unit_1_grafical.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "covi_data = pd.read_csv('data/covid_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "vaccinations_data = pd.read_csv('data/country_vaccinations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>province/state</th>\n",
       "      <th>country</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "      <th>recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>China</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>China</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>China</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>China</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Gansu</td>\n",
       "      <td>China</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date province/state country  confirmed  deaths  recovered\n",
       "0  01/22/2020          Anhui   China        1.0     0.0        0.0\n",
       "1  01/22/2020        Beijing   China       14.0     0.0        0.0\n",
       "2  01/22/2020      Chongqing   China        6.0     0.0        0.0\n",
       "3  01/22/2020         Fujian   China        1.0     0.0        0.0\n",
       "4  01/22/2020          Gansu   China        0.0     0.0        0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covi_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы будем работать со следующими столбцами:\n",
    "\n",
    "date — дата наблюдения;\n",
    "\n",
    "province/state — наименование провинции/штата;\n",
    "\n",
    "country — наименование страны;\n",
    "\n",
    "confirmed — общее число зафиксированных случаев на указанный день;\n",
    "\n",
    "deaths — общее число зафиксированных смертей на указанный день;\n",
    "\n",
    "recovered — общее число выздоровлений на указанный день."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>country</th>\n",
       "      <th>iso_code</th>\n",
       "      <th>date</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>daily_vaccinations_raw</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>total_vaccinations_per_hundred</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations_per_million</th>\n",
       "      <th>vaccines</th>\n",
       "      <th>source_name</th>\n",
       "      <th>source_website</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2021-02-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>https://app.powerbi.com/view?r=eyJrIjoiYTkyM2V...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2021-02-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>https://app.powerbi.com/view?r=eyJrIjoiYTkyM2V...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2021-02-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>https://app.powerbi.com/view?r=eyJrIjoiYTkyM2V...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2021-02-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>https://app.powerbi.com/view?r=eyJrIjoiYTkyM2V...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>AFG</td>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "      <td>World Health Organization</td>\n",
       "      <td>https://app.powerbi.com/view?r=eyJrIjoiYTkyM2V...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country iso_code        date  total_vaccinations  people_vaccinated  \\\n",
       "0  Afghanistan      AFG  2021-02-22                 0.0                0.0   \n",
       "1  Afghanistan      AFG  2021-02-23                 NaN                NaN   \n",
       "2  Afghanistan      AFG  2021-02-24                 NaN                NaN   \n",
       "3  Afghanistan      AFG  2021-02-25                 NaN                NaN   \n",
       "4  Afghanistan      AFG  2021-02-26                 NaN                NaN   \n",
       "\n",
       "   people_fully_vaccinated  daily_vaccinations_raw  daily_vaccinations  \\\n",
       "0                      NaN                     NaN                 NaN   \n",
       "1                      NaN                     NaN              1367.0   \n",
       "2                      NaN                     NaN              1367.0   \n",
       "3                      NaN                     NaN              1367.0   \n",
       "4                      NaN                     NaN              1367.0   \n",
       "\n",
       "   total_vaccinations_per_hundred  people_vaccinated_per_hundred  \\\n",
       "0                             0.0                            0.0   \n",
       "1                             NaN                            NaN   \n",
       "2                             NaN                            NaN   \n",
       "3                             NaN                            NaN   \n",
       "4                             NaN                            NaN   \n",
       "\n",
       "   people_fully_vaccinated_per_hundred  daily_vaccinations_per_million  \\\n",
       "0                                  NaN                             NaN   \n",
       "1                                  NaN                            34.0   \n",
       "2                                  NaN                            34.0   \n",
       "3                                  NaN                            34.0   \n",
       "4                                  NaN                            34.0   \n",
       "\n",
       "                                            vaccines  \\\n",
       "0  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...   \n",
       "1  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...   \n",
       "2  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...   \n",
       "3  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...   \n",
       "4  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...   \n",
       "\n",
       "                 source_name  \\\n",
       "0  World Health Organization   \n",
       "1  World Health Organization   \n",
       "2  World Health Organization   \n",
       "3  World Health Organization   \n",
       "4  World Health Organization   \n",
       "\n",
       "                                      source_website  \n",
       "0  https://app.powerbi.com/view?r=eyJrIjoiYTkyM2V...  \n",
       "1  https://app.powerbi.com/view?r=eyJrIjoiYTkyM2V...  \n",
       "2  https://app.powerbi.com/view?r=eyJrIjoiYTkyM2V...  \n",
       "3  https://app.powerbi.com/view?r=eyJrIjoiYTkyM2V...  \n",
       "4  https://app.powerbi.com/view?r=eyJrIjoiYTkyM2V...  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccinations_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'date', 'total_vaccinations', 'people_vaccinated',\n",
       "       'people_vaccinated_per_hundred', 'people_fully_vaccinated',\n",
       "       'people_fully_vaccinated_per_hundred', 'daily_vaccinations',\n",
       "       'vaccines'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccinations_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "vaccinations_data = vaccinations_data[\n",
    "    ['country', 'date', 'total_vaccinations', \n",
    "     'people_vaccinated', 'people_vaccinated_per_hundred',\n",
    "     'people_fully_vaccinated', 'people_fully_vaccinated_per_hundred',\n",
    "     'daily_vaccinations', 'vaccines']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>country</th>\n",
       "      <th>date</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>vaccines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country        date  total_vaccinations  people_vaccinated  \\\n",
       "0  Afghanistan  2021-02-22                 0.0                0.0   \n",
       "1  Afghanistan  2021-02-23                 NaN                NaN   \n",
       "2  Afghanistan  2021-02-24                 NaN                NaN   \n",
       "3  Afghanistan  2021-02-25                 NaN                NaN   \n",
       "4  Afghanistan  2021-02-26                 NaN                NaN   \n",
       "\n",
       "   people_vaccinated_per_hundred  people_fully_vaccinated  \\\n",
       "0                            0.0                      NaN   \n",
       "1                            NaN                      NaN   \n",
       "2                            NaN                      NaN   \n",
       "3                            NaN                      NaN   \n",
       "4                            NaN                      NaN   \n",
       "\n",
       "   people_fully_vaccinated_per_hundred  daily_vaccinations  \\\n",
       "0                                  NaN                 NaN   \n",
       "1                                  NaN              1367.0   \n",
       "2                                  NaN              1367.0   \n",
       "3                                  NaN              1367.0   \n",
       "4                                  NaN              1367.0   \n",
       "\n",
       "                                            vaccines  \n",
       "0  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...  \n",
       "1  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...  \n",
       "2  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...  \n",
       "3  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...  \n",
       "4  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccinations_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Данная таблица содержит следующие столбцы:**\n",
    "\n",
    "*country* — наименование страны;\n",
    "\n",
    "*date* — дата наблюдения;\n",
    "\n",
    "*total_vaccinations* — общее число введённых вакцин в стране на указанный день;\n",
    "\n",
    "*people_vaccinated* — общее число привитых первым компонентом в стране на указанный день;\n",
    "\n",
    "*people_vaccinated_per_hundred* — процент привитых первым компонентом в стране на указанный день (рассчитывается как );\n",
    "\n",
    "*people_fully_vaccinated* — общее число привитых вторым компонентом в стране на указанный день (первый компонент уже был введён им ранее);\n",
    "\n",
    "*people_fully_vaccinated_per_hundred* — процент привитых вторым компонентом в стране на указанный день (рассчитывается как );\n",
    "\n",
    "*daily_vaccination* — ежедневная вакцинация (число вакцинированных в указанный день);\n",
    "\n",
    "*vaccines* — комбинации вакцин, используемые в стране."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Группируем таблицу по дате и названию страны и рассчитываем суммарные показатели по всем регионам. Тем самым переходим от данных по регионам к данным по странам:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "covi_data = covi_data.groupby(\n",
    "    ['date', 'country'],\n",
    "    as_index=False\n",
    ")[['confirmed', 'deaths', 'recovered']].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Преобразуем даты в формат datetime с помощью функции pd.to_datetime():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "covi_data['date'] = pd.to_datetime(covi_data['date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим признак больных на данный момент (active). Для этого вычтем из общего числа зафиксированных случаев число смертей и число выздоровевших пациентов:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "covi_data['active'] = covi_data['confirmed'] - covi_data['deaths'] - covi_data['recovered']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Создадим признак ежедневного прироста числа заболевших, умерших и выздоровевших людей. Для этого отсортируем данные по названиям стран, а затем по датам. После этого произведём группировку по странам и рассчитаем разницу между «вчера и сегодня» с помощью метода diff():"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "covi_data = covi_data.sort_values(by=['country', 'date'])\n",
    "covi_data['daily_confirmed'] = covi_data.groupby('country')['confirmed'].diff()\n",
    "covi_data['daily_deaths'] = covi_data.groupby('country')['deaths'].diff()\n",
    "covi_data['daily_recovered'] = covi_data.groupby('country')['recovered'].diff()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>country</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "      <th>recovered</th>\n",
       "      <th>active</th>\n",
       "      <th>daily_confirmed</th>\n",
       "      <th>daily_deaths</th>\n",
       "      <th>daily_recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11337</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11570</th>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11807</th>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12051</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12299</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            date      country  confirmed  deaths  recovered  active  \\\n",
       "11337 2020-02-24  Afghanistan        1.0     0.0        0.0     1.0   \n",
       "11570 2020-02-25  Afghanistan        1.0     0.0        0.0     1.0   \n",
       "11807 2020-02-26  Afghanistan        1.0     0.0        0.0     1.0   \n",
       "12051 2020-02-27  Afghanistan        1.0     0.0        0.0     1.0   \n",
       "12299 2020-02-28  Afghanistan        1.0     0.0        0.0     1.0   \n",
       "\n",
       "       daily_confirmed  daily_deaths  daily_recovered  \n",
       "11337              NaN           NaN              NaN  \n",
       "11570              0.0           0.0              0.0  \n",
       "11807              0.0           0.0              0.0  \n",
       "12051              0.0           0.0              0.0  \n",
       "12299              0.0           0.0              0.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covi_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В таблице vaccinations_data достаточно будет преобразовать столбцы в формат datetime:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "vaccinations_data['date'] = pd.to_datetime(vaccinations_data['date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>country</th>\n",
       "      <th>date</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>vaccines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-22</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-23</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2021-02-26</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1367.0</td>\n",
       "      <td>Johnson&amp;Johnson, Oxford/AstraZeneca, Pfizer/Bi...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country       date  total_vaccinations  people_vaccinated  \\\n",
       "0  Afghanistan 2021-02-22                 0.0                0.0   \n",
       "1  Afghanistan 2021-02-23                 NaN                NaN   \n",
       "2  Afghanistan 2021-02-24                 NaN                NaN   \n",
       "3  Afghanistan 2021-02-25                 NaN                NaN   \n",
       "4  Afghanistan 2021-02-26                 NaN                NaN   \n",
       "\n",
       "   people_vaccinated_per_hundred  people_fully_vaccinated  \\\n",
       "0                            0.0                      NaN   \n",
       "1                            NaN                      NaN   \n",
       "2                            NaN                      NaN   \n",
       "3                            NaN                      NaN   \n",
       "4                            NaN                      NaN   \n",
       "\n",
       "   people_fully_vaccinated_per_hundred  daily_vaccinations  \\\n",
       "0                                  NaN                 NaN   \n",
       "1                                  NaN              1367.0   \n",
       "2                                  NaN              1367.0   \n",
       "3                                  NaN              1367.0   \n",
       "4                                  NaN              1367.0   \n",
       "\n",
       "                                            vaccines  \n",
       "0  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...  \n",
       "1  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...  \n",
       "2  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...  \n",
       "3  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...  \n",
       "4  Johnson&Johnson, Oxford/AstraZeneca, Pfizer/Bi...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vaccinations_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2020-01-22 00:00:00\n",
      "2021-05-29 00:00:00\n"
     ]
    }
   ],
   "source": [
    "print(covi_data['date'].min())\n",
    "print(covi_data['date'].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df = covi_data.merge(vaccinations_data, on=['date', 'country'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>country</th>\n",
       "      <th>confirmed</th>\n",
       "      <th>deaths</th>\n",
       "      <th>recovered</th>\n",
       "      <th>active</th>\n",
       "      <th>daily_confirmed</th>\n",
       "      <th>daily_deaths</th>\n",
       "      <th>daily_recovered</th>\n",
       "      <th>total_vaccinations</th>\n",
       "      <th>people_vaccinated</th>\n",
       "      <th>people_vaccinated_per_hundred</th>\n",
       "      <th>people_fully_vaccinated</th>\n",
       "      <th>people_fully_vaccinated_per_hundred</th>\n",
       "      <th>daily_vaccinations</th>\n",
       "      <th>vaccines</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date      country  confirmed  deaths  recovered  active  \\\n",
       "0 2020-02-24  Afghanistan        1.0     0.0        0.0     1.0   \n",
       "1 2020-02-25  Afghanistan        1.0     0.0        0.0     1.0   \n",
       "2 2020-02-26  Afghanistan        1.0     0.0        0.0     1.0   \n",
       "3 2020-02-27  Afghanistan        1.0     0.0        0.0     1.0   \n",
       "4 2020-02-28  Afghanistan        1.0     0.0        0.0     1.0   \n",
       "\n",
       "   daily_confirmed  daily_deaths  daily_recovered  total_vaccinations  \\\n",
       "0              NaN           NaN              NaN                 NaN   \n",
       "1              0.0           0.0              0.0                 NaN   \n",
       "2              0.0           0.0              0.0                 NaN   \n",
       "3              0.0           0.0              0.0                 NaN   \n",
       "4              0.0           0.0              0.0                 NaN   \n",
       "\n",
       "   people_vaccinated  people_vaccinated_per_hundred  people_fully_vaccinated  \\\n",
       "0                NaN                            NaN                      NaN   \n",
       "1                NaN                            NaN                      NaN   \n",
       "2                NaN                            NaN                      NaN   \n",
       "3                NaN                            NaN                      NaN   \n",
       "4                NaN                            NaN                      NaN   \n",
       "\n",
       "   people_fully_vaccinated_per_hundred  daily_vaccinations vaccines  \n",
       "0                                  NaN                 NaN      NaN  \n",
       "1                                  NaN                 NaN      NaN  \n",
       "2                                  NaN                 NaN      NaN  \n",
       "3                                  NaN                 NaN      NaN  \n",
       "4                                  NaN                 NaN      NaN  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(86785, 16)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В получившейся в задании 3.3 таблице covid_df создайте признаки death_rate — общий процент смертей среди зафиксированных случаев (летальность) и recover_rate — общий процент случаев выздоровления. Данные характеристики рассчитайте как отношение числа смертей (deaths) и числа выздоровлений (recovered) к числу зафиксированных случаев (confirmed) и умножьте результаты на 100%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Какова максимальная летальность в США (United States) за весь период? Ответ округлите до второго знака после запятой."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df['death_rate'] = covid_df['deaths'] / covid_df['confirmed'] * 100\n",
    "covid_df['recover_rate'] = covid_df['recovered'] / covid_df['confirmed'] * 100\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7.19"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(covid_df[covid_df['country'] == 'United States']['death_rate'].max(), 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2020-03-16</td>\n",
       "      <td>occupied Palestinian territory</td>\n",
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      ],
      "text/plain": [
       "            date                         country  confirmed  deaths  \\\n",
       "86780 2020-03-12  occupied Palestinian territory        0.0     0.0   \n",
       "86781 2020-03-14  occupied Palestinian territory        0.0     0.0   \n",
       "86782 2020-03-15  occupied Palestinian territory        0.0     0.0   \n",
       "86783 2020-03-16  occupied Palestinian territory        0.0     0.0   \n",
       "86784 2020-03-17  occupied Palestinian territory        0.0     0.0   \n",
       "\n",
       "       recovered  active  daily_confirmed  daily_deaths  daily_recovered  \\\n",
       "86780        0.0     0.0              0.0           0.0              0.0   \n",
       "86781        0.0     0.0              0.0           0.0              0.0   \n",
       "86782        0.0     0.0              0.0           0.0              0.0   \n",
       "86783        0.0     0.0              0.0           0.0              0.0   \n",
       "86784        0.0     0.0              0.0           0.0              0.0   \n",
       "\n",
       "       total_vaccinations  people_vaccinated  people_vaccinated_per_hundred  \\\n",
       "86780                 NaN                NaN                            NaN   \n",
       "86781                 NaN                NaN                            NaN   \n",
       "86782                 NaN                NaN                            NaN   \n",
       "86783                 NaN                NaN                            NaN   \n",
       "86784                 NaN                NaN                            NaN   \n",
       "\n",
       "       people_fully_vaccinated  people_fully_vaccinated_per_hundred  \\\n",
       "86780                      NaN                                  NaN   \n",
       "86781                      NaN                                  NaN   \n",
       "86782                      NaN                                  NaN   \n",
       "86783                      NaN                                  NaN   \n",
       "86784                      NaN                                  NaN   \n",
       "\n",
       "       daily_vaccinations vaccines  death_rate  recover_rate   recover_rate  \n",
       "86780                 NaN      NaN         NaN            NaN           NaN  \n",
       "86781                 NaN      NaN         NaN            NaN           NaN  \n",
       "86782                 NaN      NaN         NaN            NaN           NaN  \n",
       "86783                 NaN      NaN         NaN            NaN           NaN  \n",
       "86784                 NaN      NaN         NaN            NaN           NaN  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>people_fully_vaccinated_per_hundred</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [date, country, confirmed, deaths, recovered, active, daily_confirmed, daily_deaths, daily_recovered, total_vaccinations, people_vaccinated, people_vaccinated_per_hundred, people_fully_vaccinated, people_fully_vaccinated_per_hundred, daily_vaccinations, vaccines, death_rate, recover_rate , recover_rate]\n",
       "Index: []"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_df[covid_df['recover_rate'] == 'Russia']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67.06"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(covid_df[covid_df['country'] == 'Russia']['recover_rate'].mean(), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "67.06"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(covid_df[covid_df['country'] == 'Russia']['recover_rate'].mean(), 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_df.to_csv('data/covid_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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