       "1                        0.000000  ...         AGO2020-03-23   \n",
       "2                        0.000000  ...         AGO2020-03-24   \n",
       "3                        0.000000  ...         AGO2020-03-25   \n",
       "4                        0.000000  ...         AGO2020-03-26   \n",
       "...                           ...  ...                   ...   \n",
       "8438                     0.000370  ...         ZWE2020-08-27   \n",
       "8439                     0.000276  ...         ZWE2020-08-28   \n",
       "8440                     0.000646  ...         ZWE2020-08-29   \n",
       "8441                     0.000121  ...         ZWE2020-08-30   \n",
       "8442                     0.000040  ...         ZWE2020-08-31   \n",
       "\n",
       "      StringencyIndex_updated  percentage_change_stringency  \\\n",
       "0                       11.11                           0.0   \n",
       "1                       11.11                           0.0   \n",
       "2                       33.33                         200.0   \n",
       "3                       33.33                           0.0   \n",
       "4                       33.33                           0.0   \n",
       "...                       ...                           ...   \n",
       "8438                    80.56                           0.0   \n",
       "8439                    80.56                           0.0   \n",
       "8440                    80.56                           0.0   \n",
       "8441                    80.56                           0.0   \n",
       "8442                    80.56                           0.0   \n",
       "\n",
       "      StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "0                             14.29                            10.26   \n",
       "1                             14.29                            10.26   \n",
       "2                             46.67                            25.64   \n",
       "3                             46.67                            25.64   \n",
       "4                             46.67                            25.64   \n",
       "...                             ...                              ...   \n",
       "8438                          88.10                            66.03   \n",
       "8439                          88.10                            66.03   \n",
       "8440                          88.10                            66.03   \n",
       "8441                          88.10                            66.03   \n",
       "8442                          88.10                            66.03   \n",
       "\n",
       "      percentage_change_GovernmentResponse  ContainmentHealthIndex_updated  \\\n",
       "0                                 0.000000                           12.12   \n",
       "1                                 0.000000                           12.12   \n",
       "2                               149.902534                           30.30   \n",
       "3                                 0.000000                           30.30   \n",
       "4                                 0.000000                           30.30   \n",
       "...                                    ...                             ...   \n",
       "8438                              0.000000                           73.48   \n",
       "8439                              0.000000                           73.48   \n",
       "8440                              0.000000                           73.48   \n",
       "8441                              0.000000                           73.48   \n",
       "8442                              0.000000                           73.48   \n",
       "\n",
       "      percentage_change_ContainmentHealth  EconomicSupportIndex_updated  \\\n",
       "0                                     0.0                           0.0   \n",
       "1                                     0.0                           0.0   \n",
       "2                                   150.0                           0.0   \n",
       "3                                     0.0                           0.0   \n",
       "4                                     0.0                           0.0   \n",
       "...                                   ...                           ...   \n",
       "8438                                  0.0                          25.0   \n",
       "8439                                  0.0                          25.0   \n",
       "8440                                  0.0                          25.0   \n",
       "8441                                  0.0                          25.0   \n",
       "8442                                  0.0                          25.0   \n",
       "\n",
       "      percentage_change_EconomicSupport  \n",
       "0                                   0.0  \n",
       "1                                   0.0  \n",
       "2                                   0.0  \n",
       "3                                   0.0  \n",
       "4                                   0.0  \n",
       "...                                 ...  \n",
       "8438                                0.0  \n",
       "8439                                0.0  \n",
       "8440                                0.0  \n",
       "8441                                0.0  \n",
       "8442                                0.0  \n",
       "\n",
       "[8443 rows x 30 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Dataframe filtered for Continent\n",
    "merged_covid_AF_df = pd.DataFrame(merged_covid_df.loc[merged_covid_df[\"continent\"]==\"Africa\"].reset_index())\n",
    "merged_covid_AF_df = merged_covid_AF_df.drop([\"index\"],axis=1)\n",
    "merged_covid_AF_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MultipleLocator\n",
    "# Increase the size of the plot figure.\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "ax.barh(merged_covid_AF_df[\"location\"],merged_covid_AF_df[\"median_age\"])\n",
    "\n",
    "ax.set_xticks(np.arange(0, 51, step=5.0))\n",
    "\n",
    "# Create minor ticks at an increment of 1.\n",
    "ax.xaxis.set_minor_locator(MultipleLocator(5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Algeria                         29.1\n",
       "Angola                          16.8\n",
       "Benin                           18.8\n",
       "Botswana                        25.8\n",
       "Burkina Faso                    17.6\n",
       "Burundi                         17.5\n",
       "Cameroon                        18.8\n",
       "Cape Verde                      25.7\n",
       "Central African Republic        18.3\n",
       "Chad                            16.7\n",
       "Congo                           19.0\n",
       "Cote d'Ivoire                   18.7\n",
       "Democratic Republic of Congo    17.0\n",
       "Djibouti                        25.4\n",
       "Egypt                           25.3\n",
       "Eritrea                         19.3\n",
       "Ethiopia                        19.8\n",
       "Gabon                           23.1\n",
       "Gambia                          17.5\n",
       "Ghana                           21.1\n",
       "Guinea                          19.0\n",
       "Kenya                           20.0\n",
       "Lesotho                         22.2\n",
       "Liberia                         19.2\n",
       "Libya                           29.0\n",
       "Madagascar                      19.6\n",
       "Malawi                          18.1\n",
       "Mali                            16.4\n",
       "Mauritania                      20.3\n",
       "Mauritius                       37.4\n",
       "Morocco                         29.6\n",
       "Mozambique                      17.7\n",
       "Namibia                         22.0\n",
       "Niger                           15.1\n",
       "Nigeria                         18.1\n",
       "Rwanda                          20.3\n",
       "Senegal                         18.7\n",
       "Seychelles                      36.2\n",
       "Sierra Leone                    19.1\n",
       "Somalia                         16.8\n",
       "South Africa                    27.3\n",
       "Sudan                           19.7\n",
       "Swaziland                       21.5\n",
       "Tanzania                        17.7\n",
       "Togo                            19.4\n",
       "Tunisia                         32.7\n",
       "Uganda                          16.4\n",
       "Zambia                          17.7\n",
       "Zimbabwe                        19.6\n",
       "Name: median_age, dtype: float64"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average test scores.\n",
    "covid_Africa_df = merged_covid_AF_df.groupby([\"location\"]).mean()[\"median_age\"]\n",
    "covid_Africa_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(covid_Africa_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_age\n",
       "(0, 20]     31\n",
       "(20, 30]    15\n",
       "(30, 40]     3\n",
       "(40, 50]     0\n",
       "Name: median_age, dtype: int64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Median age range for different locations\n",
    "bins = [0,20,30,40,50]\n",
    "median_age_Africa=covid_Africa_df.groupby(pd.cut(covid_Africa_df,bins)).count()\n",
    "median_age_Africa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 5760x7200 with 0 Axes>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 5760x7200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plot.\n",
    "median_age_Africa.plot( marker=\"*\", color=\"green\", linewidth=2, label='Median Age')\n",
    "# Create labels for the x and y axes.\n",
    "plt.xlabel(\"Median Age\")\n",
    "plt.ylabel(\"Country Count\")\n",
    "# Set the y limit between 0 and 45.\n",
    "plt.ylim(0, 45)\n",
    "# Create a title.\n",
    "plt.title(\"Covid 19 Impacted Median Age Group in Africa\")\n",
    "# Add a grid.\n",
    "plt.grid()\n",
    "# Add the legend.\n",
    "plt.legend()\n",
    "plt.figure(figsize=(80, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<19', '20-29', '30-39', '40-50']"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Establish the  bins and group names.\n",
    "bins = [0, 20, 30,40,50]\n",
    "group_names = [\"<19\",\"20-29\",\"30-39\",\"40-50\"]\n",
    "group_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>percentage_of_Pop_New_Deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>percentage_of_Pop_New_Tests</th>\n",
       "      <th>total_cases_updated</th>\n",
       "      <th>...</th>\n",
       "      <th>percentage_of_Pop_Total_Tests</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>43851043.0</td>\n",
       "      <td>17.348</td>\n",
       "      <td>29.1</td>\n",
       "      <td>180.926230</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>6.151639</td>\n",
       "      <td>1.402849e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9993.372951</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>53.264713</td>\n",
       "      <td>1.715670</td>\n",
       "      <td>59.041557</td>\n",
       "      <td>44.894754</td>\n",
       "      <td>1.809580</td>\n",
       "      <td>48.027049</td>\n",
       "      <td>1.768089</td>\n",
       "      <td>27.663934</td>\n",
       "      <td>0.396175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>32866268.0</td>\n",
       "      <td>23.890</td>\n",
       "      <td>16.8</td>\n",
       "      <td>16.098160</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.656442</td>\n",
       "      <td>1.997311e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>501.650307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>78.040307</td>\n",
       "      <td>2.187622</td>\n",
       "      <td>83.523497</td>\n",
       "      <td>65.534540</td>\n",
       "      <td>1.891078</td>\n",
       "      <td>70.644417</td>\n",
       "      <td>1.848278</td>\n",
       "      <td>37.423313</td>\n",
       "      <td>-0.102249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>12123198.0</td>\n",
       "      <td>99.110</td>\n",
       "      <td>18.8</td>\n",
       "      <td>12.767857</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>1.963964e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>787.226190</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>49.979405</td>\n",
       "      <td>1.048231</td>\n",
       "      <td>57.584286</td>\n",
       "      <td>51.834345</td>\n",
       "      <td>0.771051</td>\n",
       "      <td>55.428988</td>\n",
       "      <td>0.627589</td>\n",
       "      <td>32.068452</td>\n",
       "      <td>-0.099206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>2351625.0</td>\n",
       "      <td>4.044</td>\n",
       "      <td>25.8</td>\n",
       "      <td>10.673203</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.039216</td>\n",
       "      <td>1.667599e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>357.836601</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>65.526078</td>\n",
       "      <td>-0.025762</td>\n",
       "      <td>71.224183</td>\n",
       "      <td>61.389673</td>\n",
       "      <td>-0.017520</td>\n",
       "      <td>61.189281</td>\n",
       "      <td>-0.034291</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>0.163399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>20903278.0</td>\n",
       "      <td>70.151</td>\n",
       "      <td>17.6</td>\n",
       "      <td>7.953488</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.319767</td>\n",
       "      <td>1.529748e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>804.494186</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>58.102326</td>\n",
       "      <td>1.689966</td>\n",
       "      <td>64.168837</td>\n",
       "      <td>50.481686</td>\n",
       "      <td>1.269640</td>\n",
       "      <td>59.657849</td>\n",
       "      <td>1.270154</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              population  population_density  median_age   new_cases  \\\n",
       "location                                                               \n",
       "Algeria       43851043.0              17.348        29.1  180.926230   \n",
       "Angola        32866268.0              23.890        16.8   16.098160   \n",
       "Benin         12123198.0              99.110        18.8   12.767857   \n",
       "Botswana       2351625.0               4.044        25.8   10.673203   \n",
       "Burkina Faso  20903278.0              70.151        17.6    7.953488   \n",
       "\n",
       "              percentage_of_Pop_New_Cases  new_deaths  \\\n",
       "location                                                \n",
       "Algeria                          0.000413    6.151639   \n",
       "Angola                           0.000049    0.656442   \n",
       "Benin                            0.000105    0.238095   \n",
       "Botswana                         0.000454    0.039216   \n",
       "Burkina Faso                     0.000038    0.319767   \n",
       "\n",
       "              percentage_of_Pop_New_Deaths  new_tests  \\\n",
       "location                                                \n",
       "Algeria                       1.402849e-07        0.0   \n",
       "Angola                        1.997311e-08        0.0   \n",
       "Benin                         1.963964e-08        0.0   \n",
       "Botswana                      1.667599e-08        0.0   \n",
       "Burkina Faso                  1.529748e-08        0.0   \n",
       "\n",
       "              percentage_of_Pop_New_Tests  total_cases_updated  ...  \\\n",
       "location                                                        ...   \n",
       "Algeria                               0.0          9993.372951  ...   \n",
       "Angola                                0.0           501.650307  ...   \n",
       "Benin                                 0.0           787.226190  ...   \n",
       "Botswana                              0.0           357.836601  ...   \n",
       "Burkina Faso                          0.0           804.494186  ...   \n",
       "\n",
       "              percentage_of_Pop_Total_Tests  StringencyIndex_updated  \\\n",
       "location                                                               \n",
       "Algeria                                 0.0                53.264713   \n",
       "Angola                                  0.0                78.040307   \n",
       "Benin                                   0.0                49.979405   \n",
       "Botswana                                0.0                65.526078   \n",
       "Burkina Faso                            0.0                58.102326   \n",
       "\n",
       "              percentage_change_stringency  StringencyLegacyIndex_updated  \\\n",
       "location                                                                    \n",
       "Algeria                           1.715670                      59.041557   \n",
       "Angola                            2.187622                      83.523497   \n",
       "Benin                             1.048231                      57.584286   \n",
       "Botswana                         -0.025762                      71.224183   \n",
       "Burkina Faso                      1.689966                      64.168837   \n",
       "\n",
       "              GovernmentResponseIndex_updated  \\\n",
       "location                                        \n",
       "Algeria                             44.894754   \n",
       "Angola                              65.534540   \n",
       "Benin                               51.834345   \n",
       "Botswana                            61.389673   \n",
       "Burkina Faso                        50.481686   \n",
       "\n",
       "              percentage_change_GovernmentResponse  \\\n",
       "location                                             \n",
       "Algeria                                   1.809580   \n",
       "Angola                                    1.891078   \n",
       "Benin                                     0.771051   \n",
       "Botswana                                 -0.017520   \n",
       "Burkina Faso                              1.269640   \n",
       "\n",
       "              ContainmentHealthIndex_updated  \\\n",
       "location                                       \n",
       "Algeria                            48.027049   \n",
       "Angola                             70.644417   \n",
       "Benin                              55.428988   \n",
       "Botswana                           61.189281   \n",
       "Burkina Faso                       59.657849   \n",
       "\n",
       "              percentage_change_ContainmentHealth  \\\n",
       "location                                            \n",
       "Algeria                                  1.768089   \n",
       "Angola                                   1.848278   \n",
       "Benin                                    0.627589   \n",
       "Botswana                                -0.034291   \n",
       "Burkina Faso                             1.270154   \n",
       "\n",
       "              EconomicSupportIndex_updated  percentage_change_EconomicSupport  \n",
       "location                                                                       \n",
       "Algeria                          27.663934                           0.396175  \n",
       "Angola                           37.423313                          -0.102249  \n",
       "Benin                            32.068452                          -0.099206  \n",
       "Botswana                         62.500000                           0.163399  \n",
       "Burkina Faso                      0.000000                           0.000000  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_age_Africa=merged_covid_AF_df.groupby(\"location\").mean()\n",
    "median_age_Africa.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>percentage_of_Pop_New_Deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>percentage_of_Pop_New_Tests</th>\n",
       "      <th>total_cases_updated</th>\n",
       "      <th>...</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "      <th>Age_Range</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Algeria</th>\n",
       "      <td>43851043.0</td>\n",
       "      <td>17.348</td>\n",
       "      <td>29.1</td>\n",
       "      <td>180.926230</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>6.151639</td>\n",
       "      <td>1.402849e-07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9993.372951</td>\n",
       "      <td>...</td>\n",
       "      <td>53.264713</td>\n",
       "      <td>1.715670</td>\n",
       "      <td>59.041557</td>\n",
       "      <td>44.894754</td>\n",
       "      <td>1.809580</td>\n",
       "      <td>48.027049</td>\n",
       "      <td>1.768089</td>\n",
       "      <td>27.663934</td>\n",
       "      <td>0.396175</td>\n",
       "      <td>20-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angola</th>\n",
       "      <td>32866268.0</td>\n",
       "      <td>23.890</td>\n",
       "      <td>16.8</td>\n",
       "      <td>16.098160</td>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.656442</td>\n",
       "      <td>1.997311e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>501.650307</td>\n",
       "      <td>...</td>\n",
       "      <td>78.040307</td>\n",
       "      <td>2.187622</td>\n",
       "      <td>83.523497</td>\n",
       "      <td>65.534540</td>\n",
       "      <td>1.891078</td>\n",
       "      <td>70.644417</td>\n",
       "      <td>1.848278</td>\n",
       "      <td>37.423313</td>\n",
       "      <td>-0.102249</td>\n",
       "      <td>&lt;19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Benin</th>\n",
       "      <td>12123198.0</td>\n",
       "      <td>99.110</td>\n",
       "      <td>18.8</td>\n",
       "      <td>12.767857</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.238095</td>\n",
       "      <td>1.963964e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>787.226190</td>\n",
       "      <td>...</td>\n",
       "      <td>49.979405</td>\n",
       "      <td>1.048231</td>\n",
       "      <td>57.584286</td>\n",
       "      <td>51.834345</td>\n",
       "      <td>0.771051</td>\n",
       "      <td>55.428988</td>\n",
       "      <td>0.627589</td>\n",
       "      <td>32.068452</td>\n",
       "      <td>-0.099206</td>\n",
       "      <td>&lt;19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Botswana</th>\n",
       "      <td>2351625.0</td>\n",
       "      <td>4.044</td>\n",
       "      <td>25.8</td>\n",
       "      <td>10.673203</td>\n",
       "      <td>0.000454</td>\n",
       "      <td>0.039216</td>\n",
       "      <td>1.667599e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>357.836601</td>\n",
       "      <td>...</td>\n",
       "      <td>65.526078</td>\n",
       "      <td>-0.025762</td>\n",
       "      <td>71.224183</td>\n",
       "      <td>61.389673</td>\n",
       "      <td>-0.017520</td>\n",
       "      <td>61.189281</td>\n",
       "      <td>-0.034291</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>0.163399</td>\n",
       "      <td>20-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Burkina Faso</th>\n",
       "      <td>20903278.0</td>\n",
       "      <td>70.151</td>\n",
       "      <td>17.6</td>\n",
       "      <td>7.953488</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>0.319767</td>\n",
       "      <td>1.529748e-08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>804.494186</td>\n",
       "      <td>...</td>\n",
       "      <td>58.102326</td>\n",
       "      <td>1.689966</td>\n",
       "      <td>64.168837</td>\n",
       "      <td>50.481686</td>\n",
       "      <td>1.269640</td>\n",
       "      <td>59.657849</td>\n",
       "      <td>1.270154</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>&lt;19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              population  population_density  median_age   new_cases  \\\n",
       "location                                                               \n",
       "Algeria       43851043.0              17.348        29.1  180.926230   \n",
       "Angola        32866268.0              23.890        16.8   16.098160   \n",
       "Benin         12123198.0              99.110        18.8   12.767857   \n",
       "Botswana       2351625.0               4.044        25.8   10.673203   \n",
       "Burkina Faso  20903278.0              70.151        17.6    7.953488   \n",
       "\n",
       "              percentage_of_Pop_New_Cases  new_deaths  \\\n",
       "location                                                \n",
       "Algeria                          0.000413    6.151639   \n",
       "Angola                           0.000049    0.656442   \n",
       "Benin                            0.000105    0.238095   \n",
       "Botswana                         0.000454    0.039216   \n",
       "Burkina Faso                     0.000038    0.319767   \n",
       "\n",
       "              percentage_of_Pop_New_Deaths  new_tests  \\\n",
       "location                                                \n",
       "Algeria                       1.402849e-07        0.0   \n",
       "Angola                        1.997311e-08        0.0   \n",
       "Benin                         1.963964e-08        0.0   \n",
       "Botswana                      1.667599e-08        0.0   \n",
       "Burkina Faso                  1.529748e-08        0.0   \n",
       "\n",
       "              percentage_of_Pop_New_Tests  total_cases_updated  ...  \\\n",
       "location                                                        ...   \n",
       "Algeria                               0.0          9993.372951  ...   \n",
       "Angola                                0.0           501.650307  ...   \n",
       "Benin                                 0.0           787.226190  ...   \n",
       "Botswana                              0.0           357.836601  ...   \n",
       "Burkina Faso                          0.0           804.494186  ...   \n",
       "\n",
       "              StringencyIndex_updated  percentage_change_stringency  \\\n",
       "location                                                              \n",
       "Algeria                     53.264713                      1.715670   \n",
       "Angola                      78.040307                      2.187622   \n",
       "Benin                       49.979405                      1.048231   \n",
       "Botswana                    65.526078                     -0.025762   \n",
       "Burkina Faso                58.102326                      1.689966   \n",
       "\n",
       "              StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "location                                                                       \n",
       "Algeria                           59.041557                        44.894754   \n",
       "Angola                            83.523497                        65.534540   \n",
       "Benin                             57.584286                        51.834345   \n",
       "Botswana                          71.224183                        61.389673   \n",
       "Burkina Faso                      64.168837                        50.481686   \n",
       "\n",
       "              percentage_change_GovernmentResponse  \\\n",
       "location                                             \n",
       "Algeria                                   1.809580   \n",
       "Angola                                    1.891078   \n",
       "Benin                                     0.771051   \n",
       "Botswana                                 -0.017520   \n",
       "Burkina Faso                              1.269640   \n",
       "\n",
       "              ContainmentHealthIndex_updated  \\\n",
       "location                                       \n",
       "Algeria                            48.027049   \n",
       "Angola                             70.644417   \n",
       "Benin                              55.428988   \n",
       "Botswana                           61.189281   \n",
       "Burkina Faso                       59.657849   \n",
       "\n",
       "              percentage_change_ContainmentHealth  \\\n",
       "location                                            \n",
       "Algeria                                  1.768089   \n",
       "Angola                                   1.848278   \n",
       "Benin                                    0.627589   \n",
       "Botswana                                -0.034291   \n",
       "Burkina Faso                             1.270154   \n",
       "\n",
       "              EconomicSupportIndex_updated  percentage_change_EconomicSupport  \\\n",
       "location                                                                        \n",
       "Algeria                          27.663934                           0.396175   \n",
       "Angola                           37.423313                          -0.102249   \n",
       "Benin                            32.068452                          -0.099206   \n",
       "Botswana                         62.500000                           0.163399   \n",
       "Burkina Faso                      0.000000                           0.000000   \n",
       "\n",
       "              Age_Range  \n",
       "location                 \n",
       "Algeria           20-29  \n",
       "Angola              <19  \n",
       "Benin               <19  \n",
       "Botswana          20-29  \n",
       "Burkina Faso        <19  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorize Age based on the bins.\n",
    "median_age_Africa[(\"Age_Range\")] = pd.cut(covid_Africa_df,bins, labels=group_names)\n",
    "\n",
    "median_age_Africa.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total Cases Per Age Group in Asia')"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "#ax2 = ax.twinx()\n",
    "ax.bar(median_age_Africa.Age_Range, median_age_Africa[\"total_cases_updated\"], color=\"red\", label=\"total_deaths_updated\")\n",
    "#ax2.plot(median_age_Asia_df.Age_Range, median_age_Asia_df[\"total_cases_updated\"], color='blue', label='total_cases_updated')\n",
    "#ax.set_xticklabels(median_age_Asia_df.Age_Range)\n",
    "#ax.legend(loc='best')\n",
    "ax.set_ylabel('Total Cases Per Age Group in Asia', color=\"red\")\n",
    "#ax2.set_ylabel('total deaths', color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a30d46a788>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9t7/9bS6nqPsIxsx2SO+88w6LFi1i4cKFANx2222MGDECgN13331Tv8batWvp3LkzXbt25fXXX9/i2WCdOnViwoQJfPGLX9x0dtrSpUu5/fbb6y3rzTffZM2aNXzsYx/j2muvZe7cuQCMGjWKH/3oR5vKLqT//e9/55BDDuGSSy6hsrKS559/vlYddtttN3r06MGMGTOALLj8/ve/55hjjtm0zF133QXA448/TteuXenatSvHHnssd9xxBwAPPPDApiC1Zs0aunXrRqdOnXj++ed54oknNpXTrl07NmzYAMDxxx/P3XffzbJlyzat9+WXX+aII47gkUceYeXKlWzYsIFf/vKXW/p4ypJbgJH0M0nLJD1bYt5XJYWkvYrSLpW0UNICSScWpQ+V9EyaN0UpzEpqL+mulD5LUp+iPOMlvZCG8Xlto1lLtr17YbZWhw4duPnmmznttNM45JBD2GWXXTj//PMBmDhxIieddBLHHXcchx56KEOGDGHAgAGcc845HH300Vss+6qrrqKiooL+/fszcOBAxo4dS0VFRb1lrVu3jpNPPplBgwYxYsQIrrnmGiBrlqqqqmLQoEH079+fH//4x0B2ltvAgQM59NBD6dixIyeddNJmdbj11lu56qqrGDx4MB/5yEeYPHkyH/zgBzfN79atG0cddRTnn38+N910EwCTJ0/mscce47DDDuPBBx9k//33B2D06NHU1NQwaNAgvvGNbzB8+PBN5UycOJFBgwZx5pln0r9/f6666ipGjRrFoEGDOOGEE1i6dCk9evTgsssu48gjj+SjH/0ohx122NZ/YKXU1zmzrQNwLHAY8Gyd9N7AH4CXgb1SWn/gaaA90Bf4O9AmzZsNHAkIeAA4KaV/HvhxGh8H3JXG9wReTK/d0ni3LdXXnfw77mBNo1QHrjWPUicn7Ah2mE7+iHgMWFVi1jXAvwPF/2nGAHdGxPqIeAlYCAyT1APoEhEz04bcCowtyjM1jd8NHJ+Obk4EpkfEqohYDUwHRm/nzTMzsy1o0k5+SacAr0bE03U6lHoCTxRNV6e0DWm8bnohz2KAiKiRtAboXpxeIk/d+kwEJgKbDjXNzJrbI4880txV2C6arJNfUifg68A3S80ukRYNpDc2T+3EiBsjojIiKisqKkotYtaqZA0FZptrzHejKc8i+yBZ/8rTkhYBvYCnJO1LdpTRu2jZXsCSlN6rRDrFeSS1BbqSNcnVV5aZNaBDhw6sXLnSQcY2E5E9D6ZDhw5bla/Jmsgi4hlg78J0CjKVEbFC0r3AzyX9ANgP6AfMjoiNktZJGg7MAs4GrktF3AuMB2YCpwIPR0RI+gPwLUmFK5BGAZfmv4VmO7devXpRXV3N8uXLm7sqtgMqPNFya+QWYCT9AhgJ7CWpGpgcETeVWjYi5kmaBswHaoALI2Jjmn0BcAvQkewsssJJ7jcBt0laSHbkMi6VtUrSlcCTabkrIqLUyQZmVqRdu3Zb9bRCsy2RD4czlZWVUeq23uXyXX/z46+o2Y5L0pyIqCw1z1fym5lZLhxgzMwsFw4wZmaWCwcYMzPLhQOMmZnlwgHGzMxy4QBjZma5cIAxM7NcOMCYmVkuHGDMzCwXDjBmZpYLBxgzM8uFA4yZmeXCAcbMzHLhAGNmZrlwgDEzs1w4wJiZWS4cYMzMLBcOMGZmlovcAoykn0laJunZorTvSnpe0l8l/UbSHkXzLpW0UNICSScWpQ+V9EyaN0WSUnp7SXel9FmS+hTlGS/phTSMz2sbzcysfnkewdwCjK6TNh0YGBGDgL8BlwJI6g+MAwakPNdLapPy3ABMBPqloVDmBGB1RBwIXAN8J5W1JzAZOAIYBkyW1C2H7TMzswbkFmAi4jFgVZ20ByOiJk0+AfRK42OAOyNifUS8BCwEhknqAXSJiJkREcCtwNiiPFPT+N3A8eno5kRgekSsiojVZEGtbqAzM7OcNWcfzDnAA2m8J7C4aF51SuuZxuum18qTgtYaoHsDZZmZWRNqlgAj6etADXBHIanEYtFAemPz1K3HRElVkqqWL1/ecKXNzGyrNHmASZ3uJwNnpmYvyI4yehct1gtYktJ7lUivlUdSW6ArWZNcfWVtJiJujIjKiKisqKjYls0yM7M6mjTASBoNXAKcEhFvFc26FxiXzgzrS9aZPzsilgLrJA1P/StnA/cU5SmcIXYq8HAKWH8ARknqljr3R6U0MzNrQm3zKljSL4CRwF6SqsnO7LoUaA9MT2cbPxER50fEPEnTgPlkTWcXRsTGVNQFZGekdSTrsyn029wE3CZpIdmRyziAiFgl6UrgybTcFRFR62QDMzPLn95vpWrdKisro6qqqtH5Varnx7YLf0XNdlyS5kREZal5vpLfzMxy4QBjZma5cIAxM7NcOMCYmVkuHGDMzCwXDjBmZpYLBxgzM8uFA4yZmeXCAcbMzHLhAGNmZrlwgDEzs1w4wJiZWS4cYMzMLBcOMGZmlgsHGDMzy4UDjJmZ5cIBxszMcuEAY2ZmuXCAMTOzXDjAmJlZLnILMJJ+JmmZpGeL0vaUNF3SC+m1W9G8SyUtlLRA0olF6UMlPZPmTZGklN5e0l0pfZakPkV5xqd1vCBpfF7baGZm9cvzCOYWYHSdtEnAjIjoB8xI00jqD4wDBqQ810tqk/LcAEwE+qWhUOYEYHVEHAhcA3wnlbUnMBk4AhgGTC4OZGZm1jRyCzAR8Riwqk7yGGBqGp8KjC1KvzMi1kfES8BCYJikHkCXiJgZEQHcWidPoay7gePT0c2JwPSIWBURq4HpbB7ozMwsZ03dB7NPRCwFSK97p/SewOKi5apTWs80Xje9Vp6IqAHWAN0bKGszkiZKqpJUtXz58m3YLDMzq2tH6eRXibRoIL2xeWonRtwYEZURUVlRUVFWRc3MrDxNHWBeT81epNdlKb0a6F20XC9gSUrvVSK9Vh5JbYGuZE1y9ZVlZmZNqKkDzL1A4ayu8cA9Renj0plhfck682enZrR1koan/pWz6+QplHUq8HDqp/kDMEpSt9S5PyqlmZlZE2qbV8GSfgGMBPaSVE12ZtfVwDRJE4BXgNMAImKepGnAfKAGuDAiNqaiLiA7I60j8EAaAG4CbpO0kOzIZVwqa5WkK4En03JXRETdkw3MzCxnyv70W2VlZVRVVTU6v0r1/Nh24a+o2Y5L0pyIqCw1b4tNZJI6S9oljR8k6RRJ7bZ3Jc3MrGUppw/mMaCDpJ5kF0d+lqzJyszMrF7lBBhFxFvAJ4DrIuKfgf75VsvMzHZ2ZQUYSUcCZwL/m9JyOznAzMxahnICzMXApcBv0tleHwD+mG+1zMxsZ7fFI5GIeBR4VFLnNP0i8MW8K2ZmZju3cs4iO1LSfOC5NH2opOtzr5mZme3Uymkiu5bsDsUrASLiaeDYPCtlZmY7v7JuFRMRi+skbSy5oJmZWVLO2WCLJR0FhKRdyfpfnsu3WmZmtrMr5wjmfOBC3n82y+A0bWZmVq9yziJbQXYNjJmZWdnKOYvsvyR1kdRO0gxJKySd1RSVMzOznVc5TWSjImItcDJZE9lBwL/lWiszM9vplRNgCndO/hjwCz9bxczMylHOWWS/k/Q88DbweUkVwDv5VsvMzHZ2WzyCiYhJwJFAZURsAP4BjMm7YmZmtnMr967IPYETJHUoSrs1h/qYmVkLscUAI2kyMJLsGTD3AycBj+MAY2ZmDSink/9U4HjgtYj4LHAo0H5bVirpS5LmSXpW0i8kdZC0p6Tpkl5Ir92Klr9U0kJJCySdWJQ+VNIzad4USUrp7SXdldJnSeqzLfU1M7OtV06AeTsi3gNqJHUBlgEfaOwK06OXv0jWpzMQaAOMAyYBMyKiH9mjmSel5fun+QOA0cD1ktqk4m4AJgL90jA6pU8AVkfEgcA1wHcaW18zM2uccgJMlaQ9gJ8Cc4CngNnbuN62QEdJbYFOwBKyEwempvlTgbFpfAxwZ0Ssj4iXgIXAMEk9gC4RMTMigqzJrjhPoay7geMLRzdmZtY0yrlVzOfT6I8l/Z5sp/7Xxq4wIl6V9D3gFbJTnx+MiAcl7RMRS9MySyXtnbL0BJ4oKqI6pW1I43XTC3kWp7JqJK0BugMriusiaSLZERD7779/YzfJzMxKqPcIRtKJkk4tTouIRcAhkk5o7ApT38oYoC+wH9B5C7eeKXXkEQ2kN5SndkLEjRFRGRGVFRUVDVfczMy2SkNNZJcDj5ZInwFcsQ3r/CjwUkQsT9fV/Bo4Cng9NXuRXpel5auB3kX5e5E1qVWn8brptfKkZriugO9AYGbWhBoKMJ0iYnndxIh4Dei8Det8BRguqVPqFzme7Pky9wLj0zLjgXvS+L3AuHRmWF+yzvzZqTltnaThqZyz6+QplHUq8HDqpzEzsybSUB9MB0ltI6KmOFFSO6BjY1cYEbMk3U12skAN8BfgRmA3YJqkCWRB6LS0/DxJ04D5afkLI6LwRM0LgFtSfR5IA8BNwG2SFpIduYxrbH3NzKxxVN8fe0lXA/sAF0XEP1JaZ2AKsCIiLmmyWjaBysrKqKqqanR+n6OWHx97mu24JM2JiMpS8xpqIvsP4HXgZUlzJM0BFgHL0zwzM7N61dtElprGJkm6HDgwJS+MiLebpGZmZrZTK+c6mLeBZ5qgLmZm1oKUcyW/mZnZVnOAMTOzXNTbRCbpsIYyRsRT2786ZmbWUjTUB/P9BuYF8JHtXBczM2tBGjqL7LimrIiZmbUsZT0yWdJAsidabnpkckT4iZZmZlYvPzLZzMxy0SyPTDYzs5avyR+ZbGZmrUM5fTB1H5n8Jtv+yGQzM2vhmvyRyWZm1jpssYlM0ozCeEQsioi/FqeZmZmV0tCV/B2ATsBekrrx/nPuuwD7NUHdzMxsJ9ZQE9l5wMVkwaT4tjBrgf/Os1JmZrbza+hK/h8CP5T0hYi4rgnrZGZmLUA5Z5H9RNIXgWPT9CPATyJiQ261MjOznV4518FcDwxNr4XxG7ZlpZL2kHS3pOclPSfpSEl7Spou6YX02q1o+UslLZS0QNKJRelDJT2T5k2RpJTeXtJdKX2WpD7bUl8zM9t69QYYSYWjm8MjYnxEPJyGzwKHb+N6fwj8PiIOJrszwHPAJGBGRPQDZqRpJPUHxgEDgNHA9ZLapHJuACYC/dIwOqVPAFZHxIHANcB3trG+Zma2lRo6gilcTLlR0gcLiZI+AGxs7ArT3QCOBW4CiIh3I+INYAwwNS02FRibxscAd0bE+oh4CVgIDJPUg+yanJkREWT3RivOUyjrbuD4wtGNmZk1jYb6YAo75K8Cf5T0YpruA3x2G9b5AWA5cLOkQ8nuDvCvwD4RsRQgIpZK2jst3xN4oih/dUrbkMbrphfyLE5l1UhaA3QHVtTaQGki2REQ+++//zZskpmZ1dVQgKmQ9OU0/hOgDfAPslv2DwH+uA3rPAz4QkTMkvRDUnNYPUodeUQD6Q3lqZ0QcSNwI0BlZeVm883MrPEaaiJrA+wG7E4WFJSm26a0xqoGqiNiVpq+myzgvJ6avUivy4qW712UvxewJKX3KpFeK0/qS+oKrNqGOpuZ2VZq6AhmaURcsb1XGBGvSVos6UMRsYDsUQDz0zAeuDq93pOy3Av8XNIPyC767AfMjoiNktZJGg7MAs4GrivKMx6YSfa4gYdTP42ZmTWRcvpg8vAF4A5JuwIvkvXp7AJMkzQBeAU4DSAi5kmaRhaAaoALI6JwksEFwC1AR+CBNEB2AsFtkhaSHbmMy3FbzMysBNX3x17SnhHRapqVKisro6qqqtH5fY5afnzsabbjkjQnIipLzau3D6Y1BRczM9v+yrmS38zMbKs5wJiZWS4cYMzMLBcOMGZmlgsHGDMzy4UDjJmZ5cIBxszMcuEAY2ZmuXCAMTOzXDjAmJlZLhxgzMwsFw4wZmaWCwcYMzPLhQOMmZnlwgHGzMxy4QBjZma5cIAxM7NcOMCYmVkumi3ASGoj6S+S7kvTe0qaLumF9NqtaNlLJS2UtEDSiUXpQyU9k+ZNkaSU3l7SXSl9lqQ+Tb19ZmatXXMewfwr8FzR9CRgRkT0A2akaST1B8YBA4DRwPWS2qQ8NwATgX5pGJ3SJwCrI+JA4BrgOypW2ZwAAAurSURBVPluipmZ1dUsAUZSL+DjwP8UJY8BpqbxqcDYovQ7I2J9RLwELASGSeoBdImImRERwK118hTKuhs4vnB0Y2ZmTaO5jmCuBf4deK8obZ+IWAqQXvdO6T2BxUXLVae0nmm8bnqtPBFRA6wButethKSJkqokVS1fvnxbt8nMzIo0eYCRdDKwLCLmlJulRFo0kN5QntoJETdGRGVEVFZUVJRZHTMzK0fbZljn0cApkj4GdAC6SLodeF1Sj4hYmpq/lqXlq4HeRfl7AUtSeq8S6cV5qiW1BboCq/LaIDMz21yTH8FExKUR0Ssi+pB13j8cEWcB9wLj02LjgXvS+L3AuHRmWF+yzvzZqRltnaThqX/l7Dp5CmWdmtax2RGMmZnlpzmOYOpzNTBN0gTgFeA0gIiYJ2kaMB+oAS6MiI0pzwXALUBH4IE0ANwE3CZpIdmRy7im2ggzM8vIf+wzlZWVUVVV1ej8PkctP/6Kmu24JM2JiMpS83wlv5mZ5cIBxszMcuEAY2ZmuXCAMTOzXDjAmJlZLhxgzMwsFw4wZmaWCwcYMzPLhQOMmZnlwgHGzMxy4QBjZma5cIAxM7NcOMCYmVkuHGDMzCwXDjBmZpYLBxgzM8uFA4yZmeXCAcbMzHLhAGNmZrlo8gAjqbekP0p6TtI8Sf+a0veUNF3SC+m1W1GeSyUtlLRA0olF6UMlPZPmTZGklN5e0l0pfZakPk29nWZmrV1zHMHUAF+JiA8Dw4ELJfUHJgEzIqIfMCNNk+aNAwYAo4HrJbVJZd0ATAT6pWF0Sp8ArI6IA4FrgO80xYaZmdn7mjzARMTSiHgqja8DngN6AmOAqWmxqcDYND4GuDMi1kfES8BCYJikHkCXiJgZEQHcWidPoay7geMLRzdmZtY0mrUPJjVdDQFmAftExFLIghCwd1qsJ7C4KFt1SuuZxuum18oTETXAGqB7HttgZmalNVuAkbQb8Cvg4ohY29CiJdKigfSG8tStw0RJVZKqli9fvqUqm5nZVmiWACOpHVlwuSMifp2SX0/NXqTXZSm9GuhdlL0XsCSl9yqRXiuPpLZAV2BV3XpExI0RURkRlRUVFdtj08zMLGmOs8gE3AQ8FxE/KJp1LzA+jY8H7ilKH5fODOtL1pk/OzWjrZM0PJV5dp08hbJOBR5O/TRmZtZE2jbDOo8GPg08I2luSvsacDUwTdIE4BXgNICImCdpGjCf7Ay0CyNiY8p3AXAL0BF4IA2QBbDbJC0kO3IZl/dGmZlZbfIf+0xlZWVUVVU1Or/PUcuPv6JmOy5JcyKistQ8X8lvZma5cIAxM7NcOMCYmVkuHGDMzCwXzXEWmVmz80kZ+fFJGVbgIxgzM8uFA4yZmeXCAcbMzHLhAGNmZrlwgDEzs1w4wJiZWS4cYMzMLBcOMGZmlgsHGDMzy4UDjJmZ5cIBxszMcuEAY2ZmuXCAMTOzXDjAmJlZLhxgzMwsFy06wEgaLWmBpIWSJjV3fczMWpMW+8AxSW2A/wZOAKqBJyXdGxHzm7dmZtYYfkhcfvJ6SFxLPoIZBiyMiBcj4l3gTmBMM9fJzKzVaLFHMEBPYHHRdDVwRPECkiYCE9Pkm5IWNFHdmttewIrmrkS5/M8V2Ik+M39em7SWz+yA+ma05ABT6i2rdSAYETcCNzZNdXYckqoiorK562Hl82e28/Fn1rKbyKqB3kXTvYAlzVQXM7NWpyUHmCeBfpL6StoVGAfc28x1MjNrNVpsE1lE1Ei6CPgD0Ab4WUTMa+Zq7ShaXbNgC+DPbOfT6j8zRV7np5mZWavWkpvIzMysGTnAmJlZLhxgdmKSekv6o6TnJM2T9K8pfU9J0yW9kF671ZP/u5Kel/RXSb+RtEdK31XSzZKekfS0pJFNuFlWRNJF6VZHIWmvovRu6TP7q6TZkgY2Zz1bMkkd0nv8dPqdXZ7Sy/2dXZk+p7mSHpS0X0pv8b8zB5idWw3wlYj4MDAcuFBSf2ASMCMi+gEz0nQp04GBETEI+BtwaUr/HEBEHEJ2q53vS/J3pYmkHU/nNPln4KPAy3UW+xowN312ZwM/bMIqtjbrgY9ExKHAYGC0pOGU/zv7bkQMiojBwH3AN1N6i/+dtaiNaW0iYmlEPJXG1wHPkd3BYAwwNS02FRhbT/4HI6ImTT5Bdq0QQH+yHwwRsQx4A2jVF4w1BUkflvR9YAFwEEBE/CUiFpVYvPgzeh7oI2mfpqpraxKZN9NkuzQE5f/O1hZNdub9C75b/O/MAaaFkNQHGALMAvaJiKWQBSFg7zKKOAd4II0/DYyR1FZSX2AotS9ate1EUmdJn5X0OPA/ZH8SBkXEX7aQ9WngE6mMYWS36+jVYA5rNEltJM0FlgHTI2KrfmeS/lPSYuBM3j+CafG/sxZ7HUxrImk34FfAxRGxVlt5YyFJXydrbrsjJf0M+DBQRdY0839pvm1/S4G/AuemI5FyXQ38MO30ngH+gj+j3ETERmBw6qf8zdb2eUXE14GvS7oUuAiYTCv4nfkIZicnqR1ZcLkjIn6dkl+X1CPN70H2r4vUoThX0v1F+ccDJwNnRrooKiJqIuJLETE4IsYAewAvNN1WtSqnAq+S7bS+KaneGwcWi4i1EfHZ1K5/NlABvJRjPQ2IiDeAR4DRbMXvrMjPgU+mslr878wBZiem7FDlJuC5iPhB0ax7gfFpfDxwD0BhhxQRH0v5RwOXAKdExFtF5XYqdDJLOgGo8XN08pH6wT4FHAOsAe6R9FBq8qyXpD3SLZAAzgUeq9PWb9uJpIqiMyw7kp108Tzl/876FRV3SsrbKn5nvpJ/JybpGOBPZE0k76Xkr5H1w0wD9gdeAU6LiFUl8i8E2gMrU9ITEXF+2rn9IZX5KjAhIuqexWQ5SX0qSyNisaQvAv8O7Ev2D/n+iDhX0pHArcBGYD7ZZ7S62SrdgkkaRNaJ34bsT/m0iLhCUnfK+539CvgQ2e/pZeD8iHi1NfzOHGDMzCwXbiIzM7NcOMCYmVkuHGDMzCwXDjBmZpYLBxgzM8uFA4zZNpL0z+luxwdvxzIvk/RqumBvvqQztlfZZk3FAcZs250BPA6M287lXpOu1B8D/CTdtcFsp+EAY7YN0n3gjgYmUBRgJO0i6fr0/JD7JN0v6dQ0b6ikRyXNkfSHwu1G6hMRLwBvAd1S/hskVRU/mySlL5J0uaSn0jNGDk7pFel5JU9J+omkl5WeLSPpLGXPOpmb5rXZzm+RtWIOMGbbZizw+4j4G7BK0mEp/RNAH+AQslu5HAmb7h13HXBqRAwlu+Hhfza0glTmC+mW7gBfj4hKYBAwIl1pXrAiIg4DbgC+mtImAw+n9N+QXXmOpA8DnwKOTkdKG8nu9mu2Xfhuymbb5gzg2jR+Z5p+iuzeYr+MiPeA1yT9MS3zIWAgMD3d9boN2R2VS/mSpM8BHyC7uWLB6ZImkv1+e5A9V+SvaV7hhqdzSLfzT3X5Z4CI+L2kwi1ljie7RfyTqS4dSTdsNNseHGDMGindi+ojwEBJQRYsQtK/A/U9M0HAvIg4soxVXBMR35P0CeBWSR8kCyhfBQ6PiNWSbgE6FOVZn1438v7vu6G6TI2IS+uZb7ZN3ERm1ninArdGxAER0SciepPdMv8Ysk7/T6a+mH2AkSnPAqAi3awSSe0kDWhoJekxDFVkd+ztAvwDWJPKPamMej4OnJ7WN4rUl0P2NMVTJe2d5u1Z7uMCzMrhIxizxjuD7MFfxX4F/AtwIVkT1LPA38jucL0mIt5Nnf1TJHUl+w1eC8zbwrquIHuWyIfJHi42D3gR+HMZ9bwc+IWkTwGPkjXJrYuIFZL+A3hQ2bPgN6R6t6g7+lrz8d2UzXIiabeIeDM1pc0m60x/rRnq0R7YGBE16cjphtSpb5YrH8GY5ee+9KCqXYErmyO4JPsD09JRyrvA55qpHtbK+AjGzMxy4U5+MzPLhQOMmZnlwgHGzMxy4QBjZma5cIAxM7Nc/H8puJm9BaN/oAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plot.\n",
    "plt.bar(median_age_Africa.Age_Range, median_age_Africa[\"total_cases_updated\"], color=\"blue\", label='Total Cases Updated')\n",
    "# Create labels for the x and y axes.\n",
    "plt.xlabel(\"Age Range\")\n",
    "plt.ylabel(\"Total Cases\")\n",
    "# Create a title.\n",
    "plt.title(\"Total Cases Per Age Group in Africa\")\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Europe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primary_key_reaction</th>\n",
       "      <th>date_updated</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>...</th>\n",
       "      <th>primary_key_response</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ALB2020-03-09</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2877800.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.000069</td>\n",
       "      <td>...</td>\n",
       "      <td>ALB2020-03-09</td>\n",
       "      <td>36.11</td>\n",
       "      <td>333.493397</td>\n",
       "      <td>41.67</td>\n",
       "      <td>31.41</td>\n",
       "      <td>157.881773</td>\n",
       "      <td>37.12</td>\n",
       "      <td>157.956915</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ALB2020-03-10</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2877800.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>...</td>\n",
       "      <td>ALB2020-03-10</td>\n",
       "      <td>41.67</td>\n",
       "      <td>15.397397</td>\n",
       "      <td>46.43</td>\n",
       "      <td>35.26</td>\n",
       "      <td>12.257243</td>\n",
       "      <td>41.67</td>\n",
       "      <td>12.257543</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ALB2020-03-11</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2877800.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>...</td>\n",
       "      <td>ALB2020-03-11</td>\n",
       "      <td>51.85</td>\n",
       "      <td>24.430046</td>\n",
       "      <td>60.71</td>\n",
       "      <td>42.31</td>\n",
       "      <td>19.994328</td>\n",
       "      <td>50.00</td>\n",
       "      <td>19.990401</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ALB2020-03-12</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2877800.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>...</td>\n",
       "      <td>ALB2020-03-12</td>\n",
       "      <td>51.85</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.71</td>\n",
       "      <td>42.31</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>50.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ALB2020-03-13</td>\n",
       "      <td>2020-03-13</td>\n",
       "      <td>ALB</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2877800.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.000417</td>\n",
       "      <td>...</td>\n",
       "      <td>ALB2020-03-13</td>\n",
       "      <td>78.70</td>\n",
       "      <td>51.783992</td>\n",
       "      <td>84.52</td>\n",
       "      <td>60.90</td>\n",
       "      <td>43.937603</td>\n",
       "      <td>71.97</td>\n",
       "      <td>43.940000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8172</th>\n",
       "      <td>UKR2020-08-27</td>\n",
       "      <td>2020-08-27</td>\n",
       "      <td>UKR</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>43733759.0</td>\n",
       "      <td>77.390</td>\n",
       "      <td>41.4</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>0.004514</td>\n",
       "      <td>...</td>\n",
       "      <td>UKR2020-08-27</td>\n",
       "      <td>54.17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.48</td>\n",
       "      <td>56.09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59.47</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8173</th>\n",
       "      <td>UKR2020-08-28</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>UKR</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>43733759.0</td>\n",
       "      <td>77.390</td>\n",
       "      <td>41.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>UKR2020-08-28</td>\n",
       "      <td>54.17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.48</td>\n",
       "      <td>56.09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59.47</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8174</th>\n",
       "      <td>UKR2020-08-29</td>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>UKR</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>43733759.0</td>\n",
       "      <td>77.390</td>\n",
       "      <td>41.4</td>\n",
       "      <td>2438.0</td>\n",
       "      <td>0.005575</td>\n",
       "      <td>...</td>\n",
       "      <td>UKR2020-08-29</td>\n",
       "      <td>54.17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.48</td>\n",
       "      <td>56.09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59.47</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8175</th>\n",
       "      <td>UKR2020-08-30</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>UKR</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>43733759.0</td>\n",
       "      <td>77.390</td>\n",
       "      <td>41.4</td>\n",
       "      <td>2481.0</td>\n",
       "      <td>0.005673</td>\n",
       "      <td>...</td>\n",
       "      <td>UKR2020-08-30</td>\n",
       "      <td>54.17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.48</td>\n",
       "      <td>56.09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59.47</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8176</th>\n",
       "      <td>UKR2020-08-31</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>UKR</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>43733759.0</td>\n",
       "      <td>77.390</td>\n",
       "      <td>41.4</td>\n",
       "      <td>2096.0</td>\n",
       "      <td>0.004793</td>\n",
       "      <td>...</td>\n",
       "      <td>UKR2020-08-31</td>\n",
       "      <td>54.17</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>60.48</td>\n",
       "      <td>56.09</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>59.47</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>37.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8177 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     primary_key_reaction date_updated iso_code continent location  \\\n",
       "0           ALB2020-03-09   2020-03-09      ALB    Europe  Albania   \n",
       "1           ALB2020-03-10   2020-03-10      ALB    Europe  Albania   \n",
       "2           ALB2020-03-11   2020-03-11      ALB    Europe  Albania   \n",
       "3           ALB2020-03-12   2020-03-12      ALB    Europe  Albania   \n",
       "4           ALB2020-03-13   2020-03-13      ALB    Europe  Albania   \n",
       "...                   ...          ...      ...       ...      ...   \n",
       "8172        UKR2020-08-27   2020-08-27      UKR    Europe  Ukraine   \n",
       "8173        UKR2020-08-28   2020-08-28      UKR    Europe  Ukraine   \n",
       "8174        UKR2020-08-29   2020-08-29      UKR    Europe  Ukraine   \n",
       "8175        UKR2020-08-30   2020-08-30      UKR    Europe  Ukraine   \n",
       "8176        UKR2020-08-31   2020-08-31      UKR    Europe  Ukraine   \n",
       "\n",
       "      population  population_density  median_age  new_cases  \\\n",
       "0      2877800.0             104.871        38.0        2.0   \n",
       "1      2877800.0             104.871        38.0        4.0   \n",
       "2      2877800.0             104.871        38.0        4.0   \n",
       "3      2877800.0             104.871        38.0        1.0   \n",
       "4      2877800.0             104.871        38.0       12.0   \n",
       "...          ...                 ...         ...        ...   \n",
       "8172  43733759.0              77.390        41.4     1974.0   \n",
       "8173  43733759.0              77.390        41.4        0.0   \n",
       "8174  43733759.0              77.390        41.4     2438.0   \n",
       "8175  43733759.0              77.390        41.4     2481.0   \n",
       "8176  43733759.0              77.390        41.4     2096.0   \n",
       "\n",
       "      percentage_of_Pop_New_Cases  ...  primary_key_response  \\\n",
       "0                        0.000069  ...         ALB2020-03-09   \n",
       "1                        0.000139  ...         ALB2020-03-10   \n",
       "2                        0.000139  ...         ALB2020-03-11   \n",
       "3                        0.000035  ...         ALB2020-03-12   \n",
       "4                        0.000417  ...         ALB2020-03-13   \n",
       "...                           ...  ...                   ...   \n",
       "8172                     0.004514  ...         UKR2020-08-27   \n",
       "8173                     0.000000  ...         UKR2020-08-28   \n",
       "8174                     0.005575  ...         UKR2020-08-29   \n",
       "8175                     0.005673  ...         UKR2020-08-30   \n",
       "8176                     0.004793  ...         UKR2020-08-31   \n",
       "\n",
       "      StringencyIndex_updated  percentage_change_stringency  \\\n",
       "0                       36.11                    333.493397   \n",
       "1                       41.67                     15.397397   \n",
       "2                       51.85                     24.430046   \n",
       "3                       51.85                      0.000000   \n",
       "4                       78.70                     51.783992   \n",
       "...                       ...                           ...   \n",
       "8172                    54.17                      0.000000   \n",
       "8173                    54.17                      0.000000   \n",
       "8174                    54.17                      0.000000   \n",
       "8175                    54.17                      0.000000   \n",
       "8176                    54.17                      0.000000   \n",
       "\n",
       "      StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "0                             41.67                            31.41   \n",
       "1                             46.43                            35.26   \n",
       "2                             60.71                            42.31   \n",
       "3                             60.71                            42.31   \n",
       "4                             84.52                            60.90   \n",
       "...                             ...                              ...   \n",
       "8172                          60.48                            56.09   \n",
       "8173                          60.48                            56.09   \n",
       "8174                          60.48                            56.09   \n",
       "8175                          60.48                            56.09   \n",
       "8176                          60.48                            56.09   \n",
       "\n",
       "      percentage_change_GovernmentResponse  ContainmentHealthIndex_updated  \\\n",
       "0                               157.881773                           37.12   \n",
       "1                                12.257243                           41.67   \n",
       "2                                19.994328                           50.00   \n",
       "3                                 0.000000                           50.00   \n",
       "4                                43.937603                           71.97   \n",
       "...                                    ...                             ...   \n",
       "8172                              0.000000                           59.47   \n",
       "8173                              0.000000                           59.47   \n",
       "8174                              0.000000                           59.47   \n",
       "8175                              0.000000                           59.47   \n",
       "8176                              0.000000                           59.47   \n",
       "\n",
       "      percentage_change_ContainmentHealth  EconomicSupportIndex_updated  \\\n",
       "0                              157.956915                           0.0   \n",
       "1                               12.257543                           0.0   \n",
       "2                               19.990401                           0.0   \n",
       "3                                0.000000                           0.0   \n",
       "4                               43.940000                           0.0   \n",
       "...                                   ...                           ...   \n",
       "8172                             0.000000                          37.5   \n",
       "8173                             0.000000                          37.5   \n",
       "8174                             0.000000                          37.5   \n",
       "8175                             0.000000                          37.5   \n",
       "8176                             0.000000                          37.5   \n",
       "\n",
       "      percentage_change_EconomicSupport  \n",
       "0                                   0.0  \n",
       "1                                   0.0  \n",
       "2                                   0.0  \n",
       "3                                   0.0  \n",
       "4                                   0.0  \n",
       "...                                 ...  \n",
       "8172                                0.0  \n",
       "8173                                0.0  \n",
       "8174                                0.0  \n",
       "8175                                0.0  \n",
       "8176                                0.0  \n",
       "\n",
       "[8177 rows x 30 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Dataframe filtered for Continent\n",
    "merged_covid_EU_df = pd.DataFrame(merged_covid_df.loc[merged_covid_df[\"continent\"]==\"Europe\"].reset_index())\n",
    "merged_covid_EU_df = merged_covid_EU_df.drop([\"index\"],axis=1)\n",
    "merged_covid_EU_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MultipleLocator\n",
    "# Increase the size of the plot figure.\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "ax.barh(merged_covid_EU_df[\"location\"],merged_covid_EU_df[\"median_age\"])\n",
    "\n",
    "ax.set_xticks(np.arange(0, 51, step=5.0))\n",
    "\n",
    "# Create minor ticks at an increment of 1.\n",
    "ax.xaxis.set_minor_locator(MultipleLocator(5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Albania                   38.0\n",
       "Austria                   44.4\n",
       "Belarus                   40.3\n",
       "Belgium                   41.8\n",
       "Bosnia and Herzegovina    42.5\n",
       "Bulgaria                  44.7\n",
       "Croatia                   44.0\n",
       "Cyprus                    37.3\n",
       "Czech Republic            43.3\n",
       "Denmark                   42.3\n",
       "Estonia                   42.7\n",
       "Finland                   42.8\n",
       "France                    42.0\n",
       "Germany                   46.6\n",
       "Greece                    45.3\n",
       "Hungary                   43.4\n",
       "Iceland                   37.3\n",
       "Ireland                   38.7\n",
       "Italy                     47.9\n",
       "Latvia                    43.9\n",
       "Lithuania                 43.5\n",
       "Luxembourg                39.7\n",
       "Moldova                   37.6\n",
       "Netherlands               43.2\n",
       "Norway                    39.7\n",
       "Poland                    41.8\n",
       "Portugal                  46.2\n",
       "Romania                   43.0\n",
       "Russia                    39.6\n",
       "Serbia                    41.2\n",
       "Slovakia                  41.2\n",
       "Slovenia                  44.5\n",
       "Spain                     45.5\n",
       "Sweden                    41.0\n",
       "Switzerland               43.1\n",
       "Ukraine                   41.4\n",
       "United Kingdom            40.8\n",
       "Name: median_age, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average test scores.\n",
    "covid_Europe_df = merged_covid_EU_df.groupby([\"location\"]).mean()[\"median_age\"]\n",
    "covid_Europe_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(covid_Europe_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_age\n",
       "(0, 20]      0\n",
       "(20, 30]     0\n",
       "(30, 40]     8\n",
       "(40, 50]    29\n",
       "Name: median_age, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Median age range for different locations\n",
    "bins = [0,20,30,40,50]\n",
    "median_age_Europe=covid_Europe_df.groupby(pd.cut(covid_Europe_df,bins)).count()\n",
    "median_age_Europe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 5760x7200 with 0 Axes>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 5760x7200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plot.\n",
    "median_age_Europe.plot( marker=\"*\", color=\"green\", linewidth=2, label='Median Age')\n",
    "# Create labels for the x and y axes.\n",
    "plt.xlabel(\"Median Age\")\n",
    "plt.ylabel(\"Count\")\n",
    "# Set the y limit between 0 and 45.\n",
    "plt.ylim(0, 45)\n",
    "# Create a title.\n",
    "plt.title(\"Covid 19 Impacted Median Age Group in Europe\")\n",
    "# Add a grid.\n",
    "plt.grid()\n",
    "# Add the legend.\n",
    "plt.legend()\n",
    "plt.figure(figsize=(80, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<19', '20-29', '30-39', '40-50']"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Establish the  bins and group names.\n",
    "bins = [0, 20, 30,40,50]\n",
    "group_names = [\"<19\",\"20-29\",\"30-39\",\"40-50\"]\n",
    "group_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>percentage_of_Pop_New_Deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>percentage_of_Pop_New_Tests</th>\n",
       "      <th>total_cases_updated</th>\n",
       "      <th>...</th>\n",
       "      <th>percentage_of_Pop_Total_Tests</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>2877800.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>53.295455</td>\n",
       "      <td>0.001852</td>\n",
       "      <td>1.590909</td>\n",
       "      <td>5.528213e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2488.715909</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.730852</td>\n",
       "      <td>2.222673</td>\n",
       "      <td>77.078068</td>\n",
       "      <td>67.661193</td>\n",
       "      <td>1.301413</td>\n",
       "      <td>69.349034</td>\n",
       "      <td>1.219378</td>\n",
       "      <td>58.380682</td>\n",
       "      <td>1.439394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>9006400.0</td>\n",
       "      <td>106.749</td>\n",
       "      <td>44.4</td>\n",
       "      <td>111.549180</td>\n",
       "      <td>0.001239</td>\n",
       "      <td>3.004098</td>\n",
       "      <td>3.335515e-07</td>\n",
       "      <td>4635.401639</td>\n",
       "      <td>0.051468</td>\n",
       "      <td>11580.057377</td>\n",
       "      <td>...</td>\n",
       "      <td>3.978611</td>\n",
       "      <td>38.756434</td>\n",
       "      <td>0.694215</td>\n",
       "      <td>43.376230</td>\n",
       "      <td>46.444057</td>\n",
       "      <td>2.084365</td>\n",
       "      <td>43.955820</td>\n",
       "      <td>1.925044</td>\n",
       "      <td>60.143443</td>\n",
       "      <td>0.307377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>9449321.0</td>\n",
       "      <td>46.858</td>\n",
       "      <td>40.3</td>\n",
       "      <td>293.799180</td>\n",
       "      <td>0.003109</td>\n",
       "      <td>2.770492</td>\n",
       "      <td>2.931948e-07</td>\n",
       "      <td>1913.385246</td>\n",
       "      <td>0.020249</td>\n",
       "      <td>27807.032787</td>\n",
       "      <td>...</td>\n",
       "      <td>4.994562</td>\n",
       "      <td>8.731107</td>\n",
       "      <td>0.245029</td>\n",
       "      <td>12.535287</td>\n",
       "      <td>14.840902</td>\n",
       "      <td>0.789217</td>\n",
       "      <td>17.540205</td>\n",
       "      <td>0.789256</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>11589616.0</td>\n",
       "      <td>375.564</td>\n",
       "      <td>41.8</td>\n",
       "      <td>348.147541</td>\n",
       "      <td>0.003004</td>\n",
       "      <td>40.549180</td>\n",
       "      <td>3.498751e-06</td>\n",
       "      <td>9337.668033</td>\n",
       "      <td>0.080569</td>\n",
       "      <td>37578.368852</td>\n",
       "      <td>...</td>\n",
       "      <td>5.982490</td>\n",
       "      <td>47.999754</td>\n",
       "      <td>1.316891</td>\n",
       "      <td>50.521270</td>\n",
       "      <td>50.084344</td>\n",
       "      <td>1.495648</td>\n",
       "      <td>48.823607</td>\n",
       "      <td>1.446438</td>\n",
       "      <td>57.018443</td>\n",
       "      <td>0.046838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>3280815.0</td>\n",
       "      <td>68.496</td>\n",
       "      <td>42.5</td>\n",
       "      <td>113.729885</td>\n",
       "      <td>0.003467</td>\n",
       "      <td>3.402299</td>\n",
       "      <td>1.037029e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5274.011494</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.054310</td>\n",
       "      <td>0.265508</td>\n",
       "      <td>74.824138</td>\n",
       "      <td>56.089368</td>\n",
       "      <td>0.111494</td>\n",
       "      <td>61.742874</td>\n",
       "      <td>0.220737</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>-0.574713</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        population  population_density  median_age  \\\n",
       "location                                                             \n",
       "Albania                  2877800.0             104.871        38.0   \n",
       "Austria                  9006400.0             106.749        44.4   \n",
       "Belarus                  9449321.0              46.858        40.3   \n",
       "Belgium                 11589616.0             375.564        41.8   \n",
       "Bosnia and Herzegovina   3280815.0              68.496        42.5   \n",
       "\n",
       "                         new_cases  percentage_of_Pop_New_Cases  new_deaths  \\\n",
       "location                                                                      \n",
       "Albania                  53.295455                     0.001852    1.590909   \n",
       "Austria                 111.549180                     0.001239    3.004098   \n",
       "Belarus                 293.799180                     0.003109    2.770492   \n",
       "Belgium                 348.147541                     0.003004   40.549180   \n",
       "Bosnia and Herzegovina  113.729885                     0.003467    3.402299   \n",
       "\n",
       "                        percentage_of_Pop_New_Deaths    new_tests  \\\n",
       "location                                                            \n",
       "Albania                                 5.528213e-07     0.000000   \n",
       "Austria                                 3.335515e-07  4635.401639   \n",
       "Belarus                                 2.931948e-07  1913.385246   \n",
       "Belgium                                 3.498751e-06  9337.668033   \n",
       "Bosnia and Herzegovina                  1.037029e-06     0.000000   \n",
       "\n",
       "                        percentage_of_Pop_New_Tests  total_cases_updated  ...  \\\n",
       "location                                                                  ...   \n",
       "Albania                                    0.000000          2488.715909  ...   \n",
       "Austria                                    0.051468         11580.057377  ...   \n",
       "Belarus                                    0.020249         27807.032787  ...   \n",
       "Belgium                                    0.080569         37578.368852  ...   \n",
       "Bosnia and Herzegovina                     0.000000          5274.011494  ...   \n",
       "\n",
       "                        percentage_of_Pop_Total_Tests  \\\n",
       "location                                                \n",
       "Albania                                      0.000000   \n",
       "Austria                                      3.978611   \n",
       "Belarus                                      4.994562   \n",
       "Belgium                                      5.982490   \n",
       "Bosnia and Herzegovina                       0.000000   \n",
       "\n",
       "                        StringencyIndex_updated  percentage_change_stringency  \\\n",
       "location                                                                        \n",
       "Albania                               71.730852                      2.222673   \n",
       "Austria                               38.756434                      0.694215   \n",
       "Belarus                                8.731107                      0.245029   \n",
       "Belgium                               47.999754                      1.316891   \n",
       "Bosnia and Herzegovina                68.054310                      0.265508   \n",
       "\n",
       "                        StringencyLegacyIndex_updated  \\\n",
       "location                                                \n",
       "Albania                                     77.078068   \n",
       "Austria                                     43.376230   \n",
       "Belarus                                     12.535287   \n",
       "Belgium                                     50.521270   \n",
       "Bosnia and Herzegovina                      74.824138   \n",
       "\n",
       "                        GovernmentResponseIndex_updated  \\\n",
       "location                                                  \n",
       "Albania                                       67.661193   \n",
       "Austria                                       46.444057   \n",
       "Belarus                                       14.840902   \n",
       "Belgium                                       50.084344   \n",
       "Bosnia and Herzegovina                        56.089368   \n",
       "\n",
       "                        percentage_change_GovernmentResponse  \\\n",
       "location                                                       \n",
       "Albania                                             1.301413   \n",
       "Austria                                             2.084365   \n",
       "Belarus                                             0.789217   \n",
       "Belgium                                             1.495648   \n",
       "Bosnia and Herzegovina                              0.111494   \n",
       "\n",
       "                        ContainmentHealthIndex_updated  \\\n",
       "location                                                 \n",
       "Albania                                      69.349034   \n",
       "Austria                                      43.955820   \n",
       "Belarus                                      17.540205   \n",
       "Belgium                                      48.823607   \n",
       "Bosnia and Herzegovina                       61.742874   \n",
       "\n",
       "                        percentage_change_ContainmentHealth  \\\n",
       "location                                                      \n",
       "Albania                                            1.219378   \n",
       "Austria                                            1.925044   \n",
       "Belarus                                            0.789256   \n",
       "Belgium                                            1.446438   \n",
       "Bosnia and Herzegovina                             0.220737   \n",
       "\n",
       "                        EconomicSupportIndex_updated  \\\n",
       "location                                               \n",
       "Albania                                    58.380682   \n",
       "Austria                                    60.143443   \n",
       "Belarus                                     0.000000   \n",
       "Belgium                                    57.018443   \n",
       "Bosnia and Herzegovina                     25.000000   \n",
       "\n",
       "                        percentage_change_EconomicSupport  \n",
       "location                                                   \n",
       "Albania                                          1.439394  \n",
       "Austria                                          0.307377  \n",
       "Belarus                                          0.000000  \n",
       "Belgium                                          0.046838  \n",
       "Bosnia and Herzegovina                          -0.574713  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_age_EU=merged_covid_EU_df.groupby(\"location\").mean()\n",
    "median_age_EU.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>percentage_of_Pop_New_Deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>percentage_of_Pop_New_Tests</th>\n",
       "      <th>total_cases_updated</th>\n",
       "      <th>...</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "      <th>Age_Range</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Albania</th>\n",
       "      <td>2877800.0</td>\n",
       "      <td>104.871</td>\n",
       "      <td>38.0</td>\n",
       "      <td>53.295455</td>\n",
       "      <td>0.001852</td>\n",
       "      <td>1.590909</td>\n",
       "      <td>5.528213e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2488.715909</td>\n",
       "      <td>...</td>\n",
       "      <td>71.730852</td>\n",
       "      <td>2.222673</td>\n",
       "      <td>77.078068</td>\n",
       "      <td>67.661193</td>\n",
       "      <td>1.301413</td>\n",
       "      <td>69.349034</td>\n",
       "      <td>1.219378</td>\n",
       "      <td>58.380682</td>\n",
       "      <td>1.439394</td>\n",
       "      <td>30-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austria</th>\n",
       "      <td>9006400.0</td>\n",
       "      <td>106.749</td>\n",
       "      <td>44.4</td>\n",
       "      <td>111.549180</td>\n",
       "      <td>0.001239</td>\n",
       "      <td>3.004098</td>\n",
       "      <td>3.335515e-07</td>\n",
       "      <td>4635.401639</td>\n",
       "      <td>0.051468</td>\n",
       "      <td>11580.057377</td>\n",
       "      <td>...</td>\n",
       "      <td>38.756434</td>\n",
       "      <td>0.694215</td>\n",
       "      <td>43.376230</td>\n",
       "      <td>46.444057</td>\n",
       "      <td>2.084365</td>\n",
       "      <td>43.955820</td>\n",
       "      <td>1.925044</td>\n",
       "      <td>60.143443</td>\n",
       "      <td>0.307377</td>\n",
       "      <td>40-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belarus</th>\n",
       "      <td>9449321.0</td>\n",
       "      <td>46.858</td>\n",
       "      <td>40.3</td>\n",
       "      <td>293.799180</td>\n",
       "      <td>0.003109</td>\n",
       "      <td>2.770492</td>\n",
       "      <td>2.931948e-07</td>\n",
       "      <td>1913.385246</td>\n",
       "      <td>0.020249</td>\n",
       "      <td>27807.032787</td>\n",
       "      <td>...</td>\n",
       "      <td>8.731107</td>\n",
       "      <td>0.245029</td>\n",
       "      <td>12.535287</td>\n",
       "      <td>14.840902</td>\n",
       "      <td>0.789217</td>\n",
       "      <td>17.540205</td>\n",
       "      <td>0.789256</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>40-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Belgium</th>\n",
       "      <td>11589616.0</td>\n",
       "      <td>375.564</td>\n",
       "      <td>41.8</td>\n",
       "      <td>348.147541</td>\n",
       "      <td>0.003004</td>\n",
       "      <td>40.549180</td>\n",
       "      <td>3.498751e-06</td>\n",
       "      <td>9337.668033</td>\n",
       "      <td>0.080569</td>\n",
       "      <td>37578.368852</td>\n",
       "      <td>...</td>\n",
       "      <td>47.999754</td>\n",
       "      <td>1.316891</td>\n",
       "      <td>50.521270</td>\n",
       "      <td>50.084344</td>\n",
       "      <td>1.495648</td>\n",
       "      <td>48.823607</td>\n",
       "      <td>1.446438</td>\n",
       "      <td>57.018443</td>\n",
       "      <td>0.046838</td>\n",
       "      <td>40-50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bosnia and Herzegovina</th>\n",
       "      <td>3280815.0</td>\n",
       "      <td>68.496</td>\n",
       "      <td>42.5</td>\n",
       "      <td>113.729885</td>\n",
       "      <td>0.003467</td>\n",
       "      <td>3.402299</td>\n",
       "      <td>1.037029e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5274.011494</td>\n",
       "      <td>...</td>\n",
       "      <td>68.054310</td>\n",
       "      <td>0.265508</td>\n",
       "      <td>74.824138</td>\n",
       "      <td>56.089368</td>\n",
       "      <td>0.111494</td>\n",
       "      <td>61.742874</td>\n",
       "      <td>0.220737</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>-0.574713</td>\n",
       "      <td>40-50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        population  population_density  median_age  \\\n",
       "location                                                             \n",
       "Albania                  2877800.0             104.871        38.0   \n",
       "Austria                  9006400.0             106.749        44.4   \n",
       "Belarus                  9449321.0              46.858        40.3   \n",
       "Belgium                 11589616.0             375.564        41.8   \n",
       "Bosnia and Herzegovina   3280815.0              68.496        42.5   \n",
       "\n",
       "                         new_cases  percentage_of_Pop_New_Cases  new_deaths  \\\n",
       "location                                                                      \n",
       "Albania                  53.295455                     0.001852    1.590909   \n",
       "Austria                 111.549180                     0.001239    3.004098   \n",
       "Belarus                 293.799180                     0.003109    2.770492   \n",
       "Belgium                 348.147541                     0.003004   40.549180   \n",
       "Bosnia and Herzegovina  113.729885                     0.003467    3.402299   \n",
       "\n",
       "                        percentage_of_Pop_New_Deaths    new_tests  \\\n",
       "location                                                            \n",
       "Albania                                 5.528213e-07     0.000000   \n",
       "Austria                                 3.335515e-07  4635.401639   \n",
       "Belarus                                 2.931948e-07  1913.385246   \n",
       "Belgium                                 3.498751e-06  9337.668033   \n",
       "Bosnia and Herzegovina                  1.037029e-06     0.000000   \n",
       "\n",
       "                        percentage_of_Pop_New_Tests  total_cases_updated  ...  \\\n",
       "location                                                                  ...   \n",
       "Albania                                    0.000000          2488.715909  ...   \n",
       "Austria                                    0.051468         11580.057377  ...   \n",
       "Belarus                                    0.020249         27807.032787  ...   \n",
       "Belgium                                    0.080569         37578.368852  ...   \n",
       "Bosnia and Herzegovina                     0.000000          5274.011494  ...   \n",
       "\n",
       "                        StringencyIndex_updated  percentage_change_stringency  \\\n",
       "location                                                                        \n",
       "Albania                               71.730852                      2.222673   \n",
       "Austria                               38.756434                      0.694215   \n",
       "Belarus                                8.731107                      0.245029   \n",
       "Belgium                               47.999754                      1.316891   \n",
       "Bosnia and Herzegovina                68.054310                      0.265508   \n",
       "\n",
       "                        StringencyLegacyIndex_updated  \\\n",
       "location                                                \n",
       "Albania                                     77.078068   \n",
       "Austria                                     43.376230   \n",
       "Belarus                                     12.535287   \n",
       "Belgium                                     50.521270   \n",
       "Bosnia and Herzegovina                      74.824138   \n",
       "\n",
       "                        GovernmentResponseIndex_updated  \\\n",
       "location                                                  \n",
       "Albania                                       67.661193   \n",
       "Austria                                       46.444057   \n",
       "Belarus                                       14.840902   \n",
       "Belgium                                       50.084344   \n",
       "Bosnia and Herzegovina                        56.089368   \n",
       "\n",
       "                        percentage_change_GovernmentResponse  \\\n",
       "location                                                       \n",
       "Albania                                             1.301413   \n",
       "Austria                                             2.084365   \n",
       "Belarus                                             0.789217   \n",
       "Belgium                                             1.495648   \n",
       "Bosnia and Herzegovina                              0.111494   \n",
       "\n",
       "                        ContainmentHealthIndex_updated  \\\n",
       "location                                                 \n",
       "Albania                                      69.349034   \n",
       "Austria                                      43.955820   \n",
       "Belarus                                      17.540205   \n",
       "Belgium                                      48.823607   \n",
       "Bosnia and Herzegovina                       61.742874   \n",
       "\n",
       "                        percentage_change_ContainmentHealth  \\\n",
       "location                                                      \n",
       "Albania                                            1.219378   \n",
       "Austria                                            1.925044   \n",
       "Belarus                                            0.789256   \n",
       "Belgium                                            1.446438   \n",
       "Bosnia and Herzegovina                             0.220737   \n",
       "\n",
       "                        EconomicSupportIndex_updated  \\\n",
       "location                                               \n",
       "Albania                                    58.380682   \n",
       "Austria                                    60.143443   \n",
       "Belarus                                     0.000000   \n",
       "Belgium                                    57.018443   \n",
       "Bosnia and Herzegovina                     25.000000   \n",
       "\n",
       "                        percentage_change_EconomicSupport  Age_Range  \n",
       "location                                                              \n",
       "Albania                                          1.439394      30-39  \n",
       "Austria                                          0.307377      40-50  \n",
       "Belarus                                          0.000000      40-50  \n",
       "Belgium                                          0.046838      40-50  \n",
       "Bosnia and Herzegovina                          -0.574713      40-50  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorize Age based on the bins.\n",
    "median_age_EU[(\"Age_Range\")] = pd.cut(covid_Europe_df,bins, labels=group_names)\n",
    "\n",
    "median_age_EU.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total Cases Per Age Group in Europe')"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = plt.subplots()\n",
    "#ax2 = ax.twinx()\n",
    "ax.bar(median_age_EU.Age_Range, median_age_EU[\"total_cases_updated\"], color=\"red\", label=\"total_deaths_updated\")\n",
    "#ax2.plot(median_age_Asia_df.Age_Range, median_age_Asia_df[\"total_cases_updated\"], color='blue', label='total_cases_updated')\n",
    "#ax.set_xticklabels(median_age_Asia_df.Age_Range)\n",
    "#ax.legend(loc='best')\n",
    "ax.set_ylabel('Total Cases Per Age Group in Europe', color=\"red\")\n",
    "#ax2.set_ylabel('total deaths', color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a30d1b2e88>"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plot.\n",
    "plt.bar(median_age_EU.Age_Range, median_age_EU[\"total_cases_updated\"], color=\"blue\", label='Total Cases Updated')\n",
    "# Create labels for the x and y axes.\n",
    "plt.xlabel(\"Age Range\")\n",
    "plt.ylabel(\"Total Cases\")\n",
    "# Create a title.\n",
    "plt.title(\"Total Cases Per Age Group in Europe\")\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Asia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primary_key_reaction</th>\n",
       "      <th>date_updated</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>...</th>\n",
       "      <th>primary_key_response</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG2020-01-01</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>AFG2020-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG2020-01-02</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>AFG2020-01-02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG2020-01-03</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>AFG2020-01-03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG2020-01-04</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>AFG2020-01-04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG2020-01-05</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>AFG2020-01-05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9023</th>\n",
       "      <td>YEM2020-08-27</td>\n",
       "      <td>2020-08-27</td>\n",
       "      <td>YEM</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>29825968.0</td>\n",
       "      <td>53.508</td>\n",
       "      <td>20.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>...</td>\n",
       "      <td>YEM2020-08-27</td>\n",
       "      <td>31.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.24</td>\n",
       "      <td>21.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9024</th>\n",
       "      <td>YEM2020-08-28</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>YEM</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>29825968.0</td>\n",
       "      <td>53.508</td>\n",
       "      <td>20.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>...</td>\n",
       "      <td>YEM2020-08-28</td>\n",
       "      <td>31.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.24</td>\n",
       "      <td>21.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9025</th>\n",
       "      <td>YEM2020-08-29</td>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>YEM</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>29825968.0</td>\n",
       "      <td>53.508</td>\n",
       "      <td>20.3</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>...</td>\n",
       "      <td>YEM2020-08-29</td>\n",
       "      <td>31.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.24</td>\n",
       "      <td>21.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9026</th>\n",
       "      <td>YEM2020-08-30</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>YEM</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>29825968.0</td>\n",
       "      <td>53.508</td>\n",
       "      <td>20.3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>...</td>\n",
       "      <td>YEM2020-08-30</td>\n",
       "      <td>31.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.24</td>\n",
       "      <td>21.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9027</th>\n",
       "      <td>YEM2020-08-31</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>YEM</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>29825968.0</td>\n",
       "      <td>53.508</td>\n",
       "      <td>20.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>...</td>\n",
       "      <td>YEM2020-08-31</td>\n",
       "      <td>31.48</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.24</td>\n",
       "      <td>21.79</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9028 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     primary_key_reaction date_updated iso_code continent     location  \\\n",
       "0           AFG2020-01-01   2020-01-01      AFG      Asia  Afghanistan   \n",
       "1           AFG2020-01-02   2020-01-02      AFG      Asia  Afghanistan   \n",
       "2           AFG2020-01-03   2020-01-03      AFG      Asia  Afghanistan   \n",
       "3           AFG2020-01-04   2020-01-04      AFG      Asia  Afghanistan   \n",
       "4           AFG2020-01-05   2020-01-05      AFG      Asia  Afghanistan   \n",
       "...                   ...          ...      ...       ...          ...   \n",
       "9023        YEM2020-08-27   2020-08-27      YEM      Asia        Yemen   \n",
       "9024        YEM2020-08-28   2020-08-28      YEM      Asia        Yemen   \n",
       "9025        YEM2020-08-29   2020-08-29      YEM      Asia        Yemen   \n",
       "9026        YEM2020-08-30   2020-08-30      YEM      Asia        Yemen   \n",
       "9027        YEM2020-08-31   2020-08-31      YEM      Asia        Yemen   \n",
       "\n",
       "      population  population_density  median_age  new_cases  \\\n",
       "0     38928341.0              54.422        18.6        0.0   \n",
       "1     38928341.0              54.422        18.6        0.0   \n",
       "2     38928341.0              54.422        18.6        0.0   \n",
       "3     38928341.0              54.422        18.6        0.0   \n",
       "4     38928341.0              54.422        18.6        0.0   \n",
       "...          ...                 ...         ...        ...   \n",
       "9023  29825968.0              53.508        20.3        6.0   \n",
       "9024  29825968.0              53.508        20.3        4.0   \n",
       "9025  29825968.0              53.508        20.3        9.0   \n",
       "9026  29825968.0              53.508        20.3        4.0   \n",
       "9027  29825968.0              53.508        20.3        6.0   \n",
       "\n",
       "      percentage_of_Pop_New_Cases  ...  primary_key_response  \\\n",
       "0                        0.000000  ...         AFG2020-01-01   \n",
       "1                        0.000000  ...         AFG2020-01-02   \n",
       "2                        0.000000  ...         AFG2020-01-03   \n",
       "3                        0.000000  ...         AFG2020-01-04   \n",
       "4                        0.000000  ...         AFG2020-01-05   \n",
       "...                           ...  ...                   ...   \n",
       "9023                     0.000020  ...         YEM2020-08-27   \n",
       "9024                     0.000013  ...         YEM2020-08-28   \n",
       "9025                     0.000030  ...         YEM2020-08-29   \n",
       "9026                     0.000013  ...         YEM2020-08-30   \n",
       "9027                     0.000020  ...         YEM2020-08-31   \n",
       "\n",
       "      StringencyIndex_updated  percentage_change_stringency  \\\n",
       "0                        0.00                           0.0   \n",
       "1                        0.00                           0.0   \n",
       "2                        0.00                           0.0   \n",
       "3                        0.00                           0.0   \n",
       "4                        0.00                           0.0   \n",
       "...                       ...                           ...   \n",
       "9023                    31.48                           0.0   \n",
       "9024                    31.48                           0.0   \n",
       "9025                    31.48                           0.0   \n",
       "9026                    31.48                           0.0   \n",
       "9027                    31.48                           0.0   \n",
       "\n",
       "      StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "0                              0.00                             0.00   \n",
       "1                              0.00                             0.00   \n",
       "2                              0.00                             0.00   \n",
       "3                              0.00                             0.00   \n",
       "4                              0.00                             0.00   \n",
       "...                             ...                              ...   \n",
       "9023                          45.24                            21.79   \n",
       "9024                          45.24                            21.79   \n",
       "9025                          45.24                            21.79   \n",
       "9026                          45.24                            21.79   \n",
       "9027                          45.24                            21.79   \n",
       "\n",
       "      percentage_change_GovernmentResponse  ContainmentHealthIndex_updated  \\\n",
       "0                                      0.0                            0.00   \n",
       "1                                      0.0                            0.00   \n",
       "2                                      0.0                            0.00   \n",
       "3                                      0.0                            0.00   \n",
       "4                                      0.0                            0.00   \n",
       "...                                    ...                             ...   \n",
       "9023                                   0.0                           25.76   \n",
       "9024                                   0.0                           25.76   \n",
       "9025                                   0.0                           25.76   \n",
       "9026                                   0.0                           25.76   \n",
       "9027                                   0.0                           25.76   \n",
       "\n",
       "      percentage_change_ContainmentHealth  EconomicSupportIndex_updated  \\\n",
       "0                                     0.0                           0.0   \n",
       "1                                     0.0                           0.0   \n",
       "2                                     0.0                           0.0   \n",
       "3                                     0.0                           0.0   \n",
       "4                                     0.0                           0.0   \n",
       "...                                   ...                           ...   \n",
       "9023                                  0.0                           0.0   \n",
       "9024                                  0.0                           0.0   \n",
       "9025                                  0.0                           0.0   \n",
       "9026                                  0.0                           0.0   \n",
       "9027                                  0.0                           0.0   \n",
       "\n",
       "      percentage_change_EconomicSupport  \n",
       "0                                   0.0  \n",
       "1                                   0.0  \n",
       "2                                   0.0  \n",
       "3                                   0.0  \n",
       "4                                   0.0  \n",
       "...                                 ...  \n",
       "9023                                0.0  \n",
       "9024                                0.0  \n",
       "9025                                0.0  \n",
       "9026                                0.0  \n",
       "9027                                0.0  \n",
       "\n",
       "[9028 rows x 30 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Dataframe filtered for Continent\n",
    "merged_covid_ASIA_df = pd.DataFrame(merged_covid_df.loc[merged_covid_df[\"continent\"]==\"Asia\"].reset_index())\n",
    "merged_covid_ASIA_df = merged_covid_ASIA_df.drop([\"index\"],axis=1)\n",
    "merged_covid_ASIA_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import MultipleLocator\n",
    "# Increase the size of the plot figure.\n",
    "fig, ax = plt.subplots(figsize=(10,10))\n",
    "ax.barh(merged_covid_ASIA_df[\"location\"],merged_covid_ASIA_df[\"median_age\"])\n",
    "\n",
    "ax.set_xticks(np.arange(0, 51, step=5.0))\n",
    "\n",
    "# Create minor ticks at an increment of 1.\n",
    "ax.xaxis.set_minor_locator(MultipleLocator(5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Afghanistan             18.6\n",
       "Azerbaijan              32.4\n",
       "Bahrain                 32.4\n",
       "Bangladesh              27.5\n",
       "Bhutan                  28.6\n",
       "Brunei                  32.4\n",
       "Cambodia                25.6\n",
       "China                   38.7\n",
       "Georgia                 38.7\n",
       "Hong Kong               44.8\n",
       "India                   28.2\n",
       "Indonesia               29.3\n",
       "Iran                    32.4\n",
       "Iraq                    20.0\n",
       "Israel                  30.6\n",
       "Japan                   48.2\n",
       "Jordan                  23.2\n",
       "Kazakhstan              30.6\n",
       "Kuwait                  33.7\n",
       "Kyrgyzstan              26.3\n",
       "Laos                    24.4\n",
       "Lebanon                 31.1\n",
       "Malaysia                29.9\n",
       "Mongolia                28.6\n",
       "Myanmar                 29.1\n",
       "Nepal                   25.0\n",
       "Oman                    30.7\n",
       "Pakistan                23.5\n",
       "Palestine               20.4\n",
       "Philippines             25.2\n",
       "Qatar                   31.9\n",
       "Saudi Arabia            31.9\n",
       "Singapore               42.4\n",
       "South Korea             43.4\n",
       "Sri Lanka               34.1\n",
       "Tajikistan              23.3\n",
       "Thailand                40.1\n",
       "Timor                   18.0\n",
       "Turkey                  31.6\n",
       "United Arab Emirates    34.0\n",
       "Uzbekistan              28.2\n",
       "Vietnam                 32.6\n",
       "Yemen                   20.3\n",
       "Name: median_age, dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average test scores.\n",
    "covid_ASIA_df = merged_covid_ASIA_df.groupby([\"location\"]).mean()[\"median_age\"]\n",
    "covid_ASIA_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(covid_ASIA_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_age\n",
       "(0, 20]      3\n",
       "(20, 30]    18\n",
       "(30, 40]    17\n",
       "(40, 50]     5\n",
       "Name: median_age, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Median age range for different locations\n",
    "bins = [0,20,30,40,50]\n",
    "median_age_Asia=covid_ASIA_df.groupby(pd.cut(covid_ASIA_df,bins)).count()\n",
    "median_age_Asia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 5760x7200 with 0 Axes>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 5760x7200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plot.\n",
    "median_age_Asia.plot( marker=\"*\", color=\"green\", linewidth=2, label='Median Age')\n",
    "# Create labels for the x and y axes.\n",
    "plt.xlabel(\"Median Age Range\")\n",
    "plt.ylabel(\"Count\")\n",
    "# Set the y limit between 0 and 45.\n",
    "plt.ylim(0, 45)\n",
    "# Create a title.\n",
    "plt.title(\"Covid 19 Impacted Median Age Group in Asia\")\n",
    "# Add a grid.\n",
    "plt.grid()\n",
    "# Add the legend.\n",
    "plt.legend()\n",
    "plt.figure(figsize=(80, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<19', '20-29', '30-39', '40-50']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Establish the  bins and group names.\n",
    "bins = [0, 20, 30,40,50]\n",
    "group_names = [\"<19\",\"20-29\",\"30-39\",\"40-50\"]\n",
    "group_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>percentage_of_Pop_New_Deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>percentage_of_Pop_New_Tests</th>\n",
       "      <th>total_cases_updated</th>\n",
       "      <th>...</th>\n",
       "      <th>percentage_of_Pop_Total_Tests</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>156.401639</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>5.745902</td>\n",
       "      <td>1.476020e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13151.081967</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>53.744918</td>\n",
       "      <td>1.262885</td>\n",
       "      <td>55.434180</td>\n",
       "      <td>42.725451</td>\n",
       "      <td>0.953768</td>\n",
       "      <td>50.494631</td>\n",
       "      <td>0.953501</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>10139175.0</td>\n",
       "      <td>119.309</td>\n",
       "      <td>32.4</td>\n",
       "      <td>148.807377</td>\n",
       "      <td>0.001468</td>\n",
       "      <td>2.176230</td>\n",
       "      <td>2.146358e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9381.430328</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>62.035287</td>\n",
       "      <td>2.537570</td>\n",
       "      <td>63.167418</td>\n",
       "      <td>57.290369</td>\n",
       "      <td>3.098554</td>\n",
       "      <td>62.267090</td>\n",
       "      <td>3.055503</td>\n",
       "      <td>29.918033</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bahrain</th>\n",
       "      <td>1701583.0</td>\n",
       "      <td>1935.907</td>\n",
       "      <td>32.4</td>\n",
       "      <td>211.368852</td>\n",
       "      <td>0.012422</td>\n",
       "      <td>0.774590</td>\n",
       "      <td>4.552174e-07</td>\n",
       "      <td>3574.372951</td>\n",
       "      <td>0.210062</td>\n",
       "      <td>13573.127049</td>\n",
       "      <td>...</td>\n",
       "      <td>17.867855</td>\n",
       "      <td>54.605902</td>\n",
       "      <td>1.122979</td>\n",
       "      <td>60.455000</td>\n",
       "      <td>59.165533</td>\n",
       "      <td>1.245380</td>\n",
       "      <td>58.589836</td>\n",
       "      <td>1.175370</td>\n",
       "      <td>62.346311</td>\n",
       "      <td>0.717213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bangladesh</th>\n",
       "      <td>164689383.0</td>\n",
       "      <td>1265.036</td>\n",
       "      <td>27.5</td>\n",
       "      <td>1717.248619</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>23.469613</td>\n",
       "      <td>1.425084e-07</td>\n",
       "      <td>8364.690608</td>\n",
       "      <td>0.005079</td>\n",
       "      <td>99124.806630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.317955</td>\n",
       "      <td>76.429448</td>\n",
       "      <td>1.560242</td>\n",
       "      <td>81.070497</td>\n",
       "      <td>69.205967</td>\n",
       "      <td>1.149209</td>\n",
       "      <td>73.073204</td>\n",
       "      <td>1.059952</td>\n",
       "      <td>47.928177</td>\n",
       "      <td>0.644567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bhutan</th>\n",
       "      <td>771612.0</td>\n",
       "      <td>21.188</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.287356</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>55.339080</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>72.954655</td>\n",
       "      <td>1.334092</td>\n",
       "      <td>81.901149</td>\n",
       "      <td>73.016034</td>\n",
       "      <td>1.516405</td>\n",
       "      <td>74.196839</td>\n",
       "      <td>1.393138</td>\n",
       "      <td>66.522989</td>\n",
       "      <td>0.095785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              population  population_density  median_age    new_cases  \\\n",
       "location                                                                \n",
       "Afghanistan   38928341.0              54.422        18.6   156.401639   \n",
       "Azerbaijan    10139175.0             119.309        32.4   148.807377   \n",
       "Bahrain        1701583.0            1935.907        32.4   211.368852   \n",
       "Bangladesh   164689383.0            1265.036        27.5  1717.248619   \n",
       "Bhutan          771612.0              21.188        28.6     1.287356   \n",
       "\n",
       "             percentage_of_Pop_New_Cases  new_deaths  \\\n",
       "location                                               \n",
       "Afghanistan                     0.000402    5.745902   \n",
       "Azerbaijan                      0.001468    2.176230   \n",
       "Bahrain                         0.012422    0.774590   \n",
       "Bangladesh                      0.001043   23.469613   \n",
       "Bhutan                          0.000167    0.000000   \n",
       "\n",
       "             percentage_of_Pop_New_Deaths    new_tests  \\\n",
       "location                                                 \n",
       "Afghanistan                  1.476020e-07     0.000000   \n",
       "Azerbaijan                   2.146358e-07     0.000000   \n",
       "Bahrain                      4.552174e-07  3574.372951   \n",
       "Bangladesh                   1.425084e-07  8364.690608   \n",
       "Bhutan                       0.000000e+00     0.000000   \n",
       "\n",
       "             percentage_of_Pop_New_Tests  total_cases_updated  ...  \\\n",
       "location                                                       ...   \n",
       "Afghanistan                     0.000000         13151.081967  ...   \n",
       "Azerbaijan                      0.000000          9381.430328  ...   \n",
       "Bahrain                         0.210062         13573.127049  ...   \n",
       "Bangladesh                      0.005079         99124.806630  ...   \n",
       "Bhutan                          0.000000            55.339080  ...   \n",
       "\n",
       "             percentage_of_Pop_Total_Tests  StringencyIndex_updated  \\\n",
       "location                                                              \n",
       "Afghanistan                       0.000000                53.744918   \n",
       "Azerbaijan                        0.000000                62.035287   \n",
       "Bahrain                          17.867855                54.605902   \n",
       "Bangladesh                        0.317955                76.429448   \n",
       "Bhutan                            0.000000                72.954655   \n",
       "\n",
       "             percentage_change_stringency  StringencyLegacyIndex_updated  \\\n",
       "location                                                                   \n",
       "Afghanistan                      1.262885                      55.434180   \n",
       "Azerbaijan                       2.537570                      63.167418   \n",
       "Bahrain                          1.122979                      60.455000   \n",
       "Bangladesh                       1.560242                      81.070497   \n",
       "Bhutan                           1.334092                      81.901149   \n",
       "\n",
       "             GovernmentResponseIndex_updated  \\\n",
       "location                                       \n",
       "Afghanistan                        42.725451   \n",
       "Azerbaijan                         57.290369   \n",
       "Bahrain                            59.165533   \n",
       "Bangladesh                         69.205967   \n",
       "Bhutan                             73.016034   \n",
       "\n",
       "             percentage_change_GovernmentResponse  \\\n",
       "location                                            \n",
       "Afghanistan                              0.953768   \n",
       "Azerbaijan                               3.098554   \n",
       "Bahrain                                  1.245380   \n",
       "Bangladesh                               1.149209   \n",
       "Bhutan                                   1.516405   \n",
       "\n",
       "             ContainmentHealthIndex_updated  \\\n",
       "location                                      \n",
       "Afghanistan                       50.494631   \n",
       "Azerbaijan                        62.267090   \n",
       "Bahrain                           58.589836   \n",
       "Bangladesh                        73.073204   \n",
       "Bhutan                            74.196839   \n",
       "\n",
       "             percentage_change_ContainmentHealth  \\\n",
       "location                                           \n",
       "Afghanistan                             0.953501   \n",
       "Azerbaijan                              3.055503   \n",
       "Bahrain                                 1.175370   \n",
       "Bangladesh                              1.059952   \n",
       "Bhutan                                  1.393138   \n",
       "\n",
       "             EconomicSupportIndex_updated  percentage_change_EconomicSupport  \n",
       "location                                                                      \n",
       "Afghanistan                      0.000000                           0.000000  \n",
       "Azerbaijan                      29.918033                           0.000000  \n",
       "Bahrain                         62.346311                           0.717213  \n",
       "Bangladesh                      47.928177                           0.644567  \n",
       "Bhutan                          66.522989                           0.095785  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_age_Asia=merged_covid_ASIA_df.groupby(\"location\").mean()\n",
    "median_age_Asia.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Afghanistan</th>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>156.401639</td>\n",
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       "      <td>1.476020e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>13151.081967</td>\n",
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       "      <td>&lt;19</td>\n",
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       "    <tr>\n",
       "      <th>Azerbaijan</th>\n",
       "      <td>10139175.0</td>\n",
       "      <td>119.309</td>\n",
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       "      <td>148.807377</td>\n",
       "      <td>0.001468</td>\n",
       "      <td>2.176230</td>\n",
       "      <td>2.146358e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>9381.430328</td>\n",
       "      <td>...</td>\n",
       "      <td>63.167418</td>\n",
       "      <td>57.290369</td>\n",
       "      <td>3.098554</td>\n",
       "      <td>62.267090</td>\n",
       "      <td>3.055503</td>\n",
       "      <td>29.918033</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30-39</td>\n",
       "      <td>30-39</td>\n",
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       "      <th>Bahrain</th>\n",
       "      <td>1701583.0</td>\n",
       "      <td>1935.907</td>\n",
       "      <td>32.4</td>\n",
       "      <td>211.368852</td>\n",
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       "      <td>30-39</td>\n",
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       "      <th>Bangladesh</th>\n",
       "      <td>164689383.0</td>\n",
       "      <td>1265.036</td>\n",
       "      <td>27.5</td>\n",
       "      <td>1717.248619</td>\n",
       "      <td>0.001043</td>\n",
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       "      <td>20-29</td>\n",
       "      <td>20-29</td>\n",
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       "      <td>20-29</td>\n",
       "      <td>20-29</td>\n",
       "      <td>20-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
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      ],
      "text/plain": [
       "              population  population_density  median_age    new_cases  \\\n",
       "location                                                                \n",
       "Afghanistan   38928341.0              54.422        18.6   156.401639   \n",
       "Azerbaijan    10139175.0             119.309        32.4   148.807377   \n",
       "Bahrain        1701583.0            1935.907        32.4   211.368852   \n",
       "Bangladesh   164689383.0            1265.036        27.5  1717.248619   \n",
       "Bhutan          771612.0              21.188        28.6     1.287356   \n",
       "\n",
       "             percentage_of_Pop_New_Cases  new_deaths  \\\n",
       "location                                               \n",
       "Afghanistan                     0.000402    5.745902   \n",
       "Azerbaijan                      0.001468    2.176230   \n",
       "Bahrain                         0.012422    0.774590   \n",
       "Bangladesh                      0.001043   23.469613   \n",
       "Bhutan                          0.000167    0.000000   \n",
       "\n",
       "             percentage_of_Pop_New_Deaths    new_tests  \\\n",
       "location                                                 \n",
       "Afghanistan                  1.476020e-07     0.000000   \n",
       "Azerbaijan                   2.146358e-07     0.000000   \n",
       "Bahrain                      4.552174e-07  3574.372951   \n",
       "Bangladesh                   1.425084e-07  8364.690608   \n",
       "Bhutan                       0.000000e+00     0.000000   \n",
       "\n",
       "             percentage_of_Pop_New_Tests  total_cases_updated  ...  \\\n",
       "location                                                       ...   \n",
       "Afghanistan                     0.000000         13151.081967  ...   \n",
       "Azerbaijan                      0.000000          9381.430328  ...   \n",
       "Bahrain                         0.210062         13573.127049  ...   \n",
       "Bangladesh                      0.005079         99124.806630  ...   \n",
       "Bhutan                          0.000000            55.339080  ...   \n",
       "\n",
       "             StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "location                                                                      \n",
       "Afghanistan                      55.434180                        42.725451   \n",
       "Azerbaijan                       63.167418                        57.290369   \n",
       "Bahrain                          60.455000                        59.165533   \n",
       "Bangladesh                       81.070497                        69.205967   \n",
       "Bhutan                           81.901149                        73.016034   \n",
       "\n",
       "             percentage_change_GovernmentResponse  \\\n",
       "location                                            \n",
       "Afghanistan                              0.953768   \n",
       "Azerbaijan                               3.098554   \n",
       "Bahrain                                  1.245380   \n",
       "Bangladesh                               1.149209   \n",
       "Bhutan                                   1.516405   \n",
       "\n",
       "             ContainmentHealthIndex_updated  \\\n",
       "location                                      \n",
       "Afghanistan                       50.494631   \n",
       "Azerbaijan                        62.267090   \n",
       "Bahrain                           58.589836   \n",
       "Bangladesh                        73.073204   \n",
       "Bhutan                            74.196839   \n",
       "\n",
       "             percentage_change_ContainmentHealth  \\\n",
       "location                                           \n",
       "Afghanistan                             0.953501   \n",
       "Azerbaijan                              3.055503   \n",
       "Bahrain                                 1.175370   \n",
       "Bangladesh                              1.059952   \n",
       "Bhutan                                  1.393138   \n",
       "\n",
       "             EconomicSupportIndex_updated  percentage_change_EconomicSupport  \\\n",
       "location                                                                       \n",
       "Afghanistan                      0.000000                           0.000000   \n",
       "Azerbaijan                      29.918033                           0.000000   \n",
       "Bahrain                         62.346311                           0.717213   \n",
       "Bangladesh                      47.928177                           0.644567   \n",
       "Bhutan                          66.522989                           0.095785   \n",
       "\n",
       "             Age Ranges (Per Country)  Age_Range_Per Country  Age_Range  \n",
       "location                                                                 \n",
       "Afghanistan                       <19                    <19        <19  \n",
       "Azerbaijan                      30-39                  30-39      30-39  \n",
       "Bahrain                         30-39                  30-39      30-39  \n",
       "Bangladesh                      20-29                  20-29      20-29  \n",
       "Bhutan                          20-29                  20-29      20-29  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorize Age based on the bins.\n",
    "median_age_Asia[(\"Age_Range\")] = pd.cut(covid_ASIA_df,bins, labels=group_names)\n",
    "\n",
    "median_age_Asia.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>38928341.0</td>\n",
       "      <td>54.422</td>\n",
       "      <td>18.6</td>\n",
       "      <td>156.401639</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>5.745902</td>\n",
       "      <td>1.476020e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>55.434180</td>\n",
       "      <td>42.725451</td>\n",
       "      <td>0.953768</td>\n",
       "      <td>50.494631</td>\n",
       "      <td>0.953501</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>&lt;19</td>\n",
       "      <td>&lt;19</td>\n",
       "      <td>&lt;19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>10139175.0</td>\n",
       "      <td>119.309</td>\n",
       "      <td>32.4</td>\n",
       "      <td>148.807377</td>\n",
       "      <td>0.001468</td>\n",
       "      <td>2.176230</td>\n",
       "      <td>2.146358e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>63.167418</td>\n",
       "      <td>57.290369</td>\n",
       "      <td>3.098554</td>\n",
       "      <td>62.267090</td>\n",
       "      <td>3.055503</td>\n",
       "      <td>29.918033</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30-39</td>\n",
       "      <td>30-39</td>\n",
       "      <td>30-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>1701583.0</td>\n",
       "      <td>1935.907</td>\n",
       "      <td>32.4</td>\n",
       "      <td>211.368852</td>\n",
       "      <td>0.012422</td>\n",
       "      <td>0.774590</td>\n",
       "      <td>4.552174e-07</td>\n",
       "      <td>3574.372951</td>\n",
       "      <td>0.210062</td>\n",
       "      <td>...</td>\n",
       "      <td>60.455000</td>\n",
       "      <td>59.165533</td>\n",
       "      <td>1.245380</td>\n",
       "      <td>58.589836</td>\n",
       "      <td>1.175370</td>\n",
       "      <td>62.346311</td>\n",
       "      <td>0.717213</td>\n",
       "      <td>30-39</td>\n",
       "      <td>30-39</td>\n",
       "      <td>30-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>164689383.0</td>\n",
       "      <td>1265.036</td>\n",
       "      <td>27.5</td>\n",
       "      <td>1717.248619</td>\n",
       "      <td>0.001043</td>\n",
       "      <td>23.469613</td>\n",
       "      <td>1.425084e-07</td>\n",
       "      <td>8364.690608</td>\n",
       "      <td>0.005079</td>\n",
       "      <td>...</td>\n",
       "      <td>81.070497</td>\n",
       "      <td>69.205967</td>\n",
       "      <td>1.149209</td>\n",
       "      <td>73.073204</td>\n",
       "      <td>1.059952</td>\n",
       "      <td>47.928177</td>\n",
       "      <td>0.644567</td>\n",
       "      <td>20-29</td>\n",
       "      <td>20-29</td>\n",
       "      <td>20-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Bhutan</td>\n",
       "      <td>771612.0</td>\n",
       "      <td>21.188</td>\n",
       "      <td>28.6</td>\n",
       "      <td>1.287356</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>81.901149</td>\n",
       "      <td>73.016034</td>\n",
       "      <td>1.516405</td>\n",
       "      <td>74.196839</td>\n",
       "      <td>1.393138</td>\n",
       "      <td>66.522989</td>\n",
       "      <td>0.095785</td>\n",
       "      <td>20-29</td>\n",
       "      <td>20-29</td>\n",
       "      <td>20-29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      location   population  population_density  median_age    new_cases  \\\n",
       "0  Afghanistan   38928341.0              54.422        18.6   156.401639   \n",
       "1   Azerbaijan   10139175.0             119.309        32.4   148.807377   \n",
       "2      Bahrain    1701583.0            1935.907        32.4   211.368852   \n",
       "3   Bangladesh  164689383.0            1265.036        27.5  1717.248619   \n",
       "4       Bhutan     771612.0              21.188        28.6     1.287356   \n",
       "\n",
       "   percentage_of_Pop_New_Cases  new_deaths  percentage_of_Pop_New_Deaths  \\\n",
       "0                     0.000402    5.745902                  1.476020e-07   \n",
       "1                     0.001468    2.176230                  2.146358e-07   \n",
       "2                     0.012422    0.774590                  4.552174e-07   \n",
       "3                     0.001043   23.469613                  1.425084e-07   \n",
       "4                     0.000167    0.000000                  0.000000e+00   \n",
       "\n",
       "     new_tests  percentage_of_Pop_New_Tests  ...  \\\n",
       "0     0.000000                     0.000000  ...   \n",
       "1     0.000000                     0.000000  ...   \n",
       "2  3574.372951                     0.210062  ...   \n",
       "3  8364.690608                     0.005079  ...   \n",
       "4     0.000000                     0.000000  ...   \n",
       "\n",
       "   StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "0                      55.434180                        42.725451   \n",
       "1                      63.167418                        57.290369   \n",
       "2                      60.455000                        59.165533   \n",
       "3                      81.070497                        69.205967   \n",
       "4                      81.901149                        73.016034   \n",
       "\n",
       "   percentage_change_GovernmentResponse  ContainmentHealthIndex_updated  \\\n",
       "0                              0.953768                       50.494631   \n",
       "1                              3.098554                       62.267090   \n",
       "2                              1.245380                       58.589836   \n",
       "3                              1.149209                       73.073204   \n",
       "4                              1.516405                       74.196839   \n",
       "\n",
       "   percentage_change_ContainmentHealth  EconomicSupportIndex_updated  \\\n",
       "0                             0.953501                      0.000000   \n",
       "1                             3.055503                     29.918033   \n",
       "2                             1.175370                     62.346311   \n",
       "3                             1.059952                     47.928177   \n",
       "4                             1.393138                     66.522989   \n",
       "\n",
       "   percentage_change_EconomicSupport  Age Ranges (Per Country)  \\\n",
       "0                           0.000000                       <19   \n",
       "1                           0.000000                     30-39   \n",
       "2                           0.717213                     30-39   \n",
       "3                           0.644567                     20-29   \n",
       "4                           0.095785                     20-29   \n",
       "\n",
       "   Age_Range_Per Country  Age_Range  \n",
       "0                    <19        <19  \n",
       "1                  30-39      30-39  \n",
       "2                  30-39      30-39  \n",
       "3                  20-29      20-29  \n",
       "4                  20-29      20-29  \n",
       "\n",
       "[5 rows x 28 columns]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_age_Asia_df=median_age_Asia.reset_index()\n",
    "median_age_Asia_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Total Cases Per Age Group in Asia')"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# New deaths vs Median Age Canada\n",
    "fig, ax = plt.subplots()\n",
    "#ax2 = ax.twinx()\n",
    "ax.bar(median_age_Asia_df.Age_Range, median_age_Asia_df[\"total_cases_updated\"], color=\"red\", label=\"total_deaths_updated\")\n",
    "#ax2.plot(median_age_Asia_df.Age_Range, median_age_Asia_df[\"total_cases_updated\"], color='blue', label='total_cases_updated')\n",
    "#ax.set_xticklabels(median_age_Asia_df.Age_Range)\n",
    "#ax.legend(loc='best')\n",
    "ax.set_ylabel('Total Cases Per Age Group in Asia', color=\"red\")\n",
    "#ax2.set_ylabel('total deaths', color=\"blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a30d0c1508>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plot.\n",
    "plt.bar(median_age_Asia.Age_Range, median_age_Asia[\"total_cases_updated\"], color=\"blue\", label='Total Cases Updated')\n",
    "# Create labels for the x and y axes.\n",
    "plt.xlabel(\"Age Range\")\n",
    "plt.ylabel(\"Total Cases\")\n",
    "# Create a title.\n",
    "plt.title(\"Total Cases Per Age Group in Asia\")\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Oceania"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primary_key_reaction</th>\n",
       "      <th>date_updated</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>...</th>\n",
       "      <th>primary_key_response</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AUS2020-01-01</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>25499881.0</td>\n",
       "      <td>3.202</td>\n",
       "      <td>37.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>AUS2020-01-01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AUS2020-01-02</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>25499881.0</td>\n",
       "      <td>3.202</td>\n",
       "      <td>37.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>AUS2020-01-02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AUS2020-01-03</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>25499881.0</td>\n",
       "      <td>3.202</td>\n",
       "      <td>37.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>AUS2020-01-03</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AUS2020-01-04</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>25499881.0</td>\n",
       "      <td>3.202</td>\n",
       "      <td>37.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>AUS2020-01-04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AUS2020-01-05</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>AUS</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>25499881.0</td>\n",
       "      <td>3.202</td>\n",
       "      <td>37.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>AUS2020-01-05</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>PNG2020-08-27</td>\n",
       "      <td>2020-08-27</td>\n",
       "      <td>PNG</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>8947027.0</td>\n",
       "      <td>18.220</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>PNG2020-08-27</td>\n",
       "      <td>43.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.9</td>\n",
       "      <td>51.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1029</th>\n",
       "      <td>PNG2020-08-28</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>PNG</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>8947027.0</td>\n",
       "      <td>18.220</td>\n",
       "      <td>22.6</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>...</td>\n",
       "      <td>PNG2020-08-28</td>\n",
       "      <td>43.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.9</td>\n",
       "      <td>51.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1030</th>\n",
       "      <td>PNG2020-08-29</td>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>PNG</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>8947027.0</td>\n",
       "      <td>18.220</td>\n",
       "      <td>22.6</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.000324</td>\n",
       "      <td>...</td>\n",
       "      <td>PNG2020-08-29</td>\n",
       "      <td>43.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.9</td>\n",
       "      <td>51.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1031</th>\n",
       "      <td>PNG2020-08-30</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>PNG</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>8947027.0</td>\n",
       "      <td>18.220</td>\n",
       "      <td>22.6</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>...</td>\n",
       "      <td>PNG2020-08-30</td>\n",
       "      <td>43.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.9</td>\n",
       "      <td>51.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>PNG2020-08-31</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>PNG</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>8947027.0</td>\n",
       "      <td>18.220</td>\n",
       "      <td>22.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>PNG2020-08-31</td>\n",
       "      <td>43.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>51.9</td>\n",
       "      <td>51.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1033 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     primary_key_reaction date_updated iso_code continent          location  \\\n",
       "0           AUS2020-01-01   2020-01-01      AUS   Oceania         Australia   \n",
       "1           AUS2020-01-02   2020-01-02      AUS   Oceania         Australia   \n",
       "2           AUS2020-01-03   2020-01-03      AUS   Oceania         Australia   \n",
       "3           AUS2020-01-04   2020-01-04      AUS   Oceania         Australia   \n",
       "4           AUS2020-01-05   2020-01-05      AUS   Oceania         Australia   \n",
       "...                   ...          ...      ...       ...               ...   \n",
       "1028        PNG2020-08-27   2020-08-27      PNG   Oceania  Papua New Guinea   \n",
       "1029        PNG2020-08-28   2020-08-28      PNG   Oceania  Papua New Guinea   \n",
       "1030        PNG2020-08-29   2020-08-29      PNG   Oceania  Papua New Guinea   \n",
       "1031        PNG2020-08-30   2020-08-30      PNG   Oceania  Papua New Guinea   \n",
       "1032        PNG2020-08-31   2020-08-31      PNG   Oceania  Papua New Guinea   \n",
       "\n",
       "      population  population_density  median_age  new_cases  \\\n",
       "0     25499881.0               3.202        37.9        0.0   \n",
       "1     25499881.0               3.202        37.9        0.0   \n",
       "2     25499881.0               3.202        37.9        0.0   \n",
       "3     25499881.0               3.202        37.9        0.0   \n",
       "4     25499881.0               3.202        37.9        0.0   \n",
       "...          ...                 ...         ...        ...   \n",
       "1028   8947027.0              18.220        22.6        0.0   \n",
       "1029   8947027.0              18.220        22.6        5.0   \n",
       "1030   8947027.0              18.220        22.6       29.0   \n",
       "1031   8947027.0              18.220        22.6        6.0   \n",
       "1032   8947027.0              18.220        22.6        0.0   \n",
       "\n",
       "      percentage_of_Pop_New_Cases  ...  primary_key_response  \\\n",
       "0                        0.000000  ...         AUS2020-01-01   \n",
       "1                        0.000000  ...         AUS2020-01-02   \n",
       "2                        0.000000  ...         AUS2020-01-03   \n",
       "3                        0.000000  ...         AUS2020-01-04   \n",
       "4                        0.000000  ...         AUS2020-01-05   \n",
       "...                           ...  ...                   ...   \n",
       "1028                     0.000000  ...         PNG2020-08-27   \n",
       "1029                     0.000056  ...         PNG2020-08-28   \n",
       "1030                     0.000324  ...         PNG2020-08-29   \n",
       "1031                     0.000067  ...         PNG2020-08-30   \n",
       "1032                     0.000000  ...         PNG2020-08-31   \n",
       "\n",
       "      StringencyIndex_updated  percentage_change_stringency  \\\n",
       "0                        0.00                           0.0   \n",
       "1                        0.00                           0.0   \n",
       "2                        0.00                           0.0   \n",
       "3                        0.00                           0.0   \n",
       "4                        0.00                           0.0   \n",
       "...                       ...                           ...   \n",
       "1028                    43.52                           0.0   \n",
       "1029                    43.52                           0.0   \n",
       "1030                    43.52                           0.0   \n",
       "1031                    43.52                           0.0   \n",
       "1032                    43.52                           0.0   \n",
       "\n",
       "      StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "0                               0.0                             0.00   \n",
       "1                               0.0                             0.00   \n",
       "2                               0.0                             0.00   \n",
       "3                               0.0                             0.00   \n",
       "4                               0.0                             0.00   \n",
       "...                             ...                              ...   \n",
       "1028                           51.9                            51.92   \n",
       "1029                           51.9                            51.92   \n",
       "1030                           51.9                            51.92   \n",
       "1031                           51.9                            51.92   \n",
       "1032                           51.9                            51.92   \n",
       "\n",
       "      percentage_change_GovernmentResponse  ContainmentHealthIndex_updated  \\\n",
       "0                                      0.0                            0.00   \n",
       "1                                      0.0                            0.00   \n",
       "2                                      0.0                            0.00   \n",
       "3                                      0.0                            0.00   \n",
       "4                                      0.0                            0.00   \n",
       "...                                    ...                             ...   \n",
       "1028                                   0.0                           47.73   \n",
       "1029                                   0.0                           47.73   \n",
       "1030                                   0.0                           47.73   \n",
       "1031                                   0.0                           47.73   \n",
       "1032                                   0.0                           47.73   \n",
       "\n",
       "      percentage_change_ContainmentHealth  EconomicSupportIndex_updated  \\\n",
       "0                                     0.0                           0.0   \n",
       "1                                     0.0                           0.0   \n",
       "2                                     0.0                           0.0   \n",
       "3                                     0.0                           0.0   \n",
       "4                                     0.0                           0.0   \n",
       "...                                   ...                           ...   \n",
       "1028                                  0.0                          75.0   \n",
       "1029                                  0.0                          75.0   \n",
       "1030                                  0.0                          75.0   \n",
       "1031                                  0.0                          75.0   \n",
       "1032                                  0.0                          75.0   \n",
       "\n",
       "      percentage_change_EconomicSupport  \n",
       "0                                   0.0  \n",
       "1                                   0.0  \n",
       "2                                   0.0  \n",
       "3                                   0.0  \n",
       "4                                   0.0  \n",
       "...                                 ...  \n",
       "1028                                0.0  \n",
       "1029                                0.0  \n",
       "1030                                0.0  \n",
       "1031                                0.0  \n",
       "1032                                0.0  \n",
       "\n",
       "[1033 rows x 30 columns]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Dataframe filtered for Continent\n",
    "merged_covid_OC_df = pd.DataFrame(merged_covid_df.loc[merged_covid_df[\"continent\"]==\"Oceania\"].reset_index())\n",
    "merged_covid_OC_df = merged_covid_OC_df.drop([\"index\"],axis=1)\n",
    "merged_covid_OC_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Australia           37.9\n",
       "Fiji                28.6\n",
       "Guam                31.4\n",
       "New Zealand         37.9\n",
       "Papua New Guinea    22.6\n",
       "Name: median_age, dtype: float64"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average test scores.\n",
    "covid_Oceania_df = merged_covid_OC_df.groupby([\"location\"]).mean()[\"median_age\"]\n",
    "covid_Oceania_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_age\n",
       "(0, 20]     0\n",
       "(20, 30]    2\n",
       "(30, 40]    3\n",
       "(40, 50]    0\n",
       "Name: median_age, dtype: int64"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Median age range for different locations\n",
    "bins = [0,20,30,40,50]\n",
    "median_age_Oceania=covid_Oceania_df.groupby(pd.cut(covid_Oceania_df,bins)).count()\n",
    "median_age_Oceania"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 5760x7200 with 0 Axes>"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 5760x7200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plot.\n",
    "median_age_Oceania.plot( marker=\"*\", color=\"green\", linewidth=2, label='Median Age')\n",
    "# Create labels for the x and y axes.\n",
    "plt.xlabel(\"Median Age Range\")\n",
    "plt.ylabel(\"Count\")\n",
    "# Set the y limit between 0 and 45.\n",
    "plt.ylim(0, 45)\n",
    "# Create a title.\n",
    "plt.title(\"Covid 19 Impacted Median Age Group in Oceania\")\n",
    "# Add a grid.\n",
    "plt.grid()\n",
    "# Add the legend.\n",
    "plt.legend()\n",
    "plt.figure(figsize=(80, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<19', '20-29', '30-39', '40-50']"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Establish the  bins and group names.\n",
    "bins = [0, 20, 30,40,50]\n",
    "group_names = [\"<19\",\"20-29\",\"30-39\",\"40-50\"]\n",
    "group_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>percentage_of_Pop_New_Deaths</th>\n",
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       "      <th>percentage_of_Pop_New_Tests</th>\n",
       "      <th>total_cases_updated</th>\n",
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       "      <th>percentage_of_Pop_Total_Tests</th>\n",
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       "      <th>percentage_change_GovernmentResponse</th>\n",
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       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>25499881.0</td>\n",
       "      <td>3.202</td>\n",
       "      <td>37.9</td>\n",
       "      <td>105.204918</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>2.504098</td>\n",
       "      <td>9.820039e-08</td>\n",
       "      <td>21593.422131</td>\n",
       "      <td>0.084680</td>\n",
       "      <td>6953.504098</td>\n",
       "      <td>...</td>\n",
       "      <td>5.900771</td>\n",
       "      <td>50.248074</td>\n",
       "      <td>1.002567</td>\n",
       "      <td>52.462869</td>\n",
       "      <td>53.009713</td>\n",
       "      <td>0.692101</td>\n",
       "      <td>53.554508</td>\n",
       "      <td>0.637179</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>1.229508</td>\n",
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       "    <tr>\n",
       "      <th>Fiji</th>\n",
       "      <td>896444.0</td>\n",
       "      <td>49.562</td>\n",
       "      <td>28.6</td>\n",
       "      <td>0.130233</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.009302</td>\n",
       "      <td>1.037692e-08</td>\n",
       "      <td>32.669767</td>\n",
       "      <td>0.003644</td>\n",
       "      <td>14.837209</td>\n",
       "      <td>...</td>\n",
       "      <td>0.329202</td>\n",
       "      <td>58.686140</td>\n",
       "      <td>1.146349</td>\n",
       "      <td>64.764093</td>\n",
       "      <td>60.867628</td>\n",
       "      <td>1.411260</td>\n",
       "      <td>61.872791</td>\n",
       "      <td>1.312049</td>\n",
       "      <td>55.348837</td>\n",
       "      <td>0.232558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Guam</th>\n",
       "      <td>168783.0</td>\n",
       "      <td>304.128</td>\n",
       "      <td>31.4</td>\n",
       "      <td>8.114458</td>\n",
       "      <td>0.004808</td>\n",
       "      <td>0.060241</td>\n",
       "      <td>3.569137e-07</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>273.108434</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>66.237108</td>\n",
       "      <td>0.302399</td>\n",
       "      <td>76.709277</td>\n",
       "      <td>59.813133</td>\n",
       "      <td>0.396288</td>\n",
       "      <td>68.493554</td>\n",
       "      <td>0.381293</td>\n",
       "      <td>12.048193</td>\n",
       "      <td>0.301205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Zealand</th>\n",
       "      <td>4822233.0</td>\n",
       "      <td>18.206</td>\n",
       "      <td>37.9</td>\n",
       "      <td>5.684426</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>0.090164</td>\n",
       "      <td>1.869755e-08</td>\n",
       "      <td>3105.389344</td>\n",
       "      <td>0.064397</td>\n",
       "      <td>742.254098</td>\n",
       "      <td>...</td>\n",
       "      <td>4.373194</td>\n",
       "      <td>39.832582</td>\n",
       "      <td>1.362936</td>\n",
       "      <td>43.916148</td>\n",
       "      <td>44.349344</td>\n",
       "      <td>1.193944</td>\n",
       "      <td>43.730369</td>\n",
       "      <td>1.183611</td>\n",
       "      <td>47.745902</td>\n",
       "      <td>0.437158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Papua New Guinea</th>\n",
       "      <td>8947027.0</td>\n",
       "      <td>18.220</td>\n",
       "      <td>22.6</td>\n",
       "      <td>2.798780</td>\n",
       "      <td>0.000031</td>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>54.725610</td>\n",
       "      <td>0.914634</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  population  population_density  median_age   new_cases  \\\n",
       "location                                                                   \n",
       "Australia         25499881.0               3.202        37.9  105.204918   \n",
       "Fiji                896444.0              49.562        28.6    0.130233   \n",
       "Guam                168783.0             304.128        31.4    8.114458   \n",
       "New Zealand        4822233.0              18.206        37.9    5.684426   \n",
       "Papua New Guinea   8947027.0              18.220        22.6    2.798780   \n",
       "\n",
       "                  percentage_of_Pop_New_Cases  new_deaths  \\\n",
       "location                                                    \n",
       "Australia                            0.000413    2.504098   \n",
       "Fiji                                 0.000015    0.009302   \n",
       "Guam                                 0.004808    0.060241   \n",
       "New Zealand                          0.000118    0.090164   \n",
       "Papua New Guinea                     0.000031    0.030488   \n",
       "\n",
       "                  percentage_of_Pop_New_Deaths     new_tests  \\\n",
       "location                                                       \n",
       "Australia                         9.820039e-08  21593.422131   \n",
       "Fiji                              1.037692e-08     32.669767   \n",
       "Guam                              3.569137e-07      0.000000   \n",
       "New Zealand                       1.869755e-08   3105.389344   \n",
       "Papua New Guinea                  3.407591e-09      0.000000   \n",
       "\n",
       "                  percentage_of_Pop_New_Tests  total_cases_updated  ...  \\\n",
       "location                                                            ...   \n",
       "Australia                            0.084680          6953.504098  ...   \n",
       "Fiji                                 0.003644            14.837209  ...   \n",
       "Guam                                 0.000000           273.108434  ...   \n",
       "New Zealand                          0.064397           742.254098  ...   \n",
       "Papua New Guinea                     0.000000            62.060976  ...   \n",
       "\n",
       "                  percentage_of_Pop_Total_Tests  StringencyIndex_updated  \\\n",
       "location                                                                   \n",
       "Australia                              5.900771                50.248074   \n",
       "Fiji                                   0.329202                58.686140   \n",
       "Guam                                   0.000000                66.237108   \n",
       "New Zealand                            4.373194                39.832582   \n",
       "Papua New Guinea                       0.000000                56.745915   \n",
       "\n",
       "                  percentage_change_stringency  StringencyLegacyIndex_updated  \\\n",
       "location                                                                        \n",
       "Australia                             1.002567                      52.462869   \n",
       "Fiji                                  1.146349                      64.764093   \n",
       "Guam                                  0.302399                      76.709277   \n",
       "New Zealand                           1.362936                      43.916148   \n",
       "Papua New Guinea                      0.987045                      64.998659   \n",
       "\n",
       "                  GovernmentResponseIndex_updated  \\\n",
       "location                                            \n",
       "Australia                               53.009713   \n",
       "Fiji                                    60.867628   \n",
       "Guam                                    59.813133   \n",
       "New Zealand                             44.349344   \n",
       "Papua New Guinea                        59.078659   \n",
       "\n",
       "                  percentage_change_GovernmentResponse  \\\n",
       "location                                                 \n",
       "Australia                                     0.692101   \n",
       "Fiji                                          1.411260   \n",
       "Guam                                          0.396288   \n",
       "New Zealand                                   1.193944   \n",
       "Papua New Guinea                              0.828489   \n",
       "\n",
       "                  ContainmentHealthIndex_updated  \\\n",
       "location                                           \n",
       "Australia                              53.554508   \n",
       "Fiji                                   61.872791   \n",
       "Guam                                   68.493554   \n",
       "New Zealand                            43.730369   \n",
       "Papua New Guinea                       59.871098   \n",
       "\n",
       "                  percentage_change_ContainmentHealth  \\\n",
       "location                                                \n",
       "Australia                                    0.637179   \n",
       "Fiji                                         1.312049   \n",
       "Guam                                         0.381293   \n",
       "New Zealand                                  1.183611   \n",
       "Papua New Guinea                             0.713079   \n",
       "\n",
       "                  EconomicSupportIndex_updated  \\\n",
       "location                                         \n",
       "Australia                            50.000000   \n",
       "Fiji                                 55.348837   \n",
       "Guam                                 12.048193   \n",
       "New Zealand                          47.745902   \n",
       "Papua New Guinea                     54.725610   \n",
       "\n",
       "                  percentage_change_EconomicSupport  \n",
       "location                                             \n",
       "Australia                                  1.229508  \n",
       "Fiji                                       0.232558  \n",
       "Guam                                       0.301205  \n",
       "New Zealand                                0.437158  \n",
       "Papua New Guinea                           0.914634  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_age_Oceania=merged_covid_OC_df.groupby(\"location\").mean()\n",
    "median_age_Oceania.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
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       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
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      ],
      "text/plain": [
       "                  population  population_density  median_age   new_cases  \\\n",
       "location                                                                   \n",
       "Australia         25499881.0               3.202        37.9  105.204918   \n",
       "Fiji                896444.0              49.562        28.6    0.130233   \n",
       "Guam                168783.0             304.128        31.4    8.114458   \n",
       "New Zealand        4822233.0              18.206        37.9    5.684426   \n",
       "Papua New Guinea   8947027.0              18.220        22.6    2.798780   \n",
       "\n",
       "                  percentage_of_Pop_New_Cases  new_deaths  \\\n",
       "location                                                    \n",
       "Australia                            0.000413    2.504098   \n",
       "Fiji                                 0.000015    0.009302   \n",
       "Guam                                 0.004808    0.060241   \n",
       "New Zealand                          0.000118    0.090164   \n",
       "Papua New Guinea                     0.000031    0.030488   \n",
       "\n",
       "                  percentage_of_Pop_New_Deaths     new_tests  \\\n",
       "location                                                       \n",
       "Australia                         9.820039e-08  21593.422131   \n",
       "Fiji                              1.037692e-08     32.669767   \n",
       "Guam                              3.569137e-07      0.000000   \n",
       "New Zealand                       1.869755e-08   3105.389344   \n",
       "Papua New Guinea                  3.407591e-09      0.000000   \n",
       "\n",
       "                  percentage_of_Pop_New_Tests  total_cases_updated  ...  \\\n",
       "location                                                            ...   \n",
       "Australia                            0.084680          6953.504098  ...   \n",
       "Fiji                                 0.003644            14.837209  ...   \n",
       "Guam                                 0.000000           273.108434  ...   \n",
       "New Zealand                          0.064397           742.254098  ...   \n",
       "Papua New Guinea                     0.000000            62.060976  ...   \n",
       "\n",
       "                  StringencyIndex_updated  percentage_change_stringency  \\\n",
       "location                                                                  \n",
       "Australia                       50.248074                      1.002567   \n",
       "Fiji                            58.686140                      1.146349   \n",
       "Guam                            66.237108                      0.302399   \n",
       "New Zealand                     39.832582                      1.362936   \n",
       "Papua New Guinea                56.745915                      0.987045   \n",
       "\n",
       "                  StringencyLegacyIndex_updated  \\\n",
       "location                                          \n",
       "Australia                             52.462869   \n",
       "Fiji                                  64.764093   \n",
       "Guam                                  76.709277   \n",
       "New Zealand                           43.916148   \n",
       "Papua New Guinea                      64.998659   \n",
       "\n",
       "                  GovernmentResponseIndex_updated  \\\n",
       "location                                            \n",
       "Australia                               53.009713   \n",
       "Fiji                                    60.867628   \n",
       "Guam                                    59.813133   \n",
       "New Zealand                             44.349344   \n",
       "Papua New Guinea                        59.078659   \n",
       "\n",
       "                  percentage_change_GovernmentResponse  \\\n",
       "location                                                 \n",
       "Australia                                     0.692101   \n",
       "Fiji                                          1.411260   \n",
       "Guam                                          0.396288   \n",
       "New Zealand                                   1.193944   \n",
       "Papua New Guinea                              0.828489   \n",
       "\n",
       "                  ContainmentHealthIndex_updated  \\\n",
       "location                                           \n",
       "Australia                              53.554508   \n",
       "Fiji                                   61.872791   \n",
       "Guam                                   68.493554   \n",
       "New Zealand                            43.730369   \n",
       "Papua New Guinea                       59.871098   \n",
       "\n",
       "                  percentage_change_ContainmentHealth  \\\n",
       "location                                                \n",
       "Australia                                    0.637179   \n",
       "Fiji                                         1.312049   \n",
       "Guam                                         0.381293   \n",
       "New Zealand                                  1.183611   \n",
       "Papua New Guinea                             0.713079   \n",
       "\n",
       "                  EconomicSupportIndex_updated  \\\n",
       "location                                         \n",
       "Australia                            50.000000   \n",
       "Fiji                                 55.348837   \n",
       "Guam                                 12.048193   \n",
       "New Zealand                          47.745902   \n",
       "Papua New Guinea                     54.725610   \n",
       "\n",
       "                  percentage_change_EconomicSupport  Age_Range  \n",
       "location                                                        \n",
       "Australia                                  1.229508      30-39  \n",
       "Fiji                                       0.232558      20-29  \n",
       "Guam                                       0.301205      30-39  \n",
       "New Zealand                                0.437158      30-39  \n",
       "Papua New Guinea                           0.914634      20-29  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorize Age based on the bins.\n",
    "median_age_Oceania[(\"Age_Range\")] = pd.cut(covid_Oceania_df,bins, labels=group_names)\n",
    "\n",
    "median_age_Oceania.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a31272bf08>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plot.\n",
    "plt.bar(median_age_Oceania.Age_Range, median_age_Oceania[\"total_cases_updated\"], color=\"blue\", label='Total Cases Updated')\n",
    "# Create labels for the x and y axes.\n",
    "plt.xlabel(\"Age Range\")\n",
    "plt.ylabel(\"Total Cases\")\n",
    "# Create a title.\n",
    "plt.title(\"Total Cases Per Age Group in Oceania\")\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# South America"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>primary_key_reaction</th>\n",
       "      <th>date_updated</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>...</th>\n",
       "      <th>primary_key_response</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARG2020-02-11</td>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>ARG</td>\n",
       "      <td>South America</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45195777.0</td>\n",
       "      <td>16.177</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ARG2020-02-11</td>\n",
       "      <td>11.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.29</td>\n",
       "      <td>7.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ARG2020-02-12</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>ARG</td>\n",
       "      <td>South America</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45195777.0</td>\n",
       "      <td>16.177</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ARG2020-02-12</td>\n",
       "      <td>11.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.29</td>\n",
       "      <td>7.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ARG2020-02-13</td>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>ARG</td>\n",
       "      <td>South America</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45195777.0</td>\n",
       "      <td>16.177</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ARG2020-02-13</td>\n",
       "      <td>11.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.29</td>\n",
       "      <td>7.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ARG2020-02-14</td>\n",
       "      <td>2020-02-14</td>\n",
       "      <td>ARG</td>\n",
       "      <td>South America</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45195777.0</td>\n",
       "      <td>16.177</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ARG2020-02-14</td>\n",
       "      <td>11.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.29</td>\n",
       "      <td>7.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ARG2020-02-15</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>ARG</td>\n",
       "      <td>South America</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>45195777.0</td>\n",
       "      <td>16.177</td>\n",
       "      <td>31.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>ARG2020-02-15</td>\n",
       "      <td>11.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.29</td>\n",
       "      <td>7.69</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2258</th>\n",
       "      <td>VEN2020-08-27</td>\n",
       "      <td>2020-08-27</td>\n",
       "      <td>VEN</td>\n",
       "      <td>South America</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>28435943.0</td>\n",
       "      <td>36.253</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>VEN2020-08-27</td>\n",
       "      <td>86.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.90</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2259</th>\n",
       "      <td>VEN2020-08-28</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>VEN</td>\n",
       "      <td>South America</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>28435943.0</td>\n",
       "      <td>36.253</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1740.0</td>\n",
       "      <td>0.006119</td>\n",
       "      <td>...</td>\n",
       "      <td>VEN2020-08-28</td>\n",
       "      <td>86.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.90</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2260</th>\n",
       "      <td>VEN2020-08-29</td>\n",
       "      <td>2020-08-29</td>\n",
       "      <td>VEN</td>\n",
       "      <td>South America</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>28435943.0</td>\n",
       "      <td>36.253</td>\n",
       "      <td>29.0</td>\n",
       "      <td>981.0</td>\n",
       "      <td>0.003450</td>\n",
       "      <td>...</td>\n",
       "      <td>VEN2020-08-29</td>\n",
       "      <td>86.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.90</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2261</th>\n",
       "      <td>VEN2020-08-30</td>\n",
       "      <td>2020-08-30</td>\n",
       "      <td>VEN</td>\n",
       "      <td>South America</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>28435943.0</td>\n",
       "      <td>36.253</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1067.0</td>\n",
       "      <td>0.003752</td>\n",
       "      <td>...</td>\n",
       "      <td>VEN2020-08-30</td>\n",
       "      <td>86.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.90</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2262</th>\n",
       "      <td>VEN2020-08-31</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>VEN</td>\n",
       "      <td>South America</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>28435943.0</td>\n",
       "      <td>36.253</td>\n",
       "      <td>29.0</td>\n",
       "      <td>922.0</td>\n",
       "      <td>0.003242</td>\n",
       "      <td>...</td>\n",
       "      <td>VEN2020-08-31</td>\n",
       "      <td>86.11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>86.90</td>\n",
       "      <td>75.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>79.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2263 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     primary_key_reaction date_updated iso_code      continent   location  \\\n",
       "0           ARG2020-02-11   2020-02-11      ARG  South America  Argentina   \n",
       "1           ARG2020-02-12   2020-02-12      ARG  South America  Argentina   \n",
       "2           ARG2020-02-13   2020-02-13      ARG  South America  Argentina   \n",
       "3           ARG2020-02-14   2020-02-14      ARG  South America  Argentina   \n",
       "4           ARG2020-02-15   2020-02-15      ARG  South America  Argentina   \n",
       "...                   ...          ...      ...            ...        ...   \n",
       "2258        VEN2020-08-27   2020-08-27      VEN  South America  Venezuela   \n",
       "2259        VEN2020-08-28   2020-08-28      VEN  South America  Venezuela   \n",
       "2260        VEN2020-08-29   2020-08-29      VEN  South America  Venezuela   \n",
       "2261        VEN2020-08-30   2020-08-30      VEN  South America  Venezuela   \n",
       "2262        VEN2020-08-31   2020-08-31      VEN  South America  Venezuela   \n",
       "\n",
       "      population  population_density  median_age  new_cases  \\\n",
       "0     45195777.0              16.177        31.9        0.0   \n",
       "1     45195777.0              16.177        31.9        0.0   \n",
       "2     45195777.0              16.177        31.9        0.0   \n",
       "3     45195777.0              16.177        31.9        0.0   \n",
       "4     45195777.0              16.177        31.9        0.0   \n",
       "...          ...                 ...         ...        ...   \n",
       "2258  28435943.0              36.253        29.0        0.0   \n",
       "2259  28435943.0              36.253        29.0     1740.0   \n",
       "2260  28435943.0              36.253        29.0      981.0   \n",
       "2261  28435943.0              36.253        29.0     1067.0   \n",
       "2262  28435943.0              36.253        29.0      922.0   \n",
       "\n",
       "      percentage_of_Pop_New_Cases  ...  primary_key_response  \\\n",
       "0                        0.000000  ...         ARG2020-02-11   \n",
       "1                        0.000000  ...         ARG2020-02-12   \n",
       "2                        0.000000  ...         ARG2020-02-13   \n",
       "3                        0.000000  ...         ARG2020-02-14   \n",
       "4                        0.000000  ...         ARG2020-02-15   \n",
       "...                           ...  ...                   ...   \n",
       "2258                     0.000000  ...         VEN2020-08-27   \n",
       "2259                     0.006119  ...         VEN2020-08-28   \n",
       "2260                     0.003450  ...         VEN2020-08-29   \n",
       "2261                     0.003752  ...         VEN2020-08-30   \n",
       "2262                     0.003242  ...         VEN2020-08-31   \n",
       "\n",
       "      StringencyIndex_updated  percentage_change_stringency  \\\n",
       "0                       11.11                           0.0   \n",
       "1                       11.11                           0.0   \n",
       "2                       11.11                           0.0   \n",
       "3                       11.11                           0.0   \n",
       "4                       11.11                           0.0   \n",
       "...                       ...                           ...   \n",
       "2258                    86.11                           0.0   \n",
       "2259                    86.11                           0.0   \n",
       "2260                    86.11                           0.0   \n",
       "2261                    86.11                           0.0   \n",
       "2262                    86.11                           0.0   \n",
       "\n",
       "      StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "0                             14.29                             7.69   \n",
       "1                             14.29                             7.69   \n",
       "2                             14.29                             7.69   \n",
       "3                             14.29                             7.69   \n",
       "4                             14.29                             7.69   \n",
       "...                             ...                              ...   \n",
       "2258                          86.90                            75.00   \n",
       "2259                          86.90                            75.00   \n",
       "2260                          86.90                            75.00   \n",
       "2261                          86.90                            75.00   \n",
       "2262                          86.90                            75.00   \n",
       "\n",
       "      percentage_change_GovernmentResponse  ContainmentHealthIndex_updated  \\\n",
       "0                                      0.0                            9.09   \n",
       "1                                      0.0                            9.09   \n",
       "2                                      0.0                            9.09   \n",
       "3                                      0.0                            9.09   \n",
       "4                                      0.0                            9.09   \n",
       "...                                    ...                             ...   \n",
       "2258                                   0.0                           79.55   \n",
       "2259                                   0.0                           79.55   \n",
       "2260                                   0.0                           79.55   \n",
       "2261                                   0.0                           79.55   \n",
       "2262                                   0.0                           79.55   \n",
       "\n",
       "      percentage_change_ContainmentHealth  EconomicSupportIndex_updated  \\\n",
       "0                                     0.0                           0.0   \n",
       "1                                     0.0                           0.0   \n",
       "2                                     0.0                           0.0   \n",
       "3                                     0.0                           0.0   \n",
       "4                                     0.0                           0.0   \n",
       "...                                   ...                           ...   \n",
       "2258                                  0.0                          50.0   \n",
       "2259                                  0.0                          50.0   \n",
       "2260                                  0.0                          50.0   \n",
       "2261                                  0.0                          50.0   \n",
       "2262                                  0.0                          50.0   \n",
       "\n",
       "      percentage_change_EconomicSupport  \n",
       "0                                   0.0  \n",
       "1                                   0.0  \n",
       "2                                   0.0  \n",
       "3                                   0.0  \n",
       "4                                   0.0  \n",
       "...                                 ...  \n",
       "2258                                0.0  \n",
       "2259                                0.0  \n",
       "2260                                0.0  \n",
       "2261                                0.0  \n",
       "2262                                0.0  \n",
       "\n",
       "[2263 rows x 30 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Dataframe filtered for Continent\n",
    "merged_covid_SA_df = pd.DataFrame(merged_covid_df.loc[merged_covid_df[\"continent\"]==\"South America\"].reset_index())\n",
    "merged_covid_SA_df = merged_covid_SA_df.drop([\"index\"],axis=1)\n",
    "merged_covid_SA_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "Argentina    31.9\n",
       "Bolivia      25.4\n",
       "Brazil       33.5\n",
       "Chile        35.4\n",
       "Colombia     32.2\n",
       "Ecuador      28.1\n",
       "Guyana       26.3\n",
       "Paraguay     26.5\n",
       "Peru         29.1\n",
       "Suriname     29.6\n",
       "Uruguay      35.6\n",
       "Venezuela    29.0\n",
       "Name: median_age, dtype: float64"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate the average test scores.\n",
    "covid_SA_df = merged_covid_SA_df.groupby([\"location\"]).mean()[\"median_age\"]\n",
    "covid_SA_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "median_age\n",
       "(0, 20]     0\n",
       "(20, 30]    7\n",
       "(30, 40]    5\n",
       "(40, 50]    0\n",
       "Name: median_age, dtype: int64"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Median age range for different locations\n",
    "bins = [0,20,30,40,50]\n",
    "median_age_SA=covid_SA_df.groupby(pd.cut(covid_SA_df,bins)).count()\n",
    "median_age_SA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 5760x7200 with 0 Axes>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 5760x7200 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plot.\n",
    "median_age_SA.plot( marker=\"*\", color=\"green\", linewidth=2, label='Median Age')\n",
    "# Create labels for the x and y axes.\n",
    "plt.xlabel(\"Median Age Range\")\n",
    "plt.ylabel(\"Count\")\n",
    "# Set the y limit between 0 and 45.\n",
    "plt.ylim(0, 45)\n",
    "# Create a title.\n",
    "plt.title(\"Covid 19 Impacted Median Age Group in South America\")\n",
    "# Add a grid.\n",
    "plt.grid()\n",
    "# Add the legend.\n",
    "plt.legend()\n",
    "plt.figure(figsize=(80, 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['<19', '20-29', '30-39', '40-50']"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Establish the  bins and group names.\n",
    "bins = [0, 20, 30,40,50]\n",
    "group_names = [\"<19\",\"20-29\",\"30-39\",\"40-50\"]\n",
    "group_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>percentage_of_Pop_New_Deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>percentage_of_Pop_New_Tests</th>\n",
       "      <th>total_cases_updated</th>\n",
       "      <th>...</th>\n",
       "      <th>percentage_of_Pop_Total_Tests</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>45195777.0</td>\n",
       "      <td>16.177</td>\n",
       "      <td>31.9</td>\n",
       "      <td>1976.482759</td>\n",
       "      <td>0.004373</td>\n",
       "      <td>41.384236</td>\n",
       "      <td>9.156660e-07</td>\n",
       "      <td>5333.394089</td>\n",
       "      <td>0.011801</td>\n",
       "      <td>67309.625616</td>\n",
       "      <td>...</td>\n",
       "      <td>0.561921</td>\n",
       "      <td>77.895764</td>\n",
       "      <td>1.440516</td>\n",
       "      <td>80.526502</td>\n",
       "      <td>72.243596</td>\n",
       "      <td>1.574035</td>\n",
       "      <td>74.585369</td>\n",
       "      <td>1.495449</td>\n",
       "      <td>59.359606</td>\n",
       "      <td>0.985222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>11673029.0</td>\n",
       "      <td>10.202</td>\n",
       "      <td>25.4</td>\n",
       "      <td>670.335260</td>\n",
       "      <td>0.005743</td>\n",
       "      <td>28.705202</td>\n",
       "      <td>2.459105e-06</td>\n",
       "      <td>1340.630058</td>\n",
       "      <td>0.011485</td>\n",
       "      <td>31420.289017</td>\n",
       "      <td>...</td>\n",
       "      <td>0.581956</td>\n",
       "      <td>90.381676</td>\n",
       "      <td>3.310613</td>\n",
       "      <td>92.838613</td>\n",
       "      <td>70.218786</td>\n",
       "      <td>3.372415</td>\n",
       "      <td>76.526879</td>\n",
       "      <td>3.333388</td>\n",
       "      <td>35.549133</td>\n",
       "      <td>-0.289017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>212559409.0</td>\n",
       "      <td>25.040</td>\n",
       "      <td>33.5</td>\n",
       "      <td>15829.143443</td>\n",
       "      <td>0.007447</td>\n",
       "      <td>495.196721</td>\n",
       "      <td>2.329686e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>814776.286885</td>\n",
       "      <td>...</td>\n",
       "      <td>0.423647</td>\n",
       "      <td>53.922377</td>\n",
       "      <td>1.442476</td>\n",
       "      <td>55.443607</td>\n",
       "      <td>46.095615</td>\n",
       "      <td>1.817239</td>\n",
       "      <td>48.516311</td>\n",
       "      <td>1.747227</td>\n",
       "      <td>32.786885</td>\n",
       "      <td>0.409836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>19116209.0</td>\n",
       "      <td>24.282</td>\n",
       "      <td>35.4</td>\n",
       "      <td>2277.633333</td>\n",
       "      <td>0.011915</td>\n",
       "      <td>62.466667</td>\n",
       "      <td>3.267733e-06</td>\n",
       "      <td>13124.900000</td>\n",
       "      <td>0.068658</td>\n",
       "      <td>161644.233333</td>\n",
       "      <td>...</td>\n",
       "      <td>4.210101</td>\n",
       "      <td>73.794000</td>\n",
       "      <td>1.552816</td>\n",
       "      <td>73.577833</td>\n",
       "      <td>70.223444</td>\n",
       "      <td>1.779723</td>\n",
       "      <td>73.558833</td>\n",
       "      <td>1.668807</td>\n",
       "      <td>51.875000</td>\n",
       "      <td>1.157407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>50882884.0</td>\n",
       "      <td>44.223</td>\n",
       "      <td>32.2</td>\n",
       "      <td>3377.433333</td>\n",
       "      <td>0.006638</td>\n",
       "      <td>107.577778</td>\n",
       "      <td>2.114223e-06</td>\n",
       "      <td>14439.261111</td>\n",
       "      <td>0.028377</td>\n",
       "      <td>121251.272222</td>\n",
       "      <td>...</td>\n",
       "      <td>1.375921</td>\n",
       "      <td>81.323778</td>\n",
       "      <td>1.486245</td>\n",
       "      <td>86.504111</td>\n",
       "      <td>78.375556</td>\n",
       "      <td>1.254592</td>\n",
       "      <td>80.422556</td>\n",
       "      <td>1.169270</td>\n",
       "      <td>67.083333</td>\n",
       "      <td>0.277778</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            population  population_density  median_age     new_cases  \\\n",
       "location                                                               \n",
       "Argentina   45195777.0              16.177        31.9   1976.482759   \n",
       "Bolivia     11673029.0              10.202        25.4    670.335260   \n",
       "Brazil     212559409.0              25.040        33.5  15829.143443   \n",
       "Chile       19116209.0              24.282        35.4   2277.633333   \n",
       "Colombia    50882884.0              44.223        32.2   3377.433333   \n",
       "\n",
       "           percentage_of_Pop_New_Cases  new_deaths  \\\n",
       "location                                             \n",
       "Argentina                     0.004373   41.384236   \n",
       "Bolivia                       0.005743   28.705202   \n",
       "Brazil                        0.007447  495.196721   \n",
       "Chile                         0.011915   62.466667   \n",
       "Colombia                      0.006638  107.577778   \n",
       "\n",
       "           percentage_of_Pop_New_Deaths     new_tests  \\\n",
       "location                                                \n",
       "Argentina                  9.156660e-07   5333.394089   \n",
       "Bolivia                    2.459105e-06   1340.630058   \n",
       "Brazil                     2.329686e-06      0.000000   \n",
       "Chile                      3.267733e-06  13124.900000   \n",
       "Colombia                   2.114223e-06  14439.261111   \n",
       "\n",
       "           percentage_of_Pop_New_Tests  total_cases_updated  ...  \\\n",
       "location                                                     ...   \n",
       "Argentina                     0.011801         67309.625616  ...   \n",
       "Bolivia                       0.011485         31420.289017  ...   \n",
       "Brazil                        0.000000        814776.286885  ...   \n",
       "Chile                         0.068658        161644.233333  ...   \n",
       "Colombia                      0.028377        121251.272222  ...   \n",
       "\n",
       "           percentage_of_Pop_Total_Tests  StringencyIndex_updated  \\\n",
       "location                                                            \n",
       "Argentina                       0.561921                77.895764   \n",
       "Bolivia                         0.581956                90.381676   \n",
       "Brazil                          0.423647                53.922377   \n",
       "Chile                           4.210101                73.794000   \n",
       "Colombia                        1.375921                81.323778   \n",
       "\n",
       "           percentage_change_stringency  StringencyLegacyIndex_updated  \\\n",
       "location                                                                 \n",
       "Argentina                      1.440516                      80.526502   \n",
       "Bolivia                        3.310613                      92.838613   \n",
       "Brazil                         1.442476                      55.443607   \n",
       "Chile                          1.552816                      73.577833   \n",
       "Colombia                       1.486245                      86.504111   \n",
       "\n",
       "           GovernmentResponseIndex_updated  \\\n",
       "location                                     \n",
       "Argentina                        72.243596   \n",
       "Bolivia                          70.218786   \n",
       "Brazil                           46.095615   \n",
       "Chile                            70.223444   \n",
       "Colombia                         78.375556   \n",
       "\n",
       "           percentage_change_GovernmentResponse  \\\n",
       "location                                          \n",
       "Argentina                              1.574035   \n",
       "Bolivia                                3.372415   \n",
       "Brazil                                 1.817239   \n",
       "Chile                                  1.779723   \n",
       "Colombia                               1.254592   \n",
       "\n",
       "           ContainmentHealthIndex_updated  \\\n",
       "location                                    \n",
       "Argentina                       74.585369   \n",
       "Bolivia                         76.526879   \n",
       "Brazil                          48.516311   \n",
       "Chile                           73.558833   \n",
       "Colombia                        80.422556   \n",
       "\n",
       "           percentage_change_ContainmentHealth  EconomicSupportIndex_updated  \\\n",
       "location                                                                       \n",
       "Argentina                             1.495449                     59.359606   \n",
       "Bolivia                               3.333388                     35.549133   \n",
       "Brazil                                1.747227                     32.786885   \n",
       "Chile                                 1.668807                     51.875000   \n",
       "Colombia                              1.169270                     67.083333   \n",
       "\n",
       "           percentage_change_EconomicSupport  \n",
       "location                                      \n",
       "Argentina                           0.985222  \n",
       "Bolivia                            -0.289017  \n",
       "Brazil                              0.409836  \n",
       "Chile                               1.157407  \n",
       "Colombia                            0.277778  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "median_age_SA=merged_covid_SA_df.groupby(\"location\").mean()\n",
    "median_age_SA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>population</th>\n",
       "      <th>population_density</th>\n",
       "      <th>median_age</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>percentage_of_Pop_New_Cases</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>percentage_of_Pop_New_Deaths</th>\n",
       "      <th>new_tests</th>\n",
       "      <th>percentage_of_Pop_New_Tests</th>\n",
       "      <th>total_cases_updated</th>\n",
       "      <th>...</th>\n",
       "      <th>StringencyIndex_updated</th>\n",
       "      <th>percentage_change_stringency</th>\n",
       "      <th>StringencyLegacyIndex_updated</th>\n",
       "      <th>GovernmentResponseIndex_updated</th>\n",
       "      <th>percentage_change_GovernmentResponse</th>\n",
       "      <th>ContainmentHealthIndex_updated</th>\n",
       "      <th>percentage_change_ContainmentHealth</th>\n",
       "      <th>EconomicSupportIndex_updated</th>\n",
       "      <th>percentage_change_EconomicSupport</th>\n",
       "      <th>Age_Range</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Argentina</th>\n",
       "      <td>45195777.0</td>\n",
       "      <td>16.177</td>\n",
       "      <td>31.9</td>\n",
       "      <td>1976.482759</td>\n",
       "      <td>0.004373</td>\n",
       "      <td>41.384236</td>\n",
       "      <td>9.156660e-07</td>\n",
       "      <td>5333.394089</td>\n",
       "      <td>0.011801</td>\n",
       "      <td>67309.625616</td>\n",
       "      <td>...</td>\n",
       "      <td>77.895764</td>\n",
       "      <td>1.440516</td>\n",
       "      <td>80.526502</td>\n",
       "      <td>72.243596</td>\n",
       "      <td>1.574035</td>\n",
       "      <td>74.585369</td>\n",
       "      <td>1.495449</td>\n",
       "      <td>59.359606</td>\n",
       "      <td>0.985222</td>\n",
       "      <td>30-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bolivia</th>\n",
       "      <td>11673029.0</td>\n",
       "      <td>10.202</td>\n",
       "      <td>25.4</td>\n",
       "      <td>670.335260</td>\n",
       "      <td>0.005743</td>\n",
       "      <td>28.705202</td>\n",
       "      <td>2.459105e-06</td>\n",
       "      <td>1340.630058</td>\n",
       "      <td>0.011485</td>\n",
       "      <td>31420.289017</td>\n",
       "      <td>...</td>\n",
       "      <td>90.381676</td>\n",
       "      <td>3.310613</td>\n",
       "      <td>92.838613</td>\n",
       "      <td>70.218786</td>\n",
       "      <td>3.372415</td>\n",
       "      <td>76.526879</td>\n",
       "      <td>3.333388</td>\n",
       "      <td>35.549133</td>\n",
       "      <td>-0.289017</td>\n",
       "      <td>20-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brazil</th>\n",
       "      <td>212559409.0</td>\n",
       "      <td>25.040</td>\n",
       "      <td>33.5</td>\n",
       "      <td>15829.143443</td>\n",
       "      <td>0.007447</td>\n",
       "      <td>495.196721</td>\n",
       "      <td>2.329686e-06</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>814776.286885</td>\n",
       "      <td>...</td>\n",
       "      <td>53.922377</td>\n",
       "      <td>1.442476</td>\n",
       "      <td>55.443607</td>\n",
       "      <td>46.095615</td>\n",
       "      <td>1.817239</td>\n",
       "      <td>48.516311</td>\n",
       "      <td>1.747227</td>\n",
       "      <td>32.786885</td>\n",
       "      <td>0.409836</td>\n",
       "      <td>30-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chile</th>\n",
       "      <td>19116209.0</td>\n",
       "      <td>24.282</td>\n",
       "      <td>35.4</td>\n",
       "      <td>2277.633333</td>\n",
       "      <td>0.011915</td>\n",
       "      <td>62.466667</td>\n",
       "      <td>3.267733e-06</td>\n",
       "      <td>13124.900000</td>\n",
       "      <td>0.068658</td>\n",
       "      <td>161644.233333</td>\n",
       "      <td>...</td>\n",
       "      <td>73.794000</td>\n",
       "      <td>1.552816</td>\n",
       "      <td>73.577833</td>\n",
       "      <td>70.223444</td>\n",
       "      <td>1.779723</td>\n",
       "      <td>73.558833</td>\n",
       "      <td>1.668807</td>\n",
       "      <td>51.875000</td>\n",
       "      <td>1.157407</td>\n",
       "      <td>30-39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colombia</th>\n",
       "      <td>50882884.0</td>\n",
       "      <td>44.223</td>\n",
       "      <td>32.2</td>\n",
       "      <td>3377.433333</td>\n",
       "      <td>0.006638</td>\n",
       "      <td>107.577778</td>\n",
       "      <td>2.114223e-06</td>\n",
       "      <td>14439.261111</td>\n",
       "      <td>0.028377</td>\n",
       "      <td>121251.272222</td>\n",
       "      <td>...</td>\n",
       "      <td>81.323778</td>\n",
       "      <td>1.486245</td>\n",
       "      <td>86.504111</td>\n",
       "      <td>78.375556</td>\n",
       "      <td>1.254592</td>\n",
       "      <td>80.422556</td>\n",
       "      <td>1.169270</td>\n",
       "      <td>67.083333</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>30-39</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            population  population_density  median_age     new_cases  \\\n",
       "location                                                               \n",
       "Argentina   45195777.0              16.177        31.9   1976.482759   \n",
       "Bolivia     11673029.0              10.202        25.4    670.335260   \n",
       "Brazil     212559409.0              25.040        33.5  15829.143443   \n",
       "Chile       19116209.0              24.282        35.4   2277.633333   \n",
       "Colombia    50882884.0              44.223        32.2   3377.433333   \n",
       "\n",
       "           percentage_of_Pop_New_Cases  new_deaths  \\\n",
       "location                                             \n",
       "Argentina                     0.004373   41.384236   \n",
       "Bolivia                       0.005743   28.705202   \n",
       "Brazil                        0.007447  495.196721   \n",
       "Chile                         0.011915   62.466667   \n",
       "Colombia                      0.006638  107.577778   \n",
       "\n",
       "           percentage_of_Pop_New_Deaths     new_tests  \\\n",
       "location                                                \n",
       "Argentina                  9.156660e-07   5333.394089   \n",
       "Bolivia                    2.459105e-06   1340.630058   \n",
       "Brazil                     2.329686e-06      0.000000   \n",
       "Chile                      3.267733e-06  13124.900000   \n",
       "Colombia                   2.114223e-06  14439.261111   \n",
       "\n",
       "           percentage_of_Pop_New_Tests  total_cases_updated  ...  \\\n",
       "location                                                     ...   \n",
       "Argentina                     0.011801         67309.625616  ...   \n",
       "Bolivia                       0.011485         31420.289017  ...   \n",
       "Brazil                        0.000000        814776.286885  ...   \n",
       "Chile                         0.068658        161644.233333  ...   \n",
       "Colombia                      0.028377        121251.272222  ...   \n",
       "\n",
       "           StringencyIndex_updated  percentage_change_stringency  \\\n",
       "location                                                           \n",
       "Argentina                77.895764                      1.440516   \n",
       "Bolivia                  90.381676                      3.310613   \n",
       "Brazil                   53.922377                      1.442476   \n",
       "Chile                    73.794000                      1.552816   \n",
       "Colombia                 81.323778                      1.486245   \n",
       "\n",
       "           StringencyLegacyIndex_updated  GovernmentResponseIndex_updated  \\\n",
       "location                                                                    \n",
       "Argentina                      80.526502                        72.243596   \n",
       "Bolivia                        92.838613                        70.218786   \n",
       "Brazil                         55.443607                        46.095615   \n",
       "Chile                          73.577833                        70.223444   \n",
       "Colombia                       86.504111                        78.375556   \n",
       "\n",
       "           percentage_change_GovernmentResponse  \\\n",
       "location                                          \n",
       "Argentina                              1.574035   \n",
       "Bolivia                                3.372415   \n",
       "Brazil                                 1.817239   \n",
       "Chile                                  1.779723   \n",
       "Colombia                               1.254592   \n",
       "\n",
       "           ContainmentHealthIndex_updated  \\\n",
       "location                                    \n",
       "Argentina                       74.585369   \n",
       "Bolivia                         76.526879   \n",
       "Brazil                          48.516311   \n",
       "Chile                           73.558833   \n",
       "Colombia                        80.422556   \n",
       "\n",
       "           percentage_change_ContainmentHealth  EconomicSupportIndex_updated  \\\n",
       "location                                                                       \n",
       "Argentina                             1.495449                     59.359606   \n",
       "Bolivia                               3.333388                     35.549133   \n",
       "Brazil                                1.747227                     32.786885   \n",
       "Chile                                 1.668807                     51.875000   \n",
       "Colombia                              1.169270                     67.083333   \n",
       "\n",
       "           percentage_change_EconomicSupport  Age_Range  \n",
       "location                                                 \n",
       "Argentina                           0.985222      30-39  \n",
       "Bolivia                            -0.289017      20-29  \n",
       "Brazil                              0.409836      30-39  \n",
       "Chile                               1.157407      30-39  \n",
       "Colombia                            0.277778      30-39  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Categorize Age based on the bins.\n",
    "median_age_SA[(\"Age_Range\")] = pd.cut(covid_SA_df,bins, labels=group_names)\n",
    "\n",
    "median_age_SA.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a310e0eec8>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the plot.\n",
    "plt.bar(median_age_SA.Age_Range, median_age_SA[\"total_cases_updated\"], color=\"blue\", label='Total Cases Updated')\n",
    "# Create labels for the x and y axes.\n",
    "plt.xlabel(\"Age Range\")\n",
    "plt.ylabel(\"Total Cases\")\n",
    "# Create a title.\n",
    "plt.title(\"Total Cases Per Age Group in South America\")\n",
    "# Add the legend.\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PythonData",
   "language": "python",
   "name": "pythondata"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}