{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "74012e00",
   "metadata": {},
   "source": [
    "# Install and import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cd7ae131",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Install libraries\n",
    "!pip install -q missingno\n",
    "!pip install -q pyod\n",
    "\n",
    "# import libraries\n",
    "import missingno as msno\n",
    "import pandas as pd\n",
    "from pyod.models.knn import KNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acc6fb0f",
   "metadata": {},
   "source": [
    "# Importing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "99953ed1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape before dropping columns: (330782, 67)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>population</th>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.51</td>\n",
       "      <td>41128772.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.51</td>\n",
       "      <td>41128772.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.51</td>\n",
       "      <td>41128772.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.51</td>\n",
       "      <td>41128772.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.51</td>\n",
       "      <td>41128772.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso_code continent     location        date  total_cases  new_cases  \\\n",
       "0      AFG      Asia  Afghanistan  2020-01-03          NaN       0.00   \n",
       "1      AFG      Asia  Afghanistan  2020-01-04          NaN       0.00   \n",
       "2      AFG      Asia  Afghanistan  2020-01-05          NaN       0.00   \n",
       "3      AFG      Asia  Afghanistan  2020-01-06          NaN       0.00   \n",
       "4      AFG      Asia  Afghanistan  2020-01-07          NaN       0.00   \n",
       "\n",
       "   new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  \\\n",
       "0                 NaN           NaN        0.00                  NaN  ...   \n",
       "1                 NaN           NaN        0.00                  NaN  ...   \n",
       "2                 NaN           NaN        0.00                  NaN  ...   \n",
       "3                 NaN           NaN        0.00                  NaN  ...   \n",
       "4                 NaN           NaN        0.00                  NaN  ...   \n",
       "\n",
       "   male_smokers  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "0           NaN                   37.75                        0.50   \n",
       "1           NaN                   37.75                        0.50   \n",
       "2           NaN                   37.75                        0.50   \n",
       "3           NaN                   37.75                        0.50   \n",
       "4           NaN                   37.75                        0.50   \n",
       "\n",
       "   life_expectancy  human_development_index   population  \\\n",
       "0            64.83                     0.51  41128772.00   \n",
       "1            64.83                     0.51  41128772.00   \n",
       "2            64.83                     0.51  41128772.00   \n",
       "3            64.83                     0.51  41128772.00   \n",
       "4            64.83                     0.51  41128772.00   \n",
       "\n",
       "   excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "0                                   NaN                          NaN   \n",
       "1                                   NaN                          NaN   \n",
       "2                                   NaN                          NaN   \n",
       "3                                   NaN                          NaN   \n",
       "4                                   NaN                          NaN   \n",
       "\n",
       "   excess_mortality  excess_mortality_cumulative_per_million  \n",
       "0               NaN                                      NaN  \n",
       "1               NaN                                      NaN  \n",
       "2               NaN                                      NaN  \n",
       "3               NaN                                      NaN  \n",
       "4               NaN                                      NaN  \n",
       "\n",
       "[5 rows x 67 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Read the dataset\n",
    "dataset_path = \"C:\\My\\Top-up Degree\\Data Science\\Data Science - Assignment\\Data set\\Kaggle\\Our World in Data - COVID-19\\owid-covid-data.csv\"\n",
    "data = pd.read_csv(dataset_path)\n",
    "print(\"Shape before dropping columns:\", data.shape)\n",
    "display(data.head(n=5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d083bab",
   "metadata": {},
   "source": [
    "## Describing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7d31bcf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>new_cases_smoothed_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>population</th>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2.933000e+05</td>\n",
       "      <td>3.216330e+05</td>\n",
       "      <td>3.203740e+05</td>\n",
       "      <td>2.721740e+05</td>\n",
       "      <td>321702.000000</td>\n",
       "      <td>320472.000000</td>\n",
       "      <td>293300.000000</td>\n",
       "      <td>321633.000000</td>\n",
       "      <td>320374.000000</td>\n",
       "      <td>272174.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>189702.000000</td>\n",
       "      <td>125583.000000</td>\n",
       "      <td>226326.000000</td>\n",
       "      <td>304234.000000</td>\n",
       "      <td>248529.000000</td>\n",
       "      <td>3.307820e+05</td>\n",
       "      <td>1.153500e+04</td>\n",
       "      <td>11535.000000</td>\n",
       "      <td>11535.000000</td>\n",
       "      <td>11535.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.253904e+06</td>\n",
       "      <td>1.013110e+04</td>\n",
       "      <td>1.016799e+04</td>\n",
       "      <td>8.357613e+04</td>\n",
       "      <td>91.896227</td>\n",
       "      <td>92.240686</td>\n",
       "      <td>95300.478343</td>\n",
       "      <td>153.109948</td>\n",
       "      <td>153.681658</td>\n",
       "      <td>843.644772</td>\n",
       "      <td>...</td>\n",
       "      <td>32.910416</td>\n",
       "      <td>50.790677</td>\n",
       "      <td>3.097145</td>\n",
       "      <td>73.716225</td>\n",
       "      <td>0.722483</td>\n",
       "      <td>1.282799e+08</td>\n",
       "      <td>4.933200e+04</td>\n",
       "      <td>9.635586</td>\n",
       "      <td>11.815644</td>\n",
       "      <td>1591.662698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.854149e+07</td>\n",
       "      <td>1.134175e+05</td>\n",
       "      <td>9.718695e+04</td>\n",
       "      <td>4.293215e+05</td>\n",
       "      <td>761.899876</td>\n",
       "      <td>597.590018</td>\n",
       "      <td>145463.270075</td>\n",
       "      <td>1196.206835</td>\n",
       "      <td>616.176235</td>\n",
       "      <td>1079.976631</td>\n",
       "      <td>...</td>\n",
       "      <td>13.574225</td>\n",
       "      <td>31.956510</td>\n",
       "      <td>2.548339</td>\n",
       "      <td>7.396354</td>\n",
       "      <td>0.148987</td>\n",
       "      <td>6.602098e+08</td>\n",
       "      <td>1.412464e+05</td>\n",
       "      <td>12.479665</td>\n",
       "      <td>25.623475</td>\n",
       "      <td>1894.037851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>1.188000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>53.280000</td>\n",
       "      <td>0.394000</td>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>-3.772610e+04</td>\n",
       "      <td>-44.230000</td>\n",
       "      <td>-95.920000</td>\n",
       "      <td>-2142.340300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.474000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>5.710000e-01</td>\n",
       "      <td>1.250000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2273.895000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>55.469500</td>\n",
       "      <td>...</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>20.859000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>69.590000</td>\n",
       "      <td>0.602000</td>\n",
       "      <td>4.490020e+05</td>\n",
       "      <td>7.479999e+01</td>\n",
       "      <td>1.070000</td>\n",
       "      <td>-1.495000</td>\n",
       "      <td>46.896362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.538100e+04</td>\n",
       "      <td>4.000000e+00</td>\n",
       "      <td>3.007150e+01</td>\n",
       "      <td>1.250000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.143000</td>\n",
       "      <td>24535.964500</td>\n",
       "      <td>0.366000</td>\n",
       "      <td>8.249000</td>\n",
       "      <td>358.698000</td>\n",
       "      <td>...</td>\n",
       "      <td>33.100000</td>\n",
       "      <td>49.839000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>75.050000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>5.882259e+06</td>\n",
       "      <td>5.352899e+03</td>\n",
       "      <td>7.960000</td>\n",
       "      <td>6.020000</td>\n",
       "      <td>1021.922500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.940385e+05</td>\n",
       "      <td>3.160000e+02</td>\n",
       "      <td>5.622860e+02</td>\n",
       "      <td>1.116475e+04</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>5.857000</td>\n",
       "      <td>122228.212250</td>\n",
       "      <td>41.737000</td>\n",
       "      <td>92.141750</td>\n",
       "      <td>1298.258000</td>\n",
       "      <td>...</td>\n",
       "      <td>41.300000</td>\n",
       "      <td>83.241000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>79.460000</td>\n",
       "      <td>0.829000</td>\n",
       "      <td>2.830170e+07</td>\n",
       "      <td>3.510710e+04</td>\n",
       "      <td>15.370000</td>\n",
       "      <td>16.920000</td>\n",
       "      <td>2615.629150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.689823e+08</td>\n",
       "      <td>8.401763e+06</td>\n",
       "      <td>6.402721e+06</td>\n",
       "      <td>6.953730e+06</td>\n",
       "      <td>121590.000000</td>\n",
       "      <td>18214.143000</td>\n",
       "      <td>737554.506000</td>\n",
       "      <td>228872.025000</td>\n",
       "      <td>37241.781000</td>\n",
       "      <td>6501.224000</td>\n",
       "      <td>...</td>\n",
       "      <td>78.100000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>86.750000</td>\n",
       "      <td>0.957000</td>\n",
       "      <td>7.975105e+09</td>\n",
       "      <td>1.281224e+06</td>\n",
       "      <td>76.550000</td>\n",
       "      <td>377.430000</td>\n",
       "      <td>10292.468000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_cases     new_cases  new_cases_smoothed  total_deaths  \\\n",
       "count  2.933000e+05  3.216330e+05        3.203740e+05  2.721740e+05   \n",
       "mean   6.253904e+06  1.013110e+04        1.016799e+04  8.357613e+04   \n",
       "std    3.854149e+07  1.134175e+05        9.718695e+04  4.293215e+05   \n",
       "min    1.000000e+00  0.000000e+00        0.000000e+00  1.000000e+00   \n",
       "25%    7.474000e+03  0.000000e+00        5.710000e-01  1.250000e+02   \n",
       "50%    6.538100e+04  4.000000e+00        3.007150e+01  1.250000e+03   \n",
       "75%    6.940385e+05  3.160000e+02        5.622860e+02  1.116475e+04   \n",
       "max    7.689823e+08  8.401763e+06        6.402721e+06  6.953730e+06   \n",
       "\n",
       "          new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "count  321702.000000        320472.000000            293300.000000   \n",
       "mean       91.896227            92.240686             95300.478343   \n",
       "std       761.899876           597.590018            145463.270075   \n",
       "min         0.000000             0.000000                 0.000000   \n",
       "25%         0.000000             0.000000              2273.895000   \n",
       "50%         0.000000             0.143000             24535.964500   \n",
       "75%         3.000000             5.857000            122228.212250   \n",
       "max    121590.000000         18214.143000            737554.506000   \n",
       "\n",
       "       new_cases_per_million  new_cases_smoothed_per_million  \\\n",
       "count          321633.000000                   320374.000000   \n",
       "mean              153.109948                      153.681658   \n",
       "std              1196.206835                      616.176235   \n",
       "min                 0.000000                        0.000000   \n",
       "25%                 0.000000                        0.120000   \n",
       "50%                 0.366000                        8.249000   \n",
       "75%                41.737000                       92.141750   \n",
       "max            228872.025000                    37241.781000   \n",
       "\n",
       "       total_deaths_per_million  ...   male_smokers  handwashing_facilities  \\\n",
       "count             272174.000000  ...  189702.000000           125583.000000   \n",
       "mean                 843.644772  ...      32.910416               50.790677   \n",
       "std                 1079.976631  ...      13.574225               31.956510   \n",
       "min                    0.000000  ...       7.700000                1.188000   \n",
       "25%                   55.469500  ...      22.600000               20.859000   \n",
       "50%                  358.698000  ...      33.100000               49.839000   \n",
       "75%                 1298.258000  ...      41.300000               83.241000   \n",
       "max                 6501.224000  ...      78.100000              100.000000   \n",
       "\n",
       "       hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "count               226326.000000    304234.000000            248529.000000   \n",
       "mean                     3.097145        73.716225                 0.722483   \n",
       "std                      2.548339         7.396354                 0.148987   \n",
       "min                      0.100000        53.280000                 0.394000   \n",
       "25%                      1.300000        69.590000                 0.602000   \n",
       "50%                      2.500000        75.050000                 0.740000   \n",
       "75%                      4.200000        79.460000                 0.829000   \n",
       "max                     13.800000        86.750000                 0.957000   \n",
       "\n",
       "         population  excess_mortality_cumulative_absolute  \\\n",
       "count  3.307820e+05                          1.153500e+04   \n",
       "mean   1.282799e+08                          4.933200e+04   \n",
       "std    6.602098e+08                          1.412464e+05   \n",
       "min    4.700000e+01                         -3.772610e+04   \n",
       "25%    4.490020e+05                          7.479999e+01   \n",
       "50%    5.882259e+06                          5.352899e+03   \n",
       "75%    2.830170e+07                          3.510710e+04   \n",
       "max    7.975105e+09                          1.281224e+06   \n",
       "\n",
       "       excess_mortality_cumulative  excess_mortality  \\\n",
       "count                 11535.000000      11535.000000   \n",
       "mean                      9.635586         11.815644   \n",
       "std                      12.479665         25.623475   \n",
       "min                     -44.230000        -95.920000   \n",
       "25%                       1.070000         -1.495000   \n",
       "50%                       7.960000          6.020000   \n",
       "75%                      15.370000         16.920000   \n",
       "max                      76.550000        377.430000   \n",
       "\n",
       "       excess_mortality_cumulative_per_million  \n",
       "count                             11535.000000  \n",
       "mean                               1591.662698  \n",
       "std                                1894.037851  \n",
       "min                               -2142.340300  \n",
       "25%                                  46.896362  \n",
       "50%                                1021.922500  \n",
       "75%                                2615.629150  \n",
       "max                               10292.468000  \n",
       "\n",
       "[8 rows x 62 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe() # print the descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5ac2c919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>new_cases_smoothed_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>population</th>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>293300.00</td>\n",
       "      <td>321633.00</td>\n",
       "      <td>320374.00</td>\n",
       "      <td>272174.00</td>\n",
       "      <td>321702.00</td>\n",
       "      <td>320472.00</td>\n",
       "      <td>293300.00</td>\n",
       "      <td>321633.00</td>\n",
       "      <td>320374.00</td>\n",
       "      <td>272174.00</td>\n",
       "      <td>...</td>\n",
       "      <td>189702.00</td>\n",
       "      <td>125583.00</td>\n",
       "      <td>226326.00</td>\n",
       "      <td>304234.00</td>\n",
       "      <td>248529.00</td>\n",
       "      <td>330782.00</td>\n",
       "      <td>11535.00</td>\n",
       "      <td>11535.00</td>\n",
       "      <td>11535.00</td>\n",
       "      <td>11535.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6253904.17</td>\n",
       "      <td>10131.10</td>\n",
       "      <td>10167.99</td>\n",
       "      <td>83576.13</td>\n",
       "      <td>91.90</td>\n",
       "      <td>92.24</td>\n",
       "      <td>95300.48</td>\n",
       "      <td>153.11</td>\n",
       "      <td>153.68</td>\n",
       "      <td>843.64</td>\n",
       "      <td>...</td>\n",
       "      <td>32.91</td>\n",
       "      <td>50.79</td>\n",
       "      <td>3.10</td>\n",
       "      <td>73.72</td>\n",
       "      <td>0.72</td>\n",
       "      <td>128279905.10</td>\n",
       "      <td>49332.00</td>\n",
       "      <td>9.64</td>\n",
       "      <td>11.82</td>\n",
       "      <td>1591.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>38541489.37</td>\n",
       "      <td>113417.45</td>\n",
       "      <td>97186.95</td>\n",
       "      <td>429321.51</td>\n",
       "      <td>761.90</td>\n",
       "      <td>597.59</td>\n",
       "      <td>145463.27</td>\n",
       "      <td>1196.21</td>\n",
       "      <td>616.18</td>\n",
       "      <td>1079.98</td>\n",
       "      <td>...</td>\n",
       "      <td>13.57</td>\n",
       "      <td>31.96</td>\n",
       "      <td>2.55</td>\n",
       "      <td>7.40</td>\n",
       "      <td>0.15</td>\n",
       "      <td>660209762.20</td>\n",
       "      <td>141246.42</td>\n",
       "      <td>12.48</td>\n",
       "      <td>25.62</td>\n",
       "      <td>1894.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>...</td>\n",
       "      <td>7.70</td>\n",
       "      <td>1.19</td>\n",
       "      <td>0.10</td>\n",
       "      <td>53.28</td>\n",
       "      <td>0.39</td>\n",
       "      <td>47.00</td>\n",
       "      <td>-37726.10</td>\n",
       "      <td>-44.23</td>\n",
       "      <td>-95.92</td>\n",
       "      <td>-2142.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7474.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.57</td>\n",
       "      <td>125.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2273.89</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.12</td>\n",
       "      <td>55.47</td>\n",
       "      <td>...</td>\n",
       "      <td>22.60</td>\n",
       "      <td>20.86</td>\n",
       "      <td>1.30</td>\n",
       "      <td>69.59</td>\n",
       "      <td>0.60</td>\n",
       "      <td>449002.00</td>\n",
       "      <td>74.80</td>\n",
       "      <td>1.07</td>\n",
       "      <td>-1.50</td>\n",
       "      <td>46.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>65381.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>30.07</td>\n",
       "      <td>1250.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.14</td>\n",
       "      <td>24535.96</td>\n",
       "      <td>0.37</td>\n",
       "      <td>8.25</td>\n",
       "      <td>358.70</td>\n",
       "      <td>...</td>\n",
       "      <td>33.10</td>\n",
       "      <td>49.84</td>\n",
       "      <td>2.50</td>\n",
       "      <td>75.05</td>\n",
       "      <td>0.74</td>\n",
       "      <td>5882259.00</td>\n",
       "      <td>5352.90</td>\n",
       "      <td>7.96</td>\n",
       "      <td>6.02</td>\n",
       "      <td>1021.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>694038.50</td>\n",
       "      <td>316.00</td>\n",
       "      <td>562.29</td>\n",
       "      <td>11164.75</td>\n",
       "      <td>3.00</td>\n",
       "      <td>5.86</td>\n",
       "      <td>122228.21</td>\n",
       "      <td>41.74</td>\n",
       "      <td>92.14</td>\n",
       "      <td>1298.26</td>\n",
       "      <td>...</td>\n",
       "      <td>41.30</td>\n",
       "      <td>83.24</td>\n",
       "      <td>4.20</td>\n",
       "      <td>79.46</td>\n",
       "      <td>0.83</td>\n",
       "      <td>28301700.00</td>\n",
       "      <td>35107.10</td>\n",
       "      <td>15.37</td>\n",
       "      <td>16.92</td>\n",
       "      <td>2615.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>768982331.00</td>\n",
       "      <td>8401763.00</td>\n",
       "      <td>6402720.57</td>\n",
       "      <td>6953730.00</td>\n",
       "      <td>121590.00</td>\n",
       "      <td>18214.14</td>\n",
       "      <td>737554.51</td>\n",
       "      <td>228872.02</td>\n",
       "      <td>37241.78</td>\n",
       "      <td>6501.22</td>\n",
       "      <td>...</td>\n",
       "      <td>78.10</td>\n",
       "      <td>100.00</td>\n",
       "      <td>13.80</td>\n",
       "      <td>86.75</td>\n",
       "      <td>0.96</td>\n",
       "      <td>7975105024.00</td>\n",
       "      <td>1281224.50</td>\n",
       "      <td>76.55</td>\n",
       "      <td>377.43</td>\n",
       "      <td>10292.47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_cases   new_cases  new_cases_smoothed  total_deaths  new_deaths  \\\n",
       "count     293300.00   321633.00           320374.00     272174.00   321702.00   \n",
       "mean     6253904.17    10131.10            10167.99      83576.13       91.90   \n",
       "std     38541489.37   113417.45            97186.95     429321.51      761.90   \n",
       "min            1.00        0.00                0.00          1.00        0.00   \n",
       "25%         7474.00        0.00                0.57        125.00        0.00   \n",
       "50%        65381.00        4.00               30.07       1250.00        0.00   \n",
       "75%       694038.50      316.00              562.29      11164.75        3.00   \n",
       "max    768982331.00  8401763.00          6402720.57    6953730.00   121590.00   \n",
       "\n",
       "       new_deaths_smoothed  total_cases_per_million  new_cases_per_million  \\\n",
       "count            320472.00                293300.00              321633.00   \n",
       "mean                 92.24                 95300.48                 153.11   \n",
       "std                 597.59                145463.27                1196.21   \n",
       "min                   0.00                     0.00                   0.00   \n",
       "25%                   0.00                  2273.89                   0.00   \n",
       "50%                   0.14                 24535.96                   0.37   \n",
       "75%                   5.86                122228.21                  41.74   \n",
       "max               18214.14                737554.51              228872.02   \n",
       "\n",
       "       new_cases_smoothed_per_million  total_deaths_per_million  ...  \\\n",
       "count                       320374.00                 272174.00  ...   \n",
       "mean                           153.68                    843.64  ...   \n",
       "std                            616.18                   1079.98  ...   \n",
       "min                              0.00                      0.00  ...   \n",
       "25%                              0.12                     55.47  ...   \n",
       "50%                              8.25                    358.70  ...   \n",
       "75%                             92.14                   1298.26  ...   \n",
       "max                          37241.78                   6501.22  ...   \n",
       "\n",
       "       male_smokers  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "count     189702.00               125583.00                   226326.00   \n",
       "mean          32.91                   50.79                        3.10   \n",
       "std           13.57                   31.96                        2.55   \n",
       "min            7.70                    1.19                        0.10   \n",
       "25%           22.60                   20.86                        1.30   \n",
       "50%           33.10                   49.84                        2.50   \n",
       "75%           41.30                   83.24                        4.20   \n",
       "max           78.10                  100.00                       13.80   \n",
       "\n",
       "       life_expectancy  human_development_index     population  \\\n",
       "count        304234.00                248529.00      330782.00   \n",
       "mean             73.72                     0.72   128279905.10   \n",
       "std               7.40                     0.15   660209762.20   \n",
       "min              53.28                     0.39          47.00   \n",
       "25%              69.59                     0.60      449002.00   \n",
       "50%              75.05                     0.74     5882259.00   \n",
       "75%              79.46                     0.83    28301700.00   \n",
       "max              86.75                     0.96  7975105024.00   \n",
       "\n",
       "       excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "count                              11535.00                     11535.00   \n",
       "mean                               49332.00                         9.64   \n",
       "std                               141246.42                        12.48   \n",
       "min                               -37726.10                       -44.23   \n",
       "25%                                   74.80                         1.07   \n",
       "50%                                 5352.90                         7.96   \n",
       "75%                                35107.10                        15.37   \n",
       "max                              1281224.50                        76.55   \n",
       "\n",
       "       excess_mortality  excess_mortality_cumulative_per_million  \n",
       "count          11535.00                                 11535.00  \n",
       "mean              11.82                                  1591.66  \n",
       "std               25.62                                  1894.04  \n",
       "min              -95.92                                 -2142.34  \n",
       "25%               -1.50                                    46.90  \n",
       "50%                6.02                                  1021.92  \n",
       "75%               16.92                                  2615.63  \n",
       "max              377.43                                 10292.47  \n",
       "\n",
       "[8 rows x 62 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.options.display.float_format = ' {:.2f}'.format # set the format for two decimal places\n",
    "data.describe() # print the descriptive statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "82663017",
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = (\"2023-01-01\", \"2023-08-01\")\n",
    "# Convert the date column of the full_df to datetime format\n",
    "data['date'] = pd.to_datetime(data['date'])\n",
    "# Filter the full_df by the date range using boolean indexing\n",
    "df = data[(data['date'] >= dates[0]) & (data['date'] <= dates[1])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0bedb277",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53733, 67)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dbb71f48",
   "metadata": {},
   "source": [
    "## Dataset information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "97b392bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 53733 entries, 1094 to 330780\n",
      "Data columns (total 67 columns):\n",
      " #   Column                                      Non-Null Count  Dtype         \n",
      "---  ------                                      --------------  -----         \n",
      " 0   iso_code                                    53733 non-null  object        \n",
      " 1   continent                                   51177 non-null  object        \n",
      " 2   location                                    53733 non-null  object        \n",
      " 3   date                                        53733 non-null  datetime64[ns]\n",
      " 4   total_cases                                 51972 non-null  float64       \n",
      " 5   new_cases                                   52338 non-null  float64       \n",
      " 6   new_cases_smoothed                          52338 non-null  float64       \n",
      " 7   total_deaths                                50526 non-null  float64       \n",
      " 8   new_deaths                                  52367 non-null  float64       \n",
      " 9   new_deaths_smoothed                         52367 non-null  float64       \n",
      " 10  total_cases_per_million                     51972 non-null  float64       \n",
      " 11  new_cases_per_million                       52338 non-null  float64       \n",
      " 12  new_cases_smoothed_per_million              52338 non-null  float64       \n",
      " 13  total_deaths_per_million                    50526 non-null  float64       \n",
      " 14  new_deaths_per_million                      52367 non-null  float64       \n",
      " 15  new_deaths_smoothed_per_million             52367 non-null  float64       \n",
      " 16  reproduction_rate                           390 non-null    float64       \n",
      " 17  icu_patients                                4181 non-null   float64       \n",
      " 18  icu_patients_per_million                    4181 non-null   float64       \n",
      " 19  hosp_patients                               4625 non-null   float64       \n",
      " 20  hosp_patients_per_million                   4625 non-null   float64       \n",
      " 21  weekly_icu_admissions                       1622 non-null   float64       \n",
      " 22  weekly_icu_admissions_per_million           1622 non-null   float64       \n",
      " 23  weekly_hosp_admissions                      2961 non-null   float64       \n",
      " 24  weekly_hosp_admissions_per_million          2961 non-null   float64       \n",
      " 25  total_tests                                 0 non-null      float64       \n",
      " 26  new_tests                                   0 non-null      float64       \n",
      " 27  total_tests_per_thousand                    0 non-null      float64       \n",
      " 28  new_tests_per_thousand                      0 non-null      float64       \n",
      " 29  new_tests_smoothed                          0 non-null      float64       \n",
      " 30  new_tests_smoothed_per_thousand             0 non-null      float64       \n",
      " 31  positive_rate                               0 non-null      float64       \n",
      " 32  tests_per_case                              0 non-null      float64       \n",
      " 33  tests_units                                 0 non-null      object        \n",
      " 34  total_vaccinations                          8646 non-null   float64       \n",
      " 35  people_vaccinated                           8490 non-null   float64       \n",
      " 36  people_fully_vaccinated                     8475 non-null   float64       \n",
      " 37  total_boosters                              7806 non-null   float64       \n",
      " 38  new_vaccinations                            7075 non-null   float64       \n",
      " 39  new_vaccinations_smoothed                   24313 non-null  float64       \n",
      " 40  total_vaccinations_per_hundred              8646 non-null   float64       \n",
      " 41  people_vaccinated_per_hundred               8490 non-null   float64       \n",
      " 42  people_fully_vaccinated_per_hundred         8475 non-null   float64       \n",
      " 43  total_boosters_per_hundred                  7806 non-null   float64       \n",
      " 44  new_vaccinations_smoothed_per_million       24313 non-null  float64       \n",
      " 45  new_people_vaccinated_smoothed              24502 non-null  float64       \n",
      " 46  new_people_vaccinated_smoothed_per_hundred  24502 non-null  float64       \n",
      " 47  stringency_index                            0 non-null      float64       \n",
      " 48  population_density                          45681 non-null  float64       \n",
      " 49  median_age                                  42470 non-null  float64       \n",
      " 50  aged_65_older                               40995 non-null  float64       \n",
      " 51  aged_70_older                               42044 non-null  float64       \n",
      " 52  gdp_per_capita                              41634 non-null  float64       \n",
      " 53  extreme_poverty                             26838 non-null  float64       \n",
      " 54  cardiovasc_death_rate                       41732 non-null  float64       \n",
      " 55  diabetes_prevalence                         43874 non-null  float64       \n",
      " 56  female_smokers                              31311 non-null  float64       \n",
      " 57  male_smokers                                30885 non-null  float64       \n",
      " 58  handwashing_facilities                      20448 non-null  float64       \n",
      " 59  hospital_beds_per_thousand                  36849 non-null  float64       \n",
      " 60  life_expectancy                             49499 non-null  float64       \n",
      " 61  human_development_index                     40466 non-null  float64       \n",
      " 62  population                                  53733 non-null  float64       \n",
      " 63  excess_mortality_cumulative_absolute        1327 non-null   float64       \n",
      " 64  excess_mortality_cumulative                 1327 non-null   float64       \n",
      " 65  excess_mortality                            1327 non-null   float64       \n",
      " 66  excess_mortality_cumulative_per_million     1327 non-null   float64       \n",
      "dtypes: datetime64[ns](1), float64(62), object(4)\n",
      "memory usage: 27.9+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "99fb3e47",
   "metadata": {},
   "source": [
    "## Checking missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5a8c5c5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the missing values matrix\n",
    "msno.matrix(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d37cbb66",
   "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>Missing Data Ratio (%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>iso_code</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>continent</th>\n",
       "      <td>4.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_cases</th>\n",
       "      <td>11.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_cases</th>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_deaths</th>\n",
       "      <td>17.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_deaths</th>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <td>11.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <td>2.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_cases_smoothed_per_million</th>\n",
       "      <td>3.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_deaths_per_million</th>\n",
       "      <td>17.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_deaths_per_million</th>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_deaths_smoothed_per_million</th>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>reproduction_rate</th>\n",
       "      <td>44.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icu_patients</th>\n",
       "      <td>88.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icu_patients_per_million</th>\n",
       "      <td>88.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hosp_patients</th>\n",
       "      <td>88.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hosp_patients_per_million</th>\n",
       "      <td>88.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekly_icu_admissions</th>\n",
       "      <td>97.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekly_icu_admissions_per_million</th>\n",
       "      <td>97.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekly_hosp_admissions</th>\n",
       "      <td>93.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weekly_hosp_admissions_per_million</th>\n",
       "      <td>93.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_tests</th>\n",
       "      <td>76.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_tests</th>\n",
       "      <td>77.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_tests_per_thousand</th>\n",
       "      <td>76.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_tests_per_thousand</th>\n",
       "      <td>77.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_tests_smoothed</th>\n",
       "      <td>68.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_tests_smoothed_per_thousand</th>\n",
       "      <td>68.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>positive_rate</th>\n",
       "      <td>71.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tests_per_case</th>\n",
       "      <td>71.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tests_units</th>\n",
       "      <td>67.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_vaccinations</th>\n",
       "      <td>76.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>people_vaccinated</th>\n",
       "      <td>77.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <td>78.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_boosters</th>\n",
       "      <td>86.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_vaccinations</th>\n",
       "      <td>80.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_vaccinations_smoothed</th>\n",
       "      <td>47.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <td>76.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <td>77.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <td>78.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>total_boosters_per_hundred</th>\n",
       "      <td>86.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_vaccinations_smoothed_per_million</th>\n",
       "      <td>47.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_people_vaccinated_smoothed</th>\n",
       "      <td>47.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_people_vaccinated_smoothed_per_hundred</th>\n",
       "      <td>47.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stringency_index</th>\n",
       "      <td>40.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population_density</th>\n",
       "      <td>15.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>median_age</th>\n",
       "      <td>21.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aged_65_older</th>\n",
       "      <td>23.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>aged_70_older</th>\n",
       "      <td>21.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <td>22.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extreme_poverty</th>\n",
       "      <td>50.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <td>22.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <td>18.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>female_smokers</th>\n",
       "      <td>41.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male_smokers</th>\n",
       "      <td>42.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <td>62.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <td>31.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>life_expectancy</th>\n",
       "      <td>8.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>human_development_index</th>\n",
       "      <td>24.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>population</th>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <td>96.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <td>96.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>excess_mortality</th>\n",
       "      <td>96.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "      <td>96.51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                            Missing Data Ratio (%)\n",
       "iso_code                                                      0.00\n",
       "continent                                                     4.75\n",
       "location                                                      0.00\n",
       "date                                                          0.00\n",
       "total_cases                                                  11.33\n",
       "new_cases                                                     2.77\n",
       "new_cases_smoothed                                            3.15\n",
       "total_deaths                                                 17.72\n",
       "new_deaths                                                    2.75\n",
       "new_deaths_smoothed                                           3.12\n",
       "total_cases_per_million                                      11.33\n",
       "new_cases_per_million                                         2.77\n",
       "new_cases_smoothed_per_million                                3.15\n",
       "total_deaths_per_million                                     17.72\n",
       "new_deaths_per_million                                        2.75\n",
       "new_deaths_smoothed_per_million                               3.12\n",
       "reproduction_rate                                            44.13\n",
       "icu_patients                                                 88.80\n",
       "icu_patients_per_million                                     88.80\n",
       "hosp_patients                                                88.50\n",
       "hosp_patients_per_million                                    88.50\n",
       "weekly_icu_admissions                                        97.01\n",
       "weekly_icu_admissions_per_million                            97.01\n",
       "weekly_hosp_admissions                                       93.15\n",
       "weekly_hosp_admissions_per_million                           93.15\n",
       "total_tests                                                  76.00\n",
       "new_tests                                                    77.20\n",
       "total_tests_per_thousand                                     76.00\n",
       "new_tests_per_thousand                                       77.20\n",
       "new_tests_smoothed                                           68.57\n",
       "new_tests_smoothed_per_thousand                              68.57\n",
       "positive_rate                                                71.00\n",
       "tests_per_case                                               71.48\n",
       "tests_units                                                  67.72\n",
       "total_vaccinations                                           76.59\n",
       "people_vaccinated                                            77.59\n",
       "people_fully_vaccinated                                      78.63\n",
       "total_boosters                                               86.15\n",
       "new_vaccinations                                             80.73\n",
       "new_vaccinations_smoothed                                    47.11\n",
       "total_vaccinations_per_hundred                               76.59\n",
       "people_vaccinated_per_hundred                                77.59\n",
       "people_fully_vaccinated_per_hundred                          78.63\n",
       "total_boosters_per_hundred                                   86.15\n",
       "new_vaccinations_smoothed_per_million                        47.11\n",
       "new_people_vaccinated_smoothed                               47.10\n",
       "new_people_vaccinated_smoothed_per_hundred                   47.10\n",
       "stringency_index                                             40.25\n",
       "population_density                                           15.13\n",
       "median_age                                                   21.07\n",
       "aged_65_older                                                23.83\n",
       "aged_70_older                                                21.87\n",
       "gdp_per_capita                                               22.65\n",
       "extreme_poverty                                              50.17\n",
       "cardiovasc_death_rate                                        22.49\n",
       "diabetes_prevalence                                          18.54\n",
       "female_smokers                                               41.86\n",
       "male_smokers                                                 42.65\n",
       "handwashing_facilities                                       62.03\n",
       "hospital_beds_per_thousand                                   31.58\n",
       "life_expectancy                                               8.03\n",
       "human_development_index                                      24.87\n",
       "population                                                    0.00\n",
       "excess_mortality_cumulative_absolute                         96.51\n",
       "excess_mortality_cumulative                                  96.51\n",
       "excess_mortality                                             96.51\n",
       "excess_mortality_cumulative_per_million                      96.51"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Import pandas as pd\n",
    "import pandas as pd\n",
    "\n",
    "# Import display from IPython.display\n",
    "from IPython.display import display\n",
    "\n",
    "# Calculate the missing data ratio for each column as percentages\n",
    "missing_data_ratio = data.isnull().mean() * 100\n",
    "\n",
    "# Convert the Series to a DataFrame for better display\n",
    "missing_data_df = pd.DataFrame({'Missing Data Ratio (%)': missing_data_ratio})\n",
    "\n",
    "# Display the missing data ratio for each column as percentages in a scrollable DataFrame\n",
    "with pd.option_context('display.max_rows', None):\n",
    "    display(missing_data_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "394bdfc0",
   "metadata": {},
   "source": [
    "## Checking outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "9175462b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outliers:\n"
     ]
    },
    {
     "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>population</th>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>16498</th>\n",
       "      <td>OWID_ASI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2022-03-08</td>\n",
       "      <td>122507617.00</td>\n",
       "      <td>791293.00</td>\n",
       "      <td>706920.57</td>\n",
       "      <td>1365917.00</td>\n",
       "      <td>1917.00</td>\n",
       "      <td>1951.86</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4721383370.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16499</th>\n",
       "      <td>OWID_ASI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2022-03-09</td>\n",
       "      <td>123354783.00</td>\n",
       "      <td>847166.00</td>\n",
       "      <td>727234.14</td>\n",
       "      <td>1367885.00</td>\n",
       "      <td>1968.00</td>\n",
       "      <td>1926.00</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4721383370.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16500</th>\n",
       "      <td>OWID_ASI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2022-03-10</td>\n",
       "      <td>124101007.00</td>\n",
       "      <td>746224.00</td>\n",
       "      <td>721573.14</td>\n",
       "      <td>1369710.00</td>\n",
       "      <td>1825.00</td>\n",
       "      <td>1916.57</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4721383370.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16501</th>\n",
       "      <td>OWID_ASI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2022-03-11</td>\n",
       "      <td>124898209.00</td>\n",
       "      <td>797202.00</td>\n",
       "      <td>730206.43</td>\n",
       "      <td>1371566.00</td>\n",
       "      <td>1856.00</td>\n",
       "      <td>1864.14</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4721383370.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16502</th>\n",
       "      <td>OWID_ASI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Asia</td>\n",
       "      <td>2022-03-12</td>\n",
       "      <td>125930442.00</td>\n",
       "      <td>1032233.00</td>\n",
       "      <td>775388.00</td>\n",
       "      <td>1373481.00</td>\n",
       "      <td>1915.00</td>\n",
       "      <td>1848.57</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4721383370.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>326850</th>\n",
       "      <td>OWID_WRL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World</td>\n",
       "      <td>2023-07-29</td>\n",
       "      <td>768632837.00</td>\n",
       "      <td>694.00</td>\n",
       "      <td>46823.57</td>\n",
       "      <td>6953094.00</td>\n",
       "      <td>11.00</td>\n",
       "      <td>75.00</td>\n",
       "      <td>...</td>\n",
       "      <td>34.63</td>\n",
       "      <td>60.13</td>\n",
       "      <td>2.71</td>\n",
       "      <td>72.58</td>\n",
       "      <td>0.74</td>\n",
       "      <td>7975105024.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326851</th>\n",
       "      <td>OWID_WRL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World</td>\n",
       "      <td>2023-07-30</td>\n",
       "      <td>768654204.00</td>\n",
       "      <td>21367.00</td>\n",
       "      <td>45918.43</td>\n",
       "      <td>6953470.00</td>\n",
       "      <td>376.00</td>\n",
       "      <td>87.00</td>\n",
       "      <td>...</td>\n",
       "      <td>34.63</td>\n",
       "      <td>60.13</td>\n",
       "      <td>2.71</td>\n",
       "      <td>72.58</td>\n",
       "      <td>0.74</td>\n",
       "      <td>7975105024.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326852</th>\n",
       "      <td>OWID_WRL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World</td>\n",
       "      <td>2023-07-31</td>\n",
       "      <td>768978837.00</td>\n",
       "      <td>324633.00</td>\n",
       "      <td>50862.86</td>\n",
       "      <td>6953676.00</td>\n",
       "      <td>206.00</td>\n",
       "      <td>97.29</td>\n",
       "      <td>...</td>\n",
       "      <td>34.63</td>\n",
       "      <td>60.13</td>\n",
       "      <td>2.71</td>\n",
       "      <td>72.58</td>\n",
       "      <td>0.74</td>\n",
       "      <td>7975105024.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326853</th>\n",
       "      <td>OWID_WRL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World</td>\n",
       "      <td>2023-08-01</td>\n",
       "      <td>768982331.00</td>\n",
       "      <td>3494.00</td>\n",
       "      <td>50703.29</td>\n",
       "      <td>6953730.00</td>\n",
       "      <td>54.00</td>\n",
       "      <td>97.00</td>\n",
       "      <td>...</td>\n",
       "      <td>34.63</td>\n",
       "      <td>60.13</td>\n",
       "      <td>2.71</td>\n",
       "      <td>72.58</td>\n",
       "      <td>0.74</td>\n",
       "      <td>7975105024.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>326854</th>\n",
       "      <td>OWID_WRL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>World</td>\n",
       "      <td>2023-08-02</td>\n",
       "      <td>768982331.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>50420.57</td>\n",
       "      <td>6953730.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>96.29</td>\n",
       "      <td>...</td>\n",
       "      <td>34.63</td>\n",
       "      <td>60.13</td>\n",
       "      <td>2.71</td>\n",
       "      <td>72.58</td>\n",
       "      <td>0.74</td>\n",
       "      <td>7975105024.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3675 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        iso_code continent location       date   total_cases   new_cases  \\\n",
       "16498   OWID_ASI       NaN     Asia 2022-03-08  122507617.00   791293.00   \n",
       "16499   OWID_ASI       NaN     Asia 2022-03-09  123354783.00   847166.00   \n",
       "16500   OWID_ASI       NaN     Asia 2022-03-10  124101007.00   746224.00   \n",
       "16501   OWID_ASI       NaN     Asia 2022-03-11  124898209.00   797202.00   \n",
       "16502   OWID_ASI       NaN     Asia 2022-03-12  125930442.00  1032233.00   \n",
       "...          ...       ...      ...        ...           ...         ...   \n",
       "326850  OWID_WRL       NaN    World 2023-07-29  768632837.00      694.00   \n",
       "326851  OWID_WRL       NaN    World 2023-07-30  768654204.00    21367.00   \n",
       "326852  OWID_WRL       NaN    World 2023-07-31  768978837.00   324633.00   \n",
       "326853  OWID_WRL       NaN    World 2023-08-01  768982331.00     3494.00   \n",
       "326854  OWID_WRL       NaN    World 2023-08-02  768982331.00        0.00   \n",
       "\n",
       "        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "16498            706920.57    1365917.00     1917.00              1951.86   \n",
       "16499            727234.14    1367885.00     1968.00              1926.00   \n",
       "16500            721573.14    1369710.00     1825.00              1916.57   \n",
       "16501            730206.43    1371566.00     1856.00              1864.14   \n",
       "16502            775388.00    1373481.00     1915.00              1848.57   \n",
       "...                    ...           ...         ...                  ...   \n",
       "326850            46823.57    6953094.00       11.00                75.00   \n",
       "326851            45918.43    6953470.00      376.00                87.00   \n",
       "326852            50862.86    6953676.00      206.00                97.29   \n",
       "326853            50703.29    6953730.00       54.00                97.00   \n",
       "326854            50420.57    6953730.00        0.00                96.29   \n",
       "\n",
       "        ...  male_smokers  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "16498   ...           NaN                     NaN                         NaN   \n",
       "16499   ...           NaN                     NaN                         NaN   \n",
       "16500   ...           NaN                     NaN                         NaN   \n",
       "16501   ...           NaN                     NaN                         NaN   \n",
       "16502   ...           NaN                     NaN                         NaN   \n",
       "...     ...           ...                     ...                         ...   \n",
       "326850  ...         34.63                   60.13                        2.71   \n",
       "326851  ...         34.63                   60.13                        2.71   \n",
       "326852  ...         34.63                   60.13                        2.71   \n",
       "326853  ...         34.63                   60.13                        2.71   \n",
       "326854  ...         34.63                   60.13                        2.71   \n",
       "\n",
       "        life_expectancy  human_development_index     population  \\\n",
       "16498               NaN                      NaN  4721383370.00   \n",
       "16499               NaN                      NaN  4721383370.00   \n",
       "16500               NaN                      NaN  4721383370.00   \n",
       "16501               NaN                      NaN  4721383370.00   \n",
       "16502               NaN                      NaN  4721383370.00   \n",
       "...                 ...                      ...            ...   \n",
       "326850            72.58                     0.74  7975105024.00   \n",
       "326851            72.58                     0.74  7975105024.00   \n",
       "326852            72.58                     0.74  7975105024.00   \n",
       "326853            72.58                     0.74  7975105024.00   \n",
       "326854            72.58                     0.74  7975105024.00   \n",
       "\n",
       "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "16498                                    NaN                          NaN   \n",
       "16499                                    NaN                          NaN   \n",
       "16500                                    NaN                          NaN   \n",
       "16501                                    NaN                          NaN   \n",
       "16502                                    NaN                          NaN   \n",
       "...                                      ...                          ...   \n",
       "326850                                   NaN                          NaN   \n",
       "326851                                   NaN                          NaN   \n",
       "326852                                   NaN                          NaN   \n",
       "326853                                   NaN                          NaN   \n",
       "326854                                   NaN                          NaN   \n",
       "\n",
       "        excess_mortality  excess_mortality_cumulative_per_million  \n",
       "16498                NaN                                      NaN  \n",
       "16499                NaN                                      NaN  \n",
       "16500                NaN                                      NaN  \n",
       "16501                NaN                                      NaN  \n",
       "16502                NaN                                      NaN  \n",
       "...                  ...                                      ...  \n",
       "326850               NaN                                      NaN  \n",
       "326851               NaN                                      NaN  \n",
       "326852               NaN                                      NaN  \n",
       "326853               NaN                                      NaN  \n",
       "326854               NaN                                      NaN  \n",
       "\n",
       "[3675 rows x 67 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Load your dataset (assuming you already have 'data' DataFrame)\n",
    "df = data.copy()\n",
    "\n",
    "# Define the column to check for outliers\n",
    "column_name = 'total_cases'\n",
    "column_data = df[column_name]\n",
    "\n",
    "# Calculate Z-scores for the 'total_cases' column\n",
    "z_scores = (column_data - column_data.mean()) / column_data.std()\n",
    "\n",
    "# Define the threshold for Z-score to identify outliers\n",
    "threshold = 3\n",
    "\n",
    "# Identify outliers based on the absolute Z-scores exceeding the threshold\n",
    "outliers = df[abs(z_scores) > threshold]\n",
    "\n",
    "# Print the outliers\n",
    "print(\"Outliers:\")\n",
    "display(outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d409dd5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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