Capstone project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone project collaboration Team-9\n",
    "# Bootcamp in Data Analysis/ Pyladies Amsterdam 2020\n",
    "# COVID-19: Myths and facts "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motivation:\n",
    "**Covid-19 is a new virus, but it has already been a subject of a few conspiracy theories. We have decided to take a role of MythBusters by using data analysis. We had a lot of ideas, but unfortunately in regards with some rumours, we were lacking the data.**\n",
    "\n",
    "These are the questions: \n",
    " *  What is the most Covid-19 affected continent in terms of infected cases and deaths?\n",
    " *  Do countries where BCG vaccine was widely implemented have higher life expectancy and lower infant mortality? \n",
    " *  Do countries where BCG vaccine was widely implemented have fewer infection rates per million?\n",
    " *  Do countries with higher life expectancy suffer more from Covid-19 in terms of deaths recorded?\n",
    " *  What country has the highest ratio of recovered cases?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "\n",
    "* [Heath indicators from Kaggle](https://www.kaggle.com/nxpnsv/country-health-indicators)\n",
    "\n",
    "* [Covid-19 data as of 31.05.20 from Worldometers ](https://www.worldometers.info/coronavirus/)\n",
    "\n",
    "**Variables we use from these datasets**\n",
    " * Country/Region and Continent\n",
    " * BCG immunization coverage among 1-year-olds (%)\n",
    " * Life expectancy at birth\n",
    " * Infant mortality rate (IMR) is the number of deaths per 1,000 live births of children under one year of age. \n",
    " * Total cases of Covid-19 by country as of 31/05/20\n",
    " * Total cases of Covid-19 per 1 million of population by country as of 31/05/20\n",
    " * Total deaths of Covid-19 by country as of 31/05/20 \n",
    " * Total reported recovered cases of Covid-19 as of 31/05/20 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 581,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the necessery libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt #visualisation\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 582,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the data and store it in chosen variable\n",
    "health_indicators = 'data_team-9/health_indicators.csv'\n",
    "health_df = pd.read_csv(health_indicators)\n",
    "covid19_file = 'data_team-9/worldometers_coronavirus.csv'\n",
    "covid19_df = pd.read_csv(covid19_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 583,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>first_1ConfirmedCases</th>\n",
       "      <th>first_1Fatalities</th>\n",
       "      <th>first_10ConfirmedCases</th>\n",
       "      <th>first_50ConfirmedCases</th>\n",
       "      <th>first_100ConfirmedCases</th>\n",
       "      <th>cases_growth</th>\n",
       "      <th>death_growth</th>\n",
       "      <th>Cardiovascular diseases (%)</th>\n",
       "      <th>Cancers (%)</th>\n",
       "      <th>...</th>\n",
       "      <th>total fertility rate</th>\n",
       "      <th>obesity - adult prevalence rate</th>\n",
       "      <th>school_shutdown_1case</th>\n",
       "      <th>school_shutdown_10case</th>\n",
       "      <th>school_shutdown_50case</th>\n",
       "      <th>school_shutdown_1death</th>\n",
       "      <th>FF_DayOfYear</th>\n",
       "      <th>case1_DayOfYear</th>\n",
       "      <th>case10_DayOfYear</th>\n",
       "      <th>case50_DayOfYear</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>2020-03-14</td>\n",
       "      <td>2020-03-24</td>\n",
       "      <td>2020-03-27</td>\n",
       "      <td>0.174179</td>\n",
       "      <td>0.083333</td>\n",
       "      <td>8.953006</td>\n",
       "      <td>3.372082</td>\n",
       "      <td>...</td>\n",
       "      <td>4.82</td>\n",
       "      <td>5.5</td>\n",
       "      <td>19.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>55</td>\n",
       "      <td>74.0</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>2020-03-23</td>\n",
       "      <td>0.068699</td>\n",
       "      <td>0.130051</td>\n",
       "      <td>27.016784</td>\n",
       "      <td>13.225390</td>\n",
       "      <td>...</td>\n",
       "      <td>1.53</td>\n",
       "      <td>21.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>69</td>\n",
       "      <td>70.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>2020-03-16</td>\n",
       "      <td>2020-03-21</td>\n",
       "      <td>0.158588</td>\n",
       "      <td>0.228578</td>\n",
       "      <td>17.005476</td>\n",
       "      <td>6.481729</td>\n",
       "      <td>...</td>\n",
       "      <td>2.59</td>\n",
       "      <td>27.4</td>\n",
       "      <td>16.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>56</td>\n",
       "      <td>64.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>2020-03-17</td>\n",
       "      <td>2020-03-19</td>\n",
       "      <td>2020-03-22</td>\n",
       "      <td>0.082774</td>\n",
       "      <td>0.345238</td>\n",
       "      <td>13.784688</td>\n",
       "      <td>17.879279</td>\n",
       "      <td>...</td>\n",
       "      <td>1.43</td>\n",
       "      <td>25.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>62</td>\n",
       "      <td>77.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Angola</td>\n",
       "      <td>2020-03-20</td>\n",
       "      <td>2020-03-29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.132143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.480300</td>\n",
       "      <td>3.011632</td>\n",
       "      <td>...</td>\n",
       "      <td>5.96</td>\n",
       "      <td>8.2</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>80</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 70 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Country_Region first_1ConfirmedCases first_1Fatalities  \\\n",
       "0    Afghanistan            2020-02-24        2020-03-22   \n",
       "1        Albania            2020-03-09        2020-03-11   \n",
       "2        Algeria            2020-02-25        2020-03-12   \n",
       "3        Andorra            2020-03-02        2020-03-22   \n",
       "4         Angola            2020-03-20        2020-03-29   \n",
       "\n",
       "  first_10ConfirmedCases first_50ConfirmedCases first_100ConfirmedCases  \\\n",
       "0             2020-03-14             2020-03-24              2020-03-27   \n",
       "1             2020-03-10             2020-03-16              2020-03-23   \n",
       "2             2020-03-04             2020-03-16              2020-03-21   \n",
       "3             2020-03-17             2020-03-19              2020-03-22   \n",
       "4                    NaN                    NaN                     NaN   \n",
       "\n",
       "   cases_growth  death_growth  Cardiovascular diseases (%)  Cancers (%)  ...  \\\n",
       "0      0.174179      0.083333                     8.953006     3.372082  ...   \n",
       "1      0.068699      0.130051                    27.016784    13.225390  ...   \n",
       "2      0.158588      0.228578                    17.005476     6.481729  ...   \n",
       "3      0.082774      0.345238                    13.784688    17.879279  ...   \n",
       "4      0.132143      0.000000                     4.480300     3.011632  ...   \n",
       "\n",
       "   total fertility rate  obesity - adult prevalence rate  \\\n",
       "0                  4.82                              5.5   \n",
       "1                  1.53                             21.7   \n",
       "2                  2.59                             27.4   \n",
       "3                  1.43                             25.6   \n",
       "4                  5.96                              8.2   \n",
       "\n",
       "   school_shutdown_1case  school_shutdown_10case  school_shutdown_50case  \\\n",
       "0                   19.0                    -0.0                   -10.0   \n",
       "1                    2.0                     1.0                    -5.0   \n",
       "2                   16.0                     8.0                    -4.0   \n",
       "3                   14.0                    -1.0                    -3.0   \n",
       "4                    4.0                     NaN                     NaN   \n",
       "\n",
       "   school_shutdown_1death  FF_DayOfYear  case1_DayOfYear  case10_DayOfYear  \\\n",
       "0                    -8.0          82.0               55              74.0   \n",
       "1                    -0.0          71.0               69              70.0   \n",
       "2                    -0.0          72.0               56              64.0   \n",
       "3                    -6.0          82.0               62              77.0   \n",
       "4                    -5.0          89.0               80               NaN   \n",
       "\n",
       "   case50_DayOfYear  \n",
       "0              84.0  \n",
       "1              76.0  \n",
       "2              76.0  \n",
       "3              79.0  \n",
       "4               NaN  \n",
       "\n",
       "[5 rows x 70 columns]"
      ]
     },
     "execution_count": 583,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the top 5 rows of Health dataset\n",
    "health_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 584,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Country_Region', 'first_1ConfirmedCases', 'first_1Fatalities',\n",
       "       'first_10ConfirmedCases', 'first_50ConfirmedCases',\n",
       "       'first_100ConfirmedCases', 'cases_growth', 'death_growth',\n",
       "       'Cardiovascular diseases (%)', 'Cancers (%)',\n",
       "       'Diabetes, blood, & endocrine diseases (%)', 'Respiratory diseases (%)',\n",
       "       'Liver disease (%)', 'Diarrhea & common infectious diseases (%)',\n",
       "       'Musculoskeletal disorders (%)', 'HIV/AIDS and tuberculosis (%)',\n",
       "       'Malaria & neglected tropical diseases (%)',\n",
       "       'Nutritional deficiencies (%)', 'pneumonia-death-rates',\n",
       "       'Share of deaths from smoking (%)', 'alcoholic_beverages',\n",
       "       'animal_fats', 'animal_products', 'aquatic_products,_other',\n",
       "       'cereals_-_excluding_beer', 'eggs', 'fish,_seafood',\n",
       "       'fruits_-_excluding_wine', 'meat', 'milk_-_excluding_butter',\n",
       "       'miscellaneous', 'offals', 'oilcrops', 'pulses', 'spices',\n",
       "       'starchy_roots', 'stimulants', 'sugar_&_sweeteners', 'treenuts',\n",
       "       'vegetable_oils', 'vegetables', 'vegetal_products',\n",
       "       'hospital_beds_per10k', 'hospital_density', 'nbr_surgeons',\n",
       "       'nbr_obstetricians', 'nbr_anaesthesiologists', 'medical_doctors_per10k',\n",
       "       'school_closure', 'bcg_coverage', 'bcg_year_delta', 'population',\n",
       "       'median age', 'population growth rate', 'birth rate', 'death rate',\n",
       "       'net migration rate', 'maternal mortality rate',\n",
       "       'infant mortality rate', 'life expectancy at birth',\n",
       "       'total fertility rate', 'obesity - adult prevalence rate',\n",
       "       'school_shutdown_1case', 'school_shutdown_10case',\n",
       "       'school_shutdown_50case', 'school_shutdown_1death', 'FF_DayOfYear',\n",
       "       'case1_DayOfYear', 'case10_DayOfYear', 'case50_DayOfYear'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 584,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying all the columns in Health dataset\n",
    "health_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 585,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a copy of our dataset before manipulating\n",
    "health_dfqs = health_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 586,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country_Region</th>\n",
       "      <th>bcg_coverage</th>\n",
       "      <th>life expectancy at birth</th>\n",
       "      <th>infant mortality rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>82.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>27.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>97.0</td>\n",
       "      <td>74.4</td>\n",
       "      <td>15.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>99.0</td>\n",
       "      <td>53.6</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>90.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>30.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Country_Region  bcg_coverage  life expectancy at birth  \\\n",
       "175           Venezuela          82.0                      71.0   \n",
       "176             Vietnam          97.0                      74.4   \n",
       "177  West Bank and Gaza           NaN                       NaN   \n",
       "178              Zambia          99.0                      53.6   \n",
       "179            Zimbabwe          90.0                      62.3   \n",
       "\n",
       "     infant mortality rate  \n",
       "175                   27.9  \n",
       "176                   15.7  \n",
       "177                    NaN  \n",
       "178                   56.0  \n",
       "179                   30.3  "
      ]
     },
     "execution_count": 586,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting the only columns we need in Health dataset\n",
    "health_dfqs = health_dfqs[[\"Country_Region\",\"bcg_coverage\",\"life expectancy at birth\",\"infant mortality rate\"]]\n",
    "health_dfqs.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country_region</th>\n",
       "      <th>bcg</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>infant_mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>86.0</td>\n",
       "      <td>52.8</td>\n",
       "      <td>104.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Albania</td>\n",
       "      <td>99.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>10.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>99.0</td>\n",
       "      <td>77.5</td>\n",
       "      <td>17.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>NaN</td>\n",
       "      <td>83.0</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Angola</td>\n",
       "      <td>79.0</td>\n",
       "      <td>61.3</td>\n",
       "      <td>62.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_region   bcg  life_expectancy  infant_mortality\n",
       "0    Afghanistan  86.0             52.8             104.3\n",
       "1        Albania  99.0             79.0              10.8\n",
       "2        Algeria  99.0             77.5              17.6\n",
       "3        Andorra   NaN             83.0               3.5\n",
       "4         Angola  79.0             61.3              62.3"
      ]
     },
     "execution_count": 587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Renaming the column names in Health dataset for convinience \n",
    "health_dfqs = health_dfqs.rename(columns={\"Country_Region\":\"country_region\", \"bcg_coverage\":\"bcg\",\n",
    "                                      \"life expectancy at birth\":\"life_expectancy\",\"infant mortality rate\":\"infant_mortality\"})\n",
    "health_dfqs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(180, 4)"
      ]
     },
     "execution_count": 588,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking how many rows and columns we have in our dataset\n",
    "health_dfqs.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 589,
   "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>continent</th>\n",
       "      <th>country_region</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>active_cases</th>\n",
       "      <th>critical</th>\n",
       "      <th>total cases/\\n1M pop</th>\n",
       "      <th>deaths/\\n1M pop</th>\n",
       "      <th>total_tests</th>\n",
       "      <th>tests/\\n1M pop</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Russia</td>\n",
       "      <td>405,843</td>\n",
       "      <td>4,693</td>\n",
       "      <td>171,883</td>\n",
       "      <td>229,267</td>\n",
       "      <td>2,300</td>\n",
       "      <td>2,781</td>\n",
       "      <td>32</td>\n",
       "      <td>10,643,124</td>\n",
       "      <td>72,933</td>\n",
       "      <td>145,929,507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Spain</td>\n",
       "      <td>286,509</td>\n",
       "      <td>27,127</td>\n",
       "      <td>196,958</td>\n",
       "      <td>62,424</td>\n",
       "      <td>617</td>\n",
       "      <td>6,128</td>\n",
       "      <td>580</td>\n",
       "      <td>4,063,843</td>\n",
       "      <td>86,921</td>\n",
       "      <td>46,753,345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Europe</td>\n",
       "      <td>UK</td>\n",
       "      <td>274,762</td>\n",
       "      <td>38,489</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1,559</td>\n",
       "      <td>4,049</td>\n",
       "      <td>567</td>\n",
       "      <td>4,285,738</td>\n",
       "      <td>63,158</td>\n",
       "      <td>67,856,881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italy</td>\n",
       "      <td>232,997</td>\n",
       "      <td>33,415</td>\n",
       "      <td>157,507</td>\n",
       "      <td>42,075</td>\n",
       "      <td>435</td>\n",
       "      <td>3,853</td>\n",
       "      <td>553</td>\n",
       "      <td>3,878,739</td>\n",
       "      <td>64,144</td>\n",
       "      <td>60,468,778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Europe</td>\n",
       "      <td>France</td>\n",
       "      <td>188,882</td>\n",
       "      <td>28,802</td>\n",
       "      <td>68,355</td>\n",
       "      <td>91,725</td>\n",
       "      <td>1,319</td>\n",
       "      <td>2,894</td>\n",
       "      <td>441</td>\n",
       "      <td>1,384,633</td>\n",
       "      <td>21,217</td>\n",
       "      <td>65,261,942</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  continent country_region total_cases total_deaths total_recovered  \\\n",
       "0    Europe         Russia     405,843        4,693         171,883   \n",
       "1    Europe          Spain     286,509       27,127         196,958   \n",
       "2    Europe             UK     274,762       38,489             NaN   \n",
       "3    Europe          Italy     232,997       33,415         157,507   \n",
       "4    Europe         France     188,882       28,802          68,355   \n",
       "\n",
       "  active_cases critical total cases/\\n1M pop deaths/\\n1M pop total_tests  \\\n",
       "0      229,267    2,300                2,781              32  10,643,124   \n",
       "1       62,424      617                6,128             580   4,063,843   \n",
       "2          NaN    1,559                4,049             567   4,285,738   \n",
       "3       42,075      435                3,853             553   3,878,739   \n",
       "4       91,725    1,319                2,894             441   1,384,633   \n",
       "\n",
       "  tests/\\n1M pop   population  \n",
       "0         72,933  145,929,507  \n",
       "1         86,921   46,753,345  \n",
       "2         63,158   67,856,881  \n",
       "3         64,144   60,468,778  \n",
       "4         21,217   65,261,942  "
      ]
     },
     "execution_count": 589,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the top 5 rows of Сovid19 dataset\n",
    "covid19_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 590,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['continent', 'country_region', 'total_cases', 'total_deaths',\n",
       "       'total_recovered', 'active_cases', 'critical', 'total cases/\\n1M pop',\n",
       "       'deaths/\\n1M pop', 'total_tests', 'tests/\\n1M pop', 'population'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 590,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying all the columns in Covid19 dataset\n",
    "covid19_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 591,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid19_dfqs = covid19_df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 592,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting the only columns we need in Covid19 dataset\n",
    "covid19_dfqs = covid19_dfqs[['continent','country_region', 'total_cases', 'total_deaths','total cases/\\n1M pop','total_recovered']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 593,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid19_dfqs = covid19_dfqs.rename(columns={\"total cases/\\n1M pop\":\"total_cases_1M_pop\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Countries are named differently in two datasets, so we rename them in order not to lose values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 594,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving countries in the first variable\n",
    "country_health = health_dfqs.country_region.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 595,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving countries in the second variable\n",
    "country_covid = covid19_dfqs.country_region.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 596,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49"
      ]
     },
     "execution_count": 596,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for the difference between the Covid19 and Health datasets\n",
    "# Displaying countries as they presented in Covid19 dataset\n",
    "country_dif = set(country_covid) - set(country_health)\n",
    "len(country_dif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 597,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Kosovo', 'Saint Vincent and the Grenadines', 'Congo (Kinshasa)', \"Cote d'Ivoire\", 'Congo (Brazzaville)', 'Burma', 'Central African Republic', 'Korea, South', 'US', 'West Bank and Gaza', 'United Arab Emirates', 'Taiwan*', 'Holy See', 'MS Zaandam', 'United Kingdom', 'Diamond Princess'}\n"
     ]
    }
   ],
   "source": [
    "# Checking for the difference between the Health and Covid19 datasets\n",
    "# Displaying countries as they presented in Health dataset\n",
    "country_dif2 = set(country_health) - set(country_covid)\n",
    "print(country_dif2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 598,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the names \n",
    "health_dfqs[\"country_region\"] = health_dfqs[\"country_region\"].replace({\"Taiwan*\":\"Taiwan\",\"Korea, South\":\"S.Korea\", \n",
    "                              \"United Kingdom\":\"UK\",\"US\":\"USA\",\"Central African Republic\":\"CAR\",\"Burma\":\"Myanmar\",\n",
    "                              \"United Arab Emirates\":\"UAE\",\"West Bank and Gaza\":\"Palestine\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 599,
   "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>bcg</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>infant_mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>82.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>27.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>97.0</td>\n",
       "      <td>74.4</td>\n",
       "      <td>15.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>Palestine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>99.0</td>\n",
       "      <td>53.6</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>90.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>30.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country_region   bcg  life_expectancy  infant_mortality\n",
       "175      Venezuela  82.0             71.0              27.9\n",
       "176        Vietnam  97.0             74.4              15.7\n",
       "177      Palestine   NaN              NaN               NaN\n",
       "178         Zambia  99.0             53.6              56.0\n",
       "179       Zimbabwe  90.0             62.3              30.3"
      ]
     },
     "execution_count": 599,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if code above worked, e.g. \"West Bank and Gaza\" has been changed to \"Palestine\"\n",
    "health_dfqs.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 600,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "213"
      ]
     },
     "execution_count": 600,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(covid19_dfqs.country_region.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 601,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180"
      ]
     },
     "execution_count": 601,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(health_dfqs.country_region.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question1: *What is the most Covid-19 affected continent in terms of infected cases and deaths?*\n",
    "\n",
    "**The virus, which causes a respiratory illness called COVID-19 has spread across the six continents. We want to see what continent has been affected the most.**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 602,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Africa           57\n",
       "Asia             49\n",
       "Europe           48\n",
       "North America    39\n",
       "South America    14\n",
       "Oceania           6\n",
       "Name: continent, dtype: int64"
      ]
     },
     "execution_count": 602,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display how many countries do we have for each continent  \n",
    "covid19_dfqs.continent.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe with total infected cases and deaths per continent: Europe\n",
    "europe_df = covid19_dfqs.loc[covid19_dfqs['continent'] == 'Europe', ['total_cases','total_deaths']]\n",
    "# europe_df.head(10)\n",
    "# europe_df.count()\n",
    "# europe_df.info()\n",
    "europe_df_int = europe_df.apply(lambda x: x.str.replace(',',''))\n",
    "europe_df_int_na = europe_df_int.dropna()\n",
    "# europe_df['total_cases'] = pd.to_numeric(europe_df['total_cases'])\n",
    "total_cases_europe = europe_df_int_na.total_cases.astype(int).sum()\n",
    "total_deaths_europe = europe_df_int_na.total_deaths.astype(int).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 604,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe with total infected cases and deaths per continent: North America\n",
    "north_america_df = covid19_dfqs.loc[covid19_dfqs['continent'] == 'North America', ['total_cases','total_deaths']]\n",
    "# north_america_df.head(10)\n",
    "# north_america_df.count()\n",
    "north_america_df_int = north_america_df.apply(lambda x: x.str.replace(',',''))\n",
    "north_america_df_int_na = north_america_df_int.dropna()\n",
    "total_cases_NA = north_america_df_int_na.total_cases.astype(int).sum()\n",
    "total_deaths_NA = north_america_df_int_na.total_deaths.astype(int).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe with total infected cases and deaths per continent: Asia\n",
    "asia_df = covid19_dfqs.loc[covid19_dfqs['continent'] == 'Asia', ['total_cases','total_deaths']]\n",
    "# asia_df.head(10)\n",
    "# asia_df.count()\n",
    "asia_df_int = asia_df.apply(lambda x: x.str.replace(',',''))\n",
    "asia_df_int_na = asia_df_int.dropna()\n",
    "total_cases_asia = asia_df_int_na.total_cases.astype(int).sum()\n",
    "total_deaths_asia = asia_df_int_na.total_deaths.astype(int).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe with total infected cases and deaths per continent: South America\n",
    "south_america_df = covid19_dfqs.loc[covid19_dfqs['continent'] == 'South America', ['total_cases','total_deaths']]\n",
    "# south_america_df.head(10)\n",
    "# south_america_df.count()\n",
    "south_america_df_int = south_america_df.apply(lambda x: x.str.replace(',',''))\n",
    "south_america_df_int_na = south_america_df_int.dropna()\n",
    "total_cases_SA = south_america_df_int_na.total_cases.astype(int).sum()\n",
    "total_deaths_SA = south_america_df_int_na.total_deaths.astype(int).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe with total infected cases and deaths per continent: Africa\n",
    "africa_df = covid19_dfqs.loc[covid19_dfqs['continent'] == 'Africa', ['total_cases','total_deaths']]\n",
    "# africa_df.head(10)\n",
    "# africa_df.count()\n",
    "africa_df_int = africa_df.apply(lambda x: x.str.replace(',',''))\n",
    "africa_df_int_na = africa_df_int.dropna()\n",
    "total_cases_africa = africa_df_int_na.total_cases.astype(int).sum()\n",
    "total_deaths_africa = africa_df_int_na.total_deaths.astype(int).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dataframe with total infected cases and deaths per continent: Oceania\n",
    "oceania_df = covid19_dfqs.loc[covid19_dfqs['continent'] == 'Oceania', ['total_cases','total_deaths']]\n",
    "# oceania_df.head(10)\n",
    "# oceania_df.count()\n",
    "oceania_df_int = oceania_df.apply(lambda x: x.str.replace(',',''))\n",
    "oceania_df_int_na = oceania_df_int.dropna()\n",
    "total_cases_oceania = oceania_df_int_na.total_cases.astype(int).sum()\n",
    "total_deaths_oceania = oceania_df_int_na.total_deaths.astype(int).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing question: What is the most Covid-19 affected continent in terms of infected cases?\n",
    "labels = 'Europe', 'North America', 'Asia', 'South America', 'Others'\n",
    "sizes = [total_cases_europe, total_cases_NA, total_cases_asia, total_cases_SA, (total_cases_afrika + total_cases_oceania)]\n",
    "explode = (0, 0.1, 0, 0, 0)\n",
    "fig, ax = plt.subplots ()\n",
    "ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax.axis('equal')\n",
    "plt.title('Percentage of infected cases per continent')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# visualizing question: What is the most Covid-19 affected continent in terms of infected cases?\n",
    "labels = 'Europe', 'North America', 'Asia', 'South America', 'Others'\n",
    "sizes = [total_deaths_europe, total_deaths_NA, total_deaths_asia, total_deaths_SA, (total_deaths_afrika + total_deaths_oceania)]\n",
    "explode = (0.1, 0, 0, 0, 0)\n",
    "fig, ax = plt.subplots ()\n",
    "ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax.axis('equal')\n",
    "plt.title('Percentage of death cases per continent')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion: \n",
    "**As of the data available on the 31st of May 2020 - Europe is the most affected continent in terms of Covid-19 related deaths and North America is the most affected continenr in terms of Covid-19 infection cases.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question2: *Do countries where BCG vaccine was widely implemented have higher life expectancy or lower infant mortality rate?*\n",
    "\n",
    "**Vaccines play a central role in increasing life expectancy. We want to see whether Bacille Calmette–Guerin (BCG) vaccine alone has any correlation with life expectancy. The BCG vaccine is the most commonly used vaccine against tuberculosis (TB) worldwide. The vaccine is commonly given to infants under 1 years old.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "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>bcg</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>infant_mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>175</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>82.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>27.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>176</td>\n",
       "      <td>Vietnam</td>\n",
       "      <td>97.0</td>\n",
       "      <td>74.4</td>\n",
       "      <td>15.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>177</td>\n",
       "      <td>Palestine</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>99.0</td>\n",
       "      <td>53.6</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>179</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>90.0</td>\n",
       "      <td>62.3</td>\n",
       "      <td>30.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country_region   bcg  life_expectancy  infant_mortality\n",
       "175      Venezuela  82.0             71.0              27.9\n",
       "176        Vietnam  97.0             74.4              15.7\n",
       "177      Palestine   NaN              NaN               NaN\n",
       "178         Zambia  99.0             53.6              56.0\n",
       "179       Zimbabwe  90.0             62.3              30.3"
      ]
     },
     "execution_count": 611,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "health_df_q2 = health_dfqs.copy()\n",
    "health_df_q2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    139.000000\n",
       "mean      89.618705\n",
       "std       14.147974\n",
       "min       24.000000\n",
       "25%       86.500000\n",
       "50%       96.000000\n",
       "75%       98.500000\n",
       "max       99.000000\n",
       "Name: bcg, dtype: float64"
      ]
     },
     "execution_count": 612,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing basic descriptive stats of BCG coverage\n",
    "health_df_q2[\"bcg\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    164.000000\n",
       "mean      20.842683\n",
       "std       20.288738\n",
       "min        1.700000\n",
       "25%        5.300000\n",
       "50%       11.650000\n",
       "75%       30.325000\n",
       "max      104.300000\n",
       "Name: infant_mortality, dtype: float64"
      ]
     },
     "execution_count": 613,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing basic descriptive stats of infant mortality\n",
    "# Infant mortality rate is the number of deaths per 1,000 live births of children under one year of age. \n",
    "health_df_q2[\"infant_mortality\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    164.000000\n",
       "mean      73.467683\n",
       "std        7.248634\n",
       "min       52.800000\n",
       "25%       68.800000\n",
       "50%       75.250000\n",
       "75%       78.525000\n",
       "max       86.000000\n",
       "Name: life_expectancy, dtype: float64"
      ]
     },
     "execution_count": 614,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing basic descriptive stats of infant mortality\n",
    "health_df_q2[\"life_expectancy\"].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {},
   "outputs": [],
   "source": [
    "health_df_q2=health_df_q2.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 616,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138"
      ]
     },
     "execution_count": 616,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking how many countries we have in Health dataset\n",
    "len(health_df_q2[\"country_region\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 617,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show distribution of the results\n",
    "plt.scatter(health_df_q2['bcg'], health_df_q2['infant_mortality'], color = 'blue')\n",
    "plt.title(\"BCG vaccine vs Infant Mortality\")\n",
    "plt.xlabel(\"BCG coverage\")\n",
    "plt.ylabel(\"Infant mortality\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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r9p5eT8PaV4+ZtWeY+gPq9ghbtQZlqUytotW+eiTNz7++GrgduAj4v0rYiLirt6ekLVzGb2ZlVaTMv5WBXhpd9fejjH8dsJbU5PJvgO/keZX5ZmYjoZPnBooM1r50aToJbN6cfs5M+tX7n1UnKy9YUDymZmbXWxARjwaQtH1E3F+9TNL23QvBzGxwZja3rDw3AMX6xN9rr9pX/EUro9esgZe+FB7IfRTXu6rfbbdi2yuiSOXudwrOMzMrpUZX9O08kFWtVmX07Nnpir/Ik7enn74l6Tdy7bXF4imi7hW/pEeQHrbaQdKTgUrJ086knjbNzEpvzRo4+WR48MH0+sYb02tIV/RFimoaqdwVVMrw582D++7bsrzy5C3A2Wdvu/6ddxbbTzc1qtxdBpwELAG+x5bEfw+wKiIu7EeA4MpdM2vfwoW1k+uCBXDHHd1/Unj27NpP2E5MwEMPbTu/lSacPa/cjYhVpP51Xh4RR0XEkXk6rp9J38ysE/WuqCvzu/3cQL1uFTZtql3U1M1K26IalvFHxGbgL/sUi5lZ33V7sPOJifrLanU6d9ZZMGdOZ9ttVZHK3Usl/bWkR0maX5m6F4KZWe/Uu6Lu1ZV2pUVQI9WVx0uXwqmnNk/sRbZbVN3K3SovzT9fUTUvgL27F4aZWW+cddbWzSUB5s5N86Hz5pwzVSpwV65s3JtmpV5hzRo455zWe97shHvnNLOR1+jJ2V53A12vshdS0VLRXkfb6Z205W6ZJR0VEV+T9Pxay92qx8xGQa+7ge5mx2vdatXTqKjn6cDXgOfU2j9pKEUzs6HW6ZO3zUxNda8//m5p1GXD2/LPk/sXjplZf61Yse0IWd3sBrrW9getyEAsCyT9i6QrJa2TdJakAbQ8NTPrvm4354Stu4g488zUX39l+91sltmuIs05PwVMAy8gDZI+DZzfy6DMzPqpWe+ZzVQn+oULUyui6sHiV61KV/6bN6ff587twYdoQZHEPz8i3hkRN+TpXcAuvQ7MzGwYVJqDVhL9nXdu2+nazE7fBt2Yskji/7qkEyTNytOLgC/2OjAzs2FQq3fPWiqdvp155pYO4walUe+c95Ja7wh4HfDxvGgCuA94W6MNS9qXrYuE9gbeSrpbeBmpyAjgTRHxpXaCNzMbtKK9eFYGWRn01T40btWzUycbjojrgQMAJE0APycN33gy8IGIeH8n2zczK4N6zUFn6vTJ3G7WCxQp6umGo4GfRkTJWrOa2ThodWjFVt5fq3fPOXNSX0BS/aEUW1VksJai+pX4TwA+WfX6lZKulnSupF1rrSDpNElrJa2dnp6u9RYzs6ZmVr7O7B2z0/fXag563nmpr//Nm5s//Vv0xNCtEwj0oa8eSXOBW4D9IuI2SbsBd5DqD94J7B4RL220DXfZYGbtarUvnm733VOky4a5c2GnneCuuxrXAfR8IJaqFT9eZF4DzwKujIjbACLitojYlPv6/zBwcAvbMjNrSatDK3Y6FONMRbp/fuCB1Ay0XxW/RW4e9qt+kStqD2xhHydSVcwjafeqZc8DrmlhW2ZmLanX50635jdz1lmDf2BrprqJX9IZuUnnEyXdk6d7gduBi4tsXNIk8Eds3aHb+yT9QNLVwJHAa9sP32z4tVrxOGjDFm+rQyt2eyjGpUvhlFM676qhqwPHRETDCfj7Zu/p9XTggQeG2ShavTpicjIi3eSnaXIyzS+jYYu3YvXqiKmpCCn9bBZvq+9vtq2Zx6ydafny1vcNrI0aObVQ5W5uebMPsH3VCeO/u3j+aciVuzaqej0ISLcNW7xlUO+YtaqdY9xOf/yVFU8FTgceCawHDgW+CxzVWghmNlO3KxJ7bdjiLYNuHZtuHuMilbunAwcBN0bEkcCT2dLdgpl1oNsVib02bPGWQbeOzfz53dkOFEv890fE/QCStouIHwH7di8E65Zhq3Sz7lck9tqwxVsGtY7ZoBVJ/DdL2gX4LHCppItJD2RZibT6tKGVQy8GAemlYYu3DGods+XLt7xesCB18dDMXXd1L6aWntyV9HTgYcAlEdHFniMac+Vuc650MyuPNWtS98sbN6ainhUrGp8cq98/a1btDt26WbnbUu8PEfHNiPhcP5O+FeNKN7Pielks2s7dd/UIYKtW9b44rV+dtFmPudLNrJheF4vWGphl5ghcjfSjOM2Jf0S40s2smE4TczPDcPddpJO2V9brOtnKw5VuZsX0OjHXu8uujMC1eDH81V/VL2paswZOPnnrO5KTT+5ucVTTyl1J7yL1p38lcC7wlWilRrgLXLlrZt3Si4YQ1ZWz8+bBffe1tv7cuXDuuelCbeHC1FPnTAsWpD7+W9F25W5EvJnUXcNHgJOAH0t6t6THtBaCmdngdbtYdGadQatJH1K3zKefnn6vlfQbzW9HoTL+fIX/izw9BOwKXCDpfd0Lxcys97pdLFqrzqAd3UzszRQp6nk1sIw0atY5wGcj4kFJs4AfR0TPr/xd1GNmZTVrVvcGUIkoSVEPsBB4fkQ8MyI+ExEPpgBjM/Ds1sIwMxst3WoyXelvv9bALXPnpvndUiTxfwn4v4eFJe0k6RCAiLiue6GY2bga5n6matUZzJ2bEnm9LhpmDpw+Z86WxL50aarorS6KqlT8dkuRxP8hoLq64td5nplZx4a9n6ladQbnnpuKZTZvTi2Fzj57y5O5d9wBH/vY1u8/77z+Nr0uUsa/PiIOmDHv6oh4Yk8jq+IyfrPR5X6mtlY5EVZXGE9OtlcB3UkZ/88kvVrSnDydDvystd2bmdU2DE+69lOvnyyGYon/5cDhwM+Bm4FDgNO6F4KZjTP3M7W1esM0dmP4xooiD3DdHhEnRMTDI2K3iPjziLi92XqS9pW0vmq6R9JrJM2XdKmkH+ef7g7CbIy5n6mtTUy0Nr8dRfrqWSTpTZJWSjq3MjVbLyKuj4gDcv3AgcBvgIuANwKXRcQ+wGX5tZmNqGYtdtzP1NZq9cXfaH47ihT1XEwafOW/gC9WTa04GvhpRNwIHAesyvNXAce3uC0zGxJFW+xU90e/YUPnSX+Ym4dOTbU2vx2zC7xnMiLe0OF+TgA+mX/fLSJuBYiIWyU9vMNtm1lJNaqo7NUV/cxWMZWTDQzHXcSKFak3zgcf3DJvzpz+D8TyBUnHtrsDSXOB5wKfaXG90yStlbR2enq63d2b2QANosVOP1rF9JrU+HWniiT+00nJ/7e5gvZeSfe0sI9nAVdGxG359W2SdgfIP2tWFEfEyohYEhFLFi1a1MLuzKwsetVip1FRzrA3Dz3zzNRbZ7UHHuhzc86I2CkiZkXEDhGxc369cwv7OJEtxTwAnyN1+kb+eXEL2zKzIdKLFjvN6g2GsXlo9YmsXrPNbp64CnXLLGlXSQdLOqIyFVxvEvgj4MKq2e8B/kjSj/Oy97QatJkNh1602GlWlHNsnYLpevMHbeaJrJ5unriKdNlwKqm455HAeuBQ4LsRcVT3wmjMXTaYWUW9bpCl1Cpo2LqAqBdvtUF02XA6cBBwY0QcCTwZcG2rmQ1Es6KcYSvjbxRXr55rKJL474+I+1MQ2i4ifgTs270QzMyKa1ZvMGxl/PXimprq3nMNMxVJ/DdL2gX4LHCppIuBW7obhplZMc3qDfrRBUSnD4hVr3/ffamdfrWed1kREYUn4OmkNvlzWlmv0+nAAw8MM7OiVq+OmJqKkNLP1au7u+3JyYhU05Cmycni+6i1/ty5EQsWdD9eYG3UyKlFKnc/HhEvaTavl1y5a2Zl0WnlcT8rnzup3N1vxoYmSJ2umVkbhrkfGeu88rgMlc91E7+kMyTdCzwxP7F7T359O37oyqwtwzDMoE9MjXVaeVyGyue6iT8i/j4idgL+IdITu5WndhdExBn9C9FsdJS9H5lhODENWqeVx2UYf6BIUc8Vkh5WeSFpF0nuStmsDb24ze/mFXrZT0xl0OnTyGUYf6Ddwdaviogn9zSyKq7ctVHR7Yq9bg7MDc2firXh0knlbq33FOnH38xm6PZtfrev0MtQ/my9VyTxr5X0T5IeI2lvSR8A1vU6MLNR1O3b/G4XHZWh/HkYDHsFeJHE/yrgAeB84NPAb4FX9DIos1HWzWEGu32FXoby53Y0S8TdTNQjUQFe66muWhOwY9H3dnvyk7tmtXX6FOkoaHYMun2Mpqa23lZlmprq1ifqHuo8udv0il/S4ZKuBa7Nr58k6ezeno7MrIhhvULvpmb1HN2uB+lG8dqgi4qKtOq5HHgh8LnILXkkXRMR+/chPsCtesysvmYtkbrdUqnTllndbonVSCeteoiIm2bM2tSVqMzMOtSsnqNIPUgrV+CdVoDXuwNZtqx/dwBFEv9Nkg4HQtJcSX8NXNfbsGxUDPqW1oZTNxNxs+WtVtZ2WrxWr0ho06Y+VhbXKvivnoCFwBrgNtLIW6uBBc3W6+bkyt3h5IpHa0c7fzfNumFutLzflbX19teL/VOncncgrXRanZz4h9MwtX6w/mmWpPv9dyPV3p9UfBut9P9f68TW6f7rqZf4i7Tq2VvS5yVNS7pd0sWS9u7hTYiNiDJ0P2vlUqRYpd9/N50+C9FpUdHERGf7b0eRMv5PkB7c2h3YA/gM8MkiG88dul0g6UeSrpN0mKS3S/q5pPV5Orb98K3M/Pi/zVSkaWW//256VVnbqLlo9UN8q1Ztu/85c9KQjD2rG6t1G1A9AZfXmPc/zdbL71sFnJp/nwvsArwd+Osi61cmF/UMJ5fx90YvhxXstSLFKoP4u+nkmHajqGj58oiJiS3rzZ7dnc9Pu2X8wHuANwKLgSngb4G3APOB+Q3W2xm4gfysQNV8J/4xMsxJqox6kRT7+R0VLb8v299NLyuHi5b5t1PH0Univ6HB9LMG6x0AXAF8FLgKOAeYlxP/BuBq4Fxg1zrrnwasBdbutdderX9isxHU7YrPfl9dd2t//Twx9LpLiKKtfNqp7G078bc7AUuAh4BD8uuzgHcCuwETpPqFFcC5zbblK36zpBvFCtUG0fKq06Td75NVkWPUi6KiXl7xF+my4Z3A2yNiU369M3BWRJzcZL1HkOoCFufXTwPeGBF/UvWexcAXokn3D+6ywSzp9kAuwzjwSrePQTO9Pkb1Pk+1drt06KTLhtmk4RefKOkZwPco0B9/RPyC9NTvvnnW0cC1knavetvzgGsKxGBmdL+//GFseTVszT2bqfWdzpkDCxb0ruO9pok/0sDqbwAuJ5XX/0lE/FvB7b8KWCPpalKZ/7uB90n6QZ53JPDadgI3G0fd7o1zGAdeGbbmns3U+k5PPRV23LE726+pVvlP9QQcAfwQOIPUpv8SYI9m63Vzchm/We+UrQVNM8PW3LOdfXXr89FBGf8VwEkRUemP//nAuyPicT08H23FZfxmVm3NmvSA1MaN6Up/xYrRGYOgm3UY9cr4iyT+icgVu1XzFkTEna2F0D4nfjMbF92sTO6kcnehpI9IuiRv6PHA8a3t3swGxV1j918nx7wfdRhFEv9Hga+Q+uoB+F/gNd0Lwcx6ZSQGBh8ynR7zflS4F7rij4hPA5sBIuIhPAKX2VDo9nizVlv1Ff6yZZ0d836Mo1wk8f9a0gIgACQdCvyqeyGYWa+4a+zem3mFv6nOZXGjYz6zaAi29N65YUP3K65nF3jP64DPAY+R9G1gEWnwdTMrub32qt1CpMwPaA2bWndVtdQ75jMHX68UDUHvWioVeYDrSuDpwOHAXwL7RcTVvQnHzLqpSHmxK387OwZF7p4aldEPpDiuVuP+sk1+gMusfY0ePvKYCb3rXXNiotgDX93ueK8a7T7AVQZux2/WG/3u8KyMOj0GM4tqoLVO1Xr5HXTSjt/MRpQrfzs/Bp22whlEf0lFBluXpBdLemt+vZekg3sXkpn1Sy8eFhq2OoNuHIPqMXRbbYXTj+abMxW54j8bOAw4Mb++F/hgzyIys77p9tXmMD4wVoYeSjs5cbSjSOI/JCJeAdwPEBF3kwZON7Mh1+2rzWF5YKz6ruTMM9NDV/284h60Iu34H5Q0wZYHuBaRn+I1s+G3dGn3kly9kaSajTDVT7Xaza9aNfrJvlqRK/5/AS4CHi5pBfAt0oAqZmZDZ1juSnqp7ghGpO0AAA0uSURBVBW/pEdHxA0RsUbSOtLQiQKOj4jr+hahmVkXuSVT46KeC4ADJV0WEUcDP+pTTGY2pCYmavdVMzHR/1jqcTcWjYt6Zkl6G/BYSa+bOfUrQDMbHpU+ZorOH4QytOIZtEaJ/wRSS57ZwE41JjPrg2FqF3/22bB8+ZYr/ImJ9PrsswcbV7VBtJsvmyJDLz4rIr7cp3hqcpcNNq467Q7AxlvLY+5KenFErJb0enJTzmoR8U8FdroLcA6wf97GS4HrgfOBxcAG4EX52YC6nPhtXLkvHetEO331zMs/d2TbYp4dC+73LOCSiHgc8CTgOuCNwGURsQ9wWX5tZjW4BUp/DFNxWjfUbdUTEf+Rf75j5jJJTcfclbQzcARwUt7OA8ADko4D/jC/bRXwDeANrYVtNh7cAqX3BjEQyqC12ztnkVY9ewPTwHmSrpJ0jqR5wG4RcStA/vnwWitLOk3SWklrp6en2wzTbLi5BUrvjeMDXe0mfhV4z2zgKcCHIuLJwK9poVgnIlZGxJKIWLJo0aI2wzQbbmVsgTJqxSLjWJxWpK+eWoqM3nIzcHNEXJ5fX0BK/LdJ2j0ibpW0O3B7mzGYjYVu9qXTqVEsFhnH4rS6V/yS7pV0T43pXmCPZhuOiF8AN0naN886GriWNHD7sjxvGXBxZx/BzPplFItFxrE4rVHlbjce0noVsEbSXOBnwMmkk82nJZ0CbAT+tAv7MbM+GMVikcqdyplnps+x114p6Q/rHUwRHnPXzArzcwXDxWPumlnHxrFYZBQ58ZsNuX62siljKyNrXbutesysBAbRyqZMrYysPb7iNxtio9jKxnrPid9siI1iKxvrPSd+syFW7yGjUX74yDrnxG82xNzKxtrhxG82xNzKxtrhVj1mQ86tbKxVvuI3MxszTvxmZmPGid/MbMw48ZuZjRknfjOzMePEb2Y2Zpz4zcy6rOzjErsdv5lZFw3DuMS+4jcz66Jh6DHVid/MrIuGocdUJ34zsy4ahh5TnfjNzLpoGHpM7Wnil7RB0g8krZe0Ns97u6Sf53nrJR3byxjMzPppGHpM7UerniMj4o4Z8z4QEe/vw77NzPqu7D2muqjHzGzM9DrxB/BVSesknVY1/5WSrpZ0rqRda60o6TRJayWtnZ6e7nGYZmbjo9eJ/w8i4inAs4BXSDoC+BDwGOAA4FbgH2utGBErI2JJRCxZtGhRj8M0MxsfPU38EXFL/nk7cBFwcETcFhGbImIz8GHg4F7GYGaNlb17Aeu+niV+SfMk7VT5HXgGcI2k3ave9jzgml7FMG78D2ytqnQvcOONELGlewH/7Yy2Xrbq2Q24SFJlP5+IiEskfVzSAaTy/w3AX/YwhrExDP2DWPk06l7AfzejSxEx6BiaWrJkSaxdu3bQYZTa4sUp2c80NQUbNvQ7GhsWs2alK/2ZJNi8uf/xWLJmTTr5btyYnvhdsaK9E7GkdRGxZOZ8N+ccEcPQP4iVzzB0LzBu+lH85sQ/IvwPbO0Yhu4Fxk0/evd04h8R/ge2dgxD9wLjph937078I8L/wNaupUtTPdDmzemn/2YGqx937078I8T/wDauRqkpcz/u3p34zWyojdqzCP24e3dzTjMbam7KXJ+bc5rZSHJT5tY58ZvZUHNT5tY58ZvZUHNT5tY58ZvZUHNT5tb1Y+hFM7OeKvtQh2XjK34zszHjxG9mNmac+M3MxowTv5nZmHHiNzMbM0PRZYOkaaDGQ9mlsRC4Y9BBNFD2+KD8MZY9Pih/jGWPD0YvxqmIWDRz5lAk/rKTtLZWfxhlUfb4oPwxlj0+KH+MZY8PxidGF/WYmY0ZJ34zszHjxN8dKwcdQBNljw/KH2PZ44Pyx1j2+GBMYnQZv5nZmPEVv5nZmHHiNzMbM078LZC0vaQrJH1f0g8lvSPPf7SkyyX9WNL5kuYOOM4JSVdJ+kJJ49sg6QeS1ktam+fNl3RpjvFSSbsOOMZdJF0g6UeSrpN0WFlilLRvPnaV6R5JrylLfFVxvjb/n1wj6ZP5/6c0f4uSTs+x/VDSa/K8gR5DSedKul3SNVXzasak5F8k/UTS1ZKeUnQ/Tvyt+R1wVEQ8CTgA+GNJhwLvBT4QEfsAdwOnDDBGgNOB66pely0+gCMj4oCq9shvBC7LMV6WXw/SWcAlEfE44Emk41mKGCPi+nzsDgAOBH4DXFSW+AAk7Qm8GlgSEfsDE8AJlORvUdL+wMuAg0nf77Ml7cPgj+FHgT+eMa9eTM8C9snTacCHCu8lIjy1MQGTwJXAIaSn6Gbn+YcBXxlgXI/MfxxHAV8AVKb4cgwbgIUz5l0P7J5/3x24foDx7QzcQG78UMYYq2J6BvDtssUH7AncBMwnjfvxBeCZZflbBP4UOKfq9VuAvy3DMQQWA9c0+7sD/gM4sdb7mk2+4m9RLkZZD9wOXAr8FPhlRDyU33Iz6Y9+UP6Z9Ae8Ob9eQLniAwjgq5LWSTotz9stIm4FyD8fPrDoYG9gGjgvF5mdI2leyWKsOAH4ZP69NPFFxM+B9wMbgVuBXwHrKM/f4jXAEZIWSJoEjgUeRYmOYZV6MVVOrhWFj6cTf4siYlOkW+xHkm4Tf7/W2/obVSLp2cDtEbGuenaNtw66De8fRMRTSLeqr5B0xIDjmWk28BTgQxHxZODXDL7oaRu5fPy5wGcGHctMuRz6OODRwB7APNL3PdNA/hYj4jpSsdOlwCXA94GHGq5UPm3/bzvxtykifgl8AzgU2EVSZRjLRwK3DCisPwCeK2kD8ClScc8/U574AIiIW/LP20ll0wcDt0naHSD/vH1wEXIzcHNEXJ5fX0A6EZQpRkiJ9MqIuC2/LlN8xwA3RMR0RDwIXAgcTon+FiPiIxHxlIg4ArgL+DHlOoYV9WK6mXSXUlH4eDrxt0DSIkm75N93IP1xXwd8HXhhftsy4OJBxBcRZ0TEIyNiMakI4GsRsbQs8QFImidpp8rvpDLqa4DP5dhgwDFGxC+AmyTtm2cdDVxLiWLMTmRLMQ+UK76NwKGSJiWJLcewTH+LD88/9wKeTzqWZTqGFfVi+hzwF7l1z6HArypFQk0NomJlWCfgicBVwNWkZPXWPH9v4ArgJ6Tb7u1KEOsfAl8oW3w5lu/n6YfAmXn+AlKl9I/zz/kDPn4HAGvzd/1ZYNcyxUhqXHAn8LCqeaWJL8fzDuBH+X/l48B2Jftb/H+kk9H3gaPLcAxJJ59bgQdJV/Sn1IuJVNTzQVI94w9ILagK7cddNpiZjRkX9ZiZjRknfjOzMePEb2Y2Zpz4zczGjBO/mdmYceK3oSRpU+6Z8vuSrpR0eNWyx0r6Uu618DpJn5a0W152sKRv5J4Or5T0RUlPGNwnMes/N+e0oSTpvojYMf/+TOBNEfF0SduT2jS/LiI+n5cfSep7Zxq4HPjziPhOXvZUUodxnx3E58gxTETEpkHt38aPr/htFOxM6uIX4M+B71aSPkBEfD0irgFeCayqJP287Fu1kr6kHSWdpzRuwNWSXpDnn5jnXSPpvXnecknvq1r3JEn/mn9/sdIYDusl/YekiTz/Pkl/J+ly4DBJb5X0vbzdlflpVyQdlPf/XUn/UOmnPXcW+A95nasl/WU3D6iNNid+G1Y75GT6I+Ac4J15/v6kXiBr2Y/UlXYRbyE9Av+EiHgi8DVJe5A69jqK9GTvQZKOJ/Xl8/yqdf8MOF/S7+ff/yBSx36bgKX5PfNIXe8eEhHfAv4tIg6K1Hf9DsCz8/vOA14eEYfl9StOyfEdBBwEvEzSowt+NhtzTvw2rH4baTCSx5EGrvhY5Sq5KKWRoK6TdFaNxceQHocHICLuJiXYb0TqeOwhYA1wRERMAz+TdKikBcC+wLdJ/dMcCHwvd+V9NKnLAkhJ/D+r9ndkjucHpBPLfrlfqJ2q7lA+UfX+Z5D6aVlPKr5aQBqQw6yp2c3fYlZuEfFdSQuBRaT+f55e560/JPWyeXFe7xBJL2TL1XU1sW0Xt41OLOcDLyL1TXNRREQ+Ea2KiDNqvP/+Srl+rpc4m9TXyk2S3g5s32R/Al4VEV9p8B6zmnzFb0NP0uNIQ/vdSboqPlzSn1Qt/+PccueDwEnVLYBInZ3V8lVSnUBlG7uSrqyfLmlhLqs/EfhmfsuFwPF53vl53mXAC6t6gZwvaarGvrbPP++QtCO598p8l3Fv7nkRUo+rFV8Blkuak7f92NzbqVlTvuK3YbVDLuaAdPW7LF9B/1ZpQJp/lvTPpF4OrwZOj4jbJP0Z8F6lMWFvJw0F+Hc1tv8u4IO5MnUT8I6IuFDSGaSuhQV8KSIqdw93S7oWeHxEXJHnXSvpzaTRxmblWF4B3Fi9o4j4paQPk1ojbQC+V7X4FODDkn5NGv/hV3n+OaQh+q7MdxbTpBOPWVNuzmlWYpJ2jIj78u9vJI2pevqAw7Ih5yt+s3L7k3yXMZt0p3DSYMOxUeArfjOzMePKXTOzMePEb2Y2Zpz4zczGjBO/mdmYceI3Mxsz/x+kMy/wudL6sAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show distribution of the results\n",
    "plt.scatter(health_df_q2['bcg'], health_df_q2['life_expectancy'], color = 'blue')\n",
    "plt.title(\"BCG vaccine vs life expectancy\")\n",
    "plt.xlabel(\"BCG coverage\")\n",
    "plt.ylabel(\"Life expectancy at birth\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "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>bcg</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>infant_mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>bcg</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.203503</td>\n",
       "      <td>-0.282999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>life_expectancy</td>\n",
       "      <td>0.203503</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.932175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>infant_mortality</td>\n",
       "      <td>-0.282999</td>\n",
       "      <td>-0.932175</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       bcg  life_expectancy  infant_mortality\n",
       "bcg               1.000000         0.203503         -0.282999\n",
       "life_expectancy   0.203503         1.000000         -0.932175\n",
       "infant_mortality -0.282999        -0.932175          1.000000"
      ]
     },
     "execution_count": 619,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing out the correlation among columns\n",
    "# Unsurpisingly, we have high negative correlation between life expectancy and infant mortality. \n",
    "# Only weak positive correlation between BCG coverage and life expectancy\n",
    "health_df_q2.corr(method = \"pearson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>country_region</th>\n",
       "      <th>bcg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>Austria</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>21</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>24</td>\n",
       "      <td>Brunei</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25</td>\n",
       "      <td>Bulgaria</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>35</td>\n",
       "      <td>Chile</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>40</td>\n",
       "      <td>Costa Rica</td>\n",
       "      <td>83.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>42</td>\n",
       "      <td>Croatia</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>Cuba</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>45</td>\n",
       "      <td>Czechia</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>49</td>\n",
       "      <td>Dominica</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>56</td>\n",
       "      <td>Estonia</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>59</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>60</td>\n",
       "      <td>Finland</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>61</td>\n",
       "      <td>France</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>67</td>\n",
       "      <td>Greece</td>\n",
       "      <td>50.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>76</td>\n",
       "      <td>Hungary</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>Ireland</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>Israel</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>86</td>\n",
       "      <td>Japan</td>\n",
       "      <td>84.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>90</td>\n",
       "      <td>S.Korea</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>92</td>\n",
       "      <td>Kuwait</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>95</td>\n",
       "      <td>Latvia</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>100</td>\n",
       "      <td>Lithuania</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>107</td>\n",
       "      <td>Malta</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>Mauritius</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>114</td>\n",
       "      <td>Montenegro</td>\n",
       "      <td>88.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>128</td>\n",
       "      <td>Panama</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>133</td>\n",
       "      <td>Poland</td>\n",
       "      <td>94.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>134</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>135</td>\n",
       "      <td>Qatar</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>136</td>\n",
       "      <td>Romania</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>137</td>\n",
       "      <td>Russia</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>Serbia</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>Seychelles</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>Singapore</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>Slovakia</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>Slovenia</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>154</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>157</td>\n",
       "      <td>Sweden</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>162</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>170</td>\n",
       "      <td>Ukraine</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>171</td>\n",
       "      <td>UAE</td>\n",
       "      <td>96.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>173</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             country_region   bcg\n",
       "6                 Argentina  99.0\n",
       "9                   Austria  90.0\n",
       "15                  Belarus  97.0\n",
       "21   Bosnia and Herzegovina  95.0\n",
       "24                   Brunei  99.0\n",
       "25                 Bulgaria  96.0\n",
       "35                    Chile  93.0\n",
       "40               Costa Rica  83.0\n",
       "42                  Croatia  98.0\n",
       "43                     Cuba  99.0\n",
       "45                  Czechia  98.0\n",
       "49                 Dominica  98.0\n",
       "56                  Estonia  95.0\n",
       "59                     Fiji  99.0\n",
       "60                  Finland  98.0\n",
       "61                   France  78.0\n",
       "67                   Greece  50.0\n",
       "76                  Hungary  99.0\n",
       "82                  Ireland  77.0\n",
       "83                   Israel  68.0\n",
       "86                    Japan  84.0\n",
       "90                  S.Korea  98.0\n",
       "92                   Kuwait  99.0\n",
       "95                   Latvia  94.0\n",
       "100               Lithuania  97.0\n",
       "107                   Malta  96.0\n",
       "109               Mauritius  98.0\n",
       "114              Montenegro  88.0\n",
       "128                  Panama  99.0\n",
       "133                  Poland  94.0\n",
       "134                Portugal  32.0\n",
       "135                   Qatar  97.0\n",
       "136                 Romania  98.0\n",
       "137                  Russia  96.0\n",
       "145                  Serbia  98.0\n",
       "146              Seychelles  99.0\n",
       "148               Singapore  99.0\n",
       "149                Slovakia  90.0\n",
       "150                Slovenia  96.0\n",
       "154               Sri Lanka  99.0\n",
       "157                  Sweden  24.0\n",
       "162                Thailand  99.0\n",
       "170                 Ukraine  39.0\n",
       "171                     UAE  96.0\n",
       "173                 Uruguay  98.0"
      ]
     },
     "execution_count": 620,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show that countries with higher bcg coverage have lower infant mortality rates\n",
    "health_df_q2.loc[(health_df_q2['infant_mortality'] <= 10), ['country_region', 'bcg']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "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>bcg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>33</td>\n",
       "      <td>CAR</td>\n",
       "      <td>74.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>34</td>\n",
       "      <td>Chad</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>116</td>\n",
       "      <td>Mozambique</td>\n",
       "      <td>95.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>122</td>\n",
       "      <td>Niger</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>90.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>Somalia</td>\n",
       "      <td>39.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>178</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country_region   bcg\n",
       "0      Afghanistan  86.0\n",
       "33             CAR  74.0\n",
       "34            Chad  70.0\n",
       "116     Mozambique  95.0\n",
       "122          Niger  77.0\n",
       "147   Sierra Leone  90.0\n",
       "151        Somalia  39.0\n",
       "178         Zambia  99.0"
      ]
     },
     "execution_count": 621,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show that even countries with higher bcg coverage often have lower life expectancy\n",
    "health_df_q2.loc[(health_df_q2['life_expectancy'] <= 60), ['country_region', 'bcg']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Conclusion:** \n",
    "**We see that BCG is really important to reduce infant mortality. However, the correlation between life expectancy and BCG is not strong. One possible explanation behind the inconclusive results is that BCG vaccine is not mandatory in more developed countries with lower risks of TB.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question3: *Do countries where BCG vaccine was widely implemented have fewer infection rates per million?*\n",
    "\n",
    "**The spread of COVID-19 and its impact varies across countries in the world. There were rumours that lower frequency of cases and mortalities can be attributed to Bacille Calmette–Guerin (BCG) vaccination. The BCG vaccine is the most commonly used vaccine against tuberculosis (TB) worldwide**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Merging the two datasets**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 622,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\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>continent</th>\n",
       "      <th>country_region</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_cases_1M_pop</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>bcg</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>infant_mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Russia</td>\n",
       "      <td>405,843</td>\n",
       "      <td>4,693</td>\n",
       "      <td>2,781</td>\n",
       "      <td>171,883</td>\n",
       "      <td>96.0</td>\n",
       "      <td>71.9</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Spain</td>\n",
       "      <td>286,509</td>\n",
       "      <td>27,127</td>\n",
       "      <td>6,128</td>\n",
       "      <td>196,958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Europe</td>\n",
       "      <td>UK</td>\n",
       "      <td>274,762</td>\n",
       "      <td>38,489</td>\n",
       "      <td>4,049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.1</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italy</td>\n",
       "      <td>232,997</td>\n",
       "      <td>33,415</td>\n",
       "      <td>3,853</td>\n",
       "      <td>157,507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.5</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Europe</td>\n",
       "      <td>France</td>\n",
       "      <td>188,882</td>\n",
       "      <td>28,802</td>\n",
       "      <td>2,894</td>\n",
       "      <td>68,355</td>\n",
       "      <td>78.0</td>\n",
       "      <td>82.2</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  continent country_region total_cases total_deaths total_cases_1M_pop  \\\n",
       "0    Europe         Russia     405,843        4,693              2,781   \n",
       "1    Europe          Spain     286,509       27,127              6,128   \n",
       "2    Europe             UK     274,762       38,489              4,049   \n",
       "3    Europe          Italy     232,997       33,415              3,853   \n",
       "4    Europe         France     188,882       28,802              2,894   \n",
       "\n",
       "  total_recovered   bcg  life_expectancy  infant_mortality  \n",
       "0         171,883  96.0             71.9               6.5  \n",
       "1         196,958   NaN             82.0               3.2  \n",
       "2             NaN   NaN             81.1               4.1  \n",
       "3         157,507   NaN             82.5               3.2  \n",
       "4          68,355  78.0             82.2               3.2  "
      ]
     },
     "execution_count": 622,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_q3 = covid19_dfqs.merge(health_dfqs, on = ['country_region']) \n",
    "merged_df_q3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 171 entries, 0 to 170\n",
      "Data columns (total 9 columns):\n",
      "continent             171 non-null object\n",
      "country_region        171 non-null object\n",
      "total_cases           171 non-null object\n",
      "total_deaths          155 non-null object\n",
      "total_cases_1M_pop    171 non-null object\n",
      "total_recovered       169 non-null object\n",
      "bcg                   136 non-null float64\n",
      "life_expectancy       161 non-null float64\n",
      "infant_mortality      161 non-null float64\n",
      "dtypes: float64(3), object(6)\n",
      "memory usage: 13.4+ KB\n"
     ]
    }
   ],
   "source": [
    "merged_df_q3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 624,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(122, 9)"
      ]
     },
     "execution_count": 624,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop NaN values\n",
    "merged_df_q3 = merged_df_q3.dropna()\n",
    "merged_df_q3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 625,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 122 entries, 0 to 164\n",
      "Data columns (total 9 columns):\n",
      "continent             122 non-null object\n",
      "country_region        122 non-null object\n",
      "total_cases           122 non-null object\n",
      "total_deaths          122 non-null object\n",
      "total_cases_1M_pop    122 non-null float64\n",
      "total_recovered       122 non-null object\n",
      "bcg                   122 non-null float64\n",
      "life_expectancy       122 non-null float64\n",
      "infant_mortality      122 non-null float64\n",
      "dtypes: float64(4), object(5)\n",
      "memory usage: 9.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# change dtype of only 1 column to float\n",
    "merged_df_q3[\"total_cases_1M_pop\"] = merged_df_q3[\"total_cases_1M_pop\"].apply(lambda x: float(x.replace(',','')))\n",
    "merged_df_q3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 626,
   "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>continent</th>\n",
       "      <th>country_region</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_cases_1M_pop</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>bcg</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>infant_mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>160</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Angola</td>\n",
       "      <td>86</td>\n",
       "      <td>4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>18</td>\n",
       "      <td>79.0</td>\n",
       "      <td>61.3</td>\n",
       "      <td>62.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>161</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Burundi</td>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33</td>\n",
       "      <td>93.0</td>\n",
       "      <td>66.7</td>\n",
       "      <td>40.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>105</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Syria</td>\n",
       "      <td>122</td>\n",
       "      <td>5</td>\n",
       "      <td>7.0</td>\n",
       "      <td>46</td>\n",
       "      <td>66.0</td>\n",
       "      <td>73.7</td>\n",
       "      <td>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>156</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Mozambique</td>\n",
       "      <td>254</td>\n",
       "      <td>2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>91</td>\n",
       "      <td>95.0</td>\n",
       "      <td>55.9</td>\n",
       "      <td>64.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Tanzania</td>\n",
       "      <td>509</td>\n",
       "      <td>21</td>\n",
       "      <td>9.0</td>\n",
       "      <td>183</td>\n",
       "      <td>99.0</td>\n",
       "      <td>63.9</td>\n",
       "      <td>36.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    continent country_region total_cases total_deaths  total_cases_1M_pop  \\\n",
       "160    Africa         Angola          86            4                 3.0   \n",
       "161    Africa        Burundi          63            1                 5.0   \n",
       "105      Asia          Syria         122            5                 7.0   \n",
       "156    Africa     Mozambique         254            2                 8.0   \n",
       "148    Africa       Tanzania         509           21                 9.0   \n",
       "\n",
       "    total_recovered   bcg  life_expectancy  infant_mortality  \n",
       "160              18  79.0             61.3              62.3  \n",
       "161              33  93.0             66.7              40.1  \n",
       "105              46  66.0             73.7              16.5  \n",
       "156              91  95.0             55.9              64.7  \n",
       "148             183  99.0             63.9              36.4  "
      ]
     },
     "execution_count": 626,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_q3= merged_df_q3.sort_values(by=['total_cases_1M_pop'])\n",
    "merged_df_q3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 627,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show distribution of the results\n",
    "plt.scatter(merged_df_q3['bcg'], merged_df_q3['total_cases_1M_pop'], color = 'green')\n",
    "\n",
    "plt.title(\"BCG vaccine vs total cases\")\n",
    "plt.xlabel(\"BCG coverage\")\n",
    "plt.ylabel(\"Total cases per mln\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "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>total_cases_1M_pop</th>\n",
       "      <th>bcg</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>infant_mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>total_cases_1M_pop</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028947</td>\n",
       "      <td>0.323699</td>\n",
       "      <td>-0.283116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>bcg</td>\n",
       "      <td>0.028947</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.183783</td>\n",
       "      <td>-0.265631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>life_expectancy</td>\n",
       "      <td>0.323699</td>\n",
       "      <td>0.183783</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.931667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>infant_mortality</td>\n",
       "      <td>-0.283116</td>\n",
       "      <td>-0.265631</td>\n",
       "      <td>-0.931667</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    total_cases_1M_pop       bcg  life_expectancy  \\\n",
       "total_cases_1M_pop            1.000000  0.028947         0.323699   \n",
       "bcg                           0.028947  1.000000         0.183783   \n",
       "life_expectancy               0.323699  0.183783         1.000000   \n",
       "infant_mortality             -0.283116 -0.265631        -0.931667   \n",
       "\n",
       "                    infant_mortality  \n",
       "total_cases_1M_pop         -0.283116  \n",
       "bcg                        -0.265631  \n",
       "life_expectancy            -0.931667  \n",
       "infant_mortality            1.000000  "
      ]
     },
     "execution_count": 628,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the correlation table\n",
    "merged_df_q3.corr(method = \"pearson\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion: \n",
    "**We find no correlation between the BCG vaccination coverage and total number of Covid-19 cases per 1 million of population**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question4: *Do countries with higher life expectancy suffer more from Covid-19 in terms of deaths recorded?*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 629,
   "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>continent</th>\n",
       "      <th>country_region</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>total_cases_1M_pop</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>bcg</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>infant_mortality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Russia</td>\n",
       "      <td>405,843</td>\n",
       "      <td>4,693</td>\n",
       "      <td>2,781</td>\n",
       "      <td>171,883</td>\n",
       "      <td>96.0</td>\n",
       "      <td>71.9</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Spain</td>\n",
       "      <td>286,509</td>\n",
       "      <td>27,127</td>\n",
       "      <td>6,128</td>\n",
       "      <td>196,958</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.0</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Europe</td>\n",
       "      <td>UK</td>\n",
       "      <td>274,762</td>\n",
       "      <td>38,489</td>\n",
       "      <td>4,049</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>81.1</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italy</td>\n",
       "      <td>232,997</td>\n",
       "      <td>33,415</td>\n",
       "      <td>3,853</td>\n",
       "      <td>157,507</td>\n",
       "      <td>NaN</td>\n",
       "      <td>82.5</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Europe</td>\n",
       "      <td>France</td>\n",
       "      <td>188,882</td>\n",
       "      <td>28,802</td>\n",
       "      <td>2,894</td>\n",
       "      <td>68,355</td>\n",
       "      <td>78.0</td>\n",
       "      <td>82.2</td>\n",
       "      <td>3.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  continent country_region total_cases total_deaths total_cases_1M_pop  \\\n",
       "0    Europe         Russia     405,843        4,693              2,781   \n",
       "1    Europe          Spain     286,509       27,127              6,128   \n",
       "2    Europe             UK     274,762       38,489              4,049   \n",
       "3    Europe          Italy     232,997       33,415              3,853   \n",
       "4    Europe         France     188,882       28,802              2,894   \n",
       "\n",
       "  total_recovered   bcg  life_expectancy  infant_mortality  \n",
       "0         171,883  96.0             71.9               6.5  \n",
       "1         196,958   NaN             82.0               3.2  \n",
       "2             NaN   NaN             81.1               4.1  \n",
       "3         157,507   NaN             82.5               3.2  \n",
       "4          68,355  78.0             82.2               3.2  "
      ]
     },
     "execution_count": 629,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging the datasets\n",
    "merged_df_q4 = covid19_dfqs.merge(health_dfqs, on = ['country_region']) \n",
    "merged_df_q4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 630,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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_deaths</th>\n",
       "      <th>life_expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>4,693</td>\n",
       "      <td>71.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>27,127</td>\n",
       "      <td>82.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>UK</td>\n",
       "      <td>38,489</td>\n",
       "      <td>81.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Italy</td>\n",
       "      <td>33,415</td>\n",
       "      <td>82.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>France</td>\n",
       "      <td>28,802</td>\n",
       "      <td>82.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_region total_deaths  life_expectancy\n",
       "0         Russia        4,693             71.9\n",
       "1          Spain       27,127             82.0\n",
       "2             UK       38,489             81.1\n",
       "3          Italy       33,415             82.5\n",
       "4         France       28,802             82.2"
      ]
     },
     "execution_count": 630,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Selecting the columns we need\n",
    "merged_df_q4 = merged_df_q4[[\"country_region\",\"total_deaths\",\"life_expectancy\"]]\n",
    "merged_df_q4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 631,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 171 entries, 0 to 170\n",
      "Data columns (total 3 columns):\n",
      "country_region     171 non-null object\n",
      "total_deaths       155 non-null object\n",
      "life_expectancy    161 non-null float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 5.3+ KB\n"
     ]
    }
   ],
   "source": [
    "merged_df_q4.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_df_q4= merged_df_q4.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 147 entries, 0 to 168\n",
      "Data columns (total 3 columns):\n",
      "country_region     147 non-null object\n",
      "total_deaths       147 non-null int32\n",
      "life_expectancy    147 non-null float64\n",
      "dtypes: float64(1), int32(1), object(1)\n",
      "memory usage: 4.0+ KB\n"
     ]
    }
   ],
   "source": [
    "merged_df_q4['total_deaths'] = merged_df_q4['total_deaths'].str.replace(',', '').astype(int)\n",
    "merged_df_q4.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {
    "scrolled": true
   },
   "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_deaths</th>\n",
       "      <th>life_expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>43</td>\n",
       "      <td>USA</td>\n",
       "      <td>106195</td>\n",
       "      <td>80.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>UK</td>\n",
       "      <td>38489</td>\n",
       "      <td>81.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Italy</td>\n",
       "      <td>33415</td>\n",
       "      <td>82.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>109</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>29314</td>\n",
       "      <td>74.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>France</td>\n",
       "      <td>28802</td>\n",
       "      <td>82.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country_region  total_deaths  life_expectancy\n",
       "43             USA        106195             80.3\n",
       "2               UK         38489             81.1\n",
       "3            Italy         33415             82.5\n",
       "109         Brazil         29314             74.7\n",
       "4           France         28802             82.2"
      ]
     },
     "execution_count": 634,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_df_q4= merged_df_q4.sort_values(\"total_deaths\",ascending=False)\n",
    "merged_df_q4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 635,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Removing the outliers #method 1\n",
    "#merged_df_q4[\"total_deaths\"] = merged_df_q4[\"total_deaths\"][merged_df_q4[\"total_deaths\"].between(merged_df_q4[\"total_deaths\"].quantile(.05), merged_df_q4[\"total_deaths\"].quantile(.95))] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 636,
   "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_deaths</th>\n",
       "      <th>life_expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>44</td>\n",
       "      <td>Canada</td>\n",
       "      <td>7295.0</td>\n",
       "      <td>83.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>5956.0</td>\n",
       "      <td>81.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>65</td>\n",
       "      <td>India</td>\n",
       "      <td>5408.0</td>\n",
       "      <td>69.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>4693.0</td>\n",
       "      <td>71.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>69</td>\n",
       "      <td>China</td>\n",
       "      <td>4634.0</td>\n",
       "      <td>76.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   country_region  total_deaths  life_expectancy\n",
       "44         Canada        7295.0             83.4\n",
       "7     Netherlands        5956.0             81.7\n",
       "65          India        5408.0             69.7\n",
       "0          Russia        4693.0             71.9\n",
       "69          China        4634.0             76.1"
      ]
     },
     "execution_count": 636,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removing the outliers #method 2\n",
    "merged_df_q4[\"total_deaths\"] = merged_df_q4[\"total_deaths\"][merged_df_q4[\"total_deaths\"].between(merged_df_q4[\"total_deaths\"].quantile(.05), merged_df_q4[\"total_deaths\"].quantile(.95))] \n",
    "q_low = merged_df_q4['total_deaths'].quantile(0.01)\n",
    "q_hi  = merged_df_q4['total_deaths'].quantile(0.99)\n",
    "\n",
    "merged_df_q4 = merged_df_q4[(merged_df_q4['total_deaths'] < q_hi) & (merged_df_q4['total_deaths'] > q_low)]\n",
    "merged_df_q4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 637,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show distribution of the results\n",
    "plt.scatter(merged_df_q4[\"total_deaths\"], merged_df_q4[\"life_expectancy\"], color = 'purple')\n",
    "\n",
    "plt.title(\"Life expectancy vs total deaths\")\n",
    "plt.xlabel(\"Total Covid-19 deaths\")\n",
    "plt.ylabel(\"Life expectancy at birth\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 638,
   "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>total_deaths</th>\n",
       "      <th>life_expectancy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>total_deaths</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.192885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>life_expectancy</td>\n",
       "      <td>0.192885</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 total_deaths  life_expectancy\n",
       "total_deaths         1.000000         0.192885\n",
       "life_expectancy      0.192885         1.000000"
      ]
     },
     "execution_count": 638,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the correlation table\n",
    "merged_df_q4.corr(method=\"pearson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 639,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>      <td>total_deaths</td>   <th>  R-squared (uncentered):</th>      <td>   0.177</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.170</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   27.29</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 19 Jun 2020</td> <th>  Prob (F-statistic):</th>          <td>6.97e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>00:04:41</td>     <th>  Log-Likelihood:    </th>          <td> -1098.4</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   128</td>      <th>  AIC:               </th>          <td>   2199.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   127</td>      <th>  BIC:               </th>          <td>   2202.</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "         <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>life_expectancy</th> <td>    8.0718</td> <td>    1.545</td> <td>    5.224</td> <td> 0.000</td> <td>    5.014</td> <td>   11.130</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>109.193</td> <th>  Durbin-Watson:     </th> <td>   0.031</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td> 755.523</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 3.217</td>  <th>  Prob(JB):          </th> <td>8.71e-165</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>13.013</td>  <th>  Cond. No.          </th> <td>    1.00</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:           total_deaths   R-squared (uncentered):                   0.177\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.170\n",
       "Method:                 Least Squares   F-statistic:                              27.29\n",
       "Date:                Fri, 19 Jun 2020   Prob (F-statistic):                    6.97e-07\n",
       "Time:                        00:04:41   Log-Likelihood:                         -1098.4\n",
       "No. Observations:                 128   AIC:                                      2199.\n",
       "Df Residuals:                     127   BIC:                                      2202.\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "===================================================================================\n",
       "                      coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-----------------------------------------------------------------------------------\n",
       "life_expectancy     8.0718      1.545      5.224      0.000       5.014      11.130\n",
       "==============================================================================\n",
       "Omnibus:                      109.193   Durbin-Watson:                   0.031\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              755.523\n",
       "Skew:                           3.217   Prob(JB):                    8.71e-165\n",
       "Kurtosis:                      13.013   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 639,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Displaying the regression table\n",
    "X = merged_df_q4[\"life_expectancy\"]\n",
    "y = merged_df_q4[\"total_deaths\"]\n",
    "\n",
    "# Note the difference in argument order\n",
    "model = sm.OLS(y, X).fit()\n",
    "predictions = model.predict(X) # make the predictions by the model\n",
    "\n",
    "# Print out the statistics\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "**We do not observe a strong correlation between the number of total deaths and the life expectancy at birth.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Question5: *What country has the highest ratio of recovered cases?*\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 640,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a copy of the dataset for the question 5\n",
    "covid19_df_q5 = covid19_dfqs.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 641,
   "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_cases</th>\n",
       "      <th>total_recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>405,843</td>\n",
       "      <td>171,883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>286,509</td>\n",
       "      <td>196,958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>UK</td>\n",
       "      <td>274,762</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Italy</td>\n",
       "      <td>232,997</td>\n",
       "      <td>157,507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>France</td>\n",
       "      <td>188,882</td>\n",
       "      <td>68,355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_region total_cases total_recovered\n",
       "0         Russia     405,843         171,883\n",
       "1          Spain     286,509         196,958\n",
       "2             UK     274,762             NaN\n",
       "3          Italy     232,997         157,507\n",
       "4         France     188,882          68,355"
      ]
     },
     "execution_count": 641,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecting the data we need\n",
    "covid19_df_q5 = covid19_df_q5[['country_region','total_cases','total_recovered',]]\n",
    "covid19_df_q5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 642,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(213, 3)"
      ]
     },
     "execution_count": 642,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of rows and columns\n",
    "covid19_df_q5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 213 entries, 0 to 212\n",
      "Data columns (total 3 columns):\n",
      "country_region     213 non-null object\n",
      "total_cases        213 non-null object\n",
      "total_recovered    211 non-null object\n",
      "dtypes: object(3)\n",
      "memory usage: 5.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# Information about the df\n",
    "covid19_df_q5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 644,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Dropping missing values\n",
    "covid19_df_q5 = covid19_df_q5.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 645,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transforming object(str) to int\n",
    "covid19_df_q5['total_cases'] = covid19_df_q5['total_cases'].str.replace(',', '').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 646,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transforming object(str) to int\n",
    "covid19_df_q5['total_recovered'] = covid19_df_q5['total_recovered'].str.replace(',', '').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 211 entries, 0 to 212\n",
      "Data columns (total 3 columns):\n",
      "country_region     211 non-null object\n",
      "total_cases        211 non-null int32\n",
      "total_recovered    211 non-null int32\n",
      "dtypes: int32(2), object(1)\n",
      "memory usage: 4.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Double checking that objects have been transformed to int\n",
    "covid19_df_q5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 648,
   "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_cases</th>\n",
       "      <th>total_recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>208</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>1504</td>\n",
       "      <td>1481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>209</td>\n",
       "      <td>French Polynesia</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>210</td>\n",
       "      <td>New Caledonia</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>211</td>\n",
       "      <td>Fiji</td>\n",
       "      <td>18</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>212</td>\n",
       "      <td>Papua New Guinea</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country_region  total_cases  total_recovered\n",
       "208       New Zealand         1504             1481\n",
       "209  French Polynesia           60               60\n",
       "210     New Caledonia           19               18\n",
       "211              Fiji           18               15\n",
       "212  Papua New Guinea            8                8"
      ]
     },
     "execution_count": 648,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can see what in some countries total cases = total recovered\n",
    "covid19_df_q5.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 649,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 211 entries, 0 to 212\n",
      "Data columns (total 4 columns):\n",
      "country_region     211 non-null object\n",
      "total_cases        211 non-null int32\n",
      "total_recovered    211 non-null int32\n",
      "Cases>5000         211 non-null bool\n",
      "dtypes: bool(1), int32(2), object(1)\n",
      "memory usage: 5.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# Checking what countries have more than 5000 total cases.\n",
    "# It allows us to consider countries that had bigger outbreaks. \n",
    "covid19_df_q5[\"Cases>5000\"]= covid19_df_q5['total_cases'] > 5000\n",
    "covid19_df_q5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 650,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 67 entries, 0 to 207\n",
      "Data columns (total 4 columns):\n",
      "country_region     67 non-null object\n",
      "total_cases        67 non-null int32\n",
      "total_recovered    67 non-null int32\n",
      "Cases>5000         67 non-null bool\n",
      "dtypes: bool(1), int32(2), object(1)\n",
      "memory usage: 1.6+ KB\n"
     ]
    }
   ],
   "source": [
    "# We are only left with 67 countries that have more than 5000 \n",
    "covid19_df_q5 = covid19_df_q5[covid19_df_q5['Cases>5000'] == True] \n",
    "covid19_df_q5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 67 entries, 0 to 207\n",
      "Data columns (total 5 columns):\n",
      "country_region     67 non-null object\n",
      "total_cases        67 non-null int32\n",
      "total_recovered    67 non-null int32\n",
      "Cases>5000         67 non-null bool\n",
      "recovery_rate      67 non-null int32\n",
      "dtypes: bool(1), int32(3), object(1)\n",
      "memory usage: 1.9+ KB\n"
     ]
    }
   ],
   "source": [
    "# Adding a new column that corresponds to recovery rate\n",
    "# Transforming it to rounded percentage number\n",
    "# Changing it from float to int\n",
    "covid19_df_q5[\"recovery_rate\"]= covid19_df_q5[\"total_recovered\"]/covid19_df_q5[\"total_cases\"]\n",
    "covid19_df_q5[\"recovery_rate\"] = (covid19_df_q5[\"recovery_rate\"] * 100).round()\n",
    "covid19_df_q5[\"recovery_rate\"] = covid19_df_q5[\"recovery_rate\"].dropna().astype('int')\n",
    "covid19_df_q5.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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_cases</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>Cases&gt;5000</th>\n",
       "      <th>recovery_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Russia</td>\n",
       "      <td>405843</td>\n",
       "      <td>171883</td>\n",
       "      <td>True</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Spain</td>\n",
       "      <td>286509</td>\n",
       "      <td>196958</td>\n",
       "      <td>True</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Italy</td>\n",
       "      <td>232997</td>\n",
       "      <td>157507</td>\n",
       "      <td>True</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>France</td>\n",
       "      <td>188882</td>\n",
       "      <td>68355</td>\n",
       "      <td>True</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Germany</td>\n",
       "      <td>183494</td>\n",
       "      <td>165200</td>\n",
       "      <td>True</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  country_region  total_cases  total_recovered  Cases>5000  recovery_rate\n",
       "0         Russia       405843           171883        True             42\n",
       "1          Spain       286509           196958        True             69\n",
       "3          Italy       232997           157507        True             68\n",
       "4         France       188882            68355        True             36\n",
       "5        Germany       183494           165200        True             90"
      ]
     },
     "execution_count": 652,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid19_df_q5.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "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_cases</th>\n",
       "      <th>total_recovered</th>\n",
       "      <th>Cases&gt;5000</th>\n",
       "      <th>recovery_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>91</td>\n",
       "      <td>China</td>\n",
       "      <td>83001</td>\n",
       "      <td>78304</td>\n",
       "      <td>True</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>Austria</td>\n",
       "      <td>16731</td>\n",
       "      <td>15593</td>\n",
       "      <td>True</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>207</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7195</td>\n",
       "      <td>6614</td>\n",
       "      <td>True</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>20</td>\n",
       "      <td>Norway</td>\n",
       "      <td>8440</td>\n",
       "      <td>7727</td>\n",
       "      <td>True</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>Switzerland</td>\n",
       "      <td>30862</td>\n",
       "      <td>28500</td>\n",
       "      <td>True</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country_region  total_cases  total_recovered  Cases>5000  recovery_rate\n",
       "91           China        83001            78304        True             94\n",
       "16         Austria        16731            15593        True             93\n",
       "207      Australia         7195             6614        True             92\n",
       "20          Norway         8440             7727        True             92\n",
       "11     Switzerland        30862            28500        True             92"
      ]
     },
     "execution_count": 653,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# First prepare your dataframe, e.g. sort so x and y will align properly\n",
    "XY = covid19_df_q5.sort_values('recovery_rate', ascending=False)\n",
    "XY.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x468 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying the results\n",
    "# In this specific case x and y axis should be treated together in a dataframe and not munipulated apart. \n",
    "\n",
    "x = XY['country_region']\n",
    "y = XY['recovery_rate'][:10]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(9, 6.5)) \n",
    "width = 0.5 # the width of the bars \n",
    "ind = np.arange(len(y))  # the x locations for the groups\n",
    "ax.barh(ind, y, width, color=\"0.4\")\n",
    "ax.set_yticks(ind+width/4)\n",
    "for i, v in enumerate(y): # adding numbers to every line\n",
    "        ax.text(v + 1, i + .1, str(v), color='black', fontweight='bold')\n",
    "ax.set_yticklabels(x,color = \"green\", fontsize = 12, minor=False)\n",
    "plt.xticks(range(0,125,15))\n",
    "plt.title(\"Recovery  rates\",color = \"purple\", fontsize = 16, fontweight=\"bold\")\n",
    "plt.xlabel(\"Percentage\", color = \"gray\")\n",
    "plt.gca().invert_yaxis()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusion\n",
    "\n",
    "**China has the highest percentage of recovered cases among countries with more than 5000 cases.**"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}