Covid-19 South Korean Story.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# They have it under control - South Korea story"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Singapore success"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"Singapore.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Korea - look on data properly"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two graphs to compare:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"Death_rate_South_Korea.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"Death_rate_South_Korea_0_1.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The reliable and interesting source of data:\n",
    "https://www.cdc.go.kr/board/board.es?mid=&bid=0030"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Korea - preparing the graph\n",
    "### The end result: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"South Korea.png\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://www.kaggle.com/sudalairajkumar/novel-corona-virus-2019-dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import the necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using pandas package we import data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "covid_19_data =  pd.read_csv('data/covid_19_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>ObservationDate</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Anhui</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Fujian</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>Gansu</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   SNo ObservationDate Province/State  Country/Region      Last Update  \\\n",
       "0    1      01/22/2020          Anhui  Mainland China  1/22/2020 17:00   \n",
       "1    2      01/22/2020        Beijing  Mainland China  1/22/2020 17:00   \n",
       "2    3      01/22/2020      Chongqing  Mainland China  1/22/2020 17:00   \n",
       "3    4      01/22/2020         Fujian  Mainland China  1/22/2020 17:00   \n",
       "4    5      01/22/2020          Gansu  Mainland China  1/22/2020 17:00   \n",
       "\n",
       "   Confirmed  Deaths  Recovered  \n",
       "0        1.0     0.0        0.0  \n",
       "1       14.0     0.0        0.0  \n",
       "2        6.0     0.0        0.0  \n",
       "3        1.0     0.0        0.0  \n",
       "4        0.0     0.0        0.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_19_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be interested only in South Korea data, so let's filter them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "south_korea_data = covid_19_data[covid_19_data['Country/Region']=='South Korea']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>SNo</th>\n",
       "      <th>ObservationDate</th>\n",
       "      <th>Province/State</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Last Update</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>1/22/2020 17:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76</td>\n",
       "      <td>01/23/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>1/23/20 17:00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>122</td>\n",
       "      <td>01/24/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>1/24/20 17:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>163</td>\n",
       "      <td>01/25/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>1/25/20 17:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>208</th>\n",
       "      <td>209</td>\n",
       "      <td>01/26/2020</td>\n",
       "      <td>NaN</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>1/26/20 16:00</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "     SNo ObservationDate Province/State Country/Region      Last Update  \\\n",
       "37    38      01/22/2020            NaN    South Korea  1/22/2020 17:00   \n",
       "75    76      01/23/2020            NaN    South Korea    1/23/20 17:00   \n",
       "121  122      01/24/2020            NaN    South Korea    1/24/20 17:00   \n",
       "162  163      01/25/2020            NaN    South Korea    1/25/20 17:00   \n",
       "208  209      01/26/2020            NaN    South Korea    1/26/20 16:00   \n",
       "\n",
       "     Confirmed  Deaths  Recovered  \n",
       "37         1.0     0.0        0.0  \n",
       "75         1.0     0.0        0.0  \n",
       "121        2.0     0.0        0.0  \n",
       "162        2.0     0.0        0.0  \n",
       "208        3.0     0.0        0.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "south_korea_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I already know, that we don't have data for the Province/State in South Korea, so we have only one record per day. But if we have data from different Provinces we would need to group the data before we will work with them further. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "south_korea_data_groupped = south_korea_data.groupby(['ObservationDate','Country/Region']).agg({'Confirmed':sum,\n",
    "                                                                'Deaths':sum,\n",
    "                                                                'Recovered':sum})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ObservationDate</th>\n",
       "      <th>Country/Region</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>01/22/2020</th>\n",
       "      <th>South Korea</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/23/2020</th>\n",
       "      <th>South Korea</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/24/2020</th>\n",
       "      <th>South Korea</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/25/2020</th>\n",
       "      <th>South Korea</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01/26/2020</th>\n",
       "      <th>South Korea</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "                                Confirmed  Deaths  Recovered\n",
       "ObservationDate Country/Region                              \n",
       "01/22/2020      South Korea           1.0     0.0        0.0\n",
       "01/23/2020      South Korea           1.0     0.0        0.0\n",
       "01/24/2020      South Korea           2.0     0.0        0.0\n",
       "01/25/2020      South Korea           2.0     0.0        0.0\n",
       "01/26/2020      South Korea           3.0     0.0        0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "south_korea_data_groupped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>ObservationDate</th>\n",
       "      <th>Country/Region</th>\n",
       "      <th>Confirmed</th>\n",
       "      <th>Deaths</th>\n",
       "      <th>Recovered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>01/22/2020</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>01/23/2020</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>01/24/2020</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>01/25/2020</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>01/26/2020</td>\n",
       "      <td>South Korea</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   index ObservationDate Country/Region  Confirmed  Deaths  Recovered\n",
       "0      0      01/22/2020    South Korea        1.0     0.0        0.0\n",
       "1      1      01/23/2020    South Korea        1.0     0.0        0.0\n",
       "2      2      01/24/2020    South Korea        2.0     0.0        0.0\n",
       "3      3      01/25/2020    South Korea        2.0     0.0        0.0\n",
       "4      4      01/26/2020    South Korea        3.0     0.0        0.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "south_korea_data_groupped = south_korea_data_groupped.reset_index()\n",
    "south_korea_data_groupped.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's try with simple graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax1.bar(south_korea_data_groupped['ObservationDate'], south_korea_data_groupped['Confirmed'])\n",
    "ax1.bar(south_korea_data_groupped['ObservationDate'], south_korea_data_groupped['Deaths'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that few things went wrong. First of all the x axis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "south_korea_data_groupped['ObservationDate'] = south_korea_data_groupped['ObservationDate'].apply(lambda row: datetime.datetime.strptime(row,\"%m/%d/%Y\").date())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.dates as mdates\n",
    "fig, ax1 = plt.subplots()\n",
    "formatter = mdates.DateFormatter(\"%Y-%m-%d\")\n",
    "ax1.xaxis.set_major_formatter(formatter)   \n",
    "dates_list = list(south_korea_data_groupped['ObservationDate'].unique())\n",
    "ax1.bar(dates_list, south_korea_data_groupped['Confirmed'])\n",
    "ax1.bar(dates_list, south_korea_data_groupped['Deaths'])\n",
    "ax1.tick_params(axis='y')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's add the title and X-axis and Y-axis names and legend. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax1.set(xlabel='Dates', ylabel='Number of cases', title='South Korea')\n",
    "formatter = mdates.DateFormatter(\"%Y-%m-%d\")\n",
    "ax1.xaxis.set_major_formatter(formatter)   \n",
    "dates_list = list(south_korea_data_groupped['ObservationDate'].unique())\n",
    "ax1.bar(dates_list, south_korea_data_groupped['Confirmed'], label='Confirmed')\n",
    "ax1.bar(dates_list, south_korea_data_groupped['Deaths'], label='Deaths')\n",
    "ax1.tick_params(axis='y')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now add two lines, which are showing the proporcion of deaths to all confirmed cases, or deaths to death + recovered"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Death_rate_1 = $\\frac{deaths}{confirmed}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Death_rate_2 = $\\frac{deaths}{deaths + recovered}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To do it we need function $shift$ which let us to take values for the one day before. I use it together with the groupby, but as we have only one country we could use simple shift function. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "skdg = south_korea_data_groupped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "#skdg['Deaths_yesterday'] = skdg.groupby(['Country/Region'])['Deaths'].shift(1)\n",
    "skdg['Deaths_yesterday'] = skdg['Deaths'].shift(1)\n",
    "skdg['Deaths_per_day'] = skdg['Deaths']- skdg['Deaths_yesterday']\n",
    "#skdg['Confirmed_yesterday'] = skdg.groupby(['Country/Region'])['Confirmed'].shift(1)\n",
    "skdg['Confirmed_yesterday'] = skdg['Confirmed'].shift(1)\n",
    "skdg['Confirmed_per_day'] = skdg['Confirmed']- skdg['Confirmed_yesterday']\n",
    "skdg['Death_rate']=skdg['Deaths']/skdg['Confirmed']\n",
    "skdg['Death_rate_ended']=skdg['Deaths']/(skdg['Deaths']+skdg['Recovered'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax1.set(xlabel='Dates', ylabel='Number of cases', title='South Korea')\n",
    "formatter = mdates.DateFormatter(\"%Y-%m-%d\")\n",
    "ax1.xaxis.set_major_formatter(formatter)   \n",
    "dates_list = list(skdg['ObservationDate'].unique())\n",
    "ax1.bar(dates_list, skdg['Confirmed'], label='Confirmed')\n",
    "ax1.bar(dates_list, skdg['Deaths'], label='Deaths')\n",
    "ax1.plot(dates_list , skdg['Death_rate_ended'], label = 'Death rate closed cases')\n",
    "ax1.plot(dates_list , skdg['Death_rate'], label = 'Death rate')\n",
    "ax1.tick_params(axis='y')\n",
    "plt.xticks(rotation=45)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax1.set(xlabel='Dates', ylabel='Number of cases', title='South Korea')\n",
    "formatter = mdates.DateFormatter(\"%Y-%m-%d\")\n",
    "ax1.xaxis.set_major_formatter(formatter)\n",
    "plt.xticks(rotation=45)\n",
    "ax2 = ax1.twinx() \n",
    "dates_list = list(skdg['ObservationDate'].unique())\n",
    "b1 = ax1.bar(dates_list, skdg['Confirmed'], label='Confirmed')\n",
    "b2 = ax1.bar(dates_list, skdg['Deaths'], label='Deaths')\n",
    "l1 = ax2.plot(dates_list , skdg['Death_rate_ended'], label = 'Death rate closed cases')\n",
    "l2 = ax2.plot(dates_list , skdg['Death_rate'], label = 'Death rate')\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "lines, labels = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines + lines2, labels + labels2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that it used the same colors for the lines and bars. We can fix it by manually setting colors or by importing some smart pallete. \n",
    "[https://docs.bokeh.org/en/latest/docs/reference/palettes.html](https://docs.bokeh.org/en/latest/docs/reference/palettes.html) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from bokeh import palettes as bh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#000003', '#410967', '#932567', '#DC5039', '#FBA40A', '#FCFEA4']"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "colors_bars = bh.viridis(4)\n",
    "colors_lines = bh.inferno(4)\n",
    "colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots()\n",
    "ax1.set(xlabel='Dates', ylabel='Number of cases', title='South Korea')\n",
    "formatter = mdates.DateFormatter(\"%Y-%m-%d\")\n",
    "ax1.xaxis.set_major_formatter(formatter)\n",
    "plt.xticks(rotation=45)\n",
    "ax2 = ax1.twinx() \n",
    "dates_list = list(skdg['ObservationDate'].unique())\n",
    "b1 = ax1.bar(dates_list, skdg['Confirmed'], label='Confirmed', color = colors_bars[3])\n",
    "b2 = ax1.bar(dates_list, skdg['Deaths'], label='Deaths', color = colors_bars[0])\n",
    "l1 = ax2.plot(dates_list , skdg['Death_rate_ended'], label = 'Death rate closed cases', color = colors_lines[1])\n",
    "l2 = ax2.plot(dates_list , skdg['Death_rate'], label = 'Death rate', color = colors_lines[2])\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "lines, labels = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines + lines2, labels + labels2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://matplotlib.org/3.1.0/gallery/style_sheets/style_sheets_reference.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as mtick\n",
    "plt.style.use('bmh')\n",
    "fig, ax1 = plt.subplots(figsize=(10,5))\n",
    "ax1.set(xlabel='Dates', ylabel='Number of cases', title='South Korea')\n",
    "\n",
    "formatter = mdates.DateFormatter(\"%Y-%m-%d\")\n",
    "ax1.xaxis.set_major_formatter(formatter)\n",
    "plt.xticks(rotation=45)\n",
    "ax2 = ax1.twinx() \n",
    "ax2.yaxis.set_major_formatter(mtick.PercentFormatter(1))\n",
    "dates_list = list(skdg['ObservationDate'].unique())\n",
    "b1 = ax1.bar(dates_list, skdg['Confirmed'], label='Confirmed', color = colors_bars[3])\n",
    "b2 = ax1.bar(dates_list, skdg['Deaths'], label='Deaths', color = colors_bars[0])\n",
    "l1 = ax2.plot(dates_list , skdg['Death_rate_ended'], label = 'Death rate closed cases', color = colors_lines[1])\n",
    "l2 = ax2.plot(dates_list , skdg['Death_rate'], label = 'Death rate', color = colors_lines[2])\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "lines, labels = ax1.get_legend_handles_labels()\n",
    "lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "ax2.legend(lines + lines2, labels + labels2,loc=2)\n",
    "ax1.set_ylim(0,10000)\n",
    "ax2.set_ylim(0,0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as mtick\n",
    "plt.style.use('Solarize_Light2')\n",
    "fig, ax2 = plt.subplots(figsize=(10,5))\n",
    "ax1.set(xlabel='Dates', ylabel='Death rate', title='South Korea')\n",
    "plt.xticks(rotation=45)\n",
    "ax2.yaxis.set_major_formatter(mtick.PercentFormatter(1))\n",
    "dates_list = list(skdg['ObservationDate'].unique())\n",
    "ax2.plot(dates_list , skdg['Death_rate'], label = 'Death rate', color = colors_lines[2])\n",
    "ax2.legend()\n",
    "fig.savefig(\"Death_rate_South_Korea.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as mtick\n",
    "plt.style.use('Solarize_Light2')\n",
    "fig, ax2 = plt.subplots(figsize=(10,5))\n",
    "ax1.set(xlabel='Dates', ylabel='Death rate', title='South Korea')\n",
    "plt.xticks(rotation=45)\n",
    "ax2.yaxis.set_major_formatter(mtick.PercentFormatter(1))\n",
    "dates_list = list(skdg['ObservationDate'].unique())\n",
    "ax2.plot(dates_list , skdg['Death_rate'], label = 'Death rate', color = colors_lines[2])\n",
    "ax2.plot(dates_list , 1-skdg['Death_rate'], label = 'Survive rate', color = colors_lines[1])\n",
    "ax2.legend()\n",
    "fig.savefig(\"Death_rate_South_Korea_0_1.png\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}