IndiasVaccination.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c062ca7b",
   "metadata": {},
   "source": [
    "# EDA PROJECT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "080d8c11",
   "metadata": {},
   "source": [
    "Team ID: 17\n",
    "Semister : IV\n",
    "Division: D\n",
    "Department: Computer Science"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "604fe5a5",
   "metadata": {},
   "source": [
    "Title ID: 4CEDA_CP_01\n",
    "Project Title: India's Vaccination (1 billion glory doses)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4b6383a",
   "metadata": {},
   "source": [
    "Problem Statement: The dataset concludes the rate of people getting vaccinated which ultimately helped us to reach the milestone of 1 billion.​"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "057512bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import all libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt \n",
    "import os\n",
    "import statsmodels.api as sm\n",
    "import pylab as py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5232e7c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#To load comma-separated values data into pandas we’ll use the pd.read_csv() function\n",
    "df = pd.read_csv('India_vaccination.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1023bec3",
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>new_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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>India</td>\n",
       "      <td>30-01-2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>India</td>\n",
       "      <td>31-01-2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>India</td>\n",
       "      <td>01-02-2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</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>NaN</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>India</td>\n",
       "      <td>02-02-2020</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</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>NaN</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>India</td>\n",
       "      <td>03-02-2020</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</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>NaN</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",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>681</th>\n",
       "      <td>India</td>\n",
       "      <td>11-12-2021</td>\n",
       "      <td>34690510</td>\n",
       "      <td>7877</td>\n",
       "      <td>9450.000</td>\n",
       "      <td>475434.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>700.571</td>\n",
       "      <td>24896.143</td>\n",
       "      <td>5.653</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>India</td>\n",
       "      <td>12-12-2021</td>\n",
       "      <td>34697860</td>\n",
       "      <td>7350</td>\n",
       "      <td>8042.714</td>\n",
       "      <td>475636.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>299.857</td>\n",
       "      <td>24901.417</td>\n",
       "      <td>5.275</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>India</td>\n",
       "      <td>13-12-2021</td>\n",
       "      <td>34703644</td>\n",
       "      <td>5784</td>\n",
       "      <td>7894.429</td>\n",
       "      <td>475888.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>304.429</td>\n",
       "      <td>24905.568</td>\n",
       "      <td>4.151</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>India</td>\n",
       "      <td>14-12-2021</td>\n",
       "      <td>34710628</td>\n",
       "      <td>6984</td>\n",
       "      <td>7686.571</td>\n",
       "      <td>476135.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>311.857</td>\n",
       "      <td>24910.581</td>\n",
       "      <td>5.012</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>India</td>\n",
       "      <td>15-12-2021</td>\n",
       "      <td>34718602</td>\n",
       "      <td>7974</td>\n",
       "      <td>7480.143</td>\n",
       "      <td>476478.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>338.143</td>\n",
       "      <td>24916.303</td>\n",
       "      <td>5.723</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>686 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    location        date  total_cases  new_cases  new_cases_smoothed  \\\n",
       "0      India  30-01-2020            1          1                 NaN   \n",
       "1      India  31-01-2020            1          0                 NaN   \n",
       "2      India  01-02-2020            1          0                 NaN   \n",
       "3      India  02-02-2020            2          1                 NaN   \n",
       "4      India  03-02-2020            3          1                 NaN   \n",
       "..       ...         ...          ...        ...                 ...   \n",
       "681    India  11-12-2021     34690510       7877            9450.000   \n",
       "682    India  12-12-2021     34697860       7350            8042.714   \n",
       "683    India  13-12-2021     34703644       5784            7894.429   \n",
       "684    India  14-12-2021     34710628       6984            7686.571   \n",
       "685    India  15-12-2021     34718602       7974            7480.143   \n",
       "\n",
       "     total_deaths  new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "0             NaN         NaN                  NaN                    0.001   \n",
       "1             NaN         NaN                  NaN                    0.001   \n",
       "2             NaN         NaN                  NaN                    0.001   \n",
       "3             NaN         NaN                  NaN                    0.001   \n",
       "4             NaN         NaN                  NaN                    0.002   \n",
       "..            ...         ...                  ...                      ...   \n",
       "681      475434.0       562.0              700.571                24896.143   \n",
       "682      475636.0       202.0              299.857                24901.417   \n",
       "683      475888.0       252.0              304.429                24905.568   \n",
       "684      476135.0       247.0              311.857                24910.581   \n",
       "685      476478.0       343.0              338.143                24916.303   \n",
       "\n",
       "     new_cases_per_million  ...  gdp_per_capita  extreme_poverty  \\\n",
       "0                    0.001  ...             NaN              NaN   \n",
       "1                    0.000  ...             NaN              NaN   \n",
       "2                    0.000  ...             NaN              NaN   \n",
       "3                    0.001  ...             NaN              NaN   \n",
       "4                    0.001  ...             NaN              NaN   \n",
       "..                     ...  ...             ...              ...   \n",
       "681                  5.653  ...        6426.674             21.2   \n",
       "682                  5.275  ...        6426.674             21.2   \n",
       "683                  4.151  ...        6426.674             21.2   \n",
       "684                  5.012  ...        6426.674             21.2   \n",
       "685                  5.723  ...        6426.674             21.2   \n",
       "\n",
       "     cardiovasc_death_rate  diabetes_prevalence  female_smokers  male_smokers  \\\n",
       "0                      NaN                  NaN             NaN           NaN   \n",
       "1                      NaN                  NaN             NaN           NaN   \n",
       "2                      NaN                  NaN             NaN           NaN   \n",
       "3                      NaN                  NaN             NaN           NaN   \n",
       "4                      NaN                  NaN             NaN           NaN   \n",
       "..                     ...                  ...             ...           ...   \n",
       "681                 282.28                10.39             1.9          20.6   \n",
       "682                 282.28                10.39             1.9          20.6   \n",
       "683                 282.28                10.39             1.9          20.6   \n",
       "684                 282.28                10.39             1.9          20.6   \n",
       "685                 282.28                10.39             1.9          20.6   \n",
       "\n",
       "     handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
       "0                       NaN                         NaN              NaN   \n",
       "1                       NaN                         NaN              NaN   \n",
       "2                       NaN                         NaN              NaN   \n",
       "3                       NaN                         NaN              NaN   \n",
       "4                       NaN                         NaN              NaN   \n",
       "..                      ...                         ...              ...   \n",
       "681                   59.55                        0.53            69.66   \n",
       "682                   59.55                        0.53            69.66   \n",
       "683                   59.55                        0.53            69.66   \n",
       "684                   59.55                        0.53            69.66   \n",
       "685                   59.55                        0.53            69.66   \n",
       "\n",
       "     human_development_index  \n",
       "0                        NaN  \n",
       "1                        NaN  \n",
       "2                        NaN  \n",
       "3                        NaN  \n",
       "4                        NaN  \n",
       "..                       ...  \n",
       "681                    0.645  \n",
       "682                    0.645  \n",
       "683                    0.645  \n",
       "684                    0.645  \n",
       "685                    0.645  \n",
       "\n",
       "[686 rows x 61 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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    "df"
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   "execution_count": 4,
   "id": "77513f58",
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     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 686 entries, 0 to 685\n",
      "Data columns (total 61 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   location                                    686 non-null    object \n",
      " 1   date                                        686 non-null    object \n",
      " 2   total_cases                                 686 non-null    int64  \n",
      " 3   new_cases                                   686 non-null    int64  \n",
      " 4   new_cases_smoothed                          681 non-null    float64\n",
      " 5   total_deaths                                645 non-null    float64\n",
      " 6   new_deaths                                  645 non-null    float64\n",
      " 7   new_deaths_smoothed                         681 non-null    float64\n",
      " 8   total_cases_per_million                     686 non-null    float64\n",
      " 9   new_cases_per_million                       686 non-null    float64\n",
      " 10  new_cases_smoothed_per_million              681 non-null    float64\n",
      " 11  total_deaths_per_million                    645 non-null    float64\n",
      " 12  new_deaths_per_million                      645 non-null    float64\n",
      " 13  new_deaths_smoothed_per_million             681 non-null    float64\n",
      " 14  reproduction_rate                           633 non-null    float64\n",
      " 15  icu_patients                                0 non-null      float64\n",
      " 16  icu_patients_per_million                    0 non-null      float64\n",
      " 17  hosp_patients                               0 non-null      float64\n",
      " 18  hosp_patients_per_million                   0 non-null      float64\n",
      " 19  weekly_icu_admissions                       0 non-null      float64\n",
      " 20  weekly_icu_admissions_per_million           0 non-null      float64\n",
      " 21  weekly_hosp_admissions                      0 non-null      float64\n",
      " 22  weekly_hosp_admissions_per_million          0 non-null      float64\n",
      " 23  new_tests                                   614 non-null    float64\n",
      " 24  total_tests                                 625 non-null    float64\n",
      " 25  total_tests_per_thousand                    625 non-null    float64\n",
      " 26  new_tests_per_thousand                      614 non-null    float64\n",
      " 27  new_tests_smoothed                          635 non-null    float64\n",
      " 28  new_tests_smoothed_per_thousand             635 non-null    float64\n",
      " 29  positive_rate                               633 non-null    float64\n",
      " 30  tests_per_case                              633 non-null    float64\n",
      " 31  tests_units                                 642 non-null    object \n",
      " 32  total_vaccinations                          323 non-null    float64\n",
      " 33  people_vaccinated                           323 non-null    float64\n",
      " 34  people_fully_vaccinated                     294 non-null    float64\n",
      " 35  total_boosters                              0 non-null      float64\n",
      " 36  new_vaccinations                            312 non-null    float64\n",
      " 37  new_vaccinations_smoothed                   333 non-null    float64\n",
      " 38  total_vaccinations_per_hundred              323 non-null    float64\n",
      " 39  people_vaccinated_per_hundred               323 non-null    float64\n",
      " 40  people_fully_vaccinated_per_hundred         294 non-null    float64\n",
      " 41  total_boosters_per_hundred                  0 non-null      float64\n",
      " 42  new_vaccinations_smoothed_per_million       333 non-null    float64\n",
      " 43  new_people_vaccinated_smoothed              32 non-null     float64\n",
      " 44  new_people_vaccinated_smoothed_per_hundred  32 non-null     float64\n",
      " 45  stringency_index                            30 non-null     float64\n",
      " 46  population                                  32 non-null     float64\n",
      " 47  population_density                          32 non-null     float64\n",
      " 48  median_age                                  32 non-null     float64\n",
      " 49  aged_65_older                               32 non-null     float64\n",
      " 50  aged_70_older                               32 non-null     float64\n",
      " 51  gdp_per_capita                              32 non-null     float64\n",
      " 52  extreme_poverty                             32 non-null     float64\n",
      " 53  cardiovasc_death_rate                       32 non-null     float64\n",
      " 54  diabetes_prevalence                         32 non-null     float64\n",
      " 55  female_smokers                              32 non-null     float64\n",
      " 56  male_smokers                                32 non-null     float64\n",
      " 57  handwashing_facilities                      32 non-null     float64\n",
      " 58  hospital_beds_per_thousand                  32 non-null     float64\n",
      " 59  life_expectancy                             32 non-null     float64\n",
      " 60  human_development_index                     32 non-null     float64\n",
      "dtypes: float64(56), int64(2), object(3)\n",
      "memory usage: 327.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#Info about all columns\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a9b21d5f",
   "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>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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.860000e+02</td>\n",
       "      <td>686.000000</td>\n",
       "      <td>681.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>681.000000</td>\n",
       "      <td>686.000000</td>\n",
       "      <td>686.000000</td>\n",
       "      <td>681.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>32.000</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.420240e+07</td>\n",
       "      <td>50610.206997</td>\n",
       "      <td>50949.971057</td>\n",
       "      <td>202926.379845</td>\n",
       "      <td>738.725581</td>\n",
       "      <td>698.326427</td>\n",
       "      <td>10192.554168</td>\n",
       "      <td>36.321153</td>\n",
       "      <td>36.564962</td>\n",
       "      <td>145.633048</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>2.120000e+01</td>\n",
       "      <td>2.822800e+02</td>\n",
       "      <td>1.039000e+01</td>\n",
       "      <td>1.900000e+00</td>\n",
       "      <td>2.060000e+01</td>\n",
       "      <td>5.955000e+01</td>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>6.966000e+01</td>\n",
       "      <td>6.450000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.311326e+07</td>\n",
       "      <td>76598.151437</td>\n",
       "      <td>76304.943119</td>\n",
       "      <td>168195.262890</td>\n",
       "      <td>977.120502</td>\n",
       "      <td>925.496166</td>\n",
       "      <td>9410.921256</td>\n",
       "      <td>54.971771</td>\n",
       "      <td>54.761353</td>\n",
       "      <td>120.707745</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>7.219121e-15</td>\n",
       "      <td>1.155059e-13</td>\n",
       "      <td>9.023902e-15</td>\n",
       "      <td>9.023902e-16</td>\n",
       "      <td>1.082868e-14</td>\n",
       "      <td>2.887649e-14</td>\n",
       "      <td>2.255975e-16</td>\n",
       "      <td>1.443824e-14</td>\n",
       "      <td>3.383963e-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>2.120000e+01</td>\n",
       "      <td>2.822800e+02</td>\n",
       "      <td>1.039000e+01</td>\n",
       "      <td>1.900000e+00</td>\n",
       "      <td>2.060000e+01</td>\n",
       "      <td>5.955000e+01</td>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>6.966000e+01</td>\n",
       "      <td>6.450000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.127489e+06</td>\n",
       "      <td>10604.750000</td>\n",
       "      <td>11278.429000</td>\n",
       "      <td>53866.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>158.857000</td>\n",
       "      <td>809.159000</td>\n",
       "      <td>7.610750</td>\n",
       "      <td>8.094000</td>\n",
       "      <td>38.658000</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>2.120000e+01</td>\n",
       "      <td>2.822800e+02</td>\n",
       "      <td>1.039000e+01</td>\n",
       "      <td>1.900000e+00</td>\n",
       "      <td>2.060000e+01</td>\n",
       "      <td>5.955000e+01</td>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>6.966000e+01</td>\n",
       "      <td>6.450000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.040435e+07</td>\n",
       "      <td>27092.500000</td>\n",
       "      <td>29539.143000</td>\n",
       "      <td>153847.000000</td>\n",
       "      <td>437.000000</td>\n",
       "      <td>417.714000</td>\n",
       "      <td>7466.829500</td>\n",
       "      <td>19.443500</td>\n",
       "      <td>21.199000</td>\n",
       "      <td>110.411000</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>2.120000e+01</td>\n",
       "      <td>2.822800e+02</td>\n",
       "      <td>1.039000e+01</td>\n",
       "      <td>1.900000e+00</td>\n",
       "      <td>2.060000e+01</td>\n",
       "      <td>5.955000e+01</td>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>6.966000e+01</td>\n",
       "      <td>6.450000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.026779e+07</td>\n",
       "      <td>53270.500000</td>\n",
       "      <td>53213.429000</td>\n",
       "      <td>405028.000000</td>\n",
       "      <td>836.000000</td>\n",
       "      <td>813.857000</td>\n",
       "      <td>21722.117250</td>\n",
       "      <td>38.230500</td>\n",
       "      <td>38.189000</td>\n",
       "      <td>290.674000</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>2.120000e+01</td>\n",
       "      <td>2.822800e+02</td>\n",
       "      <td>1.039000e+01</td>\n",
       "      <td>1.900000e+00</td>\n",
       "      <td>2.060000e+01</td>\n",
       "      <td>5.955000e+01</td>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>6.966000e+01</td>\n",
       "      <td>6.450000e-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.471860e+07</td>\n",
       "      <td>414188.000000</td>\n",
       "      <td>391232.000000</td>\n",
       "      <td>476478.000000</td>\n",
       "      <td>7374.000000</td>\n",
       "      <td>4190.000000</td>\n",
       "      <td>24916.303000</td>\n",
       "      <td>297.248000</td>\n",
       "      <td>280.773000</td>\n",
       "      <td>341.951000</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>2.120000e+01</td>\n",
       "      <td>2.822800e+02</td>\n",
       "      <td>1.039000e+01</td>\n",
       "      <td>1.900000e+00</td>\n",
       "      <td>2.060000e+01</td>\n",
       "      <td>5.955000e+01</td>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>6.966000e+01</td>\n",
       "      <td>6.450000e-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_cases      new_cases  new_cases_smoothed   total_deaths  \\\n",
       "count  6.860000e+02     686.000000          681.000000     645.000000   \n",
       "mean   1.420240e+07   50610.206997        50949.971057  202926.379845   \n",
       "std    1.311326e+07   76598.151437        76304.943119  168195.262890   \n",
       "min    1.000000e+00       0.000000            0.000000       1.000000   \n",
       "25%    1.127489e+06   10604.750000        11278.429000   53866.000000   \n",
       "50%    1.040435e+07   27092.500000        29539.143000  153847.000000   \n",
       "75%    3.026779e+07   53270.500000        53213.429000  405028.000000   \n",
       "max    3.471860e+07  414188.000000       391232.000000  476478.000000   \n",
       "\n",
       "        new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "count   645.000000           681.000000               686.000000   \n",
       "mean    738.725581           698.326427             10192.554168   \n",
       "std     977.120502           925.496166              9410.921256   \n",
       "min      -1.000000             0.000000                 0.001000   \n",
       "25%     195.000000           158.857000               809.159000   \n",
       "50%     437.000000           417.714000              7466.829500   \n",
       "75%     836.000000           813.857000             21722.117250   \n",
       "max    7374.000000          4190.000000             24916.303000   \n",
       "\n",
       "       new_cases_per_million  new_cases_smoothed_per_million  \\\n",
       "count             686.000000                      681.000000   \n",
       "mean               36.321153                       36.564962   \n",
       "std                54.971771                       54.761353   \n",
       "min                 0.000000                        0.000000   \n",
       "25%                 7.610750                        8.094000   \n",
       "50%                19.443500                       21.199000   \n",
       "75%                38.230500                       38.189000   \n",
       "max               297.248000                      280.773000   \n",
       "\n",
       "       total_deaths_per_million  ...  gdp_per_capita  extreme_poverty  \\\n",
       "count                645.000000  ...          32.000     3.200000e+01   \n",
       "mean                 145.633048  ...        6426.674     2.120000e+01   \n",
       "std                  120.707745  ...           0.000     7.219121e-15   \n",
       "min                    0.001000  ...        6426.674     2.120000e+01   \n",
       "25%                   38.658000  ...        6426.674     2.120000e+01   \n",
       "50%                  110.411000  ...        6426.674     2.120000e+01   \n",
       "75%                  290.674000  ...        6426.674     2.120000e+01   \n",
       "max                  341.951000  ...        6426.674     2.120000e+01   \n",
       "\n",
       "       cardiovasc_death_rate  diabetes_prevalence  female_smokers  \\\n",
       "count           3.200000e+01         3.200000e+01    3.200000e+01   \n",
       "mean            2.822800e+02         1.039000e+01    1.900000e+00   \n",
       "std             1.155059e-13         9.023902e-15    9.023902e-16   \n",
       "min             2.822800e+02         1.039000e+01    1.900000e+00   \n",
       "25%             2.822800e+02         1.039000e+01    1.900000e+00   \n",
       "50%             2.822800e+02         1.039000e+01    1.900000e+00   \n",
       "75%             2.822800e+02         1.039000e+01    1.900000e+00   \n",
       "max             2.822800e+02         1.039000e+01    1.900000e+00   \n",
       "\n",
       "       male_smokers  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "count  3.200000e+01            3.200000e+01                3.200000e+01   \n",
       "mean   2.060000e+01            5.955000e+01                5.300000e-01   \n",
       "std    1.082868e-14            2.887649e-14                2.255975e-16   \n",
       "min    2.060000e+01            5.955000e+01                5.300000e-01   \n",
       "25%    2.060000e+01            5.955000e+01                5.300000e-01   \n",
       "50%    2.060000e+01            5.955000e+01                5.300000e-01   \n",
       "75%    2.060000e+01            5.955000e+01                5.300000e-01   \n",
       "max    2.060000e+01            5.955000e+01                5.300000e-01   \n",
       "\n",
       "       life_expectancy  human_development_index  \n",
       "count     3.200000e+01             3.200000e+01  \n",
       "mean      6.966000e+01             6.450000e-01  \n",
       "std       1.443824e-14             3.383963e-16  \n",
       "min       6.966000e+01             6.450000e-01  \n",
       "25%       6.966000e+01             6.450000e-01  \n",
       "50%       6.966000e+01             6.450000e-01  \n",
       "75%       6.966000e+01             6.450000e-01  \n",
       "max       6.966000e+01             6.450000e-01  \n",
       "\n",
       "[8 rows x 58 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Statistical data of all columns\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d955d17d",
   "metadata": {},
   "source": [
    "# Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "156a3ac5",
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>new_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",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [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, total_boosters, new_vaccinations, new_vaccinations_smoothed, total_vaccinations_per_hundred, people_vaccinated_per_hundred, people_fully_vaccinated_per_hundred, total_boosters_per_hundred, new_vaccinations_smoothed_per_million, new_people_vaccinated_smoothed, new_people_vaccinated_smoothed_per_hundred, 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 61 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check for rows with all NaN values\n",
    "any_nan_df = df[df.isna().all(axis=1)]\n",
    "any_nan_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "793fe062",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>new_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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</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>681</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>686 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     location   date  total_cases  new_cases  new_cases_smoothed  \\\n",
       "0       False  False        False      False                True   \n",
       "1       False  False        False      False                True   \n",
       "2       False  False        False      False                True   \n",
       "3       False  False        False      False                True   \n",
       "4       False  False        False      False                True   \n",
       "..        ...    ...          ...        ...                 ...   \n",
       "681     False  False        False      False               False   \n",
       "682     False  False        False      False               False   \n",
       "683     False  False        False      False               False   \n",
       "684     False  False        False      False               False   \n",
       "685     False  False        False      False               False   \n",
       "\n",
       "     total_deaths  new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "0            True        True                 True                    False   \n",
       "1            True        True                 True                    False   \n",
       "2            True        True                 True                    False   \n",
       "3            True        True                 True                    False   \n",
       "4            True        True                 True                    False   \n",
       "..            ...         ...                  ...                      ...   \n",
       "681         False       False                False                    False   \n",
       "682         False       False                False                    False   \n",
       "683         False       False                False                    False   \n",
       "684         False       False                False                    False   \n",
       "685         False       False                False                    False   \n",
       "\n",
       "     new_cases_per_million  ...  gdp_per_capita  extreme_poverty  \\\n",
       "0                    False  ...            True             True   \n",
       "1                    False  ...            True             True   \n",
       "2                    False  ...            True             True   \n",
       "3                    False  ...            True             True   \n",
       "4                    False  ...            True             True   \n",
       "..                     ...  ...             ...              ...   \n",
       "681                  False  ...           False            False   \n",
       "682                  False  ...           False            False   \n",
       "683                  False  ...           False            False   \n",
       "684                  False  ...           False            False   \n",
       "685                  False  ...           False            False   \n",
       "\n",
       "     cardiovasc_death_rate  diabetes_prevalence  female_smokers  male_smokers  \\\n",
       "0                     True                 True            True          True   \n",
       "1                     True                 True            True          True   \n",
       "2                     True                 True            True          True   \n",
       "3                     True                 True            True          True   \n",
       "4                     True                 True            True          True   \n",
       "..                     ...                  ...             ...           ...   \n",
       "681                  False                False           False         False   \n",
       "682                  False                False           False         False   \n",
       "683                  False                False           False         False   \n",
       "684                  False                False           False         False   \n",
       "685                  False                False           False         False   \n",
       "\n",
       "     handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
       "0                      True                        True             True   \n",
       "1                      True                        True             True   \n",
       "2                      True                        True             True   \n",
       "3                      True                        True             True   \n",
       "4                      True                        True             True   \n",
       "..                      ...                         ...              ...   \n",
       "681                   False                       False            False   \n",
       "682                   False                       False            False   \n",
       "683                   False                       False            False   \n",
       "684                   False                       False            False   \n",
       "685                   False                       False            False   \n",
       "\n",
       "     human_development_index  \n",
       "0                       True  \n",
       "1                       True  \n",
       "2                       True  \n",
       "3                       True  \n",
       "4                       True  \n",
       "..                       ...  \n",
       "681                    False  \n",
       "682                    False  \n",
       "683                    False  \n",
       "684                    False  \n",
       "685                    False  \n",
       "\n",
       "[686 rows x 61 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "25964867",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location                        0\n",
       "date                            0\n",
       "total_cases                     0\n",
       "new_cases                       0\n",
       "new_cases_smoothed              5\n",
       "                             ... \n",
       "male_smokers                  654\n",
       "handwashing_facilities        654\n",
       "hospital_beds_per_thousand    654\n",
       "life_expectancy               654\n",
       "human_development_index       654\n",
       "Length: 61, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of null values in each column\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cd33b405",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop rows with all null values\n",
    "df.dropna(how='all', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d19611c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Drop duplicate values\n",
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6fb66d85",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove all the columns with all NaN values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "19f95ef0",
   "metadata": {},
   "outputs": [],
   "source": [
    "location = df.pop('location')\n",
    "del location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ea2a0ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "icu_patients = df.pop('icu_patients')\n",
    "del icu_patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "04bddca5",
   "metadata": {},
   "outputs": [],
   "source": [
    "icu_patients_per_million = df.pop('icu_patients_per_million')\n",
    "del icu_patients_per_million"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e2b52383",
   "metadata": {},
   "outputs": [],
   "source": [
    "hosp_patients = df.pop('hosp_patients')\n",
    "del hosp_patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "faa4162f",
   "metadata": {},
   "outputs": [],
   "source": [
    "hosp_patients_per_million = df.pop('hosp_patients_per_million')\n",
    "del hosp_patients_per_million"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ff475501",
   "metadata": {},
   "outputs": [],
   "source": [
    "weekly_icu_admissions = df.pop('weekly_icu_admissions')\n",
    "del weekly_icu_admissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6501582e",
   "metadata": {},
   "outputs": [],
   "source": [
    "weekly_icu_admissions_per_million = df.pop('weekly_icu_admissions_per_million')\n",
    "del weekly_icu_admissions_per_million"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bda3e938",
   "metadata": {},
   "outputs": [],
   "source": [
    "weekly_hosp_admissions = df.pop('weekly_hosp_admissions')\n",
    "del weekly_hosp_admissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3ee0b8cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "weekly_hosp_admissions_per_million = df.pop('weekly_hosp_admissions_per_million')\n",
    "del weekly_hosp_admissions_per_million"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "81935f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_boosters = df.pop('total_boosters')\n",
    "del total_boosters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d7f92eb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "total_boosters_per_hundred = df.pop('total_boosters_per_hundred')\n",
    "del total_boosters_per_hundred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4756ec0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>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>...</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30-01-2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31-01-2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-02-2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02-02-2020</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>03-02-2020</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>-1.000</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>681</th>\n",
       "      <td>11-12-2021</td>\n",
       "      <td>34690510</td>\n",
       "      <td>7877</td>\n",
       "      <td>9450.000</td>\n",
       "      <td>475434.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>700.571</td>\n",
       "      <td>24896.143</td>\n",
       "      <td>5.653</td>\n",
       "      <td>6.782</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>12-12-2021</td>\n",
       "      <td>34697860</td>\n",
       "      <td>7350</td>\n",
       "      <td>8042.714</td>\n",
       "      <td>475636.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>299.857</td>\n",
       "      <td>24901.417</td>\n",
       "      <td>5.275</td>\n",
       "      <td>5.772</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>13-12-2021</td>\n",
       "      <td>34703644</td>\n",
       "      <td>5784</td>\n",
       "      <td>7894.429</td>\n",
       "      <td>475888.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>304.429</td>\n",
       "      <td>24905.568</td>\n",
       "      <td>4.151</td>\n",
       "      <td>5.666</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>14-12-2021</td>\n",
       "      <td>34710628</td>\n",
       "      <td>6984</td>\n",
       "      <td>7686.571</td>\n",
       "      <td>476135.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>311.857</td>\n",
       "      <td>24910.581</td>\n",
       "      <td>5.012</td>\n",
       "      <td>5.516</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>15-12-2021</td>\n",
       "      <td>34718602</td>\n",
       "      <td>7974</td>\n",
       "      <td>7480.143</td>\n",
       "      <td>476478.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>338.143</td>\n",
       "      <td>24916.303</td>\n",
       "      <td>5.723</td>\n",
       "      <td>5.368</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>686 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  total_cases  new_cases  new_cases_smoothed  total_deaths  \\\n",
       "0    30-01-2020            1          1              -1.000          -1.0   \n",
       "1    31-01-2020            1          0              -1.000          -1.0   \n",
       "2    01-02-2020            1          0              -1.000          -1.0   \n",
       "3    02-02-2020            2          1              -1.000          -1.0   \n",
       "4    03-02-2020            3          1              -1.000          -1.0   \n",
       "..          ...          ...        ...                 ...           ...   \n",
       "681  11-12-2021     34690510       7877            9450.000      475434.0   \n",
       "682  12-12-2021     34697860       7350            8042.714      475636.0   \n",
       "683  13-12-2021     34703644       5784            7894.429      475888.0   \n",
       "684  14-12-2021     34710628       6984            7686.571      476135.0   \n",
       "685  15-12-2021     34718602       7974            7480.143      476478.0   \n",
       "\n",
       "     new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "0          -1.0               -1.000                    0.001   \n",
       "1          -1.0               -1.000                    0.001   \n",
       "2          -1.0               -1.000                    0.001   \n",
       "3          -1.0               -1.000                    0.001   \n",
       "4          -1.0               -1.000                    0.002   \n",
       "..          ...                  ...                      ...   \n",
       "681       562.0              700.571                24896.143   \n",
       "682       202.0              299.857                24901.417   \n",
       "683       252.0              304.429                24905.568   \n",
       "684       247.0              311.857                24910.581   \n",
       "685       343.0              338.143                24916.303   \n",
       "\n",
       "     new_cases_per_million  new_cases_smoothed_per_million  ...  \\\n",
       "0                    0.001                          -1.000  ...   \n",
       "1                    0.000                          -1.000  ...   \n",
       "2                    0.000                          -1.000  ...   \n",
       "3                    0.001                          -1.000  ...   \n",
       "4                    0.001                          -1.000  ...   \n",
       "..                     ...                             ...  ...   \n",
       "681                  5.653                           6.782  ...   \n",
       "682                  5.275                           5.772  ...   \n",
       "683                  4.151                           5.666  ...   \n",
       "684                  5.012                           5.516  ...   \n",
       "685                  5.723                           5.368  ...   \n",
       "\n",
       "     gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
       "0            -1.000             -1.0                  -1.00   \n",
       "1            -1.000             -1.0                  -1.00   \n",
       "2            -1.000             -1.0                  -1.00   \n",
       "3            -1.000             -1.0                  -1.00   \n",
       "4            -1.000             -1.0                  -1.00   \n",
       "..              ...              ...                    ...   \n",
       "681        6426.674             21.2                 282.28   \n",
       "682        6426.674             21.2                 282.28   \n",
       "683        6426.674             21.2                 282.28   \n",
       "684        6426.674             21.2                 282.28   \n",
       "685        6426.674             21.2                 282.28   \n",
       "\n",
       "     diabetes_prevalence  female_smokers  male_smokers  \\\n",
       "0                  -1.00            -1.0          -1.0   \n",
       "1                  -1.00            -1.0          -1.0   \n",
       "2                  -1.00            -1.0          -1.0   \n",
       "3                  -1.00            -1.0          -1.0   \n",
       "4                  -1.00            -1.0          -1.0   \n",
       "..                   ...             ...           ...   \n",
       "681                10.39             1.9          20.6   \n",
       "682                10.39             1.9          20.6   \n",
       "683                10.39             1.9          20.6   \n",
       "684                10.39             1.9          20.6   \n",
       "685                10.39             1.9          20.6   \n",
       "\n",
       "     handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
       "0                     -1.00                       -1.00            -1.00   \n",
       "1                     -1.00                       -1.00            -1.00   \n",
       "2                     -1.00                       -1.00            -1.00   \n",
       "3                     -1.00                       -1.00            -1.00   \n",
       "4                     -1.00                       -1.00            -1.00   \n",
       "..                      ...                         ...              ...   \n",
       "681                   59.55                        0.53            69.66   \n",
       "682                   59.55                        0.53            69.66   \n",
       "683                   59.55                        0.53            69.66   \n",
       "684                   59.55                        0.53            69.66   \n",
       "685                   59.55                        0.53            69.66   \n",
       "\n",
       "     human_development_index  \n",
       "0                     -1.000  \n",
       "1                     -1.000  \n",
       "2                     -1.000  \n",
       "3                     -1.000  \n",
       "4                     -1.000  \n",
       "..                       ...  \n",
       "681                    0.645  \n",
       "682                    0.645  \n",
       "683                    0.645  \n",
       "684                    0.645  \n",
       "685                    0.645  \n",
       "\n",
       "[686 rows x 50 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace null values with -1\n",
    "df.replace(to_replace = np.nan, value = -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6db9018e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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>new_cases_per_million</th>\n",
       "      <th>new_cases_smoothed_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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30-01-2020</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0.001</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31-01-2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-02-2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</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>NaN</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>02-02-2020</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>NaN</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>NaN</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>03-02-2020</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>NaN</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>NaN</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>681</th>\n",
       "      <td>11-12-2021</td>\n",
       "      <td>34690510</td>\n",
       "      <td>7877</td>\n",
       "      <td>9450.000</td>\n",
       "      <td>475434.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>700.571</td>\n",
       "      <td>24896.143</td>\n",
       "      <td>5.653</td>\n",
       "      <td>6.782</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>12-12-2021</td>\n",
       "      <td>34697860</td>\n",
       "      <td>7350</td>\n",
       "      <td>8042.714</td>\n",
       "      <td>475636.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>299.857</td>\n",
       "      <td>24901.417</td>\n",
       "      <td>5.275</td>\n",
       "      <td>5.772</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>13-12-2021</td>\n",
       "      <td>34703644</td>\n",
       "      <td>5784</td>\n",
       "      <td>7894.429</td>\n",
       "      <td>475888.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>304.429</td>\n",
       "      <td>24905.568</td>\n",
       "      <td>4.151</td>\n",
       "      <td>5.666</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>14-12-2021</td>\n",
       "      <td>34710628</td>\n",
       "      <td>6984</td>\n",
       "      <td>7686.571</td>\n",
       "      <td>476135.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>311.857</td>\n",
       "      <td>24910.581</td>\n",
       "      <td>5.012</td>\n",
       "      <td>5.516</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>15-12-2021</td>\n",
       "      <td>34718602</td>\n",
       "      <td>7974</td>\n",
       "      <td>7480.143</td>\n",
       "      <td>476478.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>338.143</td>\n",
       "      <td>24916.303</td>\n",
       "      <td>5.723</td>\n",
       "      <td>5.368</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>686 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  total_cases  new_cases  new_cases_smoothed  total_deaths  \\\n",
       "0    30-01-2020            1          1                 NaN           NaN   \n",
       "1    31-01-2020            1          0                 NaN           NaN   \n",
       "2    01-02-2020            1          0                 NaN           NaN   \n",
       "3    02-02-2020            2          1                 NaN           NaN   \n",
       "4    03-02-2020            3          1                 NaN           NaN   \n",
       "..          ...          ...        ...                 ...           ...   \n",
       "681  11-12-2021     34690510       7877            9450.000      475434.0   \n",
       "682  12-12-2021     34697860       7350            8042.714      475636.0   \n",
       "683  13-12-2021     34703644       5784            7894.429      475888.0   \n",
       "684  14-12-2021     34710628       6984            7686.571      476135.0   \n",
       "685  15-12-2021     34718602       7974            7480.143      476478.0   \n",
       "\n",
       "     new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "0           NaN                  NaN                    0.001   \n",
       "1           NaN                  NaN                    0.001   \n",
       "2           NaN                  NaN                    0.001   \n",
       "3           NaN                  NaN                    0.001   \n",
       "4           NaN                  NaN                    0.002   \n",
       "..          ...                  ...                      ...   \n",
       "681       562.0              700.571                24896.143   \n",
       "682       202.0              299.857                24901.417   \n",
       "683       252.0              304.429                24905.568   \n",
       "684       247.0              311.857                24910.581   \n",
       "685       343.0              338.143                24916.303   \n",
       "\n",
       "     new_cases_per_million  new_cases_smoothed_per_million  ...  \\\n",
       "0                    0.001                             NaN  ...   \n",
       "1                    0.000                             NaN  ...   \n",
       "2                    0.000                             NaN  ...   \n",
       "3                    0.001                             NaN  ...   \n",
       "4                    0.001                             NaN  ...   \n",
       "..                     ...                             ...  ...   \n",
       "681                  5.653                           6.782  ...   \n",
       "682                  5.275                           5.772  ...   \n",
       "683                  4.151                           5.666  ...   \n",
       "684                  5.012                           5.516  ...   \n",
       "685                  5.723                           5.368  ...   \n",
       "\n",
       "     gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
       "0               NaN              NaN                    NaN   \n",
       "1               NaN              NaN                    NaN   \n",
       "2               NaN              NaN                    NaN   \n",
       "3               NaN              NaN                    NaN   \n",
       "4               NaN              NaN                    NaN   \n",
       "..              ...              ...                    ...   \n",
       "681        6426.674             21.2                 282.28   \n",
       "682        6426.674             21.2                 282.28   \n",
       "683        6426.674             21.2                 282.28   \n",
       "684        6426.674             21.2                 282.28   \n",
       "685        6426.674             21.2                 282.28   \n",
       "\n",
       "     diabetes_prevalence  female_smokers  male_smokers  \\\n",
       "0                    NaN             NaN           NaN   \n",
       "1                    NaN             NaN           NaN   \n",
       "2                    NaN             NaN           NaN   \n",
       "3                    NaN             NaN           NaN   \n",
       "4                    NaN             NaN           NaN   \n",
       "..                   ...             ...           ...   \n",
       "681                10.39             1.9          20.6   \n",
       "682                10.39             1.9          20.6   \n",
       "683                10.39             1.9          20.6   \n",
       "684                10.39             1.9          20.6   \n",
       "685                10.39             1.9          20.6   \n",
       "\n",
       "     handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
       "0                       NaN                         NaN              NaN   \n",
       "1                       NaN                         NaN              NaN   \n",
       "2                       NaN                         NaN              NaN   \n",
       "3                       NaN                         NaN              NaN   \n",
       "4                       NaN                         NaN              NaN   \n",
       "..                      ...                         ...              ...   \n",
       "681                   59.55                        0.53            69.66   \n",
       "682                   59.55                        0.53            69.66   \n",
       "683                   59.55                        0.53            69.66   \n",
       "684                   59.55                        0.53            69.66   \n",
       "685                   59.55                        0.53            69.66   \n",
       "\n",
       "     human_development_index  \n",
       "0                        NaN  \n",
       "1                        NaN  \n",
       "2                        NaN  \n",
       "3                        NaN  \n",
       "4                        NaN  \n",
       "..                       ...  \n",
       "681                    0.645  \n",
       "682                    0.645  \n",
       "683                    0.645  \n",
       "684                    0.645  \n",
       "685                    0.645  \n",
       "\n",
       "[686 rows x 50 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Interpolate column values using forward linear regression method\n",
    "df.interpolate(method ='linear', limit_direction ='forward')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "419c66c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        0.0\n",
       "1        0.0\n",
       "2        0.0\n",
       "3        0.0\n",
       "4        0.0\n",
       "       ...  \n",
       "681    562.0\n",
       "682    202.0\n",
       "683    252.0\n",
       "684    247.0\n",
       "685    343.0\n",
       "Name: new_deaths, Length: 686, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace null values with 0\n",
    "df[\"new_deaths\"].replace(to_replace = np.nan, value = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f5796b60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           0.0\n",
       "1           0.0\n",
       "2           0.0\n",
       "3           0.0\n",
       "4           0.0\n",
       "         ...   \n",
       "681    475434.0\n",
       "682    475636.0\n",
       "683    475888.0\n",
       "684    476135.0\n",
       "685    476478.0\n",
       "Name: total_deaths, Length: 686, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Replace null values with 0\n",
    "df[\"total_deaths\"].replace(to_replace = np.nan, value = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fd27a85d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 686 entries, 0 to 685\n",
      "Data columns (total 50 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   date                                        686 non-null    object \n",
      " 1   total_cases                                 686 non-null    int64  \n",
      " 2   new_cases                                   686 non-null    int64  \n",
      " 3   new_cases_smoothed                          681 non-null    float64\n",
      " 4   total_deaths                                645 non-null    float64\n",
      " 5   new_deaths                                  645 non-null    float64\n",
      " 6   new_deaths_smoothed                         681 non-null    float64\n",
      " 7   total_cases_per_million                     686 non-null    float64\n",
      " 8   new_cases_per_million                       686 non-null    float64\n",
      " 9   new_cases_smoothed_per_million              681 non-null    float64\n",
      " 10  total_deaths_per_million                    645 non-null    float64\n",
      " 11  new_deaths_per_million                      645 non-null    float64\n",
      " 12  new_deaths_smoothed_per_million             681 non-null    float64\n",
      " 13  reproduction_rate                           633 non-null    float64\n",
      " 14  new_tests                                   614 non-null    float64\n",
      " 15  total_tests                                 625 non-null    float64\n",
      " 16  total_tests_per_thousand                    625 non-null    float64\n",
      " 17  new_tests_per_thousand                      614 non-null    float64\n",
      " 18  new_tests_smoothed                          635 non-null    float64\n",
      " 19  new_tests_smoothed_per_thousand             635 non-null    float64\n",
      " 20  positive_rate                               633 non-null    float64\n",
      " 21  tests_per_case                              633 non-null    float64\n",
      " 22  tests_units                                 642 non-null    object \n",
      " 23  total_vaccinations                          323 non-null    float64\n",
      " 24  people_vaccinated                           323 non-null    float64\n",
      " 25  people_fully_vaccinated                     294 non-null    float64\n",
      " 26  new_vaccinations                            312 non-null    float64\n",
      " 27  new_vaccinations_smoothed                   333 non-null    float64\n",
      " 28  total_vaccinations_per_hundred              323 non-null    float64\n",
      " 29  people_vaccinated_per_hundred               323 non-null    float64\n",
      " 30  people_fully_vaccinated_per_hundred         294 non-null    float64\n",
      " 31  new_vaccinations_smoothed_per_million       333 non-null    float64\n",
      " 32  new_people_vaccinated_smoothed              32 non-null     float64\n",
      " 33  new_people_vaccinated_smoothed_per_hundred  32 non-null     float64\n",
      " 34  stringency_index                            30 non-null     float64\n",
      " 35  population                                  32 non-null     float64\n",
      " 36  population_density                          32 non-null     float64\n",
      " 37  median_age                                  32 non-null     float64\n",
      " 38  aged_65_older                               32 non-null     float64\n",
      " 39  aged_70_older                               32 non-null     float64\n",
      " 40  gdp_per_capita                              32 non-null     float64\n",
      " 41  extreme_poverty                             32 non-null     float64\n",
      " 42  cardiovasc_death_rate                       32 non-null     float64\n",
      " 43  diabetes_prevalence                         32 non-null     float64\n",
      " 44  female_smokers                              32 non-null     float64\n",
      " 45  male_smokers                                32 non-null     float64\n",
      " 46  handwashing_facilities                      32 non-null     float64\n",
      " 47  hospital_beds_per_thousand                  32 non-null     float64\n",
      " 48  life_expectancy                             32 non-null     float64\n",
      " 49  human_development_index                     32 non-null     float64\n",
      "dtypes: float64(46), int64(2), object(2)\n",
      "memory usage: 273.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "85aa0cd2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>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>...</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",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>30-01-2020</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>NaN</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>NaN</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>31-01-2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</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>NaN</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>01-02-2020</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</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>NaN</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>02-02-2020</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>NaN</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>NaN</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>03-02-2020</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>NaN</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>NaN</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>681</th>\n",
       "      <td>11-12-2021</td>\n",
       "      <td>34690510</td>\n",
       "      <td>7877</td>\n",
       "      <td>9450.000</td>\n",
       "      <td>475434.0</td>\n",
       "      <td>562.0</td>\n",
       "      <td>700.571</td>\n",
       "      <td>24896.143</td>\n",
       "      <td>5.653</td>\n",
       "      <td>6.782</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>12-12-2021</td>\n",
       "      <td>34697860</td>\n",
       "      <td>7350</td>\n",
       "      <td>8042.714</td>\n",
       "      <td>475636.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>299.857</td>\n",
       "      <td>24901.417</td>\n",
       "      <td>5.275</td>\n",
       "      <td>5.772</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>683</th>\n",
       "      <td>13-12-2021</td>\n",
       "      <td>34703644</td>\n",
       "      <td>5784</td>\n",
       "      <td>7894.429</td>\n",
       "      <td>475888.0</td>\n",
       "      <td>252.0</td>\n",
       "      <td>304.429</td>\n",
       "      <td>24905.568</td>\n",
       "      <td>4.151</td>\n",
       "      <td>5.666</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>684</th>\n",
       "      <td>14-12-2021</td>\n",
       "      <td>34710628</td>\n",
       "      <td>6984</td>\n",
       "      <td>7686.571</td>\n",
       "      <td>476135.0</td>\n",
       "      <td>247.0</td>\n",
       "      <td>311.857</td>\n",
       "      <td>24910.581</td>\n",
       "      <td>5.012</td>\n",
       "      <td>5.516</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685</th>\n",
       "      <td>15-12-2021</td>\n",
       "      <td>34718602</td>\n",
       "      <td>7974</td>\n",
       "      <td>7480.143</td>\n",
       "      <td>476478.0</td>\n",
       "      <td>343.0</td>\n",
       "      <td>338.143</td>\n",
       "      <td>24916.303</td>\n",
       "      <td>5.723</td>\n",
       "      <td>5.368</td>\n",
       "      <td>...</td>\n",
       "      <td>6426.674</td>\n",
       "      <td>21.2</td>\n",
       "      <td>282.28</td>\n",
       "      <td>10.39</td>\n",
       "      <td>1.9</td>\n",
       "      <td>20.6</td>\n",
       "      <td>59.55</td>\n",
       "      <td>0.53</td>\n",
       "      <td>69.66</td>\n",
       "      <td>0.645</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>686 rows × 50 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           date  total_cases  new_cases  new_cases_smoothed  total_deaths  \\\n",
       "0    30-01-2020            1          1                 NaN           NaN   \n",
       "1    31-01-2020            1          0                 NaN           NaN   \n",
       "2    01-02-2020            1          0                 NaN           NaN   \n",
       "3    02-02-2020            2          1                 NaN           NaN   \n",
       "4    03-02-2020            3          1                 NaN           NaN   \n",
       "..          ...          ...        ...                 ...           ...   \n",
       "681  11-12-2021     34690510       7877            9450.000      475434.0   \n",
       "682  12-12-2021     34697860       7350            8042.714      475636.0   \n",
       "683  13-12-2021     34703644       5784            7894.429      475888.0   \n",
       "684  14-12-2021     34710628       6984            7686.571      476135.0   \n",
       "685  15-12-2021     34718602       7974            7480.143      476478.0   \n",
       "\n",
       "     new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "0           NaN                  NaN                    0.001   \n",
       "1           NaN                  NaN                    0.001   \n",
       "2           NaN                  NaN                    0.001   \n",
       "3           NaN                  NaN                    0.001   \n",
       "4           NaN                  NaN                    0.002   \n",
       "..          ...                  ...                      ...   \n",
       "681       562.0              700.571                24896.143   \n",
       "682       202.0              299.857                24901.417   \n",
       "683       252.0              304.429                24905.568   \n",
       "684       247.0              311.857                24910.581   \n",
       "685       343.0              338.143                24916.303   \n",
       "\n",
       "     new_cases_per_million  new_cases_smoothed_per_million  ...  \\\n",
       "0                    0.001                             NaN  ...   \n",
       "1                    0.000                             NaN  ...   \n",
       "2                    0.000                             NaN  ...   \n",
       "3                    0.001                             NaN  ...   \n",
       "4                    0.001                             NaN  ...   \n",
       "..                     ...                             ...  ...   \n",
       "681                  5.653                           6.782  ...   \n",
       "682                  5.275                           5.772  ...   \n",
       "683                  4.151                           5.666  ...   \n",
       "684                  5.012                           5.516  ...   \n",
       "685                  5.723                           5.368  ...   \n",
       "\n",
       "     gdp_per_capita  extreme_poverty  cardiovasc_death_rate  \\\n",
       "0               NaN              NaN                    NaN   \n",
       "1               NaN              NaN                    NaN   \n",
       "2               NaN              NaN                    NaN   \n",
       "3               NaN              NaN                    NaN   \n",
       "4               NaN              NaN                    NaN   \n",
       "..              ...              ...                    ...   \n",
       "681        6426.674             21.2                 282.28   \n",
       "682        6426.674             21.2                 282.28   \n",
       "683        6426.674             21.2                 282.28   \n",
       "684        6426.674             21.2                 282.28   \n",
       "685        6426.674             21.2                 282.28   \n",
       "\n",
       "     diabetes_prevalence  female_smokers  male_smokers  \\\n",
       "0                    NaN             NaN           NaN   \n",
       "1                    NaN             NaN           NaN   \n",
       "2                    NaN             NaN           NaN   \n",
       "3                    NaN             NaN           NaN   \n",
       "4                    NaN             NaN           NaN   \n",
       "..                   ...             ...           ...   \n",
       "681                10.39             1.9          20.6   \n",
       "682                10.39             1.9          20.6   \n",
       "683                10.39             1.9          20.6   \n",
       "684                10.39             1.9          20.6   \n",
       "685                10.39             1.9          20.6   \n",
       "\n",
       "     handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
       "0                       NaN                         NaN              NaN   \n",
       "1                       NaN                         NaN              NaN   \n",
       "2                       NaN                         NaN              NaN   \n",
       "3                       NaN                         NaN              NaN   \n",
       "4                       NaN                         NaN              NaN   \n",
       "..                      ...                         ...              ...   \n",
       "681                   59.55                        0.53            69.66   \n",
       "682                   59.55                        0.53            69.66   \n",
       "683                   59.55                        0.53            69.66   \n",
       "684                   59.55                        0.53            69.66   \n",
       "685                   59.55                        0.53            69.66   \n",
       "\n",
       "     human_development_index  \n",
       "0                        NaN  \n",
       "1                        NaN  \n",
       "2                        NaN  \n",
       "3                        NaN  \n",
       "4                        NaN  \n",
       "..                       ...  \n",
       "681                    0.645  \n",
       "682                    0.645  \n",
       "683                    0.645  \n",
       "684                    0.645  \n",
       "685                    0.645  \n",
       "\n",
       "[686 rows x 50 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "00049311",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add new columns \"Month\" and \"Year\"\n",
    "df['Month'] = df['date'].str[3:5]\n",
    "df['Year'] = df['date'].str[6:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "62c3373f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0           NaN\n",
       "1           NaN\n",
       "2           NaN\n",
       "3           NaN\n",
       "4           NaN\n",
       "         ...   \n",
       "681    0.071347\n",
       "682    0.027483\n",
       "683    0.043568\n",
       "684    0.035367\n",
       "685    0.043015\n",
       "Name: death_rate, Length: 686, dtype: float64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Add new column \"death_rate\" using \"new_cases\" and \"new_deaths\"\n",
    "df['death_rate'] = df['new_deaths']/df['new_cases']\n",
    "df['death_rate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "eabe387c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Different dataforms for different years\n",
    "df1 = df[df['Year']=='2020']\n",
    "df2 = df[df['Year']=='2021']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "51e4056f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 12 entries, 01 to 12\n",
      "Data columns (total 49 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   total_cases                                 12 non-null     int64  \n",
      " 1   new_cases                                   12 non-null     int64  \n",
      " 2   new_cases_smoothed                          12 non-null     float64\n",
      " 3   total_deaths                                12 non-null     float64\n",
      " 4   new_deaths                                  12 non-null     float64\n",
      " 5   new_deaths_smoothed                         12 non-null     float64\n",
      " 6   total_cases_per_million                     12 non-null     float64\n",
      " 7   new_cases_per_million                       12 non-null     float64\n",
      " 8   new_cases_smoothed_per_million              12 non-null     float64\n",
      " 9   total_deaths_per_million                    12 non-null     float64\n",
      " 10  new_deaths_per_million                      12 non-null     float64\n",
      " 11  new_deaths_smoothed_per_million             12 non-null     float64\n",
      " 12  reproduction_rate                           12 non-null     float64\n",
      " 13  new_tests                                   12 non-null     float64\n",
      " 14  total_tests                                 12 non-null     float64\n",
      " 15  total_tests_per_thousand                    12 non-null     float64\n",
      " 16  new_tests_per_thousand                      12 non-null     float64\n",
      " 17  new_tests_smoothed                          12 non-null     float64\n",
      " 18  new_tests_smoothed_per_thousand             12 non-null     float64\n",
      " 19  positive_rate                               12 non-null     float64\n",
      " 20  tests_per_case                              12 non-null     float64\n",
      " 21  total_vaccinations                          12 non-null     float64\n",
      " 22  people_vaccinated                           12 non-null     float64\n",
      " 23  people_fully_vaccinated                     12 non-null     float64\n",
      " 24  new_vaccinations                            12 non-null     float64\n",
      " 25  new_vaccinations_smoothed                   12 non-null     float64\n",
      " 26  total_vaccinations_per_hundred              12 non-null     float64\n",
      " 27  people_vaccinated_per_hundred               12 non-null     float64\n",
      " 28  people_fully_vaccinated_per_hundred         12 non-null     float64\n",
      " 29  new_vaccinations_smoothed_per_million       12 non-null     float64\n",
      " 30  new_people_vaccinated_smoothed              12 non-null     float64\n",
      " 31  new_people_vaccinated_smoothed_per_hundred  12 non-null     float64\n",
      " 32  stringency_index                            12 non-null     float64\n",
      " 33  population                                  12 non-null     float64\n",
      " 34  population_density                          12 non-null     float64\n",
      " 35  median_age                                  12 non-null     float64\n",
      " 36  aged_65_older                               12 non-null     float64\n",
      " 37  aged_70_older                               12 non-null     float64\n",
      " 38  gdp_per_capita                              12 non-null     float64\n",
      " 39  extreme_poverty                             12 non-null     float64\n",
      " 40  cardiovasc_death_rate                       12 non-null     float64\n",
      " 41  diabetes_prevalence                         12 non-null     float64\n",
      " 42  female_smokers                              12 non-null     float64\n",
      " 43  male_smokers                                12 non-null     float64\n",
      " 44  handwashing_facilities                      12 non-null     float64\n",
      " 45  hospital_beds_per_thousand                  12 non-null     float64\n",
      " 46  life_expectancy                             12 non-null     float64\n",
      " 47  human_development_index                     12 non-null     float64\n",
      " 48  death_rate                                  12 non-null     float64\n",
      "dtypes: float64(47), int64(2)\n",
      "memory usage: 4.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#Group and add data by Month for year 2020\n",
    "result1 = df[df['Year']== '2020'].groupby('Month').sum() \n",
    "result1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "63232f63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 12 entries, 01 to 12\n",
      "Data columns (total 49 columns):\n",
      " #   Column                                      Non-Null Count  Dtype  \n",
      "---  ------                                      --------------  -----  \n",
      " 0   total_cases                                 12 non-null     int64  \n",
      " 1   new_cases                                   12 non-null     int64  \n",
      " 2   new_cases_smoothed                          12 non-null     float64\n",
      " 3   total_deaths                                12 non-null     float64\n",
      " 4   new_deaths                                  12 non-null     float64\n",
      " 5   new_deaths_smoothed                         12 non-null     float64\n",
      " 6   total_cases_per_million                     12 non-null     float64\n",
      " 7   new_cases_per_million                       12 non-null     float64\n",
      " 8   new_cases_smoothed_per_million              12 non-null     float64\n",
      " 9   total_deaths_per_million                    12 non-null     float64\n",
      " 10  new_deaths_per_million                      12 non-null     float64\n",
      " 11  new_deaths_smoothed_per_million             12 non-null     float64\n",
      " 12  reproduction_rate                           12 non-null     float64\n",
      " 13  new_tests                                   12 non-null     float64\n",
      " 14  total_tests                                 12 non-null     float64\n",
      " 15  total_tests_per_thousand                    12 non-null     float64\n",
      " 16  new_tests_per_thousand                      12 non-null     float64\n",
      " 17  new_tests_smoothed                          12 non-null     float64\n",
      " 18  new_tests_smoothed_per_thousand             12 non-null     float64\n",
      " 19  positive_rate                               12 non-null     float64\n",
      " 20  tests_per_case                              12 non-null     float64\n",
      " 21  total_vaccinations                          12 non-null     float64\n",
      " 22  people_vaccinated                           12 non-null     float64\n",
      " 23  people_fully_vaccinated                     12 non-null     float64\n",
      " 24  new_vaccinations                            12 non-null     float64\n",
      " 25  new_vaccinations_smoothed                   12 non-null     float64\n",
      " 26  total_vaccinations_per_hundred              12 non-null     float64\n",
      " 27  people_vaccinated_per_hundred               12 non-null     float64\n",
      " 28  people_fully_vaccinated_per_hundred         12 non-null     float64\n",
      " 29  new_vaccinations_smoothed_per_million       12 non-null     float64\n",
      " 30  new_people_vaccinated_smoothed              12 non-null     float64\n",
      " 31  new_people_vaccinated_smoothed_per_hundred  12 non-null     float64\n",
      " 32  stringency_index                            12 non-null     float64\n",
      " 33  population                                  12 non-null     float64\n",
      " 34  population_density                          12 non-null     float64\n",
      " 35  median_age                                  12 non-null     float64\n",
      " 36  aged_65_older                               12 non-null     float64\n",
      " 37  aged_70_older                               12 non-null     float64\n",
      " 38  gdp_per_capita                              12 non-null     float64\n",
      " 39  extreme_poverty                             12 non-null     float64\n",
      " 40  cardiovasc_death_rate                       12 non-null     float64\n",
      " 41  diabetes_prevalence                         12 non-null     float64\n",
      " 42  female_smokers                              12 non-null     float64\n",
      " 43  male_smokers                                12 non-null     float64\n",
      " 44  handwashing_facilities                      12 non-null     float64\n",
      " 45  hospital_beds_per_thousand                  12 non-null     float64\n",
      " 46  life_expectancy                             12 non-null     float64\n",
      " 47  human_development_index                     12 non-null     float64\n",
      " 48  death_rate                                  12 non-null     float64\n",
      "dtypes: float64(47), int64(2)\n",
      "memory usage: 4.7+ KB\n"
     ]
    }
   ],
   "source": [
    "#Group and add data by Month for year 2021\n",
    "result2 = df[df['Year']== '2021'].groupby('Month').sum() \n",
    "result2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "feee5aa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Dataforms for MAY, OCTOBER and November of 2021\n",
    "month05 = df.iloc[list((df.Month == '05') & (df.Year == '2021')),  :,]\n",
    "month10 = df.iloc[list((df.Month == '10') & (df.Year == '2021')),  :,]\n",
    "month11 = df.iloc[list((df.Month == '11') & (df.Year == '2021')),  :,]\n",
    "month12 = df.iloc[list((df.Month == '12') & (df.Year == '2021')),  :,]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c5d3524",
   "metadata": {},
   "source": [
    "# Segregate Data Columns"
   ]
  },
  {
   "cell_type": "raw",
   "id": "fbb20746",
   "metadata": {},
   "source": [
    "total_cases, new_cases, total_deaths, new_deaths \n",
    "new_tests, total_tests, positive_rate\n",
    "total_vaccinations, people_vaccinated, people_fully_vaccinated, new_vaccinations\n",
    "population, population_density, gdp_per_capita, median_age, reproduction_rate\n",
    "cardiovasc_death_rate, diabetes_prevalence, female_smokers, male_smokers, extreme_poverty "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ad797cb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 686 entries, 0 to 685\n",
      "Data columns (total 4 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   total_cases   686 non-null    int64  \n",
      " 1   new_cases     686 non-null    int64  \n",
      " 2   total_deaths  645 non-null    float64\n",
      " 3   new_deaths    645 non-null    float64\n",
      "dtypes: float64(2), int64(2)\n",
      "memory usage: 26.8 KB\n"
     ]
    }
   ],
   "source": [
    "cat1 = df[['total_cases','new_cases','total_deaths','new_deaths']]\n",
    "cat1.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ae7ced95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 686 entries, 0 to 685\n",
      "Data columns (total 3 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   new_tests      614 non-null    float64\n",
      " 1   total_tests    625 non-null    float64\n",
      " 2   positive_rate  633 non-null    float64\n",
      "dtypes: float64(3)\n",
      "memory usage: 21.4 KB\n"
     ]
    }
   ],
   "source": [
    "cat2 = df[['new_tests', 'total_tests', 'positive_rate']]\n",
    "cat2.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f2fcea4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 686 entries, 0 to 685\n",
      "Data columns (total 4 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   total_vaccinations       323 non-null    float64\n",
      " 1   people_vaccinated        323 non-null    float64\n",
      " 2   people_fully_vaccinated  294 non-null    float64\n",
      " 3   new_vaccinations         312 non-null    float64\n",
      "dtypes: float64(4)\n",
      "memory usage: 26.8 KB\n"
     ]
    }
   ],
   "source": [
    "cat3 = df[['total_vaccinations', 'people_vaccinated', 'people_fully_vaccinated', 'new_vaccinations']]\n",
    "cat3.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8d43c556",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 686 entries, 0 to 685\n",
      "Data columns (total 5 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   population          32 non-null     float64\n",
      " 1   population_density  32 non-null     float64\n",
      " 2   gdp_per_capita      32 non-null     float64\n",
      " 3   median_age          32 non-null     float64\n",
      " 4   reproduction_rate   633 non-null    float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 32.2 KB\n"
     ]
    }
   ],
   "source": [
    "cat4 = df[['population', 'population_density', 'gdp_per_capita', 'median_age', 'reproduction_rate']]\n",
    "cat4.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d809317d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 686 entries, 0 to 685\n",
      "Data columns (total 5 columns):\n",
      " #   Column                 Non-Null Count  Dtype  \n",
      "---  ------                 --------------  -----  \n",
      " 0   cardiovasc_death_rate  32 non-null     float64\n",
      " 1   diabetes_prevalence    32 non-null     float64\n",
      " 2   female_smokers         32 non-null     float64\n",
      " 3   male_smokers           32 non-null     float64\n",
      " 4   extreme_poverty        32 non-null     float64\n",
      "dtypes: float64(5)\n",
      "memory usage: 32.2 KB\n"
     ]
    }
   ],
   "source": [
    "cat5 = df[['cardiovasc_death_rate', 'diabetes_prevalence', 'female_smokers', 'male_smokers', 'extreme_poverty']]\n",
    "cat5.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2a44e398",
   "metadata": {},
   "source": [
    "# VISUALIZATIONS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6ae1b479",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar graph to show new tests vs positive results in 2020\n",
    "Month = [1,2,3,4,5,6,7,8,9,10,11,12]\n",
    "new_tests = result1['new_tests'].tolist()\n",
    "positive_rate  = (result1['positive_rate']*result1['new_tests']/30).tolist()\n",
    "\n",
    "plt.bar([a-0.25 for a in Month], new_tests, width= 0.25, label = 'New tests', align='edge')\n",
    "plt.bar([a+0.25 for a in Month], positive_rate , width= -0.25, label = 'Positve Cases', align='edge')\n",
    "plt.xlabel('Month Number')\n",
    "plt.ylabel('Number of people')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.xticks(Month)\n",
    "plt.grid(True, linewidth= 1, linestyle=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "dbb8c64a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar graph to show new tests vs positive results in 2021\n",
    "new_tests = result2['new_tests'].tolist()\n",
    "positive_rate  = (result2['positive_rate']*result2['new_tests']/30).tolist()\n",
    "\n",
    "plt.bar([a-0.25 for a in Month], new_tests, width= 0.25, label = 'New tests', align='edge')\n",
    "plt.bar([a+0.25 for a in Month], positive_rate , width= -0.25, label = 'Positve Cases', align='edge')\n",
    "plt.xlabel('Month Number')\n",
    "plt.ylabel('Number of people')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.xticks(Month)\n",
    "plt.grid(True, linewidth= 1, linestyle=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b09ada50",
   "metadata": {},
   "source": [
    "From the graph, we can analyze that in the year 2020 both the cases and the deaths have reached the peak during the month of September.\n",
    "Later, there was gradual decrease in both the cases and deaths.\n",
    "In 2021 we can analyze that there is a sudden rise of cases from the month of February which indicates the emergence of second wave of Covid. New cases and new deaths have reached the peaks in the month of May."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "310de5ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line plot to show new cases vs new deaths in 2020\n",
    "newCases1  = result1['new_cases'].tolist()\n",
    "deaths1  = result1['new_deaths'].tolist()\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(Month, newCases1, color='r')\n",
    "plt.xticks(Month)\n",
    "ax1.set_ylabel('Number of new Cases', color='r')\n",
    "ax1.set_xlabel('Month number')\n",
    "\n",
    "ax2.plot(Month,deaths1, color='g')\n",
    "ax2.set_ylabel('Number of new deaths', color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8366c84c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line plot to show new cases vs new deaths in 2021\n",
    "newCases2  = result2['new_cases'].tolist()\n",
    "deaths2  = result2['new_deaths'].tolist()\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(Month, newCases2, color='r')\n",
    "plt.xticks(Month)\n",
    "ax1.set_ylabel('Number of new Cases', color='r')\n",
    "ax1.set_xlabel('Month number')\n",
    "\n",
    "ax2.plot(Month,deaths2, color='g')\n",
    "ax2.set_ylabel('Number of new deaths', color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "091e5b30",
   "metadata": {},
   "source": [
    "From the graph we can analyze that in the year 2020 both cases and the deaths have reached their peak during the month of September.\n",
    "Later, there was gradual decrease in both the cases and deaths.\n",
    "In 2021 we can analyze that there is sudden rise of cases from the month of February which indicates the emergence of second wave of covid. New cases and new deaths have reached their peaks in the month of May."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "6201b1b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter plot for new cases and new deaths for year 2020\n",
    "plt.scatter(df1[\"new_cases\"], df1[\"new_deaths\"])\n",
    "plt.title(\"Scatter Plot for New cases vs New deaths\")\n",
    "plt.xlabel(\"New cases\")\n",
    "plt.ylabel(\"New deaths\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b6c968a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Scatter plot for new cases and new deaths for year 2021\n",
    "plt.scatter(df2[\"new_cases\"], df2[\"new_deaths\"])\n",
    "plt.title(\"Scatter Plot for New cases vs New deaths\")\n",
    "plt.xlabel(\"New cases\")\n",
    "plt.ylabel(\"New deaths\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ef7c2781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar graph to show positive rate vs death rate\n",
    "dates = np.arange(0,31,1)\n",
    "plt.bar([a-0.25 for a in dates], month05[\"positive_rate\"], width= 0.25, label = 'Positive rate', align='edge')\n",
    "plt.bar([a+0.25 for a in dates], month05[\"death_rate\"] , width= -0.25, label = 'Death rate', align='edge')\n",
    "plt.xlabel('Day of the month')\n",
    "plt.ylabel('Rate')\n",
    "plt.legend(loc='upper right')\n",
    "\n",
    "plt.xticks(dates)\n",
    "plt.grid(True, linewidth= 1, linestyle=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d49e8e4",
   "metadata": {},
   "source": [
    "Earlier we have just discussed that in the month of May we had witnessed highest number of covid cases and deaths.\n",
    "From the graph of May month we can analyze that in May second week we had maximum positivity rate and last week of May had highest death rate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ba4f66fe",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line graph to show new cases and people vaccinated in year 2020\n",
    "Month = [1,2,3,4,5,6,7,8,9,10,11,12]\n",
    "newCases1  = result1['new_cases'].tolist()\n",
    "vacc  = result1['people_vaccinated'].tolist()\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(Month, newCases1, color='r')\n",
    "plt.xticks(Month)\n",
    "ax1.set_ylabel('Number of new Cases', color='r')\n",
    "ax1.set_xlabel('Month number')\n",
    "\n",
    "ax2.plot(Month, vacc, color='g')\n",
    "ax2.set_ylabel('Number of new vaccinations', color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d1285724",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line graph to show new cases and people vaccinated in year 2021\n",
    "newCases2  = result2['new_cases'].tolist()\n",
    "vacc2  = result2['people_vaccinated'].tolist()\n",
    "\n",
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(Month, newCases2, color='r')\n",
    "plt.xticks(Month)\n",
    "ax1.set_ylabel('Number of new Cases', color='r')\n",
    "ax1.set_xlabel('Month number')\n",
    "\n",
    "ax2.plot(Month, vacc2, color='g')\n",
    "ax2.set_ylabel('Number of new vaccinations', color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd6922c1",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "In 2020, the line depicting the number of vaccinations is a straight line, since there were no vaccinations released. The line depicting the number of cases has gradually increased and has reached its peak in the month of September. \n",
    "In the year 2021, the Covid cases reached the peak in the month of May and started declining later onwards.\n",
    "So, from the graph we can analyze that the people's urge to take vaccine started increasing gradually and the Covid cases also gradually decreased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "46770d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line graph to show new cases vs people vaccinated in October, 2021\n",
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(dates, month10['people_vaccinated'], color='g')\n",
    "plt.xticks(dates)\n",
    "ax1.set_ylabel('Number of people vaccinated', color='g')\n",
    "ax1.set_xlabel('Day of the month')\n",
    "\n",
    "ax2.plot(dates, month10['new_cases'], color='r')\n",
    "ax2.set_ylabel('Number of new cases', color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4886d6f8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line graph to show new cases vs people vaccinated in November, 2021\n",
    "dates1 = np.arange(1,31,1)\n",
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(dates1, month11['people_vaccinated'], color='g')\n",
    "plt.xticks(dates)\n",
    "ax1.set_ylabel('Number of people vaccinated', color='g')\n",
    "ax1.set_xlabel('Day of the month')\n",
    "\n",
    "ax2.plot(dates1, month11['new_cases'], color='r')\n",
    "ax2.set_ylabel('Number of new cases', color='r')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f463954f",
   "metadata": {},
   "source": [
    "After reaching peak in the month of May, covid cases started decreasing by a considerable amount and people getting vaccinated started increasing.\n",
    "Maximum vaccination occurred in the month of October and November.\n",
    "From the graph we can analyze that last week of November that is on 31st of November maximum number of people have received there vaccination.\n",
    "Variations in the new cases can be due to public meetings, religious festivals(Diwali, Dussehra) and weekends "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abd92187",
   "metadata": {},
   "source": [
    "#Line graph for New deaths and new vaccinations for year 2020\n",
    "deaths1  = result1['new_deaths'].tolist()\n",
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(Month, vacc, color='r')\n",
    "plt.xticks(Month)\n",
    "ax1.set_ylabel('Number of new vaccinations', color='r')\n",
    "ax1.set_xlabel('Month number')\n",
    "\n",
    "ax2.plot(Month, deaths1, color='g')\n",
    "ax2.set_ylabel('Number of new deaths', color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "81ac7e64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line graph for New deaths and new vaccinations for year 2021\n",
    "deaths2  = result2['new_deaths'].tolist()\n",
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(Month, vacc2, color='r')\n",
    "plt.xticks(Month)\n",
    "ax1.set_ylabel('Number of new vaccinations', color='r')\n",
    "ax1.set_xlabel('Month number')\n",
    "\n",
    "ax2.plot(Month, deaths2, color='g')\n",
    "ax2.set_ylabel('Number of new deaths', color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b613822",
   "metadata": {},
   "source": [
    "Here, we can see the monthly count of the number of deaths increased gradually and had reached the peak in the month of September, 2020.\n",
    "From February, 2021 we can see a rapid surge in the Covid deaths, hence can be termed as the beginning of India's second wave of Covid. Not only cases increased, but deaths were much more in this second wave of Covid \n",
    "From the month of May, 2021 the deaths rapidly decreased this may be due to vaccination."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "fd663f02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heat map to show correlation between attributes in 2020\n",
    "subset = result2[['new_tests','new_cases', 'new_deaths', 'positive_rate', 'new_vaccinations']]\n",
    "correlation = subset.corr(method='pearson')\n",
    "sns.heatmap(correlation,xticklabels=correlation.columns,\n",
    "            yticklabels=correlation.columns, annot = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2affe11e",
   "metadata": {},
   "source": [
    "New tests and new cases are strongly positively correlated i.e., almost dependent on each other.\n",
    "New cases and deaths are strongly positively correlated i.e., almost dependent on each other.\n",
    "New cases and positive rate are weakly positively correlated i.e., almost independent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "67701ec8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heat map to show correlation between attributes in 2021\n",
    "subset1 = result1[['new_tests','new_cases', 'new_deaths', 'positive_rate']]\n",
    "correlation1 = subset1.corr(method='pearson')\n",
    "sns.heatmap(correlation1,xticklabels=correlation1.columns,\n",
    "            yticklabels=correlation1.columns, annot = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a0d8876",
   "metadata": {},
   "source": [
    "New tests and new cases are weakly positively correlated i.e., almost independent. \n",
    "New cases and deaths are strongly positively correlated i.e., almost dependent on each other.\n",
    "New vaccinations and new cases are weakly negatively correlated i.e., almost independent.\n",
    "New vaccinations and deaths are weakly negatively correlated i.e., almost independent."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6cce54ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar graph to show positive results in May, 2021\n",
    "dates = np.arange(1,32,1)\n",
    "plt.bar(dates, (month05['positive_rate']*month05['new_tests_smoothed']))\n",
    "plt.xlabel('Day of the month')\n",
    "plt.ylabel('Number of Positive cases')\n",
    "plt.xticks(dates)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "211c6957",
   "metadata": {},
   "source": [
    "### Patterns in Vaccination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "ad87a8a0",
   "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>people_vaccinated</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>new_vaccinations</th>\n",
       "      <th>total_vaccinations</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01</th>\n",
       "      <td>2.832321e+07</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>3758843.0</td>\n",
       "      <td>2.832321e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>02</th>\n",
       "      <td>2.211475e+08</td>\n",
       "      <td>1.661464e+07</td>\n",
       "      <td>10078106.0</td>\n",
       "      <td>2.377621e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03</th>\n",
       "      <td>9.755784e+08</td>\n",
       "      <td>1.904285e+08</td>\n",
       "      <td>50816630.0</td>\n",
       "      <td>1.166007e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>04</th>\n",
       "      <td>2.610993e+09</td>\n",
       "      <td>4.271372e+08</td>\n",
       "      <td>77932654.0</td>\n",
       "      <td>3.038130e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>05</th>\n",
       "      <td>4.297974e+09</td>\n",
       "      <td>1.134571e+09</td>\n",
       "      <td>55496176.0</td>\n",
       "      <td>5.432545e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>06</th>\n",
       "      <td>6.464570e+09</td>\n",
       "      <td>1.465486e+09</td>\n",
       "      <td>118708244.0</td>\n",
       "      <td>7.930055e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>07</th>\n",
       "      <td>8.882182e+09</td>\n",
       "      <td>2.247516e+09</td>\n",
       "      <td>118404290.0</td>\n",
       "      <td>1.112970e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>08</th>\n",
       "      <td>1.210798e+10</td>\n",
       "      <td>3.485483e+09</td>\n",
       "      <td>140320023.0</td>\n",
       "      <td>1.559347e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>09</th>\n",
       "      <td>1.743122e+10</td>\n",
       "      <td>5.731674e+09</td>\n",
       "      <td>241626067.0</td>\n",
       "      <td>2.316289e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.155516e+10</td>\n",
       "      <td>8.744599e+09</td>\n",
       "      <td>176206059.0</td>\n",
       "      <td>3.029975e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2.199049e+10</td>\n",
       "      <td>1.105533e+10</td>\n",
       "      <td>166666754.0</td>\n",
       "      <td>3.304581e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.211476e+10</td>\n",
       "      <td>7.390237e+09</td>\n",
       "      <td>115430216.0</td>\n",
       "      <td>1.950499e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       people_vaccinated  people_fully_vaccinated  new_vaccinations  \\\n",
       "Month                                                                 \n",
       "01          2.832321e+07             0.000000e+00         3758843.0   \n",
       "02          2.211475e+08             1.661464e+07        10078106.0   \n",
       "03          9.755784e+08             1.904285e+08        50816630.0   \n",
       "04          2.610993e+09             4.271372e+08        77932654.0   \n",
       "05          4.297974e+09             1.134571e+09        55496176.0   \n",
       "06          6.464570e+09             1.465486e+09       118708244.0   \n",
       "07          8.882182e+09             2.247516e+09       118404290.0   \n",
       "08          1.210798e+10             3.485483e+09       140320023.0   \n",
       "09          1.743122e+10             5.731674e+09       241626067.0   \n",
       "10          2.155516e+10             8.744599e+09       176206059.0   \n",
       "11          2.199049e+10             1.105533e+10       166666754.0   \n",
       "12          1.211476e+10             7.390237e+09       115430216.0   \n",
       "\n",
       "       total_vaccinations  \n",
       "Month                      \n",
       "01           2.832321e+07  \n",
       "02           2.377621e+08  \n",
       "03           1.166007e+09  \n",
       "04           3.038130e+09  \n",
       "05           5.432545e+09  \n",
       "06           7.930055e+09  \n",
       "07           1.112970e+10  \n",
       "08           1.559347e+10  \n",
       "09           2.316289e+10  \n",
       "10           3.029975e+10  \n",
       "11           3.304581e+10  \n",
       "12           1.950499e+10  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Vaccination subset\n",
    "vaccinations = result2[['people_vaccinated', 'people_fully_vaccinated', 'new_vaccinations', 'total_vaccinations']]\n",
    "vaccinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9103a555",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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yg2ntsrXpWqcrXet2pX3N9vnuaCGnhaA4cBrfspO3AKWBj1X1QKCD+sMKgTHuGb1sNCOXjuTPoX9SpWQVr+MExbZD25i/ZT7zfp3H4m2LOZV4iiIFitC+Znu61elG17pdqVOujtcxcyxHhSC3sUJgjHsunnAxZYuUZfmdy72O4onTiadZ/vvyv84t/HLgFwDqlKvzV1G4ssaVFC1Y1OOkWZfTI4LewFigEr6jAsG37nypQAf1hxUCY9yxef9mGoxvwLgu4/hXq395HSdX+O3gb391IS3ZtoRTiacoWqAoHWp1oGudrvRp0CfPHDnltBBsAXqoaqwb4bLKCoEx7hizfAzDlwxn5yM7qVqqqtdxcp1TCadY9vsy5v86n3lb5rHl4BbOK34emwZvonyx8l7Hy1RGhcCfuYb25pYiYIxxT2RsJG2rt7UikI6iBYvSpU4XxnUdx68P/cqqgas4cOoAj3z1iNfRcszfpSr/T0T6i0jvlJvryYwxQbPl4BbW71mfJ+YWyi3aVm/LsMuHMfWnqcz/db7XcXLEn0JQCjgJdObv+YaudTOUMSa4Pov5DIA+Dft4nCRvefqKp2lYsSH3fnEvR+OPeh0n2zKdYkJV7wxGEGOMdyJjI2lZtSUXlLZpxLKicIHCTOk5hbZT2jLsm2GM7z7e60jZktE01I87P/8rIm+efQteRGOMm7Yd2kbUrijrFsqm1tVaM6TVEN6OepsVv6/wOk62ZNQ1lHKCOApYm8bNGJMPfBbr6xbq29AKQXY93/F5apWpxaA5gziVcMrrOFmWbteQqs51fn4YvDjGmGCLjImkRZUW1Cpby+soeVbxQsWZ3GMynaZ2YtSyUbzU6SWvI2VJpieLReRrESmT6nFZEfnK1VTGmKDYcWQHq/9cbUcDAXDVhVcxqPkgXv321Ty3frI/o4YqqurhlAeqegjfVcbGmDxuZuxMAPo0sNFCgfBq51epVLwSg+YMIiEpwes4fvOnECSlXpFMRGrgW6nMGJPHRcZE0vS8pvl+CuZgKVOkDBO6TyB6bzQvr3rZ6zh+86cQPA2sFJGpIjIVWA4My+xNIvKeiOwTkY3pvN5eRI6IyHrn9mzWohtjcuLPo3+y6o9V1i0UYL3q9+KGRjcwevloYuPyxqQMmRYCVV0AXAL8HzAd3+L1/pwj+ADoksk2K1S1mXMb7cc+jTEBMmvzLMBGC7nhv13/S4lCJRg0ZxBJyUlex8mUPyeLrwcSVPULZyRRoohcl9n7VHU5cDDnEY0xbpgRM4NGFRtRv0J9r6PkO5WKV2Jcl3F8t/M7xq/J/ReZ+dM1NEJVj6Q8cE4cjwhQ+21EJFpE5otIo/Q2EpF7RCRKRKLi4uIC1LQxoWvP8T2s+H0F/Rr28zpKvnXLxbfQtU5Xhi0axvbD272OkyF/CkFa22Q6NYUf1gE1VLUp8F9gdnobquokVY1Q1YiKFSsGoGljQtus2Fkoat1CLhIR3rn2HcIkjLvn3k1uXgTM39lHXxeR2iJyoYi8QQCuLFbVo6p63Lk/DygoIhVyul9jTOYiYyOpX6E+DSs29DpKvla9dHVe7vQy32z9hg/Wf+B1nHT5UwgeAs7gO1k8A9/6xQ/ktGERqSwi4txv6WTxZB1kY0JJ3Ik4lm5fSt8GfXF+BY2L7o24l3Y12jF04VB2H9vtdZw0+TNq6ISqPul0zbRQ1WGqeiKz94nIp8B3QD0R2Skig0TkPhG5z9mkL7BRRKKBN4GbNDcfOxmTT8zePJtkTbZuoSAJkzAm95jM6cTTDJ43OFd2EWXa1y8iFYHHgUZAkZTnVbVjRu9T1f6ZvP4W8JZ/MY0xgRIZG0mdcnVocl4Tr6OEjIvKX8So9qN44psn+Cz2s1xXhP3pGvoY2AzUAkYB24E1LmYyxrjkwMkDLNq6yLqFPDC0zVBaVGnBA/Me4MDJ3NUL7k8hKK+qU/BdS7BMVQcCrV3OZYxxwezNs0nSpFz3jTQUFAgrwJSeUzh46iBDFw71Os4/+FMIUmZO2i0i3UWkOVDNxUzGmABL1mTeXvM2D3/1MPXK1+OSKpd4HSkkNa3clCcve5KPoj9iwZYFXsf5iz+F4HkRKQ08CvwbeBd4xNVUxpiA2XJwCx0/7MgD8x6gbfW2fDXgK+sW8tDwdsNpUKEB935xL8fij3kdB/CvEKxW1SOqulFVOzgjh+a4nswYkyNJyUm88d0bNJnQhPV71jOl5xQW3LKAGmVqeB0tpKWsc/zHkT8YtijT+TuDwp9C8K2ILHSGf5Z1PZExJsc279/MFe9fwdCFQ7nqwqvYNHgTA5sPtCOBXKJN9Tb8q9W/GL9mfK5Y59if6wjqAsPxDR9dKyJfiMgA15MZY7IsMTmRsSvH0mxiMzbv38zU66cy56Y5VC1V1eto5ixjOo6hZpma3DX3Ls/XOfbniABV/UFVhwIt8c0oausYG5PLbNi7gTZT2vDkoifpflF3Yh6IYUCTAXYUkEulrHP8y4FfGL3M21n4/ZmGupSI3C4i84Fvgd34CoIxJhdISEpg9LLRtJjUgt8P/870vtOJ7BdJ5RKVvY5mMtHpwk4MbDaQV759hXW713mWQzK73FlEtuGbGXS6qn4XjFAZiYiI0KioKK9jGJMrrNu9joGfDyR6bzT9G/dnXJdxVCxuM/TmJYdPH6bh+IZUKl6JNXevoWB4QVfaEZG1qhqR1mv+dA1dqKqP5IYiYIzxiU+MZ/ji4bSc3JK9J/Yy+8bZfNLnEysCeVCZImV4u/vbRO+N5pVvX/Ekgz8ni3PfDEnGhLDVO1dzyaRLGLNiDAOaDCBmcAy96vfyOpbJgevqX0e/hv0YtWyUJ+sc+3Wy2BjjvVMJp3hs4WO0fa8tR+OPMu/meXxw3QeULWqjuvMDL9c59udkcZHMtjHGuGvljpU0ndiUV797lbua38XG+zfStW5Xr2OZADqvxHn855r/8N3O73h7zdtBbdufI4KNIrJKRF4SkW7OdBPGmCA4ceYEQ+YPod377UhITuCbW7/hnR7vULqI/RrmRwOaDPBknWN/zhHUAfoDG4BrgWgRWe9yLmNC3pJtS7h4wsW8+cObPNjyQTbcv4GrLrzK61jGRSLCxGsnIiLcM/eeoC1i40/XUDXgMuAKoDmwCd+ylcYYFxyNP8r9X9xPx486Eh4WzvI7lvNm1zcpUaiE19FMEFxQ+gLGdhrL11u/5sPo4Fy76891BMn4FqJ5QVU/D0qqDNh1BCY/+2rLV9w9927+PPYnj7R+hNEdRlOsYDGvY5kgS9Zk2n/Qng37NhAzOIYqJavkeJ85vY6gOfARcLOIfCciH4nIoBynMsb85dCpQwz8fCBdPu5CiUIlWDVwFa92ftWKQIgKkzDe7fkupxJO8eD8B11vL9M1i1U1WkR+A37D1z00AGgHTHE5mzGeWLljJb8f/p0wCSM8LJwwCfPdl1T3A/j82l1reWDeA+w7sY+nLn+KZ658hiIFbLBeqEtZ5/jJRU8SGRPp6qpy/nQNRQGF8c0ztBJYrqq/u5YoE9Y1ZNy0ef9mGr3diGRNDmq7Tc5rwns936PF+S2C2q7J3RKTE2n1bit2Ht1J7AOxlCtaLtv7yqhrKNMjAqCrqsZlu3Vj8pBnljxDsYLFWH7HcooWLEqyJpOsySQlJ/19X5PSfS47zxcrWIzeDXpTKLyQ1x/f5DIFwgrwXs/3iJgcwdCvhvLBdR+4044f24SJyBTgfFXtKiINgTbOgvbG5Btrd60lMiaSEVeOoHmV5l7HMQbwrXP8xGVPMGbFGG5qfBNd6nQJeBv+nCz+APgKON95/AvwcMCTGOOxpxY/Rfmi5RnaZqjXUYz5h2faPUPjSo3ZsHeDK/v354iggqpOF5FhAKqaKCLBnQjDGJct3b6Uhb8t5LXOr1GqcCmv4xjzD4ULFCbq7igKFyjsyv79OSI4ISLlAQUQkdbAEVfSGOMBVWXYomFULVmV+yPu9zqOMWlyqwiAf0cEQ4E5QG0RWQVUBNwbx2RMkM39ZS7f7/yeSddOomjBol7HMSbo/LmOYJ2IXAnUAwT4WVUTXE9mTBAkJSfx9OKnqVuuLnc2v9PrOMZ4wp8jAvCtUVzT2f4SEUFVP3ItlTFBMm3jNDbu28i0PtMoEObvr4Mx+Uum//JFZCpQG1gPpJwkVnzTThiTZ51JOsOzS5+lWeVm9GvUz+s4xnjGn69AEUBDW7LS5DdT1k1h66GtzLt5HmFii/WZ0OXXwjRAZbeDGBNMJxNO8tzy57j8gstduUDHmLzEr+sIgBgR+QGIT3lSVXu6lsoYl/139X/ZfXw30/tNR0S8jmOMp/wpBCOzs2MReQ/fimb7VLVxGq8LMA7oBpwE7lDVddlpy5isOHz6MGNXjaVb3W5cfsHlXscxxnP+DB9dls19fwC8RfonlbsCdZ1bK2CC89MYV72y6hUOnT7EmI5jvI5iTK7g2hkyVV0OHMxgk17AR+rzPVBGRHK+DI8xGdh7fC//Wf0fbmp8E80qN/M6jjG5gpdDJaoCf6R6vNN57hwico+IRIlIVFyczYhtsm/MijHEJ8Yzuv1or6MYk2ukWwhEZJHzc6xLbad1hi7NIaqqOklVI1Q1omLFii7FMfnd9sPbmRg1kUHNB1G3fF2v4xiTa2R0jqCKM7VETxGZxll/uANwYncnUD3V42rArhzu05h0jVw6kjAJ45krn/E6ijG5SkaF4FngSXx/oF8/6zUFOuaw7TnAg06RaQUcUdXdOdynMWmKiYth6k9TeaT1I1QrVc3rOMbkKukWAlWNBCJF5BlVfS6rOxaRT4H2QAUR2QmMAAo6+54IzMM3dHQLvuGjNuOXcc3wxcMpXrA4T17+pNdRjMl1/Bk++pyI9ATaOU8tVdUv/Hhf/0xeV+ABv1IakwM//PkDszbPYlT7UVQoVsHrOMbkOpmOGhKRF4EhQIxzG+I8Z0ye8PTip6lYrCKPtH7E6yjG5Er+XFncHWimqskAIvIh8CMwzM1gxgTC4m2L+WbrN7xxzRuULFzS6zjG5Er+XkdQJtX90i7kMCbgUpagrF6qOvdF3Od1HGNyLX+OCF4EfhSRJfiGkLbDjgZMHvD5z5/zw58/8G6PdylSoIjXcYzJtcSfZQacqR8uxVcIVqvqHreDpSciIkKjoqK8at7kEUnJSTSZ2ISk5CQ2Dt5oq4+ZkCcia1U1Iq3X/PrtcMb3zwloKmNc9PGGj4mJi2F63+lWBIzJhC3LZPKdM0lnGLF0BJdUuYQ+Dft4HceYXM++Kpl8Z/Layb55hbpPtCUojfFDhr8lIhImIhuDFcaYnDpx5gTPLX+OK2tcSefanb2OY0yekOERgaomi0i0iFygqjuCFcqY7Hpz9ZvsPbGXmTfOtCUojfGTP11DVYBNzprFJ1KetDWLTW5z6NQhXv72Za696FraVm/rdRxj8gx/CsEo11OYPOXAyQPM3jybmxrfRPFCxb2O85eXV73MkdNHbAlKY7Io0zNpzprF24GCzv01gC0yH6I27N3ApZMv5a65d9FkYhNW/L7C60gA7D62m3Grx9H/4v40Oa+J13GMyVP8mXTubiASeMd5qiow28VMJpeaFTuLNlPacDrxNJOunQTAlR9cydCvhnIq4ZSn2casGENCcoItQWlMNvgztu4B4DLgKICq/gpUcjOUyV2SNZlRS0fRe3pvGlVqRNQ9Udzd4m6i74vm/oj7eeP7N2j2TjO+3/m9J/m2HdrGpLWTuKv5XdQuV9uTDMbkZf4UgnhVPZPyQEQKkM7awib/OX7mOP1m9GPkspHc1vQ2lt2xjPNLng9AiUIlGN99PN/c+g2nE09z2XuX8cTXT3A68XRQM45YOoLwsHBbgtKYbPKnECwTkaeAoiJyNTADmOtuLJMbbD20lTZT2jB782zeuOYNPuj1QZqTt1114VVsuH8Dg5oP4uVvX6bFpBZE7QrOfFAb923kfz/9j4daPvRXgTLGZI0/heBJIA7YANyLb4nJ4W6GMt5bvG0xl06+lD+P/smCWxbwcOuHMxyXX6pwKSb1mMSCWxZw5PQRWr/bmmcWP8OZpDPpvicQhi8eTsnCJXnisidcbceY/MyfUUPJwIfAc/iGkn6o/kxZavIkVeWtH96i89TOnFf8PH64+weurn213++/ps41bBy8kVub3srzK57n0smXsn7Peleyfr/zez7/+XMea/sY5YuVd6UNY0KBP6OGugO/AW8CbwFbRKSr28FM8MUnxnP33Lt5aP5DdL+oO9/f9T11ytXJ8n7KFCnD+73eZ85Nc9h3Yh+XTr6U0ctGk5CUELCsqspTi56iUvFKPNz64YDt15hQ5E/X0GtAB1Vtr6pXAh2AN9yNZYJtz/E9dPyoI1N+nMLwK4Yz68ZZlCpcKkf77FGvB5sGb+KGRjcwYukIWk9pzcZ9gZm6atG2RSzZvoSnr3iaEoVKBGSfxoQqfwrBPlXdkurxVmCfS3mMB6J2Rf3VhTO973Se6/hcwGbtLFe0HB/3/pjPbviMP478QYtJLXhxxYskJidme58pRwM1Stfg3hb3BiSnMaEs3d92EektIr3xzTM0T0TuEJHb8Y0YWhO0hMZVH//0MVe8fwXhEs6qgavo16ifK+30btCbTYM30bNeT55a/BSXvXcZsXGx2drXrM2zWLNrDSPbj6RwgcIBTmpM6Mnoa18P51YE2AtcCbTHN4KorOvJjKuSkpN4/OvHGTBrAK2qtmLN3WtoVrmZq21WLF6RGf1mMK3PNLYc3ELzd5rz2revkZSc5Pc+kpKTGL54OPUr1GdAkwEupjUmdKQ76Zyq3hnMICZ4Dp8+zM2f3cz8LfMZHDGY/3T5DwXDCwat/Rsb38iVNa/kvi/u499f/5tZm2fxfq/3qVu+bqbvnfrTVGL3xxLZL9KWoDQmQDJdvF5EagEPATVJVTi8mobaFq/Pmc37N9NrWi+2HtrK+G7juafFPZ5lUVU+3vAxD81/iPjEeF7q9BIPtnww3fMT8YnxXPTWRVQqXokf7vrB1hswJgtyunj9bGAKvnMDyQHMZYJs3q/z6P9ZfwqHF2bxbYu5osYVnuYREQY0GUCHmh24e+7dDFkwhFmbZ/Fez/eoVbbWOdu/s/YddhzZwbs93rUiYEwA+TM05LSqvqmqS1R1WcrN9WQmYFSVsSvHcu0n11K7bG2i7onyvAikVrVUVb68+Uum9JzC2l1ruXjCxUyMmkjqo9XjZ44zZsUYOtTsQKcLO3mY1pj8x59CME5ERohIGxG5JOXmejITECcTTnLLzFt4ctGT3NDoBlYOXMkFpS/wOtY5RISBzQeycfBG2lRvw/1f3k/n/3VmxxHfCqnjvh/HvhP7eOGqF+xowJgA86dr6GLgVqAjf3cNqfPY5GJ/HPmD6/7vOn7c/SMvXvUiT1z2RK7/I3pB6QtYOGAhk9ZO4tGFj9L47cY83/F5Xvn2FXrW60nraq29jmhMvuPPyeLNQJPUU1F7yU4W+2fVjlX0nt6b04mn+aT3J3S/qLvXkbJs26FtDJwzkKXblyIIP93/E40rNfY6ljF5Uk5PFkcDZbCrifOMd9e9y+AvB1OzTE2W3r6UBhUbeB0pW2qVrcWi2xbx7rp3SUxOtCJgjEv8KQTnAZtFZA0Qn/KkP8NHRaQLMA4IB95V1ZfOer098DmwzXlqpqraWoPZlJCUwCNfPcL4NeO5pvY1fNrnU8oWzdvX/oVJmKdDXI0JBf4UghHZ2bGIhAPjgauBncAaEZmjqjFnbbpCVa/NThvmb/tP7qffjH4s3b6Ux9o+xotXvUh4WLjXsYwxeUCmhSAHQ0VbAltUdSuAiEwDegFnFwKTQ/tO7KPtlLbsPLqTqddPtakXjDFZ4s96BMdE5KhzOy0iSSJy1I99VwX+SPV4p/Pc2dqISLSIzBeRRulkuEdEokQkKi4uzo+mQ8eZpDP0nd6XP4/9yeLbF1sRMMZkmT9HBCVTPxaR6/B9289MWuMUzx6itA6ooarHRaQbvquYz5lwRlUnAZPAN2rIj7ZDgqry0LyHWLFjBZ/0/oS21dt6HckYkwdledJ5VZ2Nf9cQ7ASqp3pcDdh11r6Oqupx5/48oKCIVMhqplA1IWoCk9ZN4snLnqT/xf29jmOMyaMyPSJw1iRIEQZEcO43+7SsAeo6k9b9CdwE3HzWvisDe1VVRaSls/8DfmYPaUu3L2XIgiF0r9ud5zs+73UcY0we5s+ooR6p7icC2/Gd9M2QqiaKyIPAV/iGj76nqptE5D7n9YlAX+B+EUkETgE3aWZXuBm2HdpG3+l9qVuuLp/0+cRGBxljciTTK4tzm1C/svj4meO0ndKWP47+wQ93/eDXHP7GGJOtK4tF5NkM9qmq+lyOk5ksSdZkbpt1G5viNjH/lvlWBIwxAZFR19CJNJ4rDgwCygNWCIJs9LLRzNo8i9c7v07n2p29jmOMyScyWqrytZT7IlISGALcCUwDXkvvfcYdn8V8xqhlo7ij2R083Pphr+MYY/KRDE8Wi0g5YChwC/AhcImqHgpGMPO36D3R3Db7NlpXa83E7hNz/VTSxpi8JaNzBK8AvfFdyHVxynh/E1xxJ+LoNa0XZYuUZeYNMylcoLDXkYwx+UxGF5Q9CpwPDAd2pZpm4pifU0yYHEpISqDfjH7sOb6HWTfOokrJKl5HMsbkQxmdI8jyVccmsIYsGMKy35fxv+v/x6VVL/U6jjEmn7I/9rnUO1HvMCFqAo+3fZxbmtzidRxjTD5mhSAXWv77ch6c/yDd6nbjhate8DqOMSafs0KQy2w/vJ0+0/tQu2xtPult00cYY9xnhSAXOXHmBL2m9SIhKYE5/edQukhpryMZY0KAP5POmSBQVe74/A427tvIvJvncVH5i7yOZIwJEVYIconnlz9PZEwkr179KtfUucbrOMaYEGJdQ7nArNhZPLv0WW5tcitD2wz1Oo4xJsRYIfDYhr0buHXWrbSs2pJJPSbZ9BHGmKCzQuCh/Sf302taL0oVLsWsG2dRpEARryMZY0KQnSPwSEJSAjfMuIFdx3ax7I5lnF/yfK8jGWNClBUCjwz9aihLti/ho+s+olW1Vl7HMcaEMOsa8sDktZN5a81bPNrmUW5teqvXcYwxIc4KQZCt3LGSB+Y9wDW1r2Fsp7FexzHGGCsEwbTjyA56/19vapWtxbS+02z6CGNMrmCFIEhSpo+IT4pnzk1zKFOkjNeRjDEGsJPFQaGq3Pn5nUTvieaLm7+gXoV6Xkcyxpi/WCEIghdWvMCMmBmM7TSWbnW7eR3HGGP+wbqGXDbn5zkMXzKcWy6+hcfaPuZ1HGOMOYcVAhdt2reJW2beQsT5EUzuMdmmjzDG5EpWCFxy4OQBek3rRYlCJZh942yKFizqdSRjjEmTnSMIgONnjhO9J5r1e9bz454f+XHPj2zctxGAZXcso2qpqh4nNMaY9FkhyKK9x/f6/tjv/pH1e9fz4+4f2XJwC4oCUL5oeZpXac6QVkO4rv51tK7W2uPExhiTMSsE6UjWZLYe2sqPu33f8FO+7e85vuevbWqVqUXzKs25tcmtNK/SnGaVm1G1ZFU7F2CMyVOsEADxifFsitvk+2PvfNOP3hPNsTPHACgQVoCGFRtyTe1raFa5Gc0rN6dp5aZ2UZgxJl8IuUJw5PQR1u9Z/9c3/PV71rMpbhOJyYkAlChUgqbnNeX2prf7/uhXaU6jio0oXKCwx8mNMcYdIVMI5v06j4fmP8TWQ1v/eq5yico0q9yMbnW7/fVNv3a52oSJDaYyxoQOVwuBiHQBxgHhwLuq+tJZr4vzejfgJHCHqq5zI0ul4pVoUaUFdzW/669v+pVLVHajKWOMyVNcKwQiEg6MB64GdgJrRGSOqsak2qwrUNe5tQImOD8DLuL8CKb3m+7Gro0xJk9zsw+kJbBFVbeq6hlgGtDrrG16AR+pz/dAGRGp4mImY4wxZ3GzEFQF/kj1eKfzXFa3QUTuEZEoEYmKi4sLeFBjjAllbhaCtAbTaza2QVUnqWqEqkZUrFgxIOGMMcb4uFkIdgLVUz2uBuzKxjbGGGNc5GYhWAPUFZFaIlIIuAmYc9Y2c4DbxKc1cERVd7uYyRhjzFlcGzWkqoki8iDwFb7ho++p6iYRuc95fSIwD9/Q0S34ho/e6VYeY4wxaXP1OgJVnYfvj33q5yamuq/AA25mMMYYkzG7hNYYY0Kc+L6U5x0iEgf8ns23VwD2BzBOKLeXnz9bsNvLz58t2O3l58+W0/ZqqGqawy7zXCHICRGJUtUIay9vtZXf28vPny3Y7eXnz+Zme9Y1ZIwxIc4KgTHGhLhQKwSTrL082VZ+by8/f7Zgt5efP5tr7YXUOQJjjDHnCrUjAmOMMWexQmCMMSEuJAqBiLwnIvtEZGOQ2qsuIktEJFZENonIEBfbKiIiP4hItNPWKLfaOqvdcBH5UUS+CEJb20Vkg4isF5Eol9sqIyKRIrLZ+f/XxsW26jmfKeV2VEQedqs9p81HnH8nG0XkUxEp4mJbQ5x2NrnxudL6vRaRciLytYj86vws63J7/ZzPlywiAR3WmU57rzj/Nn8SkVkiUiYQbYVEIQA+ALoEsb1E4FFVbQC0Bh4QkYYutRUPdFTVpkAzoIszgZ/bhgCxQWgnRQdVbRaEMdvjgAWqWh9oioufUVV/dj5TM6AFvvm2ZrnVnohUBf4FRKhqY3xzgN3kUluNgbvxLVDVFLhWROoGuJkPOPf3+klgkarWBRY5j91sbyPQG1gewHYyau9roLGqNgF+AYYFoqGQKASquhw4GMT2dqesvayqx/D9MTlnwZ0AtaWqetx5WNC5uToCQESqAd2Bd91sJ9hEpBTQDpgCoKpnVPVwkJq/CvhNVbN71by/CgBFRaQAUAz3pn1vAHyvqidVNRFYBlwfyAbS+b3uBXzo3P8QuM7N9lQ1VlV/DlQbfrS30PnvCfA9vqn7cywkCoGXRKQm0BxY7WIb4SKyHtgHfK2qrrXl+A/wOJDscjspFFgoImtF5B4X27kQiAPed7q93hWR4i62l9pNwKduNqCqfwKvAjuA3fimfV/oUnMbgXYiUl5EiuGbZbh6Ju8JhPNSprJ3flYKQpteGQjMD8SOrBC4SERKAJ8BD6vqUbfaUdUkp3uhGtDSOSx3hYhcC+xT1bVutZGGy1T1EqArvm62di61UwC4BJigqs2BEwS2ayFNznodPYEZLrdTFt835lrA+UBxERngRluqGguMxdeVsQCIxtdlagJARJ7G99/z40DszwqBS0SkIL4i8LGqzgxGm043xlLcPR9yGdBTRLYD04COIvI/F9tDVXc5P/fh60Nv6VJTO4GdqY6oIvEVBrd1Bdap6l6X2+kEbFPVOFVNAGYCbd1qTFWnqOolqtoOXxfHr261lcpeEakC4PzcF4Q2g0pEbgeuBW7RAF0IZoXABSIi+PqZY1X1dZfbqpgyckBEiuL7Zd/sVnuqOkxVq6lqTXzdGYtV1ZVvlQAiUlxESqbcBzrj63YIOFXdA/whIvWcp64CYtxo6yz9cblbyLEDaC0ixZx/o1fh4slwEank/LwA3wnVYHzGOcDtzv3bgc+D0GbQiEgX4Amgp6qeDNiOVTXf3/D9A9wNJOD71jfI5fYux9ev/ROw3rl1c6mtJsCPTlsbgWeD+N+1PfCFy21ciK9bIRrYBDztcnvNgCjnv+dsoKzL7RUDDgClg/T/bBS+LwobgalAYRfbWoGvkEYDV7mw/3N+r4Hy+EYL/er8LOdye9c79+OBvcBXLre3Bfgj1d+ViYFoy6aYMMaYEGddQ8YYE+KsEBhjTIizQmCMMSHOCoExxoQ4KwTGGBPirBCYPE1EVESmpnpcQETisjsrqjP76OBUj9sHY4bVDPLcISJvedW+CQ1WCExedwJo7FxMB3A18GcO9lcGGJzZRnmFiIR7ncHkflYITH4wH99sqHDWVbrO/PSznfnbvxeRJs7zI5353peKyFYR+ZfzlpeA2s76AK84z5VItUbBx85Vuf/g7Ges+NaG+EVErnCe/8c3ehH5QkTaO/ePO+9ZKyLfiEjLVHl6ptp9dRFZICI/i8iIVPsa4LS3XkTeSfmj7+x3tIisBlxbT8HkH1YITH4wDbhJfIusNOGfM72OAn5U3/ztTwEfpXqtPnANvrmLRjjzQz2JbzroZqr6mLNdc+BhoCG+K50vSydHAVVt6Ww7Ip1tUisOLFXVFsAx4Hl8RzTXA6NTbdcSuAXfVc/9RCRCRBoAN+KbkK8ZkORsk7LfjaraSlVX+pHDhLgCXgcwJqdU9Sdnuu/+wLyzXr4c6ONst9iZFrm089qXqhoPxIvIPuC8dJr4QVV3AjjTfdcE0voDmzK54Fpnm8ycwTczJ8AGIF5VE0Rkw1nv/1pVDzjtz3Q+UyK+xWzWOAcoRfl7grUkfBMeGuMXKwQmv5iDb6799vjmm0lxTjcOfy/cE5/quSTS/33I6napt0nkn0feqZeGTNC/53hJTnm/qiY7C8ecnTf1YwE+VNW0Vqg6rapJ6WQ05hzWNWTyi/eA0aq64aznl+N0mTh98/s147UhjgElA5hrO9BMRMJEpDrZm0L7audcR1F8K26twjehWt9UM3yWE5EaAcpsQowdEZh8wem6GZfGSyPxrTj2E741gW9PY5vU+zkgIqvEt2D4fODLHEZbBWzD1/WzEViXjX2sxDdTaB3gE1WNAhCR4fhWbgvDN0PlA4DbS12afMhmHzXGmBBnXUPGGBPirBAYY0yIs0JgjDEhzgqBMcaEOCsExhgT4qwQGGNMiLNCYIwxIe7/AQnFsgkjliQyAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line graph to show the variation of new vaccinations\n",
    "plt.plot(Month, vaccinations[\"new_vaccinations\"], color='g')\n",
    "plt.xticks(Month)\n",
    "plt.xlabel(\"Month number\")\n",
    "plt.ylabel(\"Number of new vaccinations\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "bc0a1606",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Histogram to show new vaccinations for each month\n",
    "fig, ax = plt.subplots(figsize =(8, 7))\n",
    "ax.hist(vaccinations['new_vaccinations'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12350b19",
   "metadata": {},
   "source": [
    "Number of new vaccinations is increasing gradually from February due to the second wave of covid.\n",
    "There was a peak in September due to beginning of the third wave of covid.\n",
    "There was dip in number of vaccinations in May which also had peak in number of cases.\n",
    "Most monthly vaccinations lie between 1e8 and 1.2e8 or between 0.1e8 and 0.3e8.\n",
    "We can say that number of vaccinations mostly depend upon the waves of covid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9b91a17e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Box plot to show first and second dose vaccinations\n",
    "plt.boxplot([vaccinations['people_vaccinated'], vaccinations['people_fully_vaccinated']], vert = 0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "75953a45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar graph to show first and second dose vaccinations\n",
    "plt.bar([a-0.25 for a in Month], vaccinations['people_vaccinated'], width= 0.25, label = 'First dose of vaccination', align='edge')\n",
    "plt.bar([a+0.25 for a in Month], vaccinations['people_fully_vaccinated'] , width= -0.25, label = 'Second dose of vaccination', align='edge')\n",
    "plt.xlabel('Month Number')\n",
    "plt.ylabel('Number of people')\n",
    "plt.legend(loc='upper left')\n",
    "\n",
    "plt.xticks(Month)\n",
    "plt.grid(True, linewidth= 1, linestyle=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4ae0d08",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "We can see that the number of people vaccinated with first dose is more compared to people fully vaccinated.\n",
    "Both data have no outliers i.e., there are no months with vaccinations out of the range."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b8bf4fcb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Heat map to show correlation between attributes for first and second dose\n",
    "subset2 = result2[['new_cases', 'new_deaths', 'people_vaccinated', 'people_fully_vaccinated']]\n",
    "correlation2 = subset2.corr(method='pearson')\n",
    "sns.heatmap(correlation2,xticklabels=correlation2.columns,\n",
    "            yticklabels=correlation2.columns, annot = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22da4a3b",
   "metadata": {},
   "source": [
    "1st dose and 2nd dose of vaccinations varied almost equally.\n",
    "We can see that 2nd dose of vaccination had more effect on new cases and deaths as compared to that of 1st dose."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba140cc0",
   "metadata": {},
   "source": [
    "Q. How did the Government of India deal with second wave of Covid?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "f2d2e8d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Line graph to show new tests and new vaccinations during 2nd wave\n",
    "new_tests = result2['new_tests'].tolist()\n",
    "peoplevacc2  = result2['people_vaccinated'].tolist()\n",
    "fig, ax1 = plt.subplots()\n",
    "ax2 = ax1.twinx()\n",
    "ax1.plot(Month, new_tests, color='r')\n",
    "plt.xticks(Month)\n",
    "ax1.set_ylabel('Number of new tests', color='r')\n",
    "ax1.set_xlabel('Month number')\n",
    "\n",
    "ax2.plot(Month, peoplevacc2, color='g')\n",
    "ax2.set_ylabel('Number of new vaccinations', color='g')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "039c54b1",
   "metadata": {},
   "source": [
    "Analysis:\n",
    "Only way to control the pandemic was to keep increasing the testing and vaccinations in addition to lockdown. Increased testing almost certainly has resulted in the detection of a high proportion of mild cases.\n",
    "So, government started free testing facilities in all public sectors like airports, bus-stands, hospitals and providing vaccinations initially, to frontline workers, senior citizens and gradually, to the other age groups.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0a8ce8de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar graph to conclude new tests, cases and deaths in 2020\n",
    "plt.bar([a-0.25 for a in Month], result1[\"new_tests\"], width= 0.25, label = 'New tests', align='edge', color='blue')\n",
    "plt.bar([a+0.25 for a in Month], result1[\"new_cases\"] , width= -0.25, label = 'New Cases', align='edge', color = \"orange\")\n",
    "plt.bar([a+0.5 for a in Month], result1[\"new_deaths\"] , width= -0.25, label = 'New deaths', align='edge', color = \"red\")\n",
    "plt.xlabel('Month Number')\n",
    "plt.ylabel('Number of people')\n",
    "plt.legend(loc='upper left')\n",
    "plt.xticks(Month)\n",
    "plt.grid(True, linewidth= 1, linestyle=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "7c51a483",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Bar graph to conclude new tests, cases, deaths and vaccinations in 2020\n",
    "plt.bar([a-0.25 for a in Month], result2[\"new_tests\"], width= 0.25, label = 'New tests', align='edge', color='blue')\n",
    "plt.bar([a+0.25 for a in Month], result2[\"new_cases\"] , width= -0.25, label = 'New Cases', align='edge', color = \"orange\")\n",
    "plt.bar([a+0.5 for a in Month], result2[\"new_deaths\"] , width= -0.25, label = 'New deaths', align='edge', color = \"red\")\n",
    "plt.bar([a+0.75 for a in Month], result2[\"new_vaccinations\"] , width= -0.25, label = 'New vaccinations', align='edge', color = \"green\")\n",
    "plt.xlabel('Month Number')\n",
    "plt.ylabel('Number of people')\n",
    "plt.legend(loc='upper left')\n",
    "plt.xticks(Month)\n",
    "plt.grid(True, linewidth= 1, linestyle=\"--\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce98bc00",
   "metadata": {},
   "source": [
    "Testing helped to detect more cases and hence, reducing severe cases of covid.\n",
    "In general, as cases increased, deaths also increased. But deaths did not depend on cases.\n",
    "Vaccination did not affect much on the number of cases or positive rate.\n",
    "Cases mostly depended on waves of covid and other factors.\n",
    "Vaccination affected the number of deaths by increasing the immunity of the people which is seen in further days or months.\n",
    "Vaccinations depended on waves of covid and other factors.\n",
    "There were more first dose vaccinations compared to second dose vaccinations."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d809d955",
   "metadata": {},
   "source": [
    "# Statistical Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "5b29d47d",
   "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>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",
       "      <th>death_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.860000e+02</td>\n",
       "      <td>686.000000</td>\n",
       "      <td>681.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>681.000000</td>\n",
       "      <td>686.000000</td>\n",
       "      <td>686.000000</td>\n",
       "      <td>681.000000</td>\n",
       "      <td>645.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>3.200000e+01</td>\n",
       "      <td>642.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.420240e+07</td>\n",
       "      <td>50610.206997</td>\n",
       "      <td>50949.971057</td>\n",
       "      <td>202926.379845</td>\n",
       "      <td>738.725581</td>\n",
       "      <td>698.326427</td>\n",
       "      <td>10192.554168</td>\n",
       "      <td>36.321153</td>\n",
       "      <td>36.564962</td>\n",
       "      <td>145.633048</td>\n",
       "      <td>...</td>\n",
       "      <td>2.120000e+01</td>\n",
       "      <td>2.822800e+02</td>\n",
       "      <td>1.039000e+01</td>\n",
       "      <td>1.900000e+00</td>\n",
       "      <td>2.060000e+01</td>\n",
       "      <td>5.955000e+01</td>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>6.966000e+01</td>\n",
       "      <td>6.450000e-01</td>\n",
       "      <td>0.018840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.311326e+07</td>\n",
       "      <td>76598.151437</td>\n",
       "      <td>76304.943119</td>\n",
       "      <td>168195.262890</td>\n",
       "      <td>977.120502</td>\n",
       "      <td>925.496166</td>\n",
       "      <td>9410.921256</td>\n",
       "      <td>54.971771</td>\n",
       "      <td>54.761353</td>\n",
       "      <td>120.707745</td>\n",
       "      <td>...</td>\n",
       "      <td>7.219121e-15</td>\n",
       "      <td>1.155059e-13</td>\n",
       "      <td>9.023902e-15</td>\n",
       "      <td>9.023902e-16</td>\n",
       "      <td>1.082868e-14</td>\n",
       "      <td>2.887649e-14</td>\n",
       "      <td>2.255975e-16</td>\n",
       "      <td>1.443824e-14</td>\n",
       "      <td>3.383963e-16</td>\n",
       "      <td>0.016981</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.120000e+01</td>\n",
       "      <td>2.822800e+02</td>\n",
       "      <td>1.039000e+01</td>\n",
       "      <td>1.900000e+00</td>\n",
       "      <td>2.060000e+01</td>\n",
       "      <td>5.955000e+01</td>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>6.966000e+01</td>\n",
       "      <td>6.450000e-01</td>\n",
       "      <td>-0.011628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.127489e+06</td>\n",
       "      <td>10604.750000</td>\n",
       "      <td>11278.429000</td>\n",
       "      <td>53866.000000</td>\n",
       "      <td>195.000000</td>\n",
       "      <td>158.857000</td>\n",
       "      <td>809.159000</td>\n",
       "      <td>7.610750</td>\n",
       "      <td>8.094000</td>\n",
       "      <td>38.658000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.120000e+01</td>\n",
       "      <td>2.822800e+02</td>\n",
       "      <td>1.039000e+01</td>\n",
       "      <td>1.900000e+00</td>\n",
       "      <td>2.060000e+01</td>\n",
       "      <td>5.955000e+01</td>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>6.966000e+01</td>\n",
       "      <td>6.450000e-01</td>\n",
       "      <td>0.010848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.040435e+07</td>\n",
       "      <td>27092.500000</td>\n",
       "      <td>29539.143000</td>\n",
       "      <td>153847.000000</td>\n",
       "      <td>437.000000</td>\n",
       "      <td>417.714000</td>\n",
       "      <td>7466.829500</td>\n",
       "      <td>19.443500</td>\n",
       "      <td>21.199000</td>\n",
       "      <td>110.411000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.120000e+01</td>\n",
       "      <td>2.822800e+02</td>\n",
       "      <td>1.039000e+01</td>\n",
       "      <td>1.900000e+00</td>\n",
       "      <td>2.060000e+01</td>\n",
       "      <td>5.955000e+01</td>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>6.966000e+01</td>\n",
       "      <td>6.450000e-01</td>\n",
       "      <td>0.013805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.026779e+07</td>\n",
       "      <td>53270.500000</td>\n",
       "      <td>53213.429000</td>\n",
       "      <td>405028.000000</td>\n",
       "      <td>836.000000</td>\n",
       "      <td>813.857000</td>\n",
       "      <td>21722.117250</td>\n",
       "      <td>38.230500</td>\n",
       "      <td>38.189000</td>\n",
       "      <td>290.674000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.120000e+01</td>\n",
       "      <td>2.822800e+02</td>\n",
       "      <td>1.039000e+01</td>\n",
       "      <td>1.900000e+00</td>\n",
       "      <td>2.060000e+01</td>\n",
       "      <td>5.955000e+01</td>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>6.966000e+01</td>\n",
       "      <td>6.450000e-01</td>\n",
       "      <td>0.023263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3.471860e+07</td>\n",
       "      <td>414188.000000</td>\n",
       "      <td>391232.000000</td>\n",
       "      <td>476478.000000</td>\n",
       "      <td>7374.000000</td>\n",
       "      <td>4190.000000</td>\n",
       "      <td>24916.303000</td>\n",
       "      <td>297.248000</td>\n",
       "      <td>280.773000</td>\n",
       "      <td>341.951000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.120000e+01</td>\n",
       "      <td>2.822800e+02</td>\n",
       "      <td>1.039000e+01</td>\n",
       "      <td>1.900000e+00</td>\n",
       "      <td>2.060000e+01</td>\n",
       "      <td>5.955000e+01</td>\n",
       "      <td>5.300000e-01</td>\n",
       "      <td>6.966000e+01</td>\n",
       "      <td>6.450000e-01</td>\n",
       "      <td>0.182522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 49 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_cases      new_cases  new_cases_smoothed   total_deaths  \\\n",
       "count  6.860000e+02     686.000000          681.000000     645.000000   \n",
       "mean   1.420240e+07   50610.206997        50949.971057  202926.379845   \n",
       "std    1.311326e+07   76598.151437        76304.943119  168195.262890   \n",
       "min    1.000000e+00       0.000000            0.000000       1.000000   \n",
       "25%    1.127489e+06   10604.750000        11278.429000   53866.000000   \n",
       "50%    1.040435e+07   27092.500000        29539.143000  153847.000000   \n",
       "75%    3.026779e+07   53270.500000        53213.429000  405028.000000   \n",
       "max    3.471860e+07  414188.000000       391232.000000  476478.000000   \n",
       "\n",
       "        new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "count   645.000000           681.000000               686.000000   \n",
       "mean    738.725581           698.326427             10192.554168   \n",
       "std     977.120502           925.496166              9410.921256   \n",
       "min      -1.000000             0.000000                 0.001000   \n",
       "25%     195.000000           158.857000               809.159000   \n",
       "50%     437.000000           417.714000              7466.829500   \n",
       "75%     836.000000           813.857000             21722.117250   \n",
       "max    7374.000000          4190.000000             24916.303000   \n",
       "\n",
       "       new_cases_per_million  new_cases_smoothed_per_million  \\\n",
       "count             686.000000                      681.000000   \n",
       "mean               36.321153                       36.564962   \n",
       "std                54.971771                       54.761353   \n",
       "min                 0.000000                        0.000000   \n",
       "25%                 7.610750                        8.094000   \n",
       "50%                19.443500                       21.199000   \n",
       "75%                38.230500                       38.189000   \n",
       "max               297.248000                      280.773000   \n",
       "\n",
       "       total_deaths_per_million  ...  extreme_poverty  cardiovasc_death_rate  \\\n",
       "count                645.000000  ...     3.200000e+01           3.200000e+01   \n",
       "mean                 145.633048  ...     2.120000e+01           2.822800e+02   \n",
       "std                  120.707745  ...     7.219121e-15           1.155059e-13   \n",
       "min                    0.001000  ...     2.120000e+01           2.822800e+02   \n",
       "25%                   38.658000  ...     2.120000e+01           2.822800e+02   \n",
       "50%                  110.411000  ...     2.120000e+01           2.822800e+02   \n",
       "75%                  290.674000  ...     2.120000e+01           2.822800e+02   \n",
       "max                  341.951000  ...     2.120000e+01           2.822800e+02   \n",
       "\n",
       "       diabetes_prevalence  female_smokers  male_smokers  \\\n",
       "count         3.200000e+01    3.200000e+01  3.200000e+01   \n",
       "mean          1.039000e+01    1.900000e+00  2.060000e+01   \n",
       "std           9.023902e-15    9.023902e-16  1.082868e-14   \n",
       "min           1.039000e+01    1.900000e+00  2.060000e+01   \n",
       "25%           1.039000e+01    1.900000e+00  2.060000e+01   \n",
       "50%           1.039000e+01    1.900000e+00  2.060000e+01   \n",
       "75%           1.039000e+01    1.900000e+00  2.060000e+01   \n",
       "max           1.039000e+01    1.900000e+00  2.060000e+01   \n",
       "\n",
       "       handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
       "count            3.200000e+01                3.200000e+01     3.200000e+01   \n",
       "mean             5.955000e+01                5.300000e-01     6.966000e+01   \n",
       "std              2.887649e-14                2.255975e-16     1.443824e-14   \n",
       "min              5.955000e+01                5.300000e-01     6.966000e+01   \n",
       "25%              5.955000e+01                5.300000e-01     6.966000e+01   \n",
       "50%              5.955000e+01                5.300000e-01     6.966000e+01   \n",
       "75%              5.955000e+01                5.300000e-01     6.966000e+01   \n",
       "max              5.955000e+01                5.300000e-01     6.966000e+01   \n",
       "\n",
       "       human_development_index  death_rate  \n",
       "count             3.200000e+01  642.000000  \n",
       "mean              6.450000e-01    0.018840  \n",
       "std               3.383963e-16    0.016981  \n",
       "min               6.450000e-01   -0.011628  \n",
       "25%               6.450000e-01    0.010848  \n",
       "50%               6.450000e-01    0.013805  \n",
       "75%               6.450000e-01    0.023263  \n",
       "max               6.450000e-01    0.182522  \n",
       "\n",
       "[8 rows x 49 columns]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Count, mean, standard deviation, min, max and quartiles of all columns\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "1ecff9da",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[df[\"Year\"]=='2020']\n",
    "df2 = df[df[\"Year\"]=='2021']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "e8a5f9ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp/ipykernel_10124/1507822514.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df1.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "total_cases                                   3.217449e+06\n",
       "new_cases                                     3.052436e+04\n",
       "new_cases_smoothed                            3.080352e+04\n",
       "total_deaths                                  5.801216e+04\n",
       "new_deaths                                    5.033581e+02\n",
       "new_deaths_smoothed                           4.462952e+02\n",
       "total_cases_per_million                       2.309048e+03\n",
       "new_cases_per_million                         2.190625e+01\n",
       "new_cases_smoothed_per_million                2.210657e+01\n",
       "total_deaths_per_million                      4.163328e+01\n",
       "new_deaths_per_million                        3.612872e-01\n",
       "new_deaths_smoothed_per_million               3.202861e-01\n",
       "reproduction_rate                             1.195548e+00\n",
       "new_tests                                     6.203530e+05\n",
       "total_tests                                   5.222427e+07\n",
       "total_tests_per_thousand                      3.747947e+01\n",
       "new_tests_per_thousand                        4.452117e-01\n",
       "new_tests_smoothed                            5.888933e+05\n",
       "new_tests_smoothed_per_thousand               4.226098e-01\n",
       "positive_rate                                 6.313240e-02\n",
       "tests_per_case                                1.914739e+01\n",
       "total_vaccinations                                     NaN\n",
       "people_vaccinated                                      NaN\n",
       "people_fully_vaccinated                                NaN\n",
       "new_vaccinations                                       NaN\n",
       "new_vaccinations_smoothed                              NaN\n",
       "total_vaccinations_per_hundred                         NaN\n",
       "people_vaccinated_per_hundred                          NaN\n",
       "people_fully_vaccinated_per_hundred                    NaN\n",
       "new_vaccinations_smoothed_per_million                  NaN\n",
       "new_people_vaccinated_smoothed                         NaN\n",
       "new_people_vaccinated_smoothed_per_hundred             NaN\n",
       "stringency_index                                       NaN\n",
       "population                                             NaN\n",
       "population_density                                     NaN\n",
       "median_age                                             NaN\n",
       "aged_65_older                                          NaN\n",
       "aged_70_older                                          NaN\n",
       "gdp_per_capita                                         NaN\n",
       "extreme_poverty                                        NaN\n",
       "cardiovasc_death_rate                                  NaN\n",
       "diabetes_prevalence                                    NaN\n",
       "female_smokers                                         NaN\n",
       "male_smokers                                           NaN\n",
       "handwashing_facilities                                 NaN\n",
       "hospital_beds_per_thousand                             NaN\n",
       "life_expectancy                                        NaN\n",
       "human_development_index                                NaN\n",
       "Month                                                  inf\n",
       "Year                                                   inf\n",
       "death_rate                                    2.066525e-02\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mean of attributes in 2020\n",
    "df1.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "f5914930",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp/ipykernel_10124/1421482907.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df1.std()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "total_cases                                   3.735022e+06\n",
       "new_cases                                     2.954011e+04\n",
       "new_cases_smoothed                            2.944515e+04\n",
       "total_deaths                                  5.423633e+04\n",
       "new_deaths                                    3.749432e+02\n",
       "new_deaths_smoothed                           3.755454e+02\n",
       "total_cases_per_million                       2.680492e+03\n",
       "new_cases_per_million                         2.119986e+01\n",
       "new_cases_smoothed_per_million                2.113175e+01\n",
       "total_deaths_per_million                      3.892349e+01\n",
       "new_deaths_per_million                        2.690823e-01\n",
       "new_deaths_smoothed_per_million               2.695262e-01\n",
       "reproduction_rate                             3.328086e-01\n",
       "new_tests                                     4.551197e+05\n",
       "total_tests                                   5.532776e+07\n",
       "total_tests_per_thousand                      3.970675e+01\n",
       "new_tests_per_thousand                        3.266096e-01\n",
       "new_tests_smoothed                            4.536015e+05\n",
       "new_tests_smoothed_per_thousand               3.255450e-01\n",
       "positive_rate                                 2.580555e-02\n",
       "tests_per_case                                9.069926e+00\n",
       "total_vaccinations                                     NaN\n",
       "people_vaccinated                                      NaN\n",
       "people_fully_vaccinated                                NaN\n",
       "new_vaccinations                                       NaN\n",
       "new_vaccinations_smoothed                              NaN\n",
       "total_vaccinations_per_hundred                         NaN\n",
       "people_vaccinated_per_hundred                          NaN\n",
       "people_fully_vaccinated_per_hundred                    NaN\n",
       "new_vaccinations_smoothed_per_million                  NaN\n",
       "new_people_vaccinated_smoothed                         NaN\n",
       "new_people_vaccinated_smoothed_per_hundred             NaN\n",
       "stringency_index                                       NaN\n",
       "population                                             NaN\n",
       "population_density                                     NaN\n",
       "median_age                                             NaN\n",
       "aged_65_older                                          NaN\n",
       "aged_70_older                                          NaN\n",
       "gdp_per_capita                                         NaN\n",
       "extreme_poverty                                        NaN\n",
       "cardiovasc_death_rate                                  NaN\n",
       "diabetes_prevalence                                    NaN\n",
       "female_smokers                                         NaN\n",
       "male_smokers                                           NaN\n",
       "handwashing_facilities                                 NaN\n",
       "hospital_beds_per_thousand                             NaN\n",
       "life_expectancy                                        NaN\n",
       "human_development_index                                NaN\n",
       "death_rate                                    1.810244e-02\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Standard deviation of attributes in 2020\n",
    "df1.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "581cda1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp/ipykernel_10124/817985468.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df2.mean()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "total_cases                                   2.480964e+07\n",
       "new_cases                                     7.000542e+04\n",
       "new_cases_smoothed                            7.011508e+04\n",
       "total_deaths                                  3.258336e+05\n",
       "new_deaths                                    9.383496e+02\n",
       "new_deaths_smoothed                           9.380811e+02\n",
       "total_cases_per_million                       1.780499e+04\n",
       "new_cases_per_million                         5.024042e+01\n",
       "new_cases_smoothed_per_million                5.031907e+01\n",
       "total_deaths_per_million                      2.338392e+02\n",
       "new_deaths_per_million                        6.734241e-01\n",
       "new_deaths_smoothed_per_million               6.732292e-01\n",
       "reproduction_rate                             9.871554e-01\n",
       "new_tests                                     1.398453e+06\n",
       "total_tests                                   4.012411e+08\n",
       "total_tests_per_thousand                      2.879564e+02\n",
       "new_tests_per_thousand                        1.003603e+00\n",
       "new_tests_smoothed                            1.394620e+06\n",
       "new_tests_smoothed_per_thousand               1.000859e+00\n",
       "positive_rate                                 4.502428e-02\n",
       "tests_per_case                                4.924075e+01\n",
       "total_vaccinations                            4.661593e+08\n",
       "people_vaccinated                             3.364717e+08\n",
       "people_fully_vaccinated                       1.424798e+08\n",
       "new_vaccinations                              4.087962e+06\n",
       "new_vaccinations_smoothed                     3.980295e+06\n",
       "total_vaccinations_per_hundred                3.345489e+01\n",
       "people_vaccinated_per_hundred                 2.414743e+01\n",
       "people_fully_vaccinated_per_hundred           1.022520e+01\n",
       "new_vaccinations_smoothed_per_million         2.856508e+03\n",
       "new_people_vaccinated_smoothed                2.205465e+06\n",
       "new_people_vaccinated_smoothed_per_hundred    1.583125e-01\n",
       "stringency_index                              4.614133e+01\n",
       "population                                    1.393409e+09\n",
       "population_density                            4.504190e+02\n",
       "median_age                                    2.820000e+01\n",
       "aged_65_older                                 5.989000e+00\n",
       "aged_70_older                                 3.414000e+00\n",
       "gdp_per_capita                                6.426674e+03\n",
       "extreme_poverty                               2.120000e+01\n",
       "cardiovasc_death_rate                         2.822800e+02\n",
       "diabetes_prevalence                           1.039000e+01\n",
       "female_smokers                                1.900000e+00\n",
       "male_smokers                                  2.060000e+01\n",
       "handwashing_facilities                        5.955000e+01\n",
       "hospital_beds_per_thousand                    5.300000e-01\n",
       "life_expectancy                               6.966000e+01\n",
       "human_development_index                       6.450000e-01\n",
       "Month                                                  inf\n",
       "Year                                                   inf\n",
       "death_rate                                    1.727900e-02\n",
       "dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Mean of attributes in 2021\n",
    "df2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "df396077",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp/ipykernel_10124/1853903244.py:2: FutureWarning: Dropping of nuisance columns in DataFrame reductions (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  Select only valid columns before calling the reduction.\n",
      "  df2.std()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "total_cases                                   9.763134e+06\n",
       "new_cases                                     9.969264e+04\n",
       "new_cases_smoothed                            9.897955e+04\n",
       "total_deaths                                  1.297951e+05\n",
       "new_deaths                                    1.249243e+03\n",
       "new_deaths_smoothed                           1.192189e+03\n",
       "total_cases_per_million                       7.006654e+03\n",
       "new_cases_per_million                         7.154587e+01\n",
       "new_cases_smoothed_per_million                7.103412e+01\n",
       "total_deaths_per_million                      9.314929e+01\n",
       "new_deaths_per_million                        8.965443e-01\n",
       "new_deaths_smoothed_per_million               8.555744e-01\n",
       "reproduction_rate                             1.994503e-01\n",
       "new_tests                                     5.327305e+05\n",
       "total_tests                                   1.587471e+08\n",
       "total_tests_per_thousand                      1.139271e+02\n",
       "new_tests_per_thousand                        3.823365e-01\n",
       "new_tests_smoothed                            4.889185e+05\n",
       "new_tests_smoothed_per_thousand               3.508781e-01\n",
       "positive_rate                                 5.645688e-02\n",
       "tests_per_case                                3.260446e+01\n",
       "total_vaccinations                            4.222435e+08\n",
       "people_vaccinated                             2.816688e+08\n",
       "people_fully_vaccinated                       1.464808e+08\n",
       "new_vaccinations                              3.214294e+06\n",
       "new_vaccinations_smoothed                     2.554000e+06\n",
       "total_vaccinations_per_hundred                3.030292e+01\n",
       "people_vaccinated_per_hundred                 2.021432e+01\n",
       "people_fully_vaccinated_per_hundred           1.051230e+01\n",
       "new_vaccinations_smoothed_per_million         1.832897e+03\n",
       "new_people_vaccinated_smoothed                2.685742e+05\n",
       "new_people_vaccinated_smoothed_per_hundred    1.925288e-02\n",
       "stringency_index                              5.568641e+00\n",
       "population                                    0.000000e+00\n",
       "population_density                            5.775297e-14\n",
       "median_age                                    1.443824e-14\n",
       "aged_65_older                                 2.707171e-15\n",
       "aged_70_older                                 9.023902e-16\n",
       "gdp_per_capita                                0.000000e+00\n",
       "extreme_poverty                               7.219121e-15\n",
       "cardiovasc_death_rate                         1.155059e-13\n",
       "diabetes_prevalence                           9.023902e-15\n",
       "female_smokers                                9.023902e-16\n",
       "male_smokers                                  1.082868e-14\n",
       "handwashing_facilities                        2.887649e-14\n",
       "hospital_beds_per_thousand                    2.255975e-16\n",
       "life_expectancy                               1.443824e-14\n",
       "human_development_index                       3.383963e-16\n",
       "death_rate                                    1.581840e-02\n",
       "dtype: float64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Standard deviation of attributes in 2021\n",
    "df2.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e45da8af",
   "metadata": {},
   "source": [
    "The variation of new cases and new tests is much higher in 2021 when compared to 2020.\n",
    "The main reason for much varying cases and tests could be because of the emergence of second wave of Covid in 2021, which resulted in the increase in number of tests and cases."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6cb71fc",
   "metadata": {},
   "source": [
    "By analyzing the variation of deaths in 2021, we can understand how severely the second wave hit our country.\n",
    "There is a huge variation of cases and deaths in 2021 when compared to 2020.​"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78421f83",
   "metadata": {},
   "source": [
    "There was more variation in the first dose of vaccinations compared to second dose of vaccinations.\n",
    "This may be due to more number of first dose of vaccinations compared to second dose of vaccinations."
   ]
  }
 ],
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