covid19_korea.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## COVID-19\n",
    "### South Korea\n",
    "#### Data source: https://github.com/jihoo-kim/Coronavirus-Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import seaborn as sns\n",
    "from datetime import date, timedelta\n",
    "import re\n",
    "import json\n",
    "import folium\n",
    "import difflib\n",
    "%matplotlib inline\n",
    "#plt.style.use('fivethirtyeight')\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### gather data from https://github.com/jihoo-kim/Coronavirus-Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>patient_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>country</th>\n",
       "      <th>region</th>\n",
       "      <th>disease</th>\n",
       "      <th>group</th>\n",
       "      <th>infection_reason</th>\n",
       "      <th>infection_order</th>\n",
       "      <th>infected_by</th>\n",
       "      <th>contact_number</th>\n",
       "      <th>confirmed_date</th>\n",
       "      <th>released_date</th>\n",
       "      <th>deceased_date</th>\n",
       "      <th>state</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>1984.0</td>\n",
       "      <td>China</td>\n",
       "      <td>filtered at airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>visit to Wuhan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.0</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>NaT</td>\n",
       "      <td>released</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>Korea</td>\n",
       "      <td>filtered at airport</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>visit to Wuhan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.0</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>NaT</td>\n",
       "      <td>released</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Korea</td>\n",
       "      <td>capital area</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>visit to Wuhan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>NaT</td>\n",
       "      <td>released</td>\n",
       "      <td>54.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>male</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>Korea</td>\n",
       "      <td>capital area</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>visit to Wuhan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>NaT</td>\n",
       "      <td>released</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>male</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>Korea</td>\n",
       "      <td>capital area</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>visit to Wuhan</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>NaT</td>\n",
       "      <td>released</td>\n",
       "      <td>33.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "      <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>7864</th>\n",
       "      <td>7865</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Korea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>isolated</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7865</th>\n",
       "      <td>7866</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Korea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>isolated</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7866</th>\n",
       "      <td>7867</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Korea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>isolated</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7867</th>\n",
       "      <td>7868</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Korea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>isolated</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7868</th>\n",
       "      <td>7869</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Korea</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaT</td>\n",
       "      <td>isolated</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7869 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      patient_id     sex  birth_year country               region  disease  \\\n",
       "0              1  female      1984.0   China  filtered at airport      NaN   \n",
       "1              2    male      1964.0   Korea  filtered at airport      NaN   \n",
       "2              3    male      1966.0   Korea         capital area      NaN   \n",
       "3              4    male      1964.0   Korea         capital area      NaN   \n",
       "4              5    male      1987.0   Korea         capital area      NaN   \n",
       "...          ...     ...         ...     ...                  ...      ...   \n",
       "7864        7865     NaN         NaN   Korea                  NaN      NaN   \n",
       "7865        7866     NaN         NaN   Korea                  NaN      NaN   \n",
       "7866        7867     NaN         NaN   Korea                  NaN      NaN   \n",
       "7867        7868     NaN         NaN   Korea                  NaN      NaN   \n",
       "7868        7869     NaN         NaN   Korea                  NaN      NaN   \n",
       "\n",
       "     group infection_reason  infection_order  infected_by  contact_number  \\\n",
       "0      NaN   visit to Wuhan              1.0          NaN            45.0   \n",
       "1      NaN   visit to Wuhan              1.0          NaN            75.0   \n",
       "2      NaN   visit to Wuhan              1.0          NaN            16.0   \n",
       "3      NaN   visit to Wuhan              1.0          NaN            95.0   \n",
       "4      NaN   visit to Wuhan              1.0          NaN            31.0   \n",
       "...    ...              ...              ...          ...             ...   \n",
       "7864   NaN              NaN              NaN          NaN             NaN   \n",
       "7865   NaN              NaN              NaN          NaN             NaN   \n",
       "7866   NaN              NaN              NaN          NaN             NaN   \n",
       "7867   NaN              NaN              NaN          NaN             NaN   \n",
       "7868   NaN              NaN              NaN          NaN             NaN   \n",
       "\n",
       "     confirmed_date released_date deceased_date     state   age  \n",
       "0        2020-01-20    2020-02-06           NaT  released  36.0  \n",
       "1        2020-01-24    2020-02-05           NaT  released  56.0  \n",
       "2        2020-01-26    2020-02-12           NaT  released  54.0  \n",
       "3        2020-01-27    2020-02-09           NaT  released  56.0  \n",
       "4        2020-01-30    2020-03-02           NaT  released  33.0  \n",
       "...             ...           ...           ...       ...   ...  \n",
       "7864     2020-03-11           NaT           NaT  isolated   NaN  \n",
       "7865     2020-03-11           NaT           NaT  isolated   NaN  \n",
       "7866     2020-03-11           NaT           NaT  isolated   NaN  \n",
       "7867     2020-03-11           NaT           NaT  isolated   NaN  \n",
       "7868     2020-03-11           NaT           NaT  isolated   NaN  \n",
       "\n",
       "[7869 rows x 16 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_patient = pd.read_csv('https://raw.githubusercontent.com/jihoo-kim/Coronavirus-Dataset/master/patient.csv', parse_dates=['confirmed_date', 'released_date', 'deceased_date'])\n",
    "df_patient['age'] = 2020 - df_patient['birth_year']\n",
    "df_patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed_new</th>\n",
       "      <th>confirmed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-20</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-30</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-02</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-04</th>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-05</th>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-06</th>\n",
       "      <td>3</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-09</th>\n",
       "      <td>3</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-16</th>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-18</th>\n",
       "      <td>9</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-19</th>\n",
       "      <td>27</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-20</th>\n",
       "      <td>38</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>100</td>\n",
       "      <td>204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-22</th>\n",
       "      <td>229</td>\n",
       "      <td>433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-23</th>\n",
       "      <td>169</td>\n",
       "      <td>602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24</th>\n",
       "      <td>231</td>\n",
       "      <td>833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-25</th>\n",
       "      <td>143</td>\n",
       "      <td>976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>287</td>\n",
       "      <td>1263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27</th>\n",
       "      <td>504</td>\n",
       "      <td>1767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>571</td>\n",
       "      <td>2338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-29</th>\n",
       "      <td>812</td>\n",
       "      <td>3150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01</th>\n",
       "      <td>1062</td>\n",
       "      <td>4212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>600</td>\n",
       "      <td>4812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>516</td>\n",
       "      <td>5328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>438</td>\n",
       "      <td>5766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05</th>\n",
       "      <td>518</td>\n",
       "      <td>6284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-06</th>\n",
       "      <td>483</td>\n",
       "      <td>6767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-07</th>\n",
       "      <td>367</td>\n",
       "      <td>7134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-08</th>\n",
       "      <td>248</td>\n",
       "      <td>7382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-09</th>\n",
       "      <td>131</td>\n",
       "      <td>7513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-10</th>\n",
       "      <td>242</td>\n",
       "      <td>7755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-11</th>\n",
       "      <td>114</td>\n",
       "      <td>7869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            confirmed_new  confirmed\n",
       "date                                \n",
       "2020-01-20              1          1\n",
       "2020-01-24              1          2\n",
       "2020-01-26              1          3\n",
       "2020-01-27              1          4\n",
       "2020-01-30              3          7\n",
       "2020-01-31              4         11\n",
       "2020-02-01              1         12\n",
       "2020-02-02              3         15\n",
       "2020-02-04              1         16\n",
       "2020-02-05              5         21\n",
       "2020-02-06              3         24\n",
       "2020-02-09              3         27\n",
       "2020-02-10              1         28\n",
       "2020-02-16              2         30\n",
       "2020-02-18              9         39\n",
       "2020-02-19             27         66\n",
       "2020-02-20             38        104\n",
       "2020-02-21            100        204\n",
       "2020-02-22            229        433\n",
       "2020-02-23            169        602\n",
       "2020-02-24            231        833\n",
       "2020-02-25            143        976\n",
       "2020-02-26            287       1263\n",
       "2020-02-27            504       1767\n",
       "2020-02-28            571       2338\n",
       "2020-02-29            812       3150\n",
       "2020-03-01           1062       4212\n",
       "2020-03-02            600       4812\n",
       "2020-03-03            516       5328\n",
       "2020-03-04            438       5766\n",
       "2020-03-05            518       6284\n",
       "2020-03-06            483       6767\n",
       "2020-03-07            367       7134\n",
       "2020-03-08            248       7382\n",
       "2020-03-09            131       7513\n",
       "2020-03-10            242       7755\n",
       "2020-03-11            114       7869"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_korea = df_patient.groupby('confirmed_date').size().reset_index()\n",
    "df_korea.columns=['date', 'confirmed_new']\n",
    "df_korea = df_korea.set_index('date')\n",
    "df_korea['confirmed'] = np.cumsum(df_korea['confirmed_new'])\n",
    "df_korea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1,2,figsize=(20,8))\n",
    "for i, ctype in enumerate(['confirmed', 'confirmed_new'], 1):\n",
    "    plt.subplot(1,2,i)\n",
    "    ax = sns.barplot(data=df_korea, x=df_korea.index, y=ctype, color=str('C' + str(i-1)))\n",
    "    xticks = [xdate.strftime('%d.%m.') for xdate in df_korea.index]\n",
    "    _ =ax.set_xticklabels(xticks, rotation=45, ha='right')\n",
    "_ = plt.suptitle('COVID-19 - confirmed cases in South Korea',fontsize=20)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "isolated    7777\n",
       "released      56\n",
       "deceased      36\n",
       "Name: state, dtype: int64"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_patient.state.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.countplot(x=np.floor(df_patient['age'].dropna()),color='C2')\n",
    "ax.set_yscale('log')\n",
    "_ = ax.set_xticklabels(ax.get_xticklabels(), rotation=90)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8))\n",
    "plot_data = df_patient[(df_patient.state=='died') & (df_patient.age != np.nan)]\n",
    "plot_data.loc[:,'agegroup']= pd.cut(plot_data['age'], bins = [0,50, np.inf])\n",
    "ax = sns.countplot(data=plot_data, x='agegroup')\n",
    "_ = ax.set_xticklabels(ax.get_xticklabels(), rotation=90)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(20, 8))\n",
    "plot_data = df_patient[(df_patient.state=='released') & (df_patient.age != np.nan)]\n",
    "plot_data.loc[:,'agegroup']= pd.cut(plot_data['age'], bins = [0,50, np.inf])\n",
    "ax = sns.countplot(data=plot_data, x='agegroup')\n",
    "_ = ax.set_xticklabels(ax.get_xticklabels(), rotation=90)"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}