Covid_Anayze.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 한국과 미국의 코로나 수 분석\n",
    "데이터 분석의 목적 : 여행을 목적으로, 코로나 상황을 파악하려고 함"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>population</th>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>41128772.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>41128772.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>41128772.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>41128772.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>41128772.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso_code continent     location        date  total_cases  new_cases  \\\n",
       "0      AFG      Asia  Afghanistan  2020-01-03          NaN        0.0   \n",
       "1      AFG      Asia  Afghanistan  2020-01-04          NaN        0.0   \n",
       "2      AFG      Asia  Afghanistan  2020-01-05          NaN        0.0   \n",
       "3      AFG      Asia  Afghanistan  2020-01-06          NaN        0.0   \n",
       "4      AFG      Asia  Afghanistan  2020-01-07          NaN        0.0   \n",
       "\n",
       "   new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  \\\n",
       "0                 NaN           NaN         0.0                  NaN  ...   \n",
       "1                 NaN           NaN         0.0                  NaN  ...   \n",
       "2                 NaN           NaN         0.0                  NaN  ...   \n",
       "3                 NaN           NaN         0.0                  NaN  ...   \n",
       "4                 NaN           NaN         0.0                  NaN  ...   \n",
       "\n",
       "   male_smokers  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "0           NaN                  37.746                         0.5   \n",
       "1           NaN                  37.746                         0.5   \n",
       "2           NaN                  37.746                         0.5   \n",
       "3           NaN                  37.746                         0.5   \n",
       "4           NaN                  37.746                         0.5   \n",
       "\n",
       "   life_expectancy  human_development_index  population  \\\n",
       "0            64.83                    0.511  41128772.0   \n",
       "1            64.83                    0.511  41128772.0   \n",
       "2            64.83                    0.511  41128772.0   \n",
       "3            64.83                    0.511  41128772.0   \n",
       "4            64.83                    0.511  41128772.0   \n",
       "\n",
       "   excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "0                                   NaN                          NaN   \n",
       "1                                   NaN                          NaN   \n",
       "2                                   NaN                          NaN   \n",
       "3                                   NaN                          NaN   \n",
       "4                                   NaN                          NaN   \n",
       "\n",
       "   excess_mortality  excess_mortality_cumulative_per_million  \n",
       "0               NaN                                      NaN  \n",
       "1               NaN                                      NaN  \n",
       "2               NaN                                      NaN  \n",
       "3               NaN                                      NaN  \n",
       "4               NaN                                      NaN  \n",
       "\n",
       "[5 rows x 67 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('./data/covid.csv')\n",
    "# 코로나 데이터 불러오기\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 346487 entries, 0 to 346486\n",
      "Data columns (total 67 columns):\n",
      " #   Column                                      Non-Null Count   Dtype  \n",
      "---  ------                                      --------------   -----  \n",
      " 0   iso_code                                    346487 non-null  object \n",
      " 1   continent                                   330035 non-null  object \n",
      " 2   location                                    346487 non-null  object \n",
      " 3   date                                        346487 non-null  object \n",
      " 4   total_cases                                 308672 non-null  float64\n",
      " 5   new_cases                                   337028 non-null  float64\n",
      " 6   new_cases_smoothed                          335769 non-null  float64\n",
      " 7   total_deaths                                287169 non-null  float64\n",
      " 8   new_deaths                                  337072 non-null  float64\n",
      " 9   new_deaths_smoothed                         335842 non-null  float64\n",
      " 10  total_cases_per_million                     308672 non-null  float64\n",
      " 11  new_cases_per_million                       337028 non-null  float64\n",
      " 12  new_cases_smoothed_per_million              335769 non-null  float64\n",
      " 13  total_deaths_per_million                    287169 non-null  float64\n",
      " 14  new_deaths_per_million                      337072 non-null  float64\n",
      " 15  new_deaths_smoothed_per_million             335842 non-null  float64\n",
      " 16  reproduction_rate                           184817 non-null  float64\n",
      " 17  icu_patients                                37464 non-null   float64\n",
      " 18  icu_patients_per_million                    37464 non-null   float64\n",
      " 19  hosp_patients                               38695 non-null   float64\n",
      " 20  hosp_patients_per_million                   38695 non-null   float64\n",
      " 21  weekly_icu_admissions                       10139 non-null   float64\n",
      " 22  weekly_icu_admissions_per_million           10139 non-null   float64\n",
      " 23  weekly_hosp_admissions                      23103 non-null   float64\n",
      " 24  weekly_hosp_admissions_per_million          23103 non-null   float64\n",
      " 25  total_tests                                 79387 non-null   float64\n",
      " 26  new_tests                                   75403 non-null   float64\n",
      " 27  total_tests_per_thousand                    79387 non-null   float64\n",
      " 28  new_tests_per_thousand                      75403 non-null   float64\n",
      " 29  new_tests_smoothed                          103965 non-null  float64\n",
      " 30  new_tests_smoothed_per_thousand             103965 non-null  float64\n",
      " 31  positive_rate                               95927 non-null   float64\n",
      " 32  tests_per_case                              94348 non-null   float64\n",
      " 33  tests_units                                 106788 non-null  object \n",
      " 34  total_vaccinations                          78894 non-null   float64\n",
      " 35  people_vaccinated                           75519 non-null   float64\n",
      " 36  people_fully_vaccinated                     72165 non-null   float64\n",
      " 37  total_boosters                              47179 non-null   float64\n",
      " 38  new_vaccinations                            64970 non-null   float64\n",
      " 39  new_vaccinations_smoothed                   179191 non-null  float64\n",
      " 40  total_vaccinations_per_hundred              78894 non-null   float64\n",
      " 41  people_vaccinated_per_hundred               75519 non-null   float64\n",
      " 42  people_fully_vaccinated_per_hundred         72165 non-null   float64\n",
      " 43  total_boosters_per_hundred                  47179 non-null   float64\n",
      " 44  new_vaccinations_smoothed_per_million       179191 non-null  float64\n",
      " 45  new_people_vaccinated_smoothed              178841 non-null  float64\n",
      " 46  new_people_vaccinated_smoothed_per_hundred  178841 non-null  float64\n",
      " 47  stringency_index                            197651 non-null  float64\n",
      " 48  population_density                          294131 non-null  float64\n",
      " 49  median_age                                  273544 non-null  float64\n",
      " 50  aged_65_older                               263969 non-null  float64\n",
      " 51  aged_70_older                               270802 non-null  float64\n",
      " 52  gdp_per_capita                              268082 non-null  float64\n",
      " 53  extreme_poverty                             172750 non-null  float64\n",
      " 54  cardiovasc_death_rate                       268697 non-null  float64\n",
      " 55  diabetes_prevalence                         282368 non-null  float64\n",
      " 56  female_smokers                              201541 non-null  float64\n",
      " 57  male_smokers                                198799 non-null  float64\n",
      " 58  handwashing_facilities                      131618 non-null  float64\n",
      " 59  hospital_beds_per_thousand                  237187 non-null  float64\n",
      " 60  life_expectancy                             318787 non-null  float64\n",
      " 61  human_development_index                     260432 non-null  float64\n",
      " 62  population                                  346487 non-null  float64\n",
      " 63  excess_mortality_cumulative_absolute        11944 non-null   float64\n",
      " 64  excess_mortality_cumulative                 11944 non-null   float64\n",
      " 65  excess_mortality                            11944 non-null   float64\n",
      " 66  excess_mortality_cumulative_per_million     11944 non-null   float64\n",
      "dtypes: float64(62), object(5)\n",
      "memory usage: 177.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(346487, 67)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape\n",
    "# 346487행, 67열"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>population</th>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>41128772.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>41128772.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>41128772.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</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>41128772.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso_code continent     location        date  total_cases  new_cases  \\\n",
       "0      AFG      Asia  Afghanistan  2020-01-03          NaN        0.0   \n",
       "1      AFG      Asia  Afghanistan  2020-01-04          NaN        0.0   \n",
       "2      AFG      Asia  Afghanistan  2020-01-05          NaN        0.0   \n",
       "3      AFG      Asia  Afghanistan  2020-01-06          NaN        0.0   \n",
       "4      AFG      Asia  Afghanistan  2020-01-07          NaN        0.0   \n",
       "\n",
       "   new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  ...  \\\n",
       "0                 NaN           NaN         0.0                  NaN  ...   \n",
       "1                 NaN           NaN         0.0                  NaN  ...   \n",
       "2                 NaN           NaN         0.0                  NaN  ...   \n",
       "3                 NaN           NaN         0.0                  NaN  ...   \n",
       "4                 NaN           NaN         0.0                  NaN  ...   \n",
       "\n",
       "   male_smokers  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "0           NaN                  37.746                         0.5   \n",
       "1           NaN                  37.746                         0.5   \n",
       "2           NaN                  37.746                         0.5   \n",
       "3           NaN                  37.746                         0.5   \n",
       "4           NaN                  37.746                         0.5   \n",
       "\n",
       "   life_expectancy  human_development_index  population  \\\n",
       "0            64.83                    0.511  41128772.0   \n",
       "1            64.83                    0.511  41128772.0   \n",
       "2            64.83                    0.511  41128772.0   \n",
       "3            64.83                    0.511  41128772.0   \n",
       "4            64.83                    0.511  41128772.0   \n",
       "\n",
       "   excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "0                                   NaN                          NaN   \n",
       "1                                   NaN                          NaN   \n",
       "2                                   NaN                          NaN   \n",
       "3                                   NaN                          NaN   \n",
       "4                                   NaN                          NaN   \n",
       "\n",
       "   excess_mortality  excess_mortality_cumulative_per_million  \n",
       "0               NaN                                      NaN  \n",
       "1               NaN                                      NaN  \n",
       "2               NaN                                      NaN  \n",
       "3               NaN                                      NaN  \n",
       "4               NaN                                      NaN  \n",
       "\n",
       "[5 rows x 67 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>population</th>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>346482</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2023-09-30</td>\n",
       "      <td>265753.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.714</td>\n",
       "      <td>5718.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>16320539.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346483</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2023-10-01</td>\n",
       "      <td>265753.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.714</td>\n",
       "      <td>5718.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>16320539.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346484</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2023-10-02</td>\n",
       "      <td>265753.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.714</td>\n",
       "      <td>5718.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>16320539.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346485</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2023-10-03</td>\n",
       "      <td>265753.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5718.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>16320539.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346486</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2023-10-04</td>\n",
       "      <td>265753.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5718.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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>16320539.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent  location        date  total_cases  new_cases  \\\n",
       "346482      ZWE    Africa  Zimbabwe  2023-09-30     265753.0        0.0   \n",
       "346483      ZWE    Africa  Zimbabwe  2023-10-01     265753.0        0.0   \n",
       "346484      ZWE    Africa  Zimbabwe  2023-10-02     265753.0        0.0   \n",
       "346485      ZWE    Africa  Zimbabwe  2023-10-03     265753.0        0.0   \n",
       "346486      ZWE    Africa  Zimbabwe  2023-10-04     265753.0        0.0   \n",
       "\n",
       "        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "346482               0.714        5718.0         0.0                  0.0   \n",
       "346483               0.714        5718.0         0.0                  0.0   \n",
       "346484               0.714        5718.0         0.0                  0.0   \n",
       "346485               0.000        5718.0         0.0                  0.0   \n",
       "346486               0.000        5718.0         0.0                  0.0   \n",
       "\n",
       "        ...  male_smokers  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "346482  ...          30.7                  36.791                         1.7   \n",
       "346483  ...          30.7                  36.791                         1.7   \n",
       "346484  ...          30.7                  36.791                         1.7   \n",
       "346485  ...          30.7                  36.791                         1.7   \n",
       "346486  ...          30.7                  36.791                         1.7   \n",
       "\n",
       "        life_expectancy  human_development_index  population  \\\n",
       "346482            61.49                    0.571  16320539.0   \n",
       "346483            61.49                    0.571  16320539.0   \n",
       "346484            61.49                    0.571  16320539.0   \n",
       "346485            61.49                    0.571  16320539.0   \n",
       "346486            61.49                    0.571  16320539.0   \n",
       "\n",
       "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "346482                                   NaN                          NaN   \n",
       "346483                                   NaN                          NaN   \n",
       "346484                                   NaN                          NaN   \n",
       "346485                                   NaN                          NaN   \n",
       "346486                                   NaN                          NaN   \n",
       "\n",
       "        excess_mortality  excess_mortality_cumulative_per_million  \n",
       "346482               NaN                                      NaN  \n",
       "346483               NaN                                      NaN  \n",
       "346484               NaN                                      NaN  \n",
       "346485               NaN                                      NaN  \n",
       "346486               NaN                                      NaN  \n",
       "\n",
       "[5 rows x 67 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41128772.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41128772.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41128772.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41128772.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41128772.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  iso_code     location        date  total_cases  population\n",
       "0      AFG  Afghanistan  2020-01-03          NaN  41128772.0\n",
       "1      AFG  Afghanistan  2020-01-04          NaN  41128772.0\n",
       "2      AFG  Afghanistan  2020-01-05          NaN  41128772.0\n",
       "3      AFG  Afghanistan  2020-01-06          NaN  41128772.0\n",
       "4      AFG  Afghanistan  2020-01-07          NaN  41128772.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[['iso_code', 'location', 'date', 'total_cases', 'population']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    Afghanistan\n",
       "1    Afghanistan\n",
       "2    Afghanistan\n",
       "3    Afghanistan\n",
       "4    Afghanistan\n",
       "Name: location, dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Africa', 'Albania', 'Algeria', 'American Samoa',\n",
       "       'Andorra', 'Angola', 'Anguilla', 'Antigua and Barbuda',\n",
       "       'Argentina', 'Armenia', 'Aruba', 'Asia', 'Australia', 'Austria',\n",
       "       'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh', 'Barbados',\n",
       "       'Belarus', 'Belgium', 'Belize', 'Benin', 'Bermuda', 'Bhutan',\n",
       "       'Bolivia', 'Bonaire Sint Eustatius and Saba',\n",
       "       'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
       "       'British Virgin Islands', 'Brunei', 'Bulgaria', 'Burkina Faso',\n",
       "       'Burundi', 'Cambodia', 'Cameroon', 'Canada', 'Cape Verde',\n",
       "       'Cayman Islands', 'Central African Republic', 'Chad', 'Chile',\n",
       "       'China', 'Colombia', 'Comoros', 'Congo', 'Cook Islands',\n",
       "       'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Curacao',\n",
       "       'Cyprus', 'Czechia', 'Democratic Republic of Congo', 'Denmark',\n",
       "       'Djibouti', 'Dominica', 'Dominican Republic', 'Ecuador', 'Egypt',\n",
       "       'El Salvador', 'England', 'Equatorial Guinea', 'Eritrea',\n",
       "       'Estonia', 'Eswatini', 'Ethiopia', 'Europe', 'European Union',\n",
       "       'Faeroe Islands', 'Falkland Islands', 'Fiji', 'Finland', 'France',\n",
       "       'French Guiana', 'French Polynesia', 'Gabon', 'Gambia', 'Georgia',\n",
       "       'Germany', 'Ghana', 'Gibraltar', 'Greece', 'Greenland', 'Grenada',\n",
       "       'Guadeloupe', 'Guam', 'Guatemala', 'Guernsey', 'Guinea',\n",
       "       'Guinea-Bissau', 'Guyana', 'Haiti', 'High income', 'Honduras',\n",
       "       'Hong Kong', 'Hungary', 'Iceland', 'India', 'Indonesia', 'Iran',\n",
       "       'Iraq', 'Ireland', 'Isle of Man', 'Israel', 'Italy', 'Jamaica',\n",
       "       'Japan', 'Jersey', 'Jordan', 'Kazakhstan', 'Kenya', 'Kiribati',\n",
       "       'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia', 'Lebanon',\n",
       "       'Lesotho', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',\n",
       "       'Low income', 'Lower middle income', 'Luxembourg', 'Macao',\n",
       "       'Madagascar', 'Malawi', 'Malaysia', 'Maldives', 'Mali', 'Malta',\n",
       "       'Marshall Islands', 'Martinique', 'Mauritania', 'Mauritius',\n",
       "       'Mayotte', 'Mexico', 'Micronesia (country)', 'Moldova', 'Monaco',\n",
       "       'Mongolia', 'Montenegro', 'Montserrat', 'Morocco', 'Mozambique',\n",
       "       'Myanmar', 'Namibia', 'Nauru', 'Nepal', 'Netherlands',\n",
       "       'New Caledonia', 'New Zealand', 'Nicaragua', 'Niger', 'Nigeria',\n",
       "       'Niue', 'North America', 'North Korea', 'North Macedonia',\n",
       "       'Northern Cyprus', 'Northern Ireland', 'Northern Mariana Islands',\n",
       "       'Norway', 'Oceania', 'Oman', 'Pakistan', 'Palau', 'Palestine',\n",
       "       'Panama', 'Papua New Guinea', 'Paraguay', 'Peru', 'Philippines',\n",
       "       'Pitcairn', 'Poland', 'Portugal', 'Puerto Rico', 'Qatar',\n",
       "       'Reunion', 'Romania', 'Russia', 'Rwanda', 'Saint Barthelemy',\n",
       "       'Saint Helena', 'Saint Kitts and Nevis', 'Saint Lucia',\n",
       "       'Saint Martin (French part)', 'Saint Pierre and Miquelon',\n",
       "       'Saint Vincent and the Grenadines', 'Samoa', 'San Marino',\n",
       "       'Sao Tome and Principe', 'Saudi Arabia', 'Scotland', 'Senegal',\n",
       "       'Serbia', 'Seychelles', 'Sierra Leone', 'Singapore',\n",
       "       'Sint Maarten (Dutch part)', 'Slovakia', 'Slovenia',\n",
       "       'Solomon Islands', 'Somalia', 'South Africa', 'South America',\n",
       "       'South Korea', 'South Sudan', 'Spain', 'Sri Lanka', 'Sudan',\n",
       "       'Suriname', 'Sweden', 'Switzerland', 'Syria', 'Taiwan',\n",
       "       'Tajikistan', 'Tanzania', 'Thailand', 'Timor', 'Togo', 'Tokelau',\n",
       "       'Tonga', 'Trinidad and Tobago', 'Tunisia', 'Turkey',\n",
       "       'Turkmenistan', 'Turks and Caicos Islands', 'Tuvalu', 'Uganda',\n",
       "       'Ukraine', 'United Arab Emirates', 'United Kingdom',\n",
       "       'United States', 'United States Virgin Islands',\n",
       "       'Upper middle income', 'Uruguay', 'Uzbekistan', 'Vanuatu',\n",
       "       'Vatican', 'Venezuela', 'Vietnam', 'Wales', 'Wallis and Futuna',\n",
       "       'Western Sahara', 'World', 'Yemen', 'Zambia', 'Zimbabwe'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['location'].unique()\n",
    "# 중복되지 않는 데이터 출력!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 1371 entries, 2020-01-03 to 2023-10-04\n",
      "Data columns (total 4 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   iso_code     1371 non-null   object \n",
      " 1   location     1371 non-null   object \n",
      " 2   total_cases  1355 non-null   float64\n",
      " 3   population   1371 non-null   float64\n",
      "dtypes: float64(2), object(2)\n",
      "memory usage: 53.6+ KB\n"
     ]
    },
    {
     "data": {
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       "    .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>location</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-09-30</th>\n",
       "      <td>KOR</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>34571873.0</td>\n",
       "      <td>51815808.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-01</th>\n",
       "      <td>KOR</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>34571873.0</td>\n",
       "      <td>51815808.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-02</th>\n",
       "      <td>KOR</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>34571873.0</td>\n",
       "      <td>51815808.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-03</th>\n",
       "      <td>KOR</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>34571873.0</td>\n",
       "      <td>51815808.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-04</th>\n",
       "      <td>KOR</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>34571873.0</td>\n",
       "      <td>51815808.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           iso_code     location  total_cases  population\n",
       "date                                                     \n",
       "2023-09-30      KOR  South Korea   34571873.0  51815808.0\n",
       "2023-10-01      KOR  South Korea   34571873.0  51815808.0\n",
       "2023-10-02      KOR  South Korea   34571873.0  51815808.0\n",
       "2023-10-03      KOR  South Korea   34571873.0  51815808.0\n",
       "2023-10-04      KOR  South Korea   34571873.0  51815808.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# kor_df = df.loc[df.location == 'South Korea']\n",
    "kor_df = df[df['location'] == 'South Korea']\n",
    "# 두 방식 둘 다 동작함! ( df.location과 df[df['location']])은 동일함!\n",
    "kor_df = kor_df.set_index('date')\n",
    "kor_df\n",
    "# 날자가 유니크하기에 index로 사용해도 됨!\n",
    "kor_df.info()\n",
    "kor_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>location</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2023-09-30</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>103436829.0</td>\n",
       "      <td>338289856.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-01</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>103436829.0</td>\n",
       "      <td>338289856.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-02</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>103436829.0</td>\n",
       "      <td>338289856.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-03</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>103436829.0</td>\n",
       "      <td>338289856.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-04</th>\n",
       "      <td>USA</td>\n",
       "      <td>United States</td>\n",
       "      <td>103436829.0</td>\n",
       "      <td>338289856.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           iso_code       location  total_cases   population\n",
       "date                                                        \n",
       "2023-09-30      USA  United States  103436829.0  338289856.0\n",
       "2023-10-01      USA  United States  103436829.0  338289856.0\n",
       "2023-10-02      USA  United States  103436829.0  338289856.0\n",
       "2023-10-03      USA  United States  103436829.0  338289856.0\n",
       "2023-10-04      USA  United States  103436829.0  338289856.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "usa_df = df[df['location'] == 'United States']\n",
    "usa_df = usa_df.set_index('date')\n",
    "# 유니크한 값으로 인덱스 결정!\n",
    "usa_df\n",
    "usa_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "date\n",
      "2020-01-03           NaN\n",
      "2020-01-04           NaN\n",
      "2020-01-05           NaN\n",
      "2020-01-06           NaN\n",
      "2020-01-07           NaN\n",
      "                 ...    \n",
      "2023-09-30    34571873.0\n",
      "2023-10-01    34571873.0\n",
      "2023-10-02    34571873.0\n",
      "2023-10-03    34571873.0\n",
      "2023-10-04    34571873.0\n",
      "Name: total_cases, Length: 1371, dtype: float64 date\n",
      "2020-01-03            NaN\n",
      "2020-01-04            NaN\n",
      "2020-01-05            NaN\n",
      "2020-01-06            NaN\n",
      "2020-01-07            NaN\n",
      "                 ...     \n",
      "2023-09-30    103436829.0\n",
      "2023-10-01    103436829.0\n",
      "2023-10-02    103436829.0\n",
      "2023-10-03    103436829.0\n",
      "2023-10-04    103436829.0\n",
      "Name: total_cases, Length: 1371, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "# total_cases => 확진자 수\n",
    "kor_cases = kor_df['total_cases']\n",
    "usa_cases = usa_df['total_cases']\n",
    "print(kor_cases, usa_cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>KOR</th>\n",
       "      <th>USA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-30</th>\n",
       "      <td>34571873.0</td>\n",
       "      <td>103436829.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-01</th>\n",
       "      <td>34571873.0</td>\n",
       "      <td>103436829.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-02</th>\n",
       "      <td>34571873.0</td>\n",
       "      <td>103436829.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-03</th>\n",
       "      <td>34571873.0</td>\n",
       "      <td>103436829.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-04</th>\n",
       "      <td>34571873.0</td>\n",
       "      <td>103436829.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1371 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   KOR          USA\n",
       "date                               \n",
       "2020-01-03         NaN          NaN\n",
       "2020-01-04         NaN          NaN\n",
       "2020-01-05         NaN          NaN\n",
       "2020-01-06         NaN          NaN\n",
       "2020-01-07         NaN          NaN\n",
       "...                ...          ...\n",
       "2023-09-30  34571873.0  103436829.0\n",
       "2023-10-01  34571873.0  103436829.0\n",
       "2023-10-02  34571873.0  103436829.0\n",
       "2023-10-03  34571873.0  103436829.0\n",
       "2023-10-04  34571873.0  103436829.0\n",
       "\n",
       "[1371 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "final_df = pd.DataFrame({\n",
    "    'KOR' : kor_cases,\n",
    "    'USA' : usa_cases\n",
    "}, index=kor_df.index)\n",
    "# 서로 같은 날짜를 기준으로 그룹화\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='date'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_df.plot.line(figsize=(12, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 미국에 비해 확진자 수가 적어보임!\n",
    "# 데이터가 없어보이는 0인 부분이 있어서 변측치가 있어보임!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='date'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_df['2022-01-01':].plot.line(figsize=(12, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 완만하게 확진자 수가 증가 중\n",
    "# 인구 대비가 감안되지 않아, 실제로 인구 대비 확진률이 아님!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 인구 대비 확진자 수 구하기\n",
    "kor_rate = kor_df['total_cases'] / kor_df['population']\n",
    "usa_rate = usa_df['total_cases'] / usa_df['population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>KOR</th>\n",
       "      <th>USA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-06</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-07</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-09-30</th>\n",
       "      <td>0.667207</td>\n",
       "      <td>0.305764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-01</th>\n",
       "      <td>0.667207</td>\n",
       "      <td>0.305764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-02</th>\n",
       "      <td>0.667207</td>\n",
       "      <td>0.305764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-03</th>\n",
       "      <td>0.667207</td>\n",
       "      <td>0.305764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023-10-04</th>\n",
       "      <td>0.667207</td>\n",
       "      <td>0.305764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1371 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 KOR       USA\n",
       "date                          \n",
       "2020-01-03       NaN       NaN\n",
       "2020-01-04       NaN       NaN\n",
       "2020-01-05       NaN       NaN\n",
       "2020-01-06       NaN       NaN\n",
       "2020-01-07       NaN       NaN\n",
       "...              ...       ...\n",
       "2023-09-30  0.667207  0.305764\n",
       "2023-10-01  0.667207  0.305764\n",
       "2023-10-02  0.667207  0.305764\n",
       "2023-10-03  0.667207  0.305764\n",
       "2023-10-04  0.667207  0.305764\n",
       "\n",
       "[1371 rows x 2 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.DataFrame({\n",
    "    'KOR' : kor_rate,\n",
    "    'USA' : usa_rate\n",
    "}, index=kor_df.index)\n",
    "# 서로 같은 날짜를 기준으로 그룹화\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='date'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_df.plot.line(figsize=(12, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 어느 시점부터 한국의 확진률이 미국 확진률을 뛰어넘음!\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='date'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_df['2022-01-01' : ].plot.line(figsize=(12, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 미국의 데이터는 급격하게 증가한 후, 완만하게 데이터가 증가하는 중!\n",
    "# 인구를 고려해서 그려 보면, 어떤 나라가 더 안전한지 알 수 있음!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}