EDA Covid19 Jan2020_July2020_SVis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0a4482c4",
   "metadata": {},
   "source": [
    "# Global Covid 19 cases 2020 January - July"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29312b0d",
   "metadata": {},
   "source": [
    "This is a practice exploratory data analysis (EDA) on the Covid19 data from WHO for the period of January 2020 - July 2020. This data represents the pandemic's beginning and contains the number of confirmed cases, reported deaths, active cases, and recovered cases for each country. To get a more reliable estimate, I will adjust the data for newly reported cases and newly reported deaths."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87cbe1eb",
   "metadata": {},
   "source": [
    "It is important to note that the data is highly unreliable due to the inconsistency of reporting cases in many countries. The actual amounts are likely far bigger, especially in countries with bad information infrastructure."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c893339e",
   "metadata": {},
   "source": [
    "Here I check data quality, make adjustments to data structure and visualize and interpret it to my best guess."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3edeed5e",
   "metadata": {},
   "source": [
    "Data from WHO, obtained from https://www.kaggle.com/code/meeratif/covid-data-analysis-and-visualize/data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b8da61b",
   "metadata": {},
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2eb12631",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import matplotlib.gridspec as gs\n",
    "from scipy.stats import normaltest\n",
    "import warnings\n",
    "import seaborn as sns\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5521b936",
   "metadata": {},
   "source": [
    "### Importing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "dd1c8d38",
   "metadata": {},
   "outputs": [],
   "source": [
    "covid19 = pd.read_csv('covid19.csv',sep=',')\n",
    "covid19 = pd.DataFrame(covid19)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14722849",
   "metadata": {},
   "source": [
    "## Visualizing RAW data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d31f5a7",
   "metadata": {},
   "source": [
    "The first rows show the beginning of the pandemic in January, where in most countries, there were no reported cases."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "9b889810",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "      <th>Active</th>\n",
       "      <th>New cases</th>\n",
       "      <th>New deaths</th>\n",
       "      <th>New recovered</th>\n",
       "      <th>WHO Region</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Albania</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-22</td>\n",
       "      <td>Angola</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "         Date Country/Region  Confirmed  Deaths  Recovered  Active  New cases  \\\n",
       "0  2020-01-22    Afghanistan          0       0          0       0          0   \n",
       "1  2020-01-22        Albania          0       0          0       0          0   \n",
       "2  2020-01-22        Algeria          0       0          0       0          0   \n",
       "3  2020-01-22        Andorra          0       0          0       0          0   \n",
       "4  2020-01-22         Angola          0       0          0       0          0   \n",
       "\n",
       "   New deaths  New recovered             WHO Region  \n",
       "0           0              0  Eastern Mediterranean  \n",
       "1           0              0                 Europe  \n",
       "2           0              0                 Africa  \n",
       "3           0              0                 Europe  \n",
       "4           0              0                 Africa  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid19.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad4da5f4",
   "metadata": {},
   "source": [
    "The end of the data shows the reported cases in July, where most countries were already affected by the pandemic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "26b31aa6",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
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       "      <th>WHO Region</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35151</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>10621</td>\n",
       "      <td>78</td>\n",
       "      <td>3752</td>\n",
       "      <td>6791</td>\n",
       "      <td>152</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35152</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35153</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>1691</td>\n",
       "      <td>483</td>\n",
       "      <td>833</td>\n",
       "      <td>375</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35154</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>4552</td>\n",
       "      <td>140</td>\n",
       "      <td>2815</td>\n",
       "      <td>1597</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>465</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35155</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2704</td>\n",
       "      <td>36</td>\n",
       "      <td>542</td>\n",
       "      <td>2126</td>\n",
       "      <td>192</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "             Date      Country/Region  Confirmed  Deaths  Recovered  Active  \\\n",
       "35151  2020-07-27  West Bank and Gaza      10621      78       3752    6791   \n",
       "35152  2020-07-27      Western Sahara         10       1          8       1   \n",
       "35153  2020-07-27               Yemen       1691     483        833     375   \n",
       "35154  2020-07-27              Zambia       4552     140       2815    1597   \n",
       "35155  2020-07-27            Zimbabwe       2704      36        542    2126   \n",
       "\n",
       "       New cases  New deaths  New recovered             WHO Region  \n",
       "35151        152           2              0  Eastern Mediterranean  \n",
       "35152          0           0              0                 Africa  \n",
       "35153         10           4             36  Eastern Mediterranean  \n",
       "35154         71           1            465                 Africa  \n",
       "35155        192           2             24                 Africa  "
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid19.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21fc80e5",
   "metadata": {},
   "source": [
    "## Data Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "8651392c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    27138\n",
       "True      8018\n",
       "Name: Confirmed, dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# counting NULL values\n",
    "(covid19['Confirmed'] == 0).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "9aed4c3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    27138\n",
       "Name: Confirmed, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid19 = covid19[covid19['Confirmed']!=0]\n",
    "(covid19['Confirmed'] <= 0).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "ca56c40f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    27100\n",
       "True        38\n",
       "Name: New deaths, dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(covid19['New deaths'] < 0).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "40870347",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    10920\n",
       "Name: total_deaths, dtype: int64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping negative values new_deaths\n",
    "covid19[covid19['New deaths'] < 0] = 0\n",
    "covid19 = covid19[covid19['New deaths'] != 0]\n",
    "(covid19['total_deaths'] < 0).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "84c79562",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    10437\n",
       "Name: total_recovered, dtype: int64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping negative values total_recovered\n",
    "covid19[covid19['total_recovered'] < 0] = 0\n",
    "covid19 = covid19[covid19['total_recovered'] != 0]\n",
    "(covid19['total_recovered'] < 0).value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3839cca",
   "metadata": {},
   "source": [
    "### Adding newly calculated columns: total_confirmed, total_deaths, total_recovered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "59cde195",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Date</th>\n",
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       "      <th>35151</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>10621</td>\n",
       "      <td>78</td>\n",
       "      <td>3752</td>\n",
       "      <td>6791</td>\n",
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       "      <th>35152</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>Africa</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>35153</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>1691</td>\n",
       "      <td>483</td>\n",
       "      <td>833</td>\n",
       "      <td>375</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>36</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>1701</td>\n",
       "      <td>487</td>\n",
       "      <td>869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35154</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>4552</td>\n",
       "      <td>140</td>\n",
       "      <td>2815</td>\n",
       "      <td>1597</td>\n",
       "      <td>71</td>\n",
       "      <td>1</td>\n",
       "      <td>465</td>\n",
       "      <td>Africa</td>\n",
       "      <td>4623</td>\n",
       "      <td>141</td>\n",
       "      <td>3280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35155</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2704</td>\n",
       "      <td>36</td>\n",
       "      <td>542</td>\n",
       "      <td>2126</td>\n",
       "      <td>192</td>\n",
       "      <td>2</td>\n",
       "      <td>24</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2896</td>\n",
       "      <td>38</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "             Date      Country/Region  Confirmed  Deaths  Recovered  Active  \\\n",
       "35151  2020-07-27  West Bank and Gaza      10621      78       3752    6791   \n",
       "35152  2020-07-27      Western Sahara         10       1          8       1   \n",
       "35153  2020-07-27               Yemen       1691     483        833     375   \n",
       "35154  2020-07-27              Zambia       4552     140       2815    1597   \n",
       "35155  2020-07-27            Zimbabwe       2704      36        542    2126   \n",
       "\n",
       "       New cases  New deaths  New recovered             WHO Region  \\\n",
       "35151        152           2              0  Eastern Mediterranean   \n",
       "35152          0           0              0                 Africa   \n",
       "35153         10           4             36  Eastern Mediterranean   \n",
       "35154         71           1            465                 Africa   \n",
       "35155        192           2             24                 Africa   \n",
       "\n",
       "       total_confirmed  total_deaths  total_recovered  \n",
       "35151            10773            80             3752  \n",
       "35152               10             1                8  \n",
       "35153             1701           487              869  \n",
       "35154             4623           141             3280  \n",
       "35155             2896            38              566  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# total confirmed cases\n",
    "covid19['total_confirmed'] = covid19['Confirmed'] + covid19['New cases']\n",
    "covid19.tail()\n",
    "\n",
    "# total deaths\n",
    "covid19['total_deaths'] = covid19['Deaths'] + covid19['New deaths']\n",
    "covid19.tail()\n",
    "\n",
    "# total recovered\n",
    "covid19['total_recovered'] = covid19['Recovered'] + covid19['New recovered']\n",
    "covid19.tail()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa5820fe",
   "metadata": {},
   "source": [
    "#### Dropping unnecessary columns: Confirmed, Deaths, Recovered, New cases, New deaths, New recovered"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "ff8a3e7b",
   "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>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35149</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>5883</td>\n",
       "      <td>Americas</td>\n",
       "      <td>16513</td>\n",
       "      <td>150</td>\n",
       "      <td>10172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35151</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>6791</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>10773</td>\n",
       "      <td>80</td>\n",
       "      <td>3752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35153</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>375</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>1701</td>\n",
       "      <td>487</td>\n",
       "      <td>869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35154</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>1597</td>\n",
       "      <td>Africa</td>\n",
       "      <td>4623</td>\n",
       "      <td>141</td>\n",
       "      <td>3280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35155</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2126</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2896</td>\n",
       "      <td>38</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date      Country/Region  Active             WHO Region  \\\n",
       "35149  2020-07-27           Venezuela    5883               Americas   \n",
       "35151  2020-07-27  West Bank and Gaza    6791  Eastern Mediterranean   \n",
       "35153  2020-07-27               Yemen     375  Eastern Mediterranean   \n",
       "35154  2020-07-27              Zambia    1597                 Africa   \n",
       "35155  2020-07-27            Zimbabwe    2126                 Africa   \n",
       "\n",
       "       total_confirmed  total_deaths  total_recovered  \n",
       "35149            16513           150            10172  \n",
       "35151            10773            80             3752  \n",
       "35153             1701           487              869  \n",
       "35154             4623           141             3280  \n",
       "35155             2896            38              566  "
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid19 = covid19.drop(columns=['Confirmed','Deaths','Recovered','New cases','New deaths','New recovered'])\n",
    "covid19.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04dfe314",
   "metadata": {},
   "source": [
    "### Visualizing the Top 5 countries with the most confirmed cases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "231d7bff",
   "metadata": {},
   "source": [
    "The top 5 least confirmed cases are closer to 0 and are not a good visualization in comparison to the top 5 most confirmed cases. I have added them in a table just to show how little it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "id": "5667cfef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>US</td>\n",
       "      <td>228635763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>91966267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Russia</td>\n",
       "      <td>46219554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>India</td>\n",
       "      <td>42357869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>26943962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country/Region  total_confirmed\n",
       "152              US        228635763\n",
       "21           Brazil         91966267\n",
       "124          Russia         46219554\n",
       "68            India         42357869\n",
       "156  United Kingdom         26943962"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed = covid19.iloc[:,[1,4]].reset_index()\n",
    "confirmed = confirmed.drop(columns=['index'])\n",
    "\n",
    "confirmed = confirmed.sort_values(by=\"total_confirmed\",ascending=False) # sort by total confirmed amount\n",
    "confirmed2 = confirmed.groupby(['Country/Region']).agg('sum').reset_index() # sum confirmed cases for each country\n",
    "confirmed2.tail()\n",
    "confirmed2 = confirmed2.sort_values(by=\"total_confirmed\",ascending=False) # sort sum confirmed cases for each country\n",
    "confirmed.head() #check old df\n",
    "confirmed2.head() # check new df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "id": "c87b262e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>Monaco</td>\n",
       "      <td>237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88</th>\n",
       "      <td>Liechtenstein</td>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161</th>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country/Region  total_confirmed\n",
       "101               Monaco              237\n",
       "88         Liechtenstein              160\n",
       "5    Antigua and Barbuda               23\n",
       "116     Papua New Guinea               22\n",
       "161       Western Sahara                9"
      ]
     },
     "execution_count": 478,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Top 5 countries with the least confirmed cases\n",
    "confirmed2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "id": "799655bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>US</td>\n",
       "      <td>228.635763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>91.966267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>Russia</td>\n",
       "      <td>46.219554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>India</td>\n",
       "      <td>42.357869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>26.943962</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Country/Region  total_confirmed\n",
       "152              US       228.635763\n",
       "21           Brazil        91.966267\n",
       "124          Russia        46.219554\n",
       "68            India        42.357869\n",
       "156  United Kingdom        26.943962"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confirmed2['total_confirmed'] = confirmed2['total_confirmed']/1000000 # divide by million for better visualization\n",
    "confirmed2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f5f554c",
   "metadata": {},
   "source": [
    "## Visualisation top 5 countries with the most confirmed cases"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 481,
   "id": "3b043001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# top 5 countries with the most confirmed cases in the 2020 report\n",
    "top_5 = confirmed2.head(5)\n",
    "fig= plt.figure(figsize=(15,8))\n",
    "g = gs.GridSpec(ncols=1, nrows=2, figure=fig)\n",
    "plt.suptitle(\"Top 5 countries with confirmed Covid19 cases\", family='Serif', weight='bold', size=20)\n",
    "ax1 = plt.subplot(g[0,0])\n",
    "\n",
    "sns.set_theme(style=\"whitegrid\")\n",
    "barplot_top_5 = sns.barplot(x='total_confirmed', y='Country/Region', data=top_5, color=color[5])\n",
    "\n",
    "for p in barplot_top_5.patches:\n",
    "    width = p.get_width() # bar length\n",
    "    barplot_top_5.text(width + 1, # 1 unit right of the bar\n",
    "            p.get_y() + p.get_height() / 2, # Y + X  / 2\n",
    "            '{:0.2f}'.format(width), \n",
    "            ha = 'left', \n",
    "            va = 'center')  \n",
    "\n",
    "ax1.set_xlabel('Confirmed Covid19 cases in millions',fontsize=15)\n",
    "ax1.set_ylabel('Country',fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b3427a6",
   "metadata": {},
   "source": [
    "The US and Brasil both have the most reported confirmed cases. It is important to note that reported confirmed cases have not been accounted for population size or population density. As third reliability is the variable for how likely the country reported accurately. It is likely that for many countries, cases were often under-reported. Especially in countries where testing to confirm cases is not a standard procedure due to finance, infrastructure, and other variables."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d334ad2",
   "metadata": {},
   "source": [
    "## Group by WHO regions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c85b1d75",
   "metadata": {},
   "source": [
    "- All countries were grouped by region. WHO regions were compared in terms of confirmed cases, amount of deaths, and recovered.  - The reported cases were summed to a total for each region. \n",
    "- Cases are shown in millions for better visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "id": "51c45827",
   "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>WHO Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>18.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Americas</td>\n",
       "      <td>398.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>71.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>224.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>55.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>13.11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              WHO Region  total_confirmed\n",
       "0                 Africa            18.11\n",
       "1               Americas           398.47\n",
       "2  Eastern Mediterranean            71.14\n",
       "3                 Europe           224.74\n",
       "4        South-East Asia            55.96\n",
       "5        Western Pacific            13.11"
      ]
     },
     "execution_count": 447,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total confirmed cases for each WHO region\n",
    "confirmed_cases = covid19.groupby('WHO Region').total_confirmed.sum().reset_index()\n",
    "confirmed_cases = pd.DataFrame(confirmed_cases)\n",
    "confirmed_cases['total_confirmed'] = confirmed_cases['total_confirmed']/1000000\n",
    "confirmed_cases.round(2).head(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "37449ae9",
   "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>WHO Region</th>\n",
       "      <th>total_deaths</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Americas</td>\n",
       "      <td>18.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>17.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>1.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>0.46</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              WHO Region  total_deaths\n",
       "0                 Africa          0.37\n",
       "1               Americas         18.84\n",
       "2  Eastern Mediterranean          1.90\n",
       "3                 Europe         17.26\n",
       "4        South-East Asia          1.49\n",
       "5        Western Pacific          0.46"
      ]
     },
     "execution_count": 448,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total deaths for each WHO region\n",
    "deaths = covid19.groupby('WHO Region').total_deaths.sum().reset_index()\n",
    "deaths = pd.DataFrame(deaths)\n",
    "deaths['total_deaths'] = deaths['total_deaths']/1000000\n",
    "deaths.round(2).head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f159204e",
   "metadata": {},
   "source": [
    "Again, shown in millions for better visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 445,
   "id": "b92caa03",
   "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>WHO Region</th>\n",
       "      <th>total_recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Africa</td>\n",
       "      <td>9.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Americas</td>\n",
       "      <td>159.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>46.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Europe</td>\n",
       "      <td>111.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>30.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              WHO Region  total_recovered\n",
       "0                 Africa             9.30\n",
       "1               Americas           159.71\n",
       "2  Eastern Mediterranean            46.57\n",
       "3                 Europe           111.65\n",
       "4        South-East Asia            30.53"
      ]
     },
     "execution_count": 445,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total recovered for each WHO region\n",
    "recovered = covid19.groupby('WHO Region').total_recovered.sum().reset_index()\n",
    "recovered = pd.DataFrame(recovered)\n",
    "recovered['total_recovered'] = recovered['total_recovered']/1000000\n",
    "recovered.round(2).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3d8698f",
   "metadata": {},
   "source": [
    "## Proportion confirmed cases by WHO region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "id": "f2f0f92b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Proportion confirmed cases\n",
    "plt.figure(figsize=(10,6))\n",
    "colors = sns.color_palette(\"Pastel1\")\n",
    "\n",
    "ax1 = plt.subplot(1,2,1)\n",
    "who_region = confirmed_cases['WHO Region']\n",
    "plt.pie(confirmed_cases['total_confirmed'],autopct='%0.1f%%',labels=who_region, colors=colors)\n",
    "plt.axis('equal')\n",
    "plt.title('Confirmed cases proportion WHO regions',fontsize=15)\n",
    "plt.show()\n",
    "\n",
    "# Proportion deaths\n",
    "plt.figure(figsize=(10,6))\n",
    "ax2 = plt.subplot(1,2,2)\n",
    "plt.pie(deaths['total_deaths'],autopct='%0.1f%%',labels=who_region, colors=colors)\n",
    "plt.axis('equal')\n",
    "plt.title('Total deaths proportion WHO regions',fontsize=15)\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Proportion recovered\n",
    "plt.figure(figsize=(10,6))\n",
    "ax3 = plt.subplot(1,2,2)\n",
    "ax3.pie(recovered['total_recovered'],autopct='%0.1f%%',labels=who_region, colors=colors)\n",
    "ax3.axis('equal')\n",
    "plt.title('Total recovered proportion WHO regions',fontsize=15)\n",
    "\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab33672",
   "metadata": {},
   "source": [
    "# Observing data for each region"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09e9fedf",
   "metadata": {},
   "source": [
    "## Africa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "f24dc1c0",
   "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>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35115</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>3093</td>\n",
       "      <td>Africa</td>\n",
       "      <td>9847</td>\n",
       "      <td>197</td>\n",
       "      <td>6545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35123</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>South Africa</td>\n",
       "      <td>170537</td>\n",
       "      <td>Africa</td>\n",
       "      <td>459625</td>\n",
       "      <td>7365</td>\n",
       "      <td>284773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35125</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>1084</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2348</td>\n",
       "      <td>47</td>\n",
       "      <td>1175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35154</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>1597</td>\n",
       "      <td>Africa</td>\n",
       "      <td>4623</td>\n",
       "      <td>141</td>\n",
       "      <td>3280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35155</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2126</td>\n",
       "      <td>Africa</td>\n",
       "      <td>2896</td>\n",
       "      <td>38</td>\n",
       "      <td>566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Country/Region  Active WHO Region  total_confirmed  \\\n",
       "35115  2020-07-27        Senegal    3093     Africa             9847   \n",
       "35123  2020-07-27   South Africa  170537     Africa           459625   \n",
       "35125  2020-07-27    South Sudan    1084     Africa             2348   \n",
       "35154  2020-07-27         Zambia    1597     Africa             4623   \n",
       "35155  2020-07-27       Zimbabwe    2126     Africa             2896   \n",
       "\n",
       "       total_deaths  total_recovered  \n",
       "35115           197             6545  \n",
       "35123          7365           284773  \n",
       "35125            47             1175  \n",
       "35154           141             3280  \n",
       "35155            38              566  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Africa\n",
    "africa = covid19.loc[covid19['WHO Region'] == 'Africa']\n",
    "africa = pd.DataFrame(africa)\n",
    "africa.tail(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "0a57d116",
   "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>Active</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1483.0</td>\n",
       "      <td>1483.0</td>\n",
       "      <td>1483.0</td>\n",
       "      <td>1483.0</td>\n",
       "      <td>1483.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5532.0</td>\n",
       "      <td>12215.0</td>\n",
       "      <td>253.0</td>\n",
       "      <td>6269.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19542.0</td>\n",
       "      <td>45085.0</td>\n",
       "      <td>708.0</td>\n",
       "      <td>24705.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>359.0</td>\n",
       "      <td>744.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1304.0</td>\n",
       "      <td>2272.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>798.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3096.0</td>\n",
       "      <td>7262.0</td>\n",
       "      <td>155.0</td>\n",
       "      <td>3371.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>173590.0</td>\n",
       "      <td>459625.0</td>\n",
       "      <td>7365.0</td>\n",
       "      <td>284773.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Active  total_confirmed  total_deaths  total_recovered   month\n",
       "count    1483.0           1483.0        1483.0           1483.0  1483.0\n",
       "mean     5532.0          12215.0         253.0           6269.0     6.0\n",
       "std     19542.0          45085.0         708.0          24705.0     1.0\n",
       "min         2.0              5.0           2.0              1.0     3.0\n",
       "25%       359.0            744.0          20.0            200.0     5.0\n",
       "50%      1304.0           2272.0          52.0            798.0     6.0\n",
       "75%      3096.0           7262.0         155.0           3371.0     7.0\n",
       "max    173590.0         459625.0        7365.0         284773.0     7.0"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "africa['Date'] = pd.to_datetime(africa['Date'])\n",
    "# Split date, create column month\n",
    "\n",
    "africa['month'] = africa['Date'].dt.month\n",
    "africa.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "id": "98cadd18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "ax7 = plt.subplot(1,3,1)\n",
    "ax7.hist(africa['total_confirmed'],bins=100,color='maroon')\n",
    "ax7.set_xlabel('Confirmed cases',fontsize=16)\n",
    "ax7.set_ylabel('Count',fontsize=16)\n",
    "plt.xlim(xmin=0, xmax = 45000)\n",
    "plt.ylim(ymin=0, ymax = 1200)\n",
    "ax7.set_title('Count of confirmed cases for Africa',fontsize=18)\n",
    "\n",
    "ax8 = plt.subplot(1,3,2)\n",
    "ax8.hist(africa['total_deaths'],bins=100,color='maroon')\n",
    "ax8.set_xlabel('Deaths',fontsize=16)\n",
    "ax8.set_ylabel('Count',fontsize=16)\n",
    "plt.xlim(xmin=0, xmax = 1200)\n",
    "plt.ylim(ymin=0, ymax = 1200)\n",
    "ax8.set_title('Count of Deaths for Africa',fontsize=18)\n",
    "\n",
    "ax9 = plt.subplot(1,3,3)\n",
    "ax9.hist(africa['total_recovered'],bins=100,color='maroon')\n",
    "ax9.set_xlabel('Recovered',fontsize=16)\n",
    "ax9.set_ylabel('Count',fontsize=16)\n",
    "plt.xlim(xmin=0, xmax = 20000)\n",
    "plt.ylim(ymin=0, ymax = 1200)\n",
    "ax9.set_title('Count of recovered for Africa',fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e824137f",
   "metadata": {},
   "source": [
    "### D'Agostino and Pearson's Test for Africa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "7308fd53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=1898.689, p=0.000\n",
      "Sample does not look Gaussian (reject H0)\n"
     ]
    }
   ],
   "source": [
    "# Normality test, no normality expected\n",
    "\n",
    "stat, p = normaltest(africa['total_confirmed'])\n",
    "print('Statistics=%.3f, p=%.3f' % (stat,p))\n",
    "# interpret results\n",
    "alpha = 0.5\n",
    "if p >= alpha:\n",
    "    print('Sample looks Gaussian (fail to reject H0)')\n",
    "else:\n",
    "    print('Sample does not look Gaussian (reject H0)')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51d81a0e",
   "metadata": {},
   "source": [
    "## Americas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "a94af5da",
   "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>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7279</th>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>US</td>\n",
       "      <td>16</td>\n",
       "      <td>Americas</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7653</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>US</td>\n",
       "      <td>40</td>\n",
       "      <td>Americas</td>\n",
       "      <td>76</td>\n",
       "      <td>11</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7840</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>US</td>\n",
       "      <td>59</td>\n",
       "      <td>Americas</td>\n",
       "      <td>93</td>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8027</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>US</td>\n",
       "      <td>86</td>\n",
       "      <td>Americas</td>\n",
       "      <td>135</td>\n",
       "      <td>15</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8214</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>US</td>\n",
       "      <td>155</td>\n",
       "      <td>Americas</td>\n",
       "      <td>244</td>\n",
       "      <td>13</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date Country/Region  Active WHO Region  total_confirmed  \\\n",
       "7279  2020-02-29             US      16   Americas               32   \n",
       "7653  2020-03-02             US      40   Americas               76   \n",
       "7840  2020-03-03             US      59   Americas               93   \n",
       "8027  2020-03-04             US      86   Americas              135   \n",
       "8214  2020-03-05             US     155   Americas              244   \n",
       "\n",
       "      total_deaths  total_recovered  \n",
       "7279             2                7  \n",
       "7653            11                7  \n",
       "7840             8                7  \n",
       "8027            15                7  \n",
       "8214            13                7  "
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Americas\n",
    "americas = covid19.loc[covid19['WHO Region'] == 'Americas']\n",
    "americas = pd.DataFrame(americas)\n",
    "americas.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "0360f028",
   "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>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35101</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Peru</td>\n",
       "      <td>98752</td>\n",
       "      <td>Americas</td>\n",
       "      <td>403473</td>\n",
       "      <td>18993</td>\n",
       "      <td>277244</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35129</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>534</td>\n",
       "      <td>Americas</td>\n",
       "      <td>1527</td>\n",
       "      <td>25</td>\n",
       "      <td>960</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35142</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>US</td>\n",
       "      <td>2816444</td>\n",
       "      <td>Americas</td>\n",
       "      <td>4346595</td>\n",
       "      <td>149087</td>\n",
       "      <td>1353745</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35147</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>216</td>\n",
       "      <td>Americas</td>\n",
       "      <td>1212</td>\n",
       "      <td>36</td>\n",
       "      <td>954</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35149</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>5883</td>\n",
       "      <td>Americas</td>\n",
       "      <td>16513</td>\n",
       "      <td>150</td>\n",
       "      <td>10172</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date Country/Region   Active WHO Region  total_confirmed  \\\n",
       "35101 2020-07-27           Peru    98752   Americas           403473   \n",
       "35129 2020-07-27       Suriname      534   Americas             1527   \n",
       "35142 2020-07-27             US  2816444   Americas          4346595   \n",
       "35147 2020-07-27        Uruguay      216   Americas             1212   \n",
       "35149 2020-07-27      Venezuela     5883   Americas            16513   \n",
       "\n",
       "       total_deaths  total_recovered  month  \n",
       "35101         18993           277244      7  \n",
       "35129            25              960      7  \n",
       "35142        149087          1353745      7  \n",
       "35147            36              954      7  \n",
       "35149           150            10172      7  "
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "americas['Date'] = pd.to_datetime(americas['Date'])\n",
    "# Split date, create column month\n",
    "\n",
    "americas['month'] = americas['Date'].dt.month\n",
    "americas.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "7bd86bd9",
   "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>Active</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1993.0</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>1993.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>108380.0</td>\n",
       "      <td>199935.0</td>\n",
       "      <td>9451.0</td>\n",
       "      <td>80136.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>351827.0</td>\n",
       "      <td>570821.0</td>\n",
       "      <td>26025.0</td>\n",
       "      <td>227131.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1993.0</td>\n",
       "      <td>2950.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>487.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7831.0</td>\n",
       "      <td>14321.0</td>\n",
       "      <td>435.0</td>\n",
       "      <td>3574.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>29121.0</td>\n",
       "      <td>70681.0</td>\n",
       "      <td>3092.0</td>\n",
       "      <td>29960.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2816444.0</td>\n",
       "      <td>4346595.0</td>\n",
       "      <td>149087.0</td>\n",
       "      <td>1880369.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Active  total_confirmed  total_deaths  total_recovered   month\n",
       "count     1993.0           1993.0        1993.0           1993.0  1993.0\n",
       "mean    108380.0         199935.0        9451.0          80136.0     5.0\n",
       "std     351827.0         570821.0       26025.0         227131.0     1.0\n",
       "min          1.0              9.0           2.0              1.0     2.0\n",
       "25%       1993.0           2950.0          70.0            487.0     4.0\n",
       "50%       7831.0          14321.0         435.0           3574.0     5.0\n",
       "75%      29121.0          70681.0        3092.0          29960.0     6.0\n",
       "max    2816444.0        4346595.0      149087.0        1880369.0     7.0"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "americas.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "id": "90dfcb2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "ax10 = plt.subplot(1,3,1)\n",
    "ax10.hist(americas['total_confirmed'],bins='auto',color='#f9c74f')\n",
    "ax10.set_xlabel('Confirmed cases',fontsize=16)\n",
    "ax10.set_ylabel('Count',fontsize=16)\n",
    "plt.xlim(xmin=0, xmax = 400000)\n",
    "plt.ylim(ymin=0, ymax = 1200)\n",
    "ax10.set_title('Confirmed cases in Americas',fontsize=18)\n",
    "\n",
    "ax11 = plt.subplot(1,3,2)\n",
    "ax11.hist(americas['total_deaths'],bins='auto',color='#f9c74f')\n",
    "ax11.set_xlabel('Deaths',fontsize=16)\n",
    "ax11.set_ylabel('Count',fontsize=16)\n",
    "plt.xlim(xmin=0, xmax = 10000)\n",
    "plt.ylim(ymin=0, ymax = 1200)\n",
    "ax11.set_title('Deaths in Americas',fontsize=18)\n",
    "\n",
    "\n",
    "ax12 = plt.subplot(1,3,3)\n",
    "ax12.hist(americas['total_recovered'],bins='auto',color='#f9c74f')\n",
    "ax12.set_xlabel('Recovered',fontsize=16)\n",
    "plt.xlim(xmin=0, xmax = 80000)\n",
    "plt.ylim(ymin=0, ymax = 1200)\n",
    "ax12.set_ylabel('Count',fontsize=16)\n",
    "\n",
    "ax12.set_title('Recovered in Americas',fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "046d1c37",
   "metadata": {},
   "source": [
    "### D'Agostino and Pearson's Test for Americas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec4c9d3a",
   "metadata": {},
   "source": [
    "As epected, the reported confirmed cases do not follow have a gaussian distribution. All cases have a strong right skew as they are correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "757ab6b1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=1720.470, p=0.000\n",
      "Sample does not look Gaussian (reject H0)\n"
     ]
    }
   ],
   "source": [
    "# Normality test, no normality expected\n",
    "\n",
    "stat, p = normaltest(americas['total_confirmed'])\n",
    "print('Statistics=%.3f, p=%.3f' % (stat,p))\n",
    "# interpret results\n",
    "alpha = 0.5\n",
    "if p >= alpha:\n",
    "    print('Sample looks Gaussian (fail to reject H0)')\n",
    "else:\n",
    "    print('Sample does not look Gaussian (reject H0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "800fd166",
   "metadata": {},
   "source": [
    "## Eastern Mediterranean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "ca7b8ac6",
   "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>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35114</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>43238</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>270927</td>\n",
       "      <td>2787</td>\n",
       "      <td>225549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35128</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>4765</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>11463</td>\n",
       "      <td>723</td>\n",
       "      <td>5988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35145</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>6322</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>59441</td>\n",
       "      <td>346</td>\n",
       "      <td>52838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35151</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>6791</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>10773</td>\n",
       "      <td>80</td>\n",
       "      <td>3752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35153</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>375</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>1701</td>\n",
       "      <td>487</td>\n",
       "      <td>869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date        Country/Region  Active             WHO Region  \\\n",
       "35114  2020-07-27          Saudi Arabia   43238  Eastern Mediterranean   \n",
       "35128  2020-07-27                 Sudan    4765  Eastern Mediterranean   \n",
       "35145  2020-07-27  United Arab Emirates    6322  Eastern Mediterranean   \n",
       "35151  2020-07-27    West Bank and Gaza    6791  Eastern Mediterranean   \n",
       "35153  2020-07-27                 Yemen     375  Eastern Mediterranean   \n",
       "\n",
       "       total_confirmed  total_deaths  total_recovered  \n",
       "35114           270927          2787           225549  \n",
       "35128            11463           723             5988  \n",
       "35145            59441           346            52838  \n",
       "35151            10773            80             3752  \n",
       "35153             1701           487              869  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Eastern Mediterranean\n",
    "mediterranean = covid19.loc[covid19['WHO Region'] == 'Eastern Mediterranean']\n",
    "mediterranean = pd.DataFrame(mediterranean)\n",
    "mediterranean.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "1f4c0472",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35114</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>43238</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>270927</td>\n",
       "      <td>2787</td>\n",
       "      <td>225549</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35128</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Sudan</td>\n",
       "      <td>4765</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>11463</td>\n",
       "      <td>723</td>\n",
       "      <td>5988</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35145</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>6322</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>59441</td>\n",
       "      <td>346</td>\n",
       "      <td>52838</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35151</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>6791</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>10773</td>\n",
       "      <td>80</td>\n",
       "      <td>3752</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35153</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>375</td>\n",
       "      <td>Eastern Mediterranean</td>\n",
       "      <td>1701</td>\n",
       "      <td>487</td>\n",
       "      <td>869</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date        Country/Region  Active             WHO Region  \\\n",
       "35114 2020-07-27          Saudi Arabia   43238  Eastern Mediterranean   \n",
       "35128 2020-07-27                 Sudan    4765  Eastern Mediterranean   \n",
       "35145 2020-07-27  United Arab Emirates    6322  Eastern Mediterranean   \n",
       "35151 2020-07-27    West Bank and Gaza    6791  Eastern Mediterranean   \n",
       "35153 2020-07-27                 Yemen     375  Eastern Mediterranean   \n",
       "\n",
       "       total_confirmed  total_deaths  total_recovered  month  \n",
       "35114           270927          2787           225549      7  \n",
       "35128            11463           723             5988      7  \n",
       "35145            59441           346            52838      7  \n",
       "35151            10773            80             3752      7  \n",
       "35153             1701           487              869      7  "
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mediterranean['Date'] = pd.to_datetime(mediterranean['Date'])\n",
    "# Split date, create column month\n",
    "\n",
    "mediterranean['month'] = mediterranean['Date'].dt.month\n",
    "mediterranean.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "fd70b9fa",
   "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>Active</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1680.0</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1680.0</td>\n",
       "      <td>1680.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13367.0</td>\n",
       "      <td>42347.0</td>\n",
       "      <td>1133.0</td>\n",
       "      <td>27719.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18339.0</td>\n",
       "      <td>63678.0</td>\n",
       "      <td>2434.0</td>\n",
       "      <td>49571.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1322.0</td>\n",
       "      <td>2253.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>559.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5638.0</td>\n",
       "      <td>14240.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>4662.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18730.0</td>\n",
       "      <td>53380.0</td>\n",
       "      <td>662.0</td>\n",
       "      <td>30692.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>108642.0</td>\n",
       "      <td>296040.0</td>\n",
       "      <td>16124.0</td>\n",
       "      <td>257075.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Active  total_confirmed  total_deaths  total_recovered   month\n",
       "count    1680.0           1680.0        1680.0           1680.0  1680.0\n",
       "mean    13367.0          42347.0        1133.0          27719.0     5.0\n",
       "std     18339.0          63678.0        2434.0          49571.0     1.0\n",
       "min         3.0              6.0           2.0              1.0     2.0\n",
       "25%      1322.0           2253.0          50.0            559.0     4.0\n",
       "50%      5638.0          14240.0         198.0           4662.0     6.0\n",
       "75%     18730.0          53380.0         662.0          30692.0     6.0\n",
       "max    108642.0         296040.0       16124.0         257075.0     7.0"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mediterranean.describe().round()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb56152e",
   "metadata": {},
   "source": [
    "### Visualization of distribution of confirmed cases, deaths and recovered for Eastern Mediterranean."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ae6c8b9",
   "metadata": {},
   "source": [
    "- As expected most reported confirmed cases were under 50.000. \n",
    "- There are peaks above 50.000 that would suggest the pandemic's peak for the period of January to July. \n",
    "- The stability of reported cases for larger reported counts indicates that not all countries experienced the pandemic's peak as severely or simultaneously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "id": "dfee6340",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "ax13 = plt.subplot(1,3,1)\n",
    "ax13.hist(mediterranean['total_confirmed'],bins='auto',color='goldenrod')\n",
    "ax13.set_xlabel('Confirmed cases',fontsize=16)\n",
    "ax13.set_ylabel('Count',fontsize=16)\n",
    "plt.ylim(ymin=0, ymax = 1000)\n",
    "plt.xlim(xmin=0, xmax = 300000)\n",
    "ax13.set_title('Confirmed cases in Eastern Mediterranean',fontsize=18)\n",
    "\n",
    "ax14 = plt.subplot(1,3,2)\n",
    "ax14.hist(mediterranean['total_deaths'],bins='auto',color='goldenrod')\n",
    "ax14.set_xlabel('Deaths',fontsize=16)\n",
    "ax14.set_ylabel('Count',fontsize=16)\n",
    "plt.xlim(xmin=0, xmax = 5000)\n",
    "plt.ylim(ymin=0, ymax = 1000)\n",
    "ax14.set_title('Deaths in Eastern Mediterranean',fontsize=18)\n",
    "\n",
    "\n",
    "ax15 = plt.subplot(1,3,3)\n",
    "ax15.hist(mediterranean['total_recovered'],bins='auto',color='goldenrod')\n",
    "ax15.set_xlabel('Recovered',fontsize=16)\n",
    "ax15.set_ylabel('Count',fontsize=16)\n",
    "plt.ylim(ymin=0, ymax = 1000)\n",
    "plt.xlim(xmin=0, xmax = 250000)\n",
    "ax15.set_title('Recovered in Eastern Mediterranean',fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47104a14",
   "metadata": {},
   "source": [
    "### D'Agostino and Pearson's Test for Eastern Mediterranean"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11b65373",
   "metadata": {},
   "source": [
    "As epected, the reported confirmed cases do not follow have a gaussian distribution. All cases have a strong right skew as they are correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "99c2228b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=712.183, p=0.000\n",
      "Sample does not look Gaussian (reject H0)\n"
     ]
    }
   ],
   "source": [
    "# Normality test, no normality expected\n",
    "\n",
    "stat, p = normaltest(mediterranean['total_confirmed'])\n",
    "print('Statistics=%.3f, p=%.3f' % (stat,p))\n",
    "# interpret results\n",
    "alpha = 0.5\n",
    "if p >= alpha:\n",
    "    print('Sample looks Gaussian (fail to reject H0)')\n",
    "else:\n",
    "    print('Sample does not look Gaussian (reject H0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee3d51e9",
   "metadata": {},
   "source": [
    "## Europe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "4f21e98b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35134</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>1147</td>\n",
       "      <td>Europe</td>\n",
       "      <td>7278</td>\n",
       "      <td>61</td>\n",
       "      <td>6086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35141</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>10920</td>\n",
       "      <td>Europe</td>\n",
       "      <td>227938</td>\n",
       "      <td>5647</td>\n",
       "      <td>211451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35144</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>28258</td>\n",
       "      <td>Europe</td>\n",
       "      <td>67931</td>\n",
       "      <td>1647</td>\n",
       "      <td>37519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35146</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>254427</td>\n",
       "      <td>Europe</td>\n",
       "      <td>302396</td>\n",
       "      <td>45851</td>\n",
       "      <td>1440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35148</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>9414</td>\n",
       "      <td>Europe</td>\n",
       "      <td>21887</td>\n",
       "      <td>126</td>\n",
       "      <td>12243</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date  Country/Region  Active WHO Region  total_confirmed  \\\n",
       "35134  2020-07-27      Tajikistan    1147     Europe             7278   \n",
       "35141  2020-07-27          Turkey   10920     Europe           227938   \n",
       "35144  2020-07-27         Ukraine   28258     Europe            67931   \n",
       "35146  2020-07-27  United Kingdom  254427     Europe           302396   \n",
       "35148  2020-07-27      Uzbekistan    9414     Europe            21887   \n",
       "\n",
       "       total_deaths  total_recovered  \n",
       "35134            61             6086  \n",
       "35141          5647           211451  \n",
       "35144          1647            37519  \n",
       "35146         45851             1440  \n",
       "35148           126            12243  "
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Europe\n",
    "europe = covid19.loc[covid19['WHO Region'] == 'Europe']\n",
    "europe = pd.DataFrame(europe)\n",
    "europe.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "de086de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35134</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>1147</td>\n",
       "      <td>Europe</td>\n",
       "      <td>7278</td>\n",
       "      <td>61</td>\n",
       "      <td>6086</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35141</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Turkey</td>\n",
       "      <td>10920</td>\n",
       "      <td>Europe</td>\n",
       "      <td>227938</td>\n",
       "      <td>5647</td>\n",
       "      <td>211451</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35144</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>28258</td>\n",
       "      <td>Europe</td>\n",
       "      <td>67931</td>\n",
       "      <td>1647</td>\n",
       "      <td>37519</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35146</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>254427</td>\n",
       "      <td>Europe</td>\n",
       "      <td>302396</td>\n",
       "      <td>45851</td>\n",
       "      <td>1440</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35148</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Uzbekistan</td>\n",
       "      <td>9414</td>\n",
       "      <td>Europe</td>\n",
       "      <td>21887</td>\n",
       "      <td>126</td>\n",
       "      <td>12243</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Country/Region  Active WHO Region  total_confirmed  \\\n",
       "35134 2020-07-27      Tajikistan    1147     Europe             7278   \n",
       "35141 2020-07-27          Turkey   10920     Europe           227938   \n",
       "35144 2020-07-27         Ukraine   28258     Europe            67931   \n",
       "35146 2020-07-27  United Kingdom  254427     Europe           302396   \n",
       "35148 2020-07-27      Uzbekistan    9414     Europe            21887   \n",
       "\n",
       "       total_deaths  total_recovered  month  \n",
       "35134            61             6086      7  \n",
       "35141          5647           211451      7  \n",
       "35144          1647            37519      7  \n",
       "35146         45851             1440      7  \n",
       "35148           126            12243      7  "
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "europe['Date'] = pd.to_datetime(europe['Date'])\n",
    "# Split date, create column month\n",
    "\n",
    "europe['month'] = europe['Date'].dt.month\n",
    "europe.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "aca75818",
   "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>Active</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4164.0</td>\n",
       "      <td>4164.0</td>\n",
       "      <td>4164.0</td>\n",
       "      <td>4164.0</td>\n",
       "      <td>4164.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22788.0</td>\n",
       "      <td>53971.0</td>\n",
       "      <td>4144.0</td>\n",
       "      <td>26813.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>47219.0</td>\n",
       "      <td>101872.0</td>\n",
       "      <td>9211.0</td>\n",
       "      <td>64272.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1147.0</td>\n",
       "      <td>2987.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>492.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4934.0</td>\n",
       "      <td>12766.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>3432.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>17254.0</td>\n",
       "      <td>44870.0</td>\n",
       "      <td>1748.0</td>\n",
       "      <td>17316.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>254427.0</td>\n",
       "      <td>822287.0</td>\n",
       "      <td>45885.0</td>\n",
       "      <td>605326.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Active  total_confirmed  total_deaths  total_recovered   month\n",
       "count    4164.0           4164.0        4164.0           4164.0  4164.0\n",
       "mean    22788.0          53971.0        4144.0          26813.0     5.0\n",
       "std     47219.0         101872.0        9211.0          64272.0     1.0\n",
       "min        -1.0             13.0           2.0              1.0     2.0\n",
       "25%      1147.0           2987.0          81.0            492.0     4.0\n",
       "50%      4934.0          12766.0         329.0           3432.0     5.0\n",
       "75%     17254.0          44870.0        1748.0          17316.0     6.0\n",
       "max    254427.0         822287.0       45885.0         605326.0     7.0"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "europe.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "5a27e96b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "ax16 = plt.subplot(1,3,1)\n",
    "ax16.hist(europe['total_confirmed'],bins='auto',color='steelblue')\n",
    "ax16.set_xlabel('Confirmed cases',fontsize=16)\n",
    "ax16.set_ylabel('Count',fontsize=16)\n",
    "plt.xlim(xmin=0, xmax = 300000)\n",
    "plt.ylim(ymin=0, ymax = 2000)\n",
    "ax16.set_title('Confirmed cases in Europe',fontsize=18)\n",
    "\n",
    "ax17 = plt.subplot(1,3,2)\n",
    "ax17.hist(europe['total_deaths'],bins='auto',color='steelblue')\n",
    "ax17.set_xlabel('Deaths',fontsize=16)\n",
    "ax17.set_ylabel('Count',fontsize=16)\n",
    "# plt.ylim(ymin=0, ymax = 800)\n",
    "plt.xlim(xmin=0, xmax = 30000)\n",
    "plt.ylim(ymin=0, ymax = 2000)\n",
    "ax17.set_title('Deaths in Europe',fontsize=18)\n",
    "\n",
    "\n",
    "ax18 = plt.subplot(1,3,3)\n",
    "ax18.hist(europe['total_recovered'],bins='auto',color='steelblue')\n",
    "ax18.set_xlabel('Recovered',fontsize=16)\n",
    "ax18.set_ylabel('Count',fontsize=16)\n",
    "plt.ylim(ymin=0, ymax = 2000)\n",
    "plt.xlim(xmin=0, xmax = 200000)\n",
    "ax18.set_title('Recovered in Europe',fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d56efa06",
   "metadata": {},
   "source": [
    "### D'Agostino and Pearson's Test for Europe"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39b163c9",
   "metadata": {},
   "source": [
    "As epected, the reported confirmed cases do not follow have a gaussian distribution. All cases have a strong right skew as they are correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "47527c45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=3149.872, p=0.000\n",
      "Sample does not look Gaussian (reject H0)\n"
     ]
    }
   ],
   "source": [
    "# Normality test, no normality expected\n",
    "\n",
    "stat, p = normaltest(europe['total_confirmed'])\n",
    "print('Statistics=%.3f, p=%.3f' % (stat,p))\n",
    "# interpret results\n",
    "alpha = 0.5\n",
    "if p >= alpha:\n",
    "    print('Sample looks Gaussian (fail to reject H0)')\n",
    "else:\n",
    "    print('Sample does not look Gaussian (reject H0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b0ced43",
   "metadata": {},
   "source": [
    "## South-East Asia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "219c641b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34862</th>\n",
       "      <td>2020-07-26</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>37342</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>100270</td>\n",
       "      <td>4848</td>\n",
       "      <td>57956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34982</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>97577</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>228997</td>\n",
       "      <td>3002</td>\n",
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       "      <td>495499</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>1524530</td>\n",
       "      <td>34045</td>\n",
       "      <td>984764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35049</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>37292</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>101828</td>\n",
       "      <td>4895</td>\n",
       "      <td>59691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35088</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>4950</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>18891</td>\n",
       "      <td>51</td>\n",
       "      <td>14380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Country/Region  Active       WHO Region  total_confirmed  \\\n",
       "34862  2020-07-26      Indonesia   37342  South-East Asia           100270   \n",
       "34982  2020-07-27     Bangladesh   97577  South-East Asia           228997   \n",
       "35048  2020-07-27          India  495499  South-East Asia          1524530   \n",
       "35049  2020-07-27      Indonesia   37292  South-East Asia           101828   \n",
       "35088  2020-07-27          Nepal    4950  South-East Asia            18891   \n",
       "\n",
       "       total_deaths  total_recovered  \n",
       "34862          4848            57956  \n",
       "34982          3002           127484  \n",
       "35048         34045           984764  \n",
       "35049          4895            59691  \n",
       "35088            51            14380  "
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# South-East Asia\n",
    "se_asia = covid19.loc[covid19['WHO Region'] == 'South-East Asia']\n",
    "se_asia = pd.DataFrame(se_asia)\n",
    "se_asia.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "2aab56d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>month</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34862</th>\n",
       "      <td>2020-07-26</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>37342</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>100270</td>\n",
       "      <td>4848</td>\n",
       "      <td>57956</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34982</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>97577</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>228997</td>\n",
       "      <td>3002</td>\n",
       "      <td>127484</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>35048</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>India</td>\n",
       "      <td>495499</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>1524530</td>\n",
       "      <td>34045</td>\n",
       "      <td>984764</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35049</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>37292</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>101828</td>\n",
       "      <td>4895</td>\n",
       "      <td>59691</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35088</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>4950</td>\n",
       "      <td>South-East Asia</td>\n",
       "      <td>18891</td>\n",
       "      <td>51</td>\n",
       "      <td>14380</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date Country/Region  Active       WHO Region  total_confirmed  \\\n",
       "34862 2020-07-26      Indonesia   37342  South-East Asia           100270   \n",
       "34982 2020-07-27     Bangladesh   97577  South-East Asia           228997   \n",
       "35048 2020-07-27          India  495499  South-East Asia          1524530   \n",
       "35049 2020-07-27      Indonesia   37292  South-East Asia           101828   \n",
       "35088 2020-07-27          Nepal    4950  South-East Asia            18891   \n",
       "\n",
       "       total_deaths  total_recovered  month  \n",
       "34862          4848            57956      7  \n",
       "34982          3002           127484      7  \n",
       "35048         34045           984764      7  \n",
       "35049          4895            59691      7  \n",
       "35088            51            14380      7  "
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "se_asia['Date'] = pd.to_datetime(se_asia['Date'])\n",
    "# Split date, create column month\n",
    "\n",
    "se_asia['month'] = se_asia['Date'].dt.month\n",
    "se_asia.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "d017bcb2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Active</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>486.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>486.0</td>\n",
       "      <td>486.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>47950.0</td>\n",
       "      <td>115147.0</td>\n",
       "      <td>3068.0</td>\n",
       "      <td>62826.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>84595.0</td>\n",
       "      <td>247933.0</td>\n",
       "      <td>6284.0</td>\n",
       "      <td>155847.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1680.0</td>\n",
       "      <td>2874.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>512.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14119.0</td>\n",
       "      <td>19486.0</td>\n",
       "      <td>764.0</td>\n",
       "      <td>5431.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>53418.0</td>\n",
       "      <td>93106.0</td>\n",
       "      <td>2562.0</td>\n",
       "      <td>42022.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>495499.0</td>\n",
       "      <td>1524530.0</td>\n",
       "      <td>34045.0</td>\n",
       "      <td>984764.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Active  total_confirmed  total_deaths  total_recovered  month\n",
       "count     486.0            486.0         486.0            486.0  486.0\n",
       "mean    47950.0         115147.0        3068.0          62826.0    5.0\n",
       "std     84595.0         247933.0        6284.0         155847.0    1.0\n",
       "min        10.0             18.0           2.0              2.0    3.0\n",
       "25%      1680.0           2874.0          45.0            512.0    4.0\n",
       "50%     14119.0          19486.0         764.0           5431.0    5.0\n",
       "75%     53418.0          93106.0        2562.0          42022.0    6.0\n",
       "max    495499.0        1524530.0       34045.0         984764.0    7.0"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "se_asia.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "94649eb9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "ax19 = plt.subplot(1,3,1)\n",
    "ax19.hist(se_asia['total_confirmed'],bins='auto',color='#43aa8b')\n",
    "plt.xlim(xmin=0, xmax = 800000)\n",
    "ax19.set_xlabel('Confirmed cases (in millions)',fontsize=16)\n",
    "ax19.set_xticklabels(['0','1','2','3','4','5','6','7','8'],fontsize=12)\n",
    "ax19.set_ylabel('Count',fontsize=16)\n",
    "ax19.set_title('Confirmed cases in South-East Asia',fontsize=18)\n",
    "\n",
    "ax20 = plt.subplot(1,3,2)\n",
    "ax20.hist(se_asia['total_deaths'],bins='auto',color='#43aa8b')\n",
    "ax20.set_xlabel('Deaths',fontsize=16)\n",
    "# plt.xlim(xmin=0, xmax = 40000)\n",
    "# ax20.set_xticklabels(['0','5','10','15','20','25','30','35'],fontsize=12)\n",
    "ax20.set_ylabel('Count',fontsize=16)\n",
    "ax20.set_title('Deaths in South-East Asia',fontsize=18)\n",
    "\n",
    "\n",
    "ax21 = plt.subplot(1,3,3)\n",
    "ax21.hist(se_asia['total_recovered'],bins='auto',color='#43aa8b')\n",
    "# ax21.xaxis.set_ticks(np.arange(0, 400000, 100000))\n",
    "plt.xlim(xmin=0, xmax = 300000)\n",
    "ax21.set_xlabel('Recovered',fontsize=16)\n",
    "# ax21.set_xticklabels(['0','50','75','100','125','150','200','250','300'],fontsize=12)\n",
    "ax21.set_ylabel('Count',fontsize=16)\n",
    "ax21.set_title('Recovered in South-East Asia',fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8782401",
   "metadata": {},
   "source": [
    "### D'Agostino and Pearson's Test for South-East Asia"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad841b16",
   "metadata": {},
   "source": [
    "As epected, the reported confirmed cases do not follow have a gaussian distribution. All cases have a strong right skew as they are correlated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "id": "34cd51ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=371.327, p=0.000\n",
      "Sample does not look Gaussian (reject H0)\n"
     ]
    }
   ],
   "source": [
    "# Normality test, no normality expected\n",
    "\n",
    "stat, p = normaltest(se_asia['total_confirmed'])\n",
    "print('Statistics=%.3f, p=%.3f' % (stat,p))\n",
    "# interpret results\n",
    "alpha = 0.5\n",
    "if p >= alpha:\n",
    "    print('Sample looks Gaussian (fail to reject H0)')\n",
    "else:\n",
    "    print('Sample does not look Gaussian (reject H0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afd4e499",
   "metadata": {},
   "source": [
    "## Western Pacific"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "1dede3eb",
   "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>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34937</th>\n",
       "      <td>2020-07-26</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>971</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>14200</td>\n",
       "      <td>300</td>\n",
       "      <td>12920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34977</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5825</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>15671</td>\n",
       "      <td>173</td>\n",
       "      <td>9448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35005</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>China</td>\n",
       "      <td>3258</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>86996</td>\n",
       "      <td>4660</td>\n",
       "      <td>78876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35102</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>53649</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>83632</td>\n",
       "      <td>1958</td>\n",
       "      <td>26782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35124</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>896</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>14231</td>\n",
       "      <td>301</td>\n",
       "      <td>13109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Date Country/Region  Active       WHO Region  total_confirmed  \\\n",
       "34937  2020-07-26    South Korea     971  Western Pacific            14200   \n",
       "34977  2020-07-27      Australia    5825  Western Pacific            15671   \n",
       "35005  2020-07-27          China    3258  Western Pacific            86996   \n",
       "35102  2020-07-27    Philippines   53649  Western Pacific            83632   \n",
       "35124  2020-07-27    South Korea     896  Western Pacific            14231   \n",
       "\n",
       "       total_deaths  total_recovered  \n",
       "34937           300            12920  \n",
       "34977           173             9448  \n",
       "35005          4660            78876  \n",
       "35102          1958            26782  \n",
       "35124           301            13109  "
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Western Pacific\n",
    "western_pacific = covid19.loc[covid19['WHO Region'] == 'Western Pacific']\n",
    "western_pacific = pd.DataFrame(western_pacific)\n",
    "western_pacific.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "48cec074",
   "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>Date</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Active</th>\n",
       "      <th>WHO Region</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34937</th>\n",
       "      <td>2020-07-26</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>971</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>14200</td>\n",
       "      <td>300</td>\n",
       "      <td>12920</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34977</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5825</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>15671</td>\n",
       "      <td>173</td>\n",
       "      <td>9448</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35005</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>China</td>\n",
       "      <td>3258</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>86996</td>\n",
       "      <td>4660</td>\n",
       "      <td>78876</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35102</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>53649</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>83632</td>\n",
       "      <td>1958</td>\n",
       "      <td>26782</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35124</th>\n",
       "      <td>2020-07-27</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>896</td>\n",
       "      <td>Western Pacific</td>\n",
       "      <td>14231</td>\n",
       "      <td>301</td>\n",
       "      <td>13109</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date Country/Region  Active       WHO Region  total_confirmed  \\\n",
       "34937 2020-07-26    South Korea     971  Western Pacific            14200   \n",
       "34977 2020-07-27      Australia    5825  Western Pacific            15671   \n",
       "35005 2020-07-27          China    3258  Western Pacific            86996   \n",
       "35102 2020-07-27    Philippines   53649  Western Pacific            83632   \n",
       "35124 2020-07-27    South Korea     896  Western Pacific            14231   \n",
       "\n",
       "       total_deaths  total_recovered  month  \n",
       "34937           300            12920      7  \n",
       "34977           173             9448      7  \n",
       "35005          4660            78876      7  \n",
       "35102          1958            26782      7  \n",
       "35124           301            13109      7  "
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "western_pacific['Date'] = pd.to_datetime(western_pacific['Date'])\n",
    "# Split date, create column month\n",
    "\n",
    "western_pacific['month'] = western_pacific['Date'].dt.month\n",
    "western_pacific.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "8eec29c8",
   "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>Active</th>\n",
       "      <th>total_confirmed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>631.0</td>\n",
       "      <td>631.0</td>\n",
       "      <td>631.0</td>\n",
       "      <td>631.0</td>\n",
       "      <td>631.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8031.0</td>\n",
       "      <td>20777.0</td>\n",
       "      <td>731.0</td>\n",
       "      <td>11907.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11932.0</td>\n",
       "      <td>25589.0</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>19800.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1151.0</td>\n",
       "      <td>5306.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>702.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3359.0</td>\n",
       "      <td>10748.0</td>\n",
       "      <td>245.0</td>\n",
       "      <td>4837.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8672.0</td>\n",
       "      <td>20145.0</td>\n",
       "      <td>938.0</td>\n",
       "      <td>11848.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>58739.0</td>\n",
       "      <td>86996.0</td>\n",
       "      <td>5926.0</td>\n",
       "      <td>78881.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Active  total_confirmed  total_deaths  total_recovered  month\n",
       "count    631.0            631.0         631.0            631.0  631.0\n",
       "mean    8031.0          20777.0         731.0          11907.0    4.0\n",
       "std    11932.0          25589.0        1075.0          19800.0    2.0\n",
       "min        0.0             22.0           2.0              2.0    1.0\n",
       "25%     1151.0           5306.0          68.0            702.0    3.0\n",
       "50%     3359.0          10748.0         245.0           4837.0    4.0\n",
       "75%     8672.0          20145.0         938.0          11848.0    6.0\n",
       "max    58739.0          86996.0        5926.0          78881.0    7.0"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "western_pacific.describe().round()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "e9b376dd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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tWrTQq6++Kok6WbVqleV38/rrr0uiTgqr/NJPJCYmqlWrVjpy5Igk+31fc1tO9lm56Z133lHLli0VEhKi+fPn59typMmJPjO3de/eXSEhIWrdurVat26tvXv35suy5GSfjezJrX4ip9r5O9kvcdTjjJxsg+2xH+doxyE52T7eSVyOeIyydOlSSz21bt1afn5+evnll/M8LsnBjl8MjDHGnDhxwjRp0sT8888/5uLFiyY0NNQcOHAgr8Oyq++++8506tTJJCUlmatXr5oePXqYzz//3AQGBpq///7bJCcnm969e5tvvvnGGGNMSEiI2bNnjzHGmHHjxpmFCxcaY4zp27evWbNmjTHGmJkzZ5rp06cbY4x56aWXzPvvv2+MMWbFihVm2LBhuVvAbNq2bZtp2LChGTNmjLl8+XKhro+///7bPPbYY+b48ePm6tWrpkuXLuabb74p1HVy6dIlU79+fXP69GmTnJxs2rdvb77++utCXSeFVX7pJ3788UfTqlUrU6NGDXP48GG7tmu5Kaf7rNyyc+dO07lzZ5OcnGwuX75smjRpYvbv35/vypEmp/rM3JSammoee+wxk5ycbJmWH8uS0302si63+omcbOezu1/iqMcZOd0G3+l+nKMdh+R0+5jduPLDMcrvv/9umjVrZo4dO5bncTna8QtXOv2/bdu2qVGjRipdurRKlCih5s2ba8OGDXkdll15eHho7NixKlq0qFxdXVWlShXFxcWpUqVKqlixolxcXBQaGqoNGzbo6NGjunLliurUqSNJCgsL04YNG5ScnKzvv/9ezZs3t5ouSd98841CQ0MlSa1atdJ///tfJScn50lZbXX27FlFRkaqf//+kqR9+/YV6vr48ssv1bJlS5UrV06urq6KjIxU8eLFC3WdXLt2Tampqbp8+bJSUlKUkpIid3f3Ql0nhVV+6Seio6M1efJkeXp6SrJvu5abcrrPyi0NGjTQxx9/LBcXF50+fVrXrl3T+fPn8105pJztM3PTX3/9JUnq3bu3nn76aX3yySf5siw53Wcj63Krn8jJdj67+yWOepyR023wnezHOeJxSE63j9mNKz8co7z44osaMWKEDh8+nOdxOdrxC0mn/3fy5El5eHhYXnt6eio+Pj4PI7K/qlWrWr5McXFxWr9+vZycnDIs98314eHhofj4eP3zzz9yd3eXi4uL1XTJug5dXFzk7u6uM2fO5FLpsmfSpEkaMWKE7r77bkm3/h4Ulvo4dOiQrl27pv79+6t169ZatGhRoa8Td3d3DRs2TC1atFBgYKAqVKhQ6OuksMov/cSUKVNUr149y2t7fl9zU073WbnJ1dVVUVFRCgkJkb+/f779THKyz8xN58+fl7+/v2bNmqUPP/xQn376qY4dO5bvypLTfTayLrf6iZxs57O7X+LIxxk52QbfSVyOeByS0+1jduNy9GOUbdu26cqVK2rRooVDxOVoxy8knf5famqqnJycLK+NMVavC5IDBw6od+/eGj16tCpWrJhhuW9VHxnVy63qyRgjZ2fH/YotXbpU5cuXl7+/v2XarcpdGOpDup4V3759u1577TUtWbJE+/bt0+HDhwt1nfz666/67LPPtHnzZn377bdydnZWXFxcoa6Twiq/9hM52a7lhtzqs3La0KFDtX37dh0/fjxH25Ccktt9Zk7y9fXV9OnTVbJkSZUpU0bt27dXVFRUvitLbvfZuL286iccaf/VUY8zcqsNtjUuRz0Oye320da4HP0Y5dNPP9Wzzz4ryTE+R0c7fnG5bcSFRLly5bR7927L64SEBMslqwVJTEyMhg4dqvHjxyskJES7du2yGvgrrdzlypWzmn7q1Cl5enqqTJkyunDhgq5du6YiRYpY1ZOnp6dOnTqlcuXKKSUlRRcvXlTp0qVzu4g2W7dunRISEtS6dWudO3dOly5d0tGjR1WkSBHLMoWpPiSpbNmy8vf3V5kyZSRJTz31lDZs2FCo62Tr1q3y9/fXvffeK+n6paVz584t1HVSWOXXfuLm7+WdfF9zW072Wbnlzz//1NWrV1W9enUVL15cQUFBdm1Xc0tO95m5affu3UpOTrYc7BljVKFChXz33crpPhtZl1f9hD3b+TvZL3HE44ycboOzG5ejHofkdPuY3bgc+Rjl6tWr+v777zVt2jRJjvF7dLTjF06n/7/GjRtr+/btOnPmjC5fvqwvvvhCAQEBeR2WXR0/flyDBg1SRESEQkJCJEm1a9fWwYMHLZcsrlmzRgEBAapQoYLc3NwUExMj6fro9wEBAXJ1dVW9evW0bt06SdLKlSst9RQYGKiVK1dKut6Q1qtXT66urrlfUBvNnz9fa9as0apVqzR06FA1bdpUH3zwQaGtD0lq0qSJtm7dqvPnz+vatWv69ttvFRwcXKjrpFq1atq2bZsuXbokY4w2bdpUqH83hVl+7Sfs+X3NTTndZ+WWI0eOKDw8XFevXtXVq1f19ddfq3PnzvmuHDndZ+amCxcuaPr06UpKSlJiYqJWrFihkSNH5ruy5HSfjazLq37CEfZLHPU4I6fb4OzG5ajHITndPmY3Lkc+Rvntt9/04IMPqkSJEpIc43vvcMcvmQ4zXsisXr3ahISEmKCgIDNnzpy8DsfuXnnlFVOnTh3z9NNPW/4WLVpktm3bZkJDQ01QUJCZMmWKSU1NNcYYs3//ftOuXTvTvHlzM3LkSJOUlGSMMebIkSOmW7dupkWLFqZ3797m7Nmzxhhj/vnnH9OvXz/TsmVL06lTJ3P48OE8K2tWffbZZ2bMmDHGGFPo62Pp0qWW38FLL71krl27Vujr5P333zfNmzc3rVq1MuPGjTNXrlwp9HVSWOWnfqJJkyaW75O9vq+5Kaf7rNwUFRVlWrRoYVq1amWioqKMMfnzM0mTE31mbouMjDTBwcEmKCjIfPjhh/m2LDnZZyN7crOfyIl2Prv7JY58nJGTbbA99uMc7TgkJ9vHO4nLUY9R1q5da4YPH241zRHicqTjFydjjLEphQcAAAAAAADYiNvrAAAAAAAAYHcknQAAAAAAAGB3JJ0AAAAAAABgdySdAAAAAAAAYHcknQAAABwUz3sBAAD5GUkn5LmvvvpKffr0kb+/v3x9fdWmTRstXLhQycnJObK9qVOnql69eqpbt65iYmLk7e2tDRs25Mi2sqNp06Z6+eWX8zoMAMANunfvLm9vb8vfI488ooYNG+q5557T9u3b7b698+fP6/nnn9fPP/9smebt7a25c+fafVsAAGs3t/lp7X6jRo00YMAA/fnnn3kdYp5avny5vL29debMmbwOBfmAS14HgMLtpZde0qeffqo2bdqoS5cuKlGihHbt2qXXX39dO3bs0Ntvv60iRYrYbXu//fabPvzwQ/Xs2VPNmjWTl5eXlixZogcffNBu2wAAFEx169bVmDFjJEkpKSk6efKkPv30Uz377LOKiIhQq1at7Lat/fv3a82aNerVq5fd1gkAsN2Nbb4kXb16Vb/++qtmzZqlPn36aOPGjXJzc8vDCIH8gaQT8szKlSu1aNEivfzyy+rUqZNleuPGjeXl5aURI0bo888/V5s2bey2zXPnzkmSWrVqpVq1akmS6tSpY7f1AwAKrrvvvjtdnxEcHKyePXvqxRdf1OOPP65SpUrlTXAAALvKqM1v0KCBihUrpokTJ2rHjh0KDAzMm+CAfITb65Bn5s6dK29vb6uEU5qWLVuqd+/euueeeyzTjhw5omHDhqlx48by9fXVgAEDFBcXZ5k/Y8YMhYWFac2aNWrevLlq1qypdu3a6YcffrDM7969uySpQ4cO6t69u44cOWJ1e93YsWM1cOBAPf/886pbt65GjBihnTt3ytvbWzt27FCHDh1Uq1YttWrVSrt379bu3bvVpk0b1a5dW88884wOHTpkVY6PP/5YQUFB8vHxUUhIiNatW2c1PyEhQUOHDpWfn58ef/xxrVy50qa6++KLLxQWFqbatWuradOmeu+99yzjfhhj9NFHHyk0NFQ1a9aUr6+vnn32Wf32229W2x02bJgaNmxoiX3Xrl1W2/juu+8s5Q0ICNA777yja9euWeb/9ddfeu655yy3Kvbp00e//vqrTfEDQEHh7OysgQMH6sKFC5a+5PTp0xo9erQaNGggX19f9e/fX4cPH7Z637fffqtu3brJ19dXNWvWVOvWrfXFF19Iknbu3KkePXpIktq3b6+xY8da3nf27FmNHDlSvr6+atiwoV577TWr29G3bNli6R/8/f01btw4nT17NodrAQAKD3d3d6vXhw4d0sCBA+Xr66t69epp1KhR6W4727lzp7p27SpfX18FBARo2rRpSkpKssz//vvv1bVrV9WtW1eNGzfWyy+/rIsXL0q6fitbtWrVFB8fb7XO119/XU2aNLEcA9xu371p06aKiIhQx44dVa9ePX344Yc2x79y5Uo1b95ctWrV0r///W/6FWQJSSfkiZMnT+r333/P9OzAmDFjLPNPnDihDh066NChQ5o8ebKmTp2qI0eO6JlnnrFqgOPi4hQVFaXBgwdrxowZSkpK0rBhw5SSkqIOHTpo0qRJkq6P6zR58uQMt7tlyxYlJSVp1qxZVgmxUaNGqX379po5c6ZSU1M1fPhwjR8/Xr169dJrr72mP//802osppkzZ+r1119Xy5Yt9d5776lx48YaOXKk1q9fL0m6du2a+vTpo9jYWL3yyisaO3asoqKi0nUoN9u4caOGDBkib29vzZw5Uz169NDMmTP1n//8R5I0b948RUREqH379po7d64mTpyoP/74Q+PGjbOsY9y4cfr77781depUzZ49W8WLF1e/fv0sHcj27dv173//Ww888IBmzpypPn36aP78+Xr11Vct6xg0aJCuXbumyMhIRUZG6p9//lG/fv2sOjcAKAwaNGigIkWKaM+ePbpy5Yp69OihmJgYhYeHa/r06Tp16pS6detmudp237596tu3r6pWrarZs2crMjJSxYsX1/PPP68zZ86oRo0aVv3VwIEDLdv64IMPdM8992j27Nnq3LmzPvroI3366aeSpKNHj2rw4MGqW7eu5syZozFjxmjz5s2MEwgA2WCMUUpKiuXv4sWL2rlzpyIjI3X//ferXr16OnXqlJ555hkdO3ZM06dP10svvaQff/xRffr00dWrVyVdb/N79+6tkiVLKjIyUkOGDNHSpUs1ZcoUSdePPXr06CEPDw/L/LVr16pfv35KTU1VUFCQihYtqo0bN1rFt3HjRrVs2VJOTk427btL0vz58xUQEKA33nhDAQEBNsW/fv16jRkzRo8++qhmzZqlihUr6q233sqFTwAFhgHywN69e42Xl5dZtGiRTctPnTrV1KlTx5w+fdoy7fTp08bX19dMnTrVGGNMVFSU8fLyMnv37rUs89VXXxkvLy/z008/GWOM2bFjh/Hy8jL79u0zxhhz+PBh4+XlZdavX2+MMWbMmDHGy8vLajtp73n//fct0xYvXmy8vLzMihUrLNMiIyONn5+fMcaYc+fOmZo1a5q33nrLqhzjxo0zTz75pFVse/bsscyPjY01Xl5e5qWXXrplXbRu3dr06NHDatr06dNNv379jDHGvPLKK2b27NlW8+fPn2+8vLxMYmKiMcaY2rVrm/fee88yPz4+3kybNs0cO3bMGGNMx44dTefOna3WsWLFClOtWjVz+PBhc/r0aePl5WU+//xzy/zffvvNvPnmm+b8+fO3jB0A8qtu3bqZvn373nK+v7+/6dOnj1m8eLGpXr26+eOPPyzzLly4YOrVq2dmzJhhjDFm2bJlZsiQIVbv//nnn42Xl5fZtGmTMSZ9f2WMMV5eXqZ3795W72vVqpUZNGiQMcaYdevWGS8vLxMfH2+Z/8UXX5j58+dnr9AAUEh169bNeHl5pfurVauW6devn4mLizPGGBMREWH8/Pysjh3+/vtvU716dctxwsCBA01QUJBJSUmxLLNgwQITFhZmUlJSTNu2bU3Hjh2ttv/f//7XeHl5ma+//toYY8yQIUNMly5dLPP37NljvLy8zC+//GKMuf2+uzHGNGnSxISEhFgtY0v8bdq0MX369LF6X79+/dIdMwG3wpVOyBNpg4OnpqbatPz333+vhg0bqkyZMpZpZcqUkb+/v9VtYS4uLvLx8bG8LleunCTp8uXLNsdWpkwZq+2kSRsDSpLKli0rSVbbKl26tC5cuCBJ+vHHH5WUlKQnnnjC6gxJQECADh8+rMOHD+uHH35QqVKlrO4Vr1GjhipUqHDL2K5cuaL9+/erSZMmVtNHjRql9957T5IUHh6uAQMG6MyZM9q9e7eio6O1adMmSbKcsfD19VVUVJRGjhypVatWqWjRohozZozKly+vy5cva9++fWrSpEm62FNTU7Vz506VLl1aDz74oCZOnKjx48dr48aNqlChgkaOHKmSJUvaXNcAUNDs3LlTlSpVUqVKlSztZ7FixeTn56cdO3ZIktq1a6eoqChdunRJP/30kz7//HMtXLhQ0v/a6Vvx9fW1el2hQgWdP39e0vU+qWjRourQoYNef/117dy5U02bNmUwcgDIBj8/Py1btkzLli3T1KlTVbp0aTVp0kRRUVGqVKmSpOttfp06dXT33Xdb2vzy5curSpUqlieb7tmzRwEBAVYPR+rWrZs+++wzXblyRb/88ouCg4Ottp02RuD3338v6fp4tD/88IPljoj169ercuXKql69uk377mmqVKlitZ3bxX/58mXt379fAQEBVu9r3ry5nWoZhQEDiSNPlC9fXpJ0/PjxWy5z8uRJlS1bVs7Ozjp//ryqV6+ebpl7771Xf/zxh+V10aJF5ez8v1xq2v+2JrfS1pmRu+66K920YsWKZbhs2m1qnTt3znB+QkKCzp8/bzVmVRoPD49bxpZ2a8atYpSkP//8UxMnTlRMTIyKFy+uatWqWWI3/3/Pd2RkpGbNmqX169dr7dq1cnV1VVhYmMLDw3X+/HmlpqbqzTff1Jtvvplh7M7Ozvrwww81Y8YMff311/rss89UrFgx9enTR0OGDJGTk9Mt4wOAgiYpKUnnzp3Tfffdp2PHjumvv/5SjRo10i2X9qTUS5cuadKkSZbbrR966CFVq1ZN0v/a6VspXry41WtnZ2fLeypWrKgPP/xQc+bM0SeffKJ58+bJw8NDkydPVrNmze60mABQqJQsWVI1a9aUJNWsWVPly5fXs88+q6JFi2r69OmSru/z7927N8M2P22f/ty5c7fcd79w4YKMMRnOL1OmjBITEyVJTzzxhNzd3fXFF1+oW7du2rhxozp06CBJNu27p7l5O7eL//z58zLGpDtmSTsBD9iCpBPyRJkyZfTII4/o22+/1QsvvJDhMs8++6zKli2rjz76SKVKldKpU6fSLXPq1CmVLl06h6PNurSrfWbNmqX77rsv3fyHHnpIpUuX1unTp9PNy2xgvrTk0c2D+504cUKHDh1SvXr1NGDAAJUuXVqff/65Hn74YTk7O2vhwoXaunWrZfnSpUtrwoQJmjBhgvbv36/Vq1dr/vz5euCBB/TMM89IkgYMGKAnn3wyXQyenp6SricOX3vtNaWmpurHH3/U0qVLNWvWLD388MNq2bLlbWoIAAqO3bt3KyUlRX5+frpw4YKqVauWbhwN6fqJEUl65ZVX9N1332nOnDmqX7++ihYtqj/++EOff/75Hcfi5+en999/X5cvX9b27dv1wQcfaNiwYdq8eXOG/REAwDb+/v5q3769li5dquDgYDVt2lTu7u4KCAjQ0KFD0y2ftt/u7u6ebt/97Nmz+vnnn1W7dm05OTlleExw43FO0aJF1axZM33xxRfy8fHR8ePHFRISYrWd2+27Z+R28ZcqVSrD+BhIHFnB7XXIMz179tSvv/6qpUuXppu3atUq/fHHHwoNDZV0fSd6586dVg32mTNntH37dtWtWzfXYrZV7dq15erqqtOnT6tmzZqWvwMHDmjWrFmSpIYNG+rChQuWS28l6eDBg/r7779vuV53d3d5eXnpm2++sZq+YMECvfDCC/rnn3906NAhdezYUV5eXpYrvb799lvLsmfOnNETTzyhL7/8UpJUvXp1jRkzRvfff7+OHz8ud3d3VatWTYcPH7aK3dXVVW+99ZZOnDihX3/9VY899ph+/vlnOTs7q27dunr11Vfl4uKiY8eO2asaAcDhGWM0Z84clS5dWkFBQapbt66OHDmiChUqWNpPHx8fffjhh5a2+8cff9Tjjz+uRx991JKISmun065auvE2DFstXbpUTz75pJKTk1W8eHE1bdpUw4cP17Vr1277kAoAwO2lDSUxbdo0Xb16VX5+fvrrr7/k7e1tafO9vLw0c+ZMxcTESLp+W/R///tfqzsv1q1bp379+km6vi+e9vTTNN9++60uXLhgdZyTdovdZ599ppo1a1qunrVl3/1Wbhd/sWLFVKdOHX311VdW79uyZcsd1SMKF650Qp5p3bq1vvnmG02aNEn79u3Tk08+KScnJ23dulWLFy9WixYt1K5dO0lSr169tGLFCvXu3VsDBw6UMUbvvvuuihYtqp49e+ZxSdIrU6aMunfvrmnTpuncuXOqVauWfv31V0VGRurJJ5+Uu7u7Hn30UdWvX1+jRo3SCy+8oBIlSujtt9+Wq6trpuseNGiQhg0bpokTJyo4OFi///67Pv74Y40ePVply5bV/fffr48++shya+LKlSstBzqXL19WhQoVVKlSJb366qu6ePGiypcvr2+++UZHjx613H4xdOhQDRo0SO7u7mrWrJn++ecfvf3223J2dpaXl5dcXV111113acyYMRo8eLBKlSqllStXysnJSU888UQO1y4A5I3z58/rxx9/lCSlpKQoPj5eS5cu1ffff6+IiAi5u7urffv2WrBggXr37q2+ffuqdOnSWrJkib744gs9/fTTkq7fprFp0yatWLFC5cuX144dOzR37lxJ18fuk/53xeyWLVtUokSJdONwZKRevXpKSEjQsGHD9Mwzzyg5OVnvvvuuHnjggQxvUQcAZE2ZMmXUr18/RUREaMGCBXr22We1atUqPffcc+rRo4dcXV01b948/fjjjxo+fLgkqX///uratauGDh2qjh076sSJE3r77bfVrVs3ubu7a8iQIRo4cKCGDx+usLAwHT9+XG+99ZZ8fX2txlJq1KiR7rnnHi1fvlyjR4+2iut2++63Ykv8Q4YM0XPPPadx48apZcuW2rFjR7okFJAZkk7IM05OTnrrrbcUHR2t5cuX64svvtDVq1f10EMPKTw8XO3bt7eMDVS+fHktXLhQb7zxhsaMGaMiRYqoQYMGioyMtAwW7mhGjRqlMmXKKDo6WlFRUfL09FTPnj01ePBgSdfL/+677+q1117TlClT5OLiot69e1uuQLqV4OBgvf3225o9e7ZWrFih+++/X2PGjFG3bt0kSTNmzNCrr76q4cOHy93dXTVr1tT8+fPVq1cv/fjjj6pQoYLeeustTZ8+XRERETp79qweeughvfnmm2rcuLEk6cknn9Ts2bM1a9YsLV++XO7u7mrcuLFeeOEFy3gi//nPf/T666/rxRdf1KVLl+Tt7a33339fDz/8cA7WKgDknR9++EGdOnWSJLm6usrDw0M1a9bU4sWLVbt2bUnXzzgvXLhQ06dP14svvqirV6+qatWqmj17tgIDAyVJY8eO1ZUrV/Taa69Juj6w68yZM/Xaa69pz549atu2rapWrarWrVvr/fffV2xsrOVhEZl56KGH9N577ykqKspyq0TDhg0VERFx2xMaAADb9OzZU4sXL9a7776rtm3batGiRXrjjTc0atQoOTk5qUaNGpo/f74l2V+nTh3NnTtXkZGRGjRokMqWLavu3burf//+kqSmTZtq1qxZmjVrlgYOHKjSpUurVatWGjFihNVVr0WKFFFwcLAWLlyoFi1aWMVky757Ru6///7bxv/oo49qxowZeuedd7RmzRrVrFlTo0aN0ssvv2zvqkUB5WRuN2IlAAAAAAAAkEWM6QQAAAAAAAC7I+kEAAAAAAAAuyPpBAAAAAAAALsj6QQAAAAAAAC7I+kEAAAAAAAAuyPpBAAAAAAAALsj6QQAAAAAAAC7I+kEAAAAAAAAuyPpBAAAAAAAALv7Pyl253h6XT69AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x360 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "ax22 = plt.subplot(1,3,1)\n",
    "ax22.hist(western_pacific['total_confirmed'],bins='auto',color='orchid')\n",
    "ax22.set_xlabel('Confirmed cases',fontsize=16)\n",
    "ax22.set_ylabel('Count',fontsize=16)\n",
    "ax22.set_title('Confirmed cases in Western Pacific',fontsize=18)\n",
    "\n",
    "ax23 = plt.subplot(1,3,2)\n",
    "ax23.hist(western_pacific['total_deaths'],bins='auto',color='orchid')\n",
    "ax23.set_xlabel('Deaths',fontsize=16)\n",
    "ax23.set_ylabel('Count',fontsize=16)\n",
    "ax23.set_title('Deaths in Western Pacific',fontsize=18)\n",
    "\n",
    "\n",
    "ax24 = plt.subplot(1,3,3)\n",
    "ax24.hist(western_pacific['total_recovered'],bins='auto',color='orchid')\n",
    "ax24.set_xlabel('Recovered',fontsize=16)\n",
    "ax24.set_ylabel('Count',fontsize=16)\n",
    "ax24.set_title('Recovered in Western Pacific',fontsize=18)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "963bb4c2",
   "metadata": {},
   "source": [
    "### D'Agostino and Pearson's Test for Western Pacific"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "119358c1",
   "metadata": {},
   "source": [
    "- As expected, the reported confirmed cases do not have a Gaussian distribution, and all cases have a strong right skew (as they are correlated). \n",
    "- However, in comparison to the other WHO regions, the Western Pacific countries seem to be the most affected by a peak in the pandemic at the same time. \n",
    "- Interestingly, their confirmed cases are far less than in the Americas or Europe. \n",
    "- It is also important to note that many countries might have under-reported their cases more than the Americas or Europe due to a lack of information infrastructure, as often with developing countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 705,
   "id": "598810a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Statistics=130.217, p=0.000\n",
      "Sample does not look Gaussian (reject H0)\n"
     ]
    }
   ],
   "source": [
    "# Normality test, no normality expected\n",
    "\n",
    "stat, p = normaltest(western_pacific['total_confirmed'])\n",
    "print('Statistics=%.3f, p=%.3f' % (stat,p))\n",
    "# interpret results\n",
    "alpha = 0.5\n",
    "if p >= alpha:\n",
    "    print('Sample looks Gaussian (fail to reject H0)')\n",
    "else:\n",
    "    print('Sample does not look Gaussian (reject H0)')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12c72f52",
   "metadata": {},
   "source": [
    "## Comparing confirmed cases per month per WHO region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "id": "4ce68b00",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "months = [1,2,3,4,5,6,7]\n",
    "labels = ['January', 'February', 'March','April','May','June','July']\n",
    "#-------------------------------------------------------------------------------------------------------------------------- \n",
    "\n",
    "# First Subplot Africa\n",
    "\n",
    "ax1 = plt.subplot(2, 3, 1)\n",
    "plt.bar(africa['month'], africa['total_confirmed'], color='maroon')\n",
    "# plt.xlabel('Months',fontsize=16)\n",
    "plt.ylabel('Confirmed count',fontsize=16)\n",
    "start, end = ax1.get_xlim()\n",
    "ax1.yaxis.set_ticks(np.arange(0, 5000000, 500000))\n",
    "ax1.xaxis.set_ticks(np.arange(1, 8, 1))\n",
    "ax1.set_xticklabels(labels, rotation='30',fontsize=12)\n",
    "ax1.set_facecolor(\"floralwhite\")# Setting the background color of the plot\n",
    "plt.title('Africa',fontsize=20)\n",
    "\n",
    "#-------------------------------------------------------------------------------------------------------------------------- \n",
    "\n",
    "# Second Subplot Americas\n",
    "ax2 = plt.subplot(2, 3, 2)\n",
    "plt.bar(americas['month'], americas['total_confirmed'], color='gold')\n",
    "# plt.xlabel('Months',fontsize=16)\n",
    "plt.ylabel('Confirmed count',fontsize=16)\n",
    "start, end = ax2.get_xlim()\n",
    "ax2.yaxis.set_ticks(np.arange(0, 5000000, 500000))\n",
    "ax2.xaxis.set_ticks(np.arange(1, 8, 1))\n",
    "ax2.set_xticklabels(labels, rotation='30',fontsize=12)\n",
    "ax2.set_facecolor(\"floralwhite\")# Setting the background color of the plot \n",
    "plt.title('Americas',fontsize=20)\n",
    "\n",
    "#-------------------------------------------------------------------------------------------------------------------------- \n",
    "\n",
    "# Third Subplot Eastern Mediterranean\n",
    "ax3 = plt.subplot(2, 3, 3)\n",
    "plt.bar(mediterranean['month'], mediterranean['total_confirmed'], color='goldenrod')\n",
    "# plt.xlabel('Months',fontsize=16)\n",
    "plt.ylabel('Confirmed count',fontsize=16)\n",
    "start, end = ax3.get_xlim()\n",
    "ax3.yaxis.set_ticks(np.arange(0, 5000000, 500000))\n",
    "ax3.set_xticklabels(labels, rotation='30',fontsize=12)\n",
    "ax3.xaxis.set_ticks(np.arange(1, 8, 1))\n",
    "ax3.set_facecolor(\"floralwhite\")# Setting the background color of the plot\n",
    "plt.title('Eastern Mediterranean',fontsize=20)\n",
    "\n",
    "#-------------------------------------------------------------------------------------------------------------------------- \n",
    "\n",
    "# Fourth Subplot Europe\n",
    "ax4 = plt.subplot(2, 3, 4)\n",
    "plt.bar(europe['month'], europe['total_confirmed'], color='steelblue')\n",
    "# plt.xlabel('Months',fontsize=16)\n",
    "plt.ylabel('Confirmed count',fontsize=16)\n",
    "start, end = ax4.get_xlim()\n",
    "ax4.yaxis.set_ticks(np.arange(0, 5000000, 500000))\n",
    "ax4.xaxis.set_ticks(np.arange(1, 8, 1))\n",
    "ax4.set_xticklabels(labels, rotation='30',fontsize=12)\n",
    "ax4.set_facecolor(\"floralwhite\")# Setting the background color of the plot\n",
    "plt.title('Europe',fontsize=20)\n",
    "\n",
    "#-------------------------------------------------------------------------------------------------------------------------- \n",
    "\n",
    "# Fifth subplot South-East Asia\n",
    "ax5 = plt.subplot(2, 3, 5)\n",
    "plt.bar(se_asia['month'], se_asia['total_confirmed'], color='#43aa8b')\n",
    "# plt.xlabel('Months',fontsize=16)\n",
    "plt.ylabel('Confirmed count',fontsize=16)\n",
    "start, end = ax5.get_xlim()\n",
    "ax5.yaxis.set_ticks(np.arange(0, 5000000, 500000))\n",
    "ax5.xaxis.set_ticks(np.arange(1, 8, 1))\n",
    "ax5.set_xticklabels(labels, rotation='30',fontsize=12)\n",
    "ax5.set_facecolor(\"floralwhite\")# Setting the background color of the plot\n",
    "plt.title('South-East Asia',fontsize=20)\n",
    " \n",
    "#--------------------------------------------------------------------------------------------------------------------------    \n",
    "\n",
    "# Sixth subplot Western Pacific\n",
    "ax6 = plt.subplot(2, 3, 6)\n",
    "total_confirmed_wp = western_pacific['total_confirmed']\n",
    "months = western_pacific['month']\n",
    "plt.bar(months, total_confirmed_wp, color='orchid') #deaths\n",
    "# plt.xlabel('Months',fontsize=16)\n",
    "plt.ylabel('Confirmed count',fontsize=16)\n",
    "\n",
    "start, end = ax6.get_xlim()\n",
    "ax6.yaxis.set_ticks(np.arange(0, 5000000, 500000))\n",
    "ax6.xaxis.set_ticks(np.arange(1, 8, 1))\n",
    "ax6.set_xticklabels(labels, rotation='30',fontsize=12)\n",
    "ax6.set_facecolor(\"floralwhite\")# Setting the background color of the plot\n",
    "plt.title('Western Pacific',fontsize=20)  \n",
    "\n",
    "\n",
    "# Display subplots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92ca61ce",
   "metadata": {},
   "source": [
    "## Comparing deaths per month per WHO region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "id": "c2b80b53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,15))\n",
    "\n",
    "#-------------------------------------------------------------------------------------------------------------------------- \n",
    "\n",
    "# First Subplot Africa\n",
    "\n",
    "ax1 = plt.subplot(2, 3, 1)\n",
    "plt.bar(africa['month'], africa['total_deaths'], color='maroon')\n",
    "# plt.xlabel('Months',fontsize=16)\n",
    "plt.ylabel('Death count',fontsize=16)\n",
    "start, end = ax1.get_xlim()\n",
    "ax1.yaxis.set_ticks(np.arange(0, 160000, 20000))\n",
    "ax1.xaxis.set_ticks(np.arange(1, 8, 1))\n",
    "ax1.set_xticklabels(labels, rotation='30',fontsize=12)\n",
    "ax1.set_facecolor(\"floralwhite\")# Setting the background color of the plot\n",
    "plt.title('Africa',fontsize=20)\n",
    "\n",
    "#-------------------------------------------------------------------------------------------------------------------------- \n",
    "\n",
    "# Second Subplot Americas\n",
    "ax2 = plt.subplot(2, 3, 2)\n",
    "plt.bar(americas['month'], americas['total_deaths'], color='gold')\n",
    "# plt.xlabel('Months',fontsize=16)\n",
    "plt.ylabel('Death count',fontsize=16)\n",
    "start, end = ax2.get_xlim()\n",
    "ax2.yaxis.set_ticks(np.arange(0, 160000, 20000))\n",
    "ax2.xaxis.set_ticks(np.arange(1, 8, 1))\n",
    "ax2.set_xticklabels(labels, rotation='30',fontsize=12)\n",
    "ax2.set_facecolor(\"floralwhite\")# Setting the background color of the plot\n",
    "plt.title('Americas',fontsize=20)\n",
    "\n",
    "#-------------------------------------------------------------------------------------------------------------------------- \n",
    "\n",
    "# Third Subplot Eastern Mediterranean\n",
    "ax3 = plt.subplot(2, 3, 3)\n",
    "plt.bar(mediterranean['month'], mediterranean['total_deaths'], color='goldenrod')\n",
    "# plt.xlabel('Months',fontsize=16)\n",
    "plt.ylabel('Death count',fontsize=16)\n",
    "start, end = ax3.get_xlim()\n",
    "ax3.yaxis.set_ticks(np.arange(0, 160000, 20000))\n",
    "ax3.xaxis.set_ticks(np.arange(1, 8, 1))\n",
    "ax3.set_xticklabels(labels, rotation='30',fontsize=12)\n",
    "ax3.set_facecolor(\"floralwhite\")# Setting the background color of the plot\n",
    "plt.title('Eastern Mediterranean',fontsize=20)\n",
    "\n",
    "#-------------------------------------------------------------------------------------------------------------------------- \n",
    "\n",
    "# Fourth Subplot Europe\n",
    "ax4 = plt.subplot(2, 3, 4)\n",
    "plt.bar(europe['month'], europe['total_deaths'], color='steelblue')\n",
    "# plt.xlabel('Months',fontsize=16)\n",
    "plt.ylabel('Death count',fontsize=16)\n",
    "start, end = ax4.get_xlim()\n",
    "ax4.yaxis.set_ticks(np.arange(0, 160000, 20000))\n",
    "ax4.xaxis.set_ticks(np.arange(1, 8, 1))\n",
    "ax4.set_xticklabels(labels, rotation='30',fontsize=12)\n",
    "ax4.set_facecolor(\"floralwhite\")# Setting the background color of the plot\n",
    "plt.title('Europe',fontsize=20)\n",
    "\n",
    "#-------------------------------------------------------------------------------------------------------------------------- \n",
    "\n",
    "# Fifth subplot South-East Asia\n",
    "ax5 = plt.subplot(2, 3, 5)\n",
    "plt.bar(se_asia['month'], se_asia['total_deaths'], color='#43aa8b')\n",
    "# plt.xlabel('Months',fontsize=16)\n",
    "plt.ylabel('Death count',fontsize=16)\n",
    "start, end = ax5.get_xlim()\n",
    "ax5.yaxis.set_ticks(np.arange(0, 160000, 20000))\n",
    "ax5.xaxis.set_ticks(np.arange(1, 8, 1))\n",
    "ax5.set_xticklabels(labels, rotation='30',fontsize=12)\n",
    "ax5.set_facecolor(\"floralwhite\")# Setting the background color of the plot\n",
    "plt.title('South-East Asia',fontsize=20)\n",
    " \n",
    "#--------------------------------------------------------------------------------------------------------------------------    \n",
    "\n",
    "# Sixth subplot Western Pacific\n",
    "ax6 = plt.subplot(2, 3, 6)\n",
    "total_confirmed_wp = western_pacific['total_confirmed']\n",
    "total_deaths_wp = western_pacific['total_deaths']\n",
    "total_recovered_wp = western_pacific['total_recovered']\n",
    "months = western_pacific['month']\n",
    "plt.bar(months, total_deaths_wp, color='orchid') #deaths\n",
    "# plt.xlabel('Months',fontsize=16)\n",
    "plt.ylabel('Death count',fontsize=16)\n",
    "\n",
    "start, end = ax6.get_xlim()\n",
    "ax6.yaxis.set_ticks(np.arange(0, 160000, 20000))\n",
    "ax6.xaxis.set_ticks(np.arange(1, 8, 1))\n",
    "ax6.set_xticklabels(labels, rotation='30',fontsize=12)\n",
    "ax6.set_facecolor(\"floralwhite\")# Setting the background color of the plot\n",
    "plt.title('Western Pacific',fontsize=20)  \n",
    "\n",
    "# Display subplots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f91af01c",
   "metadata": {},
   "source": [
    "# Final Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b82d8181",
   "metadata": {},
   "source": [
    "### WHO Regions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23c1e5a1",
   "metadata": {},
   "source": [
    "From the visualization and summary statistics, it can be concluded that there was an increase in Covid19 cases for each month for all WHO regions. \n",
    "1. The most affected WHO region was the Americas, with the US followed by Brazil as the most affected country. \n",
    "2. The WHO region with the least confirmed cases was the Western Pacific (Interestingly, China, where Covid19 started, is part of the Western Pacific region.)\n",
    "3. The least amount of observed deaths were in Africa. However, this might be an unreliable conclusion, as Africa had fewer means of testing and validating confirmed cases. It is most likely that the actual amount is far bigger."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c71c18e",
   "metadata": {},
   "source": [
    "From simply visualizing the data, many conclusions can be made. However, knowing the data's origin and the specifics brings the results into another light. It might be that the regions Africa, South-East Asia, and Western Pacific were not, as the data suggests, that much less affected by the pandemic but instead had fewer resources to test and record positive cases. And equally for the number of deaths."
   ]
  }
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