Prophet Forecasting of Corona Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>Confirmed</th>\n",
       "      <th>Death</th>\n",
       "      <th>fatality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>China</th>\n",
       "      <td>80552</td>\n",
       "      <td>3042</td>\n",
       "      <td>3.776443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Korea</th>\n",
       "      <td>6284</td>\n",
       "      <td>40</td>\n",
       "      <td>0.636537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Italia</th>\n",
       "      <td>3858</td>\n",
       "      <td>148</td>\n",
       "      <td>3.836185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iran</th>\n",
       "      <td>3513</td>\n",
       "      <td>108</td>\n",
       "      <td>3.074295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JPN_Cruise</th>\n",
       "      <td>696</td>\n",
       "      <td>6</td>\n",
       "      <td>0.862069</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Germany</th>\n",
       "      <td>545</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>France</th>\n",
       "      <td>423</td>\n",
       "      <td>7</td>\n",
       "      <td>1.654846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Japan</th>\n",
       "      <td>364</td>\n",
       "      <td>6</td>\n",
       "      <td>1.648352</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Confirmed  Death  fatality\n",
       "China           80552   3042  3.776443\n",
       "Korea            6284     40  0.636537\n",
       "Italia           3858    148  3.836185\n",
       "Iran             3513    108  3.074295\n",
       "JPN_Cruise        696      6  0.862069\n",
       " Germany          545      0  0.000000\n",
       "France            423      7  1.654846\n",
       "Japan             364      6  1.648352"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fatality_rate = pd.DataFrame({'Confirmed':[80552, 6284, 3858, 3513, 696, 545, 423, 364], 'Death':[3042, 40, 148, 108, 6, 0, 7,  6]}) #3.67\n",
    "fatality_rate = fatality_rate.rename(index={0:\"China\",1:\"Korea\", 2:\"Italia\", 3:\"Iran\", 4: \"JPN_Cruise\", 5:  \" Germany\", 6: \"France\", 7:\"Japan\" })\n",
    "fatality_rate['fatality'] = fatality_rate['Death']  /fatality_rate['Confirmed'] *100\n",
    "fatality_rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: `Series.plot()` should not be called with positional arguments, only keyword arguments. The order of positional arguments will change in the future. Use `Series.plot(kind='bar')` instead of `Series.plot('bar',)`.\n",
      "  \n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=15,7\n",
    "ax= fatality_rate['Confirmed'].plot('bar',  title = \"International nCOV-19 Confirmed patients\", color=['slateblue'])\n",
    "for p in ax.patches:\n",
    "  left, bottom, width, height = p.get_bbox().bounds\n",
    "  ax.annotate(\"%.f\"%(height), (left+width/2, height*1.01), ha='center')\n",
    "    \n",
    "plt.sca(ax)\n",
    "plt.box(False) \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=18,7\n",
    "ax = fatality_rate['fatality'].plot(kind='barh', stacked=True, title= \"International nCOV-19 fatality_rate\", rot=0, color=['red', 'skyblue','darkgreen', 'orange','lightgreen','yellow','slateblue', 'darkblue']) \n",
    "for p in ax.patches: \n",
    "  left, bottom, width, height = p.get_bbox().bounds \n",
    "  ax.annotate(\"%.2f%%\"%(width), xy=(left+width*1.04, bottom+height/2), ha='center', va='center') \n",
    "\n",
    "plt.sca(ax)\n",
    "plt.box(False) \n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# load Korea nCov-19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df_patient = pd.read_csv('patient.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>id</th>\n",
       "      <th>sex</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>country</th>\n",
       "      <th>region</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",
       "    </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>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>NaN</td>\n",
       "      <td>released</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>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>NaN</td>\n",
       "      <td>released</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>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>NaN</td>\n",
       "      <td>released</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>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>NaN</td>\n",
       "      <td>released</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>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>NaN</td>\n",
       "      <td>released</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5761</th>\n",
       "      <td>5762</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>2020-03-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>isolated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5762</th>\n",
       "      <td>5763</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>2020-03-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>isolated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5763</th>\n",
       "      <td>5764</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>2020-03-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>isolated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5764</th>\n",
       "      <td>5765</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>2020-03-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>isolated</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5765</th>\n",
       "      <td>5766</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>2020-03-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>isolated</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5766 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id     sex  birth_year country               region group  \\\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",
       "5761  5762     NaN         NaN   Korea                  NaN   NaN   \n",
       "5762  5763     NaN         NaN   Korea                  NaN   NaN   \n",
       "5763  5764     NaN         NaN   Korea                  NaN   NaN   \n",
       "5764  5765     NaN         NaN   Korea                  NaN   NaN   \n",
       "5765  5766     NaN         NaN   Korea                  NaN   NaN   \n",
       "\n",
       "     infection_reason  infection_order  infected_by  contact_number  \\\n",
       "0      visit to Wuhan              1.0          NaN            45.0   \n",
       "1      visit to Wuhan              1.0          NaN            75.0   \n",
       "2      visit to Wuhan              1.0          NaN            16.0   \n",
       "3      visit to Wuhan              1.0          NaN            95.0   \n",
       "4      visit to Wuhan              1.0          NaN            31.0   \n",
       "...               ...              ...          ...             ...   \n",
       "5761              NaN              NaN          NaN             NaN   \n",
       "5762              NaN              NaN          NaN             NaN   \n",
       "5763              NaN              NaN          NaN             NaN   \n",
       "5764              NaN              NaN          NaN             NaN   \n",
       "5765              NaN              NaN          NaN             NaN   \n",
       "\n",
       "     confirmed_date released_date deceased_date     state  \n",
       "0        2020-01-20    2020-02-06           NaN  released  \n",
       "1        2020-01-24    2020-02-05           NaN  released  \n",
       "2        2020-01-26    2020-02-12           NaN  released  \n",
       "3        2020-01-27    2020-02-09           NaN  released  \n",
       "4        2020-01-30    2020-03-02           NaN  released  \n",
       "...             ...           ...           ...       ...  \n",
       "5761     2020-03-04           NaN           NaN  isolated  \n",
       "5762     2020-03-04           NaN           NaN  isolated  \n",
       "5763     2020-03-04           NaN           NaN  isolated  \n",
       "5764     2020-03-04           NaN           NaN  isolated  \n",
       "5765     2020-03-04           NaN           NaN  isolated  \n",
       "\n",
       "[5766 rows x 14 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from datetime import date, timedelta\n",
    "\n",
    "from fbprophet import Prophet\n",
    "from fbprophet.plot import plot_plotly, add_changepoints_to_plot\n",
    "from plotly.offline import iplot, init_notebook_mode\n",
    "import plotly.graph_objs as go\n",
    "import plotly.offline as py\n",
    "\n",
    "df_patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5766 entries, 0 to 5765\n",
      "Data columns (total 14 columns):\n",
      "id                  5766 non-null int64\n",
      "sex                 421 non-null object\n",
      "birth_year          408 non-null float64\n",
      "country             5765 non-null object\n",
      "region              409 non-null object\n",
      "group               83 non-null object\n",
      "infection_reason    146 non-null object\n",
      "infection_order     35 non-null float64\n",
      "infected_by         70 non-null float64\n",
      "contact_number      50 non-null float64\n",
      "confirmed_date      5765 non-null object\n",
      "released_date       38 non-null object\n",
      "deceased_date       23 non-null object\n",
      "state               5765 non-null object\n",
      "dtypes: float64(4), int64(1), object(9)\n",
      "memory usage: 630.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_patient.info() # most data have missing value..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get daily confirmed count \n",
    "\n",
    "daily_count = df_patient.groupby('confirmed_date').id.count()\n",
    " \n",
    "# get accumulated confirmed count\n",
    "\n",
    "accumulated_count = daily_count.cumsum()\n",
    "\n",
    "plt.figure(figsize=(18,5))\n",
    "\n",
    "color = 'tab:red'\n",
    "ax = accumulated_count.plot(title='Korea nCOV19 Confirmed individuals', rot=90, color=color)\n",
    "ax.set_ylabel('Accumulated_count', color=color)\n",
    "plt.box(False)\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "color = 'tab:blue'\n",
    "ax2 = daily_count.plot(kind='bar',color=color, alpha=0.1)\n",
    "for p in ax2.patches:\n",
    "  left, bottom, width, height = p.get_bbox().bounds\n",
    "  ax2.annotate(\"%.f\"%(height), (left+width/2, height*1.01), ha='center') # ha =horizontal align\n",
    "ax2.set_ylabel('Confirmed_count', color=color)\n",
    "ax2.tick_params(axis='y', color=color)\n",
    "\n",
    "plt.box(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>confirmed_date</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>1062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   confirmed_date    id\n",
       "0      2020-01-20     1\n",
       "1      2020-01-24     1\n",
       "2      2020-01-26     1\n",
       "3      2020-01-27     1\n",
       "4      2020-01-30     3\n",
       "5      2020-01-31     4\n",
       "6      2020-02-01     1\n",
       "7      2020-02-02     3\n",
       "8      2020-02-04     1\n",
       "9      2020-02-05     5\n",
       "10     2020-02-06     3\n",
       "11     2020-02-09     3\n",
       "12     2020-02-10     1\n",
       "13     2020-02-16     2\n",
       "14     2020-02-18     9\n",
       "15     2020-02-19    26\n",
       "16     2020-02-20    38\n",
       "17     2020-02-21   100\n",
       "18     2020-02-22   229\n",
       "19     2020-02-23   169\n",
       "20     2020-02-24   231\n",
       "21     2020-02-25   143\n",
       "22     2020-02-26   285\n",
       "23     2020-02-27   505\n",
       "24     2020-02-28   571\n",
       "25     2020-02-29   813\n",
       "26     2020-03-01  1062\n",
       "27     2020-03-02   600\n",
       "28     2020-03-03   516\n",
       "29     2020-03-04   438"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "\n",
    "daily_count = pd.DataFrame(daily_count)\n",
    "daily_count_data =daily_count.reset_index()\n",
    "daily_count_data"
   ]
  },
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   "execution_count": 9,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>confirmed_date</th>\n",
       "      <th>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>7</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>28</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>39</td>\n",
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       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>65</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>1260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>1765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>2336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>3149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>4211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>4811</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>5327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>5765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   confirmed_date    id\n",
       "0      2020-01-20     1\n",
       "1      2020-01-24     2\n",
       "2      2020-01-26     3\n",
       "3      2020-01-27     4\n",
       "4      2020-01-30     7\n",
       "5      2020-01-31    11\n",
       "6      2020-02-01    12\n",
       "7      2020-02-02    15\n",
       "8      2020-02-04    16\n",
       "9      2020-02-05    21\n",
       "10     2020-02-06    24\n",
       "11     2020-02-09    27\n",
       "12     2020-02-10    28\n",
       "13     2020-02-16    30\n",
       "14     2020-02-18    39\n",
       "15     2020-02-19    65\n",
       "16     2020-02-20   103\n",
       "17     2020-02-21   203\n",
       "18     2020-02-22   432\n",
       "19     2020-02-23   601\n",
       "20     2020-02-24   832\n",
       "21     2020-02-25   975\n",
       "22     2020-02-26  1260\n",
       "23     2020-02-27  1765\n",
       "24     2020-02-28  2336\n",
       "25     2020-02-29  3149\n",
       "26     2020-03-01  4211\n",
       "27     2020-03-02  4811\n",
       "28     2020-03-03  5327\n",
       "29     2020-03-04  5765"
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     "execution_count": 9,
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   ],
   "source": [
    "# get accumulated confirmed count\n",
    "accumulated_count = daily_count.cumsum()\n",
    "accumulated_count.reset_index(inplace=True)\n",
    "accumulated_count"
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       "        </div>"
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   ],
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    "fig = go.Figure()\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=daily_count_data.confirmed_date, y=daily_count.id,\n",
    "        name='nCOV-19 in Korea'))\n",
    "# Add figure title\n",
    "fig.update_layout(title_text=\"daily Confirmed_patient of nCOV-19 in Korea\")\n",
    "\n",
    "# Set x-axis title\n",
    "fig.update_xaxes(title_text=\"2020 01 ~03\")"
   ]
  },
  {
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   "execution_count": 11,
   "metadata": {},
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   "source": [
    "fig = go.Figure()\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=accumulated_count.confirmed_date, y=accumulated_count.id, \n",
    "               name='nCOV-19 in Korea'))\n",
    "\n",
    "# Add figure title\n",
    "fig.update_layout(title_text=\"Accumulated Confirmed_patient of nCOV-19 in Korea\")\n",
    "\n",
    "# Set x-axis title\n",
    "fig.update_xaxes(title_text=\"2020 01 ~03\")"
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  {
   "cell_type": "code",
   "execution_count": 12,
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\"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"type\": \"surface\"}], \"table\": [{\"cells\": {\"fill\": {\"color\": \"#EBF0F8\"}, \"line\": {\"color\": \"white\"}}, \"header\": {\"fill\": {\"color\": \"#C8D4E3\"}, \"line\": {\"color\": \"white\"}}, \"type\": \"table\"}]}, \"layout\": {\"annotationdefaults\": {\"arrowcolor\": \"#2a3f5f\", \"arrowhead\": 0, \"arrowwidth\": 1}, \"coloraxis\": {\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}}, \"colorscale\": {\"diverging\": [[0, \"#8e0152\"], [0.1, \"#c51b7d\"], [0.2, \"#de77ae\"], [0.3, \"#f1b6da\"], [0.4, \"#fde0ef\"], [0.5, \"#f7f7f7\"], [0.6, \"#e6f5d0\"], [0.7, \"#b8e186\"], [0.8, \"#7fbc41\"], [0.9, \"#4d9221\"], [1, \"#276419\"]], \"sequential\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]], \"sequentialminus\": [[0.0, \"#0d0887\"], [0.1111111111111111, \"#46039f\"], [0.2222222222222222, \"#7201a8\"], [0.3333333333333333, \"#9c179e\"], [0.4444444444444444, \"#bd3786\"], [0.5555555555555556, \"#d8576b\"], [0.6666666666666666, \"#ed7953\"], [0.7777777777777778, \"#fb9f3a\"], [0.8888888888888888, \"#fdca26\"], [1.0, \"#f0f921\"]]}, \"colorway\": [\"#636efa\", \"#EF553B\", \"#00cc96\", \"#ab63fa\", \"#FFA15A\", \"#19d3f3\", \"#FF6692\", \"#B6E880\", \"#FF97FF\", \"#FECB52\"], \"font\": {\"color\": \"#2a3f5f\"}, \"geo\": {\"bgcolor\": \"white\", \"lakecolor\": \"white\", \"landcolor\": \"#E5ECF6\", \"showlakes\": true, \"showland\": true, \"subunitcolor\": \"white\"}, \"hoverlabel\": {\"align\": \"left\"}, \"hovermode\": \"closest\", \"mapbox\": {\"style\": \"light\"}, \"paper_bgcolor\": \"white\", \"plot_bgcolor\": \"#E5ECF6\", \"polar\": {\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Corona19 in Korea\"}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 0.94], \"title\": {\"text\": \"2020 01 ~03\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"<b>Confirmed</b> individual\"}}, \"yaxis2\": {\"anchor\": \"x\", \"overlaying\": \"y\", \"side\": \"right\", \"title\": {\"text\": \"<b>Accumulated</b> individual\"}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('68fb38b8-b1dd-4332-8823-e952756e400a');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# Create figure with secondary y-axis\n",
    "fig = make_subplots(specs=[[{\"secondary_y\": True}]])\n",
    "\n",
    "\n",
    "# Add traces\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=daily_count_data.confirmed_date, y=daily_count.id,\n",
    "        name='Confirmed in Korea'), secondary_y=False,)\n",
    "\n",
    "fig.add_trace(\n",
    "    go.Scatter(x=accumulated_count.confirmed_date, y=accumulated_count.id,\n",
    "        name='Accumulated in Korea'), secondary_y=True,)\n",
    "\n",
    "# Add figure title\n",
    "fig.update_layout(title_text=\"Corona19 in Korea\")\n",
    "\n",
    "# Set x-axis title\n",
    "fig.update_xaxes(title_text=\"2020 01 ~03\")\n",
    "\n",
    "# Set y-axes titles\n",
    "fig.update_yaxes(title_text=\"<b>Confirmed</b> individual\", secondary_y=False)\n",
    "fig.update_yaxes(title_text=\"<b>Accumulated</b> individual\", secondary_y=True)\n",
    "\n",
    "fig.update_layout(\n",
    "    legend=dict(x=0, y=1,\n",
    "        traceorder=\"normal\",\n",
    "        font=dict(\n",
    "            family=\"sans-serif\",\n",
    "            size=14,\n",
    "            color=\"black\" ),\n",
    "        bgcolor=\"white\",\n",
    "        bordercolor=\"Black\",\n",
    "        borderwidth=2 ))\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prophet Forecasting\n",
    "\n",
    "https://facebook.github.io/prophet/docs/trend_changepoints.html\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020-02-16</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2020-02-22</td>\n",
       "      <td>432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2020-02-25</td>\n",
       "      <td>975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>1260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>1765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>2336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>3149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>4211</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>4811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>5327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>5765</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds     y\n",
       "0   2020-01-20     1\n",
       "1   2020-01-24     2\n",
       "2   2020-01-26     3\n",
       "3   2020-01-27     4\n",
       "4   2020-01-30     7\n",
       "5   2020-01-31    11\n",
       "6   2020-02-01    12\n",
       "7   2020-02-02    15\n",
       "8   2020-02-04    16\n",
       "9   2020-02-05    21\n",
       "10  2020-02-06    24\n",
       "11  2020-02-09    27\n",
       "12  2020-02-10    28\n",
       "13  2020-02-16    30\n",
       "14  2020-02-18    39\n",
       "15  2020-02-19    65\n",
       "16  2020-02-20   103\n",
       "17  2020-02-21   203\n",
       "18  2020-02-22   432\n",
       "19  2020-02-23   601\n",
       "20  2020-02-24   832\n",
       "21  2020-02-25   975\n",
       "22  2020-02-26  1260\n",
       "23  2020-02-27  1765\n",
       "24  2020-02-28  2336\n",
       "25  2020-02-29  3149\n",
       "26  2020-03-01  4211\n",
       "27  2020-03-02  4811\n",
       "28  2020-03-03  5327\n",
       "29  2020-03-04  5765"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prophet = accumulated_count.rename(columns={ 'confirmed_date': 'ds', 'id': 'y' })\n",
    "\n",
    "df_prophet"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prophet code changepoint_range=default :0.8 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>ds</th>\n",
       "      <th>yhat</th>\n",
       "      <th>yhat_lower</th>\n",
       "      <th>yhat_upper</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2020-03-05</td>\n",
       "      <td>6258.187292</td>\n",
       "      <td>6233.945973</td>\n",
       "      <td>6285.858903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>6736.780677</td>\n",
       "      <td>6624.531992</td>\n",
       "      <td>6866.127309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2020-03-07</td>\n",
       "      <td>7215.374061</td>\n",
       "      <td>6981.675617</td>\n",
       "      <td>7476.203073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2020-03-08</td>\n",
       "      <td>7693.967446</td>\n",
       "      <td>7318.793032</td>\n",
       "      <td>8096.026569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>2020-03-09</td>\n",
       "      <td>8172.560831</td>\n",
       "      <td>7641.130227</td>\n",
       "      <td>8738.473902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>8651.154216</td>\n",
       "      <td>7941.334404</td>\n",
       "      <td>9406.505942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>2020-03-11</td>\n",
       "      <td>9129.747600</td>\n",
       "      <td>8231.435511</td>\n",
       "      <td>10106.733155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           ds         yhat   yhat_lower    yhat_upper\n",
       "30 2020-03-05  6258.187292  6233.945973   6285.858903\n",
       "31 2020-03-06  6736.780677  6624.531992   6866.127309\n",
       "32 2020-03-07  7215.374061  6981.675617   7476.203073\n",
       "33 2020-03-08  7693.967446  7318.793032   8096.026569\n",
       "34 2020-03-09  8172.560831  7641.130227   8738.473902\n",
       "35 2020-03-10  8651.154216  7941.334404   9406.505942\n",
       "36 2020-03-11  9129.747600  8231.435511  10106.733155"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = Prophet(\n",
    "    changepoint_prior_scale=0.2, # increasing it will make the trend more flexible\n",
    "    changepoint_range=0.95, # place potential changepoints in the first 98% of the time series\n",
    "    yearly_seasonality=False,\n",
    "    weekly_seasonality=False,\n",
    "    daily_seasonality=True,\n",
    "    seasonality_mode='additive'\n",
    ")\n",
    "\n",
    "m.fit(df_prophet)\n",
    "\n",
    "future = m.make_future_dataframe(periods=7)\n",
    "forecast = m.predict(future)\n",
    "\n",
    "\n",
    "forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(7)"
   ]
  },
  {
   "cell_type": "code",
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l2Ddl2Df53NkzSZLw3bky1FrsaNXEm/NsFit8h9yCzud/xeFt3yD4qs6Kvp5DklBeZ8NtV7dVWHHjGuubVnMbj1gQERER6VBprRVFVeYmh2MAyJ/3Mvr8egxfzXpR8XDc0nBAJiIi4NlnnQ8t8wqMDVq4UP28ositQW99ExXbwkiShF8KqhBousxw/O0BDN/wNr6KH4Lov052T3HNAM8gExERsHev9nkFxposFvXziiK3Br31TVRsC1NSY0VJjbXJs8eW6lpcNeuvKA0IguHtFYALt58mJ15BJiIiItIRSZJworAKrXyb3lxR/MxzuOb8Geyf+yoCI9u5qbrmgQMyERERkY4UV1tQVmNt8nhF7tadGLElBf+7+S50+Mu9bqyueeCATERERKQTkiThREEVgv0aH45rS8vQ+9kncC6sHQLfelO1r22zS2gphzR4BpmIiIDoaO3zCoy119aqn1cUuTXorW+iYluIwiozKsw2RLRq/Oxx9Yy/IaY0H1tX/geR4aGqfN06mx0VdTb06xCiSj5Pxz3IVA93XirDvinDvsnHninDvinDvsknsmcOh4Rvz5TA2wD4+TR8/vjch5/grtlTsXXEBLRbtVSVr1ttsaHO6sCAmFCEBZhUyflH3INMRERERLIVVJlRbbE1OhxX5RVg0EtP45eIzgh/fZEqX7Os1gq7A7i+S7iw4dgT8YgFEREBM2c6Py5Zol1egbHBVVXAO++om1cUuTXorW+iYps5h6Pps8eSJMHx6KNoXVOOw6s+QERgwBV/zZIaKwJM3oiPDml0KG+uNBmQ33zzTbzzzjswGAzo3bs31q1bh7y8PIwfPx4lJSXo168f3nvvPZhMJpjNZkycOBEHDx5E69at8Z///AedO3cGACxcuBBr166Ft7c3li5dimHDhmnxcoiI9O/IEe3zCoz1cXWfr6g+yCG3Br31TVRsM3e+sg41VjsifBs+e3x2dQruydiJLfc9hqjBg67oa0mShKIaC9oEmHBdhxD4eLe8Awduf8U5OTlYunQpMjIycPToUdjtdmzYsAGzZ8/GrFmzkJmZibCwMKxduxYAsHbtWoSFheHkyZOYNWsWZs+eDQA4fvw4NmzYgGPHjmH79u2YPn067Ha7u18OERERkVB2h4SfC6oR4ufT4J+Xn/4VSW/Mww8x3RHx0rwr+loOSUJBlQXRIf7oGx3aIodjQKMzyDabDbW1tbDZbKipqUFkZCR27dqFMWPGAAAmTZqEzZs3AwBSU1MxadIkAMCYMWOQlpYGSZKQmpqK8ePHw9fXF126dEFsbCwOHDigxcshIiIiEia3vBZ1djt8jZeObZLDAb9pf4WfzYLzy1bDaGp4iHaFzSGhoMqMuLaB6Nk+CN5eLWWp26XcfsSiQ4cOeOqppxATEwN/f3/cfvvtiI+PR2hoKIxGZznR0dHIyckB4Lzi3LFjR2exRiNCQkJQXFyMnJwcDBr0f79CuPhz/mjNmjVYs2YNACA/Px+5ubkiX6KuFRYWal2CLrFvyrBv8onqWevffpVerPLPRzl5RcZaLRbV84oitwa99U1UrKdQ+/+jNoeEQ9ll8PfxRlntpQNr0dvrMe6nfdg4+UmEd4xAWdF5ZV/HLqHCbENcm0AEWh3Iy6u40tJl8bT/Hrh9QC4tLUVqairOnDmD0NBQ/PnPf8a2bdsuiTP8dr/whrbQGQyGRp9vSHJyMpKTkwE414VwZU3T2B9l2Ddl2Df5hPSsVy8xueXkFRhrq6lRP68ocmvQW99ExXoQNes9XVQN/1AftAm8dINE8Y8/4U8pS3Dg6v7o8vdnYfBW9ka6WqsdVrMNSdeEom0T+5VF86Tvs9sH5J07d6JLly5o27YtAOCee+7Bnj17UFZWBpvNBqPRiOzs7AtNio6Oxrlz5xAdHQ2bzYby8nKEh4dfeP53F38OERHJ9Ntv2TTNKzC2PDcXgWrnFUVuDXrrm6jYZshss+NkUTVC/S89NmG32tDmsUdg8/JG1durEaJwOK4y22B1SEjsHI6QBr5OS+X2M8gxMTHYt28fampqIEkS0tLS0KNHD9xyyy3YtGkTACAlJQWjR48GAIwaNQopKSkAgE2bNuHWW2+FwWDAqFGjsGHDBpjNZpw5cwaZmZkYOHCgu18OERERkRBZJc47GRobOAuc+48F6HfmR3w14+8IubqrovxltVYAQGKnMA7Hf+D2K8gJCQkYM2YM+vXrB6PRiL59+yI5ORl/+tOfMH78eMydOxd9+/bF1KlTAQBTp07FhAkTEBsbi/DwcGzYsAEA0LNnT4wdOxY9evSA0WjEihUr4K3wX09ERC3eb8fQVL9iJyevwNiQmhrg/ffVzSuK3Br01jdRsc1MjcWGM8XVaN3A0YqCPRkY/u9l+KZPEjpMn6oof0mNBQE+3ojvGNridhy7QpM9yPPnz8f8+fPrPde1a9cGt1D4+flh48aNDeZ5/vnn8fzzzwupkYioRfnlF+3zCow1urrPV1Qf5JBbg976Jiq2mTlTXAOjtwFef3h/lbWmDp1n/RXlfq0grXwbBi/5hwGKqi0I8/dBnw4hMDWwGYN4q2kiIiIij1JltuFsWS1CG9h7XDhnLrrnncK+5xYhsEOkrLySJOF8lRkRrUzoF83huCm81TQRERGRBzlZVAWTt9cl27lyd6RjROo67LzxTnSYME5WTockobDKgs7h/rgmIgheLXjHsSv4TwciIiIiD1Fea0VuhRkhfvWvYdaVVaDn7MeQG9wW/svekpXT7nDeHS+2bSC6t+Nw7ApeQSYiIqBPH+3zCoy1VlXBpe2uovogh9wa9NY3UbHNgCRJOFFYhUAf70uuHlfOego3FOfgs+UfIrJ1mMs5bXYHimqs6Nk+CJ3DA9QuudkySA3dcaMZ69+/PzIyMrQuw2Pl5uZyn7QC7Jsy7Jt87Jky7Jsy7Jt8V9KzkhoL9mWVoF2QX73nszd+itFPTsL2O/6CtmtWuJzPbHOgrNaKPlHBiAr1V1STuzTWN63mNl5BJiIiItKYJEn4Kb8SQb7135hXc74QA/7xJDLbxiD0jVdczldntaPCbMOAGG3vjqdXHJCJiAh44AHnR1d23orKKzA2tLYW+PhjdfOKIrcGvfVNVKzOFVSaUWG2IeIPw6zlsSfQtroU37+dgohWLt3XENUWG8w2BxI7hzd4Fz66PA7IREQEZGdrn1dgrLer+3xF9UEOuTXorW+iYnXM4ZDwc0EVgn3rj2Vn33kPd+/fji1jpyHq5utdylVtscFilzCoUziC/DjmKcUtFkREREQayq+sQ43VXu+OdhVZ2Rj86lwcje6Gdv/8h0t5qsw2WO0SBnUK43B8hTggExEREWnEZnfg54LqejcFkRwOmKY9ggBrHXKXroa376W3m/6jKrMNdglI6BSGVr4cjq8UB2QiIiIijeSU18Fit9e7q93ZN9/GDcf2YMfkWWjd/7rL5qiss8EhAQkxoRyOVcIuEhERkJiofV6BsRZX9/mK6oMccmvQW99ExeqQxebAL4XVCLvo6nHJsRMYunIBMmL7Iur5py+bo7LOBglAQqdQBJg41qmFe5CpHu68VIZ9U4Z9k489U4Z9U4Z9k09Oz04VVeFkUQ3aBDqPUNhtdnjfNgRx2b8g4/OvEdIttsnPr6izwmAwYGCM/odjT9uDzCMWRERERG5WZ7Ujs6gaYRetYct9cRHiT3+PXY+9cNnhuLzOCi+DAQmdwnQ/HHsidpSIiIB773V+dGXnrai8AmPD6uqAzz9XN68ocmvQW99ExerM6ZJqeBsM8PZy3lK6YP8h3PHeW/j22psQ/cQjTX5uWa0VPt5eGBATCv+LNl+QejggExERUFysfV6BsV6u7vMV1Qc55Nagt76JitWRarMNv5bUoU2g8+qxrc6MjjP+ikrfANhXroTBq/Ff8JfVWWEyemFAx9B6a+FIXTxiQURERORGmUVVMHkb4GVwXj0+/+w89MrNxJ45C9GqY4dGP6+s1gI/bw7H7sABmYiIiMhNymutyKswI+S3G3nk7fwaIz55B7sSRyB68n2Nfl5ZrQV+Rm/0j+Fw7A4ckImIiIjcQJIknCisQoCPNwwGAywVlbjmmcdwPqg1TMuXNvp5ZXXWC8Oxr5HDsTvwDDIREQFDhmifV2CsuaLCtX2+ovogh9wa9NY3UbE6UFJjRVGVBe2CnF0tnfU0EovO4bO33kdk29YNfk5ZrfPMcXxHDsfuxD3IVA93XirDvinDvsnHninDvinDvsnXWM8cDgl7skoAAP4+3sj+7+cYPeMBbB86Dm3XrmowV3mdFUYvLwxsAccquAeZiIiIqIUpqDKj0myDv483agpLEP/CLJxuHY2QJa82GF9RZ4W3wYABLWA49kQ8YkFERMDw4c6P27Zpl1dgbLjZDOzapW5eUeTWoLe+iYr1YHaHhJ/OVyHkt1tKmx97HO0ri/HFvzajfVDQJfFVZhsAA/cca4gDMhERAbW12ucVGGtwdZ+vqD7IIbcGvfVNVKwHyymvRZ3djmA/I86u+zfu3rsVn92bjMghN10SW2W2wS4BCc3g9tF6xiMWRERERIJY7Q6cKKhGuJ8PKs7m4MbFz+FYhzhELJh/SWyV2QabQ0JCTCgCfTkca4kDMhEREZEgZ0tr4JAkeBsA4/RpCDLXIOet1fD296sXV22xweqQkNApjMOxB+CATERERCRAndWOzKJqhPn74OzSNbjph2/wxYQZaD2wb724GosdZpsDCTFhaMXh2CPwu0BERMDIkdrnFRhb5+o+X1F9kENuDXrrm6hYD3S6pBreBgMqTpzEkBUv4WDX6xA1b3a9mFqrHbVWOwZ1DkOQH8cyT8E9yFQPd14qw74pw77Jx54pw74pw77J93vPqsw2fHO6GOF+3vC6/XZcc/Zn7E9NR1jPqy/E1lntqLLYMahTGEL8fTSsWnvcg0xERETUzJ0sqoLJ2wu5/3wNA04exq7pz9Ubjs02ByrMNiRwOPZIvJZPRERAUpLzY3q6dnkFxra2WIA9e9TNK4rcGvTWN1GxHqS81orcCjMMP/2MO1LexJ5e16PDzOkX/txmd6Cs1ooBMaEI5XDskXgFmYiIiEglkiTh54JK+NltiJ7xCGp8/GB5ezUMXs6Ry+aQUFRjRZ+oYLRt5dIJb9IAB2QiIiIilVTU2VBcbUXli/9E7+wT+PaZlxHUORoA4JAkFFWb0at9EKJC/TWulJrCIxZEREREKnA4JJwuroHl2A8Y+fEafJkwDNFTJwBwXlkuqDKjW9tW6BQeoHGldDm8gkxERESkgtzyOlSWV6LX7MdQGBgGn+XLL/xZQbUFXVsH4qo2gRpWSK7iFWQiIgLGjtU+r8DY2rIy1/b5iuqDHHJr0FvfRMVqzGJz4OfCKgS88jriCs/i0zdT0KFdGwBAQZUZ0SF+6Na2FQwGg8aVkiu4B5nq4c5LZdg3Zdg3+dgzZdg3Zdg31/1SWIWD76di3N8m4H+3jUGbf60BABRXW9A60IQ+HULg7cXhuDHcg0xERJ6npsb50DKvwFhDba36eUWRW4Pe+iYqVkPVZhuO/5KNG+c/iV/DIxH85msAgNJaC4L9jbg2KpjDsc7wiAUREQEjRjg/qr1vVk5egbHhru7zFdUHOeTWoLe+iYrVUGZRFfxmP4OoikL8981/oUtIMMrrrPAzeqNfh1D4ePN6pN7wO0ZERESkUGmNBUf/34e4Y/cWbB/9IMIGD0KV2QYDDIjvGAqTkaOWHvG7RkRERKSAwyHhu4xfcPtrz+GnyKvQZvHLMNscsNglDIgJhb+Pt9YlkkIckImIiIgUyC+vgenxxxBSV4Vfl6yCZDKh1mLHwJhQtPLlKVY944BMREREJJPV7sDXLy9H0g9f4Yv7H0d4QjxKaq3oFtEKIf4+WpdHV4j/vCEiImDyZO3zCoytKS11bZ+vqD7IIbcGvfVNVKybHdrzI+5Y8RIOd+6FdvOeRVG1BddEtIKfxa51aaQC7kGmerjzUhn2TRn2TT72TBn2TRn2rWHVtRb8eN316JV1DHtTd8HepSuiQ/zRs30Q8vLy2DMFuAeZiIg8T1GR86FlXoGxXiUl6ucVRW4NeuubqFg3+t/fXsSgzINIe2QOvK66CuH+JnSRxKmZAAAgAElEQVRvF8S75DUjPGJBRETAmDHOj2rvm5WTV2BsmKv7fEX1QQ65Neitb6Ji3eSH9O9w+zuvYl/3BIQ8Pg3w8sJ1HXgjkOaGV5CJiIiIXGCtM0OaOAlmownly1fB7ADio0Pha+Q6t+aGAzIRERGRC9IefgbXnfsJ6U++BHtkJOKjQxDkx1/GN0cckImIiIgu48fPv8KQf6/Al/1vg+kv96FnuyC0aeXSjg/SIQ7IRERERE2oq6yGaeqDKA4IQfnrb6Jr6wDEhPlrXRYJxN8LEBERMG2a9nkFxlaXlLi2z1dUH+SQW4Pe+iYqVqDdEx/HkPNnsOHlNYjt3AHd2rbixopmjnuQqR7uvFSGfVOGfZOPPVOGfVOGfQOObNiCa+8bjW2DRyN43TtIiAmDydj4L+DZM2W4B5mIiDzPuXPOh5Z5BcZ65eSon1cUuTXorW+iYgWoKCxB+PRknA1rD+nV19CvQ0iTwzE1HzxiQUREwIQJzo9q75uVk1dgrMv7fEX1QQ65Neitb6JiBTg8/mHcVHoe/37zA4zo1RGBvhybWgr+M4iIiIjoDw6s+gA37/oEm+94AIkP3InwAJPWJZEbcUAmIiIiukjxr7no9PTj+DmiM1q/thBdwgO1LoncjAMyERER0W8khwM/j5mIsJpKHFm0DP1j28GLt5FucTQZkMvKyjBmzBhcc8016N69O/bu3YuSkhIMHToUcXFxGDp0KEpLSwEAkiThiSeeQGxsLK699locOnToQp6UlBTExcUhLi4OKSkpWrwUIiIiakZ2L3wbN2Sk4b9jHsFNdw/hueMWSpPv+owZM3DHHXdg06ZNsFgsqKmpwYIFCzBkyBDMmTMHixYtwqJFi7B48WJs27YNmZmZyMzMxP79+zFt2jTs378fJSUlmD9/PjIyMmAwGBAfH49Ro0YhLCxMi5dERKRvTz6pfV6BsVXFxa7t8xXVBznk1qC3vomKVUHu8ZPo+fKzOBzTA10X/h1RIX5u/frkOdy+B7miogLXXXcdTp8+XW/Jdrdu3ZCeno7IyEjk5eUhKSkJJ06cwCOPPIKkpCTcd9999eJ+f6xevRoALolrDPcgN437G5Vh35Rh3+Rjz5Rh35RpSX2THA5k9L4ePTKPYPsHX2Dk3TfC1+gtO09L6pmaWvwe5NOnT6Nt27Z48MEH0bdvXzz00EOorq7G+fPnERkZCQCIjIxEQUEBACAnJwcdO3a88PnR0dHIyclp9HkiIlLgxAnnQ8u8AmO9T55UP68ocmvQW99ExV6hr2cvxIDj+/HpxL8haUSiouGYmg+3H7Gw2Ww4dOgQli1bhoSEBMyYMQOLFi1qNL6hC9wGg6HR5xuyZs0arFmzBgCQn5+P3NxchdU3f4WFhVqXoEvsmzLsm3yietb6wQcBAMWbNmmWV2RsoMWC3E8/VTWvKHJr0FvfRMVeidzvf0b/t17C3rh+6Pj4ZJjLi5BbriwXf64p42l9c/uAHB0djejoaCQkJAAAxowZg0WLFqFdu3bIy8u7cMQiIiLiQvy5i+6ik52djaioKERHRyP9osXh2dnZSEpKavBrJicnIzk5GYDzUj1/9dE09kcZ9k0Z9k0+IT0zmcTklpNXb7GiyK3BE3rhCbEK2SxWFD06DBZvIwqXrsKIXrEwel/ZL9j5c00ZT+qb249YtG/fHh07dsSJ335lkpaWhh49emDUqFEXNlGkpKRg9OjRAIBRo0bh3XffhSRJ2LdvH0JCQhAZGYlhw4Zhx44dKC0tRWlpKXbs2IFhw4a5++UQERGRjn3z6HO49sxRbHt0HpKS+lzxcEzNgyZbLJYtW4b7778fFosFXbt2xbp16+BwODB27FisXbsWMTEx2LhxIwBgxIgR2Lp1K2JjYxEQEIB169YBAMLDw/HCCy9gwIABAIB58+YhPDxci5dDREREOvTz/3bjhn8twZd9kpA451EE+/loXRJ5CE0G5D59+jT4jsS0tLRLnjMYDFixYkWDeaZMmYIpU6aoXh8RERE1b+bqGhgmTUSpfxAsy5ajU3iA1iWRB+H2ayIiAubO1T6vwNjKoiLX9vmK6oMccmvQW99Excq058GZuCXvNDbMX4XRCdfwbnlUj9v3IGuNe5Cbxv2NyrBvyrBv8rFnyrBvyjTXvm1+5W3cOedxfBZ/Gwbu3IzIEH/VcjfXnonW4vcgExGRBzpyxPnQMq/AWOPRo+rnFUVuDXrrm6hYF6V/sRO9Xv4HzgaGYcrPB5B1XOPvN3kkHrEgIiJg5kznx4vWZ7o9r8DYEIsFuP12dfOKIrcGvfVNVKyLip5biMGVRUhqE4Py0mykp6cjMTFRtfzUPPAKMhEREbUI3/2/DRhzaBeWdOyJPaXZMJlMjd5DgVo2XkEmIiKiZq805zyin3wMv0R0QthrC/HSqR+RlJTEq8fUIA7IRERE1Owd+/NkJFSXYe/S9Zhw7wgYvUdqXRJ5MB6xICIiomZt9+JVuHHvdnx6zyMYMm4Y75ZHl8UryEREBCxYoH1egbEVhYVoq3ZeUeTWoLe+iYptRP6JM+g+/xl83/EaXP36iwjx593y6PI4IBMREXD99drnFRhrzc1VP68ocmvQW99ExTZAcjhwdswD6G0149c3VmJkdPgV5aOWg79jICIiYM8e50PLvAJjfb77Tv28ositQW99ExXbgK+fW4yBR/dgy8RZuPXOG3m3PHIZ76RH9fAOQMqwb8qwb/IJ69nvq67U3v8rJ6/AWLPFAl9XBi1RfZBDbg1665uo2D/4NeMowm8YiOOduiP8my8R1y5Ydg4l+HNNGd5Jj4iIiEggu9WG0vEPwGEwoGjpKsRGBGldEukMB2QiIiJqVr5+fC76nPoe26fPxS239oXBwKMVJA8HZCIiImo2Tuzah+vfeR1fXTsYg557HAEm7iMg+fi3hoiIiHRv79692PW/NNy9ch3K/VrBvGIFYloHal0W6RQHZCIiApYs0T6vwNjyggJEqJ1XFLk16K1vAmL37t2LIUOG4Lk2seiRfxrLH5mLhwd159EKUowDMhERAX36aJ9XYKzN1X2+ovogh9wa9NY3AbHp6em4rlU7PJt9DOuiuiO3nQm+Rm/Xvw7RH/AMMhERATt3Oh9a5hUYa/r6a/XziiK3Br31TUBsYv8EpJhrkB0Yimcq8nDnHbe5lp+oEdyDTPVwf6My7Jsy7Jt83IOsLJZ7kJXF6mUPcvqIvyBp24eYM+ZhjHhiEgbfdMPlcwvCn2vKeNoeZB6xICIiIt3KWLcRSds+xJah4/H0+uVoHWjSuiRqBnjEgoiIiHSpLLcAUTOn42SbjohY+hqHY1INB2QiIiLSpR/HTUFEZQm+X7QU8XGRWpdDzQgHZCIiItKdPa//P9z07ef49K6HMOQvf4LRmyMNqYdnkImICFi9Wvu8AmPLzp9HO7XziiK3Br31TYXYgpO/otsLT+Foh6tx1RsvI9Tfx/WcRC7ggExEREC3btrnFRhrDwpSP68ocmvQW9+uMFZyOHBmzAO4zlKLU2+8jZExbVzPR+Siy/4+Yvny5SgtLXVHLUREpJUtW5wPLfMKjPXdsUP9vKLIrUFvfbvC2G/+/gYSvv8WWybMxK2jboa3F++WR+q77B7kuXPnYsOGDejXrx+mTJmCYcOG6frWjdyD3DTub1SGfVOGfZOPe5CVxXIPsrJYT9uDfO7wTwgZFI8THbshZPdXuLpd8OVzuBl/rinjaXuQL3sF+eWXX0ZmZiamTp2K9evXIy4uDs899xxOnTrljvqIiIiIYLfZUTT2LwCAgqWrENvWxeMfRAq49JZPg8GA9u3bo3379jAajSgtLcWYMWPwzDPPiK6PiIiICF/P/Dv6njyCrY88h1tui4cXj1aQQJd9k97SpUuRkpKCNm3a4KGHHsKrr74KHx8fOBwOxMXF4ZVXXnFHnURERNRCnfzqOySuegXf9LoRg+bNRICJOwZIrMv+DSsqKsInn3yCTp061Xvey8sLn332mbDCiIiIiBwOBywPPIAqkz9qlq1Ap9aBWpdELcBlB+QXX3yx0T/r3r27qsUQEZFG3ntP+7wCY0vz89Fe7byiyK1Bb32TGbvvibm4/ptv8OHcZbj7hp66XhRA+sHfURAREdCxo/Z5BcY6vL3VzyuK3Br01jcZsambtuJPqe9ja59bkPTUVPj5uFgP0RXifRmJiAj4z3+cDy3zCoz1S01VP68ocmvQW99cjN397be49oXnUGbyx+RTh5F1/Ihr+YlUwCvIREQErFzp/DhunHZ5BcYGWizAtGnq5hVFbg1665uLsRlL1mFGdQmOm/xRUlOJ9PR0JCYmXj4/kQp4BZmIiIg8irXOjFFfbkOV0YRCSy1MJhOSfr9pCJEbcEAmIiIij7Jn3uvoUpKH8ohIdO7SBWlpabx6TG7FIxZERETkMapLK9Bt5es41Lk3enQIgZ/RG504HJOb8QoyEREReYwDT7+I9lUlOP30C/A1cmsFaYNXkImICNi0Sfu8AmNd3ucrqg9yyK1Bb31rIrY05zz6frAa3/S6AYMnjIJh7C2u5yVSEQdkIiIC2rTRPq/AWIfFon5eUeTWoLe+NRF7ZMbzuLmuGsXPzkPbViYgyAO+H9Qi8YgFEREB69c7H1rmFRjr7+qOXlF9kENuDXrrWyOxucdPYlDqe0hLGIbBdyU575jnCd8PapE4IBMRUbMfkAM2blQ/rygeNCAL6VsjsZkznoW3wwHLC/MQHmCSn5dIRRyQiYiISFOn9x7GDWmfYMct9+CmIf21LoeIAzIRERFpK3/mM6gzmuA77wUE+/loXQ4RB2QiIiLSzvHP03H9gZ3438gJuGHQNVqXQwSAAzIRERFpqObp2Sj2D0bbF55FgInLtcgz8G8iEREBW7dqn1dgbEleHiLVziuK3Br01reLYg+mfIL+Px3ApklP4c7eMVeWl0hFHJCJiAgICNA+r8BYyd9f/byiyK1Bb337LVZyOOA7by6yg9ui6/NPNnzXPE/4flCLxAGZiIiAt992fpw+Xbu8AmMDysqA555TN68ocmvQW99+i91XZ0Li2Z+w8YmXcVfXiCvPS6QigyRJktZFuFP//v2RkZGhdRkeKzc3F1FRUVqXoTvsmzLsm3zCepaU5PyYnq5dXoGxZosFvnv2qJtXFLk16K1vSUmQHBJO/5wFOwyQjhxBt6jQK8/rIfhzTZnG+qbV3MY36REREZFblf+ajasKz+Lw9KcR2z5E63KILsEBmYiIiNymrLQM/tlncbjD1Uh8fBK8vQxal0R0CQ7IRERE5BZ79+5F8YmT8HXY8LTDgXM//6B1SUQN4oBMREREbvHlri/RwWpGqY8f0gvO4Ouvv9K6JKIGcYsFERGJexOUnLwCY4tzc+HS26Y84c1gcmvQUd96BUXAz2HDtMirYSo5g6Tf34R3hXmJ1MYryEREROQWQfsOwW7wQvDdI5GWlobExEStSyJqEAdkIiICXnvN+dAyr8DYwFWr1M8ritwadNS3Dl9+gdywdlgUFe7acOwJ3w9qkTQbkO12O/r27YuRI0cCAM6cOYOEhATExcVh3LhxsFgsAACz2Yxx48YhNjYWCQkJyMrKupBj4cKFiI2NRbdu3fDFF19o8TKIiJqHzz5zPrTMKzDWb+dO9fOKIrcGnfQt67sfcXX+afiZjPD7YptqeYlE0GxAfuutt9C9e/cL/3v27NmYNWsWMjMzERYWhrVr1wIA1q5di7CwMJw8eRKzZs3C7NmzAQDHjx/Hhg0bcOzYMWzfvh3Tp0+H3W7X5LUQERFR07LW/RsA4N22DbjYjTydJgNydnY2Pv/8czz00EMAAEmSsGvXLowZMwYAMGnSJGzevBkAkJqaikmTJgEAxowZg7S0NEiShNTUVIwfPx6+vr7o0qULYmNjceDAAS1eDhEREV1G2Bdb8VPkVWgV0krrUoguS5MtFjNnzsQrr7yCyspKAEBxcTFCQ0NhNDrLiY6ORk5ODgAgJycHHTt2dBZrNCIkJATFxcXIycnBoEGDLuS8+HP+aM2aNVizZg0AID8/H7m5ucJem94VFhZqXYIusW/KsG/yiepZ69+OtRWr/PNRTl6RsVaLRfW8ositQQ99Kz2bh96nf8R/Rz2IrgXHYbYbdPP9kIs/15TxtL65fUD+7LPPEBERgfj4eKT/tr5FkqRL4gwGQ5N/1tTn/FFycjKSk5MBOO/pzXukN439UYZ9U4Z9k09Iz0JCxOSWk1dgrGQ2q59XFLk16KBvJ5e/Dy9I8Bk7Fr7vLxFTrwfRW72ewpP65vYBeffu3fj000+xdetW1NXVoaKiAjNnzkRZWRlsNhuMRiOys7MvNCk6Ohrnzp1DdHQ0bDYbysvLER4efuH53138OUREJNM2F980JTKvwNgSV/f5iuqDHHJr0EHf/D7/FGdD26HfiBuB+4fJqoFIC24/g7xw4UJkZ2cjKysLGzZswK233ooPPvgAt9xyCzZt2gQASElJwejRowEAo0aNQkpKCgBg06ZNuPXWW2EwGDBq1Chs2LABZrMZZ86cQWZmJgYOHOjul0NERERNqCwqw7XHDuDHgbeifbC/1uUQucRj9iAvXrwYb7zxBmJjY1FcXIypU6cCAKZOnYri4mLExsbijTfewKJFiwAAPXv2xNixY9GjRw/ccccdWLFiBby9vbV8CURE+vXSS86HlnkFxrZ6803184oitwYP79vR9R/Bz26FZeSdMHp7iauXSEUGqaHDvM1Y//79kZGRoXUZHis3N5dHVRRg35Rh3+QT1rPfb/mr9q195eQVGGu2WOC7Z4+6eUWRW4OH9+3bG0ag++HdKDn1K+IiQ8XV6yH4c02Zxvqm1dzmMVeQiYiIqHmx1JrR++DXONh3MGLaBmldDpHLOCATERGRED9u2IIQczXK7/gTfI08Bkn6wQGZiIiIhKj66GNU+/gibvxorUshkkWTG4UQEZGHad1a+7wCYx11dernFUVuDR7aN4fNjqt378TBHgmIj2mruAYiLXBAJiIi4OOPtc8rMLY0NxcuLRgT1Qc55NbgoX37aVs6elYWYfdtwxHoa2wyVk5eInfgEQsiIiJSXcH7H8Fm8EL78fdqXQqRbByQiYgIePZZ50PLvAJjgxYuVD+vKHJr8NC+dUz/Aoeu6oNePTtfUQ2afz+oReIRCyIiAvbu1T6vwFiTxaJ+XlHk1uCBfTu99zBiC37Fx6PuwwA/Y5OxcvISuQuvIBMREZGqzq7fAAAIHnsvDAaDxtUQyccBmYiIiFTVesdWHIuKQ5/E3lqXQqQIB2QiIiJSzflfzqBn1jFk3nAbWgeYtC6HSBGeQSYiIiA6Wvu8AmPttbXq5xVFbg0e1rcTaz9EO0gw3nM3vLwaOF4hql4iFRkkSZK0LsKd+vfvj4yMDK3L8Fi5ubmIiorSugzdYd+UYd/kY8+UYd+UUdK373pfj7Y5v8LnVCY6hAUIqsxz8e+aMo31Tau5jUcsiIiISBUVBcW49qfvcHTQrWgX7Kd1OUSK8YgFEREBM2c6Py5Zol1egbHBVVXAO++om1cUuTV4UN+Otu+G6+02WO+8E0bvRq7BiaqXSEUckImICDhyRPu8AmN9XN3nK6oPcsitwZP6Zv4JhQEh6H33HarVQKQFHrEgIiKiK+awO9Dr0Dc43G8wOrYJ1LocoivCAZmIiIiuWHVBEYIttai8YyR8jd5al0N0RTggExER0RWzFxahyscPceNHaV0K0RXjGWQiIgKuvlr7vAJjbTU18FU7ryhya/CAvkmxcTDt3ofvel+P/tFtVK2BSAvcg0z1cH+jMuybMuybfOyZMuybMq727eh/d6DXPcOw6elXMOaVp91Qmefi3zVluAeZiIiImpUzq/8Fq5c3ynt11boUIlVwQCYiIiA52fnQMq/A2JBnnlE/ryhya9C4b3t278ZNu1Jx3hSAx/86AXv37lW1Bs2/H9Qi8QwyEREBv/yifV6BsUZX9/mK6oMccmvQuG+7127A9dY65BsMsNgtSE9PR2Jiomo1EGmBV5CJiIhIsfjvj8Fm8EKhtQ4mkwlJSUlal0R0xTggExERkSLFWTm44ftvUBoUio6dOiEtLa3pq8dEOsEBmYiIiBT58ZXl8LXbYOgQjc6dOnE4pmaDZ5CJiAjo00f7vAJjrVVVru1BFtUHOeTWoFHfJIcDnTa+jyOdeuLqpMGAycW754mql0hF3INM9XB/ozLsmzLsm3zsmTLsmzJN9e3wB6no+8Bd2DhrAe59bQ68vAxurs4z8e+aMtyDTERERLpXu/xtlPq1Qte/TuJwTM0Oj1gQERHwwAPOj++/r11egbGhtbXAxx+rm1cUuTVo0LeiM9mI/24X0m65B7d2aecZ32ciFXFAJiIiIDtb+7wCY71d3YMsqg9yyK1Bg74dXbwcSXYb7A8/DD8fb8/4PhOpiEcsiIiIyGUOuwOdNr2PQ517IWH4TVqXQyQEB2QiIiJy2ZF/f4ouxTk4dc/9aNvKpHU5REJwQCYiIiKX1a1wvjnvqr9OgsHAN+dR88QzyEREBIi6wYOcvAJjLa7uQfaEG13IrcGNfSs8fQ79v/sS/xsyBkM6R7ilBiItcA8y1cP9jcqwb8qwb/KxZ8qwb8r8sW/pj8xG0ppXsOWjnbjzz0M0rMxz8e+aMtyDTERERLrjsNnR+eMPcKhLbwwafqPW5RAJxQGZiIiAe+91PrTMKzA27OGH1c8ritwa3NS3wx9sRuff3pzXJtB0Sazm32ciFfEMMhERAcXF2ucVGOvl6h5kUX2QQ24NbuqbZcUqlPq1wtWPTLz0zXme8H0mUhGvIBMREVGTCk+dRf+DX2L34DtxTZeIy38Ckc5xQCYiIqImHV20DD4OO5D8EHyN3lqXQyQcB2QiIiJqlMNmR9ePP0BG1+swaNgNWpdD5BY8g0xERMAQQSu75OQVGGuuqHBtD7KoPsghtwbBfTv63n8RX5qHA1NnIv6Pb85zQw1EWuAeZKqH+xuVYd+UYd/kY8+UYd+Uyc3Nxa+jJ+LqY98h98df0PuqdlqX5PH4d00Z7kEmIiIiXSjJykH/Q+nYe/NIXN2pjdblELkNj1gQEREwfLjz47Zt2uUVGBtuNgO7dqmbVxS5NQjsW7sfjjvfnPfQw02/Oc8Tvs9EKuKATEREQG2t9nkFxhpc3YMsqg9yyK1BUN+kmhoEFeT/9ua8RE1q8IjvB7VIPGJBRERElyj6NRt+NgsO3nw7Wjf25jyiZooDMhEREdWzd+9eIC8PVoMXnt6wEvv27dO6JCK34oBMRERE9aR/uBltLLXI8/FDjaUW6enpWpdE5FY8g0xERMDIkdrnFRhb5+oeZFF9kENuDSr3TXI4kPRFGuq8ffAvOGAymZCUlOTWGhTFEqmIe5CpHu5vVIZ9U4Z9k489U4Z9c92Ble9j4PQJ+NfoqfipWxvcc9doJCZe5k16dAH/rinjaXuQeQWZiIiIAACWmjq0mfcsTrWOxoA3F+IOXyuHPWqReAaZiIiApCTnQ8u8AmNbjxmjfl5R5NagYt/2PPUiuhZl4+CsF9Br8p/F9E1ULJGKOCATERERCs9ko8+/lmLPNQm48dEHYDBoXRGRdjggExEREY4nz0IrSy0KX1yAyBB/rcsh0hQHZCIiohbu5/99ixvTPsH2IX9G0sibYODlY2rhOCATERG1YJLDgdpHn0CZXysEvPwiQvx9tC6JSHNu32Jx7tw5TJw4Efn5+fDy8kJycjJmzJiBkpISjBs3DllZWejcuTM++ugjhIWFQZIkzJgxA1u3bkVAQADWr1+Pfv36AQBSUlLw8ssvAwDmzp2LSZMmufvlEBE1D2PHap9XYGxtWZlre5BF9UEOuTVcYd/2LvkXrs88jI8efg6j+l1VL1ZI30TFEqnI7XuQ8/LykJeXh379+qGyshLx8fHYvHkz1q9fj/DwcMyZMweLFi1CaWkpFi9ejK1bt2LZsmXYunUr9u/fjxkzZmD//v0oKSm5sBvPYDAgPj4eBw8eRFhYWJNfn3uQm8b9jcqwb8qwb/KxZ8qwbw2rq6xGQadY1Jr8YM84iB7R4fX+nH2Tjz1TxtP2ILv9iEVkZOSFK8BBQUHo3r07cnJykJqaeuEK8KRJk7B582YAQGpqKiZOnAiDwYBBgwahrKwMeXl5+OKLLzB06FCEh4cjLCwMQ4cOxfbt2939coiImoeaGudDy7wCYw21ternFUVuDVfQt70z5yGmNB8/PD0f3aLCLokV0jdRsUQq0vRGIVlZWTh8+DASEhJw/vx5REZGAnAO0QUFBQCAnJwcdOzY8cLnREdHIycnp9HnG7JmzRqsWbMGAJCfn4/c3FxRL0n3CgsLtS5Bl9g3Zdg3+UT17Pd9t8WbNmmWV2RskMWC3E8/VTWvKHJrUNq3otPZGPDe2/iq5/XodudNOJ+fd0msiL6JivUU/LmmjKf1TbMBuaqqCvfeey+WLFmC4ODgRuMaOgFiMBgafb4hycnJSE5OBuC8VM9ffTSN/VGGfVOGfZNPSM9MJjG55eTVW6wocmtQ+PpO3fdXmOw2VPxzMQbHdb70v6Ge0GNP+H4ooLd6PYUn9U2TLRZWqxX33nsv7r//ftxzzz0AgHbt2iEvz/mv17y8PERERABwXhk+d+7chc/Nzs5GVFRUo88TERFR045tScNNX2/B9jvuw83DErjWjegP3D4gS5KEqVOnonv37vjb3/524flRo0YhJSUFgHM7xejRoy88/+6770KSJOzbtw8hISGIjIzEsGHDsGPHDpSWlqK0tBQ7duzAsGHD3P1yiIiIdEUCYJ8xEwWBoQh7+R8I9uNaN6I/cvsRi927d+O9995D79690adPHwDAggULMGfOHIwdOxZr165FTEwMNm7cCAAYMWIEtm7ditjYWAQEBGDdunUAgPDwcLzwwgsYMGAAAGDevHkIDw9v+IsSERERAKDi1wSNNA8AACAASURBVBxcm3USHz02H6N7d9K6HCKP5PYB+cYbb2zw/DAApKWlXfKcwWDAihUrGoyfMmUKpkyZomp9REQt0uTJ2ucVGFtTWuraPl9RfZBDbg0y4s1jxsLx9Gwci4rFdc89AV+jd5N5hfRNVCyRity+B1lr3IPcNO5vVIZ9U4Z9k489U4Z9c0odNRGjt7yHpc8swqMLn4G3V9Nnj9k3+dgzZVr8HmQiIvJARUXOh5Z5BcZ6lZSon1cUuTW4GL/13Y8wdOsG/LfdVZi9bD4O7N932bxC+iYqlkhFmu5BJiIiD/Hbvlmkp2uXV2BsmMUC7Nmjbl5R5NbgQrzkcCBk3oswOexoX5gFqwFIT09HYmJik3mF9E1ULJGKeAWZiIiomfv6+Vdxw6/HcNrkB4vDDpPJhKSkJK3LIvJYvIJMRETUjOUeP4k+b76IjK7XISZAgm91JdI++KDpq8dELRyvIBMRETVTksOBs3+ZApPdhrzXlqFt6zB0ionhcEx0GRyQiYiImqk9r6zGoO+/wWfjH0XS8ETwfnlEruERCyIiAqZN0z6vwNjqkhLX9vmK6oMccmtoJL4oKwdXv/Qsfojuhh4L5iLIz+gZfRMVS6Qi7kGmeri/URn2TRn2TT72TJmW2LdvbxiBgfv+h63vfY47xw+97M7jhrTEvl0p9kwZ7kEmIiLPc+6c86FlXoGxXjk56ucVRW4NDcTvf/t93LhnGz6/awpuufuW/xuOPaFvomKJVMQjFkREBEyY4Pyo9r5ZOXkFxrq8z1dUH+SQW8Mf4svyixAzZxZORHRG51dfQoi/j7LcovomKpZIRbyCTERE1Ix8P2EaIqpK8OPLb6J3pzZal0OkSxyQiYiImolD76fi5p2b8Pnt43Hr/SNg9OZ/5omU4P9ziIiImoHq0gq0njEdWWGRiHhjMcIDTFqXRKRbHJCJiIiagQMPPoFOJbn4bt4riO/GLQpEV4Jv0iMiIuDJJ7XPKzC2qrjYtX2+ovogh9wannwSWfuOYPDCf2Db4Ltwy0N/hk9jRys8oW+iYolUxD3IVA/3NyrDvinDvsnHninTnPtmrqnF2a49EFhThcwv9+Hm+KtUy92c+yYKe6YM9yATEZHnOXHC+dAyr8BY75Mn1c8riswavh05HnHns/DJhOlIvK6LerlF9U1ULJGKeMSCiIiARx5xflR736ycvAJjQ13d5yuqD3LIqGHTUy/ini8/xXmTP55e9yriH7gDiYmJquQW1jdRsUQq4hVkIiIinXHY7Nh1/6MY8/rfUWX0RabVDKvFgnQOkkSq4IBMRESkIxWFJdg38Dbc+u+3saXfEHzvsMIuOWAymZCUlKR1eUTNAgdkIiIincjKOIr83v0x8MjX+M+kp9F92ye49rrr0LlLF6SlpTV9vIKIXMYzyERERDpwcP3H6DL9QRgkCRsXrcVt0+9D21a+QHAwQoKD0YnDMZFqOCATEREwd672eQXGVhYVubbPV1Qf5PhDDZLDgfSZ/8BNy/+JU21j8NPqd3Hn8ES08jU2GC8n9+VihfRNVCyRirgHmerh/kZl2Ddl2Df52DNl9Nq3uqoaHLjzLxicnoqvet0Ix/p3ccN1nWAyuueEpF77piX2TBlP24PMK8hERAQcOeL82KePdnkFxhoLCgBXhhZRfZDjtxryA0JRMGwkBmcdw8d3PYyrl7+CnpEh8PIyNBivm76JiiVSEQdkIiICZs50flR7TZicvAJjQywW4Pbb1c0rSPmUKSjPL4SxqhZX1VXh33PexM2zH0GHUP+GP0FvfRMVS6QibrEgIiLyEF/t/BIlx39Bh7wcWCRg9XOvYsS8RxsfjolICA7IREREGpMcDnz76mp0vnccupirUeLjh4EOK6qMFQj199G6PKIWh0csiIiINHR8+zcwP/Y4bjz1PY5HdMb3tZUos9aiyggMHXKr1uURtUi8gkxERKSBglNn8dWQe3HN8JvRMfcMPkyeC/OBDHTqeQ1v/EGkMV5BJiIiYMEC7fMKjK0oLERbtfPKsHfvXqSnpyMpKQn9ruuD3X97EfHrl+J6qxmf3v7/27vz+KbqdH/gn5O2oRQoWwVKi5TSsrTQ1rYIBdllFculOBRkGOYyinqZizJ3UGe4KuPIIPz0XllGEAcc7wgIIgpIhWFEFKFsHZYBBBRZurG0pS1laZqc5/dHmjTdkzRpcuDzfr3ySnPynO/55tMsT9OTkykI+MNrGBvbBS2b+gF//jNaAfZ/8YfWcnNXLZELsUEmIiKgf3/Pj+vG2rKcHNePa6f09HQMHz4cpaWlGN+xB966XYhhBTnYG5WEa/MXYMCoJLRv0QSKojg3B63l5q5aIhfiLhZERATs328+eXJcN9b6HT7s+nHttGfPHoQ3bYO0oC7YnHUaBihY96f30fGbv2N8ymB0CPSvaI6dmYPWcnNXLZEL8R1kIiICfv9787mrjzfryLhurA00GIDx4107rh3yL+ciZscBzL2Ri5t6f7zQqRdi/3cRJiePQlM/H9fMQWu5uauWyIXYIBMREblYWakB3730J8S99zZGl97GZ31HY/dD0fi3SY9j5JCBnp4eEdWDDTIREZELHV69Ea1+/yKGXruEA5HxuPzqnzDw8UGYWHVXCiLyWmyQiYiIXOCn9KO4/ux/ou+JfbjQOhjrXlmOxFnTkBDUHH4+/MgPkZawQSYiImqAwit5OPrsbzFg21oE+fph45TZ6PzaS/i3Lu0QoOfLLJEW8ZFLRETAO+94flw31hZdu4Z2Lh73u717cXzRe5j09RcYfLsYOweMg/r66xjRLwqtA/T2z68Bc3C43gtyc1stkQuxQSYiIiAuzvPjurHWaO/xfO0cd/OCJQh/+/9h1o1s7A0Kw+H/mo/U3zyD4EB/6HQN3M/Y0d+FhnJzay2RC3GnKCIiAv7xD/PJk+O6sVb/7bcuGffy0dPY32c4Uv77BbQuu4PUDt0wpOAybje5jZBWTRveHNsxhwbVeyi3RqklciFFRMTTk2hMiYmJOHLkiKen4bVycnLQsWNHT09Dc5ibc5ib49yW2ZAh5nNXH2/WkXHdWFtqMKCJPV84Ucu4N/MKcfjXL6H/pg9g0umwaehEzP5uG26V3oZer8dXX32FJHu/GtrJObikvpFza9RaL8HnNefUlpun+jbuYkFERFQL1WjCd28sQ+T/vIFhN/Oxq89I3Hn9DYwbGItuJzKwZ88eDBkyxHXNMRF5BTbIRERENTixeSd0c+Zg0OXvcSK0O755cwUGTHkMHVs2hY9OQVJSEhtjonsUG2QiIqJyRcXFKMzLxw+Jw/Foxm5cad4G655fgJi5z2FC+0A08a3l66GJ6J7CBpmIiAjA3m++Rcjpc+hcehvtc3Lx0dCfIeStRXg8qhNa+PPlkuh+wkc8EREB773n+XHdWFt49Sra11Fy5h/7Efjzf0d46S3sCuqMZ0pvYcqg3pj6UFjjfz20o78LD+bWGHMg8gQ2yEREBHTv7vlx3VhratGixqtKb9/B/plzMWD9eyj0b4bU0Ch8mnsWer0e40Y92vjNMeD478IDuTXmHIg8gcdBJiIiYNs288mT47qxtsnf/15t8cltu3Gxay8MXftn7EkYjoyd+/C756Zh/ZNPuvawbY5y9HfRyLk19hzccr8kqgePg0yV8PiNzmFuzmFujuNxkJ2rtT2e7+2imzgwYw4Gf/YBrjZvgz3/9Tr6/+d0PNiqKXTDhto/rrvwOMjO1XoJPq85h8dBJiIi8pBjG9MQOOsZDMvLQtoj4+H39mIkx4ajeRO+HBJRBT4jEBHRPa2ouBj516/j+6ETMGbPFmS1aof1iz7AgKcnIbSli74emojuKWyQiYjonvX1jl3ocPoswowGhGVtwadJjyFo+VKM79UJAXq+BBJRzfjsQERE95SCrKs4+Zd18Pv8MySdPAB/Uxlu+/hhcFBnDHi0DxZ64tBtRKQpbJCJiAj42988P24Daq/+lIkzqz5C021b8NCZIxikmpAdGIRtfUdh0w//wsG8TFy7dRWLx4youzl2Vw6OcHQObsz4xpUr6ODhORB5AhtkIiICOnXy/LgO1KZnZWH3Z2nokVuILge+Q+z5ExgsKi60DsYXo55E2fgURI0bgmGtAhB87AjCv9iG5OTk+g/d5q4cHOHoHNyUMTp1gupj51dru3EORJ7ABpmIiIANG8znqakeG/fcH/+I06dPo/3s2UhKSsKtwpu48v2PuHH2PG6fv4Syixfhk5UFffZldM46j3l3igAAZ4M64bPkGZAJExA9cgAGt2yKlv5+8Cn/8N0jWZmIevBBtLHnuMbuysERjs7BkXoHa/0LCoDnnvPoHOyuJXIhzTfIO3bswPPPPw+TyYSnnnoKL7/8sqenREQelJ6ejj179mDIkCH1vlvoSO29rmjRIhQWFiLnwQddmkXVce+U3EbB5VwUZ1/Brexc3L1yDYar11H043k8vGszBoiKS3/fh+t3i/HA7SJ0rTLe1eatkd2sNfRGA37SB+CxgBYYM3MGXv3veQhs4lvzESlWrEAzg8G+Rm/FCvO5JxsyR+fgSL2DtW7JzV21RC6k6QbZZDJh1qxZ2LVrF0JDQ9GnTx8kJycjKirK01MDoL0X3/T0dGzdutW+f0M6OK4jOXhDbo42Wfbm1tjNm4hABFBVFSIC1WTCgQMHsffbbzGg/wAkxidANZnMdSYTVKP5Z1VVISYTjh09hiOHDyMhPgG9oqIhMNeJah5LVNV8MglEBGdOn8a/Tv4LvaKi0S0yEmLeOFRVAFUgUM3n5ctvFNxAXsuW5jFEIGJ7vblGRIDy60QFIOZtwrJctVwvyMrMxIZ162FSTfjnirVITU01H3je8n1I5XkoIsjJzcWnn26CyaTixMp1yJqQguAOwQBUc61asY4lS+sYAKCW1wmq1KmVL6u238Uk9Y5d57kquHP3Ds74+5dfrjwH67Zt1lHq3QZQeOMG4k6fgwIgbcpzyI2JRWCzZoBqgmIyQcqMUFQTYDJBManW5YrJBEVVbX62WaaaYCotRdSF79FaBMZR41FcWoJAwx2EAAhBdWWKDgadD675NcGPHWPg07M3TCEhUDp3RsvILnigWzhatm6OrH8eQcmY4RBVxSUfwc/GjUKrpn41jEhE5BxNN8iHDh1CREQEwsPDAQCTJ0/Gli1bvKJBTk9Px/uTn0EzFdiwcgOyBz6CNm3b2rxYSvUXqvLFFcvML8QVL3BApfdGypsCpbwO5bXmuurLbLelSMX1EEFxcRGOHTsGX1XFjvVfml8gAwMrz6/SPOtYVjEySoqL8f3p04AIvlm5AVd79ECLFs1rGQsoKSnBj+fOQi+CAys/Rl7XrggIaGaer6qasyjPxtxkSMUyESiqWr758su211l/VqEIzOeW7dtcX2YwoKggH1EiyFv2V+wNbAm9rw8gML/ww7wdRQQmYxkMJSUYKwLT6o9x1N8fPjodFFWsdYBApwpENaGZoRTjBNAtXoZzvr7wAaCIeSxd+e3SqeY5hppMmAaB7o3FuKIo0EGgU81ztl1HgflcV357dOW3z3LZR9RK3yk/uPxkj1HlJ3tFAnjcgXp3eML2wluv2l+75KQbZqM9f7x0HLh0HABgUnQwKToYdT4w6cw/q+XnJp0OquJTfm5ebtT5QNX5QC1fZlBV+IrAqChIb94WatfeCIjoDlPbIEhQEHweCEKz0A4IDA3G5dxLCE15DGIqw8TibGz9eA0eGdAfeh+ddVcJi4iRQ1AUG4vCwkJ8tXatJt6AICJt0XSDnJ2djU42O/CHhobi4MGD1epWrVqFVatWAQCuXLmCnJwct89t69ateKUgB11K8s0L1h53+zYbKsX2wiX756vatO1S/ulw2/fMHrNdlnUKUl4utaw3ovw6ASC55yCKAlXRmddXFPN1Svn11suAKLqKn6vUoPyyqigV26oylmVso6qitckEUcy3TTGVwcfPF6qiA2AZQwdRAGOZEQJBmaJAhQITdPDTNzWPaT2Z29Pbd+/ijnILAgUmRUHTZs3h37yFtcZ2nZKSmyguKoQAUBUFzVu1QYvWbcxzt9wGy3o6HWC5/YoCKBWXoSgV1+t0uJabgys5mVBFIIoO7Tt1RodOD5prdRXriwJkX76Ii+fPldcq6BzZAw9GdgegADrz9qHzgegUKFDw49nTOHPyKFQRADp0j30Ikb3jzEcMsBw1QGfO0DIfo+Eu/JoEQPEBAJ15XEudZT0FgKKDYrndOvPPiuU6nQIdzHPPycnEx2tWwmgywsfHB1N/9R8I6dLFfE+zHrnAvG7mpQv4vxVLUGYywtfHF//+H8/jwfAIc4XOcn9Uyi9bVlWqnOvKL1ouV65TqtbXsMx6WVcxP9s5VK27VVKMgMBW1svV16+gs83TthaV1zl7+iQemjUDqioY26QJFq9Yg5iEBCiKzubXoJh/5bD8ahRL/BV1NttQAJw+moGbqU9ARPD0jYv4v+UL0adPInx0CnRVjiQR3ikG+ugoFBcVYcPy5Yju1hU3rl+tdpss2vr7o0OHDvDr3Lne5/S2BgPKDAbk2/Hc39ZgAAC7at3F0Tk4Uu9orTtyc1ett7h+/bqnp6BJ3pabphtksX0XslxNh++ZOXMmZs6cCcD8nd6N8R3pycnJGPDnVTA2bQFfXz8sWPoeevaOKZ+j5cWqojGseLGzfYExN3u2DYa511MqWktro2BuUizrocY6y48667Yty47/8zCeSp0AQ5kBej89Vm/8HHEP97N5vddZx7E5Qw1x28wD+Ofhg5iWMg5lZQb4+enxt81fIL5P31pzq1q/9vPtleoVVHkX3ZpZzfOo+iJcc11lRw4dwM+Sx6LMYICfXo9NG79En779qq2vADh88ABSHh9jrf18ww70ttRat2n+6fDBA5g6dhQMBgP0ej22rtuJh/v1g1JtBsChg+mYPqai9ou1K9G3X1L1OVS9fbXcVss2DqSn48VRI6zj7ly/Ev2SkmrIVEF6ejrmj3jUWrvrjfeRlJRU6/bT09OxePhwa/2c1z6o9529nJwclz8e+48fbPeuKf3GDvD47jyOckdmIwcloGjVEhQWFmKXC9+RjY7ojKKFcSgsLMRue8Zt2xZBbdsiPDm5/sH1egCwLwt31bqLo3Pwhiy8odaLaG2+3sKbclOkpi5TI9LT0zF//nzs3LkTALBw4UIAwO9+97ta10lMTMSRI0cabX5aevHlPsjOzcGb90Fu7Dk4Wu+OZu9e57bM8vLM50FBnhvXjbVXrlxBh169XDuuuzg6B63l5q5aL8HnNefUlltj9m22NN0gG41GdOvWDV999RVCQkLQp08frFu3DtHR0bWu46mgtYIPbOcwN+cwN8cxM+cwN+cwN8cxM+d4W4Osq7/Ee/n6+mL58uUYNWoUevbsiUmTJtXZHBMRUS3++lfzyZPjurG2qeV4uq4c110cnYPWcnNXLZELafodZGfwHeS68S9f5zA35zA3x7ktsyFDzOd79nhuXDfWlhoMaLJ/v2vHdRdH56C13NxV6yX4vOYcvoNMREREROTF2CATEREREdlgg0xEREREZIMNMhERERGRDU1/UQgREblIWprnx3VjbUFuLoJdPa67ODoHreXmrloiF2KDTEREQECA58d1Y600ber6cd3F0TloLTd31RK5EHexICIi4N13zSdPjuvG2gB7j6Xrrhwc4egctJabu2qJXIgNMhERARs3mk+eHNeNtU2/+ML147qLo3PQWm7uqiVyITbIREREREQ22CATEREREdlgg0xEREREZIMNMhERERGRDUVExNOTaExBQUEICwvz9DS81vXr1/HAAw94ehqaw9ycw9wcx8ycw9ycw9wcx8ycU1tuFy9eRF5eXqPP575rkKluiYmJOHLkiKenoTnMzTnMzXHMzDnMzTnMzXHMzDnelht3sSAiIiIissEGmYiIiIjIhs/8+fPne3oS5F0SEhI8PQVNYm7OYW6OY2bOYW7OYW6OY2bO8abcuA8yEREREZEN7mJBRERERGSDDTIRERERkQ02yF4uMzMTQ4cORc+ePREdHY0lS5YAAAoKCjBixAhERkZixIgRuHHjBgBg7dq1iImJQUxMDPr374/jx49bx9qxYwe6d++OiIgIvPnmm7Vu88MPP0RkZCQiIyPx4YcfWpfPmzcPnTp1QvPmzWtd9/bt23jsscfQo0cPREdH4+WXX65Ws2nTJiiK4tbDuWgtNwAYPXo0YmNjER0djWeffRYmkwkA8MknnyA6Oho6nc7th8DxltzsuR9ZZGRkoHfv3oiIiMDs2bNh2WssNTUVcXFxiIuLQ1hYGOLi4hqcT020mFl998n76TFqb2511X377beIj4+Hr68vNm3a1OBs6nIv5Xb58mUMHToUDz30EGJiYpCWltbgfGriLZkBtT/PV1XbdkQE8+bNQ7du3dCzZ08sXbq0QdnURYu5zZgxA+3atUOvXr0qLX/llVcQExODuLg4jBw5Ejk5OfUHIOTVcnJyJCMjQ0REiouLJTIyUk6dOiVz586VhQsXiojIwoUL5cUXXxQRkX379klBQYGIiKSlpcnDDz8sIiJGo1HCw8Pl/PnzUlpaKjExMXLq1Klq28vPz5cuXbpIfn6+FBQUSJcuXazjpaenS05OjjRr1qzW+d66dUt2794tIiKlpaXyyCOPSFpamvX64uJiGThwoPTt21cOHz7c0HhqpbXcRESKiopERERVVUlJSZH169eLiMjp06flzJkzMnjwYLdmJuI9udV3P7LVp08f2b9/v6iqKqNHj66x7je/+Y384Q9/aGA6NdNiZnXdJ++3x6i9udVVd+HCBTl+/LhMmzZNPvnkE1fGVM29lNvTTz8t7777roiInDp1Sjp37uyqmCrxlsxEan+et1XXdtasWSPTpk0Tk8kkIiJXr151WU5VaS03EZFvvvlGMjIyJDo6utJyy/oiIkuWLJFnnnmm3tvPd5C9XHBwMOLj4wEALVq0QM+ePZGdnY0tW7Zg+vTpAIDp06fj888/BwD0798frVu3BgD069cPWVlZAIBDhw4hIiIC4eHh0Ov1mDx5MrZs2VJtezt37sSIESPQpk0btG7dGiNGjMCOHTus4wUHB9c534CAAAwdOhQAoNfrER8fb50DYP4r7sUXX4S/v39DYqmX1nIDgMDAQACA0WiEwWCAoigAgJ49e6J79+4NicNu3pJbffcji9zcXBQXFyMpKQmKouAXv/iFdW4WIoKNGzdiypQpLkqpMq1lZtlubffJ++0xam9uddWFhYUhJiYGOp37X1LvpdwURUFxcTEAoKioCB07dnRZTra8JTOg9ud5W3VtZ8WKFXj11Vet97V27dq5LKeqtJYbAAwaNAht2rSpttyyPgDcunWr1vVtsUHWkIsXL+Lo0aPo27cvrl69an2BCw4OxrVr16rVr169GmPGjAEAZGdno1OnTtbrQkNDkZ2dXW0de+vsUVhYiG3btmH48OEAgKNHjyIzMxPjxo1zajxnaSm3UaNGoV27dmjRogWeeOIJh9d3JW/Jrer9qOr6oaGhda6/d+9etG/fHpGRkfbc7AbRQmZ1ud8fo/bm5my+rqb13ObPn4+PPvoIoaGhGDt2LJYtW2bnLXeeN2RW3/N8XeufP38eGzZsQGJiIsaMGYMffvjB0QicooXc6mPZtWzt2rV4/fXX661ng6wRJSUlmDhxIt55551KfwnV5uuvv8bq1auxaNEiALDul2mrpr+g7K2rj9FoxJQpUzB79myEh4dDVVXMmTMHb7/9tsNjNYTWctu5cydyc3NRWlqK3bt3O7y+q3hLblXvR46uDwDr169327vHtrSSWW3u98eovbk5m6+r3Qu5rV+/Hr/85S+RlZWFtLQ0TJs2Daqq1ntbnOUtmdX3PF/X+qWlpfD398eRI0fw9NNPY8aMGfXejobSSm71WbBgATIzMzF16lQsX7683no2yBpQVlaGiRMnYurUqUhJSQEAtG/fHrm5uQDM/2a2/TfLiRMn8NRTT2HLli1o27YtAPNfYpmZmdaarKwsdOzYEQcPHrR+kGnr1q211tXGZDJZ13/11Vety2fOnInIyEi88MILAICbN2/i5MmTGDJkCMLCwnDgwAEkJye79UNAWswNAPz9/ZGcnFzjv6AagzflVvV+VDW30NDQSv/Wrbq+0WjE5s2bkZqa6sqIqtFSZrW53x+j9uZWtc4T7pXcVq9ejUmTJgEAkpKScPfuXeTl5bksJ1velBlQ+Xk+MzPTuv7KlSvrXD80NBQTJ04EAEyYMAEnTpxwZUzVaCk3ez355JP49NNP6y+sdy9l8ihVVWXatGny/PPPV1r+29/+ttJO8nPnzhURkUuXLknXrl1l3759lerLysqkS5cu8tNPP1l3kj958mS17eXn50tYWJgUFBRIQUGBhIWFSX5+fqWa+j5sNm/ePElJSbF+iKAm7v7AmdZyu3nzpuTk5Fi3OWnSJFm2bFmlmsb4kJ435WbP/UhEJDExUdLT060f0tu+fbv1ui+//FIGDRrkeBAO0GJmFnXdJ++nx6i9udVXN336dLd/SO9eym306NHywQcfiIj5w8jBwcGiqqr9YdjJWzKz53m+vu289NJLsnr1ahER+frrryUxMbGB6dROa7lZXLhwodqH9M6dO2f9eenSpTJx4sR6bz8bZC+3d+9eASC9e/eW2NhYiY2Nle3bt0teXp4MGzZMIiIiZNiwYdYnrF/96lfSqlUra21CQoJ1rO3bt0tkZKSEh4fLG2+8Ues2V69eLV27dpWuXbvKmjVrrMvnzp0rISEhoiiKhISEyGuvvVZt3czMTAEgPXr0sM7h/fffr1bn7hdfreV25coVSUxMlN69e0tUVJT8+te/lrKyMhER2bx5s4SEhIher5d27drJyJEjXZRSdd6Sm733IxGRw4cPS3R0tISHh8usWbMqvcBOYLcwvwAAAj5JREFUnz5dVqxY4YpoaqXFzOy5T94vj1F7c6ur7tChQxISEiIBAQHSpk0biYqKcmVUldxLuZ06dUr69+8vMTExEhsbKzt37nRlVFbeklldz/NV1badGzduyNixY6VXr17Sr18/OXbsmCsiqpEWc5s8ebJ06NBBfH19JSQkRP7yl7+IiEhKSopER0dL7969Zdy4cZKVlVXv7edXTRMRERER2eA+yERERERENtggExERERHZYINMRERERGSDDTIRERERkQ02yERERERENtggExFp3Pz58/HWW295ehpERPcMNshERERERDbYIBMRadCCBQvQvXt3PProozh79iwAYOnSpYiKikJMTAwmT57s4RkSEWmXr6cnQEREjsnIyMDHH3+Mo0ePwmg0Ij4+HgkJCXjzzTdx4cIFNGnSBIWFhZ6eJhGRZvEdZCIijdm7dy8mTJiAgIAABAYGIjk5GQAQExODqVOn4qOPPoKvL9//ICJyFhtkIiINUhSl2rLt27dj1qxZyMjIQEJCAoxGowdmRkSkfWyQiYg0ZtCgQfjss89w584d3Lx5E9u2bYOqqsjMzMTQoUOxePFiFBYWoqSkxNNTJSLSJP4PjohIY+Lj45Gamoq4uDh07twZAwcOhKIo+PnPf46ioiKICObMmYNWrVp5eqpERJqkiIh4ehJERERERN6Cu1gQEREREdlgg0xEREREZIMNMhERERGRDTbIREREREQ22CATEREREdlgg0xEREREZIMNMhERERGRjf8Py9a4UUKDmn0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_plotly(m, forecast)\n",
    "py.iplot(fig) \n",
    "\n",
    "fig = m.plot(forecast)\n",
    "a = add_changepoints_to_plot(fig.gca(), m, forecast)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://datascienceschool.net/view-notebook/8903aa20770746e78fb5b1834ab5334b/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WDZIkSeV4TQ6VTZ37vvixvk6s7nHYvYHc0vrELYcORjBJkvrLskGSJGmQxRhbgXPydjs/RPv4zr7Or+593ILc0r6ysnyet1JIkoYFywZJkqTBdwqwS3WXg+5u6uxKS65N2uO+sO65mWx4fsyAJpMkqQCWDZIkSYPvXGBt54yXPtzsBbXdZ90bcq2tbd7NhwxgLkmSCmHZIEmSNIhijG3AnwE/Zsx2m5u9rnOfE+flStuayrK53kohSRryLBskSZIG18uBKcB3tuqqlract9/tvrB22Sw2rW0bkGSSJBXEskGSJGlwnQesAq7f2guru8+8N+TamNZ5N88sPpYkScWxbJAkSRokMcaxwGuAH6SUNm3t9Z37vnhurrSubfldOqb4dJIkFceyQZIkafCcAWzP1t5C0aV1TDVP2uOesHbpoWH9SlelkCQNWZYNkiRJg+dc4Dngl9v6Bp1Tj7gz5Fp76+M3Hl5cLEmSimXZIEmSNAhijOOBVwPfTyk1vQpFT9V9Tnwit455rrLssWOLSydJUrEsGyRJkgbHK4AJbOstFF0qLbk2edpdYd1zB4dVi7crJJkkSQWzbJAkSRoc5wFLgFv6+0bVvY+7M0Cldd5NR/U/liRJxbNskCRJGmAxxonA2dRvoaj29/2qex7xTG4f/0zL8ie8lUKSNCRZNkiSJA28VwFjgW8X9Ya1KfvcGTau2r+y7PEdi3pPSZKKYtkgSZI08N4IPA3cXtQbdu5z0l0ArfN/fUxR7ylJUlEsGyRJkgZQjHEP4HTgGymlWlHvW9vlgOfy2O2fqKxY4K0UkqQhx7JBkiRpYL2B+neubxT9xtWd9rszdKyb2rLovqlFv7ckSf1h2SBJkjRAYowBeDNwe0rp8aLfv3O/U+7JUGt5+k5vpZAkDSmWDZIkSQPnCGAmcNVAvHmePHVNHr9jqqx8+lhq1TAQnyFJ0rawbJAkSRo4bwI2Ad8dqA+o7XrgHaFz046tT9xywEB9hiRJW8uyQZIkaQDEGNuA1wNXp5RWDtTnbD7w9PtypWVDy6L7Thyoz5AkaWtZNkiSJA2Ms4CdGYCJIf/ImO0250lT7wxrlhwZ1j03bkA/S5KkJjVVNoQQzgwhPBZCmBdCuGALxz8XQri/sc0NITzf7Vi127GriwwvSZI0hL0JWApcN9Af1LnXMbeFXGtvfeznRw/0Z0mS1IzWvk4IIbQAXwJOAxYBd4UQrs45P9p1Ts75A93Ofw/1yZC6bMg5H15cZEmSpKEtxjgFeBXw7ymlzQP9edW9j1uQ008Xtyyde9Jm+NVAf54kSX1pZmTDscC8nPP8nHMH8G3gnBc4/3zgW0WEkyRJGqbOBdoZoFUo/kSoUNtp/1vDptX7VJ55YPdB+UxJkl5AM2XDVGBht9eLGvv+RAhhb2Af4Jfddo8NIdwdQrgjhPCabU4qSZI0fLwZeDCldP9gfeDmGaf+JhOqrU/e5kSRkqTSNVM2bGnN5tzLuecB3885V7vt2yvnfDT12ZgvDyHst8UPCWF2o5S4O4Qwu4lckiRJQ06McSZwHAM9MWQPefLUNXm7nR6qrHz6BDo3tQzmZ0uS1FMzZcMiYFq313sCi3s59zx63EKRc17ceJwP3MQfz+fQ/bw5OeejG9ucJnJJkiQNRbOBDgbrFopuqrsfemuobZ7YOveGmYP92ZIkdddM2XAXMCOEsE8IoZ16ofAnq0qEEA4EdgBu77ZvhxDCmMbznYCTgEd7XitJkjQSxBjHUV+F4n9TSssH+/M7Dzj14dzStqrl2YdOGuzPliSpuz7LhpxzJ/B31JdtSsB3c86PhBAuCSG8utup5wPfzjl3v8UiAneHEB4AbgQu7b6KhSRJ0gjzOmAyUM4ozdb2Wm2HvX4T1i47NKxcuH0pGSRJoomlLwFyztcC1/bYd1GP15/cwnW3AbP6kU+SJGk4eRfwGHBzWQE69znp1jHLnzi97fEbju849i3Xl5VDkjS6NXMbhSRJkvoQYzwEOBGYk1LqbTLtAVfb49AleeykeZVl815Mrbqlib4lSRpwlg2SJEnF6JoYclBXodiS6u6H3Bw6N+7S+vgvY9lZJEmjk2WDJElSP8UYx1OfGPL7ZUwM2dPmg864N1fa1rQsuvfksrNIkkYnywZJkqT+ex0wibImhuypfXxnbcfpvw5rlx1WWf7ElLLjSJJGH8sGSZKk/nsX8FvglrKDdOmcceotAK1zb3hR2VkkSaOPZYMkSVI/xBhnASdQ8sSQPdV23n9FnrDTQ5UVT76YzRtbys4jSRpdLBskSZL656+BTQyBiSF7qu555E2hunn7tt9ed0TZWSRJo4tlgyRJ0jaKMU4G3gx8J6X0XNl5euo84NRHc+vYZZVnHzql7CySpNHFskGSJGnbvQ2YAFxedpAtqrTk2s4zbq5seH5Gy6L79yg7jiRp9LBskCRJ2gYxxhbgPcCvUkr3lZ2nN5sPOuO2HEJny/xfuQymJGnQWDZIkiRtm1cB04HPl5zjBeXtd1uXt9/jrsrzC08I61aMLTuPJGl0sGyQJEnaNu8HngZ+XHaQvnTuc+KNIdfGtD56zUllZ5EkjQ6WDZIkSVspxng4cDLwxZRSZ9l5+lLd+7inamMnPd6y9Len0tnh9z9J0oDzDxtJkqSt915gPfAfZQdpVnXaUT8PnZt2bEs/dRlMSdKAs2yQJEnaCjHGXYDXA1ellFaWnadZnQed8WBuG7e05Zn7TyPXyo4jSRrhLBskSZK2zruAMcAXyg6yVSotubpr/EXYtGaflvm37l92HEnSyGbZIEmS1KQYYzvwt8DPUkq/LTvP1to881W350rr2tYFt51WdhZJ0shm2SBJktS81wO7McSXu+zV2IkdtR33vTmsXXZYZcmju5QdR5I0clk2SJIkNSHGWAE+CjwAXFdynG22OZ51E4Rq69wbTi07iyRp5LJskCRJas45wEHApSmlXHaYbZV3mLY6T5r6m8rzC08Mq5dMKDuPJGlksmyQJEnqQ4wxABcCTwDfLzlOv3Xuf8ovQq61tz167UvKziJJGpksGyRJkvr2MuAY4LKUUmfZYfqruufhi2vjd3yksuzxU9m4pr3sPJKkkceyQZIkqW8XAs8CV5UdpCid+77o2lDbPLHt4R+9qOwskqSRx7JBkiTpBcQYjwFOBT6XUtpUdp6iVPd78bzauMmPtSxJZ9CxrrXsPJKkkcWyQZIk6YVdADwPfKXsIEWr7nPiNaHaMbntoR+fVHYWSdLIYtkgSZLUixjjQcBrgStSSmvKzlO0zv1PeSyP3X5+y5JHzqRzU0vZeSRJI4dlgyRJUu8+CmwEvlB2kAERKnTufdz/hc5NU9oe+vFxZceRJI0clg2SJElbEGPcH3gjMCeltKzsPAOl88DTHsljtnuqZfGDr4gxOneDJKkQlg2SJElbdhHQAVxadpABFSp0Tjvm2tC5cWfgvLLjSJJGBssGSZKkHhpzNbyB+lwNS8rOM9A645kP5PYJzwAfjzH6/VCS1G/+YSJJkvSnPgmsBy4rOcfgqLTk6p5HXgMcBPxl2XEkScOfZYMkSVI3McZZwLnAF1JKy8vOM1g2H/yKe4GHgUtijG1l55EkDW+WDZIkSX/sU8Bq4F/LDjKoWtoy8HFgBvCWcsNIkoY7ywZJkqSGGOORwGuBf0sprSg7Twl+AtwOXBxjHFd2GEnS8GXZIEmS9AeXACuBy8sOUoaUUgYuBKYC7y45jiRpGLNskCRJAmKMxwNnA59NKa0qO09ZUko3A9cBF8YYJ5WdR5I0PFk2SJKkUS/GGIDPAr8DvlhynKHgY8AU4ENlB5EkDU+WDZIkSfAa4EXARSmltWWHKVtK6V7gu8AHY4y7lp1HkjT8WDZIkqRRLcbYDlwGPApcWXKcoeQfgLHURzlIkrRVLBskSdJo9y5gf+DvU0qdZYcZKlJKc6mXL38TY5xRdh5J0vDSVNkQQjgzhPBYCGFeCOGCLRx/SwhhWQjh/sb2jm7H3hxCeLyxvbnI8JIkSf0RY5wMXAzcAPy05DhD0UXAJurzWUiS1LQ+y4YQQgvwJeAs4GDg/BDCwVs49Ts558Mb29ca106h/gf4ccCxwMUhhB0KSy9JktQ/F1KfCPHDjWUf1U1KaQnwj8A5McZTy84jSRo+mhnZcCwwL+c8P+fcAXwbOKfJ9z8D+HnOeUXOeSXwc+DMbYsqSZJUnBjjdOB9wDdSSveXHGcouxx4Erg8xthadhhJ0vDQTNkwFVjY7fWixr6e/jyE8GAI4fshhGlbea0kSdJg+0egBnyi7CBDWUppI/Bh4BDgnSXHkSQNE82UDWEL+3oOM/wJMD3nfCjwC+Cqrbi2fmIIs0MIdze22U3kkiRJ2iYxxhcBrwf+NaW0qOw8w8APgZuATzfmuZAk6QU1UzYsAqZ1e70nsLj7CTnn53LOmxov/x9wVLPXdnuPOTnnoxvbnGbCS5Ikba3GrQD/DjwNXFpynGGhMZ/FB6jPb3FRyXEkScNAM2XDXcCMEMI+IYR24Dzg6u4nhBB27/by1UBqPL8OOD2EsENjYsjTG/skSZLK8m5gFvD+lNK6ssMMF415Lb4GvCfGeGDZeSRJQ1ufZUPOuRP4O+olQQK+m3N+JIRwSQjh1Y3T3htCeCSE8ADwXuAtjWtXAJ+mXljcBVzS2CdJkjToYoy7A5cAPwN+VHKc4egTwHrgizHGLd0uK0kSAE3NKJxzvha4tse+i7o9v5D60lFbuvZK4Mp+ZJQkSSrKZ4GxwHtd6nLrpZSWxhg/DnyR+mjXb5UcSZI0RDVzG4UkSdKwF2M8GXgDcFlK6fGy8wxjX6Y+YvVzThYpSeqNZYMkSRrxYoxtwJeAp4DPlBxnWEspVYF3ATvj/5aSpF5YNkiSpNHgfcBM4H0ppfVlhxnuUkr3AZ8H3hVjPKHsPJKkoceyQZIkjWgxxv2pT1j9E3qsqKV+uQh4BvhqY+SIJEm/Z9kgSZJGrBhjhfpyjR3A3zgpZHFSSmuB99BYRrTkOJKkIcayQZIkjWSzgZOBD6WUnik7zEiTUvoR9dEin4wx7lt2HknS0GHZIEmSRqQY4zTgMuAG4D9KjjOS/R3QCfxnYySJJEmWDZIkaeSJMQbgq0AL8E5vnxg4KaWF1CfgfAn12yokSbJskCRJI9JfAWcBF6aUniw7zChwFfB/wGdijAeUHUaSVD7LBkmSNKLEGHcHLgduA64oOc6o0Bg5MhvYCHw9xthSciRJUsksGyRJ0ojRmDPg68A44G0ppVq5iUaPlNKz1OdvOAH4UMlxJEkls2yQJEkjyXuA04EPppQeKzvMKPQt4AfAp2OMM8sOI0kqj2WDJEkaEWKMs4B/Bn5CfXJIDbLG7RR/A6wG/jvGOKbkSJKkklg2SJKkYS/GOBb4H+B54B2uPlGelNJS4G3A4dTLH0nSKGTZIEmSRoLPALOAtzb+sqsSpZR+AnwBeF+M8ZVl55EkDT7LBkmSNKzFGE8H3g9ckVL6adl59HsfAe6nvjrF1LLDSJIGl2WDJEkatmKMewD/BTxK/S+3GiJSSpuA84CxwP+4HKYkjS6WDZIkaViKMbYB3wEmAK9LKW0oOZJ6aKwI8m7gZOBjJceRJA0iywZJkjRcfQZ4ETA7pfRo2WHUq29Qn7zzkzHGk8sOI0kaHJYNkiRp2Ikx/hnwIeDfU0rfLDuPetdtOcy5wPdijNNKjiRJGgStZQeQJEnaGjHGGcB/AncCHyw5jpqQUloTY3wt9f/P/jfG+JKU0sayc6k40y+4ZnbZGbpbcOnZc8rOII12jmyQJEnDRoxxPPB9oBP4y8YkhBoGUkq/Bd4EHANcEWMMJUeSJA0gywZJkjQsNP5yeiUwC/irlNJTJUfSVkop/Qj4R+DtwJD6JVySVCzLBkmSNFz8A3AucGFK6adlh9E2uxj4GfDFGOMJZYeRJA0MywZJkjTkxRhfB3yK+soGl5UcR/2QUqoCrwcWAj+IMe5VciRJ0gCwbJAkSUNajPEo4CrgNurLXOaSI6mfUkorgVcD44H/izFuX3IkSVLBLBskSdKQFWPcA7gaWAa81gkhR46U0iPAnwOR+pKYbSVHkiQVyLJBkiQNSTHG7YAfA5OAV6WUlpYcSQXHeLRoAAAYMklEQVRLKf0C+GvgdFyhQpJGFMsGSZI05MQY26kvcXkkcH5K6cGSI2mApJT+A/gM9dUp/r7kOJKkglg2SJKkISXGWAH+AziD+hwNPyk5kgbeJ4DvAP8cYzy37DCSpP6zbJAkSUPNZcBfAZ9o/OqtES6lVAPeAvwa+K8Y45nlJpIk9ZdlgyRJGjJijB8GPgRcAfxTyXE0iFJKG4FXAg9TXxLzRSVHkiT1g2WDJEkaEmKMbwE+C3wPeL9LXI4+KaVVwJnAQupLYh5eciRJ0jaybJAkSaWLMb4BuBL4BfDGlFK15EgqSWPVkdOA1cD1McYDSo4kSdoGlg2SJKlUjQkBvwHcDJyTUtpUciSVLKX0NPDyxstfxBj3LTOPJGnrWTZIkqTSxBj/HPgf4FbgVSml9SVH0hCRUpoLnA5MAG6KMe5XciRJ0lawbJAkSaWIMZ4DfBv4DXB2SmltyZE0xKSU7gdeBowHbo4xzig5kiSpSZYNkiRp0DVGNHwPuBc4K6W0puRIGqJSSg8ALwXaqY9wOLDkSJKkJlg2SJKkQRVjfCvwXeAu4IyU0uqSI2mISyk9RL1waKVeOMSSI0mS+mDZIEmSBk2M8X3UV524ATg9pfR8yZE0TKSUHgFOabz8VYzx2BLjSJL60FTZEEI4M4TwWAhhXgjhgi0c/2AI4dEQwoMhhBtCCHt3O1YNIdzf2K4uMrwkSRoeYowhxngxcDnwA+qTQa4rOZaGmZRSAl4MrAJ+GWM8veRIkqRe9Fk2hBBagC8BZwEHA+eHEA7ucdp9wNE550OB7wOXdTu2Ied8eGN7dUG5JUnSMBFjbKFeMnwSuAo41+Utta1SSvOAk4B5wDUxxteXHEmStAXNjGw4FpiXc56fc+6gPmv0Od1PyDnfmHPuWqrqDmDPYmNKkqThKMY4nvoPEe+lXji8LaXUWW4qDXcppSXAycCvgf9p3J4jSRpCmikbpgILu71e1NjXm7cDP+32emwI4e4Qwh0hhNdsQ0ZJkjQMxRh3BW6i/iPF+1JKH0gp1cpNpZEipbSK+sjbHwCXxxgvjzG2lhxLktTQzH+Qwxb25S2eGMJfAUdTb5q77JVzXhxC2Bf4ZQjhoZzzE1u4djYwu/FyTs55ThPZJEnSENRYLeBaYFfgtSmlH5ccSU2YfsE1s/s+awg551+guvn69tvmTGpZ8eT7ahN2PHWf93zja3nCjhsG4uMWXHq2308lqUnNjGxYBEzr9npPYHHPk0IILwc+Drw65/z7+zBzzosbj/Op/7pxxJY+JOc8J+d8dGPzP+SSJA1TjUn7bgPGASdbNGhAtbTljhe/+7ud047+77BuRRxz8+c/Wlk6d8eyY0nSaNdM2XAXMCOEsE8IoR04D/ijVSVCCEcAX6VeNCzttn+HEMKYxvOdqE/m82hR4SVJ0tDRWHHiI9Rvp1wIHJ9SuqvkWBolNh953q86Dz7r81Q7JrX/5sqPtTxxy/5lZ5Kk0azPsiHn3An8HXAdkIDv5pwfCSFcEkLoWl3is8B2wPd6LHEZgbtDCA8ANwKX5pwtGyRJGmFijBOoTyL9z9QnhDwhpbSg1FAadTpnvOyxjqPf+Bla2ta1PfyTD7bd/d8vIztNiCSVoalJdHLO11K/77L7vou6PX95L9fdBszqT0BJkjS0xRj3A34IzAQ+Cnw2pbTF+Z2kgVbbfebSTRPf+5n2O658a+sz959bWb1kv00nvOMbjJvscquSNIiauY1CkiRpi2KMfwHcQ31Op7NSSpdZNKhsebudN2x62Ye/XN3tkB+ENUuOGnvjv36s8swDu5edS5JGE8sGSZK01WKM42KMXwa+BzwGHJVSur7kWNIfVFpyx3Fvua7z4LM/R7VzQvs9/3Nh2wPfP97bKiRpcFg2SJKkrRJjPBi4E/hr6vM2vSil9GS5qaQt65zx0sc6TnzX/5fHTHy6dcEdbx1z47+9I6xdNq7sXJI00lk2SJKkpjRWm/hr4G5gV+q3TXwkpbS55GjSC6rtOP35TS+/4F+rux78o7BmyVFjbr78IlerkKSBZdkgSZL6FGOcBvwM+DJwK3B4Suln5aaStkJLW+44/m0/3Xzon10Godr28NUfbr9tzjls3thSdjRJGoksGyRJUq8aoxneBDwEvAj4W+D0lNLicpNJ26a6z4lPbnrphz6dJ029vWXZ3FeM/cU/fbzlqd/sXXYuSRppLBskSdIWxRj3BH4EXEW9bDg0pfRlV5vQcJfH77Bp0ykfuGrzAadeQbVzQtv937uw/davvJaOdU0tCy9J6ptlgyRJ+iMxxpYY43uBBJwGfBg4JaX0RLnJpGJ1xrMe2nTKBz6ZJ+95a8vyeWeO/cWl/9DyxK+cy0GSCmDZIEmSfi/GeARwB/B56nMzHJJS+teUUrXcZNLAyNvtvGHTye//r83xzMvJtbb2h3/892Nu+txbwvPPTCw7myQNZ5YNkiSJGOMOMcbLqa80MQ04n/pqE/PLTSYNjs4DXp42vuwjn6zuPOOnYdXiY8f86guXtN3zzZOpbg5lZ5Ok4cj70iRJGsVijK3AO4FPAzsAc4CPpZRWlhpMKsO4SR0dJ77rR5XFD93R9vDV57cuuvf1LcvmnrR5xsu+W93vJfPKjidJw4llgyRJo1SM8WXA5cAs4Cbg/SmlB0oNJQ0BtT1mLdm0+8zPtT189dEtT935uvaHr/772oI77ovxwzemlB4vO58kDQeWDZIkjTIxxsOBfwLOAhYAfwH8wFUmpG5Chc2zXnP35hmnPth+/3dfXln62JnAozHGLwGfTik9V3ZESRrKLBskSRolYoz7Ub9d4nxgJfBR4AsppY2lBpOGsrETOzqOf/u1YeXCX4+95fMHAO8B3hpj/Dfgcyml1SUnlKQhyQkiJUka4WKMe8cYvwz8FngN8Blg35TSZRYNUnPyDtNWp5RmA4cBNwCfBJ6MMX40xjih1HCSNAQ5skGSpBEqxrgvcCHwFiAD/4/68O9ny8wlDWcppYeBP4sxHgVcAlwKfDDG+C/AV1JKa0oNKElDhGWDJEkjTIzxYOAjwF8BncBXgMtSSgtLDSaNICmle4CzY4wnAp8CLgM+FmO8Avh8Sml5qQElqWSWDZIkjQAxxgCcAnwIOBvYAHwB+KwjGaSBk1K6DTgtxngM9ZFEn6A+0uFr1OdEeaLUgJJUEssGSZKGsRhjO/A64IPAkcBS4CLgy/6yKg2elNJd1G+viMAFwN8C74kxXk19idmbXfFF0mhi2SBJ0jAUY9wLmA28E9iF+uSP7wT+20kfpfKklBLw5hjjhdQLh78GzgEeaNxi8a2U0royM0rSYLBskEaZ6RdcM7vsDF0WXHr2nLIzSEUYtH+vqptD6+M3HtzyzH0vCfUZ8ckTdn6wOu3Ib3XOeFmi0lIB3gT479YLGEr/HdTIlVJaDHwixviPwBuA91GfpPXfYoz/DXw1pfRAmRklaSBZNkiSNMRVfvfbnVufuOXEyooFJ4Rqxw650ramtvOMn3XOOPWW2s77ryg7n6TepZQ2AF+LMf4HcCLwLuBtwN/EGH8D/CfwnZTS8yXGlKTCWTZIkjQEhbXLxrU+fuORlWVzj6tseP7ADDlP2PGRzt0O+U7ngac9SNvYatkZJTWvMV/DrcCtMcb3Ux+F9E7qq8V8Psb4Y+Aq4PqUUmd5SSWpGJYNkiQNFR3rWlvn/nJWy+/ScWHtslmB3Jrbxi2t7nbwjzr3f+nttR338ZdPaQRIKa0ALo8xfh44Cngz8HrgL4GlMcbvAd8Gbksp1cpLKknbzrJBkqQybVjV3vrEzYe0LJ17ZFi79NCQa2NyS9uq2pTpN1enHfWb6t7HPkWolJ1S0gBojHa4G7g7xti1bO35wNuBdwOLYozfBX4I3J5SckSTpGHDskGSpEEWVi7cvvWpO2ZWnnvysLBu+cyQa+250ro2T5p6Z+fuM+/p3Pclj9Ha7q+Z0iiSUuqgXir8MMY4EXgVcC7wHupL2z4XY7wG+AlwXUppTWlhJakJlg2SJA206ubQ8vRd01uWPDKrsuqZQ8KmtXsD5Jb2VbXJ026r7X7IvZ37nPS4BYMkgEaR8E3gmzHG7YEzqJcPr6Q+10NHjPFG6sXDT1JKT5cWVpJ6YdkgSdIACKuXTGhdcPvMynPzDwlrl80Mtc7tMmTGTnqiuuvBP6ruMevh6rSjFnqLhKQXklJaDXwP+F6MsZX6ihavbmxXAFfEGB8ArgN+Cfw6pbSurLyS1MWyQZKkAoQ1vxvfsvCeGZUVC2ZU1i49gE1r9woQcqV1bd5ul4erO+37UOf0Ex7NE3ddX3ZWScNTY5WKWxrbh2OMB1If8fAq4APAR4DNMcY7qBcPvwR+k1LaVFJkSaOYZYMkSdsgxrgz8BLg5LHtE15Lx7qpAUIOoTOPnTQ/73LgNdXdD3moutexT1FpyWXnlTTypJQeAx4D/iXGOAE4CXhZY/sH4GJgQ4zx19SLh18B96SUNpYUWdIoYtkgSVIfGkOXZwLHAMdS/0J/cOPw+tza/lTeYa+f1Hbaf27nXkc/SfuEzrKyShqdGrdOXN/YiDFOBk7mD+XDZxqnbo4x3g/c0W17srEyhiQVxrJBkqRuYowB2I96sdBVLhwJjGuc8jz1L+f/BdwM3LPptI+/ZfCTSlLvUkrPAz9ubMQYdwFOAI5vbG+nvtIFwNLGrRd3AHcB96eUlg96aEkjimWDJGnUijGOoz5CYVZjO5R6sTClccpG4F7gq9S/gN8JPNHzF8DpF1wzWJElaZuklJbyx+VD14it47ttr+46P8b4DPAAcH+3x3kpJVfNkdQUywZJ0ojX+FI9HTiEP5QKs4AZQNdyEBuBR4AfUC8V7gIeSSltHuy8kjTQGpNNPtDYvgoQY5wCHAEcDhzWeDwDaGlcti7G+FDjmgT8tvG4iHP+ZVDzSxr6LBskSSNC4/aHnYADgAMbW9fz/YG2xqkZmA88BHyn8fgg9REL1UGOLUlDRkppBXBDYwMgxjiW+giwrvLhcOBcYHK3S9eP/dknl+cxE5fkcZOfrW2385I8ea8l1Z32W87YiR2D+I8gaQixbJAkDRsxxnZgGrBPt206sC/1YmGHbqdvBuZRn6n9amAu9ZELj6SU1g5eakkavhorV9zb2IDfl7s7AxE4CIi5pf3lYf2K/Surnz225Xfp99fnlvZVtI1blsdMWJbHbL88j5+yrDZxl2W1KdOX5Ul7rCFUkDQyWTZIkoaExpfXHYE9gamNxz3543JhKn+47QGgE3gaeJL6KIWuZeDmAk81hglLkgrUmLdmaWO7GWD6BdfMBmDDqvaWZXN3rTy/aLewfuVOYeOqXehYt1NYu/ygsGrx5ACh631yqGyideyK3DZ2Je3jV+T27VbmcZNW5PFTVtQm7rqytsPeKx0ZIQ1flg2SpAEVY2yj/gvYro1tl27Pd+ePi4UxPS6vAYuplwk3NR67tgXAMxYKkjSEjJvUUd3rmIXVvY5Z+CfHOta1VpbP36myevFOYe2yncOGVTuHjnVTwuYNO7Dmd1Mr1UWTel6SK63raB3zfG5tX03r2NW5bfwq2sevzmMnrspjJ6/OE6asqk3cbXXebuf1VFpcvlMaQiwbJElNa4w+GEd9tYYp1G9bmEJ9RMKWyoRd+cPKDj1tAJYAi6hPyPiDxvNnGo+LgN9ZJkjSCNE+obO2x6wltT1mLdni8Y71rZXnF06urH52h7BuxZSw8fkpYdPaHdi8YVLo3LQ961fsH6pLJoWc/+TvMJlQpaVtDa3tq3Lr2NUxfvg4YDnwHLCi8fhc99cpJUdNSAPIskGSRpFGWTAG2L6XbRL1AqGrROi+de3rOfqgu9XA7xrbo9RHI/yux7a08bi25xKSkqRRrH18Z22XA5fXdjlwea/n5Bph/cpxYfWz21fWLd8+bFg1iU2rtw8d67cPHesn0blx+7B54yTgLOpFeHtvbxVjXMuflhErgFWNbfULPF9tGS69sKbKhhDCmcDnqS9787Wc86U9jo8BvgEcRf1f0nNzzgsaxy4E3g5UgffmnK8rLL0kjVCNUmAsMAEY3+Rjz30T2XKh0Ebf1gErqX/pWkF9ebMV3baVPV6vAJY2JhKTJGlghAp5wo4b8oQdN9TqxfUWLbj07DmNP0sn8IcReDv2eN7z9d6N1039WRljXMcfSog11P/s7NrW9njd9ObKSBop+iwbQggtwJeA06gPab0rhHB1zvnRbqe9HViZc94/hHAe8M/AuSGEg4HzgJnAHsAvQggH5Jz9F0hS6RpfQlobW1tja+Z5G/Vf97u2sf14vqVj4xvb7yfRalIH9S8q6xvbaupffp5qPG9mWwOsTClt2srPliRpSGmMnlvb2J5u9rpuhX/XiL9JTTyfSL3Y2J0//ADQtfU6uqKXz98MbNzCtqmX/b1tHY1tc5OPfZ1TdUSitkYzIxuOBeblnOcDhBC+DZxDfXhsl3OATzaefx+4IoQQGvu/nXPeBDwZQpjXeL/bi4mvLo3/KHa3Na8H6lw/p75Vuj3vbV8z52zrdX/0uvXA0w4h1wKZALmeM+cAOZAzkAMQQn0f9WN0e57r1+ZaIOdK/XWtQq5VIIeQc/15JkCtQs6VxnWN/V3Pc4jxw5H6iKmWRs7+PO8qA5otC7pKhoFS4w9fDDb1eN71ej31EQI9j3X9urF+Kx7XO5xTkqT+a/yFekNj63X0RLMaEyWPB7bjT4uILW1dPz70to2nPgJjS8fGsfU/VjQrN4qQDuqrQXVSH73+Qo/9PafreW0rt225ZmveO/fYav3Y98hI/Q7XzBftqUD32WQXAcf1dk7OuTOEsIr6UKSpwB09rp26zWmHqBjjp4CPdts1WH+Zlf7/9u4mNK4qDOP4/6EfIigIuhBswC66sKgglCq4EKwfVUQ3CiqUgkg2rVhQRO2uW0E3ClKoIFqQQhWzUGpFlyqV0gohVqqIpiooXehGTezr4t7SUKZJSidzm5v/D4a5586ZzBPImZt559xzL9qaE4c7e+2CM5AzJGffbG/n3Jv2f5e4PUPzoXuG5sA0M2B7ofZiH5vlwoWEv4F/+nrQkCRJF2dqamqGc+s9LKk5szavpJlRseYC9/M9ttD92vY1Vi1wP2jf+c9d7M85+wXaoNtydy3N6ai9k6r5Z8IkeQy4v6qebtvbgM1V9cycPpNtn+m2/T3NDIY9wBdV9W67fx/wUVUdHPA648B429xbVXsv9ZdTvyUZ9+9EGg3HmzRajjlpdBxv0tJYTCVoGhib015Hc83zgX2SrKY5b+n0Ip8LQFXtrapN7c3BrsUYX7iLpCFxvEmj5ZiTRsfxJi2BxRQbjgAbkqxPspZmwceJ8/pMANvb7UeBz6qZMjEBPJ7kiiTrgQ0011KXJEmSJEk9teCaDe0aDDuBQzTnzbxVVZNJ9gBfV9UEsA94p10A8jRNQYK23wGaxSRngR1eiUKSJEmSpH5bcM0G6XLl+XXS6DjepNFyzEmj43iTlobFBkmSJEmSNFR9uFSIJEmSJEm6jFhs0LKX5PkkleS6rrNIfZbklSTfJvkmyQdJruk6k9Q3SbYmOZHkZJIXu84j9VmSsSSfJ5lKMpnk2a4zSX1isUHLWpIx4F7gp66zSCvAYeDmqroV+A54qeM8Uq8kWQW8ATwAbASeSLKx21RSr80Cz1XVTcAdwA7HnDQ8Fhu03L0GvAC4+Ii0xKrqk6qabZtfAuu6zCP10GbgZFX9UFX/Au8Bj3ScSeqtqvq1qo62238BU8AN3aaS+sNig5atJA8Dp6rqeNdZpBXoKeDjrkNIPXMD8POc9jR+8JFGIsmNwG3AV90mkfpjddcBpPkk+RS4fsBDu4GXgftGm0jqt/nGXFV92PbZTTP1dP8os0krQAbsc+aetMSSXAUcBHZV1Z9d55H6wmKDLmtVdc+g/UluAdYDx5NAM537aJLNVfXbCCNKvXKhMXdWku3AQ8CW8trJ0rBNA2Nz2uuAXzrKIq0ISdbQFBr2V9X7XeeR+iT+r6g+SPIjsKmq/ug6i9RXSbYCrwJ3VdXvXeeR+ibJaprFV7cAp4AjwJNVNdlpMKmn0nxj9TZwuqp2dZ1H6hvXbJAkLdbrwNXA4STHkrzZdSCpT9oFWHcCh2gWqjtgoUFaUncC24C72+PasSQPdh1K6gtnNkiSJEmSpKFyZoMkSZIkSRoqiw2SJEmSJGmoLDZIkiRJkqShstggSZIkSZKGymKDJEmSJEkaKosNkiRJkiRpqCw2SJIkSZKkobLYIEmSJEmShup/b2W+Q1iE83sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import seaborn as sns\n",
    "import scipy as sp\n",
    "sns.distplot(m.params[\"delta\"], kde=False, fit=sp.stats.laplace)\n",
    "plt.box(False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prediction line + daily Confirmed_count bar graph\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning:\n",
      "\n",
      "Sorting because non-concatenation axis is not aligned. A future version\n",
      "of pandas will change to not sort by default.\n",
      "\n",
      "To accept the future behavior, pass 'sort=False'.\n",
      "\n",
      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "day_plus = pd.DataFrame(index=['2020-03-05','2020-03-06','2020-03-07','2020-03-08','2020-03-09','2020-03-10','2020-03-11' ]) \n",
    "daily_count = pd.concat([daily_count, day_plus])\n",
    "  \n",
    "accumulated_count = daily_count.cumsum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Blue line : 03-04 real accumulated line\n",
    "after 03-04, \n",
    "## Red line : prediction line \n",
    "### bar graphs is daily Confirmed_count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(25,8))\n",
    "color = 'tab:red'\n",
    "forecast.yhat.plot(color=color)\n",
    "plt.ylabel('Accumulated_Confirmed', color=color)\n",
    "plt.xlabel('days')\n",
    "plt.title('Prediction by Prophet', color=color, fontdict ={'fontsize':15})\n",
    "plt.ylim=[0,14000,2000]\n",
    "plt.box(False)\n",
    "\n",
    "accumulated_count['id'].plot()\n",
    "\n",
    "\n",
    "daily_count['id'].plot(kind='bar', color=['coral'] )\n",
    "plt.text(x=19, y=200, s='169',color='red', horizontalalignment='center') ;plt.text(x=20, y=300, s='231',color='red' ,horizontalalignment='center') \n",
    "plt.text(x=21, y=200, s='143',color='red',horizontalalignment='center') ;plt.text(x=22, y=330, s='285',color='red',horizontalalignment='center') \n",
    "plt.text(x=23, y=560, s='505',color='red',horizontalalignment='center') ;plt.text(x=24, y=610, s='571',color='red',horizontalalignment='center') \n",
    "plt.text(x=25, y=900, s='813',color='red',horizontalalignment='center') ;plt.text(x=26, y=1100, s='1061',color='red',horizontalalignment='center')\n",
    "plt.text(x=27, y=650, s='600',color='red',horizontalalignment='center') ;plt.text(x=28, y=600, s='516',color='red',horizontalalignment='center')\n",
    "plt.text(x=29, y=500, s='438',color='red',horizontalalignment='center')\n",
    "\n",
    "\n",
    "plt.annotate('',xy=(14,10), xytext=(14,100), arrowprops=dict(facecolor='black'))\n",
    "plt.text(x=14, y=1000, s='secodary infection by 31 patient',color='green', fontweight='bold')\n",
    "plt.annotate('',xy=(19,1000), xytext=(19,5000), arrowprops=dict(facecolor='black'))\n",
    "plt.text(x=19, y=5500, s='president\\'s Declaration',color='blue',fontweight='bold')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-05</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-06</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-09</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-10</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-12</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-15</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-17</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-18</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-19</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-21</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-24</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-26</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-27</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-28</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-29</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-01</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-02</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05</th>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id\n",
       "2020-02-05   1\n",
       "2020-02-06   1\n",
       "2020-02-09   1\n",
       "2020-02-10   1\n",
       "2020-02-12   3\n",
       "2020-02-15   2\n",
       "2020-02-17   1\n",
       "2020-02-18   2\n",
       "2020-02-19   4\n",
       "2020-02-21   1\n",
       "2020-02-24   4\n",
       "2020-02-26   2\n",
       "2020-02-27   4\n",
       "2020-02-28   1\n",
       "2020-02-29   2\n",
       "2020-03-01   1\n",
       "2020-03-02   3\n",
       "2020-03-03   2\n",
       "2020-03-04   2\n",
       "2020-03-03   3\n",
       "2020-03-04   7\n",
       "2020-03-05  47"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_released_count = df_patient.groupby('released_date').id.count()\n",
    "daily_released_count = pd.DataFrame(daily_released_count)\n",
    "daily_plus = pd.DataFrame([3,7,47],index=['2020-03-03', '2020-03-04', '2020-03-05'], columns=['id'])\n",
    "daily_released_count =pd.concat( [daily_released_count ,daily_plus] )\n",
    "daily_released_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x7fc16ec5ec18>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "accumulated_released_count = daily_released_count.cumsum()\n",
    "plt.rcParams['figure.figsize']=10,6\n",
    "color = 'tab:blue'\n",
    "ax = daily_released_count.plot(kind='bar', title = 'Korea nCOV-19 Recovered individu1es', color=color, alpha=0.1, legend=False)\n",
    "for p in ax.patches:\n",
    "  left, bottom, width, height = p.get_bbox().bounds\n",
    "  ax.annotate(\"%.f\"%(height), (left+width/2, height*1.01), ha='center') \n",
    "ax.set_ylabel('Recovered_count', color=color)\n",
    "ax.tick_params(axis='y', color=color)\n",
    "ax.hlines(y = 10, xmin=0, xmax=22, colors=color, linestyles='dotted')\n",
    "ax.hlines(y = 40, xmin=0, xmax=22, colors=color, linestyles='dotted')\n",
    "plt.box(False)\n",
    "\n",
    "\n",
    "color='tab:green'\n",
    "ax2= accumulated_released_count.plot( color=color, legend=False)\n",
    "ax2.set_ylabel('Accumulated_count', color=color)\n",
    "ax2.tick_params(axis='y', color=color)\n",
    "ax2.hlines(y = 40, xmin=0, xmax=22, colors='green', linestyles='dotted')\n",
    "ax2.hlines(y = 80, xmin=0, xmax=22, colors='green', linestyles='dotted')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-03</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-04</th>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-03-05</th>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            id\n",
       "2020-03-03  36\n",
       "2020-03-04  38\n",
       "2020-03-03  41\n",
       "2020-03-04  48\n",
       "2020-03-05  95"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " accumulated_released_count.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accumulated Released individules count was doubled on 2020-03-05 compared 2020-03-04\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>sex</th>\n",
       "      <th>birth_year</th>\n",
       "      <th>confirmed_date</th>\n",
       "      <th>released_date</th>\n",
       "      <th>state</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>2020-01-20</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>male</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>male</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>male</td>\n",
       "      <td>1964.0</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>male</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>female</td>\n",
       "      <td>1957.0</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>female</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>female</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>male</td>\n",
       "      <td>1995.0</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>male</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>male</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>female</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>male</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>female</td>\n",
       "      <td>1977.0</td>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>male</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>female</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>male</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>female</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>male</td>\n",
       "      <td>1992.0</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>female</td>\n",
       "      <td>1989.0</td>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>female</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>male</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>female</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>male</td>\n",
       "      <td>1944.0</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>female</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63</td>\n",
       "      <td>female</td>\n",
       "      <td>1968.0</td>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>female</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>83</td>\n",
       "      <td>male</td>\n",
       "      <td>1944.0</td>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>589</td>\n",
       "      <td>female</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>1013</td>\n",
       "      <td>male</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>1129</td>\n",
       "      <td>male</td>\n",
       "      <td>1963.0</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>1255</td>\n",
       "      <td>female</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>1256</td>\n",
       "      <td>female</td>\n",
       "      <td>1980.0</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>released</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id     sex  birth_year confirmed_date released_date     state\n",
       "0        1  female      1984.0     2020-01-20    2020-02-06  released\n",
       "1        2    male      1964.0     2020-01-24    2020-02-05  released\n",
       "2        3    male      1966.0     2020-01-26    2020-02-12  released\n",
       "3        4    male      1964.0     2020-01-27    2020-02-09  released\n",
       "4        5    male      1987.0     2020-01-30    2020-03-02  released\n",
       "5        6    male      1964.0     2020-01-30    2020-02-19  released\n",
       "6        7    male      1991.0     2020-01-30    2020-02-15  released\n",
       "7        8  female      1957.0     2020-01-31    2020-02-12  released\n",
       "8        9  female      1992.0     2020-01-31    2020-02-24  released\n",
       "9       10  female      1966.0     2020-01-31    2020-02-19  released\n",
       "10      11    male      1995.0     2020-01-31    2020-02-10  released\n",
       "11      12    male      1971.0     2020-02-01    2020-02-18  released\n",
       "12      13    male      1992.0     2020-02-02    2020-02-24  released\n",
       "13      14  female      1980.0     2020-02-02    2020-02-18  released\n",
       "14      15    male      1977.0     2020-02-02    2020-02-24  released\n",
       "15      16  female      1977.0     2020-02-04    2020-02-19  released\n",
       "16      17    male      1982.0     2020-02-05    2020-02-12  released\n",
       "17      18  female      1999.0     2020-02-05    2020-02-19  released\n",
       "18      19    male      1983.0     2020-02-05    2020-02-21  released\n",
       "19      20  female      1978.0     2020-02-05    2020-02-24  released\n",
       "20      21  female      1960.0     2020-02-05    2020-02-29  released\n",
       "21      22    male      1973.0     2020-02-06    2020-02-15  released\n",
       "22      23  female      1962.0     2020-02-06    2020-02-29  released\n",
       "23      24    male      1992.0     2020-02-06    2020-02-27  released\n",
       "27      28  female      1989.0     2020-02-10    2020-02-17  released\n",
       "31      32  female      2009.0     2020-02-18    2020-03-04  released\n",
       "36      37    male      1973.0     2020-02-18    2020-02-26  released\n",
       "38      39  female      1959.0     2020-02-18    2020-03-03  released\n",
       "49      50    male      1944.0     2020-02-19    2020-03-02  released\n",
       "50      51  female      1959.0     2020-02-19    2020-02-26  released\n",
       "62      63  female      1968.0     2020-02-20    2020-02-28  released\n",
       "64      65  female      1970.0     2020-02-19    2020-02-27  released\n",
       "82      83    male      1944.0     2020-02-20    2020-03-01  released\n",
       "588    589  female      1985.0     2020-02-23    2020-02-27  released\n",
       "1012  1013    male      1981.0     2020-02-26    2020-02-27  released\n",
       "1128  1129    male      1963.0     2020-02-26    2020-03-03  released\n",
       "1254  1255  female      1985.0     2020-02-26    2020-03-04  released\n",
       "1255  1256  female      1980.0     2020-02-26    2020-03-02  released"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df_patient[[\"id\",\"sex\",\"birth_year\",\"confirmed_date\",\"released_date\",\"deceased_date\",\"state\"]]\n",
    "released_base_data = data.dropna(subset=['released_date'])\n",
    "released_base_data = released_base_data.drop(['deceased_date'], axis=1)\n",
    "released_base_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 38 entries, 0 to 1255\n",
      "Data columns (total 6 columns):\n",
      "id                38 non-null int64\n",
      "sex               38 non-null object\n",
      "birth_year        38 non-null float64\n",
      "confirmed_date    38 non-null object\n",
      "released_date     38 non-null object\n",
      "state             38 non-null object\n",
      "dtypes: float64(1), int64(1), object(4)\n",
      "memory usage: 2.1+ KB\n"
     ]
    }
   ],
   "source": [
    "released_base_data.info() # 03-04 data / not updated on 03-05 / 47 peoples data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>sex</th>\n",
       "      <th>confirmed_date</th>\n",
       "      <th>released_date</th>\n",
       "      <th>state</th>\n",
       "      <th>recovered_period</th>\n",
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       "      <th>age_class</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-01-20</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>released</td>\n",
       "      <td>17</td>\n",
       "      <td>36.0</td>\n",
       "      <td>[30, 40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-01-24</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>released</td>\n",
       "      <td>12</td>\n",
       "      <td>56.0</td>\n",
       "      <td>[50, 60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-01-26</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>released</td>\n",
       "      <td>17</td>\n",
       "      <td>54.0</td>\n",
       "      <td>[50, 60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-01-27</td>\n",
       "      <td>2020-02-09</td>\n",
       "      <td>released</td>\n",
       "      <td>13</td>\n",
       "      <td>56.0</td>\n",
       "      <td>[50, 60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>released</td>\n",
       "      <td>32</td>\n",
       "      <td>33.0</td>\n",
       "      <td>[30, 40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>released</td>\n",
       "      <td>20</td>\n",
       "      <td>56.0</td>\n",
       "      <td>[50, 60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>released</td>\n",
       "      <td>16</td>\n",
       "      <td>29.0</td>\n",
       "      <td>[20, 30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>released</td>\n",
       "      <td>12</td>\n",
       "      <td>63.0</td>\n",
       "      <td>[60, 70)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>released</td>\n",
       "      <td>24</td>\n",
       "      <td>28.0</td>\n",
       "      <td>[20, 30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>released</td>\n",
       "      <td>19</td>\n",
       "      <td>54.0</td>\n",
       "      <td>[50, 60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>released</td>\n",
       "      <td>10</td>\n",
       "      <td>25.0</td>\n",
       "      <td>[20, 30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>released</td>\n",
       "      <td>17</td>\n",
       "      <td>49.0</td>\n",
       "      <td>[40, 50)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>released</td>\n",
       "      <td>22</td>\n",
       "      <td>28.0</td>\n",
       "      <td>[20, 30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>released</td>\n",
       "      <td>16</td>\n",
       "      <td>40.0</td>\n",
       "      <td>[40, 50)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-02-02</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>released</td>\n",
       "      <td>22</td>\n",
       "      <td>43.0</td>\n",
       "      <td>[40, 50)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-04</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>released</td>\n",
       "      <td>15</td>\n",
       "      <td>43.0</td>\n",
       "      <td>[40, 50)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>2020-02-12</td>\n",
       "      <td>released</td>\n",
       "      <td>7</td>\n",
       "      <td>38.0</td>\n",
       "      <td>[30, 40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>released</td>\n",
       "      <td>14</td>\n",
       "      <td>21.0</td>\n",
       "      <td>[20, 30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>2020-02-21</td>\n",
       "      <td>released</td>\n",
       "      <td>16</td>\n",
       "      <td>37.0</td>\n",
       "      <td>[30, 40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>released</td>\n",
       "      <td>19</td>\n",
       "      <td>42.0</td>\n",
       "      <td>[40, 50)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>released</td>\n",
       "      <td>24</td>\n",
       "      <td>60.0</td>\n",
       "      <td>[60, 70)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>released</td>\n",
       "      <td>9</td>\n",
       "      <td>47.0</td>\n",
       "      <td>[40, 50)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>2020-02-29</td>\n",
       "      <td>released</td>\n",
       "      <td>23</td>\n",
       "      <td>58.0</td>\n",
       "      <td>[50, 60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-02-06</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>released</td>\n",
       "      <td>21</td>\n",
       "      <td>28.0</td>\n",
       "      <td>[20, 30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-10</td>\n",
       "      <td>2020-02-17</td>\n",
       "      <td>released</td>\n",
       "      <td>7</td>\n",
       "      <td>31.0</td>\n",
       "      <td>[30, 40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>released</td>\n",
       "      <td>15</td>\n",
       "      <td>11.0</td>\n",
       "      <td>[10, 20)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>released</td>\n",
       "      <td>8</td>\n",
       "      <td>47.0</td>\n",
       "      <td>[40, 50)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-18</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>released</td>\n",
       "      <td>14</td>\n",
       "      <td>61.0</td>\n",
       "      <td>[60, 70)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>released</td>\n",
       "      <td>12</td>\n",
       "      <td>76.0</td>\n",
       "      <td>[70, 80)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>51</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>released</td>\n",
       "      <td>7</td>\n",
       "      <td>61.0</td>\n",
       "      <td>[60, 70)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>63</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>2020-02-28</td>\n",
       "      <td>released</td>\n",
       "      <td>8</td>\n",
       "      <td>52.0</td>\n",
       "      <td>[50, 60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>released</td>\n",
       "      <td>8</td>\n",
       "      <td>50.0</td>\n",
       "      <td>[50, 60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>83</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-02-20</td>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>released</td>\n",
       "      <td>10</td>\n",
       "      <td>76.0</td>\n",
       "      <td>[70, 80)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>588</th>\n",
       "      <td>589</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-23</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>released</td>\n",
       "      <td>4</td>\n",
       "      <td>35.0</td>\n",
       "      <td>[30, 40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1012</th>\n",
       "      <td>1013</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>2020-02-27</td>\n",
       "      <td>released</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>[30, 40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1128</th>\n",
       "      <td>1129</td>\n",
       "      <td>male</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>2020-03-03</td>\n",
       "      <td>released</td>\n",
       "      <td>6</td>\n",
       "      <td>57.0</td>\n",
       "      <td>[50, 60)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1254</th>\n",
       "      <td>1255</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>2020-03-04</td>\n",
       "      <td>released</td>\n",
       "      <td>7</td>\n",
       "      <td>35.0</td>\n",
       "      <td>[30, 40)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255</th>\n",
       "      <td>1256</td>\n",
       "      <td>female</td>\n",
       "      <td>2020-02-26</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>released</td>\n",
       "      <td>5</td>\n",
       "      <td>40.0</td>\n",
       "      <td>[40, 50)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id     sex confirmed_date released_date     state  recovered_period  \\\n",
       "0        1  female     2020-01-20    2020-02-06  released                17   \n",
       "1        2    male     2020-01-24    2020-02-05  released                12   \n",
       "2        3    male     2020-01-26    2020-02-12  released                17   \n",
       "3        4    male     2020-01-27    2020-02-09  released                13   \n",
       "4        5    male     2020-01-30    2020-03-02  released                32   \n",
       "5        6    male     2020-01-30    2020-02-19  released                20   \n",
       "6        7    male     2020-01-30    2020-02-15  released                16   \n",
       "7        8  female     2020-01-31    2020-02-12  released                12   \n",
       "8        9  female     2020-01-31    2020-02-24  released                24   \n",
       "9       10  female     2020-01-31    2020-02-19  released                19   \n",
       "10      11    male     2020-01-31    2020-02-10  released                10   \n",
       "11      12    male     2020-02-01    2020-02-18  released                17   \n",
       "12      13    male     2020-02-02    2020-02-24  released                22   \n",
       "13      14  female     2020-02-02    2020-02-18  released                16   \n",
       "14      15    male     2020-02-02    2020-02-24  released                22   \n",
       "15      16  female     2020-02-04    2020-02-19  released                15   \n",
       "16      17    male     2020-02-05    2020-02-12  released                 7   \n",
       "17      18  female     2020-02-05    2020-02-19  released                14   \n",
       "18      19    male     2020-02-05    2020-02-21  released                16   \n",
       "19      20  female     2020-02-05    2020-02-24  released                19   \n",
       "20      21  female     2020-02-05    2020-02-29  released                24   \n",
       "21      22    male     2020-02-06    2020-02-15  released                 9   \n",
       "22      23  female     2020-02-06    2020-02-29  released                23   \n",
       "23      24    male     2020-02-06    2020-02-27  released                21   \n",
       "27      28  female     2020-02-10    2020-02-17  released                 7   \n",
       "31      32  female     2020-02-18    2020-03-04  released                15   \n",
       "36      37    male     2020-02-18    2020-02-26  released                 8   \n",
       "38      39  female     2020-02-18    2020-03-03  released                14   \n",
       "49      50    male     2020-02-19    2020-03-02  released                12   \n",
       "50      51  female     2020-02-19    2020-02-26  released                 7   \n",
       "62      63  female     2020-02-20    2020-02-28  released                 8   \n",
       "64      65  female     2020-02-19    2020-02-27  released                 8   \n",
       "82      83    male     2020-02-20    2020-03-01  released                10   \n",
       "588    589  female     2020-02-23    2020-02-27  released                 4   \n",
       "1012  1013    male     2020-02-26    2020-02-27  released                 1   \n",
       "1128  1129    male     2020-02-26    2020-03-03  released                 6   \n",
       "1254  1255  female     2020-02-26    2020-03-04  released                 7   \n",
       "1255  1256  female     2020-02-26    2020-03-02  released                 5   \n",
       "\n",
       "       age age_class  \n",
       "0     36.0  [30, 40)  \n",
       "1     56.0  [50, 60)  \n",
       "2     54.0  [50, 60)  \n",
       "3     56.0  [50, 60)  \n",
       "4     33.0  [30, 40)  \n",
       "5     56.0  [50, 60)  \n",
       "6     29.0  [20, 30)  \n",
       "7     63.0  [60, 70)  \n",
       "8     28.0  [20, 30)  \n",
       "9     54.0  [50, 60)  \n",
       "10    25.0  [20, 30)  \n",
       "11    49.0  [40, 50)  \n",
       "12    28.0  [20, 30)  \n",
       "13    40.0  [40, 50)  \n",
       "14    43.0  [40, 50)  \n",
       "15    43.0  [40, 50)  \n",
       "16    38.0  [30, 40)  \n",
       "17    21.0  [20, 30)  \n",
       "18    37.0  [30, 40)  \n",
       "19    42.0  [40, 50)  \n",
       "20    60.0  [60, 70)  \n",
       "21    47.0  [40, 50)  \n",
       "22    58.0  [50, 60)  \n",
       "23    28.0  [20, 30)  \n",
       "27    31.0  [30, 40)  \n",
       "31    11.0  [10, 20)  \n",
       "36    47.0  [40, 50)  \n",
       "38    61.0  [60, 70)  \n",
       "49    76.0  [70, 80)  \n",
       "50    61.0  [60, 70)  \n",
       "62    52.0  [50, 60)  \n",
       "64    50.0  [50, 60)  \n",
       "82    76.0  [70, 80)  \n",
       "588   35.0  [30, 40)  \n",
       "1012  39.0  [30, 40)  \n",
       "1128  57.0  [50, 60)  \n",
       "1254  35.0  [30, 40)  \n",
       "1255  40.0  [40, 50)  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "released_base_data.confirmed_date = pd.to_datetime(df_patient.confirmed_date)\n",
    "released_base_data.released_date = pd.to_datetime(df_patient.released_date)\n",
    "\n",
    "# \n",
    "released_base_data['recovered_period'] = released_base_data['released_date'].dt.date - released_base_data['confirmed_date'].dt.date\n",
    "released_base_data['recovered_period'] = released_base_data['recovered_period'].dt.days.astype(int)\n",
    "# Age\n",
    "released_base_data[\"age\"] = released_base_data[\"confirmed_date\"].dt.year - released_base_data[\"birth_year\"]\n",
    "released_base_data = released_base_data.drop(['birth_year'], axis=1)\n",
    "# Age_class\n",
    "released_base_data[\"age_class\"] = pd.cut(released_base_data[\"age\"], np.arange(0, 100, 10), include_lowest=True, right=False)\n",
    "released_base_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>recovered_period</th>\n",
       "      <td>38.0</td>\n",
       "      <td>13.921053</td>\n",
       "      <td>6.771888</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>18.5</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count       mean       std  min  25%   50%   75%   max\n",
       "recovered_period   38.0  13.921053  6.771888  1.0  8.0  14.0  18.5  32.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "boxplot = pd.DataFrame(released_base_data['recovered_period'].describe()).T\n",
    "boxplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'whiskers': [<matplotlib.lines.Line2D at 0x7fc16eba4be0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc16eba4f28>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x7fc16ebb12b0>,\n",
       "  <matplotlib.lines.Line2D at 0x7fc16ebb15f8>],\n",
       " 'boxes': [<matplotlib.lines.Line2D at 0x7fc16eba4a90>],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x7fc16ebb19e8>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x7fc16ebb1cf8>],\n",
       " 'means': []}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "red_square = dict(markerfacecolor='r', marker='s')\n",
    "fig, ax1 = plt.subplots(figsize=(8,2))\n",
    "ax1.set_title('Recovered time on Korea nCOV-19 patient')\n",
    "ax1.set_ylabel('Period')\n",
    "ax1.boxplot(boxplot,  vert=False, flierprops=red_square)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Classification by age_generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>state</th>\n",
       "      <th>age_class</th>\n",
       "      <th>released</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[10, 20)</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[20, 30)</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[30, 40)</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[40, 50)</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[50, 60)</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[60, 70)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[70, 80)</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "state age_class  released\n",
       "0      [10, 20)         1\n",
       "1      [20, 30)         6\n",
       "2      [30, 40)         8\n",
       "3      [40, 50)         8\n",
       "4      [50, 60)         9\n",
       "5      [60, 70)         4\n",
       "6      [70, 80)         2"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "released_period= released_base_data.pivot_table( index=\"state\", columns='age_class', values='recovered_period', aggfunc='count').fillna('0')\n",
    "released_period = released_period.T\n",
    "released_period.reset_index(inplace=True)\n",
    "released_period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "        \n",
       "        \n",
       "            <div id=\"ad77e29b-290c-4c49-8430-e856add4bb52\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
       "            <script type=\"text/javascript\">\n",
       "                require([\"plotly\"], function(Plotly) {\n",
       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    \n",
       "                if (document.getElementById(\"ad77e29b-290c-4c49-8430-e856add4bb52\")) {\n",
       "                    Plotly.newPlot(\n",
       "                        'ad77e29b-290c-4c49-8430-e856add4bb52',\n",
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\"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('ad77e29b-290c-4c49-8430-e856add4bb52');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "released_period.index=['10-19','20-29','30-39','40-49','50-59','60-69', '70-']\n",
    "\n",
    "import plotly.express as px\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Bar(name='All', x=released_period.index, y=released_period.released)])\n",
    "\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "# Classification by sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>age_class</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[10, 20)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[20, 30)</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[30, 40)</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[40, 50)</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[50, 60)</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[60, 70)</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[70, 80)</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "sex age_class female male\n",
       "0    [10, 20)      1    0\n",
       "1    [20, 30)      2    4\n",
       "2    [30, 40)      4    4\n",
       "3    [40, 50)      4    4\n",
       "4    [50, 60)      4    5\n",
       "5    [60, 70)      4    0\n",
       "6    [70, 80)      0    2"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "released_by_age= released_base_data.pivot_table( index=\"sex\", columns='age_class', values='released_date', aggfunc='count').fillna('0')\n",
    "released_by_age = released_by_age.T\n",
    "released_by_age.reset_index(inplace=True)\n",
    "released_by_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>sex</th>\n",
       "      <th>age_class</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[10, 20)</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[20, 30)</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[30, 40)</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[40, 50)</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[50, 60)</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[60, 70)</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[70, 80)</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "sex age_class female male\n",
       "0    [10, 20)      1    0\n",
       "1    [20, 30)      2    4\n",
       "2    [30, 40)      4    4\n",
       "3    [40, 50)      4    4\n",
       "4    [50, 60)      4    5\n",
       "5    [60, 70)      4    0\n",
       "6    [70, 80)      0    2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "released_by_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
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\"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('1676a415-9bf9-4c26-a943-def9e411ca99');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "released_by_age.index=['10-19','20-29','30-39','40-49','50-59','60-69', '70-']\n",
    "\n",
    "import plotly.express as px\n",
    "\n",
    "fig = go.Figure(data=[\n",
    "    go.Bar(name='Female', x=released_by_age.index, y=released_by_age.female),\n",
    "    go.Bar(name='Male', x=released_by_age.index, y=released_by_age.male)])\n",
    "\n",
    "fig.update_layout(barmode='group')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>sex</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>3617</td>\n",
       "      <td>62.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>2149</td>\n",
       "      <td>37.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         sex  rate\n",
       "female  3617  62.7\n",
       "male    2149  37.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Confirmed_sex = pd.DataFrame({'sex':[3617,2149], 'rate':[62.7,37.]})\n",
    "Confirmed_sex = Confirmed_sex.rename(index={0:\"female\", 1:\"male\"})\n",
    "Confirmed_sex"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.rcParams['figure.figsize']=12,8\n",
    "group_explodes = (0.05, 0) #explode 1st slice\n",
    "plt.pie(Confirmed_sex['sex'], explode=group_explodes,labels=[\"female\",\"male\"], colors=['lightcoral', 'lightskyblue'],autopct='%1.2f%%', shadow=True, startangle=90, textprops={'fontsize':14})\n",
    "plt.axis('equal')\n",
    "plt.title('Confirmed individual by sex')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>Confirmed</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0-9</th>\n",
       "      <td>38</td>\n",
       "      <td>0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10-19</th>\n",
       "      <td>257</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20-29</th>\n",
       "      <td>1727</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30-39</th>\n",
       "      <td>659</td>\n",
       "      <td>14.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40-49</th>\n",
       "      <td>847</td>\n",
       "      <td>19.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50-59</th>\n",
       "      <td>1127</td>\n",
       "      <td>19.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60-69</th>\n",
       "      <td>699</td>\n",
       "      <td>12.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70-79</th>\n",
       "      <td>288</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80-</th>\n",
       "      <td>124</td>\n",
       "      <td>2.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Confirmed  rate\n",
       "0-9           38   0.7\n",
       "10-19        257   4.5\n",
       "20-29       1727  30.0\n",
       "30-39        659  14.7\n",
       "40-49        847  19.8\n",
       "50-59       1127  19.5\n",
       "60-69        699  12.1\n",
       "70-79        288   5.0\n",
       "80-          124   2.2"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Confirmed_age = pd.DataFrame({'Confirmed':[38,257,1727,659,847,1127,699,288,124], 'rate':[0.7, 4.5, 30.0, 14.7, 19.8, 19.5, 12.1, 5.0, 2.2]})\n",
    "Confirmed_age = Confirmed_age.rename(index={0:\"0-9\", 1:\"10-19\", 2:\"20-29\", 3:\"30-39\", 4:\"40-49\", 5:\"50-59\", 6:\"60-69\", 7:\"70-79\", 8:\"80-\"})\n",
    "Confirmed_age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=12,5\n",
    "color = 'tab:red'\n",
    "ax= Confirmed_age['Confirmed'].plot( title = \" nCOV19 - Confirmed individule by Age\", rot=0,color=color)\n",
    "ax.set_ylabel('Confirmed_count',color=color)\n",
    "ax.tick_params(axis='y',color=color)\n",
    "ax.legend()\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "color = 'tab:blue'\n",
    "ax2 = Confirmed_age['rate'].plot(kind='bar', color=color, alpha=0.1)\n",
    "ax2.set_ylabel(' %')\n",
    "\n",
    "for p in ax2.patches: \n",
    "  left1, bottom1, width1, height1 = p.get_bbox().bounds \n",
    "  ax2.annotate(\"%.f%%\"%(height1), xy=(left1+width1/2, bottom1+height1/2), ha='center', va='center')\n",
    "\n",
    "plt.sca(ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5766 entries, 0 to 5765\n",
      "Data columns (total 14 columns):\n",
      "id                  5766 non-null int64\n",
      "sex                 421 non-null object\n",
      "birth_year          408 non-null float64\n",
      "country             5765 non-null object\n",
      "region              409 non-null object\n",
      "group               83 non-null object\n",
      "infection_reason    146 non-null object\n",
      "infection_order     35 non-null float64\n",
      "infected_by         70 non-null float64\n",
      "contact_number      50 non-null float64\n",
      "confirmed_date      5765 non-null object\n",
      "released_date       38 non-null object\n",
      "deceased_date       23 non-null object\n",
      "state               5765 non-null object\n",
      "dtypes: float64(4), int64(1), object(9)\n",
      "memory usage: 630.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_patient.info() # infection_reason    146 non-null object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>infection_reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>contact with patient</th>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visit to Daegu</th>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visit to Wuhan</th>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pilgrimage to Israel</th>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visit to Thailand</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact with patient in Singapore</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residence in Wuhan</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visit to Shincheonji Church</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visit to Cheongdo Daenam Hospital</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visit to Vietnam</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visit to Italy</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visit to Japan</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visit to China</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>contact with patient in Japan</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visit to China</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   infection_reason\n",
       "contact with patient                             75\n",
       "visit to Daegu                                   43\n",
       "visit to Wuhan                                    8\n",
       "pilgrimage to Israel                              6\n",
       "visit to Thailand                                 2\n",
       "contact with patient in Singapore                 2\n",
       "residence in Wuhan                                2\n",
       "visit to Shincheonji Church                       1\n",
       "visit to Cheongdo Daenam Hospital                 1\n",
       "visit to Vietnam                                  1\n",
       "visit to Italy                                    1\n",
       "visit to Japan                                    1\n",
       " visit to China                                   1\n",
       "contact with patient in Japan                     1\n",
       "visit to China                                    1"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(df_patient[\"infection_reason\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>infection_reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>contact with patient</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visit to other Country</th>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visit to China</th>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>residence in Wuhan</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        infection_reason\n",
       "contact with patient                 120\n",
       "visit to other Country                14\n",
       "visit to China                        10\n",
       "residence in Wuhan                     2"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reason_dict = {   \n",
    "    \"contact with the patient\": \"contact with patient\", \n",
    "    \"visit to Shincheonji Church\" : \"contact with patient\", \n",
    "    \"contact with patient in Japan\":  \"visit to other Country\",\n",
    "    \"contact with patient in Singapore\":  \"visit to other Country\",\n",
    "    \"visit to Daegu\" : \"contact with patient\",  \n",
    "    \"visit to Shincheonji Church\" : \"contact with patient\",\n",
    "\n",
    "    \"visit to Wuhan\" : \"visit to China\",\n",
    "    \" visit to China\" :  \"visit to China\",\n",
    "    \"visit to China\": \"visit to China\",\n",
    "    \"residence in Wuhan\" : \"residence in Wuhan\",\n",
    "\n",
    "    \"visit to Thailand\":  \"visit to other Country\",\n",
    "    \"visit to Japan\": \"visit to other Country\",\n",
    "    \"visit to Italy\":  \"visit to other Country\",\n",
    "    \"visit to Vietnam\":  \"visit to other Country\",\n",
    "\n",
    "    \"contact with patient in Japan\":  \"visit to other Country\",\n",
    "    \"visit to Cheongdo Daenam Hospital\" :\"contact with patient\",\n",
    "    \"pilgrimage to Israel\":  \"visit to other Country\"\n",
    "\n",
    "}\n",
    "infection_reason =pd.DataFrame(df_patient[\"infection_reason\"].replace(reason_dict).value_counts())\n",
    "infection_reason\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>infection_reason</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>contact with patient</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>visit to other Country</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>visit to China</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>residence in Wuhan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    index  infection_reason\n",
       "0    contact with patient               120\n",
       "1  visit to other Country                14\n",
       "2          visit to China                10\n",
       "3      residence in Wuhan                 2"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "infection_reason.reset_index(inplace=True)\n",
    "infection_reason"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# select data by region\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>region</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>capital area</td>\n",
       "      <td>191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Gyeongsangbuk-do</td>\n",
       "      <td>122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Daegu</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Daejeon</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Gwangju</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Gangwon-do</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>filtered at airport</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Jeollabuk-do</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Jeollanam-do</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Jeju-do</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Ulsan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Busan</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Chungcheongbuk-do</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Chungcheongnam-do</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  index  region\n",
       "0          capital area     191\n",
       "1      Gyeongsangbuk-do     122\n",
       "2                 Daegu      48\n",
       "3               Daejeon      13\n",
       "4               Gwangju      11\n",
       "5            Gangwon-do       5\n",
       "6   filtered at airport       4\n",
       "7          Jeollabuk-do       3\n",
       "8          Jeollanam-do       3\n",
       "9               Jeju-do       2\n",
       "10                Ulsan       2\n",
       "11                Busan       2\n",
       "12    Chungcheongbuk-do       2\n",
       "13    Chungcheongnam-do       1"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region = pd.DataFrame(df_patient[\"region\"].value_counts())\n",
    "region.reset_index(inplace=True)\n",
    "region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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       "<div>\n",
       "        \n",
       "        \n",
       "            <div id=\"77e80312-bcdb-470d-b791-68f1b3dc2d50\" class=\"plotly-graph-div\" style=\"height:1000px; width:100%;\"></div>\n",
       "            <script type=\"text/javascript\">\n",
       "                require([\"plotly\"], function(Plotly) {\n",
       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
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{\"angularaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0]}, \"xaxis2\": {\"anchor\": \"y2\", \"domain\": [0.0, 1.0]}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.575, 1.0]}, \"yaxis2\": {\"anchor\": \"x2\", \"domain\": [0.0, 0.425]}},\n",
       "                        {\"responsive\": true}\n",
       "                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('77e80312-bcdb-470d-b791-68f1b3dc2d50');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
       "// Listen for the clearing of the current output cell\n",
       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
       "}}\n",
       "\n",
       "                        })\n",
       "                };\n",
       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "infection_reason.index=['contact with patient','visit to other Country','visit to China','residence in Wuhan']\n",
    "region.index = ['capital area','Gyeongsangbuk-do','Daegu','Daejeon','Gwangju','Gangwon-do','filtered at airport','Jeollabuk-do','Jeollanam-do', 'Busan','Jeju-do','Ulsan','Chungcheongbuk-do','Chungcheongnam-do']\n",
    "\n",
    "fig = make_subplots(rows=2, cols=1)\n",
    "\n",
    "fig.add_trace(go.Bar(x=infection_reason.index, y=infection_reason.infection_reason,\n",
    "                    marker=dict(color=infection_reason.infection_reason, coloraxis=\"coloraxis\")),  1, 1)\n",
    "\n",
    "\n",
    "fig.add_trace(go.Bar(x=region.index, y=region.region,\n",
    "                    marker=dict(color=region.region, coloraxis=\"coloraxis\")),  2, 1)\n",
    "\n",
    "fig.update_layout(coloraxis=dict(colorscale='Bluered_r'), showlegend=False)\n",
    "fig.update_layout(height=1000, showlegend=False)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>patient</th>\n",
       "      <th>rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Deagu</th>\n",
       "      <td>4327</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gyeongbuk</th>\n",
       "      <td>861</td>\n",
       "      <td>14.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gyeonggi</th>\n",
       "      <td>110</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seoul</th>\n",
       "      <td>103</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Busan</th>\n",
       "      <td>92</td>\n",
       "      <td>1.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Chungnam</th>\n",
       "      <td>86</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gyeongnam</th>\n",
       "      <td>74</td>\n",
       "      <td>1.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           patient  rate\n",
       "Deagu         4327  75.0\n",
       "Gyeongbuk      861  14.9\n",
       "Gyeonggi       110   1.9\n",
       "Seoul          103   1.8\n",
       "Busan           92   1.6\n",
       "Chungnam        86   1.5\n",
       "Gyeongnam       74   1.3"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "region_by3_5 = pd.DataFrame({'patient':[4327,861,110,103,92,86,74], 'rate':[75.0,14.9,1.9,1.8,1.6,1.5,1.3]})\n",
    "region_by3_5 = region_by3_5.rename(index={0:\"Deagu\", 1:\"Gyeongbuk\", 2:\"Gyeonggi\", 3:\"Seoul\", 4:\"Busan\", 5:\"Chungnam\", 6:\"Gyeongnam\"})\n",
    "region_by3_5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=15,4\n",
    "color = 'tab:red'\n",
    "ax= region_by3_5['patient'].plot( title = \" nCOV19 - Confirmed individule by region\", rot=0,color=color)\n",
    "ax.set_ylabel('Confirmed_count',color=color)\n",
    "ax.tick_params(axis='y',color=color)\n",
    "ax.legend()\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "color = 'tab:blue'\n",
    "ax2 = region_by3_5['rate'].plot(kind='bar', color=color, alpha=0.1)\n",
    "ax2.set_ylabel(' %')\n",
    "\n",
    "for p in ax2.patches: \n",
    "  left1, bottom1, width1, height1 = p.get_bbox().bounds \n",
    "  ax2.annotate(\"%.f%%\"%(height1), xy=(left1+width1/2, bottom1+height1/2), ha='center', va='center')\n",
    "\n",
    "plt.sca(ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# display pie chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "        \n",
       "        \n",
       "            <div id=\"e24e07b1-2a52-41e7-81a0-c00d892a5313\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
       "            <script type=\"text/javascript\">\n",
       "                require([\"plotly\"], function(Plotly) {\n",
       "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
       "                    \n",
       "                if (document.getElementById(\"e24e07b1-2a52-41e7-81a0-c00d892a5313\")) {\n",
       "                    Plotly.newPlot(\n",
       "                        'e24e07b1-2a52-41e7-81a0-c00d892a5313',\n",
       "                        [{\"domain\": {\"x\": [0.0, 1.0], \"y\": [0.0, 1.0]}, \"hoverlabel\": {\"namelength\": 0}, \"hovertemplate\": \"index=%{label}<br>rate=%{value}\", \"labels\": [\"Deagu\", \"Gyeongbuk\", \"Gyeonggi\", \"Seoul\", \"Busan\", \"Chungnam\", \"Gyeongnam\"], \"legendgroup\": \"\", \"name\": \"\", \"showlegend\": true, \"type\": \"pie\", \"values\": [75.0, 14.9, 1.9, 1.8, 1.6, 1.5, 1.3]}],\n",
       "                        {\"legend\": {\"tracegroupgap\": 0}, \"margin\": {\"t\": 60}, \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, 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       "            Plotly.purge(gd);\n",
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       "\n",
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       "\n",
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       "                });\n",
       "            </script>\n",
       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "region_by3_5.reset_index(inplace=True)\n",
    "region_by3_5.index=[\"Deagu\",\"Gyeongbuk\",\"Gyeonggi\",\"Seoul\",\"Busan\",\"Chungnam\",\"Gyeongnam\"]\n",
    "fig = px.pie(region_by3_5, values='rate', names='index')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# classification by sex\n",
    "\n",
    "id                  5328 non-null int64\n",
    "\n",
    "sex                 383 non-null object < 3%.. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
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       "\n",
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       "        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sex = pd.DataFrame(df_patient[\"sex\"].value_counts())\n",
    "sex.reset_index(inplace=True)\n",
    "sex.index=['female','male']\n",
    "import plotly.express as px\n",
    "fig = px.pie(sex, values='sex', names='index')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# It doesn't matched on real data\n",
    "bottom Pie graph is based on 03-05 data by CDC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.rcParams['figure.figsize']=12,8\n",
    "group_explodes = (0.05, 0) #explode 1st slice\n",
    "plt.pie(Confirmed_sex['sex'], explode=group_explodes,labels=[\"female\",\"male\"], colors=['lightcoral', 'lightskyblue'],autopct='%1.2f%%', shadow=True, startangle=90, textprops={'fontsize':14})\n",
    "plt.axis('equal')\n",
    "plt.title('Confirmed individual by sex')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>isolated</th>\n",
       "      <td>5702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>released</th>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>deceased</th>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          state\n",
       "isolated   5702\n",
       "released     40\n",
       "deceased     23"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state = pd.DataFrame(df_patient[\"state\"].value_counts())\n",
    "state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "state.reset_index(inplace=True)\n",
    "state.index=['isolated','released','deceased']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import plotly.express as px\n",
    "fig = px.pie(state, values='state', names='index')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# from 2019 Novel Coronavirus COVID-19 (2019-nCoV) Data Repository by Johns Hopkins CSSE\n",
    "\n",
    "https://github.com/CSSEGISandData/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "ename": "HTTPError",
     "evalue": "HTTP Error 404: Not Found",
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      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, escapechar, comment, encoding, dialect, tupleize_cols, error_bad_lines, warn_bad_lines, skipfooter, doublequote, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    676\u001b[0m                     skip_blank_lines=skip_blank_lines)\n\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 678\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    679\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    680\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    422\u001b[0m     \u001b[0mcompression\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_infer_compression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    423\u001b[0m     filepath_or_buffer, _, compression, should_close = get_filepath_or_buffer(\n\u001b[1;32m--> 424\u001b[1;33m         filepath_or_buffer, encoding, compression)\n\u001b[0m\u001b[0;32m    425\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'compression'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompression\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    426\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\tensorflow\\lib\\site-packages\\pandas\\io\\common.py\u001b[0m in \u001b[0;36mget_filepath_or_buffer\u001b[1;34m(filepath_or_buffer, encoding, compression, mode)\u001b[0m\n\u001b[0;32m    193\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    194\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0m_is_url\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 195\u001b[1;33m         \u001b[0mreq\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_urlopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    196\u001b[0m         \u001b[0mcontent_encoding\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheaders\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'Content-Encoding'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    197\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mcontent_encoding\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m'gzip'\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\tensorflow\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36murlopen\u001b[1;34m(url, data, timeout, cafile, capath, cadefault, context)\u001b[0m\n\u001b[0;32m    221\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    222\u001b[0m         \u001b[0mopener\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_opener\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 223\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mopener\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0murl\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    224\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    225\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0minstall_opener\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopener\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\tensorflow\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mopen\u001b[1;34m(self, fullurl, data, timeout)\u001b[0m\n\u001b[0;32m    530\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mprocessor\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mprocess_response\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    531\u001b[0m             \u001b[0mmeth\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mprocessor\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 532\u001b[1;33m             \u001b[0mresponse\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmeth\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    533\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    534\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\tensorflow\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttp_response\u001b[1;34m(self, request, response)\u001b[0m\n\u001b[0;32m    640\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m200\u001b[0m \u001b[1;33m<=\u001b[0m \u001b[0mcode\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;36m300\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    641\u001b[0m             response = self.parent.error(\n\u001b[1;32m--> 642\u001b[1;33m                 'http', request, response, code, msg, hdrs)\n\u001b[0m\u001b[0;32m    643\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    644\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\tensorflow\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36merror\u001b[1;34m(self, proto, *args)\u001b[0m\n\u001b[0;32m    568\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mhttp_err\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    569\u001b[0m             \u001b[0margs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'default'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'http_error_default'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0morig_args\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 570\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_chain\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    571\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    572\u001b[0m \u001b[1;31m# XXX probably also want an abstract factory that knows when it makes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\tensorflow\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36m_call_chain\u001b[1;34m(self, chain, kind, meth_name, *args)\u001b[0m\n\u001b[0;32m    502\u001b[0m         \u001b[1;32mfor\u001b[0m \u001b[0mhandler\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mhandlers\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    503\u001b[0m             \u001b[0mfunc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandler\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmeth_name\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 504\u001b[1;33m             \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    505\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mresult\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    506\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\envs\\tensorflow\\lib\\urllib\\request.py\u001b[0m in \u001b[0;36mhttp_error_default\u001b[1;34m(self, req, fp, code, msg, hdrs)\u001b[0m\n\u001b[0;32m    648\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mHTTPDefaultErrorHandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBaseHandler\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    649\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mhttp_error_default\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreq\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 650\u001b[1;33m         \u001b[1;32mraise\u001b[0m \u001b[0mHTTPError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mreq\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfull_url\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcode\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhdrs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfp\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    651\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    652\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mHTTPRedirectHandler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mBaseHandler\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mHTTPError\u001b[0m: HTTP Error 404: Not Found"
     ]
    }
   ],
   "source": [
    "patient = pd.read_csv('https://github.com/mossydidar/COVID-19/tree/master/csse_covid_19_data/csse_covid_19_time_series.csv')\n",
    "patient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = patient.keys()\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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