{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "009c1cd3",
   "metadata": {},
   "source": [
    "# API & Data Collection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cf55f656",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f9473805",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{'updated': 1790263278491, 'country': 'Afghanistan', 'countryInfo': {'_id': 4, 'iso2': 'AF', 'iso3': 'AFG', 'lat': 33, 'long': 65, 'flag': 'https://disease.sh/assets/img/flags/af.png'}, 'cases': 234174, 'todayCases': 0, 'deaths': 7996, 'todayDeaths': 0, 'recovered': 211080, 'todayRecovered': 0, 'active': 15098, 'critical': 0, 'casesPerOneMillion': 5746, 'deathsPerOneMillion': 196, 'tests': 1390730, 'testsPerOneMillion': 34125, 'population': 40754388, 'continent': 'Asia', 'oneCasePerPeople': 174, 'oneDeathPerPeople': 5097, 'oneTestPerPeople': 29, 'activePerOneMillion': 370.46, 'recoveredPerOneMillion': 5179.32, 'criticalPerOneMillion': 0}, {'updated': 1790263278479, 'country': 'Albania', 'countryInfo': {'_id': 8, 'iso2': 'AL', 'iso3': 'ALB', 'lat': 41, 'long': 20, 'flag': 'https://disease.sh/assets/img/flags/al.png'}, 'cases': 334863, 'todayCases': 0, 'deaths': 3605, 'todayDeaths': 0, 'recovered': 330233, 'todayRecovered': 0, 'active': 1025, 'critical': 0, 'casesPerOneMillion': 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'Turks and Caicos Islands', 'countryInfo': {'_id': 796, 'iso2': 'TC', 'iso3': 'TCA', 'lat': 21.75, 'long': -71.5833, 'flag': 'https://disease.sh/assets/img/flags/tc.png'}, 'cases': 6752, 'todayCases': 0, 'deaths': 40, 'todayDeaths': 0, 'recovered': 6709, 'todayRecovered': 0, 'active': 3, 'critical': 4, 'casesPerOneMillion': 169900, 'deathsPerOneMillion': 1007, 'tests': 616788, 'testsPerOneMillion': 15520193, 'population': 39741, 'continent': 'North America', 'oneCasePerPeople': 6, 'oneDeathPerPeople': 994, 'oneTestPerPeople': 0, 'activePerOneMillion': 75.49, 'recoveredPerOneMillion': 168818.1, 'criticalPerOneMillion': 100.65}, {'updated': 1790263278607, 'country': 'Tuvalu', 'countryInfo': {'_id': 798, 'iso2': 'TV', 'iso3': 'TUV', 'lat': -8, 'long': 178, 'flag': 'https://disease.sh/assets/img/flags/tv.png'}, 'cases': 2943, 'todayCases': 0, 'deaths': 1, 'todayDeaths': 0, 'recovered': 0, 'todayRecovered': 0, 'active': 2942, 'critical': 0, 'casesPerOneMillion': 243909, 'deathsPerOneMillion': 83, 'tests': 0, 'testsPerOneMillion': 0, 'population': 12066, 'continent': 'Australia-Oceania', 'oneCasePerPeople': 4, 'oneDeathPerPeople': 12066, 'oneTestPerPeople': 0, 'activePerOneMillion': 243825.63, 'recoveredPerOneMillion': 0, 'criticalPerOneMillion': 0}, {'updated': 1790263278442, 'country': 'UAE', 'countryInfo': {'_id': 784, 'iso2': 'AE', 'iso3': 'ARE', 'lat': 24, 'long': 54, 'flag': 'https://disease.sh/assets/img/flags/ae.png'}, 'cases': 1067030, 'todayCases': 0, 'deaths': 2349, 'todayDeaths': 0, 'recovered': 0, 'todayRecovered': 0, 'active': 1064681, 'critical': 0, 'casesPerOneMillion': 105837, 'deathsPerOneMillion': 233, 'tests': 200732262, 'testsPerOneMillion': 19910389, 'population': 10081785, 'continent': 'Asia', 'oneCasePerPeople': 9, 'oneDeathPerPeople': 4292, 'oneTestPerPeople': 0, 'activePerOneMillion': 105604.41, 'recoveredPerOneMillion': 0, 'criticalPerOneMillion': 0}, {'updated': 1790263278363, 'country': 'UK', 'countryInfo': {'_id': 826, 'iso2': 'GB', 'iso3': 'GBR', 'lat': 54, 'long': -2, 'flag': 'https://disease.sh/assets/img/flags/gb.png'}, 'cases': 24910387, 'todayCases': 0, 'deaths': 232112, 'todayDeaths': 0, 'recovered': 24678275, 'todayRecovered': 0, 'active': 0, 'critical': 0, 'casesPerOneMillion': 363666, 'deathsPerOneMillion': 3389, 'tests': 522526476, 'testsPerOneMillion': 7628357, 'population': 68497907, 'continent': 'Europe', 'oneCasePerPeople': 3, 'oneDeathPerPeople': 295, 'oneTestPerPeople': 0, 'activePerOneMillion': 0, 'recoveredPerOneMillion': 360277.8, 'criticalPerOneMillion': 0}, {'updated': 1790263278351, 'country': 'USA', 'countryInfo': {'_id': 840, 'iso2': 'US', 'iso3': 'USA', 'lat': 38, 'long': -97, 'flag': 'https://disease.sh/assets/img/flags/us.png'}, 'cases': 111820082, 'todayCases': 0, 'deaths': 1219487, 'todayDeaths': 0, 'recovered': 109814428, 'todayRecovered': 0, 'active': 786167, 'critical': 940, 'casesPerOneMillion': 333985, 'deathsPerOneMillion': 3642, 'tests': 1186851502, 'testsPerOneMillion': 3544901, 'population': 334805269, 'continent': 'North America', 'oneCasePerPeople': 3, 'oneDeathPerPeople': 275, 'oneTestPerPeople': 0, 'activePerOneMillion': 2348.13, 'recoveredPerOneMillion': 327994.92, 'criticalPerOneMillion': 2.81}, {'updated': 1790263278502, 'country': 'Uganda', 'countryInfo': {'_id': 800, 'iso2': 'UG', 'iso3': 'UGA', 'lat': 1, 'long': 32, 'flag': 'https://disease.sh/assets/img/flags/ug.png'}, 'cases': 172149, 'todayCases': 0, 'deaths': 3632, 'todayDeaths': 0, 'recovered': 100431, 'todayRecovered': 0, 'active': 68086, 'critical': 0, 'casesPerOneMillion': 3554, 'deathsPerOneMillion': 75, 'tests': 3012408, 'testsPerOneMillion': 62198, 'population': 48432863, 'continent': 'Africa', 'oneCasePerPeople': 281, 'oneDeathPerPeople': 13335, 'oneTestPerPeople': 16, 'activePerOneMillion': 1405.78, 'recoveredPerOneMillion': 2073.61, 'criticalPerOneMillion': 0}, {'updated': 1790263278387, 'country': 'Ukraine', 'countryInfo': {'_id': 804, 'iso2': 'UA', 'iso3': 'UKR', 'lat': 49, 'long': 32, 'flag': 'https://disease.sh/assets/img/flags/ua.png'}, 'cases': 5557995, 'todayCases': 0, 'deaths': 112418, 'todayDeaths': 0, 'recovered': 5445577, 'todayRecovered': 0, 'active': 0, 'critical': 0, 'casesPerOneMillion': 128681, 'deathsPerOneMillion': 2603, 'tests': 32603805, 'testsPerOneMillion': 754855, 'population': 43192122, 'continent': 'Europe', 'oneCasePerPeople': 8, 'oneDeathPerPeople': 384, 'oneTestPerPeople': 1, 'activePerOneMillion': 0, 'recoveredPerOneMillion': 126078.01, 'criticalPerOneMillion': 0}, {'updated': 1790263278444, 'country': 'Uruguay', 'countryInfo': {'_id': 858, 'iso2': 'UY', 'iso3': 'URY', 'lat': -33, 'long': -56, 'flag': 'https://disease.sh/assets/img/flags/uy.png'}, 'cases': 1041111, 'todayCases': 0, 'deaths': 7664, 'todayDeaths': 0, 'recovered': 1030944, 'todayRecovered': 0, 'active': 2503, 'critical': 18, 'casesPerOneMillion': 297799, 'deathsPerOneMillion': 2192, 'tests': 6114822, 'testsPerOneMillion': 1749083, 'population': 3496016, 'continent': 'South America', 'oneCasePerPeople': 3, 'oneDeathPerPeople': 456, 'oneTestPerPeople': 1, 'activePerOneMillion': 715.96, 'recoveredPerOneMillion': 294891.1, 'criticalPerOneMillion': 5.15}, {'updated': 1790263278490, 'country': 'Uzbekistan', 'countryInfo': {'_id': 860, 'iso2': 'UZ', 'iso3': 'UZB', 'lat': 41, 'long': 64, 'flag': 'https://disease.sh/assets/img/flags/uz.png'}, 'cases': 253662, 'todayCases': 0, 'deaths': 1637, 'todayDeaths': 0, 'recovered': 241486, 'todayRecovered': 0, 'active': 10539, 'critical': 23, 'casesPerOneMillion': 7378, 'deathsPerOneMillion': 48, 'tests': 1377915, 'testsPerOneMillion': 40077, 'population': 34382084, 'continent': 'Asia', 'oneCasePerPeople': 136, 'oneDeathPerPeople': 21003, 'oneTestPerPeople': 25, 'activePerOneMillion': 306.53, 'recoveredPerOneMillion': 7023.6, 'criticalPerOneMillion': 0.67}, {'updated': 1790263278580, 'country': 'Vanuatu', 'countryInfo': {'_id': 548, 'iso2': 'VU', 'iso3': 'VUT', 'lat': -16, 'long': 167, 'flag': 'https://disease.sh/assets/img/flags/vu.png'}, 'cases': 12019, 'todayCases': 0, 'deaths': 14, 'todayDeaths': 0, 'recovered': 11976, 'todayRecovered': 0, 'active': 29, 'critical': 0, 'casesPerOneMillion': 37346, 'deathsPerOneMillion': 44, 'tests': 24976, 'testsPerOneMillion': 77606, 'population': 321832, 'continent': 'Australia-Oceania', 'oneCasePerPeople': 27, 'oneDeathPerPeople': 22988, 'oneTestPerPeople': 13, 'activePerOneMillion': 90.11, 'recoveredPerOneMillion': 37211.96, 'criticalPerOneMillion': 0}, {'updated': 1790263278461, 'country': 'Venezuela', 'countryInfo': {'_id': 862, 'iso2': 'VE', 'iso3': 'VEN', 'lat': 8, 'long': -66, 'flag': 'https://disease.sh/assets/img/flags/ve.png'}, 'cases': 552695, 'todayCases': 0, 'deaths': 5856, 'todayDeaths': 0, 'recovered': 546537, 'todayRecovered': 0, 'active': 302, 'critical': 31, 'casesPerOneMillion': 18885, 'deathsPerOneMillion': 200, 'tests': 3359014, 'testsPerOneMillion': 114771, 'population': 29266991, 'continent': 'South America', 'oneCasePerPeople': 53, 'oneDeathPerPeople': 4998, 'oneTestPerPeople': 9, 'activePerOneMillion': 10.32, 'recoveredPerOneMillion': 18674.18, 'criticalPerOneMillion': 1.06}, {'updated': 1790263278371, 'country': 'Vietnam', 'countryInfo': {'_id': 704, 'iso2': 'VN', 'iso3': 'VNM', 'lat': 21, 'long': 105.8, 'flag': 'https://disease.sh/assets/img/flags/vn.png'}, 'cases': 11625195, 'todayCases': 0, 'deaths': 43206, 'todayDeaths': 0, 'recovered': 10640971, 'todayRecovered': 0, 'active': 941018, 'critical': 0, 'casesPerOneMillion': 117481, 'deathsPerOneMillion': 437, 'tests': 85826548, 'testsPerOneMillion': 867342, 'population': 98953541, 'continent': 'Asia', 'oneCasePerPeople': 9, 'oneDeathPerPeople': 2290, 'oneTestPerPeople': 1, 'activePerOneMillion': 9509.7, 'recoveredPerOneMillion': 107535.02, 'criticalPerOneMillion': 0}, {'updated': 1790263278605, 'country': 'Wallis and Futuna', 'countryInfo': {'_id': 876, 'iso2': 'WF', 'iso3': 'WLF', 'lat': -13.3, 'long': -176.2, 'flag': 'https://disease.sh/assets/img/flags/wf.png'}, 'cases': 3550, 'todayCases': 0, 'deaths': 8, 'todayDeaths': 0, 'recovered': 438, 'todayRecovered': 0, 'active': 3104, 'critical': 0, 'casesPerOneMillion': 323256, 'deathsPerOneMillion': 728, 'tests': 20508, 'testsPerOneMillion': 1867419, 'population': 10982, 'continent': 'Australia-Oceania', 'oneCasePerPeople': 3, 'oneDeathPerPeople': 1373, 'oneTestPerPeople': 1, 'activePerOneMillion': 282644.33, 'recoveredPerOneMillion': 39883.45, 'criticalPerOneMillion': 0}, {'updated': 1790263278618, 'country': 'Western Sahara', 'countryInfo': {'_id': 732, 'iso2': 'EH', 'iso3': 'ESH', 'lat': 24.5, 'long': -13, 'flag': 'https://disease.sh/assets/img/flags/eh.png'}, 'cases': 10, 'todayCases': 0, 'deaths': 1, 'todayDeaths': 0, 'recovered': 9, 'todayRecovered': 0, 'active': 0, 'critical': 0, 'casesPerOneMillion': 16, 'deathsPerOneMillion': 2, 'tests': 0, 'testsPerOneMillion': 0, 'population': 626161, 'continent': 'Africa', 'oneCasePerPeople': 62616, 'oneDeathPerPeople': 626161, 'oneTestPerPeople': 0, 'activePerOneMillion': 0, 'recoveredPerOneMillion': 14.37, 'criticalPerOneMillion': 0}, {'updated': 1790263278582, 'country': 'Yemen', 'countryInfo': {'_id': 887, 'iso2': 'YE', 'iso3': 'YEM', 'lat': 15, 'long': 48, 'flag': 'https://disease.sh/assets/img/flags/ye.png'}, 'cases': 11945, 'todayCases': 0, 'deaths': 2159, 'todayDeaths': 0, 'recovered': 9124, 'todayRecovered': 0, 'active': 662, 'critical': 0, 'casesPerOneMillion': 383, 'deathsPerOneMillion': 69, 'tests': 329592, 'testsPerOneMillion': 10579, 'population': 31154867, 'continent': 'Asia', 'oneCasePerPeople': 2608, 'oneDeathPerPeople': 14430, 'oneTestPerPeople': 95, 'activePerOneMillion': 21.25, 'recoveredPerOneMillion': 292.86, 'criticalPerOneMillion': 0}, {'updated': 1790263278473, 'country': 'Zambia', 'countryInfo': {'_id': 894, 'iso2': 'ZM', 'iso3': 'ZMB', 'lat': -15, 'long': 30, 'flag': 'https://disease.sh/assets/img/flags/zm.png'}, 'cases': 349304, 'todayCases': 0, 'deaths': 4069, 'todayDeaths': 0, 'recovered': 341316, 'todayRecovered': 0, 'active': 3919, 'critical': 0, 'casesPerOneMillion': 17940, 'deathsPerOneMillion': 209, 'tests': 4112961, 'testsPerOneMillion': 211244, 'population': 19470234, 'continent': 'Africa', 'oneCasePerPeople': 56, 'oneDeathPerPeople': 4785, 'oneTestPerPeople': 5, 'activePerOneMillion': 201.28, 'recoveredPerOneMillion': 17530.14, 'criticalPerOneMillion': 0}, {'updated': 1790263278489, 'country': 'Zimbabwe', 'countryInfo': {'_id': 716, 'iso2': 'ZW', 'iso3': 'ZWE', 'lat': -20, 'long': 30, 'flag': 'https://disease.sh/assets/img/flags/zw.png'}, 'cases': 266359, 'todayCases': 0, 'deaths': 5740, 'todayDeaths': 0, 'recovered': 258888, 'todayRecovered': 0, 'active': 1731, 'critical': 12, 'casesPerOneMillion': 17373, 'deathsPerOneMillion': 374, 'tests': 2525756, 'testsPerOneMillion': 164744, 'population': 15331428, 'continent': 'Africa', 'oneCasePerPeople': 58, 'oneDeathPerPeople': 2671, 'oneTestPerPeople': 6, 'activePerOneMillion': 112.91, 'recoveredPerOneMillion': 16886.1, 'criticalPerOneMillion': 0.78}]\n"
     ]
    }
   ],
   "source": [
    "url = \"https://disease.sh/v3/covid-19/countries\"\n",
    "response = requests.get(url)\n",
    "data = response.json()\n",
    "print (data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c9912cd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response.status_code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7c6de3b",
   "metadata": {},
   "source": [
    "-------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32b15eb5",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7b042dce",
   "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>updated</th>\n",
       "      <th>country</th>\n",
       "      <th>countryInfo</th>\n",
       "      <th>cases</th>\n",
       "      <th>todayCases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>todayDeaths</th>\n",
       "      <th>recovered</th>\n",
       "      <th>todayRecovered</th>\n",
       "      <th>active</th>\n",
       "      <th>...</th>\n",
       "      <th>tests</th>\n",
       "      <th>testsPerOneMillion</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>oneCasePerPeople</th>\n",
       "      <th>oneDeathPerPeople</th>\n",
       "      <th>oneTestPerPeople</th>\n",
       "      <th>activePerOneMillion</th>\n",
       "      <th>recoveredPerOneMillion</th>\n",
       "      <th>criticalPerOneMillion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1790263278491</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>{'_id': 4, 'iso2': 'AF', 'iso3': 'AFG', 'lat':...</td>\n",
       "      <td>234174</td>\n",
       "      <td>0</td>\n",
       "      <td>7996</td>\n",
       "      <td>0</td>\n",
       "      <td>211080</td>\n",
       "      <td>0</td>\n",
       "      <td>15098</td>\n",
       "      <td>...</td>\n",
       "      <td>1390730</td>\n",
       "      <td>34125</td>\n",
       "      <td>40754388</td>\n",
       "      <td>Asia</td>\n",
       "      <td>174</td>\n",
       "      <td>5097</td>\n",
       "      <td>29</td>\n",
       "      <td>370.46</td>\n",
       "      <td>5179.32</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1790263278479</td>\n",
       "      <td>Albania</td>\n",
       "      <td>{'_id': 8, 'iso2': 'AL', 'iso3': 'ALB', 'lat':...</td>\n",
       "      <td>334863</td>\n",
       "      <td>0</td>\n",
       "      <td>3605</td>\n",
       "      <td>0</td>\n",
       "      <td>330233</td>\n",
       "      <td>0</td>\n",
       "      <td>1025</td>\n",
       "      <td>...</td>\n",
       "      <td>1941032</td>\n",
       "      <td>677173</td>\n",
       "      <td>2866374</td>\n",
       "      <td>Europe</td>\n",
       "      <td>9</td>\n",
       "      <td>795</td>\n",
       "      <td>1</td>\n",
       "      <td>357.59</td>\n",
       "      <td>115209.32</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1790263278487</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>{'_id': 12, 'iso2': 'DZ', 'iso3': 'DZA', 'lat'...</td>\n",
       "      <td>272010</td>\n",
       "      <td>0</td>\n",
       "      <td>6881</td>\n",
       "      <td>0</td>\n",
       "      <td>183061</td>\n",
       "      <td>0</td>\n",
       "      <td>82068</td>\n",
       "      <td>...</td>\n",
       "      <td>230960</td>\n",
       "      <td>5093</td>\n",
       "      <td>45350148</td>\n",
       "      <td>Africa</td>\n",
       "      <td>167</td>\n",
       "      <td>6591</td>\n",
       "      <td>196</td>\n",
       "      <td>1809.65</td>\n",
       "      <td>4036.61</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1790263278534</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>{'_id': 20, 'iso2': 'AD', 'iso3': 'AND', 'lat'...</td>\n",
       "      <td>48015</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47850</td>\n",
       "      <td>...</td>\n",
       "      <td>249838</td>\n",
       "      <td>3225256</td>\n",
       "      <td>77463</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>469</td>\n",
       "      <td>0</td>\n",
       "      <td>617714.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1790263278510</td>\n",
       "      <td>Angola</td>\n",
       "      <td>{'_id': 24, 'iso2': 'AO', 'iso3': 'AGO', 'lat'...</td>\n",
       "      <td>107327</td>\n",
       "      <td>0</td>\n",
       "      <td>1937</td>\n",
       "      <td>0</td>\n",
       "      <td>103419</td>\n",
       "      <td>0</td>\n",
       "      <td>1971</td>\n",
       "      <td>...</td>\n",
       "      <td>1499795</td>\n",
       "      <td>42818</td>\n",
       "      <td>35027343</td>\n",
       "      <td>Africa</td>\n",
       "      <td>326</td>\n",
       "      <td>18083</td>\n",
       "      <td>23</td>\n",
       "      <td>56.27</td>\n",
       "      <td>2952.52</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         updated      country  \\\n",
       "0  1790263278491  Afghanistan   \n",
       "1  1790263278479      Albania   \n",
       "2  1790263278487      Algeria   \n",
       "3  1790263278534      Andorra   \n",
       "4  1790263278510       Angola   \n",
       "\n",
       "                                         countryInfo   cases  todayCases  \\\n",
       "0  {'_id': 4, 'iso2': 'AF', 'iso3': 'AFG', 'lat':...  234174           0   \n",
       "1  {'_id': 8, 'iso2': 'AL', 'iso3': 'ALB', 'lat':...  334863           0   \n",
       "2  {'_id': 12, 'iso2': 'DZ', 'iso3': 'DZA', 'lat'...  272010           0   \n",
       "3  {'_id': 20, 'iso2': 'AD', 'iso3': 'AND', 'lat'...   48015           0   \n",
       "4  {'_id': 24, 'iso2': 'AO', 'iso3': 'AGO', 'lat'...  107327           0   \n",
       "\n",
       "   deaths  todayDeaths  recovered  todayRecovered  active  ...    tests  \\\n",
       "0    7996            0     211080               0   15098  ...  1390730   \n",
       "1    3605            0     330233               0    1025  ...  1941032   \n",
       "2    6881            0     183061               0   82068  ...   230960   \n",
       "3     165            0          0               0   47850  ...   249838   \n",
       "4    1937            0     103419               0    1971  ...  1499795   \n",
       "\n",
       "   testsPerOneMillion  population  continent  oneCasePerPeople  \\\n",
       "0               34125    40754388       Asia               174   \n",
       "1              677173     2866374     Europe                 9   \n",
       "2                5093    45350148     Africa               167   \n",
       "3             3225256       77463     Europe                 2   \n",
       "4               42818    35027343     Africa               326   \n",
       "\n",
       "   oneDeathPerPeople oneTestPerPeople  activePerOneMillion  \\\n",
       "0               5097               29               370.46   \n",
       "1                795                1               357.59   \n",
       "2               6591              196              1809.65   \n",
       "3                469                0            617714.26   \n",
       "4              18083               23                56.27   \n",
       "\n",
       "   recoveredPerOneMillion  criticalPerOneMillion  \n",
       "0                 5179.32                    0.0  \n",
       "1               115209.32                    0.0  \n",
       "2                 4036.61                    0.0  \n",
       "3                    0.00                    0.0  \n",
       "4                 2952.52                    0.0  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df= pd.DataFrame(data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e479bc45",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 231 entries, 0 to 230\n",
      "Data columns (total 23 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   updated                 231 non-null    int64  \n",
      " 1   country                 231 non-null    str    \n",
      " 2   countryInfo             231 non-null    object \n",
      " 3   cases                   231 non-null    int64  \n",
      " 4   todayCases              231 non-null    int64  \n",
      " 5   deaths                  231 non-null    int64  \n",
      " 6   todayDeaths             231 non-null    int64  \n",
      " 7   recovered               231 non-null    int64  \n",
      " 8   todayRecovered          231 non-null    int64  \n",
      " 9   active                  231 non-null    int64  \n",
      " 10  critical                231 non-null    int64  \n",
      " 11  casesPerOneMillion      231 non-null    int64  \n",
      " 12  deathsPerOneMillion     231 non-null    int64  \n",
      " 13  tests                   231 non-null    int64  \n",
      " 14  testsPerOneMillion      231 non-null    int64  \n",
      " 15  population              231 non-null    int64  \n",
      " 16  continent               231 non-null    str    \n",
      " 17  oneCasePerPeople        231 non-null    int64  \n",
      " 18  oneDeathPerPeople       231 non-null    int64  \n",
      " 19  oneTestPerPeople        231 non-null    int64  \n",
      " 20  activePerOneMillion     231 non-null    float64\n",
      " 21  recoveredPerOneMillion  231 non-null    float64\n",
      " 22  criticalPerOneMillion   231 non-null    float64\n",
      "dtypes: float64(3), int64(17), object(1), str(2)\n",
      "memory usage: 41.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d6788392",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(231, 23)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "709b8288",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4761aa4",
   "metadata": {},
   "source": [
    "# Data Cleaning & Wrangling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "dcac7354",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'_id': 4,\n",
       " 'iso2': 'AF',\n",
       " 'iso3': 'AFG',\n",
       " 'lat': 33,\n",
       " 'long': 65,\n",
       " 'flag': 'https://disease.sh/assets/img/flags/af.png'}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"countryInfo\"][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "460a1a09",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"iso3\"]=df[\"countryInfo\"].str.get(\"iso3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "293547a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"lat\"]=df[\"countryInfo\"].str.get(\"lat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "86dda6e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"long\"]=df[\"countryInfo\"].str.get(\"long\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "0a667d8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(\"countryInfo\", axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "c57a6dd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['updated', 'country', 'cases', 'todayCases', 'deaths', 'todayDeaths',\n",
       "       'recovered', 'todayRecovered', 'active', 'critical',\n",
       "       'casesPerOneMillion', 'deathsPerOneMillion', 'tests',\n",
       "       'testsPerOneMillion', 'population', 'continent', 'oneCasePerPeople',\n",
       "       'oneDeathPerPeople', 'oneTestPerPeople', 'activePerOneMillion',\n",
       "       'recoveredPerOneMillion', 'criticalPerOneMillion', 'iso3', 'lat',\n",
       "       'long'],\n",
       "      dtype='str')"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "1e9a5bfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={\"todayCases\":\"today_cases\",\n",
    "                    \"todayDeaths\":\"today_deaths\",\n",
    "                    \"todayRecovered\":\"today_recovered\",\n",
    "                    \"casesPerOneMillion\":\"cases_per_million\",\n",
    "                    \"deathsPerOneMillion\":\"deaths_per_million\",\n",
    "                    \"testsPerOneMillion\":\"tests_per_million\",\n",
    "                    \"oneCasePerPeople\":\"case_per_people\",\n",
    "                    \"oneDeathPerPeople\":\"death_per_people\",\n",
    "                    \"oneTestPerPeople\":\"test_per_people\",\n",
    "                    \"activePerOneMillion\":\"active_per_million\",\n",
    "                    \"recoveredPerOneMillion\":\"recovered_per_million\",\n",
    "                    \"criticalPerOneMillion\":\"critical_per_million\"},inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "86aaed50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 231 entries, 0 to 230\n",
      "Data columns (total 25 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   updated                231 non-null    int64  \n",
      " 1   country                231 non-null    str    \n",
      " 2   cases                  231 non-null    int64  \n",
      " 3   today_cases            231 non-null    int64  \n",
      " 4   deaths                 231 non-null    int64  \n",
      " 5   today_deaths           231 non-null    int64  \n",
      " 6   recovered              231 non-null    int64  \n",
      " 7   today_recovered        231 non-null    int64  \n",
      " 8   active                 231 non-null    int64  \n",
      " 9   critical               231 non-null    int64  \n",
      " 10  cases_per_million      231 non-null    int64  \n",
      " 11  deaths_per_million     231 non-null    int64  \n",
      " 12  tests                  231 non-null    int64  \n",
      " 13  tests_per_million      231 non-null    int64  \n",
      " 14  population             231 non-null    int64  \n",
      " 15  continent              231 non-null    str    \n",
      " 16  case_per_people        231 non-null    int64  \n",
      " 17  death_per_people       231 non-null    int64  \n",
      " 18  test_per_people        231 non-null    int64  \n",
      " 19  active_per_million     231 non-null    float64\n",
      " 20  recovered_per_million  231 non-null    float64\n",
      " 21  critical_per_million   231 non-null    float64\n",
      " 22  iso3                   229 non-null    object \n",
      " 23  lat                    231 non-null    float64\n",
      " 24  long                   231 non-null    float64\n",
      "dtypes: float64(5), int64(17), object(1), str(2)\n",
      "memory usage: 45.2+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "c443a46b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "updated                  0\n",
       "country                  0\n",
       "cases                    0\n",
       "today_cases              0\n",
       "deaths                   0\n",
       "today_deaths             0\n",
       "recovered                0\n",
       "today_recovered          0\n",
       "active                   0\n",
       "critical                 0\n",
       "cases_per_million        0\n",
       "deaths_per_million       0\n",
       "tests                    0\n",
       "tests_per_million        0\n",
       "population               0\n",
       "continent                0\n",
       "case_per_people          0\n",
       "death_per_people         0\n",
       "test_per_people          0\n",
       "active_per_million       0\n",
       "recovered_per_million    0\n",
       "critical_per_million     0\n",
       "iso3                     2\n",
       "lat                      0\n",
       "long                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "a21b4da4",
   "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>updated</th>\n",
       "      <th>country</th>\n",
       "      <th>cases</th>\n",
       "      <th>today_cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>today_deaths</th>\n",
       "      <th>recovered</th>\n",
       "      <th>today_recovered</th>\n",
       "      <th>active</th>\n",
       "      <th>critical</th>\n",
       "      <th>...</th>\n",
       "      <th>continent</th>\n",
       "      <th>case_per_people</th>\n",
       "      <th>death_per_people</th>\n",
       "      <th>test_per_people</th>\n",
       "      <th>active_per_million</th>\n",
       "      <th>recovered_per_million</th>\n",
       "      <th>critical_per_million</th>\n",
       "      <th>iso3</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>1790263278613</td>\n",
       "      <td>Diamond Princess</td>\n",
       "      <td>712</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>699</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>1790263278619</td>\n",
       "      <td>MS Zaandam</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           updated           country  cases  today_cases  deaths  \\\n",
       "56   1790263278613  Diamond Princess    712            0      13   \n",
       "119  1790263278619        MS Zaandam      9            0       2   \n",
       "\n",
       "     today_deaths  recovered  today_recovered  active  critical  ...  \\\n",
       "56              0        699                0       0         0  ...   \n",
       "119             0          7                0       0         0  ...   \n",
       "\n",
       "     continent  case_per_people  death_per_people  test_per_people  \\\n",
       "56                            0                 0                0   \n",
       "119                           0                 0                0   \n",
       "\n",
       "     active_per_million recovered_per_million  critical_per_million  iso3  \\\n",
       "56                  0.0                   0.0                   0.0  None   \n",
       "119                 0.0                   0.0                   0.0  None   \n",
       "\n",
       "     lat  long  \n",
       "56   0.0   0.0  \n",
       "119  0.0   0.0  \n",
       "\n",
       "[2 rows x 25 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df[\"iso3\"].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "3211209e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# since MS Zaandam & Diamond Princess are only cruise ships,and iso will be 1ry key and can't be null\n",
    "# population is 0 and continent is empty, which would distort per-capita metrics so they'll be deleted\n",
    "df.dropna(inplace =True)\n",
    "df.reset_index(drop=True, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "4860f6f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "updated                  0\n",
       "country                  0\n",
       "cases                    0\n",
       "today_cases              0\n",
       "deaths                   0\n",
       "today_deaths             0\n",
       "recovered                0\n",
       "today_recovered          0\n",
       "active                   0\n",
       "critical                 0\n",
       "cases_per_million        0\n",
       "deaths_per_million       0\n",
       "tests                    0\n",
       "tests_per_million        0\n",
       "population               0\n",
       "continent                0\n",
       "case_per_people          0\n",
       "death_per_people         0\n",
       "test_per_people          0\n",
       "active_per_million       0\n",
       "recovered_per_million    0\n",
       "critical_per_million     0\n",
       "iso3                     0\n",
       "lat                      0\n",
       "long                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "fbe52212",
   "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>updated</th>\n",
       "      <th>country</th>\n",
       "      <th>cases</th>\n",
       "      <th>today_cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>today_deaths</th>\n",
       "      <th>recovered</th>\n",
       "      <th>today_recovered</th>\n",
       "      <th>active</th>\n",
       "      <th>critical</th>\n",
       "      <th>...</th>\n",
       "      <th>continent</th>\n",
       "      <th>case_per_people</th>\n",
       "      <th>death_per_people</th>\n",
       "      <th>test_per_people</th>\n",
       "      <th>active_per_million</th>\n",
       "      <th>recovered_per_million</th>\n",
       "      <th>critical_per_million</th>\n",
       "      <th>iso3</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1790263278491</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>234174</td>\n",
       "      <td>0</td>\n",
       "      <td>7996</td>\n",
       "      <td>0</td>\n",
       "      <td>211080</td>\n",
       "      <td>0</td>\n",
       "      <td>15098</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Asia</td>\n",
       "      <td>174</td>\n",
       "      <td>5097</td>\n",
       "      <td>29</td>\n",
       "      <td>370.46</td>\n",
       "      <td>5179.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>AFG</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1790263278479</td>\n",
       "      <td>Albania</td>\n",
       "      <td>334863</td>\n",
       "      <td>0</td>\n",
       "      <td>3605</td>\n",
       "      <td>0</td>\n",
       "      <td>330233</td>\n",
       "      <td>0</td>\n",
       "      <td>1025</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>9</td>\n",
       "      <td>795</td>\n",
       "      <td>1</td>\n",
       "      <td>357.59</td>\n",
       "      <td>115209.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ALB</td>\n",
       "      <td>41.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1790263278487</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>272010</td>\n",
       "      <td>0</td>\n",
       "      <td>6881</td>\n",
       "      <td>0</td>\n",
       "      <td>183061</td>\n",
       "      <td>0</td>\n",
       "      <td>82068</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Africa</td>\n",
       "      <td>167</td>\n",
       "      <td>6591</td>\n",
       "      <td>196</td>\n",
       "      <td>1809.65</td>\n",
       "      <td>4036.61</td>\n",
       "      <td>0.00</td>\n",
       "      <td>DZA</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1790263278534</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>48015</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47850</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>469</td>\n",
       "      <td>0</td>\n",
       "      <td>617714.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>AND</td>\n",
       "      <td>42.5</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1790263278510</td>\n",
       "      <td>Angola</td>\n",
       "      <td>107327</td>\n",
       "      <td>0</td>\n",
       "      <td>1937</td>\n",
       "      <td>0</td>\n",
       "      <td>103419</td>\n",
       "      <td>0</td>\n",
       "      <td>1971</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Africa</td>\n",
       "      <td>326</td>\n",
       "      <td>18083</td>\n",
       "      <td>23</td>\n",
       "      <td>56.27</td>\n",
       "      <td>2952.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>AGO</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>1790263278605</td>\n",
       "      <td>Wallis and Futuna</td>\n",
       "      <td>3550</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>438</td>\n",
       "      <td>0</td>\n",
       "      <td>3104</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Australia-Oceania</td>\n",
       "      <td>3</td>\n",
       "      <td>1373</td>\n",
       "      <td>1</td>\n",
       "      <td>282644.33</td>\n",
       "      <td>39883.45</td>\n",
       "      <td>0.00</td>\n",
       "      <td>WLF</td>\n",
       "      <td>-13.3</td>\n",
       "      <td>-176.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>1790263278618</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62616</td>\n",
       "      <td>626161</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>14.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ESH</td>\n",
       "      <td>24.5</td>\n",
       "      <td>-13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>1790263278582</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>11945</td>\n",
       "      <td>0</td>\n",
       "      <td>2159</td>\n",
       "      <td>0</td>\n",
       "      <td>9124</td>\n",
       "      <td>0</td>\n",
       "      <td>662</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2608</td>\n",
       "      <td>14430</td>\n",
       "      <td>95</td>\n",
       "      <td>21.25</td>\n",
       "      <td>292.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>YEM</td>\n",
       "      <td>15.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>227</th>\n",
       "      <td>1790263278473</td>\n",
       "      <td>Zambia</td>\n",
       "      <td>349304</td>\n",
       "      <td>0</td>\n",
       "      <td>4069</td>\n",
       "      <td>0</td>\n",
       "      <td>341316</td>\n",
       "      <td>0</td>\n",
       "      <td>3919</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Africa</td>\n",
       "      <td>56</td>\n",
       "      <td>4785</td>\n",
       "      <td>5</td>\n",
       "      <td>201.28</td>\n",
       "      <td>17530.14</td>\n",
       "      <td>0.00</td>\n",
       "      <td>ZMB</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>1790263278489</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>266359</td>\n",
       "      <td>0</td>\n",
       "      <td>5740</td>\n",
       "      <td>0</td>\n",
       "      <td>258888</td>\n",
       "      <td>0</td>\n",
       "      <td>1731</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>Africa</td>\n",
       "      <td>58</td>\n",
       "      <td>2671</td>\n",
       "      <td>6</td>\n",
       "      <td>112.91</td>\n",
       "      <td>16886.10</td>\n",
       "      <td>0.78</td>\n",
       "      <td>ZWE</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>229 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           updated            country   cases  today_cases  deaths  \\\n",
       "0    1790263278491        Afghanistan  234174            0    7996   \n",
       "1    1790263278479            Albania  334863            0    3605   \n",
       "2    1790263278487            Algeria  272010            0    6881   \n",
       "3    1790263278534            Andorra   48015            0     165   \n",
       "4    1790263278510             Angola  107327            0    1937   \n",
       "..             ...                ...     ...          ...     ...   \n",
       "224  1790263278605  Wallis and Futuna    3550            0       8   \n",
       "225  1790263278618     Western Sahara      10            0       1   \n",
       "226  1790263278582              Yemen   11945            0    2159   \n",
       "227  1790263278473             Zambia  349304            0    4069   \n",
       "228  1790263278489           Zimbabwe  266359            0    5740   \n",
       "\n",
       "     today_deaths  recovered  today_recovered  active  critical  ...  \\\n",
       "0               0     211080                0   15098         0  ...   \n",
       "1               0     330233                0    1025         0  ...   \n",
       "2               0     183061                0   82068         0  ...   \n",
       "3               0          0                0   47850         0  ...   \n",
       "4               0     103419                0    1971         0  ...   \n",
       "..            ...        ...              ...     ...       ...  ...   \n",
       "224             0        438                0    3104         0  ...   \n",
       "225             0          9                0       0         0  ...   \n",
       "226             0       9124                0     662         0  ...   \n",
       "227             0     341316                0    3919         0  ...   \n",
       "228             0     258888                0    1731        12  ...   \n",
       "\n",
       "             continent  case_per_people  death_per_people  test_per_people  \\\n",
       "0                 Asia              174              5097               29   \n",
       "1               Europe                9               795                1   \n",
       "2               Africa              167              6591              196   \n",
       "3               Europe                2               469                0   \n",
       "4               Africa              326             18083               23   \n",
       "..                 ...              ...               ...              ...   \n",
       "224  Australia-Oceania                3              1373                1   \n",
       "225             Africa            62616            626161                0   \n",
       "226               Asia             2608             14430               95   \n",
       "227             Africa               56              4785                5   \n",
       "228             Africa               58              2671                6   \n",
       "\n",
       "     active_per_million recovered_per_million  critical_per_million  iso3  \\\n",
       "0                370.46               5179.32                  0.00   AFG   \n",
       "1                357.59             115209.32                  0.00   ALB   \n",
       "2               1809.65               4036.61                  0.00   DZA   \n",
       "3             617714.26                  0.00                  0.00   AND   \n",
       "4                 56.27               2952.52                  0.00   AGO   \n",
       "..                  ...                   ...                   ...   ...   \n",
       "224           282644.33              39883.45                  0.00   WLF   \n",
       "225                0.00                 14.37                  0.00   ESH   \n",
       "226               21.25                292.86                  0.00   YEM   \n",
       "227              201.28              17530.14                  0.00   ZMB   \n",
       "228              112.91              16886.10                  0.78   ZWE   \n",
       "\n",
       "      lat   long  \n",
       "0    33.0   65.0  \n",
       "1    41.0   20.0  \n",
       "2    28.0    3.0  \n",
       "3    42.5    1.6  \n",
       "4   -12.5   18.5  \n",
       "..    ...    ...  \n",
       "224 -13.3 -176.2  \n",
       "225  24.5  -13.0  \n",
       "226  15.0   48.0  \n",
       "227 -15.0   30.0  \n",
       "228 -20.0   30.0  \n",
       "\n",
       "[229 rows x 25 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "96e91e7e",
   "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>updated</th>\n",
       "      <th>country</th>\n",
       "      <th>cases</th>\n",
       "      <th>today_cases</th>\n",
       "      <th>deaths</th>\n",
       "      <th>today_deaths</th>\n",
       "      <th>recovered</th>\n",
       "      <th>today_recovered</th>\n",
       "      <th>active</th>\n",
       "      <th>critical</th>\n",
       "      <th>...</th>\n",
       "      <th>continent</th>\n",
       "      <th>case_per_people</th>\n",
       "      <th>death_per_people</th>\n",
       "      <th>test_per_people</th>\n",
       "      <th>active_per_million</th>\n",
       "      <th>recovered_per_million</th>\n",
       "      <th>critical_per_million</th>\n",
       "      <th>iso3</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2026-09-24</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>234174</td>\n",
       "      <td>0</td>\n",
       "      <td>7996</td>\n",
       "      <td>0</td>\n",
       "      <td>211080</td>\n",
       "      <td>0</td>\n",
       "      <td>15098</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Asia</td>\n",
       "      <td>174</td>\n",
       "      <td>5097</td>\n",
       "      <td>29</td>\n",
       "      <td>370.46</td>\n",
       "      <td>5179.32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AFG</td>\n",
       "      <td>33.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2026-09-24</td>\n",
       "      <td>Albania</td>\n",
       "      <td>334863</td>\n",
       "      <td>0</td>\n",
       "      <td>3605</td>\n",
       "      <td>0</td>\n",
       "      <td>330233</td>\n",
       "      <td>0</td>\n",
       "      <td>1025</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>9</td>\n",
       "      <td>795</td>\n",
       "      <td>1</td>\n",
       "      <td>357.59</td>\n",
       "      <td>115209.32</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ALB</td>\n",
       "      <td>41.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2026-09-24</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>272010</td>\n",
       "      <td>0</td>\n",
       "      <td>6881</td>\n",
       "      <td>0</td>\n",
       "      <td>183061</td>\n",
       "      <td>0</td>\n",
       "      <td>82068</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Africa</td>\n",
       "      <td>167</td>\n",
       "      <td>6591</td>\n",
       "      <td>196</td>\n",
       "      <td>1809.65</td>\n",
       "      <td>4036.61</td>\n",
       "      <td>0.0</td>\n",
       "      <td>DZA</td>\n",
       "      <td>28.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2026-09-24</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>48015</td>\n",
       "      <td>0</td>\n",
       "      <td>165</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47850</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2</td>\n",
       "      <td>469</td>\n",
       "      <td>0</td>\n",
       "      <td>617714.26</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AND</td>\n",
       "      <td>42.5</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2026-09-24</td>\n",
       "      <td>Angola</td>\n",
       "      <td>107327</td>\n",
       "      <td>0</td>\n",
       "      <td>1937</td>\n",
       "      <td>0</td>\n",
       "      <td>103419</td>\n",
       "      <td>0</td>\n",
       "      <td>1971</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>Africa</td>\n",
       "      <td>326</td>\n",
       "      <td>18083</td>\n",
       "      <td>23</td>\n",
       "      <td>56.27</td>\n",
       "      <td>2952.52</td>\n",
       "      <td>0.0</td>\n",
       "      <td>AGO</td>\n",
       "      <td>-12.5</td>\n",
       "      <td>18.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     updated      country   cases  today_cases  deaths  today_deaths  \\\n",
       "0 2026-09-24  Afghanistan  234174            0    7996             0   \n",
       "1 2026-09-24      Albania  334863            0    3605             0   \n",
       "2 2026-09-24      Algeria  272010            0    6881             0   \n",
       "3 2026-09-24      Andorra   48015            0     165             0   \n",
       "4 2026-09-24       Angola  107327            0    1937             0   \n",
       "\n",
       "   recovered  today_recovered  active  critical  ...  continent  \\\n",
       "0     211080                0   15098         0  ...       Asia   \n",
       "1     330233                0    1025         0  ...     Europe   \n",
       "2     183061                0   82068         0  ...     Africa   \n",
       "3          0                0   47850         0  ...     Europe   \n",
       "4     103419                0    1971         0  ...     Africa   \n",
       "\n",
       "   case_per_people  death_per_people  test_per_people  active_per_million  \\\n",
       "0              174              5097               29              370.46   \n",
       "1                9               795                1              357.59   \n",
       "2              167              6591              196             1809.65   \n",
       "3                2               469                0           617714.26   \n",
       "4              326             18083               23               56.27   \n",
       "\n",
       "  recovered_per_million  critical_per_million  iso3   lat  long  \n",
       "0               5179.32                   0.0   AFG  33.0  65.0  \n",
       "1             115209.32                   0.0   ALB  41.0  20.0  \n",
       "2               4036.61                   0.0   DZA  28.0   3.0  \n",
       "3                  0.00                   0.0   AND  42.5   1.6  \n",
       "4               2952.52                   0.0   AGO -12.5  18.5  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"updated\"] = pd.to_datetime(\n",
    "    df[\"updated\"],\n",
    "    unit=\"ms\"\n",
    ").dt.normalize()\n",
    "\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "595625f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 229 entries, 0 to 228\n",
      "Data columns (total 25 columns):\n",
      " #   Column                 Non-Null Count  Dtype         \n",
      "---  ------                 --------------  -----         \n",
      " 0   updated                229 non-null    datetime64[ms]\n",
      " 1   country                229 non-null    str           \n",
      " 2   cases                  229 non-null    int64         \n",
      " 3   today_cases            229 non-null    int64         \n",
      " 4   deaths                 229 non-null    int64         \n",
      " 5   today_deaths           229 non-null    int64         \n",
      " 6   recovered              229 non-null    int64         \n",
      " 7   today_recovered        229 non-null    int64         \n",
      " 8   active                 229 non-null    int64         \n",
      " 9   critical               229 non-null    int64         \n",
      " 10  cases_per_million      229 non-null    int64         \n",
      " 11  deaths_per_million     229 non-null    int64         \n",
      " 12  tests                  229 non-null    int64         \n",
      " 13  tests_per_million      229 non-null    int64         \n",
      " 14  population             229 non-null    int64         \n",
      " 15  continent              229 non-null    str           \n",
      " 16  case_per_people        229 non-null    int64         \n",
      " 17  death_per_people       229 non-null    int64         \n",
      " 18  test_per_people        229 non-null    int64         \n",
      " 19  active_per_million     229 non-null    float64       \n",
      " 20  recovered_per_million  229 non-null    float64       \n",
      " 21  critical_per_million   229 non-null    float64       \n",
      " 22  iso3                   229 non-null    object        \n",
      " 23  lat                    229 non-null    float64       \n",
      " 24  long                   229 non-null    float64       \n",
      "dtypes: datetime64[ms](1), float64(5), int64(16), object(1), str(2)\n",
      "memory usage: 44.9+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "71c81546",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "a705fe10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(229, 25)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "c7856123",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "today_cases        229\n",
       "today_deaths       229\n",
       "today_recovered    224\n",
       "dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df[[\"today_cases\",\"today_deaths\",\"today_recovered\"]]==0).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e4d6e4d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# today_cases, today_deaths are all zeros, and today_recovered has very few non-zero values, so all are dropped.\n",
    "df.drop([\"today_cases\",\"today_deaths\",\"today_recovered\"],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e11b3d2",
   "metadata": {},
   "source": [
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1abeb0ab",
   "metadata": {},
   "source": [
    "# PostgreSQL Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "1d24673b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The sql extension is already loaded. To reload it, use:\n",
      "  %reload_ext sql\n"
     ]
    }
   ],
   "source": [
    "%load_ext sql\n",
    "from sqlalchemy import create_engine\n",
    "%config SqlMagic.feedback = False\n",
    "%config SqlMagic.displaycon = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "51fb06dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "HOST = \"<your-host>\"\n",
    "PORT = \"<port>\"\n",
    "USER = \"<your-user>\"\n",
    "PASSWORD = \"<your-password>\"\n",
    "DATABASE = \"pandemic\"\n",
    "\n",
    "engine = create_engine(\n",
    "    f\"postgresql+psycopg2://{USER}:{PASSWORD}@{HOST}:{PORT}/{DATABASE}?sslmode=require\"\n",
    ")\n",
    "%sql postgresql+psycopg2://<your-user>:<your-password>@<your-host>:<port>/pandemic?sslmode=require"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "b0c5cee2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "++\n",
       "||\n",
       "++\n",
       "++"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "create schema if not exists covid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "9456fefc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "229"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.to_sql(\n",
    "    name=\"database\",\n",
    "    schema=\"covid\",\n",
    "    con=engine,\n",
    "    if_exists= \"replace\",\n",
    "    index= False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5861e251",
   "metadata": {},
   "source": [
    "----------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "641b9883",
   "metadata": {},
   "source": [
    "# Data Normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "26eb65a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>updated</th>\n",
       "            <th>country</th>\n",
       "            <th>cases</th>\n",
       "            <th>deaths</th>\n",
       "            <th>recovered</th>\n",
       "            <th>active</th>\n",
       "            <th>critical</th>\n",
       "            <th>cases_per_million</th>\n",
       "            <th>deaths_per_million</th>\n",
       "            <th>tests</th>\n",
       "            <th>tests_per_million</th>\n",
       "            <th>population</th>\n",
       "            <th>continent</th>\n",
       "            <th>case_per_people</th>\n",
       "            <th>death_per_people</th>\n",
       "            <th>test_per_people</th>\n",
       "            <th>active_per_million</th>\n",
       "            <th>recovered_per_million</th>\n",
       "            <th>critical_per_million</th>\n",
       "            <th>iso3</th>\n",
       "            <th>lat</th>\n",
       "            <th>long</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>2026-09-24 00:00:00</td>\n",
       "            <td>Afghanistan</td>\n",
       "            <td>234174</td>\n",
       "            <td>7996</td>\n",
       "            <td>211080</td>\n",
       "            <td>15098</td>\n",
       "            <td>0</td>\n",
       "            <td>5746</td>\n",
       "            <td>196</td>\n",
       "            <td>1390730</td>\n",
       "            <td>34125</td>\n",
       "            <td>40754388</td>\n",
       "            <td>Asia</td>\n",
       "            <td>174</td>\n",
       "            <td>5097</td>\n",
       "            <td>29</td>\n",
       "            <td>370.46</td>\n",
       "            <td>5179.32</td>\n",
       "            <td>0.0</td>\n",
       "            <td>AFG</td>\n",
       "            <td>33.0</td>\n",
       "            <td>65.0</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>2026-09-24 00:00:00</td>\n",
       "            <td>Albania</td>\n",
       "            <td>334863</td>\n",
       "            <td>3605</td>\n",
       "            <td>330233</td>\n",
       "            <td>1025</td>\n",
       "            <td>0</td>\n",
       "            <td>116825</td>\n",
       "            <td>1258</td>\n",
       "            <td>1941032</td>\n",
       "            <td>677173</td>\n",
       "            <td>2866374</td>\n",
       "            <td>Europe</td>\n",
       "            <td>9</td>\n",
       "            <td>795</td>\n",
       "            <td>1</td>\n",
       "            <td>357.59</td>\n",
       "            <td>115209.32</td>\n",
       "            <td>0.0</td>\n",
       "            <td>ALB</td>\n",
       "            <td>41.0</td>\n",
       "            <td>20.0</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "+---------------------+-------------+--------+--------+-----------+--------+----------+-------------------+--------------------+---------+-------------------+------------+-----------+-----------------+------------------+-----------------+--------------------+-----------------------+----------------------+------+------+------+\n",
       "|       updated       |   country   | cases  | deaths | recovered | active | critical | cases_per_million | deaths_per_million |  tests  | tests_per_million | population | continent | case_per_people | death_per_people | test_per_people | active_per_million | recovered_per_million | critical_per_million | iso3 | lat  | long |\n",
       "+---------------------+-------------+--------+--------+-----------+--------+----------+-------------------+--------------------+---------+-------------------+------------+-----------+-----------------+------------------+-----------------+--------------------+-----------------------+----------------------+------+------+------+\n",
       "| 2026-09-24 00:00:00 | Afghanistan | 234174 |  7996  |   211080  | 15098  |    0     |        5746       |        196         | 1390730 |       34125       |  40754388  |    Asia   |       174       |       5097       |        29       |       370.46       |        5179.32        |         0.0          | AFG  | 33.0 | 65.0 |\n",
       "| 2026-09-24 00:00:00 |   Albania   | 334863 |  3605  |   330233  |  1025  |    0     |       116825      |        1258        | 1941032 |       677173      |  2866374   |   Europe  |        9        |       795        |        1        |       357.59       |       115209.32       |         0.0          | ALB  | 41.0 | 20.0 |\n",
       "+---------------------+-------------+--------+--------+-----------+--------+----------+-------------------+--------------------+---------+-------------------+------------+-----------+-----------------+------------------+-----------------+--------------------+-----------------------+----------------------+------+------+------+"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select * from covid.database\n",
    "limit 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "c1dfe2bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "%config SqlMagic.displaylimit = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "ece24087",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>column_name</th>\n",
       "            <th>data_type</th>\n",
       "            <th>ordinal_position</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>updated</td>\n",
       "            <td>timestamp without time zone</td>\n",
       "            <td>1</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>country</td>\n",
       "            <td>text</td>\n",
       "            <td>2</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>cases</td>\n",
       "            <td>bigint</td>\n",
       "            <td>3</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>deaths</td>\n",
       "            <td>bigint</td>\n",
       "            <td>4</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>recovered</td>\n",
       "            <td>bigint</td>\n",
       "            <td>5</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>active</td>\n",
       "            <td>bigint</td>\n",
       "            <td>6</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>critical</td>\n",
       "            <td>bigint</td>\n",
       "            <td>7</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>cases_per_million</td>\n",
       "            <td>bigint</td>\n",
       "            <td>8</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>deaths_per_million</td>\n",
       "            <td>bigint</td>\n",
       "            <td>9</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>tests</td>\n",
       "            <td>bigint</td>\n",
       "            <td>10</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>tests_per_million</td>\n",
       "            <td>bigint</td>\n",
       "            <td>11</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>population</td>\n",
       "            <td>bigint</td>\n",
       "            <td>12</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>continent</td>\n",
       "            <td>text</td>\n",
       "            <td>13</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>case_per_people</td>\n",
       "            <td>bigint</td>\n",
       "            <td>14</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>death_per_people</td>\n",
       "            <td>bigint</td>\n",
       "            <td>15</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>test_per_people</td>\n",
       "            <td>bigint</td>\n",
       "            <td>16</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>active_per_million</td>\n",
       "            <td>double precision</td>\n",
       "            <td>17</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>recovered_per_million</td>\n",
       "            <td>double precision</td>\n",
       "            <td>18</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>critical_per_million</td>\n",
       "            <td>double precision</td>\n",
       "            <td>19</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>iso3</td>\n",
       "            <td>text</td>\n",
       "            <td>20</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>lat</td>\n",
       "            <td>double precision</td>\n",
       "            <td>21</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>long</td>\n",
       "            <td>double precision</td>\n",
       "            <td>22</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "+-----------------------+-----------------------------+------------------+\n",
       "|      column_name      |          data_type          | ordinal_position |\n",
       "+-----------------------+-----------------------------+------------------+\n",
       "|        updated        | timestamp without time zone |        1         |\n",
       "|        country        |             text            |        2         |\n",
       "|         cases         |            bigint           |        3         |\n",
       "|         deaths        |            bigint           |        4         |\n",
       "|       recovered       |            bigint           |        5         |\n",
       "|         active        |            bigint           |        6         |\n",
       "|        critical       |            bigint           |        7         |\n",
       "|   cases_per_million   |            bigint           |        8         |\n",
       "|   deaths_per_million  |            bigint           |        9         |\n",
       "|         tests         |            bigint           |        10        |\n",
       "|   tests_per_million   |            bigint           |        11        |\n",
       "|       population      |            bigint           |        12        |\n",
       "|       continent       |             text            |        13        |\n",
       "|    case_per_people    |            bigint           |        14        |\n",
       "|    death_per_people   |            bigint           |        15        |\n",
       "|    test_per_people    |            bigint           |        16        |\n",
       "|   active_per_million  |       double precision      |        17        |\n",
       "| recovered_per_million |       double precision      |        18        |\n",
       "|  critical_per_million |       double precision      |        19        |\n",
       "|          iso3         |             text            |        20        |\n",
       "|          lat          |       double precision      |        21        |\n",
       "|          long         |       double precision      |        22        |\n",
       "+-----------------------+-----------------------------+------------------+"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select column_name, data_type, ordinal_position\n",
    "from information_schema.columns\n",
    "where table_schema = 'covid'\n",
    "  and table_name = 'database'\n",
    "order by ordinal_position"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "8791930c",
   "metadata": {
    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "++\n",
       "||\n",
       "++\n",
       "++"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "alter table covid.database\n",
    "alter column updated type date using updated::date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "e9d58f17",
   "metadata": {
    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>updated</th>\n",
       "            <th>country</th>\n",
       "            <th>cases</th>\n",
       "            <th>deaths</th>\n",
       "            <th>recovered</th>\n",
       "            <th>active</th>\n",
       "            <th>critical</th>\n",
       "            <th>cases_per_million</th>\n",
       "            <th>deaths_per_million</th>\n",
       "            <th>tests</th>\n",
       "            <th>tests_per_million</th>\n",
       "            <th>population</th>\n",
       "            <th>continent</th>\n",
       "            <th>case_per_people</th>\n",
       "            <th>death_per_people</th>\n",
       "            <th>test_per_people</th>\n",
       "            <th>active_per_million</th>\n",
       "            <th>recovered_per_million</th>\n",
       "            <th>critical_per_million</th>\n",
       "            <th>iso3</th>\n",
       "            <th>lat</th>\n",
       "            <th>long</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "+---------+---------+-------+--------+-----------+--------+----------+-------------------+--------------------+-------+-------------------+------------+-----------+-----------------+------------------+-----------------+--------------------+-----------------------+----------------------+------+-----+------+\n",
       "| updated | country | cases | deaths | recovered | active | critical | cases_per_million | deaths_per_million | tests | tests_per_million | population | continent | case_per_people | death_per_people | test_per_people | active_per_million | recovered_per_million | critical_per_million | iso3 | lat | long |\n",
       "+---------+---------+-------+--------+-----------+--------+----------+-------------------+--------------------+-------+-------------------+------------+-----------+-----------------+------------------+-----------------+--------------------+-----------------------+----------------------+------+-----+------+\n",
       "+---------+---------+-------+--------+-----------+--------+----------+-------------------+--------------------+-------+-------------------+------------+-----------+-----------------+------------------+-----------------+--------------------+-----------------------+----------------------+------+-----+------+"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select * from covid.database\n",
    "limit 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "483b10ba",
   "metadata": {
    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "++\n",
       "||\n",
       "++\n",
       "++"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "create or replace view covid.dim_country as(\n",
    "    select iso3,\n",
    "           country,\n",
    "           continent,\n",
    "           population\n",
    "    from covid.database\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "45039410",
   "metadata": {
    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "++\n",
       "||\n",
       "++\n",
       "++"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "create or replace view covid.fact_cases as(\n",
    "    select iso3,\n",
    "           cases,\n",
    "           deaths,\n",
    "           recovered,\n",
    "           active,\n",
    "           critical,\n",
    "           tests\n",
    "    from covid.database\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d43bf035",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5993cff",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46623a03",
   "metadata": {},
   "source": [
    "1- what are the highest 3 conuntries with recorded death cases?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c4be8998",
   "metadata": {
    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>country</th>\n",
       "            <th>continent</th>\n",
       "            <th>deaths</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>USA</td>\n",
       "            <td>North America</td>\n",
       "            <td>1219487</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Brazil</td>\n",
       "            <td>South America</td>\n",
       "            <td>711380</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>India</td>\n",
       "            <td>Asia</td>\n",
       "            <td>533570</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "+---------+---------------+---------+\n",
       "| country |   continent   |  deaths |\n",
       "+---------+---------------+---------+\n",
       "|   USA   | North America | 1219487 |\n",
       "|  Brazil | South America |  711380 |\n",
       "|  India  |      Asia     |  533570 |\n",
       "+---------+---------------+---------+"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select country,continent,deaths\n",
    "from covid.fact_cases fc\n",
    "join covid.dim_country dc\n",
    "on fc.iso3 = dc.iso3\n",
    "order by deaths desc\n",
    "limit 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66355a0c",
   "metadata": {},
   "source": [
    "2- what are the highest 3 conuntries with recorded cases and lowest ones?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "8620132e",
   "metadata": {
    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>country</th>\n",
       "            <th>continent</th>\n",
       "            <th>cases</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>USA</td>\n",
       "            <td>North America</td>\n",
       "            <td>111820082</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>India</td>\n",
       "            <td>Asia</td>\n",
       "            <td>45035393</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>France</td>\n",
       "            <td>Europe</td>\n",
       "            <td>40138560</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "+---------+---------------+-----------+\n",
       "| country |   continent   |   cases   |\n",
       "+---------+---------------+-----------+\n",
       "|   USA   | North America | 111820082 |\n",
       "|  India  |      Asia     |  45035393 |\n",
       "|  France |     Europe    |  40138560 |\n",
       "+---------+---------------+-----------+"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select country,continent, cases\n",
    "from covid.fact_cases fc\n",
    "join covid.dim_country dc\n",
    "on fc.iso3 = dc.iso3\n",
    "order by cases desc\n",
    "limit 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "976c9fb3",
   "metadata": {
    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>country</th>\n",
       "            <th>continent</th>\n",
       "            <th>cases</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Western Sahara</td>\n",
       "            <td>Africa</td>\n",
       "            <td>10</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Holy See (Vatican City State)</td>\n",
       "            <td>Europe</td>\n",
       "            <td>29</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Tokelau</td>\n",
       "            <td>Australia-Oceania</td>\n",
       "            <td>80</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "+-------------------------------+-------------------+-------+\n",
       "|            country            |     continent     | cases |\n",
       "+-------------------------------+-------------------+-------+\n",
       "|         Western Sahara        |       Africa      |   10  |\n",
       "| Holy See (Vatican City State) |       Europe      |   29  |\n",
       "|            Tokelau            | Australia-Oceania |   80  |\n",
       "+-------------------------------+-------------------+-------+"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select country,continent, cases\n",
    "from covid.fact_cases fc\n",
    "join covid.dim_country dc\n",
    "on fc.iso3 = dc.iso3\n",
    "order by cases\n",
    "limit 3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7ab72a1a",
   "metadata": {},
   "source": [
    "3- How does population relate to total cases?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "6b390db4",
   "metadata": {
    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>corr</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>0.3677406773254463</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "+--------------------+\n",
       "|        corr        |\n",
       "+--------------------+\n",
       "| 0.3677406773254463 |\n",
       "+--------------------+"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select corr(population, cases)\n",
    "from covid.fact_cases fc\n",
    "join covid.dim_country dc\n",
    "on fc.iso3 = dc.iso3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "712ce57b",
   "metadata": {
    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>country</th>\n",
       "            <th>continent</th>\n",
       "            <th>population</th>\n",
       "            <th>cases</th>\n",
       "            <th>cases_per_population</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Brunei</td>\n",
       "            <td>Asia</td>\n",
       "            <td>445431</td>\n",
       "            <td>343719</td>\n",
       "            <td>77.17</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>San Marino</td>\n",
       "            <td>Europe</td>\n",
       "            <td>34085</td>\n",
       "            <td>26185</td>\n",
       "            <td>76.82</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Faroe Islands</td>\n",
       "            <td>Europe</td>\n",
       "            <td>49233</td>\n",
       "            <td>34658</td>\n",
       "            <td>70.40</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>S. Korea</td>\n",
       "            <td>Asia</td>\n",
       "            <td>51329899</td>\n",
       "            <td>34571873</td>\n",
       "            <td>67.35</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Austria</td>\n",
       "            <td>Europe</td>\n",
       "            <td>9066710</td>\n",
       "            <td>6081287</td>\n",
       "            <td>67.07</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Niue</td>\n",
       "            <td>Australia-Oceania</td>\n",
       "            <td>1622</td>\n",
       "            <td>1059</td>\n",
       "            <td>65.29</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Slovenia</td>\n",
       "            <td>Europe</td>\n",
       "            <td>2078034</td>\n",
       "            <td>1356546</td>\n",
       "            <td>65.28</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Andorra</td>\n",
       "            <td>Europe</td>\n",
       "            <td>77463</td>\n",
       "            <td>48015</td>\n",
       "            <td>61.98</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Martinique</td>\n",
       "            <td>North America</td>\n",
       "            <td>374087</td>\n",
       "            <td>230354</td>\n",
       "            <td>61.58</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>France</td>\n",
       "            <td>Europe</td>\n",
       "            <td>65584518</td>\n",
       "            <td>40138560</td>\n",
       "            <td>61.20</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "+---------------+-------------------+------------+----------+----------------------+\n",
       "|    country    |     continent     | population |  cases   | cases_per_population |\n",
       "+---------------+-------------------+------------+----------+----------------------+\n",
       "|     Brunei    |        Asia       |   445431   |  343719  |        77.17         |\n",
       "|   San Marino  |       Europe      |   34085    |  26185   |        76.82         |\n",
       "| Faroe Islands |       Europe      |   49233    |  34658   |        70.40         |\n",
       "|    S. Korea   |        Asia       |  51329899  | 34571873 |        67.35         |\n",
       "|    Austria    |       Europe      |  9066710   | 6081287  |        67.07         |\n",
       "|      Niue     | Australia-Oceania |    1622    |   1059   |        65.29         |\n",
       "|    Slovenia   |       Europe      |  2078034   | 1356546  |        65.28         |\n",
       "|    Andorra    |       Europe      |   77463    |  48015   |        61.98         |\n",
       "|   Martinique  |   North America   |   374087   |  230354  |        61.58         |\n",
       "|     France    |       Europe      |  65584518  | 40138560 |        61.20         |\n",
       "+---------------+-------------------+------------+----------+----------------------+"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select country,\n",
    "       continent,\n",
    "       population,\n",
    "       cases,\n",
    "       round(100*(cases::numeric/population),2) as cases_per_population\n",
    "from covid.fact_cases fc\n",
    "join covid.dim_country dc\n",
    "on fc.iso3 = dc.iso3\n",
    "order by cases_per_population desc\n",
    "limit 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "120c787b",
   "metadata": {},
   "source": [
    "4- Which countries have the highest case fatality rate?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "ac42ab95",
   "metadata": {
    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>country</th>\n",
       "            <th>continent</th>\n",
       "            <th>fatality_rate</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Yemen</td>\n",
       "            <td>Asia</td>\n",
       "            <td>18.07</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Western Sahara</td>\n",
       "            <td>Africa</td>\n",
       "            <td>10.00</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Sudan</td>\n",
       "            <td>Africa</td>\n",
       "            <td>7.89</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Syrian Arab Republic</td>\n",
       "            <td>Asia</td>\n",
       "            <td>5.48</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Somalia</td>\n",
       "            <td>Africa</td>\n",
       "            <td>4.98</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "+----------------------+-----------+---------------+\n",
       "|       country        | continent | fatality_rate |\n",
       "+----------------------+-----------+---------------+\n",
       "|        Yemen         |    Asia   |     18.07     |\n",
       "|    Western Sahara    |   Africa  |     10.00     |\n",
       "|        Sudan         |   Africa  |      7.89     |\n",
       "| Syrian Arab Republic |    Asia   |      5.48     |\n",
       "|       Somalia        |   Africa  |      4.98     |\n",
       "+----------------------+-----------+---------------+"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select country,continent,round(100*(deaths::numeric / cases),2) as fatality_rate\n",
    "from covid.fact_cases fc\n",
    "join covid.dim_country dc\n",
    "on fc.iso3 = dc.iso3\n",
    "order by fatality_rate desc\n",
    "limit 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be029bb9",
   "metadata": {},
   "source": [
    "5- What share of the world's total cases does each continent account for?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "96f67049",
   "metadata": {
    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>continent</th>\n",
       "            <th>total_cases</th>\n",
       "            <th>share_pct</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Europe</td>\n",
       "            <td>253406198</td>\n",
       "            <td>35.96</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Asia</td>\n",
       "            <td>221500265</td>\n",
       "            <td>31.43</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>North America</td>\n",
       "            <td>131889132</td>\n",
       "            <td>18.71</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>South America</td>\n",
       "            <td>70200879</td>\n",
       "            <td>9.96</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Australia-Oceania</td>\n",
       "            <td>14895771</td>\n",
       "            <td>2.11</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Africa</td>\n",
       "            <td>12860924</td>\n",
       "            <td>1.82</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "+-------------------+-------------+-----------+\n",
       "|     continent     | total_cases | share_pct |\n",
       "+-------------------+-------------+-----------+\n",
       "|       Europe      |  253406198  |   35.96   |\n",
       "|        Asia       |  221500265  |   31.43   |\n",
       "|   North America   |  131889132  |   18.71   |\n",
       "|   South America   |   70200879  |    9.96   |\n",
       "| Australia-Oceania |   14895771  |    2.11   |\n",
       "|       Africa      |   12860924  |    1.82   |\n",
       "+-------------------+-------------+-----------+"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select continent, \n",
    "       sum(cases) as total_cases,\n",
    "       round(100* sum(cases)/sum(sum(cases)) over () ,2) as share_pct\n",
    "from covid.fact_cases fc\n",
    "join covid.dim_country dc\n",
    "on fc.iso3 = dc.iso3\n",
    "group by continent\n",
    "order by total_cases desc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db622cee",
   "metadata": {},
   "source": [
    "6- Which countries have more than 1 million cases but a fatality rate under 5%?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "2cf6b33e",
   "metadata": {
    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>country</th>\n",
       "            <th>continent</th>\n",
       "            <th>cases</th>\n",
       "            <th>fatality_rate</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Peru</td>\n",
       "            <td>South America</td>\n",
       "            <td>4572667</td>\n",
       "            <td>4.86</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Mexico</td>\n",
       "            <td>North America</td>\n",
       "            <td>7702809</td>\n",
       "            <td>4.35</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Ecuador</td>\n",
       "            <td>South America</td>\n",
       "            <td>1070188</td>\n",
       "            <td>3.37</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Bulgaria</td>\n",
       "            <td>Europe</td>\n",
       "            <td>1339851</td>\n",
       "            <td>2.89</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Tunisia</td>\n",
       "            <td>Africa</td>\n",
       "            <td>1153361</td>\n",
       "            <td>2.55</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>South Africa</td>\n",
       "            <td>Africa</td>\n",
       "            <td>4076463</td>\n",
       "            <td>2.52</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Indonesia</td>\n",
       "            <td>Asia</td>\n",
       "            <td>6829221</td>\n",
       "            <td>2.37</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Colombia</td>\n",
       "            <td>South America</td>\n",
       "            <td>6400173</td>\n",
       "            <td>2.24</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Hungary</td>\n",
       "            <td>Europe</td>\n",
       "            <td>2230232</td>\n",
       "            <td>2.20</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Ukraine</td>\n",
       "            <td>Europe</td>\n",
       "            <td>5557995</td>\n",
       "            <td>2.02</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "+--------------+---------------+---------+---------------+\n",
       "|   country    |   continent   |  cases  | fatality_rate |\n",
       "+--------------+---------------+---------+---------------+\n",
       "|     Peru     | South America | 4572667 |      4.86     |\n",
       "|    Mexico    | North America | 7702809 |      4.35     |\n",
       "|   Ecuador    | South America | 1070188 |      3.37     |\n",
       "|   Bulgaria   |     Europe    | 1339851 |      2.89     |\n",
       "|   Tunisia    |     Africa    | 1153361 |      2.55     |\n",
       "| South Africa |     Africa    | 4076463 |      2.52     |\n",
       "|  Indonesia   |      Asia     | 6829221 |      2.37     |\n",
       "|   Colombia   | South America | 6400173 |      2.24     |\n",
       "|   Hungary    |     Europe    | 2230232 |      2.20     |\n",
       "|   Ukraine    |     Europe    | 5557995 |      2.02     |\n",
       "+--------------+---------------+---------+---------------+"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "with fatality as(\n",
    "            select country,\n",
    "                   continent,\n",
    "                   cases,\n",
    "                   round(100*(deaths::numeric / cases),2) as fatality_rate\n",
    "            from covid.fact_cases fc\n",
    "            join covid.dim_country dc\n",
    "            on fc.iso3 = dc.iso3\n",
    "            order by fatality_rate desc\n",
    ")\n",
    "select country, continent, cases,fatality_rate\n",
    "from fatality\n",
    "where (cases > 1000000) and (fatality_rate < 5)\n",
    "order by fatality_rate desc\n",
    "limit 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "633458a5",
   "metadata": {},
   "source": [
    "7- How many countries have a case fatality rate above the world average? (unweighted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "7f8b4546",
   "metadata": {
    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>country_count</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>75</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "+---------------+\n",
       "| country_count |\n",
       "+---------------+\n",
       "|       75      |\n",
       "+---------------+"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "with fatality as(\n",
    "            select country,\n",
    "                   continent,\n",
    "                   cases,\n",
    "                   round(100*(deaths::numeric / cases),2) as fatality_rate\n",
    "            from covid.fact_cases fc\n",
    "            join covid.dim_country dc\n",
    "            on fc.iso3 = dc.iso3\n",
    "),\n",
    "avg_fatality as(\n",
    "            select round(avg(fatality_rate),2) avg_fatality_rate\n",
    "            from fatality\n",
    ")\n",
    "select count(country) country_count\n",
    "from fatality\n",
    "cross join avg_fatality\n",
    "where fatality_rate > avg_fatality_rate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3cbdc48",
   "metadata": {},
   "source": [
    "-----------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "875efad4",
   "metadata": {},
   "source": [
    "# Metric View"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "2201182c",
   "metadata": {
    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "++\n",
       "||\n",
       "++\n",
       "++"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "create view covid.metric_per_continent as\n",
    "\n",
    "with fatality as(\n",
    "            select continent,\n",
    "                   sum(cases)as total_cases,\n",
    "                   sum(population) as total_population,\n",
    "                   sum(critical) as total_critical_cases,\n",
    "                   sum(deaths) as total_deaths,\n",
    "                   sum(recovered) as total_recovered,\n",
    "                   round(100 * sum(deaths)::numeric / sum(cases), 2) as fatality_rate\n",
    "            from covid.fact_cases fc\n",
    "            join covid.dim_country dc\n",
    "            on fc.iso3 = dc.iso3\n",
    "            group by continent\n",
    "),\n",
    "\n",
    "avg_fatality as(\n",
    "            select \n",
    "                  round(avg(fatality_rate),2) avg_fatality_rate\n",
    "            from fatality\n",
    ")\n",
    "\n",
    "select continent,\n",
    "       total_cases,\n",
    "       total_population,\n",
    "       total_critical_cases,\n",
    "       total_deaths,\n",
    "       total_recovered,\n",
    "       fatality_rate,\n",
    "       case \n",
    "           when fatality_rate > avg_fatality_rate then 'High'\n",
    "           when fatality_rate = avg_fatality_rate then 'Average'\n",
    "           else 'Low'\n",
    "       end as fatality_category\n",
    "from fatality\n",
    "cross join avg_fatality "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "54b0c822",
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    "vscode": {
     "languageId": "sql"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>continent</th>\n",
       "            <th>total_cases</th>\n",
       "            <th>total_population</th>\n",
       "            <th>total_critical_cases</th>\n",
       "            <th>total_deaths</th>\n",
       "            <th>total_recovered</th>\n",
       "            <th>fatality_rate</th>\n",
       "            <th>fatality_category</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Europe</td>\n",
       "            <td>253406198</td>\n",
       "            <td>747543837</td>\n",
       "            <td>401</td>\n",
       "            <td>2101824</td>\n",
       "            <td>235797174</td>\n",
       "            <td>0.83</td>\n",
       "            <td>Low</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Asia</td>\n",
       "            <td>221500265</td>\n",
       "            <td>4711356783</td>\n",
       "            <td>370</td>\n",
       "            <td>1553662</td>\n",
       "            <td>108524029</td>\n",
       "            <td>0.70</td>\n",
       "            <td>Low</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>North America</td>\n",
       "            <td>131889132</td>\n",
       "            <td>598140916</td>\n",
       "            <td>1083</td>\n",
       "            <td>1695941</td>\n",
       "            <td>126483239</td>\n",
       "            <td>1.29</td>\n",
       "            <td>High</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>South America</td>\n",
       "            <td>70200879</td>\n",
       "            <td>437694443</td>\n",
       "            <td>628</td>\n",
       "            <td>1367332</td>\n",
       "            <td>65935495</td>\n",
       "            <td>1.95</td>\n",
       "            <td>High</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Australia-Oceania</td>\n",
       "            <td>14895771</td>\n",
       "            <td>43470408</td>\n",
       "            <td>6</td>\n",
       "            <td>33015</td>\n",
       "            <td>14615509</td>\n",
       "            <td>0.22</td>\n",
       "            <td>Low</td>\n",
       "        </tr>\n",
       "        <tr>\n",
       "            <td>Africa</td>\n",
       "            <td>12860924</td>\n",
       "            <td>1406728744</td>\n",
       "            <td>389</td>\n",
       "            <td>258892</td>\n",
       "            <td>9211514</td>\n",
       "            <td>2.01</td>\n",
       "            <td>High</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "+-------------------+-------------+------------------+----------------------+--------------+-----------------+---------------+-------------------+\n",
       "|     continent     | total_cases | total_population | total_critical_cases | total_deaths | total_recovered | fatality_rate | fatality_category |\n",
       "+-------------------+-------------+------------------+----------------------+--------------+-----------------+---------------+-------------------+\n",
       "|       Europe      |  253406198  |    747543837     |         401          |   2101824    |    235797174    |      0.83     |        Low        |\n",
       "|        Asia       |  221500265  |    4711356783    |         370          |   1553662    |    108524029    |      0.70     |        Low        |\n",
       "|   North America   |  131889132  |    598140916     |         1083         |   1695941    |    126483239    |      1.29     |        High       |\n",
       "|   South America   |   70200879  |    437694443     |         628          |   1367332    |     65935495    |      1.95     |        High       |\n",
       "| Australia-Oceania |   14895771  |     43470408     |          6           |    33015     |     14615509    |      0.22     |        Low        |\n",
       "|       Africa      |   12860924  |    1406728744    |         389          |    258892    |     9211514     |      2.01     |        High       |\n",
       "+-------------------+-------------+------------------+----------------------+--------------+-----------------+---------------+-------------------+"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "select * from covid.metric_per_continent\n",
    "order by total_cases desc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c5a5649",
   "metadata": {},
   "source": [
    "--------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cd30343",
   "metadata": {},
   "source": [
    "## Insights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58c516ac",
   "metadata": {},
   "source": [
    "### 1. Geographic Distribution & Concentration of Death\n",
    "* **Total Deaths:** The United States, Brazil, and India recorded the highest number of global death from COVID-19, heavily driven by their large absolute population.\n",
    "* **Continents Share:** **Europe** accounted for the largest percentage share of total recorded cases globally when compared directly against **Asia** (2nd) and **North America** (3rd).\n",
    "\n",
    "\n",
    "### 2. Population vs. Case Volume Analysis\n",
    "* **Mild Linear Relationship ($r = 0.36$):** The correlation coefficient of **0.36** indicates only a mild positive relationship between population size and total confirmed cases.\n",
    "* **Population Size Alone Was Not Enough:** Large case counts were not only determined by population size. Non-demographic factors—such as local public health policies, testing capabilities, urban density, and lockdown stringency—played a far greater role in shaping final case counts.\n",
    "\n",
    "\n",
    "### 3. Infection Rate Dynamics & Surveillance Bias\n",
    "* **Unexpected Top Infection Rates:** Contrary to the expectation that heavily populated major countries would experience the highest relative infection rates, the highest percentage of infected residents occurred within smaller states and micro-nations.\n",
    "* **Case Study (Brunei):** Brunei registered a **77.17% infection rate**, logging $343,719$ confirmed infections out of a total population of $445,431$.\n",
    "* **Surveillance Efficiency & Data Artifacts:** This pattern reflects differences in resources and reporting rather than actual transmission dynamics. Small, well-resourced micro-nations maintained superior surveillance mechanisms, allowing them to detect and log a significantly higher proportion of mild and asymptomatic cases compared to larger, under-resourced nations where many cases went untracked.\n",
    "\n",
    "\n",
    "### 4. Fatality Rate\n",
    "* **High-Volume Outliers:** Among countries reporting over 1 million total cases, nations such as Peru, Mexico, and Ecuador recorded notable Case Fatality Rates (CFRs) hovering around or slightly below 5%.\n",
    "* **Global Benchmark Disparities:** A total of **75 countries maintained a Case Fatality Rate above the global average(unweighted)**, highlighting widespread regional disparities in healthcare infrastructure, ICU capacity, and early testing access."
   ]
  }
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