07.03. Covid19.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Аппроксимируем эпидемиологические данные (начало эпидемии)\n",
    "\n",
    "## 1. Подключаем необходимые библиотеки"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from scipy import optimize\n",
    "import pandas as pd\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Загружаем данные\n",
    "\n",
    "Данные о распространении эпидемии возьмем [здесь](https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series). Данные обновляются каждый день.\n",
    "\n",
    "Будем использовать [pandas](https://pandas.pydata.org/) - библиотеку, в которой реализованы полезные инструменты для анализа данных. Основной тип данных в этой библиотеке - это таблицы (`dataframe`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/\"\n",
    "covid19_confirmed = pd.read_csv(url + \"time_series_covid19_confirmed_global.csv\")\n",
    "covid19_deaths    = pd.read_csv(url + \"time_series_covid19_deaths_global.csv\")\n",
    "covid19_recovered = pd.read_csv(url + \"time_series_covid19_recovered_global.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Внимание!** *Данные обновляются ежедневно, поэтому вы можете получить другой результат.*\n",
    "\n",
    "Мы загрузили 3 таблицы, которые содержат данные по странам на данный день:\n",
    "* `covid19_confirmed` о количестве подтвержденных случаев\n",
    "* `covid19_deaths`    - суммарное количество\n",
    "* `covid19_recovered` - суммарное количество выздоровевших\n",
    "\n",
    "Рассмотрим, например, `covid19_confirmed`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>5/8/20</th>\n",
       "      <th>5/9/20</th>\n",
       "      <th>5/10/20</th>\n",
       "      <th>5/11/20</th>\n",
       "      <th>5/12/20</th>\n",
       "      <th>5/13/20</th>\n",
       "      <th>5/14/20</th>\n",
       "      <th>5/15/20</th>\n",
       "      <th>5/16/20</th>\n",
       "      <th>5/17/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3778</td>\n",
       "      <td>4033</td>\n",
       "      <td>4402</td>\n",
       "      <td>4687</td>\n",
       "      <td>4963</td>\n",
       "      <td>5226</td>\n",
       "      <td>5639</td>\n",
       "      <td>6053</td>\n",
       "      <td>6402</td>\n",
       "      <td>6664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Albania</td>\n",
       "      <td>41.153300</td>\n",
       "      <td>20.168300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>850</td>\n",
       "      <td>856</td>\n",
       "      <td>868</td>\n",
       "      <td>872</td>\n",
       "      <td>876</td>\n",
       "      <td>880</td>\n",
       "      <td>898</td>\n",
       "      <td>916</td>\n",
       "      <td>933</td>\n",
       "      <td>946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Algeria</td>\n",
       "      <td>28.033900</td>\n",
       "      <td>1.659600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5369</td>\n",
       "      <td>5558</td>\n",
       "      <td>5723</td>\n",
       "      <td>5891</td>\n",
       "      <td>6067</td>\n",
       "      <td>6253</td>\n",
       "      <td>6442</td>\n",
       "      <td>6629</td>\n",
       "      <td>6821</td>\n",
       "      <td>7019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Andorra</td>\n",
       "      <td>42.506300</td>\n",
       "      <td>1.521800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>752</td>\n",
       "      <td>754</td>\n",
       "      <td>755</td>\n",
       "      <td>755</td>\n",
       "      <td>758</td>\n",
       "      <td>760</td>\n",
       "      <td>761</td>\n",
       "      <td>761</td>\n",
       "      <td>761</td>\n",
       "      <td>761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Angola</td>\n",
       "      <td>-11.202700</td>\n",
       "      <td>17.873900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>43</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>17.060800</td>\n",
       "      <td>-61.796400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>-38.416100</td>\n",
       "      <td>-63.616700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5611</td>\n",
       "      <td>5776</td>\n",
       "      <td>6034</td>\n",
       "      <td>6278</td>\n",
       "      <td>6563</td>\n",
       "      <td>6879</td>\n",
       "      <td>7134</td>\n",
       "      <td>7479</td>\n",
       "      <td>7805</td>\n",
       "      <td>8068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Armenia</td>\n",
       "      <td>40.069100</td>\n",
       "      <td>45.038200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3029</td>\n",
       "      <td>3175</td>\n",
       "      <td>3313</td>\n",
       "      <td>3392</td>\n",
       "      <td>3538</td>\n",
       "      <td>3718</td>\n",
       "      <td>3860</td>\n",
       "      <td>4044</td>\n",
       "      <td>4283</td>\n",
       "      <td>4472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-35.473500</td>\n",
       "      <td>149.012400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>New South Wales</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-33.868800</td>\n",
       "      <td>151.209300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>3051</td>\n",
       "      <td>3053</td>\n",
       "      <td>3053</td>\n",
       "      <td>3053</td>\n",
       "      <td>3059</td>\n",
       "      <td>3063</td>\n",
       "      <td>3071</td>\n",
       "      <td>3074</td>\n",
       "      <td>3075</td>\n",
       "      <td>3076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-12.463400</td>\n",
       "      <td>130.845600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-28.016700</td>\n",
       "      <td>153.400000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1045</td>\n",
       "      <td>1045</td>\n",
       "      <td>1045</td>\n",
       "      <td>1051</td>\n",
       "      <td>1052</td>\n",
       "      <td>1051</td>\n",
       "      <td>1054</td>\n",
       "      <td>1055</td>\n",
       "      <td>1055</td>\n",
       "      <td>1057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>South Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-34.928500</td>\n",
       "      <td>138.600700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "      <td>439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Tasmania</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-41.454500</td>\n",
       "      <td>145.970700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>227</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Victoria</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-37.813600</td>\n",
       "      <td>144.963100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>1468</td>\n",
       "      <td>1487</td>\n",
       "      <td>1496</td>\n",
       "      <td>1511</td>\n",
       "      <td>1514</td>\n",
       "      <td>1521</td>\n",
       "      <td>1540</td>\n",
       "      <td>1551</td>\n",
       "      <td>1558</td>\n",
       "      <td>1564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Western Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>-31.950500</td>\n",
       "      <td>115.860500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>552</td>\n",
       "      <td>552</td>\n",
       "      <td>552</td>\n",
       "      <td>553</td>\n",
       "      <td>553</td>\n",
       "      <td>552</td>\n",
       "      <td>552</td>\n",
       "      <td>552</td>\n",
       "      <td>553</td>\n",
       "      <td>554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Austria</td>\n",
       "      <td>47.516200</td>\n",
       "      <td>14.550100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15774</td>\n",
       "      <td>15833</td>\n",
       "      <td>15871</td>\n",
       "      <td>15882</td>\n",
       "      <td>15961</td>\n",
       "      <td>15997</td>\n",
       "      <td>16058</td>\n",
       "      <td>16109</td>\n",
       "      <td>16201</td>\n",
       "      <td>16242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Azerbaijan</td>\n",
       "      <td>40.143100</td>\n",
       "      <td>47.576900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2279</td>\n",
       "      <td>2422</td>\n",
       "      <td>2519</td>\n",
       "      <td>2589</td>\n",
       "      <td>2693</td>\n",
       "      <td>2758</td>\n",
       "      <td>2879</td>\n",
       "      <td>2980</td>\n",
       "      <td>3138</td>\n",
       "      <td>3274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahamas</td>\n",
       "      <td>25.034300</td>\n",
       "      <td>-77.396300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>93</td>\n",
       "      <td>93</td>\n",
       "      <td>94</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bahrain</td>\n",
       "      <td>26.027500</td>\n",
       "      <td>50.550000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>4444</td>\n",
       "      <td>4774</td>\n",
       "      <td>4941</td>\n",
       "      <td>5236</td>\n",
       "      <td>5531</td>\n",
       "      <td>5816</td>\n",
       "      <td>6198</td>\n",
       "      <td>6583</td>\n",
       "      <td>6747</td>\n",
       "      <td>6956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bangladesh</td>\n",
       "      <td>23.685000</td>\n",
       "      <td>90.356300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13134</td>\n",
       "      <td>13770</td>\n",
       "      <td>14657</td>\n",
       "      <td>15691</td>\n",
       "      <td>16660</td>\n",
       "      <td>17822</td>\n",
       "      <td>18863</td>\n",
       "      <td>20065</td>\n",
       "      <td>20995</td>\n",
       "      <td>22268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Barbados</td>\n",
       "      <td>13.193900</td>\n",
       "      <td>-59.543200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>83</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>85</td>\n",
       "      <td>86</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Belarus</td>\n",
       "      <td>53.709800</td>\n",
       "      <td>27.953400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>21101</td>\n",
       "      <td>22052</td>\n",
       "      <td>22973</td>\n",
       "      <td>23906</td>\n",
       "      <td>24873</td>\n",
       "      <td>25825</td>\n",
       "      <td>26772</td>\n",
       "      <td>27730</td>\n",
       "      <td>28681</td>\n",
       "      <td>29650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>50.833300</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>52011</td>\n",
       "      <td>52596</td>\n",
       "      <td>53081</td>\n",
       "      <td>53449</td>\n",
       "      <td>53779</td>\n",
       "      <td>53981</td>\n",
       "      <td>54288</td>\n",
       "      <td>54644</td>\n",
       "      <td>54989</td>\n",
       "      <td>55280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Benin</td>\n",
       "      <td>9.307700</td>\n",
       "      <td>2.315800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>242</td>\n",
       "      <td>284</td>\n",
       "      <td>319</td>\n",
       "      <td>319</td>\n",
       "      <td>327</td>\n",
       "      <td>327</td>\n",
       "      <td>339</td>\n",
       "      <td>339</td>\n",
       "      <td>339</td>\n",
       "      <td>339</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bhutan</td>\n",
       "      <td>27.514200</td>\n",
       "      <td>90.433600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>-16.290200</td>\n",
       "      <td>-63.588700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2266</td>\n",
       "      <td>2437</td>\n",
       "      <td>2556</td>\n",
       "      <td>2831</td>\n",
       "      <td>2964</td>\n",
       "      <td>3148</td>\n",
       "      <td>3372</td>\n",
       "      <td>3577</td>\n",
       "      <td>3826</td>\n",
       "      <td>4088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Bosnia and Herzegovina</td>\n",
       "      <td>43.915900</td>\n",
       "      <td>17.679100</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>2070</td>\n",
       "      <td>2090</td>\n",
       "      <td>2117</td>\n",
       "      <td>2141</td>\n",
       "      <td>2158</td>\n",
       "      <td>2181</td>\n",
       "      <td>2218</td>\n",
       "      <td>2236</td>\n",
       "      <td>2267</td>\n",
       "      <td>2290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>-14.235000</td>\n",
       "      <td>-51.925300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>146894</td>\n",
       "      <td>156061</td>\n",
       "      <td>162699</td>\n",
       "      <td>169594</td>\n",
       "      <td>178214</td>\n",
       "      <td>190137</td>\n",
       "      <td>203165</td>\n",
       "      <td>220291</td>\n",
       "      <td>233511</td>\n",
       "      <td>241080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Brunei</td>\n",
       "      <td>4.535300</td>\n",
       "      <td>114.727700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</td>\n",
       "      <td>141</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>236</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Timor-Leste</td>\n",
       "      <td>-8.874217</td>\n",
       "      <td>125.727539</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Belize</td>\n",
       "      <td>13.193900</td>\n",
       "      <td>-59.543200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Laos</td>\n",
       "      <td>19.856270</td>\n",
       "      <td>102.495496</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>239</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Libya</td>\n",
       "      <td>26.335100</td>\n",
       "      <td>17.228331</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>NaN</td>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>31.952200</td>\n",
       "      <td>35.233200</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>375</td>\n",
       "      <td>376</td>\n",
       "      <td>381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>241</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Guinea-Bissau</td>\n",
       "      <td>11.803700</td>\n",
       "      <td>-15.180400</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>594</td>\n",
       "      <td>641</td>\n",
       "      <td>726</td>\n",
       "      <td>761</td>\n",
       "      <td>820</td>\n",
       "      <td>836</td>\n",
       "      <td>913</td>\n",
       "      <td>913</td>\n",
       "      <td>969</td>\n",
       "      <td>990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Mali</td>\n",
       "      <td>17.570692</td>\n",
       "      <td>-3.996166</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>668</td>\n",
       "      <td>692</td>\n",
       "      <td>704</td>\n",
       "      <td>712</td>\n",
       "      <td>730</td>\n",
       "      <td>758</td>\n",
       "      <td>779</td>\n",
       "      <td>806</td>\n",
       "      <td>835</td>\n",
       "      <td>860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Saint Kitts and Nevis</td>\n",
       "      <td>17.357822</td>\n",
       "      <td>-62.782998</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>Northwest Territories</td>\n",
       "      <td>Canada</td>\n",
       "      <td>64.825500</td>\n",
       "      <td>-124.845700</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>Yukon</td>\n",
       "      <td>Canada</td>\n",
       "      <td>64.282300</td>\n",
       "      <td>-135.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Kosovo</td>\n",
       "      <td>42.602636</td>\n",
       "      <td>20.902977</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>861</td>\n",
       "      <td>862</td>\n",
       "      <td>870</td>\n",
       "      <td>884</td>\n",
       "      <td>919</td>\n",
       "      <td>919</td>\n",
       "      <td>944</td>\n",
       "      <td>944</td>\n",
       "      <td>944</td>\n",
       "      <td>955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Burma</td>\n",
       "      <td>21.916200</td>\n",
       "      <td>95.956000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>177</td>\n",
       "      <td>178</td>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>180</td>\n",
       "      <td>181</td>\n",
       "      <td>181</td>\n",
       "      <td>182</td>\n",
       "      <td>182</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>Anguilla</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>18.220600</td>\n",
       "      <td>-63.068600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>British Virgin Islands</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>18.420700</td>\n",
       "      <td>-64.640000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>250</th>\n",
       "      <td>Turks and Caicos Islands</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>21.694000</td>\n",
       "      <td>-71.797900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>NaN</td>\n",
       "      <td>MS Zaandam</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Botswana</td>\n",
       "      <td>-22.328500</td>\n",
       "      <td>24.684900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Burundi</td>\n",
       "      <td>-3.373100</td>\n",
       "      <td>29.918900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>8.460555</td>\n",
       "      <td>-11.779889</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>257</td>\n",
       "      <td>291</td>\n",
       "      <td>307</td>\n",
       "      <td>338</td>\n",
       "      <td>338</td>\n",
       "      <td>387</td>\n",
       "      <td>408</td>\n",
       "      <td>447</td>\n",
       "      <td>462</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>Bonaire, Sint Eustatius and Saba</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>12.178400</td>\n",
       "      <td>-68.238500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>256</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Malawi</td>\n",
       "      <td>-13.254308</td>\n",
       "      <td>34.301525</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>43</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>57</td>\n",
       "      <td>57</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>63</td>\n",
       "      <td>65</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>Falkland Islands (Malvinas)</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>-51.796300</td>\n",
       "      <td>-59.523600</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258</th>\n",
       "      <td>Saint Pierre and Miquelon</td>\n",
       "      <td>France</td>\n",
       "      <td>46.885200</td>\n",
       "      <td>-56.315900</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259</th>\n",
       "      <td>NaN</td>\n",
       "      <td>South Sudan</td>\n",
       "      <td>6.877000</td>\n",
       "      <td>31.307000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>156</td>\n",
       "      <td>194</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>236</td>\n",
       "      <td>236</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Western Sahara</td>\n",
       "      <td>24.215500</td>\n",
       "      <td>-12.885800</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Sao Tome and Principe</td>\n",
       "      <td>0.186360</td>\n",
       "      <td>6.613081</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>208</td>\n",
       "      <td>208</td>\n",
       "      <td>208</td>\n",
       "      <td>208</td>\n",
       "      <td>208</td>\n",
       "      <td>220</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "      <td>235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Yemen</td>\n",
       "      <td>15.552727</td>\n",
       "      <td>48.516388</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>51</td>\n",
       "      <td>56</td>\n",
       "      <td>65</td>\n",
       "      <td>70</td>\n",
       "      <td>85</td>\n",
       "      <td>106</td>\n",
       "      <td>122</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Comoros</td>\n",
       "      <td>-11.645500</td>\n",
       "      <td>43.333300</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Tajikistan</td>\n",
       "      <td>38.861034</td>\n",
       "      <td>71.276093</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>522</td>\n",
       "      <td>612</td>\n",
       "      <td>612</td>\n",
       "      <td>661</td>\n",
       "      <td>729</td>\n",
       "      <td>801</td>\n",
       "      <td>907</td>\n",
       "      <td>1118</td>\n",
       "      <td>1322</td>\n",
       "      <td>1524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>265</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Lesotho</td>\n",
       "      <td>-29.609988</td>\n",
       "      <td>28.233608</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>266 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Province/State          Country/Region        Lat  \\\n",
       "0                                 NaN             Afghanistan  33.000000   \n",
       "1                                 NaN                 Albania  41.153300   \n",
       "2                                 NaN                 Algeria  28.033900   \n",
       "3                                 NaN                 Andorra  42.506300   \n",
       "4                                 NaN                  Angola -11.202700   \n",
       "5                                 NaN     Antigua and Barbuda  17.060800   \n",
       "6                                 NaN               Argentina -38.416100   \n",
       "7                                 NaN                 Armenia  40.069100   \n",
       "8        Australian Capital Territory               Australia -35.473500   \n",
       "9                     New South Wales               Australia -33.868800   \n",
       "10                 Northern Territory               Australia -12.463400   \n",
       "11                         Queensland               Australia -28.016700   \n",
       "12                    South Australia               Australia -34.928500   \n",
       "13                           Tasmania               Australia -41.454500   \n",
       "14                           Victoria               Australia -37.813600   \n",
       "15                  Western Australia               Australia -31.950500   \n",
       "16                                NaN                 Austria  47.516200   \n",
       "17                                NaN              Azerbaijan  40.143100   \n",
       "18                                NaN                 Bahamas  25.034300   \n",
       "19                                NaN                 Bahrain  26.027500   \n",
       "20                                NaN              Bangladesh  23.685000   \n",
       "21                                NaN                Barbados  13.193900   \n",
       "22                                NaN                 Belarus  53.709800   \n",
       "23                                NaN                 Belgium  50.833300   \n",
       "24                                NaN                   Benin   9.307700   \n",
       "25                                NaN                  Bhutan  27.514200   \n",
       "26                                NaN                 Bolivia -16.290200   \n",
       "27                                NaN  Bosnia and Herzegovina  43.915900   \n",
       "28                                NaN                  Brazil -14.235000   \n",
       "29                                NaN                  Brunei   4.535300   \n",
       "..                                ...                     ...        ...   \n",
       "236                               NaN             Timor-Leste  -8.874217   \n",
       "237                               NaN                  Belize  13.193900   \n",
       "238                               NaN                    Laos  19.856270   \n",
       "239                               NaN                   Libya  26.335100   \n",
       "240                               NaN      West Bank and Gaza  31.952200   \n",
       "241                               NaN           Guinea-Bissau  11.803700   \n",
       "242                               NaN                    Mali  17.570692   \n",
       "243                               NaN   Saint Kitts and Nevis  17.357822   \n",
       "244             Northwest Territories                  Canada  64.825500   \n",
       "245                             Yukon                  Canada  64.282300   \n",
       "246                               NaN                  Kosovo  42.602636   \n",
       "247                               NaN                   Burma  21.916200   \n",
       "248                          Anguilla          United Kingdom  18.220600   \n",
       "249            British Virgin Islands          United Kingdom  18.420700   \n",
       "250          Turks and Caicos Islands          United Kingdom  21.694000   \n",
       "251                               NaN              MS Zaandam   0.000000   \n",
       "252                               NaN                Botswana -22.328500   \n",
       "253                               NaN                 Burundi  -3.373100   \n",
       "254                               NaN            Sierra Leone   8.460555   \n",
       "255  Bonaire, Sint Eustatius and Saba             Netherlands  12.178400   \n",
       "256                               NaN                  Malawi -13.254308   \n",
       "257       Falkland Islands (Malvinas)          United Kingdom -51.796300   \n",
       "258         Saint Pierre and Miquelon                  France  46.885200   \n",
       "259                               NaN             South Sudan   6.877000   \n",
       "260                               NaN          Western Sahara  24.215500   \n",
       "261                               NaN   Sao Tome and Principe   0.186360   \n",
       "262                               NaN                   Yemen  15.552727   \n",
       "263                               NaN                 Comoros -11.645500   \n",
       "264                               NaN              Tajikistan  38.861034   \n",
       "265                               NaN                 Lesotho -29.609988   \n",
       "\n",
       "           Long  1/22/20  1/23/20  1/24/20  1/25/20  1/26/20  1/27/20  ...  \\\n",
       "0     65.000000        0        0        0        0        0        0  ...   \n",
       "1     20.168300        0        0        0        0        0        0  ...   \n",
       "2      1.659600        0        0        0        0        0        0  ...   \n",
       "3      1.521800        0        0        0        0        0        0  ...   \n",
       "4     17.873900        0        0        0        0        0        0  ...   \n",
       "5    -61.796400        0        0        0        0        0        0  ...   \n",
       "6    -63.616700        0        0        0        0        0        0  ...   \n",
       "7     45.038200        0        0        0        0        0        0  ...   \n",
       "8    149.012400        0        0        0        0        0        0  ...   \n",
       "9    151.209300        0        0        0        0        3        4  ...   \n",
       "10   130.845600        0        0        0        0        0        0  ...   \n",
       "11   153.400000        0        0        0        0        0        0  ...   \n",
       "12   138.600700        0        0        0        0        0        0  ...   \n",
       "13   145.970700        0        0        0        0        0        0  ...   \n",
       "14   144.963100        0        0        0        0        1        1  ...   \n",
       "15   115.860500        0        0        0        0        0        0  ...   \n",
       "16    14.550100        0        0        0        0        0        0  ...   \n",
       "17    47.576900        0        0        0        0        0        0  ...   \n",
       "18   -77.396300        0        0        0        0        0        0  ...   \n",
       "19    50.550000        0        0        0        0        0        0  ...   \n",
       "20    90.356300        0        0        0        0        0        0  ...   \n",
       "21   -59.543200        0        0        0        0        0        0  ...   \n",
       "22    27.953400        0        0        0        0        0        0  ...   \n",
       "23     4.000000        0        0        0        0        0        0  ...   \n",
       "24     2.315800        0        0        0        0        0        0  ...   \n",
       "25    90.433600        0        0        0        0        0        0  ...   \n",
       "26   -63.588700        0        0        0        0        0        0  ...   \n",
       "27    17.679100        0        0        0        0        0        0  ...   \n",
       "28   -51.925300        0        0        0        0        0        0  ...   \n",
       "29   114.727700        0        0        0        0        0        0  ...   \n",
       "..          ...      ...      ...      ...      ...      ...      ...  ...   \n",
       "236  125.727539        0        0        0        0        0        0  ...   \n",
       "237  -59.543200        0        0        0        0        0        0  ...   \n",
       "238  102.495496        0        0        0        0        0        0  ...   \n",
       "239   17.228331        0        0        0        0        0        0  ...   \n",
       "240   35.233200        0        0        0        0        0        0  ...   \n",
       "241  -15.180400        0        0        0        0        0        0  ...   \n",
       "242   -3.996166        0        0        0        0        0        0  ...   \n",
       "243  -62.782998        0        0        0        0        0        0  ...   \n",
       "244 -124.845700        0        0        0        0        0        0  ...   \n",
       "245 -135.000000        0        0        0        0        0        0  ...   \n",
       "246   20.902977        0        0        0        0        0        0  ...   \n",
       "247   95.956000        0        0        0        0        0        0  ...   \n",
       "248  -63.068600        0        0        0        0        0        0  ...   \n",
       "249  -64.640000        0        0        0        0        0        0  ...   \n",
       "250  -71.797900        0        0        0        0        0        0  ...   \n",
       "251    0.000000        0        0        0        0        0        0  ...   \n",
       "252   24.684900        0        0        0        0        0        0  ...   \n",
       "253   29.918900        0        0        0        0        0        0  ...   \n",
       "254  -11.779889        0        0        0        0        0        0  ...   \n",
       "255  -68.238500        0        0        0        0        0        0  ...   \n",
       "256   34.301525        0        0        0        0        0        0  ...   \n",
       "257  -59.523600        0        0        0        0        0        0  ...   \n",
       "258  -56.315900        0        0        0        0        0        0  ...   \n",
       "259   31.307000        0        0        0        0        0        0  ...   \n",
       "260  -12.885800        0        0        0        0        0        0  ...   \n",
       "261    6.613081        0        0        0        0        0        0  ...   \n",
       "262   48.516388        0        0        0        0        0        0  ...   \n",
       "263   43.333300        0        0        0        0        0        0  ...   \n",
       "264   71.276093        0        0        0        0        0        0  ...   \n",
       "265   28.233608        0        0        0        0        0        0  ...   \n",
       "\n",
       "     5/8/20  5/9/20  5/10/20  5/11/20  5/12/20  5/13/20  5/14/20  5/15/20  \\\n",
       "0      3778    4033     4402     4687     4963     5226     5639     6053   \n",
       "1       850     856      868      872      876      880      898      916   \n",
       "2      5369    5558     5723     5891     6067     6253     6442     6629   \n",
       "3       752     754      755      755      758      760      761      761   \n",
       "4        43      43       45       45       45       45       48       48   \n",
       "5        25      25       25       25       25       25       25       25   \n",
       "6      5611    5776     6034     6278     6563     6879     7134     7479   \n",
       "7      3029    3175     3313     3392     3538     3718     3860     4044   \n",
       "8       107     107      107      107      107      107      107      107   \n",
       "9      3051    3053     3053     3053     3059     3063     3071     3074   \n",
       "10       29      29       29       29       29       29       29       29   \n",
       "11     1045    1045     1045     1051     1052     1051     1054     1055   \n",
       "12      439     439      439      439      439      439      439      439   \n",
       "13      227     227      227      227      227      227      227      228   \n",
       "14     1468    1487     1496     1511     1514     1521     1540     1551   \n",
       "15      552     552      552      553      553      552      552      552   \n",
       "16    15774   15833    15871    15882    15961    15997    16058    16109   \n",
       "17     2279    2422     2519     2589     2693     2758     2879     2980   \n",
       "18       92      92       92       93       93       94       96       96   \n",
       "19     4444    4774     4941     5236     5531     5816     6198     6583   \n",
       "20    13134   13770    14657    15691    16660    17822    18863    20065   \n",
       "21       83      84       84       84       85       85       85       85   \n",
       "22    21101   22052    22973    23906    24873    25825    26772    27730   \n",
       "23    52011   52596    53081    53449    53779    53981    54288    54644   \n",
       "24      242     284      319      319      327      327      339      339   \n",
       "25        7       7        7        9       11       15       20       21   \n",
       "26     2266    2437     2556     2831     2964     3148     3372     3577   \n",
       "27     2070    2090     2117     2141     2158     2181     2218     2236   \n",
       "28   146894  156061   162699   169594   178214   190137   203165   220291   \n",
       "29      141     141      141      141      141      141      141      141   \n",
       "..      ...     ...      ...      ...      ...      ...      ...      ...   \n",
       "236      24      24       24       24       24       24       24       24   \n",
       "237      18      18       18       18       18       18       18       18   \n",
       "238      19      19       19       19       19       19       19       19   \n",
       "239      64      64       64       64       64       64       64       64   \n",
       "240     375     375      375      375      375      375      375      375   \n",
       "241     594     641      726      761      820      836      913      913   \n",
       "242     668     692      704      712      730      758      779      806   \n",
       "243      15      15       15       15       15       15       15       15   \n",
       "244       5       5        5        5        5        5        5        5   \n",
       "245      11      11       11       11       11       11       11       11   \n",
       "246     861     862      870      884      919      919      944      944   \n",
       "247     177     178      180      180      180      181      181      182   \n",
       "248       3       3        3        3        3        3        3        3   \n",
       "249       7       7        7        7        7        7        7        8   \n",
       "250      12      12       12       12       12       12       12       12   \n",
       "251       9       9        9        9        9        9        9        9   \n",
       "252      23      23       23       24       24       24       24       24   \n",
       "253      15      15       15       15       15       15       15       15   \n",
       "254     257     291      307      338      338      387      408      447   \n",
       "255       6       6        6        6        6        6        6        6   \n",
       "256      43      56       56       57       57       63       63       63   \n",
       "257      13      13       13       13       13       13       13       13   \n",
       "258       1       1        1        1        1        1        1        1   \n",
       "259     120     120      120      156      194      203      203      236   \n",
       "260       6       6        6        6        6        6        6        6   \n",
       "261     208     208      208      208      208      220      235      235   \n",
       "262      34      34       51       56       65       70       85      106   \n",
       "263       8      11       11       11       11       11       11       11   \n",
       "264     522     612      612      661      729      801      907     1118   \n",
       "265       0       0        0        0        0        1        1        1   \n",
       "\n",
       "     5/16/20  5/17/20  \n",
       "0       6402     6664  \n",
       "1        933      946  \n",
       "2       6821     7019  \n",
       "3        761      761  \n",
       "4         48       48  \n",
       "5         25       25  \n",
       "6       7805     8068  \n",
       "7       4283     4472  \n",
       "8        107      107  \n",
       "9       3075     3076  \n",
       "10        29       29  \n",
       "11      1055     1057  \n",
       "12       439      439  \n",
       "13       228      228  \n",
       "14      1558     1564  \n",
       "15       553      554  \n",
       "16     16201    16242  \n",
       "17      3138     3274  \n",
       "18        96       96  \n",
       "19      6747     6956  \n",
       "20     20995    22268  \n",
       "21        86       88  \n",
       "22     28681    29650  \n",
       "23     54989    55280  \n",
       "24       339      339  \n",
       "25        21       21  \n",
       "26      3826     4088  \n",
       "27      2267     2290  \n",
       "28    233511   241080  \n",
       "29       141      141  \n",
       "..       ...      ...  \n",
       "236       24       24  \n",
       "237       18       18  \n",
       "238       19       19  \n",
       "239       65       65  \n",
       "240      376      381  \n",
       "241      969      990  \n",
       "242      835      860  \n",
       "243       15       15  \n",
       "244        5        5  \n",
       "245       11       11  \n",
       "246      944      955  \n",
       "247      182      184  \n",
       "248        3        3  \n",
       "249        8        8  \n",
       "250       12       12  \n",
       "251        9        9  \n",
       "252       24       25  \n",
       "253       15       23  \n",
       "254      462      505  \n",
       "255        6        6  \n",
       "256       65       70  \n",
       "257       13       13  \n",
       "258        1        1  \n",
       "259      236      290  \n",
       "260        6        6  \n",
       "261      235      235  \n",
       "262      122      128  \n",
       "263       11       11  \n",
       "264     1322     1524  \n",
       "265        1        1  \n",
       "\n",
       "[266 rows x 121 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid19_confirmed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Мы видим, что данные по каждой стране или провинции расположены по строкам. `NaN` соответствует пропущенным значениям. Для каждой страны указаны географические координаты, а затем, начиная с 4-го столбца (столбцы нумеруются с нуля) - конкретные значения зарегестрированных случаев."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Названия столбцов можно получить следующим образом:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Province/State', 'Country/Region', 'Lat', 'Long', '1/22/20', '1/23/20',\n",
       "       '1/24/20', '1/25/20', '1/26/20', '1/27/20',\n",
       "       ...\n",
       "       '5/8/20', '5/9/20', '5/10/20', '5/11/20', '5/12/20', '5/13/20',\n",
       "       '5/14/20', '5/15/20', '5/16/20', '5/17/20'],\n",
       "      dtype='object', length=121)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid19_confirmed.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Извлечь данные по конкретной стране можно так:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>1/22/20</th>\n",
       "      <th>1/23/20</th>\n",
       "      <th>1/24/20</th>\n",
       "      <th>1/25/20</th>\n",
       "      <th>1/26/20</th>\n",
       "      <th>1/27/20</th>\n",
       "      <th>...</th>\n",
       "      <th>5/8/20</th>\n",
       "      <th>5/9/20</th>\n",
       "      <th>5/10/20</th>\n",
       "      <th>5/11/20</th>\n",
       "      <th>5/12/20</th>\n",
       "      <th>5/13/20</th>\n",
       "      <th>5/14/20</th>\n",
       "      <th>5/15/20</th>\n",
       "      <th>5/16/20</th>\n",
       "      <th>5/17/20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Russia</td>\n",
       "      <td>60.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>187859</td>\n",
       "      <td>198676</td>\n",
       "      <td>209688</td>\n",
       "      <td>221344</td>\n",
       "      <td>232243</td>\n",
       "      <td>242271</td>\n",
       "      <td>252245</td>\n",
       "      <td>262843</td>\n",
       "      <td>272043</td>\n",
       "      <td>281752</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 121 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Province/State Country/Region   Lat  Long  1/22/20  1/23/20  1/24/20  \\\n",
       "187            NaN         Russia  60.0  90.0        0        0        0   \n",
       "\n",
       "     1/25/20  1/26/20  1/27/20  ...  5/8/20  5/9/20  5/10/20  5/11/20  \\\n",
       "187        0        0        0  ...  187859  198676   209688   221344   \n",
       "\n",
       "     5/12/20  5/13/20  5/14/20  5/15/20  5/16/20  5/17/20  \n",
       "187   232243   242271   252245   262843   272043   281752  \n",
       "\n",
       "[1 rows x 121 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid19_confirmed[covid19_confirmed['Country/Region']=='Russia']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Скопируем данные для России, получим три `numpy`-массива:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "Russia_confirmed = np.array(covid19_confirmed[covid19_confirmed['Country/Region']=='Russia'].iloc[0,4:], dtype=float)\n",
    "Russia_deaths    = np.array(covid19_deaths[   covid19_deaths[   'Country/Region']=='Russia'].iloc[0,4:], dtype=float)\n",
    "Russia_recovered = np.array(covid19_recovered[covid19_recovered['Country/Region']=='Russia'].iloc[0,4:], dtype=float)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Следующие заклинания переводят дату из американского формата (месяц/число/год) в более привычный: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "dates = covid19_confirmed.columns[4:]\n",
    "dates = np.array([datetime.datetime.strptime(x, '%m/%d/%y').strftime('%d/%m') for x in dates])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Визуализируем и анализируем данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYkAAAD4CAYAAAAZ1BptAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAaK0lEQVR4nO3df4xd5Z3f8fcHY6Jh82PMYlI8NjVNLHbJotowBSq2VUIUbKwmOGyiwG6Dm1J5m0I3UdM0ppGWKNkVTqMFLdsNKxK8mC0JQUCMq0IcK1CljYAwxuaH47CeELLM2AtmbRNULILNt3/c55Dj6/trZu6Pc879vKSrufd7z69n5sx873me73NGEYGZmVkjJwz6AMzMrLicJMzMrCknCTMza8pJwszMmnKSMDOzpk4c9AF026mnnhpLly4d9GGYmZXK9u3bX46IhfXxyiWJpUuXMjExMejDMDMrFUm/aBR3d5OZmTXlJGFmZk05SZiZWVNOEmZm1pSThJmZNVW56iYzs2Gzecc0X9v6LHsPHWbR6AifX3kWa1aMdWXbThJmZiW2ecc01933NIffOArA9KHDXHff0wBdSRTubjIzK7GvbX32rQSROfzGUb629dmubN9JwsysxPYeOjyj+Ew5SZiZldii0ZEZxWfKScLMrMQ+v/IsRubPOyY2Mn8en195Vle274FrM7MSywanXd1kZmZv6WXZa56ThJlZyfS67DXPYxJmZiXT67LXPCcJM7OS6XXZa56ThJlZyfS67DXPScLMrGR6Xfaa54FrM7OS6XXZa56ThJlZSfSr7DXPScLMrAT6Wfaa5zEJM7MS6GfZa56ThJlZCfSz7DXPScLMrAT6Wfaa5yRhZlYC/Sx7zfPAtZlZCfSz7DXPScLMrODqS19v+sTynieHTNvuJklLJD0sabekXZI+k+JfkjQtaWd6rM6tc52kSUnPSlqZi69KsUlJ63PxMyU9JmmPpO9IOinF35ZeT6b3l3az8WZmRZeVvk4fOkzw69LXzTum+7L/TsYkjgCfi4jfBi4ErpF0dnrvpohYnh4PAKT3rgDeB6wCvi5pnqR5wF8ClwJnA1fmtvPVtK1lwEHg6hS/GjgYEe8FbkrLmZkNjUGVvmbaJomI2BcRT6TnrwK7gVbXOZcBd0XE6xHxc2ASOD89JiPiuYj4FXAXcJkkARcD96T1NwFrctvalJ7fA3wwLW9mNhQGVfqamVF1U+ruWQE8lkLXSnpK0kZJC1JsDHght9pUijWL/yZwKCKO1MWP2VZ6/5W0vJnZUBhU6Wum4yQh6e3AvcBnI+KXwC3Ae4DlwD7gz7JFG6wes4i32lb9sa2TNCFpYv/+/S3bYWZWJoMqfc10lCQkzaeWIO6MiPsAIuLFiDgaEW8C36DWnQS1K4EludUXA3tbxF8GRiWdWBc/Zlvp/XcBB+qPLyJujYjxiBhfuHBhJ00yMyuFNSvGuOHycxgbHUHA2OgIN1x+Tt+qm9qWwKYxgNuA3RFxYy5+ekTsSy8/CjyTnm8BviXpRmARsAz4MbWrgmWSzgSmqQ1u/35EhKSHgY9RG6dYC9yf29Za4JH0/kMRcdyVhJlZ1Qzijq+NdDJP4iLgk8DTknam2H+lVp20nFr3z/PAHwJExC5JdwM/oVYZdU1EHAWQdC2wFZgHbIyIXWl7XwDukvQnwA5qSYn09W8kTVK7grhiDm01MyuFQd3xtRFV7YP5+Ph4TExMDPowzMxm7aINDzHdoHppbHSEH62/uCf7lLQ9Isbr4753k5lZwQy67DXPScLMrGAGXfaa5yRhZlYwgy57zfMN/szMCmZQd3xtxEnCzKwgilL2muckYWZWAEUqe83zmISZWQEM+m6vzThJmJkVQJHKXvOcJMzMCqBIZa95ThJmZgVQpLLXPA9cm5kVQJHKXvOcJMzMCmLNirGBJ4V6ThJmZgNWxPkRGScJM7MBKur8iIwHrs3MBqio8yMyThJmZgNU1PkRGScJM7MBKur8iIyThJnZABV1fkTGA9dmZgNU1PkRGScJM7MBK+L8iIyThJnZABR5bkSek4SZWZ8VfW5Engeuzcz6rOhzI/KcJMzM+qzocyPynCTMzPqs6HMj8pwkzMz6rOhzI/LaJglJSyQ9LGm3pF2SPpPip0jaJmlP+rogxSXpZkmTkp6SdG5uW2vT8nskrc3Fz5P0dFrnZklqtQ8zszJbs2KMGy4/h7HREQSMjY5ww+XnFG7QGkAR0XoB6XTg9Ih4QtI7gO3AGuDfAAciYoOk9cCCiPiCpNXAfwRWAxcAfx4RF0g6BZgAxoFI2zkvIg5K+jHwGeBR4AHg5oh4UNJ/a7SPVsc7Pj4eExMTs/x2mJkNJ0nbI2K8Pt72SiIi9kXEE+n5q8BuYAy4DNiUFttELXGQ4ndEzaPAaEo0K4FtEXEgIg4C24BV6b13RsQjUctYd9Rtq9E+zMxKafOOaS7a8BBnrv9fXLThITbvmB70IbU0o3kSkpYCK4DHgHdHxD6oJRJJp6XFxoAXcqtNpVir+FSDOC32YWZWOmWaH5HpeOBa0tuBe4HPRsQvWy3aIBaziHdM0jpJE5Im9u/fP5NVzcz6pkzzIzIdJQlJ86kliDsj4r4UfjF1FWXjFi+l+BSwJLf6YmBvm/jiBvFW+zhGRNwaEeMRMb5w4cJOmmRm1ndlmh+R6aS6ScBtwO6IuDH31hYgq1BaC9yfi1+VqpwuBF5JXUZbgUskLUhVSpcAW9N7r0q6MO3rqrptNdqHmVnplGl+RKaTK4mLgE8CF0vamR6rgQ3AhyTtAT6UXkOtOuk5YBL4BvAfACLiAPAV4PH0+HKKAXwa+GZa52fAgynebB9mZqVTpvkRmbYlsGXjElgzK7Ki3v21WQms7wJrZtZjRU0MnXCSMDProTKWveb53k1mZj1UxrLXPCcJM7MeKmPZa56ThJlZD5Wx7DXPScLMrIfKWPaa54FrM7MeyganXd1kZmYNrVkxVpqkUM9JwsysB8o8NyLPScLMrMvKPjcizwPXZmZdVva5EXlOEmZmXVb2uRF5ThJmZl1W9rkReU4SZmZdVva5EXkeuDYz67Kyz43Ic5IwM+uBMs+NyHOSMDProqrMj8g4SZiZdUmV5kdkPHBtZtYlVZofkXGSMDPrkirNj8g4SZiZdUmV5kdknCTMzLqkSvMjMh64NjPrkirNj8g4SZiZzVHVyl7znCTMzOagimWveR6TMDObgyqWveY5SZiZzUEVy17z2iYJSRslvSTpmVzsS5KmJe1Mj9W5966TNCnpWUkrc/FVKTYpaX0ufqakxyTtkfQdSSel+NvS68n0/tJuNdrMrFuqWPaa18mVxO3AqgbxmyJieXo8ACDpbOAK4H1pna9LmidpHvCXwKXA2cCVaVmAr6ZtLQMOAlen+NXAwYh4L3BTWs7MrFCqWPaa1zZJRMQPgQMdbu8y4K6IeD0ifg5MAuenx2REPBcRvwLuAi6TJOBi4J60/iZgTW5bm9Lze4APpuXNzApjzYoxbrj8HMZGRxAwNjrCDZefU4lBa5hbddO1kq4CJoDPRcRBYAx4NLfMVIoBvFAXvwD4TeBQRBxpsPxYtk5EHJH0Slr+5foDkbQOWAdwxhlnzKFJZmadqXLZa95sB65vAd4DLAf2AX+W4o0+6ccs4q22dXww4taIGI+I8YULF7Y6bjOzOcvKXqcPHSb4ddnr5h3Tgz60rptVkoiIFyPiaES8CXyDWncS1K4EluQWXQzsbRF/GRiVdGJd/JhtpfffRefdXmZmPVP1ste8WSUJSafnXn4UyCqftgBXpMqkM4FlwI+Bx4FlqZLpJGqD21siIoCHgY+l9dcC9+e2tTY9/xjwUFrezGygql72mtd2TELSt4H3A6dKmgKuB94vaTm17p/ngT8EiIhdku4GfgIcAa6JiKNpO9cCW4F5wMaI2JV28QXgLkl/AuwAbkvx24C/kTRJ7Qriijm31sysCxaNjjDdICFUpew1T1X7cD4+Ph4TExODPgwzq7D6W3FArey1zFVNkrZHxHh93PduMjOboSre7bUZJwkzsxmoL3296RPLK5kcMk4SZmYdqvodXxvxDf7MzDo0TKWvGScJM7MODVPpa8ZJwsysQ1W/42sjThJmZh2q+h1fG/HAtZlZh4ap9DXjJGFmNgNrVoxVOinUc5IwM2tjWG4L3oiThJlZC8M4NyLPA9dmZi0M49yIPCcJM7MWhnFuRJ6ThJlZC8M4NyLPScLMrIVhnBuR54FrM7MWhnFuRJ6ThJlZE8N2W/BGnCTMzBoY9tLXjMckzMwaGPbS14yThJlZA8Ne+ppxkjAza2DYS18zHpMwM8vJBqunDx1GQOTeG6bS14yThJlZUj9YHfBWohgbstLXjJOEmVnSaLA6SxA/Wn/xYA5qwDwmYWaWeLD6eE4SZmaJB6uP1zZJSNoo6SVJz+Rip0jaJmlP+rogxSXpZkmTkp6SdG5unbVp+T2S1ubi50l6Oq1zsyS12oeZWbdt3jHNRRseemuwOm8YB6vzOrmSuB1YVRdbD/wgIpYBP0ivAS4FlqXHOuAWqP3BB64HLgDOB67P/dG/JS2brbeqzT7MzLomG6yeTl1K2WA11MYibrj8nKEbrM5rmyQi4ofAgbrwZcCm9HwTsCYXvyNqHgVGJZ0OrAS2RcSBiDgIbANWpffeGRGPREQAd9Rtq9E+zMy6pt1g9TAnCJj9mMS7I2IfQPp6WoqPAS/klptKsVbxqQbxVvs4jqR1kiYkTezfv3+WTTKzYeTB6ta6PXBd350Hx169dRqfkYi4NSLGI2J84cKFM13dzIbU5h3TnKBGf4aGe7A6b7ZJ4sXUVUT6+lKKTwFLcsstBva2iS9uEG+1DzOzOcvGIo7G8Z9Lh32wOm+2SWILkFUorQXuz8WvSlVOFwKvpK6ircAlkhakAetLgK3pvVclXZiqmq6q21ajfZiZzVmjsQiAedLQD1bntZ1xLenbwPuBUyVNUatS2gDcLelq4O+Aj6fFHwBWA5PAa8CnACLigKSvAI+n5b4cEdlg+KepVVCNAA+mBy32YWY2Z83GHN6McILIaZskIuLKJm99sMGyAVzTZDsbgY0N4hPA7zSI/0OjfZiZdcOi0ZG3yl7r4/ZrvneTmQ0V3+V1ZpwkzGxo+C6vM+ckYWZDw3d5nTnf4M/MhoYnzs2cryTMrPKycYhmM3U9WN2ck4SZVVr9OEQ9D1a35iRhZpXWbNIceLC6E04SZlZpzcYbBB6s7oAHrs2s0vzf5ubGScLMKmvzjmn+3+tHjot7HKJz7m4ys0pqNmC94OT5XP/h93kcokO+kjCzSmo2YH3ySSc6QcyAk4SZVZInznWHu5vMrFI8ca67nCTMrDI8ca77nCTMrDI8ca77nCTMrDI8ca77PHBtZpWwecc0J0gN3/M4xOw5SZhZ6WVjEUfj+OFqj0PMjZOEmZVes7GIeRI3XH6OxyHmwGMSZlZa+f9X3cibEU4Qc+QkYWal1K7cFTwW0Q3ubjKzUmpV7goei+gWX0mYWSm1ur2G50R0j5OEmZVKu9tujI2OeE5EFzlJmFlp+LYb/TenMQlJz0t6WtJOSRMpdoqkbZL2pK8LUlySbpY0KekpSefmtrM2Lb9H0tpc/Ly0/cm0buOZMmY2FNrddsPlrt3XjYHrD0TE8ogYT6/XAz+IiGXAD9JrgEuBZemxDrgFakkFuB64ADgfuD5LLGmZdbn1VnXheM2spNrddsMJovt6Ud10GbApPd8ErMnF74iaR4FRSacDK4FtEXEgIg4C24BV6b13RsQjERHAHbltmdmQ8W03BmOuSSKA70vaLmldir07IvYBpK+npfgY8EJu3akUaxWfahA/jqR1kiYkTezfv3+OTTKzovFtNwZnrgPXF0XEXkmnAdsk/bTFso0+AsQs4scHI24FbgUYHx9vVvRgZiXl224MzpyuJCJib/r6EvBdamMKL6auItLXl9LiU8CS3OqLgb1t4osbxM1sSGzeMc1FGx7ybTcGaNZJQtJvSHpH9hy4BHgG2AJkFUprgfvT8y3AVanK6ULgldQdtRW4RNKCNGB9CbA1vfeqpAtTVdNVuW2ZWcVlXUzNEgR4LKIf5tLd9G7gu6kq9UTgWxHxPUmPA3dLuhr4O+DjafkHgNXAJPAa8CmAiDgg6SvA42m5L0fEgfT808DtwAjwYHqY2RDwbTeKYdZJIiKeA/5pg/g/AB9sEA/gmibb2ghsbBCfAH5ntsdoZuXl224Ug2dcm1mh+LYbxeIkYWaF4dtuFI+ThJkVRrvbbriLqf+cJMysEDbvmG5ayZTddsP6z/90yMwGLutmasalroPjJGFmA9eqm8njEIPl7iYzG5iskqnVhDnfdmOwnCTMbCDaVTJBbbDaCWKw3N1kZgPhGdXl4CsJM+u7VpVM4HLXInGSMLO+alfJ5BnVxeIkYWZ90ckgtbuYisdJwsx6rpNBanAlUxE5SZhZz3Ry9ZBxJVMxOUmYWU90evUA7mYqMicJM+u6zTum+dzdT3I02v/LeVcyFZuThJl1Rb5rSdD0/0FkRubP8xhECThJmNmc1XcttUsQvnooDycJM5u1mQxMg68eyshJwsxmbPOOab60ZReHDr/R8TrzJCeIEnKSMLOOzHTMIc9XEOXlJGFmx8kSwt5Dh3nXyHx+deQor73x5lvvd5IgskTi8Ydyc5IwM6D5lcJMupQyTgzV4SRhNiTqrw4kOPTaG7O+UmjE3UrV4yRhVkHtuovyVwezuVJoZMHJ87n+w+9zgqgYJwmzAmr1qV+Cg6+9wTyJoxGMtrkq6FYSqOcxh+FQ+CQhaRXw58A84JsRsaHb+2j0C9nql3C273ub5TyOfm+z00/92S0venFV0I6vGoaHooN7qwyKpHnA3wIfAqaAx4ErI+InzdYZHx+PiYmJjvcxk5uQmQ2T7Eohn9gW+aqhsiRtj4jx+njRryTOByYj4jkASXcBlwFNk8RMtfs/u2bDyFcKlil6khgDXsi9ngIuqF9I0jpgHcAZZ5wxox3s7fB2AmZVcvL8E3jb/HnHdHv5SsEaKXqSUIPYcf1jEXErcCvUuptmsoNFoyMd33fGrIzyCcFJwGaq6EliCliSe70Y2NvNHXx+5Vkek7BCa/Spv91guBOCdUvRk8TjwDJJZwLTwBXA73dzB9kvkKubirfNohzHILbpP/JWFIVOEhFxRNK1wFZqJbAbI2JXt/ezZsWYfxHNzBoodJIAiIgHgAcGfRxmZsPohEEfgJmZFZeThJmZNeUkYWZmTTlJmJlZU4W+d9NsSNoP/GKWq58KvNzFwymCqrWpau2B6rWpau2Barap3j+OiIX1wcolibmQNNHoBldlVrU2Va09UL02Va09UM02dcrdTWZm1pSThJmZNeUkcaxbB30APVC1NlWtPVC9NlWtPVDNNnXEYxJmZtaUryTMzKwpJwkzM2uqsklC0hJJD0vaLWmXpM+k+Nck/VTSU5K+K2m0br3tkk6SdJ6kpyVNSrpZktL7H0/be1NS30riJG2U9JKkZ3KxlsfSQVtafi/60KZVkp5Nx7U+xe5MsWdSm+fnlp8vaXuzdVP8NklPpjbdI+ntfWxPr865r6R1d0r6vqRFfWxT18+73HL/WVJIOrUfbcntt9F5d216fdzxdHje3S7p5+lntFPS8n62qaciopIP4HTg3PT8HcDfAmcDlwAnpvhXga/m1lkKbEnPfwz8c2r/He9B4NIU/23gLOB/A+N9bM+/BM4FnsnFmh5Lh21p+r3oQ3vmAT8D/glwEvBk+vmsTscp4NvAp3PrfAD4i2brpmXemVv+RmB9Bc65fJv+CPirMp936b0l1P4FwC+AUwtw3q1Ix/58/fF0eN7dDnysX+3o56OyVxIRsS8inkjPXwV2A2MR8f2IOJIWe5Taf7vLXAp8T9Lp1H4xH4naGXAHsCZta3dEPNu3hiQR8UPgQF2s1bF00pZW34teOx+YjIjnIuJXwF3AZRHxQCTU/sjkj2kVtT82DdcFiIhfAqRPrSM0+He3vdLDc+6XueV/g/62qevnXXIT8F/oY1uSZufdjoh4vsk6bc+7KqtsksiTtJTaJ4XH6t76t9R++JlVwPeAMWr/OjUzlWJlMtO21H8vem0MeCH3+pjjSt1Mn6TWhswHqH16bbfuXwN/D/wWtU+Afdftc07Sn0p6AfgD4I+7f8Rd07Y9kj4CTEfEk/0/vNbnThMdnXfAn6ZuwZskva0Lx1oIlU8SqU/6XuCz+U9kkr4IHAHuTK9PAhZHxHPULo/rlaZWeKZtqf9e9Em74/o68MOI+D8AqR/+QES81m7diPgUsIjaJ/lPdO2IO9SLcy4ivhgRS9K61/bw8Getk/ZIOhn4IoNLdDP63Z7BeXcdtQ8l/ww4BfjCHI+zMCqdJNKn0XuBOyPivlx8LfCvgD9Il8IA/wL4v+n5FMd2CSwG9vb+iLum47Y0+V70wxS1funjjkvS9cBC4D/l3r+UWh92y3UzEXEU+A7we1096jb6cM59iz63aQY6ac97gDOBJyU9n+JPSPpHfTrGtudOnY7Ou9TVGBHxOvDX1LqmKqGySSL1Sd8G7I6IG3PxVdSy/EfSp4NM1u9IROwDXpV0YdrOVcD9fTv4ueuoLS2+F/3wOLBM0pnpE+gVwBZJ/w5YCVwZEW/mln+rTS3WlaT3wls//w8DP+1Te3p2zklallvnI/SxTTPUtj0R8XREnBYRSyNiKbU/vOdGxN/36Rgbnjstlm973gGkMZjsHFgDPNNgW+U06JHzXj2A36V2KfgUsDM9VgOT1PoVs9hfpeUfB0Zy649T+0H/DPjv/Hp2+kepndivAy8CW/vUnm8D+4A30v6vbnYsM2hLw+9FH39Gq6lVAP0M+GKKHUmvs2P6Y2pVJTs7WPcE4EfA06m9d5KrDCrxOXdvij8F/E9qg+H9alPXz7u67T9PH6ubWpw7f5TadITa1cE3Oz3vUvyh3Hn3P4C397NNvXz4thyApMXANyLi0kEfy1xVqS0ZSb8L/OuI+PeDPpZuqdrPqWrtgWqed7PhJGFmZk1VdkzCzMzmzknCzMyacpIwM7OmnCTMzKwpJwkzM2vKScLMzJr6/6cMoyWrMdjGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "T = len(Russia_confirmed)\n",
    "t = np.arange(T)\n",
    "plt.plot(t, Russia_confirmed, 'o')\n",
    "plt.xticks(t[0::20], dates[0::20])\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Наблюдается экспоненциальный рост. Удобнее использовать полулогарифмический масштаб: обычный линейный по горизонтальной оси и логарифмический по вертикальной."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogy(t, Russia_confirmed, 'o')\n",
    "plt.xticks(t[::15], dates[::15])\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Начиная примерно с 6 марта до 6 апреля данные расположились вдоль прямой, что говорит об экспоненциальном росте. Это закономерное развитие эпидемии.\n",
    "\n",
    "Выделим эти данные. Вначале найдем индекс, соответствующий дате 6 марта:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((array([44], dtype=int64),), (array([], dtype=int64),))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(dates=='06/03'), np.where(dates=='1/04')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выделяем данные:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "t_start = 44\n",
    "t_end = 70\n",
    "C = Russia_confirmed[t_start:t_end]\n",
    "D = Russia_deaths[t_start:t_end]\n",
    "R = Russia_recovered[t_start:t_end]\n",
    "I = Russia_confirmed[t_start:t_end] - Russia_deaths[t_start-1:t_end-1] - Russia_recovered[t_start-1:t_end-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`C`, `D`, `R` - это массивы, содержащие количества подтвержденных случаев, смертей и количество выздоровевших на каждый день.\n",
    "\n",
    "`I` - количество зарегистрированных инфицированных.\n",
    "\n",
    "Теперь можем изобразить эти данные:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "T = len(C)\n",
    "t = np.arange(T)\n",
    "dates = dates[44:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.semilogy(C, label='Confirmed')\n",
    "plt.semilogy(I, label='Infected')\n",
    "plt.semilogy(R, label='Recovered')\n",
    "plt.semilogy(D, label='Deaths')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.xticks(t[0::10], dates[0::10])\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Графики `C` и `I` практически совпадают (особенно в логарифмическом масштабе), так как в начале эпидемии еще мало выздоровевших"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Настройка параметров модели\n",
    "\n",
    "Как уже отмечалось, количество зараженных `I` (и количество новых случаев `С`) в начале эпидемии растет экспоненциально: \n",
    "\n",
    "$$\n",
    "I = I_0 e^{\\alpha t}. \\tag{$*$}\n",
    "$$\n",
    "\n",
    "Параметры $I_0$ и $\\alpha$ это *модели экспоненциального роста* подберем по данным.\n",
    "\n",
    "Рассмотрим два подхода.\n",
    "\n",
    "### 4.1. Сведение к линейной задаче наименьших квадратов\n",
    "\n",
    "Параметр $\\alpha$ входит в модель нелинейно, поэтому напрямую метод наименьших квадратов в данном случае применить нельзя. Однако, прологарифмировав уравнение ($*$), получаем\n",
    "$$\n",
    "\\ln I = \\ln I_0 + \\alpha t.\n",
    "$$\n",
    "Обозначим\n",
    "$$\n",
    "y = \\ln I, \\qquad y_0 = \\ln I_0.\n",
    "$$\n",
    "Теперь зависимость выглядит так:\n",
    "$$\n",
    "y = y_0 + \\alpha t.\n",
    "$$\n",
    "Теперь неизвестные параметры входят линейно.\n",
    "Найдем из методом наименьших квадратов с помощью функции `polyfit`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.22820502051800898, 2.107962936637556)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha, y0 = np.polyfit(t, np.log(I), 1)\n",
    "alpha, y0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.231456196449551"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I0 = np.exp(y0)\n",
    "I0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Изобразим график построенной линейной зависимости:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXYAAAD4CAYAAAD4k815AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO3deXxU5dn/8c+dEMIelkBYEggQtgBBIaKolSBaQQyIKGJbt0dF2kfR1kdEQRZ3ra2VarVo1Z+tlSKbBFFxixZEWRRDWEVECPs6kJB97t8fM2AIMyFDltm+79crL2bOMrluT7xycp/rXMdYaxERkdAR4e8ARESkeimxi4iEGCV2EZEQo8QuIhJilNhFREJMHX8HABAbG2sTExPPat+8vDwaNmxYvQEFqHAZa7iME8JnrOEyTqjdsa5evfqAtbZl+eUBkdgTExNZtWrVWe2bmZlJWlpa9QYUoMJlrOEyTgifsYbLOKF2x2qM+cnTcr9OxRhj0o0xMx0Ohz/DEBEJKX5N7NbaDGvt2JiYGH+GISISUnTxVEQkxCixi4iEGCV2EZEQo8QuIhJilNhFREKMEruIiB8czitiesY6jhYUV/tnB8QNSiIi4cJay+K1e5i6MJsjx4u5qHMslyXHVev38GtiN8akA+lJSUn+DENEpFbsO1rA5AXZLFm/l97tYvjnbefTo02Tav8+fk3s1toMICM1NfUOf8YhIlKTrLW8syqHR99bT1GJkweHdue2iztSJ7JmZsM1FSMiUoO2HzzOg/OzWLblIP07NufpUSl0jK3ZJmFK7CIiNaDUaXnjy208++EmIiMMj13di1/1b09EhKnx763ELiJSzb7fe4wJc7P4dvsRBnVryeMje9O2af1a+/5K7CIi1aSoxMm7W4p476OlNIyO5C/Xn8OIc9piTM2fpZelxC4iUg2yco4wYU4WG/cUk96nLVPTk4ltFO2XWJTYRUSqIL+olL98vJm9y97k9ajZtK53ELMnHrZOgZTRfolJiV1E5Cx9tfUgE+dmkXJ4Cc9G/4O6ttC1wrEDMsa7XvshuaulgIiIj44VFDNp/lrGzPwKp4Vnmi34OamfUJwPnzzil/h0xi4i4oNPN+5l0vxs9h4t4PaLO3LfL7tR74ndnjd25NRucG5K7CIilXAor4hHMtaxYM0uusY14m+/vpBz2zdzrYyJd02/lBcTX7tBuimxi4hUwFpLRtZupi1cx7GCYu69rAu/S0uibp0yM9mDp7jm1Ivzf14WVd+13A+U2EVEvNjjKGDygrV8vGEffRKa8syoFLq1bnz6hicukH7yCNaRg4mJdyX1cKyKUXdHEQlE1lpmrdzBE+9toNjpZPKwHtx6UUciK2oHkDIaUkbzeWYmaWlptRarJ+ruKCJSxk8H85g4dy0tt73Lp/XnEFu6H7MqHmL8dwbuK03FiIjgatr1+rIfeXbJJkZELOPxeq9Rp7TAtdLPdem+Uh27iIS9TXuOcc1LX/LYexu4OCmWx5vMp46z4NSN/FiX7iudsYtI2CoqcfLiZ1v4W+YWmtSL4q83nMtVKW0w03d63sFPdem+UmIXkbC0ZscRJsz5js17c7n6nLZMSe9J84Z1XSsDrC7dV0rsIhJW8otK+dOSTby27EfimtTjtVtSubR7uYdJB1hduq+U2EUkPGTNpuDDqUTn7eYWZwu6Jv2Oob8eT+N6UadvW6YuHUeO60zdj3XpvlJiF5GQd3z129RZdA/13I264iMOMHrPH2Fzgvdk7a5LD0aqihGRkPbx+r04MiYHVPfFmqYzdhEJSQdyC5mesZ6M73axtd5BzxsFSZWLr5TYRSSkWGt5d80upmesI6+wlPsu74rJauc5iQdJlYuvlNhFJGTsOpLP5AXZfLpxH+e2dzXt6hLXGFpODeoqF18psYtI0HM6Lf9esZ2n3t9IqdMy5apkbr4w8eemXUFe5eIrJXYRCV5Zsyn+aBqRx3aR5myBo8VtDL/xXhKaNzh92yCucvGVEruIBKXSNf/BuXA8Ue6eLvERB/hd7gxMThI0D48E7o3KHUUk6KzfdZSDCyedTOonmBAuYfSFzthFJGgUlpTywqdbeCnzBzbXPeB5oxAtYfSFEruIBIXVPx3mgblZbNmXyzV922Fz2sHR8Clh9IUSu4gEtONFJfzxw0288eU22jSpx+u3nsegbq0gK7xKGH1R7YndGNMDuAeIBT6x1r5U3d9DREJU1uxTShI39vw9t3/bkZzD+dw0oAMThnSnUbQ7bYVZCaMvKpXYjTGvAVcB+6y1vcosHwI8D0QCr1prn7LWbgDGGWMigFdqIGYRCUVZs089A3fsoP2yiQypdxe/vPNu+ndsfvo+YVTC6IvKVsW8AQwpu8AYEwm8CAwFkoEbjDHJ7nXDgaXAJ9UWqYiEtk8eOXVaBWhgingoerbnpC5eGWtt5TY0JhFYdOKM3RgzAJhmrb3C/f5BAGvtk2X2ec9aO8zL540FxgLExcX1mzVr1lkNIDc3l0aNGp3VvsEmXMYaLuOE8BlrZcY5MPNqDKfnI4vh87QFNRVatavNYzpo0KDV1trU8surMsfeDij77Kgc4HxjTBpwDRANLPa2s7V2JjATIDU11aalpZ1VEJmZmZztvsEmXMYaLuOE8BlrReO01jLvm53spgVtOb2E0cTEB9V/o0A4plVJ7MbDMmutzQQyq/C5IhImdh7J56F5a/l8837uaXUr9xx/gYgSVblUVVUSew6QUOZ9PLCrauGISDhwOi3/+vonnn5/IxaYPrwnN15wJRHZXVXlUg2qkthXAl2MMR2BncAY4Fe+fIAxJh1IT0pKqkIYIhKw3OWLAx058K0rUf/Q5komzs1i5bbD/KJLLE+M7P1z0y5VuVSLypY7vg2kAbHGmBxgqrX2H8aYu4APcZU7vmatXefLN7fWZgAZqampd/gWtogEvDLliwbAsYPiBXfzYvF3bK4zkGev68Oovu0wxtOsrlRFpRK7tfYGL8sXU8EFUhEJYx7KF6OcBUyKns3Eex6mVeN6fgos9Pm1u6MxJt0YM9PhcPgzDBGpCV6acbUo2a+kXsP8mtittRnW2rExMTH+DENEakBhw7aeV6hJV41TP3YRqVZ5hSVMW7iOCUdGkE/0qStVvlgrlNhFpNp8vnk/v3zuC/7f8m00O/83mOEzICYBi4GYBEifoaqXWqC2vSJSZUeOF/Hoog3M/SaHzi0b8s6dA0hNbA70hL5j+DwA7sYMJ35N7KpjFwlC5Vrrruk6ntu/7cTh40XcNSiJuy5Nol5UpL+jDGu6eCoilXeiNt2xA7Dg2EHXFZMYHf0lC++6iP+7opuSegDQHLuIVJ6X1rr31/kPPdvqBC1QKLGLSKVZL7XpxrGzliORiiixi8gZlTotbyz7kV22hecNVJseUHTnqYhUaMu+Y4z++3KmZaxnYYvbcdapf+oGqk0POLp4KiIeFZc6eeHT77ny+aX8sD+XP4/uw7i7JxLhrk1HtekBS3XsInKa7J0O7p+TxYbdRxmW0oZp6T1p2dh9F6la6wY8JXYROamguJS/fPw9r/x3Ky0a1uXvN/bjip6t/R2W+EiJXUQAWPHjISbOzWLrgTyuT03goWE9iKkf5e+w5CwosYuEufzVb1PwwVRSi/by74iWOAY/SLfLh/k7LKkCVcWIhLF1H74KGffQrHgvEQZa2/10WzHJdYepBC1VxYiEocN5RfzhP2uIWfYk9Sk8dWVxvusOUwlamooRCSPWWt5bu5up767DkV/Mn+oe9LyhlztMJTjozlORUJM1G57rBdOauv51T6vsPVrAnf9czV3//pZ2zeqTcffFGG93jOpO0qCmM3aRUHKi++KJRl2OHdiM8Xy19SBj13SiqMTJQ1d2538u6kidyAjXHaNltwfdSRoClNhFQomH7oumOJ+Eb54lud2/eHpUComxDX9eeeJGozL91Rk8RTcgBTkldpFQ4mVuvF3EQd6+4wIiIszpK3UnacjRHLtIKPEyN25i4j0ndQlJqmMXCRFFJU4+aD2W47buqSs0Zx52VMcuEui8VLmU9d2OIwx/YSnjvuvMO23up7RJPOq+GL40xy4SyDxUuZAx3vU6ZTT5RaU89/FmXv3vVlo1rserN6VyWfIwYILfQhb/U2IXCWQeqlxO3Bm6vOFgHpyXxbaDx7mhf3sevLI7TeqpaZcosYsENi9VLtaRww2vfEWHFg349x3nc2Hn2FoOTAKZErtIIIuJd02/lLPTtuCOX3TkD5d3o37dSD8EJoFM5Y4igWzwFFdVSxkFRFOS9jCThiUrqYtHSuwi/uCudBmYebXXShcA2/s6VveZzi5icVrD0ejW1Ln6ryQOuqV245WgoqkYkdpWptLFwGmVLifsduTz8IJsPt4QT5+EN3lmVArdWjf2S8gSXPya2I0x6UB6UlKSP8MQqV0VVLqQMhqn0zJr5Q6eXLyBYqeTycN6cOtFHYnUnaNSSX5N7NbaDCAjNTX1Dn/GIVKrvPU6d+Sw7UAeE+dl8dXWQwzo1IKnRvWmQ4uGnrcX8UJTMSK1zUuly7Ho1gx5/guiIiJ46preXH9eAsboLF18p4unIrXNS6XLpGMjuTipJR/9YSBj+rdXUpezpjN2keqQNbvyPc3dy+0n08Gxk122BX+L+BWXX/9brkppo4QuVabELlJVZ+jn4sm3TS/nAeLYXJDLyHPb8fBVyTRvWNfjtiK+0lSMSFVVVOVSzvGiEh5dtJ5rXvqSYwUl3Ns3mueuP0dJXaqVzthFqqqCKpeyvtxygInz1rL90HF+c0F7HhjSndVfLauFACXcKLGLVJWXKpcTTzNy5Bfz5OINzFq5g8QWDZg19gIu6NSiloOUcKLELlJVg6ecOscOJ59a9NH6vUxesJb9xwq5c2Anfn9ZV+pFqb+L1CwldpGqOnGBtExVzNGLHuKh7CQWZa2ie+vGvHJTKinxTf0bp4QNJXaR6pAyGlJGY61lwZqdTM9Yz/HCvdx3eVfGpXUmKlJ1ClJ7lNhFPPGlLt1t15F8Js1fy2eb9nNu+6Y8MyqFLnFq2iW1T4ldpDwf69KdTstbK7bz9PsbKXVaplyVzM0XJqppl/iNujuKlHeG7otl/XggjwfmZrHix0NcnBTLk9f0JqF5g1oMVuR06u4oUl4l6tJLSp28uvRHnvtoM9F1Injm2hSu6xevdgASEDQVI1LeGerS1+86ygNzs1i708EVPeN4dEQvWjWpV8tBininxC5Snpe69OJBk5mxZBMvZf5A0wZR/O3XfRnaq7XO0iXgKLGLlOehLn1rn/sY+2kbtuzbwjV92/HwsGSaqb+LBCgldhFP3HXpeYUlPLtkE298tI22MaW8cet5pHVr5e/oRCqkxC7ixX+/38+D89aSczifmwd04P4h3WkUrf9lJPDpp1SkHMfxYh5fvJ7Zq3Lo1LIh74wbwHmJzf0dlkilKbGLlPFB9h4efjebQ3lF/C6tM+MHd1HTLgk6SuwSPipoE7DvWAHTFq5j8do9JLdpwuu3nEevdjF+Dljk7CixS3jw0ibAAvOKL+SRRevJLy7l/iu6MfaSTmraJUFNiV3Cg5c2AQffncR9eX+hX4dmPD0qhaRWjfwTn0g1UmKX8OClTUDzkv1MH96TGy/oQISadkmI0N+bEh7c7QDKczZpx80XJiqpS0hRYpewUDLoYYojTu3nYqPqU+fyqX6KSKTmKLFLyMve6WDEF225r+B/OFinFRYDMQmY9BlnfHiGSDDSHLuErILiUmZ88j1//2IrzRrU5e5fjadFryf9HZZIjVNil5C0atshJszNYuv+PK7rF8/kYcnENIjyd1gitUKJXYKXhxuOcrtdwx8/2MibX/1Eu6b1efN/+nNJ15b+jlSkVlV7YjfGXA0MA1oBL1prl1T39xDxdMNR6bt386xZx5t5/bl5QCL3X9GNhmraJWGoUhdPjTGvGWP2GWOyyy0fYozZZIzZYoyZCGCtXWCtvQO4Bbi+2iMWAY83HEWWFjCu9C3mjBvAtOE9ldQlbFW2KuYNYEjZBcaYSOBFYCiQDNxgjEkus8lk93qR6uflhqM4e4B+HdSJUcJbpRK7tfYL4FC5xf2BLdbardbaImAWMMK4PA28b639pnrDFXEpbdzO43Lj5UYkkXBirLWV29CYRGCRtbaX+/21wBBr7e3u9zcC5wObgZuBlcAaa+3LXj5vLDAWIC4urt+sWbPOagC5ubk0ahQe/T3CZawVjdNay9KdJRzd9CnTI16lgSk6ua40IppN3f6XfXEDayvUKtMxDT21OdZBgwatttamll9elUlIT/dgW2vtDGDGmXa21s4EZgKkpqbatLS0swoiMzOTs9032ITLWL2Nc8eh4zw0fy3//f4A/ROHc6x3DxqseOZkVUzk4Ckkp4wm+fSPDFjhfkxDUSCMtSqJPQdIKPM+HthVtXBETlfqtLy5fBt//HATBnj06l78un97IiIGwEU3+Ts8kYBTlcS+EuhijOkI7ATGAL/y5QOMMelAelJSUhXCkJDhrksf6MiBb1116VtaD2XCnCy+2X6EtG4teXxkb9o1re/vSEUCWmXLHd8GlgPdjDE5xpjbrLUlwF3Ah8AGYLa1dp0v39xam2GtHRsToyfVhL0TdemOHRgsOHZQvOBu/jbjKbYeyOO56/vw+i3nKamLVEKlztittTd4Wb4YWFytEUl48lCXHuUsYFK9d3jo3inENor2U2AiwUd3cEhg8FKX3qJkHyipi/jEr217jTHpxpiZDofDn2FIAChs2MbzCtWli/jMr4ldc+xyrKCYyQvWcv/hq8mn3Jl5VH0YPMU/gYkEMT1oQ/zms437uOK5L3jr6+20vPBGIobPgJiEkw/CQA/CEDkrmmOXWncor4hHF61n/rc76dKqEXN/eyF92zcDkqHvGD4PgBs8RIKZErvUGmst763dzdR31+HIL2b84C7876DORNeJ9HdoIiHFr4ldNyiFj71HC5i8IJuP1u8lJT6Gf91+Pj3aNPF3WCIhya+J3VqbAWSkpqbe4c84pOZYa/nPyh08vngDRSVOJl3Zg1svSqROpC7viNQUTcVIjdl+8DgT52Xx5Q8HOb9jc54elUJibEN/hyUS8pTYpdqVOi2vL/uRZ5dsok5EBE+M7M2Y8xKIiPDUEFREqpsSu/jGwwOky5Ykbt57jAlzsliz4wiXdm/F4yN70SZG/V1EapMunkrleXiANBnjAShKvpaXMn/ghc++p3G9KJ4fcw7D+7TFrH2nwl8EIlL9dPFUKs9Doy6K8yn6cBrDP23Nxj3HGHFOW6ZclUyLRtEV/iJQchepOZqKkcrz0qirTu4ujkQX8+pNqVyWHPfzCi+/CPjkESV2kRqkxC6VFxPvOusux1G3FUv+cAlN6kWVW+H5F4HX5SJSLVRMLJU3eAq2zqkXQksj69Ms/bHTkzp478yojo0iNUqJXSrtk6iBTLV3kmNjsRicTeKJHFFBo67BU1wdGstSx0aRGqeqGDmjg7mFTM9Yz8LvdtG99WBGjfo98QlNOWNV+omEr6oYkVqlqhjxylrLwu92MT1jPccKivn9ZV35bVpn6tbx4Q+9lNFK5CK1TBdPxaPdjnwmz8/mk437OCehKc9cm0LXuMb+DktEKkGJPdyVu5PUeekU3i44nycXb6TE6WTysB7celFHItUOQCRoKLGHMw83EBUvuIuvC28jpeMInromhfYtGvg3RhHxmRJ7OPNwA1G0LeTJJvNpcPsTGKOzdJFgpMQezrzcKNSwYA8oqYsELb/WsRtj0o0xMx0Ohz/DCEuFJaUcjY7zvFI3EIkENb8mdmtthrV2bExMjD/DCDvfbD/MVTOWMvnYNRSZ6FNX6gYikaCnO0/DyPGiEh5dtJ5RL31JbmEJI2/6PXVHvgAxCYBx/ZtewZ2kIhIUNMceJpZtOcDEeVnsOJTPby5ozwNDutO4XhSgG4hEQo0Se4hz5Bfz5OINzFq5g46xDfnP2As4v1MLf4clIjVIiT2ELVm3h8kLsjmYV8S4gZ2597Iu1IuK9HdYIlLDlNhD0IHcQqYtXMeirN30aNOEf9x8Hr3jdYFaJFwosYcQay0L1uxkesZ6jheW8n+/7MqdAzsTFalr5CLhRIk9ROw8ks+k+WvJ3LSfvu1dTbuSWqlpl0g4UmIPck6n5a0V23lq8QacFqamJ3PTgEQ17RIJY3rQRhDbuj+XiXPXsmLbIS5OiuXJa3qT0FxNu0TCnR60EYRKSp28uvRHnvtoM9F1Injm2hSu6xevpl0iAmgqJuis33WUCXO/I3vnUa7oGcejI3rRqkk9f4clIgFEiT1IFBSXMndzEe8vWUrTBnV56dd9Gdq7zekblntwhp4xKhJ+lNiDwOqfDjFhThY/7C9mVN94Hr6qB00b1D19Qw8PziBjvOu1krtI2FCBcwDLKyxh2sJ1XPvycgqKndzXL5o/je7jOamDxwdnUJzvWi4iYUNn7AHqv9/v58F5a9l5JJ+bLujA/UO6s2r50op38vLgDK/LRSQkKbEHGMfxYh57bz3vrM6hU8uGzL5zAOclNq/czjHxrukXT8tFJGwosQeQD7J38/C76ziUV8Tv0jozfrCPTbsGTzl1jh304AyRMKTEHgD2HStg6rvreD97D8ltmvD6LefRq91ZNO06cYFUVTEiYU2J3Y+stcz9ZiePLlpPfnEp91/RjbGXdKpa064UPThDJNwpsftJzuHjPDQ/my827ye1QzOeGpVCUqtG/g5LREKAEnstczot//zqJ57+YCMGeGRET35zfgci1LRLRKqJmoDVoi37cpk4N4tVPx3mkq4teWJkL+KbqWmXiFQvNQGrBcWlTmZ+sZXnP/6e+nUj+dN1fbimbzs17RKRGqGpmBqWvdPBhDlZrN99lCt7t2b68F60bBzt77BEJIQpsdeQguJSnv/ke2Z+sZXmDevy8m/6MqSXu2mXr4263NsPdOTAtyphFJGKKbHXgJXbDvHAnCy2Hsjjun7xTB6WTEyDKNdKXxt1ldneVGZ7EQl7agJWjXILS5jybjbXvbycolIn/7ytP3+8rs/PSR18b9Slxl4i4iOdsVeTzE37mDQ/m12OfG69KJH/+2U3GkZ7+M/ra6MuNfYSER8psXtTyXnww3lFPPreeuZ9s5OkVo2YM+5C+nVo5v1zfW3UpcZeIuIjTcV4cmJe27EDsD/Pa2fNPrmJtZbFa3dz+XOfs3DNLu6+NIn3xl9ccVIH1y+IqPqnLquoUZev24tI2FNi9+QM89r7jhYw7l+r+d1b39Ampj6ZV+znvnWjiH6sBTzX65RfAKdJGQ3pMyAmATCuf9NneL8QWmZ7W5ntRSTsaSrGEy/z19aRwzurdvDYovUUljiZOLQ7d8SsIvK9B3x7HJ2vjbrc23+emUlaWlrl9xORsKQzdk+8zF8fiGzJhDlZdG/ThPfv+QXjBnYm8rNHVbUiIgFFid0TD/Pa+bYufyy5nseu7sWsOy6gU0t3J0ZVrYhIgNFUjCfuaZLiJdOIzN3FLmcLFrW8nXtvvJe2TctdyFTViogEGCV2D4pLnby8/1z+evhPNIyOZOqIntx5TlvPTbv0ODoRCTBK7OWszXFw/5zv2LjnGFeltGHa8J7ENqqgaZceRyciAUaJ3a2guJTnPt7MK19spWXjaGbe2I9f9mxduZ31ODoRCSDBm9h97XhYwZ2kX209yIPz1vLjgTxu6J/AxKE9iKkf5f2zREQCWHAmdl87HnrpqJhfXMpj23vx1tfbad+8Af++/XwuTIqtrVGIiNSI4Cx3rKYOiUcyHubtFdu5/eKOfHDvL5TURSQkVPsZuzGmEzAJiLHWXlvdnw9UWyfEOA4w97cXcm77M/R3EREJIpU6YzfGvGaM2WeMyS63fIgxZpMxZosxZiKAtXartfa2mgj2pIo6Ifqw3MS0U1IXkZBT2amYN4AhZRcYYyKBF4GhQDJwgzEmuVqj88bHjoeOCx+k0ESftr0ZPLWGAhQR8R9jra3chsYkAoustb3c7wcA06y1V7jfPwhgrX3S/X5ORVMxxpixwFiAuLi4frNmzfIp8FZ7P6fT1n8SXbifwuiWbO10I/viBp6yjbWWL3JKmLWpiKEs5eHo2TQtPUhhdKzH7QNdbm4ujRo18ncYNS5cxgnhM9ZwGSfU7lgHDRq02lqbetoKa22lvoBEILvM+2uBV8u8vxF4AWgBvAz8ADxYmc/u16+fPVufffaZx+XbDuTaMX9fbjs8sMhe//cv7Y/7c8/6ewQKb2MNNeEyTmvDZ6zhMk5ra3eswCrrIadW5eKph/vrsdbag8C4KnxulZQ6La8v+5Fnl2wiKiKCJ0b2Zsx5CUREeApXRCT0VCWx5wAJZd7HA7uqFk7VbNpzjAlzs/huxxEGd2/FYyN70Sam/pl3FBEJIVVJ7CuBLsaYjsBOYAzwK18+wBiTDqQnJSVVIQwoKnHyt8wtvPjZFhrXi+L5MecwvI+Xpl0iIiGusuWObwPLgW7GmBxjzG3W2hLgLuBDYAMw21q7zpdvbq3NsNaOjYmJ8TXuk7Y6Skn/61L+8vH3XNm7DR/9/hJGnNNOSV1EwlalztittTd4Wb4YWFytEfnghU+/50/LC4hrAv+4OZXBPeL8FYqISMAIzl4xbu1bNGRgQh1m3HYJTeqpaZeICPg5sVd1jn14n7Y0ObxZSV1EpAy/NgGrjjl2ERE5VXB2dxQREa+U2EVEQoxfE7sxJt0YM9PhcPgzDBGRkKI5dhGREKOpGBGREKPELiISYpTYRURCTKUftFGjQRizH/jpLHePBQ5UYziBLFzGGi7jhPAZa7iME2p3rB2stS3LLwyIxF4VxphV1tMTREJQuIw1XMYJ4TPWcBknBMZYNRUjIhJilNhFREJMKCT2mf4OoBaFy1jDZZwQPmMNl3FCAIw16OfYRUTkVKFwxi4iImUosYuIhJiAS+zGmCHGmE3GmC3GmInuZcYY87gxZrMxZoMxZnyZ7aOMMau97ete/g9jzHfGmCxjzBxjTKPaH9mpjDGvGWP2GWOyyyy7zhizzhjjNMacVi5ljFltjKlrjOlnjFnrHucM437AqzHmUfcY1xhjlhhj2tbmmDzxNHPWK08AAAQSSURBVE738rvdx2qdMeaZcuuCcZwJxpjP3D+f64wx97iXh+Ix9ThW97qQOa7GmHrGmBXu3LHOGDPdvfwud+zWGBNbbp/AyEfW2oD5AiKBH4BOQF3gOyAZuBV4E4hwb9eqzD6DgL9629e9TZMy2/8ZmBgAY70E6Atkl1nWA+gGZAKp5bZPBBa6X68ABgAGeB8Y6mGc44GXA3Scg4CPgWgPxzNYx9kG6Ot+3RjY7P7ZDcVj6m2sIXVc3fE1cr+OAr4GLgDOdY9nGxBbbp+AyEeBdsbeH9hird1qrS0CZgEjgN8Cj1hrnQDW2n1l9hmC64fD275Ya4+C68wfqA/4/YqxtfYL4FC5ZRustZu87DIU+MAY0wbXD8Zy6/rJeBO42r3/0TLbNyRAx4nreD5lrS10b1P2eAbrOHdba79xvz4GbADahegx9ThWQuy4Wpdc99so95e11n5rrd3mZbeAyEeBltjbATvKvM9xL+sMXG+MWWWMed8Y06XMNoNwnQ152xcAY8zrwB6gO67fqMFmCPABrjHllFlefpyPG2N2AL8GptRqhJXXFfiFMeZrY8znxpjzyqwL+nEaYxJxndV9fYZNQ22sIXdcjTGRxpg1wD7gI2vtmY5pQOSjQEvsxsMyC0QDBdZ1m+4rwGsA7jm4Q9ba4xXs63ph7a1AW1xnF9dXc9w1yhhTF4i31m7lzOOcZK1NAN4C7qqlEH1VB2iG68/a+4HZxiXox+meL50L3FvuDLT8dqE41pA7rtbaUmvtOUA80N8Y08vbtoGUjwItsecACWXexwO73MvnupfNB1Lcr4cCH55h35OstaXAf4BR1Rp1zfsFsNT9OgfX2E44bZxu/yZwx5kDzHP/qbsCcOJqnBTU4zTGROH6OX3LWjvvDJuH4lhD8rgCWGuP4DoTH1LBZgGTjwItsa8EuhhjOrp/y48BFgILgEvd2wzEdbEGfp7P8rqv+4whCU7OaaUDG2tlNNXn5DittbuBY8aYC9zjuQl4F6DcFNVwAnecJ4+nMaYrrotLBwjicbpj/AewwVr750rsEopjDanjaoxpaYxp6n5dH7jsDDEFTj6q7quxVf0CrsSVuH8AJrmXNQXeA9YCy4E+uK46r6nEvhHAMve+2bj+xGtSm2PyMs63gd1AMa7f7rcBI92vC4G9wIfubVcC9cvsm+oeyw/AC/x8B/Fc9/IsIAPXxbtAHGdd4F/uWL8BLg2BcV6M60/tLGCN++vKED2m3sYaUscV18zAt+54soEp7uXj3ce0BNdZ+KsEWD4K2pYCxpiLgd9Ya8f5O5aaZIyJB16x1g71dyw1KVzGCRprKAq0fBS0iV1ERDwLtDl2ERGpIiV2EZEQo8QuIhJilNhFREKMEruISIhRYhcRCTH/H71jr2RZwr5wAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tt = np.linspace(0, T, 500)\n",
    "II = I0*np.exp(alpha*tt)\n",
    "plt.semilogy(tt, II)\n",
    "plt.semilogy(t, I, 'o')\n",
    "plt.grid()\n",
    "plt.xticks(t[0::5], dates[0::5])\n",
    "\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Нелинейный метод наименьших квадратов\n",
    "\n",
    "Другой способ подобрать значения параметров в зависимости ($*$) - использовать *нелинейный метод наименьших квадратов*.\n",
    "\n",
    "Как и в линейном методе наименьших квадратов, будем минимизировать сумму квадратов невязок - отклонений предсказанного значения от истинного:\n",
    "$$\n",
    "\\sum_{t=0}^{N-1} \\left(I_0 e^{\\alpha t} - I[t]\\right)^2 \\to \\min\n",
    "$$\n",
    "где $I[t]$ - количество инфицированных в $t$-й день. Теперь, к сожалению, полученная задача минимизации не сводится к решению системы линейных уравнений (как в линейном методе наименьших квадратов), а решается численными методами.\n",
    "\n",
    "Нелинейный метод найименьших квадратов реализован в функции `curve_fit` модуля `numpy.optimize`.\n",
    "\n",
    "Вначале нужно создать функцию, реализующую искому зависимость:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exponential_growth(t, I0, alpha):\n",
    "    return I0 * np.exp(alpha*t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Теперь вызываем функцию `curve_fit`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "params, _ = optimize.curve_fit(exponential_growth, t, I)\n",
    "I0, alpha = params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12.02779235771787, 0.20910087329716093)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "I0, alpha"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Значения параметров $\\alpha$, найденные двумя способами, близки друг к другу.\n",
    "Значения параметров $I_0$ отличаются заметнее.\n",
    "\n",
    "Изобразим график зависимости, построенной вторым способом:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tt = np.linspace(0, T, 500)\n",
    "II = exponential_growth(tt, I0, alpha)\n",
    "plt.semilogy(tt, II)\n",
    "plt.semilogy(t, I, 'o')\n",
    "plt.grid()\n",
    "plt.xticks(t[0::5], dates[0::5])\n",
    "\n",
    "pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Конечно же, для больш*и*х значений $I$ второй метод аппроксимирует данные лучше, чем первый метод.\n",
    "\n",
    "Построенную зависимость можно использовать для предсказания развития эпидемии на ее начальном этапе.\n",
    "\n",
    "В частности, делаем вывод, что сейчас каждые сутки количество зарегистрированных инфицированных увеличивается примерно на 23%: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.232569325591361"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(alpha) - 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Это примерно такое же (или даже меньшее) значение, как было в начале эпидемии в Италии, США и некоторых других странах. Введение карантина должно снизить эту скорость. Заметим, что если карантин не вводить, то экспоненциальный рост все равно прекратится и эпидемия закончится. Но карантин и другие меры сгладит эпидемиологическую кривую и уменьшит общее количество переболевших и количество больных в пик эпидемии, что очень важно. Карантин в России введен на более ранней стадии, чем в других странах.\n",
    "\n",
    "По найденному параметру $\\alpha$ можно оценить *интенсивность заражения* $\\beta$ и *базовое репродуктивное число* $R_0$, если известна интенсивность выздоравливания $\\gamma$.\n",
    "\n",
    "$R0$ – среднее количество людей, инфицированных одним больным, если все контактные были восприимчивы;\n",
    "$\\beta$ – количество людей, инфицированных одним инфицированным человеком за единицу времени (за день)\n",
    "$\\gamma$ – доля от общего числа инфицированных, выздоровевших или умерших за сутки.\n",
    "\n",
    "Например, если болезнь длится $8$ дней, то $\\gamma = 1/8$.\n",
    "\n",
    "$$\n",
    "R_0 = \\frac{\\beta}{\\gamma} = 1+ \\frac{\\alpha}{\\gamma}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.6728069863772874"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gamma = 1/8\n",
    "R0 = 1 + alpha/gamma\n",
    "R0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Зная найденные параметры, можно смоделировать дальнеший ход эпидемии, используя модель SIR и различные ее уточнения."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}