cpa01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "5dd80911",
   "metadata": {},
   "source": [
    "The complete COVID-19 dataset is a collection of the COVID-19 data maintained by Our World in Data. They will update it daily throughout the duration of the COVID-19 pandemic.\n",
    "Download the dataset here: https://github.com/owid/covid-19-data/raw/master/public/data/owid-covid-data.csv\n",
    "Q1. What is their data source?\n",
    "Q2. What algorithm do they use to smooth the new_cases and new_deaths data?\n",
    "Q3. Are there strong relationships between death rate and variables like life_expectancy, hospital or handwashing?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c58231c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8abeb0ad",
   "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>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>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>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.746</td>\n",
       "      <td>0.5</td>\n",
       "      <td>64.83</td>\n",
       "      <td>0.511</td>\n",
       "      <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>167469</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-03-06</td>\n",
       "      <td>239209.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>486.571</td>\n",
       "      <td>5399.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.857</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167470</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-03-07</td>\n",
       "      <td>239710.0</td>\n",
       "      <td>501.0</td>\n",
       "      <td>475.714</td>\n",
       "      <td>5399.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.571</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167471</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-03-08</td>\n",
       "      <td>240343.0</td>\n",
       "      <td>633.0</td>\n",
       "      <td>496.000</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.714</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167472</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-03-09</td>\n",
       "      <td>240343.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>405.714</td>\n",
       "      <td>5400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.571</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167473</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2022-03-10</td>\n",
       "      <td>241548.0</td>\n",
       "      <td>1205.0</td>\n",
       "      <td>577.857</td>\n",
       "      <td>5408.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1.714</td>\n",
       "      <td>...</td>\n",
       "      <td>1.6</td>\n",
       "      <td>30.7</td>\n",
       "      <td>36.791</td>\n",
       "      <td>1.7</td>\n",
       "      <td>61.49</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>167474 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location       date  total_cases  new_cases  \\\n",
       "0           AFG      Asia  Afghanistan 2020-02-24          5.0        5.0   \n",
       "1           AFG      Asia  Afghanistan 2020-02-25          5.0        0.0   \n",
       "2           AFG      Asia  Afghanistan 2020-02-26          5.0        0.0   \n",
       "3           AFG      Asia  Afghanistan 2020-02-27          5.0        0.0   \n",
       "4           AFG      Asia  Afghanistan 2020-02-28          5.0        0.0   \n",
       "...         ...       ...          ...        ...          ...        ...   \n",
       "167469      ZWE    Africa     Zimbabwe 2022-03-06     239209.0      190.0   \n",
       "167470      ZWE    Africa     Zimbabwe 2022-03-07     239710.0      501.0   \n",
       "167471      ZWE    Africa     Zimbabwe 2022-03-08     240343.0      633.0   \n",
       "167472      ZWE    Africa     Zimbabwe 2022-03-09     240343.0        0.0   \n",
       "167473      ZWE    Africa     Zimbabwe 2022-03-10     241548.0     1205.0   \n",
       "\n",
       "        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\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",
       "167469             486.571        5399.0         2.0                0.857   \n",
       "167470             475.714        5399.0         0.0                0.571   \n",
       "167471             496.000        5400.0         1.0                0.714   \n",
       "167472             405.714        5400.0         0.0                0.571   \n",
       "167473             577.857        5408.0         8.0                1.714   \n",
       "\n",
       "        ...  female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0       ...             NaN           NaN                  37.746   \n",
       "1       ...             NaN           NaN                  37.746   \n",
       "2       ...             NaN           NaN                  37.746   \n",
       "3       ...             NaN           NaN                  37.746   \n",
       "4       ...             NaN           NaN                  37.746   \n",
       "...     ...             ...           ...                     ...   \n",
       "167469  ...             1.6          30.7                  36.791   \n",
       "167470  ...             1.6          30.7                  36.791   \n",
       "167471  ...             1.6          30.7                  36.791   \n",
       "167472  ...             1.6          30.7                  36.791   \n",
       "167473  ...             1.6          30.7                  36.791   \n",
       "\n",
       "        hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "0                              0.5            64.83                    0.511   \n",
       "1                              0.5            64.83                    0.511   \n",
       "2                              0.5            64.83                    0.511   \n",
       "3                              0.5            64.83                    0.511   \n",
       "4                              0.5            64.83                    0.511   \n",
       "...                            ...              ...                      ...   \n",
       "167469                         1.7            61.49                    0.571   \n",
       "167470                         1.7            61.49                    0.571   \n",
       "167471                         1.7            61.49                    0.571   \n",
       "167472                         1.7            61.49                    0.571   \n",
       "167473                         1.7            61.49                    0.571   \n",
       "\n",
       "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "0                                        NaN                          NaN   \n",
       "1                                        NaN                          NaN   \n",
       "2                                        NaN                          NaN   \n",
       "3                                        NaN                          NaN   \n",
       "4                                        NaN                          NaN   \n",
       "...                                      ...                          ...   \n",
       "167469                                   NaN                          NaN   \n",
       "167470                                   NaN                          NaN   \n",
       "167471                                   NaN                          NaN   \n",
       "167472                                   NaN                          NaN   \n",
       "167473                                   NaN                          NaN   \n",
       "\n",
       "        excess_mortality  excess_mortality_cumulative_per_million  \n",
       "0                    NaN                                      NaN  \n",
       "1                    NaN                                      NaN  \n",
       "2                    NaN                                      NaN  \n",
       "3                    NaN                                      NaN  \n",
       "4                    NaN                                      NaN  \n",
       "...                  ...                                      ...  \n",
       "167469               NaN                                      NaN  \n",
       "167470               NaN                                      NaN  \n",
       "167471               NaN                                      NaN  \n",
       "167472               NaN                                      NaN  \n",
       "167473               NaN                                      NaN  \n",
       "\n",
       "[167474 rows x 67 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('owid-covid-data-2.csv')\n",
    "df['date'] = pd.to_datetime(df['date'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fce223b9",
   "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>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>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.644330e+05</td>\n",
       "      <td>1.643000e+05</td>\n",
       "      <td>1.623130e+05</td>\n",
       "      <td>1.465460e+05</td>\n",
       "      <td>146589.000000</td>\n",
       "      <td>144483.000000</td>\n",
       "      <td>163670.000000</td>\n",
       "      <td>163537.000000</td>\n",
       "      <td>161556.000000</td>\n",
       "      <td>145796.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>106780.000000</td>\n",
       "      <td>105315.000000</td>\n",
       "      <td>69044.000000</td>\n",
       "      <td>124514.000000</td>\n",
       "      <td>156344.000000</td>\n",
       "      <td>137198.000000</td>\n",
       "      <td>5.747000e+03</td>\n",
       "      <td>5747.000000</td>\n",
       "      <td>5747.000000</td>\n",
       "      <td>5747.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.576723e+06</td>\n",
       "      <td>1.170708e+04</td>\n",
       "      <td>1.169588e+04</td>\n",
       "      <td>5.808979e+04</td>\n",
       "      <td>170.857363</td>\n",
       "      <td>172.439660</td>\n",
       "      <td>30093.568749</td>\n",
       "      <td>169.429856</td>\n",
       "      <td>168.508069</td>\n",
       "      <td>514.016131</td>\n",
       "      <td>...</td>\n",
       "      <td>10.626834</td>\n",
       "      <td>32.778931</td>\n",
       "      <td>50.785148</td>\n",
       "      <td>3.027633</td>\n",
       "      <td>73.575682</td>\n",
       "      <td>0.725566</td>\n",
       "      <td>3.822301e+04</td>\n",
       "      <td>9.478975</td>\n",
       "      <td>15.992426</td>\n",
       "      <td>985.436167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.572703e+07</td>\n",
       "      <td>8.514464e+04</td>\n",
       "      <td>8.324379e+04</td>\n",
       "      <td>3.042782e+05</td>\n",
       "      <td>830.906121</td>\n",
       "      <td>815.876016</td>\n",
       "      <td>53364.726096</td>\n",
       "      <td>694.061649</td>\n",
       "      <td>540.927121</td>\n",
       "      <td>790.089492</td>\n",
       "      <td>...</td>\n",
       "      <td>10.557776</td>\n",
       "      <td>13.524335</td>\n",
       "      <td>31.811196</td>\n",
       "      <td>2.449924</td>\n",
       "      <td>7.491314</td>\n",
       "      <td>0.149961</td>\n",
       "      <td>1.059208e+05</td>\n",
       "      <td>16.469687</td>\n",
       "      <td>30.056700</td>\n",
       "      <td>1434.548662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>1.188000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>53.280000</td>\n",
       "      <td>0.394000</td>\n",
       "      <td>-3.772610e+04</td>\n",
       "      <td>-28.450000</td>\n",
       "      <td>-95.920000</td>\n",
       "      <td>-1826.595723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.034000e+03</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>7.000000e+00</td>\n",
       "      <td>8.000000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.143000</td>\n",
       "      <td>630.882000</td>\n",
       "      <td>0.042000</td>\n",
       "      <td>1.633750</td>\n",
       "      <td>18.772000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>21.600000</td>\n",
       "      <td>19.351000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>0.602000</td>\n",
       "      <td>-6.730000e+01</td>\n",
       "      <td>-0.680000</td>\n",
       "      <td>-0.745000</td>\n",
       "      <td>-28.581156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.648300e+04</td>\n",
       "      <td>7.900000e+01</td>\n",
       "      <td>1.077140e+02</td>\n",
       "      <td>7.880000e+02</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.429000</td>\n",
       "      <td>4813.831000</td>\n",
       "      <td>11.478000</td>\n",
       "      <td>18.970000</td>\n",
       "      <td>130.369000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.300000</td>\n",
       "      <td>31.400000</td>\n",
       "      <td>49.839000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>75.050000</td>\n",
       "      <td>0.743000</td>\n",
       "      <td>3.482200e+03</td>\n",
       "      <td>6.130000</td>\n",
       "      <td>7.230000</td>\n",
       "      <td>478.555739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.032700e+05</td>\n",
       "      <td>1.069000e+03</td>\n",
       "      <td>1.157714e+03</td>\n",
       "      <td>7.353000e+03</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.286000</td>\n",
       "      <td>38554.521000</td>\n",
       "      <td>101.931000</td>\n",
       "      <td>122.029000</td>\n",
       "      <td>721.471000</td>\n",
       "      <td>...</td>\n",
       "      <td>19.300000</td>\n",
       "      <td>41.300000</td>\n",
       "      <td>83.241000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>78.930000</td>\n",
       "      <td>0.845000</td>\n",
       "      <td>2.568215e+04</td>\n",
       "      <td>14.595000</td>\n",
       "      <td>23.015000</td>\n",
       "      <td>1667.347537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.534182e+08</td>\n",
       "      <td>4.115804e+06</td>\n",
       "      <td>3.445357e+06</td>\n",
       "      <td>6.029592e+06</td>\n",
       "      <td>18021.000000</td>\n",
       "      <td>14689.286000</td>\n",
       "      <td>706541.904000</td>\n",
       "      <td>51427.491000</td>\n",
       "      <td>16052.608000</td>\n",
       "      <td>6335.962000</td>\n",
       "      <td>...</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>78.100000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>86.750000</td>\n",
       "      <td>0.957000</td>\n",
       "      <td>1.111864e+06</td>\n",
       "      <td>111.010000</td>\n",
       "      <td>375.000000</td>\n",
       "      <td>9153.060433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_cases     new_cases  new_cases_smoothed  total_deaths  \\\n",
       "count  1.644330e+05  1.643000e+05        1.623130e+05  1.465460e+05   \n",
       "mean   2.576723e+06  1.170708e+04        1.169588e+04  5.808979e+04   \n",
       "std    1.572703e+07  8.514464e+04        8.324379e+04  3.042782e+05   \n",
       "min    1.000000e+00  0.000000e+00        0.000000e+00  1.000000e+00   \n",
       "25%    2.034000e+03  1.000000e+00        7.000000e+00  8.000000e+01   \n",
       "50%    2.648300e+04  7.900000e+01        1.077140e+02  7.880000e+02   \n",
       "75%    3.032700e+05  1.069000e+03        1.157714e+03  7.353000e+03   \n",
       "max    4.534182e+08  4.115804e+06        3.445357e+06  6.029592e+06   \n",
       "\n",
       "          new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "count  146589.000000        144483.000000            163670.000000   \n",
       "mean      170.857363           172.439660             30093.568749   \n",
       "std       830.906121           815.876016             53364.726096   \n",
       "min         0.000000             0.000000                 0.001000   \n",
       "25%         0.000000             0.143000               630.882000   \n",
       "50%         2.000000             2.429000              4813.831000   \n",
       "75%        20.000000            21.286000             38554.521000   \n",
       "max     18021.000000         14689.286000            706541.904000   \n",
       "\n",
       "       new_cases_per_million  new_cases_smoothed_per_million  \\\n",
       "count          163537.000000                   161556.000000   \n",
       "mean              169.429856                      168.508069   \n",
       "std               694.061649                      540.927121   \n",
       "min                 0.000000                        0.000000   \n",
       "25%                 0.042000                        1.633750   \n",
       "50%                11.478000                       18.970000   \n",
       "75%               101.931000                      122.029000   \n",
       "max             51427.491000                    16052.608000   \n",
       "\n",
       "       total_deaths_per_million  ...  female_smokers   male_smokers  \\\n",
       "count             145796.000000  ...   106780.000000  105315.000000   \n",
       "mean                 514.016131  ...       10.626834      32.778931   \n",
       "std                  790.089492  ...       10.557776      13.524335   \n",
       "min                    0.000000  ...        0.100000       7.700000   \n",
       "25%                   18.772000  ...        1.900000      21.600000   \n",
       "50%                  130.369000  ...        6.300000      31.400000   \n",
       "75%                  721.471000  ...       19.300000      41.300000   \n",
       "max                 6335.962000  ...       44.000000      78.100000   \n",
       "\n",
       "       handwashing_facilities  hospital_beds_per_thousand  life_expectancy  \\\n",
       "count            69044.000000               124514.000000    156344.000000   \n",
       "mean                50.785148                    3.027633        73.575682   \n",
       "std                 31.811196                    2.449924         7.491314   \n",
       "min                  1.188000                    0.100000        53.280000   \n",
       "25%                 19.351000                    1.300000        69.500000   \n",
       "50%                 49.839000                    2.400000        75.050000   \n",
       "75%                 83.241000                    4.000000        78.930000   \n",
       "max                100.000000                   13.800000        86.750000   \n",
       "\n",
       "       human_development_index  excess_mortality_cumulative_absolute  \\\n",
       "count            137198.000000                          5.747000e+03   \n",
       "mean                  0.725566                          3.822301e+04   \n",
       "std                   0.149961                          1.059208e+05   \n",
       "min                   0.394000                         -3.772610e+04   \n",
       "25%                   0.602000                         -6.730000e+01   \n",
       "50%                   0.743000                          3.482200e+03   \n",
       "75%                   0.845000                          2.568215e+04   \n",
       "max                   0.957000                          1.111864e+06   \n",
       "\n",
       "       excess_mortality_cumulative  excess_mortality  \\\n",
       "count                  5747.000000       5747.000000   \n",
       "mean                      9.478975         15.992426   \n",
       "std                      16.469687         30.056700   \n",
       "min                     -28.450000        -95.920000   \n",
       "25%                      -0.680000         -0.745000   \n",
       "50%                       6.130000          7.230000   \n",
       "75%                      14.595000         23.015000   \n",
       "max                     111.010000        375.000000   \n",
       "\n",
       "       excess_mortality_cumulative_per_million  \n",
       "count                              5747.000000  \n",
       "mean                                985.436167  \n",
       "std                                1434.548662  \n",
       "min                               -1826.595723  \n",
       "25%                                 -28.581156  \n",
       "50%                                 478.555739  \n",
       "75%                                1667.347537  \n",
       "max                                9153.060433  \n",
       "\n",
       "[8 rows x 62 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e2be09e9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[     0      1      2 ... 167471 167472 167473]\n"
     ]
    }
   ],
   "source": [
    "print(df.index.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "88381fdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['iso_code' 'continent' 'location' 'date' 'total_cases' 'new_cases'\n",
      " 'new_cases_smoothed' 'total_deaths' 'new_deaths' 'new_deaths_smoothed'\n",
      " 'total_cases_per_million' 'new_cases_per_million'\n",
      " 'new_cases_smoothed_per_million' 'total_deaths_per_million'\n",
      " 'new_deaths_per_million' 'new_deaths_smoothed_per_million'\n",
      " 'reproduction_rate' 'icu_patients' 'icu_patients_per_million'\n",
      " 'hosp_patients' 'hosp_patients_per_million' 'weekly_icu_admissions'\n",
      " 'weekly_icu_admissions_per_million' 'weekly_hosp_admissions'\n",
      " 'weekly_hosp_admissions_per_million' 'new_tests' 'total_tests'\n",
      " 'total_tests_per_thousand' 'new_tests_per_thousand' 'new_tests_smoothed'\n",
      " 'new_tests_smoothed_per_thousand' 'positive_rate' 'tests_per_case'\n",
      " 'tests_units' 'total_vaccinations' 'people_vaccinated'\n",
      " 'people_fully_vaccinated' 'total_boosters' 'new_vaccinations'\n",
      " 'new_vaccinations_smoothed' 'total_vaccinations_per_hundred'\n",
      " 'people_vaccinated_per_hundred' 'people_fully_vaccinated_per_hundred'\n",
      " 'total_boosters_per_hundred' 'new_vaccinations_smoothed_per_million'\n",
      " 'new_people_vaccinated_smoothed'\n",
      " 'new_people_vaccinated_smoothed_per_hundred' 'stringency_index'\n",
      " 'population' 'population_density' 'median_age' 'aged_65_older'\n",
      " 'aged_70_older' 'gdp_per_capita' 'extreme_poverty'\n",
      " 'cardiovasc_death_rate' 'diabetes_prevalence' 'female_smokers'\n",
      " 'male_smokers' 'handwashing_facilities' 'hospital_beds_per_thousand'\n",
      " 'life_expectancy' 'human_development_index'\n",
      " 'excess_mortality_cumulative_absolute' 'excess_mortality_cumulative'\n",
      " 'excess_mortality' 'excess_mortality_cumulative_per_million']\n"
     ]
    }
   ],
   "source": [
    "print(df.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f205700",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>location</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Africa</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Anguilla</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>...</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Vatican</th>\n",
       "      <th>Venezuela</th>\n",
       "      <th>Vietnam</th>\n",
       "      <th>Wallis and Futuna</th>\n",
       "      <th>World</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-28</th>\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",
       "      <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>717.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-29</th>\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",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>801.714</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-30</th>\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",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1082.857</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\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",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1283.286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\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",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.571</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1514.429</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>2022-03-06</th>\n",
       "      <td>169.571</td>\n",
       "      <td>6472.000</td>\n",
       "      <td>97.571</td>\n",
       "      <td>58.571</td>\n",
       "      <td>62.143</td>\n",
       "      <td>15.000</td>\n",
       "      <td>3.857</td>\n",
       "      <td>2.571</td>\n",
       "      <td>5632.000</td>\n",
       "      <td>235.429</td>\n",
       "      <td>...</td>\n",
       "      <td>85.714</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>357.429</td>\n",
       "      <td>159099.286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1528275.857</td>\n",
       "      <td>1.143</td>\n",
       "      <td>159.143</td>\n",
       "      <td>486.571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-07</th>\n",
       "      <td>191.571</td>\n",
       "      <td>5920.000</td>\n",
       "      <td>98.143</td>\n",
       "      <td>51.571</td>\n",
       "      <td>88.714</td>\n",
       "      <td>9.286</td>\n",
       "      <td>2.714</td>\n",
       "      <td>2.571</td>\n",
       "      <td>6033.143</td>\n",
       "      <td>224.143</td>\n",
       "      <td>...</td>\n",
       "      <td>80.286</td>\n",
       "      <td>3.429</td>\n",
       "      <td>0.0</td>\n",
       "      <td>241.857</td>\n",
       "      <td>162653.286</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1541542.429</td>\n",
       "      <td>1.429</td>\n",
       "      <td>165.714</td>\n",
       "      <td>475.714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-08</th>\n",
       "      <td>210.571</td>\n",
       "      <td>7143.857</td>\n",
       "      <td>90.714</td>\n",
       "      <td>44.714</td>\n",
       "      <td>77.857</td>\n",
       "      <td>11.857</td>\n",
       "      <td>2.714</td>\n",
       "      <td>1.143</td>\n",
       "      <td>6455.143</td>\n",
       "      <td>197.857</td>\n",
       "      <td>...</td>\n",
       "      <td>70.429</td>\n",
       "      <td>4.857</td>\n",
       "      <td>0.0</td>\n",
       "      <td>263.143</td>\n",
       "      <td>174177.714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1585029.286</td>\n",
       "      <td>1.429</td>\n",
       "      <td>169.857</td>\n",
       "      <td>496.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-09</th>\n",
       "      <td>207.429</td>\n",
       "      <td>6922.143</td>\n",
       "      <td>83.857</td>\n",
       "      <td>38.143</td>\n",
       "      <td>77.857</td>\n",
       "      <td>15.571</td>\n",
       "      <td>2.714</td>\n",
       "      <td>1.714</td>\n",
       "      <td>6163.000</td>\n",
       "      <td>156.286</td>\n",
       "      <td>...</td>\n",
       "      <td>64.143</td>\n",
       "      <td>11.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>226.000</td>\n",
       "      <td>190365.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1617683.429</td>\n",
       "      <td>1.429</td>\n",
       "      <td>205.429</td>\n",
       "      <td>405.714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-03-10</th>\n",
       "      <td>239.857</td>\n",
       "      <td>8484.000</td>\n",
       "      <td>93.429</td>\n",
       "      <td>33.714</td>\n",
       "      <td>64.571</td>\n",
       "      <td>15.571</td>\n",
       "      <td>2.714</td>\n",
       "      <td>1.714</td>\n",
       "      <td>5722.714</td>\n",
       "      <td>136.714</td>\n",
       "      <td>...</td>\n",
       "      <td>53.143</td>\n",
       "      <td>12.000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>253.857</td>\n",
       "      <td>196409.143</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1607302.714</td>\n",
       "      <td>1.571</td>\n",
       "      <td>208.000</td>\n",
       "      <td>577.857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>773 rows × 228 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "location    Afghanistan    Africa  Albania  Algeria  Andorra  Angola  \\\n",
       "date                                                                   \n",
       "2020-01-28          NaN       NaN      NaN      NaN      NaN     NaN   \n",
       "2020-01-29          NaN       NaN      NaN      NaN      NaN     NaN   \n",
       "2020-01-30          NaN       NaN      NaN      NaN      NaN     NaN   \n",
       "2020-01-31          NaN       NaN      NaN      NaN      NaN     NaN   \n",
       "2020-02-01          NaN       NaN      NaN      NaN      NaN     NaN   \n",
       "...                 ...       ...      ...      ...      ...     ...   \n",
       "2022-03-06      169.571  6472.000   97.571   58.571   62.143  15.000   \n",
       "2022-03-07      191.571  5920.000   98.143   51.571   88.714   9.286   \n",
       "2022-03-08      210.571  7143.857   90.714   44.714   77.857  11.857   \n",
       "2022-03-09      207.429  6922.143   83.857   38.143   77.857  15.571   \n",
       "2022-03-10      239.857  8484.000   93.429   33.714   64.571  15.571   \n",
       "\n",
       "location    Anguilla  Antigua and Barbuda  Argentina  Armenia  ...  \\\n",
       "date                                                           ...   \n",
       "2020-01-28       NaN                  NaN        NaN      NaN  ...   \n",
       "2020-01-29       NaN                  NaN        NaN      NaN  ...   \n",
       "2020-01-30       NaN                  NaN        NaN      NaN  ...   \n",
       "2020-01-31       NaN                  NaN        NaN      NaN  ...   \n",
       "2020-02-01       NaN                  NaN        NaN      NaN  ...   \n",
       "...              ...                  ...        ...      ...  ...   \n",
       "2022-03-06     3.857                2.571   5632.000  235.429  ...   \n",
       "2022-03-07     2.714                2.571   6033.143  224.143  ...   \n",
       "2022-03-08     2.714                1.143   6455.143  197.857  ...   \n",
       "2022-03-09     2.714                1.714   6163.000  156.286  ...   \n",
       "2022-03-10     2.714                1.714   5722.714  136.714  ...   \n",
       "\n",
       "location    Uzbekistan  Vanuatu  Vatican  Venezuela     Vietnam  \\\n",
       "date                                                              \n",
       "2020-01-28         NaN      NaN      NaN        NaN         NaN   \n",
       "2020-01-29         NaN      NaN      NaN        NaN       0.286   \n",
       "2020-01-30         NaN      NaN      NaN        NaN       0.000   \n",
       "2020-01-31         NaN      NaN      NaN        NaN       0.000   \n",
       "2020-02-01         NaN      NaN      NaN        NaN       0.571   \n",
       "...                ...      ...      ...        ...         ...   \n",
       "2022-03-06      85.714    0.000      0.0    357.429  159099.286   \n",
       "2022-03-07      80.286    3.429      0.0    241.857  162653.286   \n",
       "2022-03-08      70.429    4.857      0.0    263.143  174177.714   \n",
       "2022-03-09      64.143   11.000      0.0    226.000  190365.000   \n",
       "2022-03-10      53.143   12.000      0.0    253.857  196409.143   \n",
       "\n",
       "location    Wallis and Futuna        World  Yemen   Zambia  Zimbabwe  \n",
       "date                                                                  \n",
       "2020-01-28                NaN      717.571    NaN      NaN       NaN  \n",
       "2020-01-29                NaN      801.714    NaN      NaN       NaN  \n",
       "2020-01-30                NaN     1082.857    NaN      NaN       NaN  \n",
       "2020-01-31                NaN     1283.286    NaN      NaN       NaN  \n",
       "2020-02-01                NaN     1514.429    NaN      NaN       NaN  \n",
       "...                       ...          ...    ...      ...       ...  \n",
       "2022-03-06                0.0  1528275.857  1.143  159.143   486.571  \n",
       "2022-03-07                0.0  1541542.429  1.429  165.714   475.714  \n",
       "2022-03-08                0.0  1585029.286  1.429  169.857   496.000  \n",
       "2022-03-09                0.0  1617683.429  1.429  205.429   405.714  \n",
       "2022-03-10                0.0  1607302.714  1.571  208.000   577.857  \n",
       "\n",
       "[773 rows x 228 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncs = pd.pivot_table(df, values='new_cases_smoothed', index=['date'], columns=['location'])\n",
    "ncs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "95c36967",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ncs.plot(y=['United States','France','India','Japan'], figsize=(20,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eccc3d46",
   "metadata": {},
   "outputs": [],
   "source": [
    "dfg = df.groupby(['date']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "49b20b63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "dfg.plot(y='new_cases_smoothed', label='world_new_cases_smoothed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "37dd8f13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAESCAYAAAD9gqKNAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAABK30lEQVR4nO3dd3zV1f348dc7eyeQASFhhL23DBEKDsSJtg4cVVtHq7autq7aOn7FVa2jVq3W1davlLprnQUREAXC3huSQEhCyN7j/P64n3tJQsa9N/fm3gvv5+ORR+49n3HPvfnkvj9nizEGpZRSKsjXGVBKKeUfNCAopZQCNCAopZSyaEBQSikFaEBQSill0YCglFIKgBBfZ8BdSUlJpl+/fr7OhlJKBZQ1a9YcMcYkt7YtYANCv379yMzM9HU2lFIqoIjIgba2aZWRUkopQAOCUkopiwYEpZRSgAYEpZRSFg0ISimlAA0ISimlLBoQlOqk2vpGGhp1GnkV+DQgKNUJxhimP7mY2xes83VWlOo0DQhKdcKGnBLySmv4ZGMuaw4U+To7SnWKBgSlOmHroVLH41eX7vVhTpTqPA0ISrnJGMOmgyVEhgZz7dS+/G9bHlsOlfg6W0q5zamAICIJIvKuiGwXkW0iMlVEuovIVyKyy/rdrcn+94nIbhHZISJnN0mfICKbrG3Pi4hY6eEi8i8rfaWI9PP4O1XKw95Zlc07q7KY3L87d541mPCQIN78dr+vs6WU25wtITwHfG6MGQqMAbYB9wKLjDGDgEXWc0RkODAPGAHMAV4UkWDrPC8BNwGDrJ85Vvr1QJExZiDwDPBEJ9+XUl63eHs+/RKjePnqCSREhXHh2F58sjGXsuo6X2dNKbd0GBBEJA6YAbwGYIypNcYUA3OBt6zd3gIush7PBRYYY2qMMfuA3cAkEUkF4owx3xljDPD3FsfYz/UucIa99KCUv9p8sIRxfboREWq735k9oidVdQ1sP1zm45wp5R5nSgj9gQLgDRFZJyJ/E5FooIcxJhfA+p1i7Z8GZDc5PsdKS7Met0xvdowxph4oARJbZkREbhKRTBHJLCgocPItKuV5FTX1HC6tZmBKjCOtd7coAA4WVfkqW0p1ijMBIQQYD7xkjBkHVGBVD7WhtTt70056e8c0TzDmFWPMRGPMxOTkVtd3UKpLHCisBKBvYpQjLS0hEoCDxRoQVGByJiDkADnGmJXW83exBYg8qxoI63d+k/17Nzk+HThkpae3kt7sGBEJAeKBo66+GaW6Qm5JFf/dZLt0+3aPdqRHhgWTFBNGlhUslAo0HQYEY8xhIFtEhlhJZwBbgY+Ba620a4GPrMcfA/OsnkMZ2BqPV1nVSmUiMsVqH7imxTH2c10CLLbaGZTyKwVlNUx9bDF/+XoPQLMqI/vzXfnahqACk7NLaP4SeFtEwoC9wE+wBZOFInI9kAVcCmCM2SIiC7EFjXrgVmNMg3Wem4E3gUjgM+sHbA3W/xCR3dhKBvM6+b6U8riy6jpOmf+/ZmmRYcHNng/uEcv7aw9ijEH7RahA41RAMMasBya2sumMNvafD8xvJT0TGNlKejVWQFHKX327+4jj8W/OHkJ6t8jj9klLiKS8pp7K2gaiwwN2yXJ1ktIrViknZR21tQ18eecMBveIbXUfexCoqKn3i4CwZEc+PeMjGNozztdZUQFAp65QyknZR6uIiwhpMxgAxEbYgkBZTX1XZatd172xmjnPLvN1NlSA0ICglJOyiyrp3T2q3X1irFLBuqziLshR+yprjwWlCj8JUMq/aUBQyknZRysdg8/aYg8Iv/73hq7IUrtymgyQ+91Hm32YExUoNCAo5QRjDDlFVfTufnxDclMxEcfaDXzdc9o+eG5gSgzvrz3INzt1dL9qnwYEpZzw1or91NQ30sfJKiOAkirfTnK35VAJIrDgpikArNpX6NP8KP+nAUEpJ3y7x/Zlev7oXu3uFxV2LCDkl9V4NU8dWb3/KAOSY0iKCWdYahybDpZ2fJA6qWlAUMoJWw+VcuGYXnSLDmt3v+TYcK6Z2heA3JLqrshaq3bnl/Pt7kIuHGMLYKPT4tl8sMTn1VjKv2lAUKoDxhhySzpuP7D7xekDAdhXUO7NbLXr/bU5hAYLV07uA8DI9HiOVtTqxHuqXRoQlOpATX0jjaZ5dVB7kmPCiY0IYU9BhZdz1radeWX0T7JVFwGMSosH4Ia3MnlnVZbP8qX8mwYEpTpQVWubiisyNLiDPW1EhME9Ytma67s6+70FFfRPPjYT69CetsF02w+Xcd/7m3h3TU5bh6qTmAYEpTpQWWcLCFFhzgUEgPF9ElhzoKhLqmjKquuaTbmdVVjJ/sKKZtNVRIQG8+crxvHr2YMBeG35Pq/nSwUeDQhKdaDKGvHbcmbT9lw8zrb0xxtd8MV75asrmfHHrx0Nxl/vyKfRwA/HpzXb74IxvfjF6YO4cXoGewrKqa1v9HreVGDRgKBUBypr7SUE5yerG94rjgHJ0V1SQth0sASA4krbuIecokrCQ4JanY0VYEzvBGrrG9mZp+s2qOY0ICjVgWMBwfkSAtgaoz/bfJhF2/K8ka3jHLBmYz1YXEVaQmSb6zGMSU8AYH12cZfkSwUODQhKdcDRqOxiQLDPJbS8yToKntbYeGxcwe78ckoq6/h+71HS2igdAKR3iyQ2PIRdWkJQLWhAUKoD7pYQnr9iHGAbrOYtBeXHRkOv2H2E/1uVxdGKWm6ZObDNY0SEfknR7NO1n1ULGhCU6oB9GumoUNcWvLlgdCrBQeLVqaezrWqiqLBgVuwp5JWlezhtYBJTByS2e1y/pGj2HfHdwDnlnzQgKNWBqjr3qoxEhOiwYCpqGjre2U32aqlzR6VyuLSaoso6rpjUp8PjBqfEkH20irJq307Ap/yLBgSlOuBulRFAbEQoZdXeLyFcNrG3I21Er46XyxyZbhu5/Jt/byS/1HdzLin/4vtFX5Xyc5UujlRuKiY8xLtVRkWVpMSGMymjOzfPHMDBoqoOp+gGmNSvO8NT4/h8y2FqGxp5/bpTvJZHFTg0ICjVgaraeiJCgwgKar0bZ3uiw4Mp92obQpVjvME9c4Y6fVx0eAif3j6dp7/cwQtf72ZPQTkDkmO8lU0VILTKSCmguLKW5buOtDo9dFVdg0uD0pqKjwylsKK2s9lrU15ZNakJzs3C2pqrp/QlLDiIXy30/ZKfyvecCggisl9ENonIehHJtNK6i8hXIrLL+t2tyf73ichuEdkhImc3SZ9gnWe3iDwv1sgZEQkXkX9Z6StFpJ+H36dSbapraOQnb67m6tdW8ummw8dtr6xtcKu6CGwjlnfllTnGMnhaaVU98ZGhbh/fIy6CW2cNZH12MXnalnDSc6WEMMsYM9YYM9F6fi+wyBgzCFhkPUdEhgPzgBHAHOBFEbH/N70E3AQMsn7mWOnXA0XGmIHAM8AT7r8lpZyXVVjJuc8tY11WMXBsGoimqmob3GpQBpjYtzv1jYZV+492JpttKq2uIzaiczW/Z4/oCcCH6w56IksqgHWmymgu8Jb1+C3goibpC4wxNcaYfcBuYJKIpAJxxpjvjK1c/vcWx9jP9S5whrQ17l4pD/rth5vYlV/ODadl0C8xiuyi4wdrVXYiIEwdkEhUWLBXpq+ormugtr6RuAj3SwgAQ3rGMiw1zqsjqlVgcDYgGOBLEVkjIjdZaT2MMbkA1u8UKz0NyG5ybI6VlmY9bpne7BhjTD1QAhw3skZEbhKRTBHJLCgocDLrSrWupr6B7/cWMn1QEvefO4ze3aPIOXp8QKiqbXB5DIJdRGgwA5JjOOCFUcH27qxxnSwhAKQlRFLg4zWgle85GxCmGWPGA+cAt4rIjHb2be3O3rST3t4xzROMecUYM9EYMzE5ObmjPCvVrqzCSuoaDJdMSCcoSEiOCedo5fENwGU19cSEu/+l2zM+gsNeWF/ZPqgsrhNtCHbJseEcKdeAcLJzKiAYYw5Zv/OBD4BJQJ5VDYT1O9/aPQfo3eTwdOCQlZ7eSnqzY0QkBIgHvFPpqpTFvsRlRpJtZbGYiBDKWxlElldaTUpchNuv0zMugsNeaLAttfLa2TYEsAWEwopa6ht0jYSTWYcBQUSiRSTW/hiYDWwGPgautXa7FvjIevwxMM/qOZSBrfF4lVWtVCYiU6z2gWtaHGM/1yXAYtNa/z+lPGjfkRYBITyE8pr6Zl1P//DJVo5W1JLWia6dPeMjKKmqo7rOsz2NSqtsJYTYTrYhgC0gGNN8sjx18nHm1qIH8IHVxhsC/J8x5nMRWQ0sFJHrgSzgUgBjzBYRWQhsBeqBW40x9v+Em4E3gUjgM+sH4DXgHyKyG1vJYJ4H3ptS7dpbUE5ybLjjCzUmIoS6BkNNfSMRocG8tGQPf7NWPOtMFwd7lU5pdR0RbnZfbc2xNoTOB4ThqbbpLjZkF5Ma737wU4Gtw4BgjNkLjGklvRA4o41j5gPzW0nPBEa2kl6NFVCU6ip7j1TQP+nYQvT2doLymnpE4InPtwO2wWXnjUp1+3Viwm1BoKKmAWI7keEWSqvtJYTOVxmNTIsjIjSIFXsKmTPS/feqApuOVFYnjcZGw9KdBdTWN7J0ZwFrs4oY2yfBsd0eECpq6tlsjUd4/opxbHhwNn0To1s7pVNiwm138K21T3SGJxuVw0OCmTk4hf9uzPXq3EvKv2lAUCeNBauzueb1VTz0ny3ctXADveIjuf60DMd2e0Aoq65n9f4iAE7tYF0BZzjOW+PZqaZLq+oJEoh2s0tsS9dPz6CwopaP1h/qeGd1QtKAoE4an2y0fdH938osjpTX8OQlo0mJPdZ7KCbiWJXR2gNFZCRFkxTT+dXOjpU8PNuoXFZdR2xEaJtrJ7tqYt9uJMWEkemlUdXK/+lsp+qkUNfQyLqsYqb2T+RIeQ094iKY2r/53X9sk6qdvUcqGNzDM7N/Hgs0Hi4hVNd7pP3ATkSYnJHIst1HaGw0bs3uqgKbBgR1Uth6qJSqugaumtKH80f3anUf+xd3aXUd2UcrmTXEM4Mfo61G5XIPlxAKK2rpHh3m0XOeNbwH/92Uy5ZDpYyyFtFRJw+tMlInhTUHbG0CE/t2b3Mfe9XOviMV1NQ3OrXQjDPs3ULt4wY85XBJFT07MWCuNSPTbEFgV36ZR8+rAoMGBHVS2F1QTkJUKD3j2/4CtVe/bD1UCkBvDwWEiNBg4iNDPTJ9xWebcnnS6g6bW1JNajvvxx19E6MICRJ25pV79LzKO4wx1NZ7bnS5BgR1Uth/pIJ+HXQdDQ8JIiRI2JprCwieKiEApMZHkFtS1enz3Pz2Wl5csofK2nrKquvp6eFBZKHBQYzpncC/VmdRp9NY+LXGRsPZzy5lzrNLqan3THWkBgR1wttbUM6KPYX0T24/IIgIMREh5JZUIwJp3Tz3ZWsLCJ0rITQdH2CfPTU5tvO9oFq6aFwaRZV1Otmdn9uVX87OvHL2HqlgyAOfU9zKxIyu0oCgTlhPfr6dfvf+l9Of/gaAi8amdXDEsXaEPt2jCA/x3DQTqQmRna4yarqi2c48Wx1/oocblQGSra62heXeW/pTdd66rKJmzx/5z9ZOn1MDgjohlVbX8eKSPY7nD5w3jBmDO+41ZJ/EbkKfbh3s6Zpe8REUVtR2aoK7vNJjd+w7DtsCQjcvBITEGNs5j3pxLWjVeWuziugWFcoXd9hWI2ht6nZXabdTdUJaby2J+dJV4zl7RE+n+9Q/Mnckz3y1kysn9/Fofux1/Xml1W5Ng7F0ZwH//P6A4/lqa/BY9ygvBAQryBRWaJWRP1uXVcy4Pt0Y0jOW8X0SqG/o/ATRGhDUCSnLWvlsbJ8ElwZYDekZy8s/nuDx/PSyegMdKnYvIFzz+ioAggQaDY6pNbpFd34eo5YSrSojXUHNf5VU1bErv5wLx9jG1HSLCmP74TLySqvp0YmuyFplpE5I2UcrCQsOokesZ7tluiu9m63HUnYrS3S64tQBSdx/7lDH886s5NaWuIgQesSFs8Xqfqv8z79WZwEwoa+tarNbdBgHi6uY/OiiTp1XSwjqhLTtcBl9E6P8ZvqFtG6RhAYL+worXD62qEld/l2zBzO+Tzem9E+kR1yEx+YxakpEGNe7GxtzSjx+btV5RRW1PP3lTmYNSWaKNf1K0ylMGhoNwW5e91pCUCec8pp6vt9TyA+caETuKsFBQp/uUewrcD0gHLLGL7x89XjGW43do9MTOlU10JG0bpHNejUp/7E2q4ia+kZunjnQccPzk1MzHIscHSp2f7yLBgR1wnnkP1uobWhk9oievs5KMxlJ0ex3o4Rg/2L2ZgBoqXt0GJW1DVTVenb+JdV5G3JKCBLbokZ2fRKjeOJHowGY9dQSt3uzaUBQJxRjjGM+f3v9qr/olxjNviMV/N/KLP76zZ6OD7DYu5t2ZUBIitGeRv5qY04xg1JiiQprXuM/Kj2eQSkx1DcatzsEaEBQJ5TS6npq6hv57bnD3K5H9Zb+yTHU1Ddy/webeOyz7U4fd9gaOe2NUclt6R5tey0di+B/NuWUtDkT7a/PHgLYeiG5QwOCOqE4qlc8POmbJ8wd24s5Taqxipz8st18sISMxGhCg7vu39U+OE27nvqXkso6Citq21yrwzGzbrUGBKUc00N4ehZQT4gOD+HlH0/grZ9OAmBbbsfdOjcfLGHR9nxO6df2tN3eMCDZ9oWjs576lwNHbW1Qfbq3PpYlLtJa06PKvXWxNSCoE8reAtsXmCdnKvW0sb0TCBL4cmseWYXtj0t4b20OALefOagrsuYQHxlKWkKkY+ZX5R/skxr2TWz9+u6yEoKIBIvIOhH5xHreXUS+EpFd1u9uTfa9T0R2i8gOETm7SfoEEdlkbXterE7UIhIuIv+y0leKSD+33o066W0+VEpSTDgpXVjf7qr4yFBOG5TMmyv2M+OPX2NM61MONDYavt6ez7SBifRK8Ow0184Y3iuOrYd8PxZhd345pz+9hAc+3OTrrPicfQR+mwEhsnOLMblSQrgd2Nbk+b3AImPMIGCR9RwRGQ7MA0YAc4AXRcQ+beRLwE3AIOtnjpV+PVBkjBkIPAM84da7USe9zQdLGJkW55UBW57053njHI8L2phmesuhUvYXVnLxuPSuylYzw1Pj2Hukgspa96ofPOWPX2xnb0EF//w+i0xrDqeT1YHCCpJjw4/rYWQXGx5CeEgQmfuLWt3eEacCgoikA+cBf2uSPBd4y3r8FnBRk/QFxpgaY8w+YDcwSURSgThjzHfGdkv09xbH2M/1LnCG+Pt/tPI71XUN7MovZ2Qv/18LOD4qlP+7cTIA23NbX67ysNVA3lYDorcN7xWHMcdmVvWFhauz+WJLHjfN6E9wkLBkR4HP8uIPDhRW0red6tCgIOGyib1ZtD2PhkbXJ7tztoTwLHA30HQJpR7GmFwA63eKlZ4GZDfZL8dKS7Met0xvdowxph4oARKdfRNKAWw/XEZDo2k2YMefjU5PICw4iEXb8lrdXmiVHOyTzXU1+8jXbS0CVltVXJ721or9PPDhZk4bmMTdZw9hZFo8i7bnd9nr+6Pd+eUdLvQ0olccdQ3GrRHLHQYEETkfyDfGrHHynK3d2Zt20ts7pmVebhKRTBHJLCg4ue8U1PE2H7TVd48IgBIC2Cammz2iB3///gBf78g/brt9xTJvLILjjPRukXSLCmXZrmP/a19uOUzGfZ+y/4jrI66dVVlbz13/Ws+DH2+hd/dI/nLleEKCg7h0QjrbckvZ7sMSiy8VlNVQWFHLkJ7t3/D0S7IFjH1u/I2cKSFMAy4Ukf3AAuB0EfknkGdVA2H9tl/ROUDvJsenA4es9PRW0psdIyIhQDxwXGWhMeYVY8xEY8zE5GT/madG+YcN2cUkRIWS7sGlL73tj5eMYVBKDL//aPNx6+IWlNUQGxFCRKjnVm5zhYgwZ2RPPtt8mK+22kox/92UC8CaA+7VUbdn88ESXlm6h/9sOMT76w4CtvUp4qNsDaXj+iQAcM5zy7jqb997/PX9nX0NjJG92g8IGVZAcGealA4DgjHmPmNMujGmH7bG4sXGmKuBj4Frrd2uBT6yHn8MzLN6DmVgazxeZVUrlYnIFKt94JoWx9jPdYn1GidvuVC5zBjDij2FTMlI9PsG5aYiw4L53fnDyT5axZAHPncMBKutb+SLLXkMTPFN+4HdvecMA2CTVfqyN2ZWdmLlt7bc9/4mHv10O/e8d6w30cR+x6YfabqOxLe7C9mdf3KVFL7enk+3qNAOp2RJiQ0nKizYayWEtjwOnCUiu4CzrOcYY7YAC4GtwOfArcYY+9VzM7aG6d3AHuAzK/01IFFEdgN3YfVYUspZ67KLOVhcxelDUzre2c9MH5TMkB6xAHy3txCwfQEfLq3m+tMyfJk1x3gE+zoO0WG20kpljWd7HjU0Gnbn28aQTO2fyM0zB7Dud2c1W9favvZDSmw4kaHBnPmnpTy/aJdH8+HPso5WMiA5hpAORqyLCH0To92q1nNpPQRjzBJgifW4EDijjf3mA/NbSc8ERraSXg1c6kpelGpq+a4jAMwZ5V8znDrr3ZunMuqhLx3/xEt32urtJ2f4vm9Fn+5Rjv7v4aG2L6NyDwYEW+nuCFV1DfzpsjH8cHzb3Ww3PTSb8JBgfvTSCjYdLOFPX+3ktjO6dtCer+SWVDOmd4JT+/buFtnlJQSl/MaegnLSEiIdIzUDTWxEKL3iI1i8PZ91WUW8umwvPxic3KUT2rWle3QYxdYC7jV1to6G+aWem+Po4w2H+PFrtiVCZw5pv4QXGxFKWEgQjVaNcqSP2le6mjGGwyXVjqVYO9IrIZJDxVUu98jSgKBOCDvzyhng4/r2zrpqSl/WZxdz8YsrqK1v5N5zhnZ8UBeICgt2rItgbzvI6uRSoE19tukwAC9dNZ7uTvaoumJSHwC6RQXmDYCrCitqqW1opKeTASEtIZKK2gZKq10ryWlAUAEvv7SabbmlnOJn6x+46uopfR2P//PL0xiW6h/jKaLCgh2BwB4YDrjRg6U19Q2NfLvnCJdP7M05o1KdPu6qyX04f3Tq8X3TT1DHJm10rgddaoItcOSWuDYWQQOCCngr9tgaYk8fFngNyk3FR4by5Z0z2PLw2X4TDAAiw0KorGlg35EKxzz7h0qqqa1v7ODI9pVU1vHnxbspq67nB0Nc60YuIoztnUBuSTWbToK1n+2DzHolOF9l1PQ4Z7nUqKyUP1qXVURUWDBDOxiwEwgGW72N/ElUWDC1DY3MempJs/SjFbVOV2G0tDu/nAtfWE5lbQNpCZGcNbyHy+f44fh0/vDfbby98gATDndj+qBkt/Pj73KtEoKz769XvD0guLYutgYEFfDWZRczJj3B71ZIO1FEhTVvuA0LCaK2vpGCshq3v4CX7MinsraB+MhQHrpwhFuL/9jbGxaszmbBattsOR/ccirj+gR21WFrNh8sIT4ylKRo5zoZJMeGExIkLpcQtMpIBbTquga2Hip1jGJVntd0Zs33bp7KP6+3Tcp3pI1ZWp2xLbeM5NhwNjw4263SQVv++MUOj53LXzQ0GpbuKuDUAYkEOXnTExwk9E2M4ts9hTS6MMmdBgQV0DYfLKG+0TDWyf7ZynVNSwiDesQ6VqPrzPKau/LLHIPxOuOLO2bQu3skD14wnIvHpTkWkDmRfLenkLzSGi4c08ul48b16caG7GI+sKYBcYYGBBXQ1mUVAzBWSwheU2X1MJo1JJm4iFB6xkcQERrEjjz3po74eMMhNuaUMKCDWTudMaRnLMvuPp2fTMsgJS6cg8VV/OP7A50+rz/ZkFMMwGmDklw67nZrwN5OF6b40ICgAtr67GLSu0WSEntiNib6A/s02LfMGghAaHAQo9MT3JrgrriyltveWQdA/2TPjhuxL5v6uw83e/S8vrYtt5Te3SOJdXHQZe/uUfSKj3CpJKcBQQW0dVlFJ2Qjoj8Z0zuBPY+eyyn9ujvSBveIcWs2TfskeUCH8/q76rKJvZk+KInQYKGootaj5/al7KIq+iW691klx2lAUCeJwyXVHCqpZpy2H3hdyx5cvRIiKa6so8LFOY1WN1na0T5Ns6eEBgdx/7nDqGswjumzTwRHymrcnsIkJTbcpWlGNCCogLU+uxjQ9gNfSLMGPh10sVvjNzuPLbbTy8lRt64YlhrHmN4JLFyd7ZOV1dYcOMr/tra+Ap47jDEUlNeQ7Oaqed2iQh2DCZ2hAUEFrB2HyxCBYSfAgLRAM8Cq/3d2lHBDo2FhZjYbsou5YEwv3rlxitNdKF11yYR0duSVOaYS70o/euk7bvh7psdWlCutrqe2vtHtEkJMeKhLM9NqQFABa7c1w2lk2Mkx46U/GZ4aR6/4CMcKah15ffk+7n53IwAzBiUxdYD3pvX+4bg0esSF89qyfV57jY5c/dpKj5znU+vz7RHnXqeJ2IgQymvqaXByLIIGBBWwdueX+3xFsZNVUJBw3uhUFm/P560V+zvcP6fINj7gikl9mD3Cu2tWRIeHMGNQMmuzirq82ijWWsSnpNL5apq21DU0ct/7ttXjZgxyb8ng2AhbfipqnSslaEBQAamh0bC3oJyBHu66qJx344z+hAUH8eaK/e1+8e7KK2N9djG9u0fy2A9HER/p/SmrTx2YSFFlHcushZO6wnd7CimrqSdIoKymvtNBwT7D6RWT+jjWlXaVfZW5MienwdaAoAJSTlElNfWNDOqhAcFXUmIjmH/xSPYdqWh1TMLhkmo2HyzhrGeWsiGnpEsXLzp3VCo94yK47/1NnRpR7YqH/7MFgOG9bG1a2w+Xdup89gntzhnpfonKPnahXAOCOpEdLLL1bultDUZSvnHuqFSiwoJ5d01Os3RjDD96aQXn/3m5Iy0spOu+bsJDgnl47ggOFlfxwuKuWXfZPuX0Hy8ZA8Dlr3zPjsPujeaGY2sZ2M/rjhiryqi8xrnSigYEFZDyymx3T+42tinPiA4P4dxRqfxnw6Fmd+JbDpUe1yXVnTV+O+PsET05Z2RP3vruAH/9Zo/XXy+vtJpZQ5IZ2vPYHE1f78h3+3x7CyoQgfRu7gcEe/VcUYUGBHUCy7MG22hA8L1rpvalpr6RuS8s54sth7n8r9/x6KfbAFj92zPZ8OBswPl6bE+anGEbXf3YZ9u9XnWUW1JNakIkIsKXd84AcKz97I6deWX0S4wmohPrRqdY3VXznXzvGhC6SH1DI3mlri1WodqWX1pDdFiwo9FM+c7o9AQevGA4h0qq+dk/1rBy31FW7ClkUkZ3kmPDiY8M5e45Q3jnxildnrdLJ/Z2zKq6+aBnVlbLL60+bmqM6roGjlbUkmrdoAzuEUtUWDCF5a5PodHYaHhnVRafbT7c6Rlh7eMXnP3u0YDQBYwxXP3aSiY/usjR/U51Tl5ZtZYO/Mh5o5tPzXznmYP5+08nOZ7fMnMgkzK6tzzM66LDQ3j/llMJDwli8Xb3q2+amvToIs565huW7Srgyy2HgSZrHjep7+8eHcZ/N+Zy17/Wu9T99dPNuY7uptMGdm68RmhwEEkxYZ4rIYhIhIisEpENIrJFRB620ruLyFcissv63a3JMfeJyG4R2SEiZzdJnyAim6xtz4uIWOnhIvIvK32liPRz9Y37q6+359P//k/5fu9RAE574mtW+mAE5Ykmv7SalDj3Rm8qz+seHeaYziLzgTO5/cxBnarq8KTo8BBmDUlhyc7OBwR7u8iR8lp+/Noqbl+wnsLyGkePoNQmK8glRodxuLSa99cdpMCFxYTsg9GuO7UfF45N63Se07tFsXJvIfUNHa+B7UwJoQY43RgzBhgLzBGRKcC9wCJjzCBgkfUcERkOzANGAHOAF0XEfmW8BNwEDLJ+5ljp1wNFxpiBwDPAE07ky++9uyaHn7y5GvvNgX2hkU0eKrqezPJKa7SE4Gf+/fOpfPLL00hyc94db8pIjuZwSbXTI3Zb2n+kgkteWsG0xxc3S6+ub+CtFfvZXVAOHJvjCeD8JqWmLCcX7mlsNKzYU8glE9J56MIRHhmz8ZNp/djbRtfgljoMCMam3Hoaav0YYC7wlpX+FnCR9XgusMAYU2OM2QfsBiaJSCoQZ4z5ztjKT39vcYz9XO8CZ9hLD4Hs1//e0Oz5hgdnExka7LibUO4xxpBXqlVG/qZXQiQj0+J9nY1WpSVEUtdgOmxY3phTzJWvfs9jn22j2loY6PXl+5j51BIym3yhfnrbdN65cQrjeifw3tqDPPnZdvonR9M38Vg36J9M6+d4vO9IBSt2H+GBDze1O0Ps1txSiivrOl1V1NTpQ1MQgWteX0VZdfu9jZxqQxCRYBFZD+QDXxljVgI9jDG5ANbvFGv3NCC7yeE5Vlqa9bhlerNjjDH1QAngvclOuoB9lGIPq1pjVFo8ocFB9EqIcHnha9VcaVU9NfWNjh4USnXEfud+1jPftDvZ28P/2cqKPYX89Zu9vLJ0LwCfbDwEwIMXDOez26ez7O5ZDO8Vx9QBiYzoFc/B4iqq6hp4+tIxNL2PDQkOYvf8cwgLCWJnXhkPfLiZf36fxYfr256ae6M1WeDEvp5rb4mNCGXumF7U1DeyMDOn3X2dCgjGmAZjzFggHdvd/sh2dm/tzt60k97eMc1PLHKTiGSKSGZBQUErh/iP3QW2ASm/P38Eg3vE8NvzhgEwKCWWtVlFbhddFeRbYxBStISgnDS5f3dG9IqjrLqeVftab8NraDQcKKxkfJ8EThuYxL/XZLNybyFrs4r5+Q8G8JNpGQxLjWs2GPKicWkkRodxz5yhrS7UFBIcRM+4CF5dto+91jiMLYfaHsG8v7CCsJCgZlVPnvDkJWMIko57WrnUy8gYUwwswVb3n2dVA2H9trfY5AC9mxyWDhyy0tNbSW92jIiEAPHA0VZe/xVjzERjzMTkZPcme+oqh4ptX1oDU2L48s4fMKW/rcBzzqie5JXWsC23c8PaT2aOMQhaQlBOigoL4d2fn2rNvXTguAbW/NJqBtz/KUfKa7hsYm8uGpdG9tEqLn/lewDmndK7tdMyoW831vzuLG6c0b/N1/7ZD45tCxLY3s7//r4jFfTtHuXxqcHDQoI4Z1QqX3WwVoMzvYySRSTBehwJnAlsBz4GrrV2uxb4yHr8MTDP6jmUga3xeJVVrVQmIlOs9oFrWhxjP9clwGLji9UtPMjeDa1nfPO7WPs88tr91H32PtXahqBcERkWTG1DI0t3FvByi5HL97xnm5q7W1Qo541OZfaIHo5t9587lH6dWN3tqsl92f/4eex//DwumZBOTlHbVcbbD5d6bQbfa6f263BtBGdKCKnA1yKyEViNrQ3hE+Bx4CwR2QWcZT3HGLMFWAhsBT4HbjXGNFjnuhn4G7aG5j3AZ1b6a0CiiOwG7sLqsRTIDpVUERUWTFxE84FT9mHo7V0Uqn278ssJDhINCMplf7jIVtv91Jc7+XiDrYIi+2glS3YWcNsZg1j3+9nERoQ2m4jvsomtlw7c0SshkvyyGmrqG47bVlRRS/bRKkanJ3js9ZoamtrxILcOh3kaYzYC41pJLwTOaOOY+cD8VtIzgePaH4wx1cClHeY2gHy3p5BhqXG07CwVHxlKTHhIpya9Otkt2pbH1P6JujCOctnVU/rywIebAfjndwc4cKSCp7/aCcBZw3o02/f35w8n62glCVFhHnt9e9tAbnH1caWOnXm27wT7bKmeFhcRSo+4cA60s4+OVPaCytp6th8uY+bg49s5RIRzR/Xkw/UHqao9/i5Bta+2vpG9RyoYp+soKze9fcNkhvaMZdX+o45gkBAVyrAWd9A/PS2Dhy4c4dHXtn/Zf7vn+HUaDhy1VSP3S/TeDL7PXDa23e0aELygtMpWT5fYxgCd04emUNdg2JWvpQRXZR2tpKHRkNGJOl11cps2MImXrp7geP7OjVNYdvcsQoK9/3U4PDWOoT1j+es3eyltMSZgb0EFIUHi8R5GTZ06MKnd7RoQvMD+h46NaL1Gbqi1KPz2XA0IrrIvOjJAV0pTnZCRFM3/7voBmx6azdQBiY6FZLxNRHj4whFkHa1k4epjw7Vq6ht4d002Y3sndElgaosGBC8o6yAg9OkeRWRoMNs6uaLSyWjpzgLiIkIY4aV6VnXyGJgS02WBoKnJ/ROJjQhp1rFkxZ5CjpTXcsusAV2en6Y0IHhBqTXve1wb85AEBQlDesbqWAQXGWP4ZmcB0wcl+/QuSqnO6hkXwdqsItZm2abD+H5vIWHBQZw6oP0qHW/T/yovsC8E0rLLaVNj0uPZkF2iDcsu2HekgrzSGqYP8u0/jVKddbSilo05JfzwxRX8bdlevtlRwLBecT6fIVYDghccqzJquzg6Z2QqVXUNvNQFS/udKHbm2eZY9Fa3PKW6yiUTjk3a8If/bmP74TLGpPt+YkANCF5gX02pvalrp/TvzqSM7o4FNlTH7GM3tEFZBbp7zxnKzj+cw/mjUx1pfbp7r7upszQgeMHB4iqSYsLaLf6JCJMzurMrv9wxza4/+3+fbGXeK9+5tPKTJxljeG9tDuP6JBCty2aqACcihIUE8fRlY3jv5lOZNjCRuR5YDKezNCB4QU5RlVN9iYf0jKWh0bC/sKILcuW+wvIa/vHdAb7fe9Qx3L+r7TtSQdbRSi6d4LlpBJTytfCQYCb07cbbN0xxrH/sSxoQvOBgURXp3Tou/tmLiAecXE2pq1TU1DtKAkt25DPjya+pbWikW1Qoty9Yz0V/+bbLSzWr9tkmv53cv+vX5VXqZKEBwcMaGw05xVWkdeu4hGAPCMt2+cfaDpsPlpB9tJIRD37Bk1/s4FcLN3DdG6sJCwniuXlj+fT26Vw4phfrs4sZ+rvPedVaQKQrLN99hKSYMPrrCGWlvEYDgocdqaihtr7RMatpexKiwhjbO4EFq7LJL/Xtspqfbz7M+X9ezsUvfgvAS0v28N5a2+pKvzt/OHPHppEaH8lz88Y6jpn/6TbHyGFv+mLLYT7ZmMvMISnHTRaolPIcDQgeZh996Ox8JM9ePpb6RsOC1dkd7+xF/92UC8CRclsPqVtmDuCXpw/krrMGN2vsEhHe+ukkHr14FN2iQpnz7LIOF93oDGMML369m4ykaMfUxUop79DuGh6W18bCOG3plxTNaQOT+HDdQW47Y5A3s9auPfnljsfRYcHcPWdom/v+wJrFNSI0iLsWbuDGv2eS+cCZJLUxmV97fv3vDaTGR/Cr2UNa3f7a8n1syCnhJ9P6+XzQjlInOi0heFihNQYh2YUvx1lDU9h7pILx/+8rNuYUeylnbbvm9VVszS3lJ9P6MX1QEg/Pde5O/Ifj03nlxxMIErjzX+vdWif63TU5/Hnxbg600dPq/bW2Bcl/Oi3D5XMrpVyjAcHDCq0ql27Rzi+q8cNxtiqZoxW1PP3lTq/kqy3bcktZutPWqH3G0B784/rJzUZRdmT2iJ48eMEIlu06wrDff847q7IA2JhTTH5Z++0iTcc0/OCPSygoq2m2vaa+gR15Zfxi1sBmC5srpbxDA4KHHa2oIT4ylFAXJl/rFh3Gl3fOAGyrJnXl4K81B2yTa/35inGc5uYcQddM7cufLhuDMYb73t/Eve9t5MIXvuXiv6xo97iW67ve9s66Zs/3FlTQ0GgY3LPjpf+UUp2nAcHDjlTUkhjj+pJ7g3vEctrAJHJLqrt08NeOw2XEhoc0G0LvKhHhh+PTWXTXTPp0j3I0kB8sPja9b2tBrrjSNufTHy8Zza/OGsx3ewubHWPv1jpMA4JSXUIDgocdLa8l0YXqoqYemWtbru/Fr/dQ19DoyWy1acfhMgb3jPVId84+iVEsvXsWKU1GXN7490zOe34Zp8xfREFZDSWVdfw7M5vTn1rCxS/aShAJUWGM6Z0AwKHiKhobDeuyinh/na39YGCKzl2kVFfQXkYeVlhRQ/8k977A+ifH8PgPR3Hv+5vYnlvGKC/PfmiMYfvhUs4f08uj5/309um8sHg3S3cW8M2OAmqt4HbK/P859hEBe6Ghe3QoUWG2S/HVpXu5tEk31jd/coqOPVCqi2hA8LDC8lpO6edeCQFgXJ9uAOwrrPBqQNh6qJSnv9xBaXU9Qz1cJZMUE+5YnHxTTgkXvLAcgKE9Y9luzVi67ZE5rD1QxK78csakJ1BkVR992SQY/ObsIcwckuLRvCml2qYBwYMaGg1Fle5XGQH0TbT1pvnPhkNc6OE7d7u6hkYu++t3jkbdC0Z753UAhqbags0Np2XwwPnDbdVGVXVEhAZz6sAkx6Lf3Zt8Zkt/M4uk2DBHqUEp1TU6bEMQkd4i8rWIbBORLSJyu5XeXUS+EpFd1u9uTY65T0R2i8gOETm7SfoEEdlkbXterLoAEQkXkX9Z6StFpJ8X3qvXFVfW0mgg0Y0BWnYRocFcMiGdr7bmccvbaygsr+n4oBbWZhXxt2V72xwX8PryfZTX1HPmsBT+e9tpLnWRdVVocBDbHpnD/ecOAyA5NrzVNoHgIOHcUT15+MIR9EmM0mCglA84819XD/zKGLNWRGKBNSLyFXAdsMgY87iI3AvcC9wjIsOBecAIoBfwPxEZbIxpAF4CbgK+Bz4F5gCfAdcDRcaYgSIyD3gCuNyTb7Qr2Ks9EqI6t3D3oxeP4mhFLZ9uOkxlbQNv/mSSS8ff8+5GduWX0y8xmjOH92i27Uh5DS99s4dZQ5J59ZqJXVI/Hxnm3AjjF6+a4OWcKKXa02EJwRiTa4xZaz0uA7YBacBc4C1rt7eAi6zHc4EFxpgaY8w+YDcwSURSgThjzHfG1gfx7y2OsZ/rXeAM6YpvKg8rqbINSkuI6twdd1hIEC9eNR6AJTsKjuuv354vthxmlzUNxY68suO2/2rhBqpqG7h7zlBtrFVKNeNSt1OrKmccsBLoYYzJBVvQAOytf2lA05nacqy0NOtxy/Rmxxhj6oESINGVvPkDe7/6hHaWznRWRGgwb98wGbAN2HJmWohF2/L4jTU3ENjWMsgvrXYcW1FTz7JdBVx/WgbDUnVdYqVUc04HBBGJAd4D7jDGtDfncWu3naad9PaOaZmHm0QkU0QyCwr8Yw2BpkqqPFNlZDdtYBKnDUxi8fZ8zn52KSVWwGnLq8v2Ulpdz9s3TObcUT1Zvb+ISY8u4t01tvj83025NBqY0Ldbu+dRSp2cnAoIIhKKLRi8bYx530rOs6qBsH7nW+k5QNN1DtOBQ1Z6eivpzY4RkRAgHjjaMh/GmFeMMRONMROTk5OdyXqXspcQ4j1QQrD7w0UjmTYwkd355Xy3t/C47ZsPljgC0aHiai4c04v+yTE8P28cd5xpmz31nvc2ceWr33P3uxsZlhrHlP4BV/hSSnUBZ3oZCfAasM0Y86cmmz4GrrUeXwt81CR9ntVzKAMYBKyyqpXKRGSKdc5rWhxjP9clwGLjq9XcO6G4qg4RiI3wXEDolxTN69edQlhwEJn7j8XITzYe4rZ31nH+n5dz6mOLWLIjn6yjlaQm2KqLQoKDuOPMwVw20RaDV+wp5Gcz+vPRrdN0kXqlVKuc+WaYBvwY2CQi6620+4HHgYUicj2QBVwKYIzZIiILga3YeijdavUwArgZeBOIxNa76DMr/TXgHyKyG1vJYF7n3pZvlFbVERseQnCQZxtrw0OCmdy/O39bvo/k2HBmDknhjgXrqbe3DdQ2cN0bqwGIadFd8w8XjeLyU3qzLquYn07LIMjDeVNKnTgkAG/EAZg4caLJzMz0dTaauWPBOtZmFbP07lkeP/fi7Xn89M1j7zchKpSnLx2DMbaeRf9ek8OY9Hje/Mkkr44rUEoFNhFZY4yZ2No2rTvwoJKqOo+2HzR1+tAerP7tmfzopRVkHa3kp9MyOGOYbYzBaYOSuHBsL04bmKRdSZVSbtOA4EHFVXUe62HUmuTYcN6/5VQWb8vnh+OPrXMcERrM9EH+18iulAosOv21B5VU1hHnpRKCXVJMOJed0psQFxbgUUopZ+i3igeVVNV5ZFCaUkr5ggYED2lsNF6vMlJKKW/SgOAhhRW1NDQaUmIjfJ0VpZRyiwYED8krrQagR5z7U18rpZQvaUDwkIIy27oFyVpCUEoFKA0IHpJfZishNF1gXimlAokGBA/JL7WXEDQgKKUCkwYED8kvqyE+MpSIUOdWB1NKKX+jAcFD8suqtbpIKRXQNCB4SH5ZDSnaw0gpFcA0IHhIfmkNPbSHkVIqgGlA8ABjDAVlNSRrCUEpFcA0IHhAcWUdtQ2NOkpZKRXQNCB4QL41KE0blZVSgUwDggfooDSl1IlAA4IH2AelpcRplZFSKnBpQPAArTJSSp0INCB4QH5ZNdFhwUSH64qkSqnApQHBA/LLauih1UVKqQCnAcEDCkprdFI7pVTA6zAgiMjrIpIvIpubpHUXka9EZJf1u1uTbfeJyG4R2SEiZzdJnyAim6xtz4uIWOnhIvIvK32liPTz8Hv0uryyam1QVkoFPGdKCG8Cc1qk3QssMsYMAhZZzxGR4cA8YIR1zIsiYp/+8yXgJmCQ9WM/5/VAkTFmIPAM8IS7b8YXjDHkl9Zog7JSKuB1GBCMMUuBoy2S5wJvWY/fAi5qkr7AGFNjjNkH7AYmiUgqEGeM+c4YY4C/tzjGfq53gTPspYdAUF5TT1VdgwYEpVTAc7cNoYcxJhfA+p1ipacB2U32y7HS0qzHLdObHWOMqQdKgMTWXlREbhKRTBHJLCgocDPrnuXocqrzGCmlApynG5Vbu7M37aS3d8zxica8YoyZaIyZmJyc7GYWPcsxKE3nMVJKBTh3A0KeVQ2E9TvfSs8BejfZLx04ZKWnt5Le7BgRCQHiOb6Kym/Zp63ooSUEpVSAczcgfAxcaz2+FvioSfo8q+dQBrbG41VWtVKZiEyx2geuaXGM/VyXAIutdoaAUFBmX0tZSwhKqcDW4dBaEXkHmAkkiUgO8CDwOLBQRK4HsoBLAYwxW0RkIbAVqAduNcY0WKe6GVuPpUjgM+sH4DXgHyKyG1vJYJ5H3lkXyS+rITwkiLgIHaWslApsHX6LGWOuaGPTGW3sPx+Y30p6JjCylfRqrIASiPJKq0mJCyeAOkYppVSrdKRyJ9nGIGh1kVIq8GlA6KT8smodg6CUOiFoQOiExkbDoeJqUuMjfZ0VpZTqNA0InZBXVk1VXQMZydG+zopSSnWaBoRO2FtQAcCAJA0ISqnApwGhE/YesQWE/skxPs6JUkp1ngaETthbUE5UWLCOUlZKnRA0IHTC3oIKMpKidQyCUuqEoAGhE/YdsQUEpZQ6EWhAcFNNfQM5RZXafqCUOmFoQHBTVmEljQYGaJdTpdQJQgOCm/ZYXU61ykgpdaLQKTrdtPdIOaAB4URQV1dHTk4O1dXVvs6KUh4TERFBeno6oaGhTh+jAcFNOw6X0TMugtgI5z9s5Z9ycnKIjY2lX79+2mNMnRCMMRQWFpKTk0NGRobTx2mVkZvWZxczpne8r7OhPKC6uprExEQNBuqEISIkJia6XOrVgOCGoxW1HCisZGzvbr7OivIQDQbqROPONa0BwQ0bsosBGNs7waf5UEopT9KA4IYlO/IJDwlidLpWGSmlThwaEFxU39DIJxtzOXNYD6LDtU1e+Y+ZM2eSmZl5XPqbb77JL37xCx/kqG2dydP69ev59NNPHc8feughnnrqKU9lzeseffRRx+P9+/czcuRxKwu7JCbGc4NjNSC46H/b8imsqOXCsb18nRWlHBoaGnydhS7TMiAEmqYBwd/oLa4LjDG8uGQ3fROjOGNoiq+zo7zg4f9sYeuhUo+ec3ivOB68YESb25988kkiIiK47bbbuPPOO9mwYQOLFy9m0aJFvPHGG5x33nk8+uijGGM477zzeOKJJwDbneFdd93FF198wdNPP93snG+88QaPPfYYqampDB48mPDwtmfkve6664iLiyMzM5PDhw/z5JNPcskllwDwxz/+kYULF1JTU8PFF1/Mww8/3GF+//nPf7b6Om3lqaCggJ///OdkZWUB8OyzzzJt2jRWrVrFHXfcQVVVFZGRkbzxxhtkZGTw+9//nqqqKpYvX859990HwNatW5k5cyZZWVnccccd3HbbbVRUVHDZZZeRk5NDQ0MDv/vd77j88stbzdu9997Lxx9/TEhICLNnz+app57iuuuuIzIyku3bt3PgwAHeeOMN3nrrLb777jsmT57Mm2++CcA777zT6t+ntfR7772Xqqoqxo4dy4gRI5g/fz4NDQ3ceOONrFixgrS0ND766CMiIyPZs2cPt956KwUFBURFRfHqq68ydOhQ9u3bx5VXXkl9fT1z5sxp8+/qDi0huGDpriNszCnhlpkDCAnWj055xowZM1i2bBkAmZmZlJeXU1dXx/Llyxk0aBD33HMPixcvZv369axevZoPP/wQgIqKCkaOHMnKlSs57bTTHOfLzc3lwQcf5Ntvv+Wrr75i69atHeYhNzeX5cuX88knn3DvvfcC8OWXX7Jr1y5WrVrF+vXrWbNmDUuXLm03v9OnT2/z/G3l6fbbb+fOO+9k9erVvPfee9xwww0ADB06lKVLl7Ju3ToeeeQR7r//fsLCwnjkkUe4/PLLWb9+veMLfvv27XzxxResWrWKhx9+mLq6Oj7//HN69erFhg0b2Lx5c5tfnkePHuWDDz5gy5YtbNy4kQceeMCxraioiMWLF/PMM89wwQUXcOedd7JlyxY2bdrE+vXrOXToUKt/n7bSH3/8cSIjI1m/fj1vv/02ALt27eLWW29ly5YtJCQk8N577wFw00038ec//5k1a9bw1FNPccsttzg+r5tvvpnVq1fTs2fPDv+2rtASgpPqGxp55qud9IqP4OJx6b7OjvKS9u7kvWXChAmsWbOGsrIywsPDGT9+PJmZmSxbtowLLriAmTNnkpycDMBVV13F0qVLueiiiwgODuZHP/rRcedbuXJls2Muv/xydu7c2W4eLrroIoKCghg+fDh5eXmALSB8+eWXjBs3DoDy8nJ27drFNddc02Z+n3/++VbP316e/ve//zULEKWlpZSVlVFSUsK1117Lrl27EBHq6urazP95551HeHg44eHhpKSkkJeXx6hRo/j1r3/NPffcw/nnn99msIqLiyMiIoIbbriB8847j/PPP9+x7YILLkBEGDVqFD169GDUqFEAjBgxgv3793PgwIFW/z4i0ubfraWMjAzGjh0L2K6F/fv3U15ezooVK7j00ksd+9XU1ADw7bffOoLGj3/8Y+655542PxdX+U1AEJE5wHNAMPA3Y8zjXfG6xhhq6hvJKaqkb2I0oa3c+Tc2Gn730WbWZxfzzOVjCAvR0oHynNDQUPr168cbb7zBqaeeyujRo/n666/Zs2cPffr0Yc2aNa0eFxERQXBwcKvbXO2D3rRKyRjj+H3ffffxs5/97Lj928rvsGHD2nyNtvLU2NjId999R2RkZLP0X/7yl8yaNYsPPviA/fv3M3PmTKfyHxwcTH19PYMHD2bNmjV8+umn3HfffcyePZvf//73xx0bEhLCqlWrWLRoEQsWLOCFF15g8eLFzc4bFBTU7DWCgoKor68nJKT1r1D7Z+iMlnmvqqqisbGRhIQE1q9f3+ox3ho34xffbCISDPwFOAcYDlwhIsM9/TrGGA4VV7FkRz6vLt3LrW+v5ZT5ixj6u885809LGf//vuL8Py/jroXr+dOXO/hw3UEWrMpi3ivf886qbG6ZOUBLB8orZsyYwVNPPcWMGTOYPn06L7/8MmPHjmXKlCl88803HDlyhIaGBt555x1+8IMftHuuyZMns2TJEgoLC6mrq+Pf//63W3k6++yzef311ykvt83bdfDgQfLz89vNb1tfVO3lafbs2bzwwguO5/YvwZKSEtLS0gAc9fUAsbGxlJWVdZj/Q4cOERUVxdVXX82vf/1r1q5d2+p+5eXllJSUcO655/Lss8+2+SXc1vtq7e/TVjrYbgDaK+2ArdSSkZHh+JyMMWzYsAGAadOmsWDBAgBHtZOn+EsJYRKw2xizF0BEFgBzgTYrP4+U1/D68n0dnthgW+pyW24pu/LKKaupd2wLDhJmDUlmXJ9u1NQ1sPlQKSVVdSzenk9x5bE/WGJ0GP9v7giuntLX7TeoVHumT5/O/PnzmTp1KtHR0URERDB9+nRSU1N57LHHmDVrFsYYzj33XObOndvuuVJTU3nooYeYOnUqqampjB8/3q1eSLNnz2bbtm1MnToVsDVi//Of/yQlJaXN/LqTp+eff55bb72V0aNHU19fz4wZM3j55Ze5++67ufbaa/nTn/7E6aef7jjXrFmzePzxxxk7dqyjUbk1mzZt4je/+Q1BQUGEhoby0ksvtbpfWVkZc+fOpbq6GmMMzzzzjNOfUXt/n7bSb7rpJkaPHs348eOZP39+m+d+++23ufnmm/nDH/5AXV0d8+bNY8yYMTz33HNceeWVPPfcc61WGXaGuFK08RYRuQSYY4y5wXr+Y2CyMeYXLfa7CbgJIKznwAmp1z7r1PljwkMY0SuOIT1jGdQjlsEpMQzuEUtCVGirdzTGGI5W1JJbUk1MeAh9E6N0aoMT2LZt29qt6lAqULV2bYvIGmPMxNb295cSQmvftsdFKmPMK8ArAOPGTzBLfj/bqZPHRIQQHOT8F7qIkBgTTmJM2131lFLqROMvASEH6N3keTpwqL0DgoOE+CidelopZ82fP/+49oRLL72U3/72tx59ncmTJzt6xNj94x//cPTQ8aWLL76YffuaVzU/8cQTnH322T7KkX/xlyqjEGAncAZwEFgNXGmM2dLWMRMnTjStDdNXylXbtm1j6NChWi2oTijGGLZv3+5SlZFf9DIyxtQDvwC+ALYBC9sLBkp5UkREBIWFhS51FVTKn9kXyImIiHDpOH+pMsIY8ykQuBOUqICVnp5OTk4OBQUFvs6KUh5jX0LTFX4TEJTyldDQUJeWGVTqROUXVUZKKaV8TwOCUkopQAOCUkopi190O3WHiJQBO1w8LB4o8eL+AEnAERePced1XD3GX/MFXZM3zZdeYyfr37Ll/kOMMbGt7mmMCcgfINONY17x5v5dlS8334tf5utk/1v6a77cfC9+ma+T/W/Zcv/2XvNkqzL6j5f3d5c7r9MVefPXfLnzOpqvrjmmK17DXz+zgM9XIFcZZZo2Rtv5kubLdf6aN82Xa/w1X+C/efNFvtp7zUAuIbzi6wy0QfPlOn/Nm+bLNf6aL/DfvPkiX22+ZsCWEJRSSnlWIJcQlFJKeZAGBKWUUkAABAQRKfd1HlojIheLiBGRob7OS2s6+txEZImIdFljloiki8hHIrJLRPaIyHMiEtbO/neISFQX5U2vMTfoNeZS3vzyGmvJ7wOCH7sCWA7Mc+UgEQn2Tnb8l9gWGngf+NAYMwgYDMQAbS8oC3cAXfLP6sf0GnOSXmOeERABQURiRGSRiKwVkU0iMtdK7yci20TkVRHZIiJfikhkV+QHmAZcj/XPKiIzRWSpiHwgIltF5GURCbK2lYvIIyKyEpjq7fw1yedMEfmkyfMXROS6rnr9Jk4Hqo0xbwAYYxqAO4Gfiki0iDxl/V03isgvReQ2oBfwtYh83RUZ1GvM7XzqNeYkf7vGWhMQAQGoBi42xowHZgFPW3cEAIOAvxhjRgDFwI+6ID8XAZ8bY3YCR0VkvJU+CfgVMAoYAPzQSo8GNhtjJhtjlndB/vzNCGBN0wRjTCmQBdwAZADjjDGjgbeNMc9jW0J1ljFmVhflUa+xwKbXmAcESkAQ4FER2Qj8D0gDeljb9hlj1luP1wD9uiA/VwALrMcLrOcAq4wxe627k3eA06z0BuC9LsiXvxKgtf7NAswAXja2VfMwxhztyoy1yIteY4FLrzEPCJQFcq4CkoEJxpg6EdkP2NeGa7qadwPg1aKWiCRiK56OFBEDBGO7ED/l+AvS/rza+gfuavU0D/qurafnOVtocccjInFAb2Avrf8jdzW9xtyj15jz/OYaa0uglBDigXzrQ5wF9PVhXi4B/m6M6WuM6WeM6Q3sw3anNklEMqx63cuxNQj60gFguIiEi0g8cIaP8rEIiBKRa8DR6Pk08CbwJfBzEQmxtnW3jikDWp+R0Tv0GnOPXmPO86drrFV+HRCsP2AN8DYwUUQysUXZ7T7M1hXABy3S3gOuBL4DHgc2Y/sHbrlfl7B/bsaYbGAhsBHbZ7jOF/kxtuHwFwOXisguYCe2+tT7gb9hq+fdKCIbsH2OYBte/5m3G/z0GnOPXmPO89NrrFV+PXWFiIwBXjXGTPJ1XjoiIjOBXxtjzvdxVgLqc/O1QPqs9BoLTIH0WfltCUFEfo6t0ewBX+clkOjn5jz9rNyjn5vzAu2z8usSglJKqa7jNyUEEektIl9bAzS2iMjtVnp3EflKbMPRvxKRblb6WSKyxhrgsUZETm9yrglW+m4Reb5JX191EvPwNTZfRLIlQKYkUF3DU9eYiESJyH9FZLt1nse75A24unybt36AVGC89TgWW6PQcOBJ4F4r/V7gCevxOKCX9XgkcLDJuVZhG60pwGfAOb5+f/rj+x8PX2NTrPOV+/p96Y///HjqGsM2pcYs63EYsKwrvsf8tspIRD4CXrB+ZhpjckUkFVhijBnSYl/BtlB1L6A78LUxZqi17Qrr+J916RtQfs/da8wYU9MkvdwYE9OV+VaBwxPXmLXtOWwj0V/1Zn79psqoKRHphy1yrgR6GGNyAazfKa0c8iNgnfUhpgE5TbblWGlKOXTyGlOqQ566xkQkAbgA21gLr/K7kcpim9TrPeAOY0xpR9X/IjICeAKYbU9qZTf/LAYpn/DANaZUuzx1jVljGN4BnjfG7PVSdh38qoQgIqHYPsS3jTHvW8l5VhEL63d+k/3TsQ3MucYYs8dKzgHSm5w2HdskVkp56hpTqk0evsZeAXYZY571esbxo4Bg1Z+9BmwzxvypyaaPgWutx9cCH1n7JwD/Be4zxnxr39kqjpWJyBTrnNfYj1EnN09dY0q1xZPXmIj8Adt0F3d4N9dNXtNfGpVF5DRsLembgEYr+X5s9W8LgT7Yhp9faow5KiIPAPcBu5qcZrYxJl9sqzS9iW2CqM+AXxp/eaPKZzx8jT2JbQqEXthKoH8zxjzUJW9E+S1PXWPYehZlY5vewt6m8IIx5m9ezb9+TyqllAI/qjJSSinlWxoQlFJKARoQlFJKWTQgKKWUAjQgKKWUsmhAUMpNIvKQiPy6ne0XicjwrsyTUp2hAUEp77kI20yXSgUEHYeglAtE5LfYRr9nAwXAGqAEuAnbYKLdwI+BscAn1rYSbBOXAfwFSAYqgRuNMX63rq46eWlAUMpJIjIB2wj4ydgmhlwLvAy8YYwptPb5A5BnjPmziLwJfGKMedfatgj4uTFml4hMBh4zxpx+/Csp5Rt+N9upUn5sOvCBMaYSQEQ+ttJHWoEgAYgBvmh5oDX75anAv5vMfBnu7Qwr5QoNCEq5prUi9ZvARcaYDSJyHTCzlX2CgGJjzFiv5UypTtJGZaWctxS4WEQiRSQW26IlYFsqMdea9viqJvuXWdswxpQC+0TkUrDNiikiY7ou60p1TNsQlHJBk0blA9jW3tgKVAB3W2mbgFhjzHUiMg14FdtslZdgm/3yJWzr7oYCC4wxj3T5m1CqDRoQlFJKAVplpJRSyqIBQSmlFKABQSmllEUDglJKKUADglJKKYsGBKWUUoAGBKWUUhYNCEoppQD4/wY8cwDTmJ94AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dfg.plot(y='new_deaths_smoothed', label='world_new_deaths_smoothed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "acff704c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='date'>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "dfg['death_rate'] = dfg['new_deaths_smoothed']/dfg['new_cases_smoothed']\n",
    "dfg.plot(y='death_rate')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ec573af8",
   "metadata": {},
   "source": [
    "#### What I find\n",
    "1. Among four countries I choose, the United States has a highest daily new cases peak, while Japan has a lowest one.\n",
    "2. The peak of world new cases appears in Jan 2022.\n",
    "3. The peak of world new deaths appears in Jan 2021.\n",
    "4. The world death rate has been decreasing for a long time."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}