Progetto python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "77a7e1d1-d4a6-4524-bed2-0d8543d0714e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as datetime\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "1c7bfcc9-da98-480b-8eda-4e8e785a0cfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(r\"C:\\Users\\User\\Downloads\\owid-covid-data.csv\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bf7f90cc-0e18-4f80-b4aa-5475b051b150",
   "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>2020-01-05</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>1</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>2</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-08</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>2020-01-09</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  2020-01-05          NaN        0.0   \n",
       "1      AFG      Asia  Afghanistan  2020-01-06          NaN        0.0   \n",
       "2      AFG      Asia  Afghanistan  2020-01-07          NaN        0.0   \n",
       "3      AFG      Asia  Afghanistan  2020-01-08          NaN        0.0   \n",
       "4      AFG      Asia  Afghanistan  2020-01-09          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": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Visualizza le prime righe del dataset\n",
    "df.head(5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eaee310c-b52b-4ca2-a2cf-653219fa7b8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "388922"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimensioni del dataset\n",
    "df.shape[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "baf03fea-29c2-430e-a350-4dcf4cd49a0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "67"
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     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "45328917-09f3-4edf-af5a-341451d3a2eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 388922 entries, 0 to 388921\n",
      "Data columns (total 67 columns):\n",
      " #   Column                                      Non-Null Count   Dtype  \n",
      "---  ------                                      --------------   -----  \n",
      " 0   iso_code                                    388922 non-null  object \n",
      " 1   continent                                   370331 non-null  object \n",
      " 2   location                                    388922 non-null  object \n",
      " 3   date                                        388922 non-null  object \n",
      " 4   total_cases                                 350041 non-null  float64\n",
      " 5   new_cases                                   377974 non-null  float64\n",
      " 6   new_cases_smoothed                          376744 non-null  float64\n",
      " 7   total_deaths                                327775 non-null  float64\n",
      " 8   new_deaths                                  378290 non-null  float64\n",
      " 9   new_deaths_smoothed                         377060 non-null  float64\n",
      " 10  total_cases_per_million                     350041 non-null  float64\n",
      " 11  new_cases_per_million                       377974 non-null  float64\n",
      " 12  new_cases_smoothed_per_million              376744 non-null  float64\n",
      " 13  total_deaths_per_million                    327775 non-null  float64\n",
      " 14  new_deaths_per_million                      378290 non-null  float64\n",
      " 15  new_deaths_smoothed_per_million             377060 non-null  float64\n",
      " 16  reproduction_rate                           184817 non-null  float64\n",
      " 17  icu_patients                                38670 non-null   float64\n",
      " 18  icu_patients_per_million                    38670 non-null   float64\n",
      " 19  hosp_patients                               40210 non-null   float64\n",
      " 20  hosp_patients_per_million                   40210 non-null   float64\n",
      " 21  weekly_icu_admissions                       10706 non-null   float64\n",
      " 22  weekly_icu_admissions_per_million           10706 non-null   float64\n",
      " 23  weekly_hosp_admissions                      24182 non-null   float64\n",
      " 24  weekly_hosp_admissions_per_million          24182 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                          83395 non-null   float64\n",
      " 35  people_vaccinated                           79263 non-null   float64\n",
      " 36  people_fully_vaccinated                     76149 non-null   float64\n",
      " 37  total_boosters                              51578 non-null   float64\n",
      " 38  new_vaccinations                            69129 non-null   float64\n",
      " 39  new_vaccinations_smoothed                   190129 non-null  float64\n",
      " 40  total_vaccinations_per_hundred              83395 non-null   float64\n",
      " 41  people_vaccinated_per_hundred               79263 non-null   float64\n",
      " 42  people_fully_vaccinated_per_hundred         76149 non-null   float64\n",
      " 43  total_boosters_per_hundred                  51578 non-null   float64\n",
      " 44  new_vaccinations_smoothed_per_million       190129 non-null  float64\n",
      " 45  new_people_vaccinated_smoothed              187480 non-null  float64\n",
      " 46  new_people_vaccinated_smoothed_per_hundred  187480 non-null  float64\n",
      " 47  stringency_index                            197292 non-null  float64\n",
      " 48  population_density                          330708 non-null  float64\n",
      " 49  median_age                                  307401 non-null  float64\n",
      " 50  aged_65_older                               296806 non-null  float64\n",
      " 51  aged_70_older                               304319 non-null  float64\n",
      " 52  gdp_per_capita                              301424 non-null  float64\n",
      " 53  extreme_poverty                             194301 non-null  float64\n",
      " 54  cardiovasc_death_rate                       302010 non-null  float64\n",
      " 55  diabetes_prevalence                         317585 non-null  float64\n",
      " 56  female_smokers                              226694 non-null  float64\n",
      " 57  male_smokers                                223612 non-null  float64\n",
      " 58  handwashing_facilities                      147980 non-null  float64\n",
      " 59  hospital_beds_per_thousand                  266760 non-null  float64\n",
      " 60  life_expectancy                             358254 non-null  float64\n",
      " 61  human_development_index                     292929 non-null  float64\n",
      " 62  population                                  388922 non-null  float64\n",
      " 63  excess_mortality_cumulative_absolute        13187 non-null   float64\n",
      " 64  excess_mortality_cumulative                 13187 non-null   float64\n",
      " 65  excess_mortality                            13187 non-null   float64\n",
      " 66  excess_mortality_cumulative_per_million     13187 non-null   float64\n",
      "dtypes: float64(62), object(5)\n",
      "memory usage: 198.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Informazioni sulle colonne\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "e0f790b7-ad78-4f56-96b1-5954a33b2c07",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>new_cases_smoothed_per_million</th>\n",
       "      <th>total_deaths_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>population</th>\n",
       "      <th>excess_mortality_cumulative_absolute</th>\n",
       "      <th>excess_mortality_cumulative</th>\n",
       "      <th>excess_mortality</th>\n",
       "      <th>excess_mortality_cumulative_per_million</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3.500410e+05</td>\n",
       "      <td>3.779740e+05</td>\n",
       "      <td>3.767440e+05</td>\n",
       "      <td>3.277750e+05</td>\n",
       "      <td>378290.000000</td>\n",
       "      <td>377060.000000</td>\n",
       "      <td>350041.000000</td>\n",
       "      <td>377974.000000</td>\n",
       "      <td>376744.000000</td>\n",
       "      <td>327775.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>223612.000000</td>\n",
       "      <td>147980.000000</td>\n",
       "      <td>266760.000000</td>\n",
       "      <td>358254.000000</td>\n",
       "      <td>292929.000000</td>\n",
       "      <td>3.889220e+05</td>\n",
       "      <td>1.318700e+04</td>\n",
       "      <td>13187.000000</td>\n",
       "      <td>13187.000000</td>\n",
       "      <td>13187.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.416469e+06</td>\n",
       "      <td>8.691576e+03</td>\n",
       "      <td>8.719559e+03</td>\n",
       "      <td>9.013788e+04</td>\n",
       "      <td>77.826382</td>\n",
       "      <td>78.072265</td>\n",
       "      <td>112814.104531</td>\n",
       "      <td>132.070821</td>\n",
       "      <td>132.497956</td>\n",
       "      <td>920.326030</td>\n",
       "      <td>...</td>\n",
       "      <td>32.910555</td>\n",
       "      <td>50.791666</td>\n",
       "      <td>3.097361</td>\n",
       "      <td>73.710996</td>\n",
       "      <td>0.722544</td>\n",
       "      <td>1.290199e+08</td>\n",
       "      <td>5.579910e+04</td>\n",
       "      <td>9.770265</td>\n",
       "      <td>10.979027</td>\n",
       "      <td>1777.206419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.412071e+07</td>\n",
       "      <td>2.391682e+05</td>\n",
       "      <td>9.018375e+04</td>\n",
       "      <td>4.574260e+05</td>\n",
       "      <td>1421.325514</td>\n",
       "      <td>533.206099</td>\n",
       "      <td>160411.084685</td>\n",
       "      <td>1540.907612</td>\n",
       "      <td>570.307845</td>\n",
       "      <td>1132.928263</td>\n",
       "      <td>...</td>\n",
       "      <td>13.573094</td>\n",
       "      <td>31.954302</td>\n",
       "      <td>2.548236</td>\n",
       "      <td>7.395007</td>\n",
       "      <td>0.148996</td>\n",
       "      <td>6.625423e+08</td>\n",
       "      <td>1.559496e+05</td>\n",
       "      <td>12.022926</td>\n",
       "      <td>24.585788</td>\n",
       "      <td>1992.786873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>1.188000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>53.280000</td>\n",
       "      <td>0.394000</td>\n",
       "      <td>4.700000e+01</td>\n",
       "      <td>-3.772610e+04</td>\n",
       "      <td>-44.230000</td>\n",
       "      <td>-95.920000</td>\n",
       "      <td>-2936.453100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.501000e+03</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.370000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2956.236000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>65.785000</td>\n",
       "      <td>...</td>\n",
       "      <td>22.600000</td>\n",
       "      <td>20.859000</td>\n",
       "      <td>1.300000</td>\n",
       "      <td>69.590000</td>\n",
       "      <td>0.602000</td>\n",
       "      <td>4.490020e+05</td>\n",
       "      <td>1.781000e+02</td>\n",
       "      <td>2.080000</td>\n",
       "      <td>-1.490000</td>\n",
       "      <td>120.205410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.003600e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.742900e+01</td>\n",
       "      <td>1.395000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.143000</td>\n",
       "      <td>32595.574000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.240000</td>\n",
       "      <td>417.464000</td>\n",
       "      <td>...</td>\n",
       "      <td>33.100000</td>\n",
       "      <td>49.839000</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>75.050000</td>\n",
       "      <td>0.740000</td>\n",
       "      <td>5.882259e+06</td>\n",
       "      <td>6.694300e+03</td>\n",
       "      <td>8.150000</td>\n",
       "      <td>5.660000</td>\n",
       "      <td>1248.656900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.609760e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>4.005710e+02</td>\n",
       "      <td>1.260750e+04</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>155877.104000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>68.012000</td>\n",
       "      <td>1459.484000</td>\n",
       "      <td>...</td>\n",
       "      <td>41.300000</td>\n",
       "      <td>82.502000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>79.460000</td>\n",
       "      <td>0.829000</td>\n",
       "      <td>2.830170e+07</td>\n",
       "      <td>3.877775e+04</td>\n",
       "      <td>15.050000</td>\n",
       "      <td>15.690000</td>\n",
       "      <td>2878.521250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.751321e+08</td>\n",
       "      <td>4.423623e+07</td>\n",
       "      <td>6.319461e+06</td>\n",
       "      <td>7.042222e+06</td>\n",
       "      <td>103719.000000</td>\n",
       "      <td>14817.000000</td>\n",
       "      <td>770693.756000</td>\n",
       "      <td>240325.866000</td>\n",
       "      <td>34332.267000</td>\n",
       "      <td>6485.570000</td>\n",
       "      <td>...</td>\n",
       "      <td>78.100000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>13.800000</td>\n",
       "      <td>86.750000</td>\n",
       "      <td>0.957000</td>\n",
       "      <td>7.975105e+09</td>\n",
       "      <td>1.345330e+06</td>\n",
       "      <td>78.080000</td>\n",
       "      <td>377.830000</td>\n",
       "      <td>10293.515000</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  3.500410e+05  3.779740e+05        3.767440e+05  3.277750e+05   \n",
       "mean   7.416469e+06  8.691576e+03        8.719559e+03  9.013788e+04   \n",
       "std    4.412071e+07  2.391682e+05        9.018375e+04  4.574260e+05   \n",
       "min    1.000000e+00  0.000000e+00        0.000000e+00  1.000000e+00   \n",
       "25%    9.501000e+03  0.000000e+00        0.000000e+00  1.370000e+02   \n",
       "50%    8.003600e+04  0.000000e+00        1.742900e+01  1.395000e+03   \n",
       "75%    8.609760e+05  0.000000e+00        4.005710e+02  1.260750e+04   \n",
       "max    7.751321e+08  4.423623e+07        6.319461e+06  7.042222e+06   \n",
       "\n",
       "          new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "count  378290.000000        377060.000000            350041.000000   \n",
       "mean       77.826382            78.072265            112814.104531   \n",
       "std      1421.325514           533.206099            160411.084685   \n",
       "min         0.000000             0.000000                 0.000000   \n",
       "25%         0.000000             0.000000              2956.236000   \n",
       "50%         0.000000             0.143000             32595.574000   \n",
       "75%         0.000000             4.000000            155877.104000   \n",
       "max    103719.000000         14817.000000            770693.756000   \n",
       "\n",
       "       new_cases_per_million  new_cases_smoothed_per_million  \\\n",
       "count          377974.000000                   376744.000000   \n",
       "mean              132.070821                      132.497956   \n",
       "std              1540.907612                      570.307845   \n",
       "min                 0.000000                        0.000000   \n",
       "25%                 0.000000                        0.000000   \n",
       "50%                 0.000000                        4.240000   \n",
       "75%                 0.000000                       68.012000   \n",
       "max            240325.866000                    34332.267000   \n",
       "\n",
       "       total_deaths_per_million  ...   male_smokers  handwashing_facilities  \\\n",
       "count             327775.000000  ...  223612.000000           147980.000000   \n",
       "mean                 920.326030  ...      32.910555               50.791666   \n",
       "std                 1132.928263  ...      13.573094               31.954302   \n",
       "min                    0.000000  ...       7.700000                1.188000   \n",
       "25%                   65.785000  ...      22.600000               20.859000   \n",
       "50%                  417.464000  ...      33.100000               49.839000   \n",
       "75%                 1459.484000  ...      41.300000               82.502000   \n",
       "max                 6485.570000  ...      78.100000              100.000000   \n",
       "\n",
       "       hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "count               266760.000000    358254.000000            292929.000000   \n",
       "mean                     3.097361        73.710996                 0.722544   \n",
       "std                      2.548236         7.395007                 0.148996   \n",
       "min                      0.100000        53.280000                 0.394000   \n",
       "25%                      1.300000        69.590000                 0.602000   \n",
       "50%                      2.500000        75.050000                 0.740000   \n",
       "75%                      4.200000        79.460000                 0.829000   \n",
       "max                     13.800000        86.750000                 0.957000   \n",
       "\n",
       "         population  excess_mortality_cumulative_absolute  \\\n",
       "count  3.889220e+05                          1.318700e+04   \n",
       "mean   1.290199e+08                          5.579910e+04   \n",
       "std    6.625423e+08                          1.559496e+05   \n",
       "min    4.700000e+01                         -3.772610e+04   \n",
       "25%    4.490020e+05                          1.781000e+02   \n",
       "50%    5.882259e+06                          6.694300e+03   \n",
       "75%    2.830170e+07                          3.877775e+04   \n",
       "max    7.975105e+09                          1.345330e+06   \n",
       "\n",
       "       excess_mortality_cumulative  excess_mortality  \\\n",
       "count                 13187.000000      13187.000000   \n",
       "mean                      9.770265         10.979027   \n",
       "std                      12.022926         24.585788   \n",
       "min                     -44.230000        -95.920000   \n",
       "25%                       2.080000         -1.490000   \n",
       "50%                       8.150000          5.660000   \n",
       "75%                      15.050000         15.690000   \n",
       "max                      78.080000        377.830000   \n",
       "\n",
       "       excess_mortality_cumulative_per_million  \n",
       "count                             13187.000000  \n",
       "mean                               1777.206419  \n",
       "std                                1992.786873  \n",
       "min                               -2936.453100  \n",
       "25%                                 120.205410  \n",
       "50%                                1248.656900  \n",
       "75%                                2878.521250  \n",
       "max                               10293.515000  \n",
       "\n",
       "[8 rows x 62 columns]"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistiche descrittive\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8adb6798-ea84-48b4-8e70-9dc3bc8b12ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Si chiede poi per ogni continente di trovare il numero di casi totali avvenuti in quello stesso continente;\n",
    "#si chiede di non considerare eventuali locazioni che nel dataset non appartengono ad alcun continente;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "38b72cf5-0b97-470a-bf9e-94302c9dc51a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa           1.286876e+10\n",
       "Asia             2.120759e+11\n",
       "Europe           2.031561e+11\n",
       "North America    1.105376e+11\n",
       "Oceania          9.609695e+09\n",
       "South America    6.434761e+10\n",
       "Name: total_cases, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Raggruppa per continente e somma il numero di casi\n",
    "casi_per_continent= df.groupby('continent')['total_cases'].sum()\n",
    "casi_per_continent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "ff1c6258-7576-488e-b7c9-03a8728a9719",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>2020-01-05</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>1</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>2</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-08</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>2020-01-09</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>...</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>388917</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-20</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</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>388918</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-21</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</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>388919</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-22</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</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>388920</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-23</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</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>388921</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-24</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</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>388922 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location        date  total_cases  new_cases  \\\n",
       "0           AFG      Asia  Afghanistan  2020-01-05          NaN        0.0   \n",
       "1           AFG      Asia  Afghanistan  2020-01-06          NaN        0.0   \n",
       "2           AFG      Asia  Afghanistan  2020-01-07          NaN        0.0   \n",
       "3           AFG      Asia  Afghanistan  2020-01-08          NaN        0.0   \n",
       "4           AFG      Asia  Afghanistan  2020-01-09          NaN        0.0   \n",
       "...         ...       ...          ...         ...          ...        ...   \n",
       "388917      ZWE    Africa     Zimbabwe  2024-03-20     266359.0        0.0   \n",
       "388918      ZWE    Africa     Zimbabwe  2024-03-21     266359.0        0.0   \n",
       "388919      ZWE    Africa     Zimbabwe  2024-03-22     266359.0        0.0   \n",
       "388920      ZWE    Africa     Zimbabwe  2024-03-23     266359.0        0.0   \n",
       "388921      ZWE    Africa     Zimbabwe  2024-03-24     266359.0        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",
       "388917                 0.0        5740.0         0.0                  0.0   \n",
       "388918                 0.0        5740.0         0.0                  0.0   \n",
       "388919                 0.0        5740.0         0.0                  0.0   \n",
       "388920                 0.0        5740.0         0.0                  0.0   \n",
       "388921                 0.0        5740.0         0.0                  0.0   \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",
       "388917  ...          30.7                  36.791                         1.7   \n",
       "388918  ...          30.7                  36.791                         1.7   \n",
       "388919  ...          30.7                  36.791                         1.7   \n",
       "388920  ...          30.7                  36.791                         1.7   \n",
       "388921  ...          30.7                  36.791                         1.7   \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",
       "388917            61.49                    0.571  16320539.0   \n",
       "388918            61.49                    0.571  16320539.0   \n",
       "388919            61.49                    0.571  16320539.0   \n",
       "388920            61.49                    0.571  16320539.0   \n",
       "388921            61.49                    0.571  16320539.0   \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",
       "388917                                   NaN                          NaN   \n",
       "388918                                   NaN                          NaN   \n",
       "388919                                   NaN                          NaN   \n",
       "388920                                   NaN                          NaN   \n",
       "388921                                   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",
       "388917               NaN                                      NaN  \n",
       "388918               NaN                                      NaN  \n",
       "388919               NaN                                      NaN  \n",
       "388920               NaN                                      NaN  \n",
       "388921               NaN                                      NaN  \n",
       "\n",
       "[388922 rows x 67 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtra le locazioni che hanno informazioni sul continente\n",
    "df_filtrato = df.dropna(subset=[\"location\"])\n",
    "df_filtrato"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "223bea08-5316-41c2-bc88-fc43cf08e629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "continent\n",
       "Africa           1.286876e+10\n",
       "Asia             2.120759e+11\n",
       "Europe           2.031561e+11\n",
       "North America    1.105376e+11\n",
       "Oceania          9.609695e+09\n",
       "South America    6.434761e+10\n",
       "Name: total_cases, dtype: float64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Raggruppa per continente e somma il numero di casi\n",
    "casi_per_continente = df_filtrato.groupby('continent')['total_cases'].sum()\n",
    "casi_per_continente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "1141c409-a3b0-4ebc-888f-b29636a4ddc0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2596068215126.0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcola i casi totali nel mondo (considerando anche le locazioni senza continente)\n",
    "casi_totali_mondo = df[\"total_cases\"].sum()\n",
    "casi_totali_mondo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "2211c191-be05-499c-8c81-f707f531a00a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Africa'"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Definisci i due continenti di interesse\n",
    "continent_1=\"Africa\"\n",
    "continent_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "5269add8-a688-4e7f-846b-54cda8b587d2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Asia'"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "continent_2=\"Asia\"\n",
    "continent_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "57aa07dc-0f98-4e49-be6a-ef1aac549a93",
   "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>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>4623</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Algeria</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>30.4</td>\n",
       "      <td>83.741</td>\n",
       "      <td>1.9</td>\n",
       "      <td>76.88</td>\n",
       "      <td>0.748</td>\n",
       "      <td>44903228.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4624</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Algeria</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>30.4</td>\n",
       "      <td>83.741</td>\n",
       "      <td>1.9</td>\n",
       "      <td>76.88</td>\n",
       "      <td>0.748</td>\n",
       "      <td>44903228.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4625</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Algeria</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>30.4</td>\n",
       "      <td>83.741</td>\n",
       "      <td>1.9</td>\n",
       "      <td>76.88</td>\n",
       "      <td>0.748</td>\n",
       "      <td>44903228.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4626</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2020-01-08</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>30.4</td>\n",
       "      <td>83.741</td>\n",
       "      <td>1.9</td>\n",
       "      <td>76.88</td>\n",
       "      <td>0.748</td>\n",
       "      <td>44903228.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4627</th>\n",
       "      <td>DZA</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>2020-01-09</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>30.4</td>\n",
       "      <td>83.741</td>\n",
       "      <td>1.9</td>\n",
       "      <td>76.88</td>\n",
       "      <td>0.748</td>\n",
       "      <td>44903228.0</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>388917</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-20</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</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>388918</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-21</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</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>388919</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-22</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</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>388920</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-23</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</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>388921</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-24</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</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>87838 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent  location        date  total_cases  new_cases  \\\n",
       "4623        DZA    Africa   Algeria  2020-01-05          NaN        0.0   \n",
       "4624        DZA    Africa   Algeria  2020-01-06          NaN        0.0   \n",
       "4625        DZA    Africa   Algeria  2020-01-07          NaN        0.0   \n",
       "4626        DZA    Africa   Algeria  2020-01-08          NaN        0.0   \n",
       "4627        DZA    Africa   Algeria  2020-01-09          NaN        0.0   \n",
       "...         ...       ...       ...         ...          ...        ...   \n",
       "388917      ZWE    Africa  Zimbabwe  2024-03-20     266359.0        0.0   \n",
       "388918      ZWE    Africa  Zimbabwe  2024-03-21     266359.0        0.0   \n",
       "388919      ZWE    Africa  Zimbabwe  2024-03-22     266359.0        0.0   \n",
       "388920      ZWE    Africa  Zimbabwe  2024-03-23     266359.0        0.0   \n",
       "388921      ZWE    Africa  Zimbabwe  2024-03-24     266359.0        0.0   \n",
       "\n",
       "        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "4623                   NaN           NaN         0.0                  NaN   \n",
       "4624                   NaN           NaN         0.0                  NaN   \n",
       "4625                   NaN           NaN         0.0                  NaN   \n",
       "4626                   NaN           NaN         0.0                  NaN   \n",
       "4627                   NaN           NaN         0.0                  NaN   \n",
       "...                    ...           ...         ...                  ...   \n",
       "388917                 0.0        5740.0         0.0                  0.0   \n",
       "388918                 0.0        5740.0         0.0                  0.0   \n",
       "388919                 0.0        5740.0         0.0                  0.0   \n",
       "388920                 0.0        5740.0         0.0                  0.0   \n",
       "388921                 0.0        5740.0         0.0                  0.0   \n",
       "\n",
       "        ...  male_smokers  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "4623    ...          30.4                  83.741                         1.9   \n",
       "4624    ...          30.4                  83.741                         1.9   \n",
       "4625    ...          30.4                  83.741                         1.9   \n",
       "4626    ...          30.4                  83.741                         1.9   \n",
       "4627    ...          30.4                  83.741                         1.9   \n",
       "...     ...           ...                     ...                         ...   \n",
       "388917  ...          30.7                  36.791                         1.7   \n",
       "388918  ...          30.7                  36.791                         1.7   \n",
       "388919  ...          30.7                  36.791                         1.7   \n",
       "388920  ...          30.7                  36.791                         1.7   \n",
       "388921  ...          30.7                  36.791                         1.7   \n",
       "\n",
       "        life_expectancy  human_development_index  population  \\\n",
       "4623              76.88                    0.748  44903228.0   \n",
       "4624              76.88                    0.748  44903228.0   \n",
       "4625              76.88                    0.748  44903228.0   \n",
       "4626              76.88                    0.748  44903228.0   \n",
       "4627              76.88                    0.748  44903228.0   \n",
       "...                 ...                      ...         ...   \n",
       "388917            61.49                    0.571  16320539.0   \n",
       "388918            61.49                    0.571  16320539.0   \n",
       "388919            61.49                    0.571  16320539.0   \n",
       "388920            61.49                    0.571  16320539.0   \n",
       "388921            61.49                    0.571  16320539.0   \n",
       "\n",
       "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "4623                                     NaN                          NaN   \n",
       "4624                                     NaN                          NaN   \n",
       "4625                                     NaN                          NaN   \n",
       "4626                                     NaN                          NaN   \n",
       "4627                                     NaN                          NaN   \n",
       "...                                      ...                          ...   \n",
       "388917                                   NaN                          NaN   \n",
       "388918                                   NaN                          NaN   \n",
       "388919                                   NaN                          NaN   \n",
       "388920                                   NaN                          NaN   \n",
       "388921                                   NaN                          NaN   \n",
       "\n",
       "        excess_mortality  excess_mortality_cumulative_per_million  \n",
       "4623                 NaN                                      NaN  \n",
       "4624                 NaN                                      NaN  \n",
       "4625                 NaN                                      NaN  \n",
       "4626                 NaN                                      NaN  \n",
       "4627                 NaN                                      NaN  \n",
       "...                  ...                                      ...  \n",
       "388917               NaN                                      NaN  \n",
       "388918               NaN                                      NaN  \n",
       "388919               NaN                                      NaN  \n",
       "388920               NaN                                      NaN  \n",
       "388921               NaN                                      NaN  \n",
       "\n",
       "[87838 rows x 67 columns]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtra i dati per i due continenti specificati\n",
    "df_continent_1 = df[df[\"continent\"] == \"Africa\"]\n",
    "df_continent_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "707724b6-298a-4617-a0ab-54d1927e9e28",
   "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>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>2020-01-05</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>1</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>2</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",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-08</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>2020-01-09</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>...</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>385835</th>\n",
       "      <td>YEM</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>2024-03-20</td>\n",
       "      <td>11945.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2159.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.2</td>\n",
       "      <td>49.542</td>\n",
       "      <td>0.7</td>\n",
       "      <td>66.12</td>\n",
       "      <td>0.470</td>\n",
       "      <td>33696612.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385836</th>\n",
       "      <td>YEM</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>2024-03-21</td>\n",
       "      <td>11945.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2159.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.2</td>\n",
       "      <td>49.542</td>\n",
       "      <td>0.7</td>\n",
       "      <td>66.12</td>\n",
       "      <td>0.470</td>\n",
       "      <td>33696612.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385837</th>\n",
       "      <td>YEM</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>2024-03-22</td>\n",
       "      <td>11945.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2159.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.2</td>\n",
       "      <td>49.542</td>\n",
       "      <td>0.7</td>\n",
       "      <td>66.12</td>\n",
       "      <td>0.470</td>\n",
       "      <td>33696612.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385838</th>\n",
       "      <td>YEM</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>2024-03-23</td>\n",
       "      <td>11945.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2159.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.2</td>\n",
       "      <td>49.542</td>\n",
       "      <td>0.7</td>\n",
       "      <td>66.12</td>\n",
       "      <td>0.470</td>\n",
       "      <td>33696612.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385839</th>\n",
       "      <td>YEM</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>2024-03-24</td>\n",
       "      <td>11945.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2159.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>29.2</td>\n",
       "      <td>49.542</td>\n",
       "      <td>0.7</td>\n",
       "      <td>66.12</td>\n",
       "      <td>0.470</td>\n",
       "      <td>33696612.0</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>76811 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location        date  total_cases  new_cases  \\\n",
       "0           AFG      Asia  Afghanistan  2020-01-05          NaN        0.0   \n",
       "1           AFG      Asia  Afghanistan  2020-01-06          NaN        0.0   \n",
       "2           AFG      Asia  Afghanistan  2020-01-07          NaN        0.0   \n",
       "3           AFG      Asia  Afghanistan  2020-01-08          NaN        0.0   \n",
       "4           AFG      Asia  Afghanistan  2020-01-09          NaN        0.0   \n",
       "...         ...       ...          ...         ...          ...        ...   \n",
       "385835      YEM      Asia        Yemen  2024-03-20      11945.0        0.0   \n",
       "385836      YEM      Asia        Yemen  2024-03-21      11945.0        0.0   \n",
       "385837      YEM      Asia        Yemen  2024-03-22      11945.0        0.0   \n",
       "385838      YEM      Asia        Yemen  2024-03-23      11945.0        0.0   \n",
       "385839      YEM      Asia        Yemen  2024-03-24      11945.0        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",
       "385835                 0.0        2159.0         0.0                  0.0   \n",
       "385836                 0.0        2159.0         0.0                  0.0   \n",
       "385837                 0.0        2159.0         0.0                  0.0   \n",
       "385838                 0.0        2159.0         0.0                  0.0   \n",
       "385839                 0.0        2159.0         0.0                  0.0   \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",
       "385835  ...          29.2                  49.542                         0.7   \n",
       "385836  ...          29.2                  49.542                         0.7   \n",
       "385837  ...          29.2                  49.542                         0.7   \n",
       "385838  ...          29.2                  49.542                         0.7   \n",
       "385839  ...          29.2                  49.542                         0.7   \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",
       "385835            66.12                    0.470  33696612.0   \n",
       "385836            66.12                    0.470  33696612.0   \n",
       "385837            66.12                    0.470  33696612.0   \n",
       "385838            66.12                    0.470  33696612.0   \n",
       "385839            66.12                    0.470  33696612.0   \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",
       "385835                                   NaN                          NaN   \n",
       "385836                                   NaN                          NaN   \n",
       "385837                                   NaN                          NaN   \n",
       "385838                                   NaN                          NaN   \n",
       "385839                                   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",
       "385835               NaN                                      NaN  \n",
       "385836               NaN                                      NaN  \n",
       "385837               NaN                                      NaN  \n",
       "385838               NaN                                      NaN  \n",
       "385839               NaN                                      NaN  \n",
       "\n",
       "[76811 rows x 67 columns]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_continent_2 = df[df['continent'] == \"Asia\"]\n",
    "df_continent_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "f2df2a70-39fa-4307-a5ff-1b403d0e257b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4072636.0"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcola i descrittori statistici per il primo continente\n",
    "max_cas_1 = df_continent_1['total_cases'].max()\n",
    "max_cas_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "37545d74-b6a1-4089-a23a-cc93c48cba91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "155741.43"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_cas_1 = df_continent_1['total_cases'].mean().round(2)\n",
    "mean_cas_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "id": "6be08526-2d62-4f30-8d26-e129e53a4487",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentuale_cas_1 = ((df_continent_1['total_cases'].sum() / casi_totali_mondo) * 100).round(2)\n",
    "percentuale_cas_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "8f2e191e-e0fe-4f0e-97ac-27ff05fb81a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99345680.0"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcola i descrittori statistici per il secondo continente\n",
    "max_cas_2 = df_continent_2['total_cases'].max()\n",
    "max_cas_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "9be3b9aa-9024-4731-ba10-2f8c909f8b85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3199745.48"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_cas_2 = df_continent_2['total_cases'].mean().round(2)\n",
    "mean_cas_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "b197b6f0-8504-4a70-b359-446ea915aa8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.17"
      ]
     },
     "execution_count": 305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "percentuale_cas_2 = ((df_continent_2[\"total_cases\"].sum() / casi_totali_mondo) * 100).round(2)\n",
    "percentuale_cas_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "d9881163-920a-46cf-b69d-e60d1075e616",
   "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>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",
       "      <th>YEAR</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>2020-01-05</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>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",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-06</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>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",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-07</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>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",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-08</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>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",
       "      <td>2020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-09</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>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",
       "      <td>2020</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>388917</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-20</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388918</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-21</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388919</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-22</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388920</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-23</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388921</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-24</td>\n",
       "      <td>266359.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5740.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</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",
       "      <td>2024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>388922 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location        date  total_cases  new_cases  \\\n",
       "0           AFG      Asia  Afghanistan  2020-01-05          NaN        0.0   \n",
       "1           AFG      Asia  Afghanistan  2020-01-06          NaN        0.0   \n",
       "2           AFG      Asia  Afghanistan  2020-01-07          NaN        0.0   \n",
       "3           AFG      Asia  Afghanistan  2020-01-08          NaN        0.0   \n",
       "4           AFG      Asia  Afghanistan  2020-01-09          NaN        0.0   \n",
       "...         ...       ...          ...         ...          ...        ...   \n",
       "388917      ZWE    Africa     Zimbabwe  2024-03-20     266359.0        0.0   \n",
       "388918      ZWE    Africa     Zimbabwe  2024-03-21     266359.0        0.0   \n",
       "388919      ZWE    Africa     Zimbabwe  2024-03-22     266359.0        0.0   \n",
       "388920      ZWE    Africa     Zimbabwe  2024-03-23     266359.0        0.0   \n",
       "388921      ZWE    Africa     Zimbabwe  2024-03-24     266359.0        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",
       "388917                 0.0        5740.0         0.0                  0.0   \n",
       "388918                 0.0        5740.0         0.0                  0.0   \n",
       "388919                 0.0        5740.0         0.0                  0.0   \n",
       "388920                 0.0        5740.0         0.0                  0.0   \n",
       "388921                 0.0        5740.0         0.0                  0.0   \n",
       "\n",
       "        ...  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "0       ...                  37.746                         0.5   \n",
       "1       ...                  37.746                         0.5   \n",
       "2       ...                  37.746                         0.5   \n",
       "3       ...                  37.746                         0.5   \n",
       "4       ...                  37.746                         0.5   \n",
       "...     ...                     ...                         ...   \n",
       "388917  ...                  36.791                         1.7   \n",
       "388918  ...                  36.791                         1.7   \n",
       "388919  ...                  36.791                         1.7   \n",
       "388920  ...                  36.791                         1.7   \n",
       "388921  ...                  36.791                         1.7   \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",
       "388917            61.49                    0.571  16320539.0   \n",
       "388918            61.49                    0.571  16320539.0   \n",
       "388919            61.49                    0.571  16320539.0   \n",
       "388920            61.49                    0.571  16320539.0   \n",
       "388921            61.49                    0.571  16320539.0   \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",
       "388917                                   NaN                          NaN   \n",
       "388918                                   NaN                          NaN   \n",
       "388919                                   NaN                          NaN   \n",
       "388920                                   NaN                          NaN   \n",
       "388921                                   NaN                          NaN   \n",
       "\n",
       "        excess_mortality  excess_mortality_cumulative_per_million  YEAR  \n",
       "0                    NaN                                      NaN  2020  \n",
       "1                    NaN                                      NaN  2020  \n",
       "2                    NaN                                      NaN  2020  \n",
       "3                    NaN                                      NaN  2020  \n",
       "4                    NaN                                      NaN  2020  \n",
       "...                  ...                                      ...   ...  \n",
       "388917               NaN                                      NaN  2024  \n",
       "388918               NaN                                      NaN  2024  \n",
       "388919               NaN                                      NaN  2024  \n",
       "388920               NaN                                      NaN  2024  \n",
       "388921               NaN                                      NaN  2024  \n",
       "\n",
       "[388922 rows x 68 columns]"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['YEAR'] = pd.DatetimeIndex(df['date']).year\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "id": "976f6399-231c-4f27-9355-fe8fded849ac",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
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       "      <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",
       "      <th>YEAR</th>\n",
       "      <th>month</th>\n",
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       "      <td>AFG</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AFG</td>\n",
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       "      <td>2020-01-08</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2020-01-09</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024</td>\n",
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       "    <tr>\n",
       "      <th>388919</th>\n",
       "      <td>ZWE</td>\n",
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       "      <td>Zimbabwe</td>\n",
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       "    <tr>\n",
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       "      <td>ZWE</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>388921</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2024-03-24</td>\n",
       "      <td>266359.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>5740.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>16320539.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>388922 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location        date  total_cases  new_cases  \\\n",
       "0           AFG      Asia  Afghanistan  2020-01-05          NaN        0.0   \n",
       "1           AFG      Asia  Afghanistan  2020-01-06          NaN        0.0   \n",
       "2           AFG      Asia  Afghanistan  2020-01-07          NaN        0.0   \n",
       "3           AFG      Asia  Afghanistan  2020-01-08          NaN        0.0   \n",
       "4           AFG      Asia  Afghanistan  2020-01-09          NaN        0.0   \n",
       "...         ...       ...          ...         ...          ...        ...   \n",
       "388917      ZWE    Africa     Zimbabwe  2024-03-20     266359.0        0.0   \n",
       "388918      ZWE    Africa     Zimbabwe  2024-03-21     266359.0        0.0   \n",
       "388919      ZWE    Africa     Zimbabwe  2024-03-22     266359.0        0.0   \n",
       "388920      ZWE    Africa     Zimbabwe  2024-03-23     266359.0        0.0   \n",
       "388921      ZWE    Africa     Zimbabwe  2024-03-24     266359.0        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",
       "388917                 0.0        5740.0         0.0                  0.0   \n",
       "388918                 0.0        5740.0         0.0                  0.0   \n",
       "388919                 0.0        5740.0         0.0                  0.0   \n",
       "388920                 0.0        5740.0         0.0                  0.0   \n",
       "388921                 0.0        5740.0         0.0                  0.0   \n",
       "\n",
       "        ...  hospital_beds_per_thousand  life_expectancy  \\\n",
       "0       ...                         0.5            64.83   \n",
       "1       ...                         0.5            64.83   \n",
       "2       ...                         0.5            64.83   \n",
       "3       ...                         0.5            64.83   \n",
       "4       ...                         0.5            64.83   \n",
       "...     ...                         ...              ...   \n",
       "388917  ...                         1.7            61.49   \n",
       "388918  ...                         1.7            61.49   \n",
       "388919  ...                         1.7            61.49   \n",
       "388920  ...                         1.7            61.49   \n",
       "388921  ...                         1.7            61.49   \n",
       "\n",
       "        human_development_index  population  \\\n",
       "0                         0.511  41128772.0   \n",
       "1                         0.511  41128772.0   \n",
       "2                         0.511  41128772.0   \n",
       "3                         0.511  41128772.0   \n",
       "4                         0.511  41128772.0   \n",
       "...                         ...         ...   \n",
       "388917                    0.571  16320539.0   \n",
       "388918                    0.571  16320539.0   \n",
       "388919                    0.571  16320539.0   \n",
       "388920                    0.571  16320539.0   \n",
       "388921                    0.571  16320539.0   \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",
       "388917                                   NaN                          NaN   \n",
       "388918                                   NaN                          NaN   \n",
       "388919                                   NaN                          NaN   \n",
       "388920                                   NaN                          NaN   \n",
       "388921                                   NaN                          NaN   \n",
       "\n",
       "        excess_mortality  excess_mortality_cumulative_per_million  YEAR  month  \n",
       "0                    NaN                                      NaN  2020      1  \n",
       "1                    NaN                                      NaN  2020      1  \n",
       "2                    NaN                                      NaN  2020      1  \n",
       "3                    NaN                                      NaN  2020      1  \n",
       "4                    NaN                                      NaN  2020      1  \n",
       "...                  ...                                      ...   ...    ...  \n",
       "388917               NaN                                      NaN  2024      3  \n",
       "388918               NaN                                      NaN  2024      3  \n",
       "388919               NaN                                      NaN  2024      3  \n",
       "388920               NaN                                      NaN  2024      3  \n",
       "388921               NaN                                      NaN  2024      3  \n",
       "\n",
       "[388922 rows x 69 columns]"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df['month'] = pd.DatetimeIndex(df['date']).month\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "id": "0c1ef017-91d9-4bac-84a8-668811c2676d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Italy'"
      ]
     },
     "execution_count": 303,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location=\"Italy\"\n",
    "location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "75f10f6a-6012-46b9-8765-e4e4d6e5e5f4",
   "metadata": {},
   "outputs": [
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       "      <td>2022-01-02</td>\n",
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       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.18</td>\n",
       "      <td>83.51</td>\n",
       "      <td>0.892</td>\n",
       "      <td>59037472.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167081</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2022-01-04</td>\n",
       "      <td>6267035.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92086.286</td>\n",
       "      <td>137513.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140.429</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.18</td>\n",
       "      <td>83.51</td>\n",
       "      <td>0.892</td>\n",
       "      <td>59037472.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167082</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>6267035.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>92086.286</td>\n",
       "      <td>137513.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>140.429</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.18</td>\n",
       "      <td>83.51</td>\n",
       "      <td>0.892</td>\n",
       "      <td>59037472.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</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>167438</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2022-12-27</td>\n",
       "      <td>25060503.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18918.143</td>\n",
       "      <td>184168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>114.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.18</td>\n",
       "      <td>83.51</td>\n",
       "      <td>0.892</td>\n",
       "      <td>59037472.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167439</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2022-12-28</td>\n",
       "      <td>25060503.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18918.143</td>\n",
       "      <td>184168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>114.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.18</td>\n",
       "      <td>83.51</td>\n",
       "      <td>0.892</td>\n",
       "      <td>59037472.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167440</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2022-12-29</td>\n",
       "      <td>25060503.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18918.143</td>\n",
       "      <td>184168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>114.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.18</td>\n",
       "      <td>83.51</td>\n",
       "      <td>0.892</td>\n",
       "      <td>59037472.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167441</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2022-12-30</td>\n",
       "      <td>25060503.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18918.143</td>\n",
       "      <td>184168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>114.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.18</td>\n",
       "      <td>83.51</td>\n",
       "      <td>0.892</td>\n",
       "      <td>59037472.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167442</th>\n",
       "      <td>ITA</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Italy</td>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>25060503.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18918.143</td>\n",
       "      <td>184168.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>114.000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.18</td>\n",
       "      <td>83.51</td>\n",
       "      <td>0.892</td>\n",
       "      <td>59037472.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 68 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent location        date  total_cases  new_cases  \\\n",
       "167078      ITA    Europe    Italy  2022-01-01    5622431.0        0.0   \n",
       "167079      ITA    Europe    Italy  2022-01-02    6267035.0   644604.0   \n",
       "167080      ITA    Europe    Italy  2022-01-03    6267035.0        0.0   \n",
       "167081      ITA    Europe    Italy  2022-01-04    6267035.0        0.0   \n",
       "167082      ITA    Europe    Italy  2022-01-05    6267035.0        0.0   \n",
       "...         ...       ...      ...         ...          ...        ...   \n",
       "167438      ITA    Europe    Italy  2022-12-27   25060503.0        0.0   \n",
       "167439      ITA    Europe    Italy  2022-12-28   25060503.0        0.0   \n",
       "167440      ITA    Europe    Italy  2022-12-29   25060503.0        0.0   \n",
       "167441      ITA    Europe    Italy  2022-12-30   25060503.0        0.0   \n",
       "167442      ITA    Europe    Italy  2022-12-31   25060503.0        0.0   \n",
       "\n",
       "        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "167078           36797.000      136530.0         0.0              140.857   \n",
       "167079           92086.286      137513.0       983.0              140.429   \n",
       "167080           92086.286      137513.0         0.0              140.429   \n",
       "167081           92086.286      137513.0         0.0              140.429   \n",
       "167082           92086.286      137513.0         0.0              140.429   \n",
       "...                    ...           ...         ...                  ...   \n",
       "167438           18918.143      184168.0         0.0              114.000   \n",
       "167439           18918.143      184168.0         0.0              114.000   \n",
       "167440           18918.143      184168.0         0.0              114.000   \n",
       "167441           18918.143      184168.0         0.0              114.000   \n",
       "167442           18918.143      184168.0         0.0              114.000   \n",
       "\n",
       "        ...  handwashing_facilities  hospital_beds_per_thousand  \\\n",
       "167078  ...                     NaN                        3.18   \n",
       "167079  ...                     NaN                        3.18   \n",
       "167080  ...                     NaN                        3.18   \n",
       "167081  ...                     NaN                        3.18   \n",
       "167082  ...                     NaN                        3.18   \n",
       "...     ...                     ...                         ...   \n",
       "167438  ...                     NaN                        3.18   \n",
       "167439  ...                     NaN                        3.18   \n",
       "167440  ...                     NaN                        3.18   \n",
       "167441  ...                     NaN                        3.18   \n",
       "167442  ...                     NaN                        3.18   \n",
       "\n",
       "        life_expectancy  human_development_index  population  \\\n",
       "167078            83.51                    0.892  59037472.0   \n",
       "167079            83.51                    0.892  59037472.0   \n",
       "167080            83.51                    0.892  59037472.0   \n",
       "167081            83.51                    0.892  59037472.0   \n",
       "167082            83.51                    0.892  59037472.0   \n",
       "...                 ...                      ...         ...   \n",
       "167438            83.51                    0.892  59037472.0   \n",
       "167439            83.51                    0.892  59037472.0   \n",
       "167440            83.51                    0.892  59037472.0   \n",
       "167441            83.51                    0.892  59037472.0   \n",
       "167442            83.51                    0.892  59037472.0   \n",
       "\n",
       "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "167078                                   NaN                          NaN   \n",
       "167079                              168226.3                        12.99   \n",
       "167080                                   NaN                          NaN   \n",
       "167081                                   NaN                          NaN   \n",
       "167082                                   NaN                          NaN   \n",
       "...                                      ...                          ...   \n",
       "167438                                   NaN                          NaN   \n",
       "167439                                   NaN                          NaN   \n",
       "167440                                   NaN                          NaN   \n",
       "167441                                   NaN                          NaN   \n",
       "167442                                   NaN                          NaN   \n",
       "\n",
       "        excess_mortality  excess_mortality_cumulative_per_million  YEAR  \n",
       "167078               NaN                                      NaN  2022  \n",
       "167079              8.06                                2849.4834  2022  \n",
       "167080               NaN                                      NaN  2022  \n",
       "167081               NaN                                      NaN  2022  \n",
       "167082               NaN                                      NaN  2022  \n",
       "...                  ...                                      ...   ...  \n",
       "167438               NaN                                      NaN  2022  \n",
       "167439               NaN                                      NaN  2022  \n",
       "167440               NaN                                      NaN  2022  \n",
       "167441               NaN                                      NaN  2022  \n",
       "167442               NaN                                      NaN  2022  \n",
       "\n",
       "[365 rows x 68 columns]"
      ]
     },
     "execution_count": 329,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtra i dati relativi all'Italia nel 2022\n",
    "\n",
    "dati_italia_2022 = df[(df.location == \"Italy\") & (df.YEAR == 2022)]\n",
    "dati_italia_2022"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "3f555ee1-2b73-4a6d-9ba0-993879ddbb09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico dell'evoluzione dei casi totali\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(dati_italia_2022[\"total_cases\"], label=\"total cases\")\n",
    "plt.title(\"Evolution of total cases in Italy in 2022\")\n",
    "plt.xlabel(\"month\")\n",
    "plt.ylabel(\"total cases\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "id": "63825802-d3de-464a-a636-bf2037dea163",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_23060\\1066599674.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dati_italia_2022[\"new_cases\"] = dati_italia_2022[\"total_cases\"].diff()\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "167078         NaN\n",
       "167079    644604.0\n",
       "167080         0.0\n",
       "167081         0.0\n",
       "167082         0.0\n",
       "            ...   \n",
       "167438         0.0\n",
       "167439         0.0\n",
       "167440         0.0\n",
       "167441         0.0\n",
       "167442         0.0\n",
       "Name: new_cases, Length: 365, dtype: float64"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcola i nuovi casi giornalieri\n",
    "dati_italia_2022[\"new_cases\"] = dati_italia_2022[\"total_cases\"].diff()\n",
    "dati_italia_2022[\"new_cases\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "c2eda784-c709-44d1-bb42-22c1a9af97ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico dell'andamento dei nuovi casi\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(dati_italia_2022[\"new_cases\"], label=\"New cases\")\n",
    "plt.title(\"Trend of new cases in Italy in 2022\")\n",
    "plt.xlabel(\"month\")\n",
    "plt.ylabel(\"Total new cases\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "39037635-f0df-448e-8028-451a44168fc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_23060\\916306595.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dati_italia_2022[\"cumulative_new_cases\"] = dati_italia_2022[\"new_cases\"].cumsum()\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Grafico della somma cumulativa dei nuovi casi\n",
    "dati_italia_2022[\"cumulative_new_cases\"] = dati_italia_2022[\"new_cases\"].cumsum()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(dati_italia_2022[\"cumulative_new_cases\"], label=\"total cumulative new cases\")\n",
    "plt.plot(dati_italia_2022['new_cases'], label='Total cases')\n",
    "plt.title(\"Trend in the cumulative sum of new cases vs total cases in Italy in 2022\")\n",
    "plt.xlabel(\"month\")\n",
    "plt.ylabel(\"Total cases\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "86f6022e-b33d-4eb2-8290-140624d556bd",
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "Unable to allocate 1.10 TiB for an array with shape (388922, 388922) and data type object",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[345], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Filtra i dati per le nazioni di Italia, Germania e Francia e per il periodo specificato\u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m dati_selezionati \u001b[38;5;241m=\u001b[39m df[(df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlocation\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39misin([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mItaly\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGermany\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFrance\u001b[39m\u001b[38;5;124m\"\u001b[39m])) \u001b[38;5;241m&\u001b[39m\n\u001b[0;32m      3\u001b[0m                         df[df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdate\u001b[39m\u001b[38;5;124m\"\u001b[39m]\u001b[38;5;241m.\u001b[39misin(pd\u001b[38;5;241m.\u001b[39mdate_range(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m2022-05-1\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m2023-04-30\u001b[39m\u001b[38;5;124m\"\u001b[39m))]]\n\u001b[0;32m      4\u001b[0m dati_selezionati\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\frame.py:3880\u001b[0m, in \u001b[0;36mDataFrame.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   3878\u001b[0m \u001b[38;5;66;03m# Do we have a (boolean) DataFrame?\u001b[39;00m\n\u001b[0;32m   3879\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, DataFrame):\n\u001b[1;32m-> 3880\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mwhere(key)\n\u001b[0;32m   3882\u001b[0m \u001b[38;5;66;03m# Do we have a (boolean) 1d indexer?\u001b[39;00m\n\u001b[0;32m   3883\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m com\u001b[38;5;241m.\u001b[39mis_bool_indexer(key):\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:10615\u001b[0m, in \u001b[0;36mNDFrame.where\u001b[1;34m(self, cond, other, inplace, axis, level)\u001b[0m\n\u001b[0;32m  10609\u001b[0m             warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[0;32m  10610\u001b[0m                 _chained_assignment_method_msg,\n\u001b[0;32m  10611\u001b[0m                 ChainedAssignmentError,\n\u001b[0;32m  10612\u001b[0m                 stacklevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m,\n\u001b[0;32m  10613\u001b[0m             )\n\u001b[0;32m  10614\u001b[0m other \u001b[38;5;241m=\u001b[39m common\u001b[38;5;241m.\u001b[39mapply_if_callable(other, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m> 10615\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_where(cond, other, inplace, axis, level)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:10299\u001b[0m, in \u001b[0;36mNDFrame._where\u001b[1;34m(self, cond, other, inplace, axis, level)\u001b[0m\n\u001b[0;32m  10294\u001b[0m         cond \u001b[38;5;241m=\u001b[39m cond\u001b[38;5;241m.\u001b[39m_constructor_expanddim(\n\u001b[0;32m  10295\u001b[0m             {i: cond \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns))},\n\u001b[0;32m  10296\u001b[0m             copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m,\n\u001b[0;32m  10297\u001b[0m         )\n\u001b[0;32m  10298\u001b[0m         cond\u001b[38;5;241m.\u001b[39mcolumns \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcolumns\n\u001b[1;32m> 10299\u001b[0m     cond \u001b[38;5;241m=\u001b[39m cond\u001b[38;5;241m.\u001b[39malign(\u001b[38;5;28mself\u001b[39m, join\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mright\u001b[39m\u001b[38;5;124m\"\u001b[39m, copy\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m  10300\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m  10301\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(cond, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mshape\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:10090\u001b[0m, in \u001b[0;36mNDFrame.align\u001b[1;34m(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis, broadcast_axis)\u001b[0m\n\u001b[0;32m  10088\u001b[0m     axis \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_axis_number(axis)\n\u001b[0;32m  10089\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, ABCDataFrame):\n\u001b[1;32m> 10090\u001b[0m     left, _right, join_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_align_frame(\n\u001b[0;32m  10091\u001b[0m         other,\n\u001b[0;32m  10092\u001b[0m         join\u001b[38;5;241m=\u001b[39mjoin,\n\u001b[0;32m  10093\u001b[0m         axis\u001b[38;5;241m=\u001b[39maxis,\n\u001b[0;32m  10094\u001b[0m         level\u001b[38;5;241m=\u001b[39mlevel,\n\u001b[0;32m  10095\u001b[0m         copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[0;32m  10096\u001b[0m         fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[0;32m  10097\u001b[0m         method\u001b[38;5;241m=\u001b[39mmethod,\n\u001b[0;32m  10098\u001b[0m         limit\u001b[38;5;241m=\u001b[39mlimit,\n\u001b[0;32m  10099\u001b[0m         fill_axis\u001b[38;5;241m=\u001b[39mfill_axis,\n\u001b[0;32m  10100\u001b[0m     )\n\u001b[0;32m  10102\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, ABCSeries):\n\u001b[0;32m  10103\u001b[0m     left, _right, join_index \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_align_series(\n\u001b[0;32m  10104\u001b[0m         other,\n\u001b[0;32m  10105\u001b[0m         join\u001b[38;5;241m=\u001b[39mjoin,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m  10112\u001b[0m         fill_axis\u001b[38;5;241m=\u001b[39mfill_axis,\n\u001b[0;32m  10113\u001b[0m     )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:10174\u001b[0m, in \u001b[0;36mNDFrame._align_frame\u001b[1;34m(self, other, join, axis, level, copy, fill_value, method, limit, fill_axis)\u001b[0m\n\u001b[0;32m  10171\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m  10172\u001b[0m     reindexers \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m0\u001b[39m: [join_index, ilidx], \u001b[38;5;241m1\u001b[39m: [join_columns, clidx]}\n\u001b[1;32m> 10174\u001b[0m left \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reindex_with_indexers(\n\u001b[0;32m  10175\u001b[0m     reindexers, copy\u001b[38;5;241m=\u001b[39mcopy, fill_value\u001b[38;5;241m=\u001b[39mfill_value, allow_dups\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m  10176\u001b[0m )\n\u001b[0;32m  10177\u001b[0m \u001b[38;5;66;03m# other must be always DataFrame\u001b[39;00m\n\u001b[0;32m  10178\u001b[0m right \u001b[38;5;241m=\u001b[39m other\u001b[38;5;241m.\u001b[39m_reindex_with_indexers(\n\u001b[0;32m  10179\u001b[0m     {\u001b[38;5;241m0\u001b[39m: [join_index, iridx], \u001b[38;5;241m1\u001b[39m: [join_columns, cridx]},\n\u001b[0;32m  10180\u001b[0m     copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[0;32m  10181\u001b[0m     fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[0;32m  10182\u001b[0m     allow_dups\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[0;32m  10183\u001b[0m )\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\generic.py:5597\u001b[0m, in \u001b[0;36mNDFrame._reindex_with_indexers\u001b[1;34m(self, reindexers, fill_value, copy, allow_dups)\u001b[0m\n\u001b[0;32m   5594\u001b[0m     indexer \u001b[38;5;241m=\u001b[39m ensure_platform_int(indexer)\n\u001b[0;32m   5596\u001b[0m \u001b[38;5;66;03m# TODO: speed up on homogeneous DataFrame objects (see _reindex_multi)\u001b[39;00m\n\u001b[1;32m-> 5597\u001b[0m new_data \u001b[38;5;241m=\u001b[39m new_data\u001b[38;5;241m.\u001b[39mreindex_indexer(\n\u001b[0;32m   5598\u001b[0m     index,\n\u001b[0;32m   5599\u001b[0m     indexer,\n\u001b[0;32m   5600\u001b[0m     axis\u001b[38;5;241m=\u001b[39mbaxis,\n\u001b[0;32m   5601\u001b[0m     fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[0;32m   5602\u001b[0m     allow_dups\u001b[38;5;241m=\u001b[39mallow_dups,\n\u001b[0;32m   5603\u001b[0m     copy\u001b[38;5;241m=\u001b[39mcopy,\n\u001b[0;32m   5604\u001b[0m )\n\u001b[0;32m   5605\u001b[0m \u001b[38;5;66;03m# If we've made a copy once, no need to make another one\u001b[39;00m\n\u001b[0;32m   5606\u001b[0m copy \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:670\u001b[0m, in \u001b[0;36mBaseBlockManager.reindex_indexer\u001b[1;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, only_slice, use_na_proxy)\u001b[0m\n\u001b[0;32m    663\u001b[0m     new_blocks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slice_take_blocks_ax0(\n\u001b[0;32m    664\u001b[0m         indexer,\n\u001b[0;32m    665\u001b[0m         fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[0;32m    666\u001b[0m         only_slice\u001b[38;5;241m=\u001b[39monly_slice,\n\u001b[0;32m    667\u001b[0m         use_na_proxy\u001b[38;5;241m=\u001b[39muse_na_proxy,\n\u001b[0;32m    668\u001b[0m     )\n\u001b[0;32m    669\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 670\u001b[0m     new_blocks \u001b[38;5;241m=\u001b[39m [\n\u001b[0;32m    671\u001b[0m         blk\u001b[38;5;241m.\u001b[39mtake_nd(\n\u001b[0;32m    672\u001b[0m             indexer,\n\u001b[0;32m    673\u001b[0m             axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m    674\u001b[0m             fill_value\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    675\u001b[0m                 fill_value \u001b[38;5;28;01mif\u001b[39;00m fill_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m blk\u001b[38;5;241m.\u001b[39mfill_value\n\u001b[0;32m    676\u001b[0m             ),\n\u001b[0;32m    677\u001b[0m         )\n\u001b[0;32m    678\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks\n\u001b[0;32m    679\u001b[0m     ]\n\u001b[0;32m    681\u001b[0m new_axes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n\u001b[0;32m    682\u001b[0m new_axes[axis] \u001b[38;5;241m=\u001b[39m new_axis\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\managers.py:671\u001b[0m, in \u001b[0;36m<listcomp>\u001b[1;34m(.0)\u001b[0m\n\u001b[0;32m    663\u001b[0m     new_blocks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_slice_take_blocks_ax0(\n\u001b[0;32m    664\u001b[0m         indexer,\n\u001b[0;32m    665\u001b[0m         fill_value\u001b[38;5;241m=\u001b[39mfill_value,\n\u001b[0;32m    666\u001b[0m         only_slice\u001b[38;5;241m=\u001b[39monly_slice,\n\u001b[0;32m    667\u001b[0m         use_na_proxy\u001b[38;5;241m=\u001b[39muse_na_proxy,\n\u001b[0;32m    668\u001b[0m     )\n\u001b[0;32m    669\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    670\u001b[0m     new_blocks \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m--> 671\u001b[0m         blk\u001b[38;5;241m.\u001b[39mtake_nd(\n\u001b[0;32m    672\u001b[0m             indexer,\n\u001b[0;32m    673\u001b[0m             axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m    674\u001b[0m             fill_value\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m    675\u001b[0m                 fill_value \u001b[38;5;28;01mif\u001b[39;00m fill_value \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m blk\u001b[38;5;241m.\u001b[39mfill_value\n\u001b[0;32m    676\u001b[0m             ),\n\u001b[0;32m    677\u001b[0m         )\n\u001b[0;32m    678\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m blk \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mblocks\n\u001b[0;32m    679\u001b[0m     ]\n\u001b[0;32m    681\u001b[0m new_axes \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maxes)\n\u001b[0;32m    682\u001b[0m new_axes[axis] \u001b[38;5;241m=\u001b[39m new_axis\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\internals\\blocks.py:1061\u001b[0m, in \u001b[0;36mBlock.take_nd\u001b[1;34m(self, indexer, axis, new_mgr_locs, fill_value)\u001b[0m\n\u001b[0;32m   1058\u001b[0m     allow_fill \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m   1060\u001b[0m \u001b[38;5;66;03m# Note: algos.take_nd has upcast logic similar to coerce_to_target_dtype\u001b[39;00m\n\u001b[1;32m-> 1061\u001b[0m new_values \u001b[38;5;241m=\u001b[39m algos\u001b[38;5;241m.\u001b[39mtake_nd(\n\u001b[0;32m   1062\u001b[0m     values, indexer, axis\u001b[38;5;241m=\u001b[39maxis, allow_fill\u001b[38;5;241m=\u001b[39mallow_fill, fill_value\u001b[38;5;241m=\u001b[39mfill_value\n\u001b[0;32m   1063\u001b[0m )\n\u001b[0;32m   1065\u001b[0m \u001b[38;5;66;03m# Called from three places in managers, all of which satisfy\u001b[39;00m\n\u001b[0;32m   1066\u001b[0m \u001b[38;5;66;03m#  these assertions\u001b[39;00m\n\u001b[0;32m   1067\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m, ExtensionBlock):\n\u001b[0;32m   1068\u001b[0m     \u001b[38;5;66;03m# NB: in this case, the 'axis' kwarg will be ignored in the\u001b[39;00m\n\u001b[0;32m   1069\u001b[0m     \u001b[38;5;66;03m#  algos.take_nd call above.\u001b[39;00m\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\array_algos\\take.py:118\u001b[0m, in \u001b[0;36mtake_nd\u001b[1;34m(arr, indexer, axis, fill_value, allow_fill)\u001b[0m\n\u001b[0;32m    115\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m arr\u001b[38;5;241m.\u001b[39mtake(indexer, fill_value\u001b[38;5;241m=\u001b[39mfill_value, allow_fill\u001b[38;5;241m=\u001b[39mallow_fill)\n\u001b[0;32m    117\u001b[0m arr \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39masarray(arr)\n\u001b[1;32m--> 118\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _take_nd_ndarray(arr, indexer, axis, fill_value, allow_fill)\n",
      "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pandas\\core\\array_algos\\take.py:158\u001b[0m, in \u001b[0;36m_take_nd_ndarray\u001b[1;34m(arr, indexer, axis, fill_value, allow_fill)\u001b[0m\n\u001b[0;32m    156\u001b[0m     out \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty(out_shape, dtype\u001b[38;5;241m=\u001b[39mdtype, order\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mF\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    157\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 158\u001b[0m     out \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty(out_shape, dtype\u001b[38;5;241m=\u001b[39mdtype)\n\u001b[0;32m    160\u001b[0m func \u001b[38;5;241m=\u001b[39m _get_take_nd_function(\n\u001b[0;32m    161\u001b[0m     arr\u001b[38;5;241m.\u001b[39mndim, arr\u001b[38;5;241m.\u001b[39mdtype, out\u001b[38;5;241m.\u001b[39mdtype, axis\u001b[38;5;241m=\u001b[39maxis, mask_info\u001b[38;5;241m=\u001b[39mmask_info\n\u001b[0;32m    162\u001b[0m )\n\u001b[0;32m    163\u001b[0m func(arr, indexer, out, fill_value)\n",
      "\u001b[1;31mMemoryError\u001b[0m: Unable to allocate 1.10 TiB for an array with shape (388922, 388922) and data type object"
     ]
    }
   ],
   "source": [
    "# Filtra i dati per le nazioni di Italia, Germania e Francia e per il periodo specificato\n",
    "dati_selezionati = df[(df[\"location\"].isin([\"Italy\", \"Germany\", \"France\"])) &\n",
    "                        df[df[\"date\"].isin(pd.date_range(\"2022-05-1\", \"2023-04-30\"))]]\n",
    "dati_selezionati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb64b7ae-27c3-41c7-bcad-7d448edbd47c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "id": "4ae05f3d-a536-4601-9984-b60d56310435",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iso_code</th>\n",
       "      <th>continent</th>\n",
       "      <th>location</th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>...</th>\n",
       "      <th>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",
       "      <th>YEAR</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2022-05-10</td>\n",
       "      <td>178923.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.286</td>\n",
       "      <td>7684.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>...</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",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>857</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2022-05-11</td>\n",
       "      <td>178923.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.286</td>\n",
       "      <td>7684.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>...</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",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2022-05-12</td>\n",
       "      <td>178923.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.286</td>\n",
       "      <td>7684.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>...</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",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2022-05-13</td>\n",
       "      <td>178923.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.286</td>\n",
       "      <td>7684.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>...</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",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>AFG</td>\n",
       "      <td>Asia</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2022-05-14</td>\n",
       "      <td>178923.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.286</td>\n",
       "      <td>7684.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>...</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",
       "      <td>2022</td>\n",
       "      <td>5</td>\n",
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       "    <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>388588</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2023-04-26</td>\n",
       "      <td>264637.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.857</td>\n",
       "      <td>5685.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>...</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",
       "      <td>2023</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388589</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2023-04-27</td>\n",
       "      <td>264637.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.857</td>\n",
       "      <td>5685.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>...</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",
       "      <td>2023</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388590</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2023-04-28</td>\n",
       "      <td>264637.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.857</td>\n",
       "      <td>5685.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>...</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",
       "      <td>2023</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388591</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2023-04-29</td>\n",
       "      <td>264637.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.857</td>\n",
       "      <td>5685.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>...</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",
       "      <td>2023</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>388592</th>\n",
       "      <td>ZWE</td>\n",
       "      <td>Africa</td>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2023-04-30</td>\n",
       "      <td>264683.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>6.571</td>\n",
       "      <td>5686.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.143</td>\n",
       "      <td>...</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",
       "      <td>2023</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90261 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent     location        date  total_cases  new_cases  \\\n",
       "856         AFG      Asia  Afghanistan  2022-05-10     178923.0        0.0   \n",
       "857         AFG      Asia  Afghanistan  2022-05-11     178923.0        0.0   \n",
       "858         AFG      Asia  Afghanistan  2022-05-12     178923.0        0.0   \n",
       "859         AFG      Asia  Afghanistan  2022-05-13     178923.0        0.0   \n",
       "860         AFG      Asia  Afghanistan  2022-05-14     178923.0        0.0   \n",
       "...         ...       ...          ...         ...          ...        ...   \n",
       "388588      ZWE    Africa     Zimbabwe  2023-04-26     264637.0        0.0   \n",
       "388589      ZWE    Africa     Zimbabwe  2023-04-27     264637.0        0.0   \n",
       "388590      ZWE    Africa     Zimbabwe  2023-04-28     264637.0        0.0   \n",
       "388591      ZWE    Africa     Zimbabwe  2023-04-29     264637.0        0.0   \n",
       "388592      ZWE    Africa     Zimbabwe  2023-04-30     264683.0       46.0   \n",
       "\n",
       "        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "856                  6.286        7684.0         0.0                0.143   \n",
       "857                  6.286        7684.0         0.0                0.143   \n",
       "858                  6.286        7684.0         0.0                0.143   \n",
       "859                  6.286        7684.0         0.0                0.143   \n",
       "860                  6.286        7684.0         0.0                0.143   \n",
       "...                    ...           ...         ...                  ...   \n",
       "388588               3.857        5685.0         0.0                0.143   \n",
       "388589               3.857        5685.0         0.0                0.143   \n",
       "388590               3.857        5685.0         0.0                0.143   \n",
       "388591               3.857        5685.0         0.0                0.143   \n",
       "388592               6.571        5686.0         1.0                0.143   \n",
       "\n",
       "        ...  hospital_beds_per_thousand  life_expectancy  \\\n",
       "856     ...                         0.5            64.83   \n",
       "857     ...                         0.5            64.83   \n",
       "858     ...                         0.5            64.83   \n",
       "859     ...                         0.5            64.83   \n",
       "860     ...                         0.5            64.83   \n",
       "...     ...                         ...              ...   \n",
       "388588  ...                         1.7            61.49   \n",
       "388589  ...                         1.7            61.49   \n",
       "388590  ...                         1.7            61.49   \n",
       "388591  ...                         1.7            61.49   \n",
       "388592  ...                         1.7            61.49   \n",
       "\n",
       "        human_development_index  population  \\\n",
       "856                       0.511  41128772.0   \n",
       "857                       0.511  41128772.0   \n",
       "858                       0.511  41128772.0   \n",
       "859                       0.511  41128772.0   \n",
       "860                       0.511  41128772.0   \n",
       "...                         ...         ...   \n",
       "388588                    0.571  16320539.0   \n",
       "388589                    0.571  16320539.0   \n",
       "388590                    0.571  16320539.0   \n",
       "388591                    0.571  16320539.0   \n",
       "388592                    0.571  16320539.0   \n",
       "\n",
       "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "856                                      NaN                          NaN   \n",
       "857                                      NaN                          NaN   \n",
       "858                                      NaN                          NaN   \n",
       "859                                      NaN                          NaN   \n",
       "860                                      NaN                          NaN   \n",
       "...                                      ...                          ...   \n",
       "388588                                   NaN                          NaN   \n",
       "388589                                   NaN                          NaN   \n",
       "388590                                   NaN                          NaN   \n",
       "388591                                   NaN                          NaN   \n",
       "388592                                   NaN                          NaN   \n",
       "\n",
       "        excess_mortality  excess_mortality_cumulative_per_million  YEAR  month  \n",
       "856                  NaN                                      NaN  2022      5  \n",
       "857                  NaN                                      NaN  2022      5  \n",
       "858                  NaN                                      NaN  2022      5  \n",
       "859                  NaN                                      NaN  2022      5  \n",
       "860                  NaN                                      NaN  2022      5  \n",
       "...                  ...                                      ...   ...    ...  \n",
       "388588               NaN                                      NaN  2023      4  \n",
       "388589               NaN                                      NaN  2023      4  \n",
       "388590               NaN                                      NaN  2023      4  \n",
       "388591               NaN                                      NaN  2023      4  \n",
       "388592               NaN                                      NaN  2023      4  \n",
       "\n",
       "[90261 rows x 69 columns]"
      ]
     },
     "execution_count": 341,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dati_selezionati = df.loc[df[\"date\"].between(\"2022-05-1\", \"2023-04-30\")]\n",
    "dati_selezionati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "25472375-4734-4e83-9206-7924a7f728d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crea un boxplot per la differenza nel numero di pazienti in terapia intensiva (ICU)\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.title(\"Difference in the number of patients in intensive care unit (ICU) from May 2022 to April 2023\")\n",
    "plt.xlabel(\"Location\")\n",
    "plt.ylabel(\"Number of icu_patients\")\n",
    "plt.grid(True)\n",
    "plt.boxplot([dati_selezionati[dati_selezionati[\"location\"] == \"Italy\"][\"icu_patients\"],\n",
    "             dati_selezionati[dati_selezionati[\"location\"] == \"Germany\"][\"icu_patients\"],\n",
    "             dati_selezionati[dati_selezionati[\"location\"] == \"France\"][\"icu_patients\"]],\n",
    "            labels=[\"Italy\", \"Germany\", \"France\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "86c43500-5b31-4fbf-b562-ceca0b5cef7d",
   "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>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",
       "      <th>YEAR</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>118124</th>\n",
       "      <td>FRA</td>\n",
       "      <td>Europe</td>\n",
       "      <td>France</td>\n",
       "      <td>2023-01-01</td>\n",
       "      <td>38141254.0</td>\n",
       "      <td>151707.0</td>\n",
       "      <td>21672.429</td>\n",
       "      <td>162475.0</td>\n",
       "      <td>808.0</td>\n",
       "      <td>115.429</td>\n",
       "      <td>...</td>\n",
       "      <td>5.98</td>\n",
       "      <td>82.66</td>\n",
       "      <td>0.901</td>\n",
       "      <td>67813000.0</td>\n",
       "      <td>122180.83</td>\n",
       "      <td>6.64</td>\n",
       "      <td>25.88</td>\n",
       "      <td>1886.7708</td>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118125</th>\n",
       "      <td>FRA</td>\n",
       "      <td>Europe</td>\n",
       "      <td>France</td>\n",
       "      <td>2023-01-02</td>\n",
       "      <td>38141254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21672.429</td>\n",
       "      <td>162475.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.429</td>\n",
       "      <td>...</td>\n",
       "      <td>5.98</td>\n",
       "      <td>82.66</td>\n",
       "      <td>0.901</td>\n",
       "      <td>67813000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118126</th>\n",
       "      <td>FRA</td>\n",
       "      <td>Europe</td>\n",
       "      <td>France</td>\n",
       "      <td>2023-01-03</td>\n",
       "      <td>38141254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21672.429</td>\n",
       "      <td>162475.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.429</td>\n",
       "      <td>...</td>\n",
       "      <td>5.98</td>\n",
       "      <td>82.66</td>\n",
       "      <td>0.901</td>\n",
       "      <td>67813000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118127</th>\n",
       "      <td>FRA</td>\n",
       "      <td>Europe</td>\n",
       "      <td>France</td>\n",
       "      <td>2023-01-04</td>\n",
       "      <td>38141254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21672.429</td>\n",
       "      <td>162475.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.429</td>\n",
       "      <td>...</td>\n",
       "      <td>5.98</td>\n",
       "      <td>82.66</td>\n",
       "      <td>0.901</td>\n",
       "      <td>67813000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118128</th>\n",
       "      <td>FRA</td>\n",
       "      <td>Europe</td>\n",
       "      <td>France</td>\n",
       "      <td>2023-01-05</td>\n",
       "      <td>38141254.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21672.429</td>\n",
       "      <td>162475.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>115.429</td>\n",
       "      <td>...</td>\n",
       "      <td>5.98</td>\n",
       "      <td>82.66</td>\n",
       "      <td>0.901</td>\n",
       "      <td>67813000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023</td>\n",
       "      <td>1</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>329228</th>\n",
       "      <td>ESP</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2023-12-27</td>\n",
       "      <td>13980340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121852.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.97</td>\n",
       "      <td>83.56</td>\n",
       "      <td>0.904</td>\n",
       "      <td>47558632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329229</th>\n",
       "      <td>ESP</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2023-12-28</td>\n",
       "      <td>13980340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121852.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.97</td>\n",
       "      <td>83.56</td>\n",
       "      <td>0.904</td>\n",
       "      <td>47558632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329230</th>\n",
       "      <td>ESP</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2023-12-29</td>\n",
       "      <td>13980340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121852.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.97</td>\n",
       "      <td>83.56</td>\n",
       "      <td>0.904</td>\n",
       "      <td>47558632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329231</th>\n",
       "      <td>ESP</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2023-12-30</td>\n",
       "      <td>13980340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121852.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.97</td>\n",
       "      <td>83.56</td>\n",
       "      <td>0.904</td>\n",
       "      <td>47558632.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>329232</th>\n",
       "      <td>ESP</td>\n",
       "      <td>Europe</td>\n",
       "      <td>Spain</td>\n",
       "      <td>2023-12-31</td>\n",
       "      <td>13980340.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>121852.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>2.97</td>\n",
       "      <td>83.56</td>\n",
       "      <td>0.904</td>\n",
       "      <td>47558632.0</td>\n",
       "      <td>148088.22</td>\n",
       "      <td>8.75</td>\n",
       "      <td>18.45</td>\n",
       "      <td>3116.3590</td>\n",
       "      <td>2023</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       iso_code continent location        date  total_cases  new_cases  \\\n",
       "118124      FRA    Europe   France  2023-01-01   38141254.0   151707.0   \n",
       "118125      FRA    Europe   France  2023-01-02   38141254.0        0.0   \n",
       "118126      FRA    Europe   France  2023-01-03   38141254.0        0.0   \n",
       "118127      FRA    Europe   France  2023-01-04   38141254.0        0.0   \n",
       "118128      FRA    Europe   France  2023-01-05   38141254.0        0.0   \n",
       "...         ...       ...      ...         ...          ...        ...   \n",
       "329228      ESP    Europe    Spain  2023-12-27   13980340.0        NaN   \n",
       "329229      ESP    Europe    Spain  2023-12-28   13980340.0        NaN   \n",
       "329230      ESP    Europe    Spain  2023-12-29   13980340.0        NaN   \n",
       "329231      ESP    Europe    Spain  2023-12-30   13980340.0        NaN   \n",
       "329232      ESP    Europe    Spain  2023-12-31   13980340.0        NaN   \n",
       "\n",
       "        new_cases_smoothed  total_deaths  new_deaths  new_deaths_smoothed  \\\n",
       "118124           21672.429      162475.0       808.0              115.429   \n",
       "118125           21672.429      162475.0         0.0              115.429   \n",
       "118126           21672.429      162475.0         0.0              115.429   \n",
       "118127           21672.429      162475.0         0.0              115.429   \n",
       "118128           21672.429      162475.0         0.0              115.429   \n",
       "...                    ...           ...         ...                  ...   \n",
       "329228                 NaN      121852.0         NaN                  NaN   \n",
       "329229                 NaN      121852.0         NaN                  NaN   \n",
       "329230                 NaN      121852.0         NaN                  NaN   \n",
       "329231                 NaN      121852.0         NaN                  NaN   \n",
       "329232                 NaN      121852.0         NaN                  NaN   \n",
       "\n",
       "        ...  hospital_beds_per_thousand  life_expectancy  \\\n",
       "118124  ...                        5.98            82.66   \n",
       "118125  ...                        5.98            82.66   \n",
       "118126  ...                        5.98            82.66   \n",
       "118127  ...                        5.98            82.66   \n",
       "118128  ...                        5.98            82.66   \n",
       "...     ...                         ...              ...   \n",
       "329228  ...                        2.97            83.56   \n",
       "329229  ...                        2.97            83.56   \n",
       "329230  ...                        2.97            83.56   \n",
       "329231  ...                        2.97            83.56   \n",
       "329232  ...                        2.97            83.56   \n",
       "\n",
       "        human_development_index  population  \\\n",
       "118124                    0.901  67813000.0   \n",
       "118125                    0.901  67813000.0   \n",
       "118126                    0.901  67813000.0   \n",
       "118127                    0.901  67813000.0   \n",
       "118128                    0.901  67813000.0   \n",
       "...                         ...         ...   \n",
       "329228                    0.904  47558632.0   \n",
       "329229                    0.904  47558632.0   \n",
       "329230                    0.904  47558632.0   \n",
       "329231                    0.904  47558632.0   \n",
       "329232                    0.904  47558632.0   \n",
       "\n",
       "        excess_mortality_cumulative_absolute  excess_mortality_cumulative  \\\n",
       "118124                             122180.83                         6.64   \n",
       "118125                                   NaN                          NaN   \n",
       "118126                                   NaN                          NaN   \n",
       "118127                                   NaN                          NaN   \n",
       "118128                                   NaN                          NaN   \n",
       "...                                      ...                          ...   \n",
       "329228                                   NaN                          NaN   \n",
       "329229                                   NaN                          NaN   \n",
       "329230                                   NaN                          NaN   \n",
       "329231                                   NaN                          NaN   \n",
       "329232                             148088.22                         8.75   \n",
       "\n",
       "        excess_mortality  excess_mortality_cumulative_per_million  YEAR  month  \n",
       "118124             25.88                                1886.7708  2023      1  \n",
       "118125               NaN                                      NaN  2023      1  \n",
       "118126               NaN                                      NaN  2023      1  \n",
       "118127               NaN                                      NaN  2023      1  \n",
       "118128               NaN                                      NaN  2023      1  \n",
       "...                  ...                                      ...   ...    ...  \n",
       "329228               NaN                                      NaN  2023     12  \n",
       "329229               NaN                                      NaN  2023     12  \n",
       "329230               NaN                                      NaN  2023     12  \n",
       "329231               NaN                                      NaN  2023     12  \n",
       "329232             18.45                                3116.3590  2023     12  \n",
       "\n",
       "[1460 rows x 69 columns]"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filtra i dati per le nazioni di Italia, Germania, Francia e Spagna e per il 2023\n",
    "dati_selezionati = df[(df['location'].isin(['Italy', 'Germany', 'France', 'Spain'])) & (df['YEAR'] == 2023)]\n",
    "dati_selezionati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "eeb72b5f-650c-4e14-9f77-4a227820750b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are null values ​​in the column 'hosp_patients'.\n",
      "You can handle null values ​​by replacing them with 0 or with the average of the non-null values.\n",
      "True\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_23060\\2871125157.py:8: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  dati_selezionati['hosp_patients'].fillna(0, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# Verifica se ci sono valori nulli nella colonna 'pazienti_ospedalizzati'\n",
    "valori_nulli = dati_selezionati['hosp_patients'].isnull().any()\n",
    "if valori_nulli:\n",
    "    # Se ci sono valori nulli, suggerisci un'opzione per gestirli\n",
    "    print(\"There are null values ​​in the column 'hosp_patients'.\")\n",
    "    print(\"You can handle null values ​​by replacing them with 0 or with the average of the non-null values.\")\n",
    "    # Sostituisci i valori nulli con 0\n",
    "    dati_selezionati['hosp_patients'].fillna(0, inplace=True)\n",
    "print(valori_nulli)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "1abc79a6-9621-41fd-a06a-74b2eeb133b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location\n",
       "France     1382574.0\n",
       "Germany          0.0\n",
       "Italy      1175272.0\n",
       "Spain       354602.0\n",
       "Name: hosp_patients, dtype: float64"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calcola la somma dei pazienti ospedalizzati per ogni nazione\n",
    "somma_pazienti_ospedalizzati = dati_selezionati.groupby('location')['hosp_patients'].sum()\n",
    "somma_pazienti_ospedalizzati"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9188996-66f6-409a-99af-fcac8c737ba9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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