Clean data of covid - 19.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing necessary libraries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The following code is written in Python 3.x. libraries provide pre-written functionally to perform necessary tasks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Reading the Data\n",
    "data = pd.read_csv('C:/Users/Public/Downloads/owid-covid-data.csv', header = 0, index_col = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(81905, 58)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing no. of rows and no. of columns of data\n",
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>total_cases_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iso_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.026</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.026</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.026</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.026</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.026</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         continent     location        date  total_cases  new_cases  \\\n",
       "iso_code                                                              \n",
       "AFG           Asia  Afghanistan  2020-02-24          1.0        1.0   \n",
       "AFG           Asia  Afghanistan  2020-02-25          1.0        0.0   \n",
       "AFG           Asia  Afghanistan  2020-02-26          1.0        0.0   \n",
       "AFG           Asia  Afghanistan  2020-02-27          1.0        0.0   \n",
       "AFG           Asia  Afghanistan  2020-02-28          1.0        0.0   \n",
       "\n",
       "          new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "iso_code                                                                      \n",
       "AFG                      NaN           NaN         NaN                  NaN   \n",
       "AFG                      NaN           NaN         NaN                  NaN   \n",
       "AFG                      NaN           NaN         NaN                  NaN   \n",
       "AFG                      NaN           NaN         NaN                  NaN   \n",
       "AFG                      NaN           NaN         NaN                  NaN   \n",
       "\n",
       "          total_cases_per_million  ...  gdp_per_capita  extreme_poverty  \\\n",
       "iso_code                           ...                                    \n",
       "AFG                         0.026  ...        1803.987              NaN   \n",
       "AFG                         0.026  ...        1803.987              NaN   \n",
       "AFG                         0.026  ...        1803.987              NaN   \n",
       "AFG                         0.026  ...        1803.987              NaN   \n",
       "AFG                         0.026  ...        1803.987              NaN   \n",
       "\n",
       "          cardiovasc_death_rate  diabetes_prevalence  female_smokers  \\\n",
       "iso_code                                                               \n",
       "AFG                     597.029                 9.59             NaN   \n",
       "AFG                     597.029                 9.59             NaN   \n",
       "AFG                     597.029                 9.59             NaN   \n",
       "AFG                     597.029                 9.59             NaN   \n",
       "AFG                     597.029                 9.59             NaN   \n",
       "\n",
       "          male_smokers  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "iso_code                                                                     \n",
       "AFG                NaN                  37.746                         0.5   \n",
       "AFG                NaN                  37.746                         0.5   \n",
       "AFG                NaN                  37.746                         0.5   \n",
       "AFG                NaN                  37.746                         0.5   \n",
       "AFG                NaN                  37.746                         0.5   \n",
       "\n",
       "          life_expectancy  human_development_index  \n",
       "iso_code                                            \n",
       "AFG                 64.83                    0.511  \n",
       "AFG                 64.83                    0.511  \n",
       "AFG                 64.83                    0.511  \n",
       "AFG                 64.83                    0.511  \n",
       "AFG                 64.83                    0.511  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Printing top 5 rows\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 81905 entries, AFG to ZWE\n",
      "Data columns (total 58 columns):\n",
      " #   Column                                 Non-Null Count  Dtype  \n",
      "---  ------                                 --------------  -----  \n",
      " 0   continent                              77929 non-null  object \n",
      " 1   location                               81905 non-null  object \n",
      " 2   date                                   81905 non-null  object \n",
      " 3   total_cases                            80087 non-null  float64\n",
      " 4   new_cases                              80085 non-null  float64\n",
      " 5   new_cases_smoothed                     79084 non-null  float64\n",
      " 6   total_deaths                           70548 non-null  float64\n",
      " 7   new_deaths                             70706 non-null  float64\n",
      " 8   new_deaths_smoothed                    79084 non-null  float64\n",
      " 9   total_cases_per_million                79653 non-null  float64\n",
      " 10  new_cases_per_million                  79651 non-null  float64\n",
      " 11  new_cases_smoothed_per_million         78655 non-null  float64\n",
      " 12  total_deaths_per_million               70127 non-null  float64\n",
      " 13  new_deaths_per_million                 70285 non-null  float64\n",
      " 14  new_deaths_smoothed_per_million        78655 non-null  float64\n",
      " 15  reproduction_rate                      66030 non-null  float64\n",
      " 16  icu_patients                           8456 non-null   float64\n",
      " 17  icu_patients_per_million               8456 non-null   float64\n",
      " 18  hosp_patients                          10127 non-null  float64\n",
      " 19  hosp_patients_per_million              10127 non-null  float64\n",
      " 20  weekly_icu_admissions                  743 non-null    float64\n",
      " 21  weekly_icu_admissions_per_million      743 non-null    float64\n",
      " 22  weekly_hosp_admissions                 1240 non-null   float64\n",
      " 23  weekly_hosp_admissions_per_million     1240 non-null   float64\n",
      " 24  new_tests                              37654 non-null  float64\n",
      " 25  total_tests                            37363 non-null  float64\n",
      " 26  total_tests_per_thousand               37363 non-null  float64\n",
      " 27  new_tests_per_thousand                 37654 non-null  float64\n",
      " 28  new_tests_smoothed                     43073 non-null  float64\n",
      " 29  new_tests_smoothed_per_thousand        43073 non-null  float64\n",
      " 30  positive_rate                          41415 non-null  float64\n",
      " 31  tests_per_case                         40823 non-null  float64\n",
      " 32  tests_units                            44484 non-null  object \n",
      " 33  total_vaccinations                     7673 non-null   float64\n",
      " 34  people_vaccinated                      7000 non-null   float64\n",
      " 35  people_fully_vaccinated                5031 non-null   float64\n",
      " 36  new_vaccinations                       6520 non-null   float64\n",
      " 37  new_vaccinations_smoothed              12240 non-null  float64\n",
      " 38  total_vaccinations_per_hundred         7673 non-null   float64\n",
      " 39  people_vaccinated_per_hundred          7000 non-null   float64\n",
      " 40  people_fully_vaccinated_per_hundred    5031 non-null   float64\n",
      " 41  new_vaccinations_smoothed_per_million  12240 non-null  float64\n",
      " 42  stringency_index                       69674 non-null  float64\n",
      " 43  population                             81380 non-null  float64\n",
      " 44  population_density                     76277 non-null  float64\n",
      " 45  median_age                             73893 non-null  float64\n",
      " 46  aged_65_older                          73047 non-null  float64\n",
      " 47  aged_70_older                          73478 non-null  float64\n",
      " 48  gdp_per_capita                         74212 non-null  float64\n",
      " 49  extreme_poverty                        50590 non-null  float64\n",
      " 50  cardiovasc_death_rate                  74843 non-null  float64\n",
      " 51  diabetes_prevalence                    75854 non-null  float64\n",
      " 52  female_smokers                         58689 non-null  float64\n",
      " 53  male_smokers                           57822 non-null  float64\n",
      " 54  handwashing_facilities                 37582 non-null  float64\n",
      " 55  hospital_beds_per_thousand             68348 non-null  float64\n",
      " 56  life_expectancy                        77738 non-null  float64\n",
      " 57  human_development_index                74741 non-null  float64\n",
      "dtypes: float64(54), object(4)\n",
      "memory usage: 36.9+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent                                 3976\n",
       "location                                     0\n",
       "date                                         0\n",
       "total_cases                               1818\n",
       "new_cases                                 1820\n",
       "new_cases_smoothed                        2821\n",
       "total_deaths                             11357\n",
       "new_deaths                               11199\n",
       "new_deaths_smoothed                       2821\n",
       "total_cases_per_million                   2252\n",
       "new_cases_per_million                     2254\n",
       "new_cases_smoothed_per_million            3250\n",
       "total_deaths_per_million                 11778\n",
       "new_deaths_per_million                   11620\n",
       "new_deaths_smoothed_per_million           3250\n",
       "reproduction_rate                        15875\n",
       "icu_patients                             73449\n",
       "icu_patients_per_million                 73449\n",
       "hosp_patients                            71778\n",
       "hosp_patients_per_million                71778\n",
       "weekly_icu_admissions                    81162\n",
       "weekly_icu_admissions_per_million        81162\n",
       "weekly_hosp_admissions                   80665\n",
       "weekly_hosp_admissions_per_million       80665\n",
       "new_tests                                44251\n",
       "total_tests                              44542\n",
       "total_tests_per_thousand                 44542\n",
       "new_tests_per_thousand                   44251\n",
       "new_tests_smoothed                       38832\n",
       "new_tests_smoothed_per_thousand          38832\n",
       "positive_rate                            40490\n",
       "tests_per_case                           41082\n",
       "tests_units                              37421\n",
       "total_vaccinations                       74232\n",
       "people_vaccinated                        74905\n",
       "people_fully_vaccinated                  76874\n",
       "new_vaccinations                         75385\n",
       "new_vaccinations_smoothed                69665\n",
       "total_vaccinations_per_hundred           74232\n",
       "people_vaccinated_per_hundred            74905\n",
       "people_fully_vaccinated_per_hundred      76874\n",
       "new_vaccinations_smoothed_per_million    69665\n",
       "stringency_index                         12231\n",
       "population                                 525\n",
       "population_density                        5628\n",
       "median_age                                8012\n",
       "aged_65_older                             8858\n",
       "aged_70_older                             8427\n",
       "gdp_per_capita                            7693\n",
       "extreme_poverty                          31315\n",
       "cardiovasc_death_rate                     7062\n",
       "diabetes_prevalence                       6051\n",
       "female_smokers                           23216\n",
       "male_smokers                             24083\n",
       "handwashing_facilities                   44323\n",
       "hospital_beds_per_thousand               13557\n",
       "life_expectancy                           4167\n",
       "human_development_index                   7164\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for missing values in any columns\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing null values with O\n",
    "data.fillna(value = 0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>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>total_cases_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iso_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.026</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.026</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.026</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.026</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AFG</th>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.026</td>\n",
       "      <td>...</td>\n",
       "      <td>1803.987</td>\n",
       "      <td>0.0</td>\n",
       "      <td>597.029</td>\n",
       "      <td>9.59</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         continent     location        date  total_cases  new_cases  \\\n",
       "iso_code                                                              \n",
       "AFG           Asia  Afghanistan  2020-02-24          1.0        1.0   \n",
       "AFG           Asia  Afghanistan  2020-02-25          1.0        0.0   \n",
       "AFG           Asia  Afghanistan  2020-02-26          1.0        0.0   \n",
       "AFG           Asia  Afghanistan  2020-02-27          1.0        0.0   \n",
       "AFG           Asia  Afghanistan  2020-02-28          1.0        0.0   \n",
       "\n",
       "          new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "iso_code                                                                      \n",
       "AFG                      0.0           0.0         0.0                  0.0   \n",
       "AFG                      0.0           0.0         0.0                  0.0   \n",
       "AFG                      0.0           0.0         0.0                  0.0   \n",
       "AFG                      0.0           0.0         0.0                  0.0   \n",
       "AFG                      0.0           0.0         0.0                  0.0   \n",
       "\n",
       "          total_cases_per_million  ...  gdp_per_capita  extreme_poverty  \\\n",
       "iso_code                           ...                                    \n",
       "AFG                         0.026  ...        1803.987              0.0   \n",
       "AFG                         0.026  ...        1803.987              0.0   \n",
       "AFG                         0.026  ...        1803.987              0.0   \n",
       "AFG                         0.026  ...        1803.987              0.0   \n",
       "AFG                         0.026  ...        1803.987              0.0   \n",
       "\n",
       "          cardiovasc_death_rate  diabetes_prevalence  female_smokers  \\\n",
       "iso_code                                                               \n",
       "AFG                     597.029                 9.59             0.0   \n",
       "AFG                     597.029                 9.59             0.0   \n",
       "AFG                     597.029                 9.59             0.0   \n",
       "AFG                     597.029                 9.59             0.0   \n",
       "AFG                     597.029                 9.59             0.0   \n",
       "\n",
       "          male_smokers  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "iso_code                                                                     \n",
       "AFG                0.0                  37.746                         0.5   \n",
       "AFG                0.0                  37.746                         0.5   \n",
       "AFG                0.0                  37.746                         0.5   \n",
       "AFG                0.0                  37.746                         0.5   \n",
       "AFG                0.0                  37.746                         0.5   \n",
       "\n",
       "          life_expectancy  human_development_index  \n",
       "iso_code                                            \n",
       "AFG                 64.83                    0.511  \n",
       "AFG                 64.83                    0.511  \n",
       "AFG                 64.83                    0.511  \n",
       "AFG                 64.83                    0.511  \n",
       "AFG                 64.83                    0.511  \n",
       "\n",
       "[5 rows x 58 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent                                0\n",
       "location                                 0\n",
       "date                                     0\n",
       "total_cases                              0\n",
       "new_cases                                0\n",
       "new_cases_smoothed                       0\n",
       "total_deaths                             0\n",
       "new_deaths                               0\n",
       "new_deaths_smoothed                      0\n",
       "total_cases_per_million                  0\n",
       "new_cases_per_million                    0\n",
       "new_cases_smoothed_per_million           0\n",
       "total_deaths_per_million                 0\n",
       "new_deaths_per_million                   0\n",
       "new_deaths_smoothed_per_million          0\n",
       "reproduction_rate                        0\n",
       "icu_patients                             0\n",
       "icu_patients_per_million                 0\n",
       "hosp_patients                            0\n",
       "hosp_patients_per_million                0\n",
       "weekly_icu_admissions                    0\n",
       "weekly_icu_admissions_per_million        0\n",
       "weekly_hosp_admissions                   0\n",
       "weekly_hosp_admissions_per_million       0\n",
       "new_tests                                0\n",
       "total_tests                              0\n",
       "total_tests_per_thousand                 0\n",
       "new_tests_per_thousand                   0\n",
       "new_tests_smoothed                       0\n",
       "new_tests_smoothed_per_thousand          0\n",
       "positive_rate                            0\n",
       "tests_per_case                           0\n",
       "tests_units                              0\n",
       "total_vaccinations                       0\n",
       "people_vaccinated                        0\n",
       "people_fully_vaccinated                  0\n",
       "new_vaccinations                         0\n",
       "new_vaccinations_smoothed                0\n",
       "total_vaccinations_per_hundred           0\n",
       "people_vaccinated_per_hundred            0\n",
       "people_fully_vaccinated_per_hundred      0\n",
       "new_vaccinations_smoothed_per_million    0\n",
       "stringency_index                         0\n",
       "population                               0\n",
       "population_density                       0\n",
       "median_age                               0\n",
       "aged_65_older                            0\n",
       "aged_70_older                            0\n",
       "gdp_per_capita                           0\n",
       "extreme_poverty                          0\n",
       "cardiovasc_death_rate                    0\n",
       "diabetes_prevalence                      0\n",
       "female_smokers                           0\n",
       "male_smokers                             0\n",
       "handwashing_facilities                   0\n",
       "hospital_beds_per_thousand               0\n",
       "life_expectancy                          0\n",
       "human_development_index                  0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data.to_csv('C:/Users/Public/Downloads/cleaned-covid-data.csv', header = True, index = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Handling Duplicate Records"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\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",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>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>total_cases_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>gdp_per_capita</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iso_code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [continent, location, date, total_cases, new_cases, new_cases_smoothed, total_deaths, new_deaths, new_deaths_smoothed, total_cases_per_million, new_cases_per_million, new_cases_smoothed_per_million, total_deaths_per_million, new_deaths_per_million, new_deaths_smoothed_per_million, reproduction_rate, icu_patients, icu_patients_per_million, hosp_patients, hosp_patients_per_million, weekly_icu_admissions, weekly_icu_admissions_per_million, weekly_hosp_admissions, weekly_hosp_admissions_per_million, new_tests, total_tests, total_tests_per_thousand, new_tests_per_thousand, new_tests_smoothed, new_tests_smoothed_per_thousand, positive_rate, tests_per_case, tests_units, total_vaccinations, people_vaccinated, people_fully_vaccinated, new_vaccinations, new_vaccinations_smoothed, total_vaccinations_per_hundred, people_vaccinated_per_hundred, people_fully_vaccinated_per_hundred, new_vaccinations_smoothed_per_million, stringency_index, population, population_density, median_age, aged_65_older, aged_70_older, gdp_per_capita, extreme_poverty, cardiovasc_death_rate, diabetes_prevalence, female_smokers, male_smokers, handwashing_facilities, hospital_beds_per_thousand, life_expectancy, human_development_index]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 58 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for duplicate data\n",
    "duplicate = data.duplicated()\n",
    "print(duplicate.sum())\n",
    "data[duplicate]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}