COVID19-daily-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "데이터 분석시점: 06-04 00:00 현재\n"
     ]
    }
   ],
   "source": [
    "import datetime\n",
    "today = datetime.date.today()\n",
    "datestr = today.strftime('%m-%d 00:00 현재')\n",
    "print(f'데이터 분석시점: {datestr}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 코로나바이러스감염증-19 감염현황 데이터 일일 분석\n",
    "\n",
    "질병관리본부에서 매일 발표하고 있는 일일 집계 현황을 다음의 구글 시트에 별도로 저장한 후, 이를 분석합니다.\n",
    "\n",
    "https://docs.google.com/spreadsheets/d/1nqc9A9M5QJgSnjkErWeyDS4KTiLB1N9VjHd3T6k69hE/\n",
    "\n",
    "활용 가능한 데이터가 추가되는대로 계속 보완합니다. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from io import BytesIO\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "import requests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib 차트 한글폰트 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rc('font', family='AppleGothic')\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "구글시트에 정리된 데이터를 가져옵니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "r = requests.get('https://docs.google.com/spreadsheet/ccc?key=1nqc9A9M5QJgSnjkErWeyDS4KTiLB1N9VjHd3T6k69hE&output=csv')\n",
    "df = pd.read_csv(BytesIO(r.content), index_col=0, skiprows=1).iloc[2:, :-4]\n",
    "df.columns = [c.replace('\\n', ' ') for c in df.columns]\n",
    "df.index = pd.to_datetime([f'2020-{i}' for i in df.index])\n",
    "\n",
    "for comma_str in ('대구', '경북', '확진자', '검사수', '격리  해제', '지역발생'):\n",
    "    df[comma_str] = df[comma_str].str.replace(',', '').astype(float)\n",
    "\n",
    "df = df.replace(np.nan, 0).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>서울</th>\n",
       "      <th>부산</th>\n",
       "      <th>대구</th>\n",
       "      <th>인천</th>\n",
       "      <th>광주</th>\n",
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       "      <th>...</th>\n",
       "      <th>제주</th>\n",
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       "      <th>확진자</th>\n",
       "      <th>사망</th>\n",
       "      <th>격리  해제</th>\n",
       "      <th>검사수</th>\n",
       "      <th>양성  비율(%)</th>\n",
       "      <th>해외유입</th>\n",
       "      <th>지역발생</th>\n",
       "    </tr>\n",
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       "      <th>2020-01-23</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>22.0</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "      <th>2020-01-24</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>25.0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>2.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>0.0</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1.75</td>\n",
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       "             서울   부산   대구   인천   광주   대전   울산   세종   경기   강원  ...   제주   검역  \\\n",
       "2020-01-23  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2020-01-24  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2020-01-25  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2020-01-26  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "2020-01-27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0   \n",
       "\n",
       "            지역  모름  확진자   사망  격리  해제   검사수  양성  비율(%)  해외유입  지역발생  \n",
       "2020-01-23     1.0  1.0  0.0     0.0  22.0       4.55   0.0   1.0  \n",
       "2020-01-24     1.0  1.0  0.0     0.0  25.0       4.00   0.0   1.0  \n",
       "2020-01-25     0.0  0.0  0.0     0.0   0.0       0.00   0.0   0.0  \n",
       "2020-01-26     1.0  1.0  0.0     0.0  48.0       2.08   0.0   1.0  \n",
       "2020-01-27     1.0  1.0  0.0     0.0  57.0       1.75   0.0   1.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>서울</th>\n",
       "      <th>부산</th>\n",
       "      <th>대구</th>\n",
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       "      <th>...</th>\n",
       "      <th>제주</th>\n",
       "      <th>검역</th>\n",
       "      <th>지역  모름</th>\n",
       "      <th>확진자</th>\n",
       "      <th>사망</th>\n",
       "      <th>격리  해제</th>\n",
       "      <th>검사수</th>\n",
       "      <th>양성  비율(%)</th>\n",
       "      <th>해외유입</th>\n",
       "      <th>지역발생</th>\n",
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       "      <th>2020-05-30</th>\n",
       "      <td>6.0</td>\n",
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       "      <td>2.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>27.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7921.0</td>\n",
       "      <td>0.34</td>\n",
       "      <td>12.0</td>\n",
       "      <td>15.0</td>\n",
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       "      <th>2020-05-31</th>\n",
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       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>35.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>10569.0</td>\n",
       "      <td>0.33</td>\n",
       "      <td>5.0</td>\n",
       "      <td>30.0</td>\n",
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       "      <th>2020-06-01</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>18460.0</td>\n",
       "      <td>0.21</td>\n",
       "      <td>2.0</td>\n",
       "      <td>36.0</td>\n",
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       "      <th>2020-06-02</th>\n",
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       "      <td>49.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>17001.0</td>\n",
       "      <td>0.29</td>\n",
       "      <td>3.0</td>\n",
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       "      <th>2020-06-03</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
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       "      <td>15.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>17006.0</td>\n",
       "      <td>0.23</td>\n",
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       "              서울   부산   대구    인천   광주   대전   울산   세종    경기   강원  ...   제주  \\\n",
       "2020-05-30   6.0  1.0  1.0   3.0  0.0  1.0  2.0  0.0  12.0  0.0  ...  1.0   \n",
       "2020-05-31   1.0  0.0  1.0  18.0  0.0  0.0  0.0  0.0  12.0  0.0  ...  0.0   \n",
       "2020-06-01  14.0  0.0  0.0   8.0  0.0  0.0  0.0  0.0  15.0  0.0  ...  0.0   \n",
       "2020-06-02  19.0  0.0  1.0  17.0  0.0  0.0  0.0  0.0  12.0  0.0  ...  0.0   \n",
       "2020-06-03  14.0  0.0  0.0   7.0  0.0  0.0  0.0  0.0  15.0  0.0  ...  0.0   \n",
       "\n",
       "             검역  지역  모름   확진자   사망  격리  해제      검사수  양성  비율(%)  해외유입  지역발생  \n",
       "2020-05-30  0.0     0.0  27.0  1.0     7.0   7921.0       0.34  12.0  15.0  \n",
       "2020-05-31  2.0     0.0  35.0  1.0    17.0  10569.0       0.33   5.0  30.0  \n",
       "2020-06-01  1.0     0.0  38.0  1.0    24.0  18460.0       0.21   2.0  36.0  \n",
       "2020-06-02  0.0     0.0  49.0  1.0    21.0  17001.0       0.29   3.0  46.0  \n",
       "2020-06-03  2.0     0.0  39.0  0.0    32.0  17006.0       0.23   6.0  33.0  \n",
       "\n",
       "[5 rows x 26 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions = [\n",
    "    '서울', '부산', '대구', '인천', '광주', '대전', '울산', '세종', '경기', '강원', \n",
    "    '충북', '충남', '전북', '전남', '경북', '경남', '제주', '검역',\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "누적값을 별도 컬럼에 추가합니다."
   ]
  },
  {
   "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>서울</th>\n",
       "      <th>부산</th>\n",
       "      <th>대구</th>\n",
       "      <th>인천</th>\n",
       "      <th>광주</th>\n",
       "      <th>대전</th>\n",
       "      <th>울산</th>\n",
       "      <th>세종</th>\n",
       "      <th>경기</th>\n",
       "      <th>강원</th>\n",
       "      <th>...</th>\n",
       "      <th>제주 누적</th>\n",
       "      <th>검역 누적</th>\n",
       "      <th>지역  모름 누적</th>\n",
       "      <th>확진자 누적</th>\n",
       "      <th>사망 누적</th>\n",
       "      <th>격리  해제 누적</th>\n",
       "      <th>검사수 누적</th>\n",
       "      <th>양성  비율(%) 누적</th>\n",
       "      <th>해외유입 누적</th>\n",
       "      <th>지역발생 누적</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-01-23</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>4.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-24</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-25</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>8.55</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-26</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>10.63</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>152.0</td>\n",
       "      <td>12.38</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 52 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             서울   부산   대구   인천   광주   대전   울산   세종   경기   강원  ...  제주 누적  \\\n",
       "2020-01-23  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0   \n",
       "2020-01-24  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0   \n",
       "2020-01-25  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0   \n",
       "2020-01-26  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0   \n",
       "2020-01-27  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...    0.0   \n",
       "\n",
       "            검역 누적  지역  모름 누적  확진자 누적  사망 누적  격리  해제 누적  검사수 누적  양성  비율(%) 누적  \\\n",
       "2020-01-23    0.0        1.0     1.0    0.0        0.0    22.0          4.55   \n",
       "2020-01-24    0.0        2.0     2.0    0.0        0.0    47.0          8.55   \n",
       "2020-01-25    0.0        2.0     2.0    0.0        0.0    47.0          8.55   \n",
       "2020-01-26    0.0        3.0     3.0    0.0        0.0    95.0         10.63   \n",
       "2020-01-27    0.0        4.0     4.0    0.0        0.0   152.0         12.38   \n",
       "\n",
       "            해외유입 누적  지역발생 누적  \n",
       "2020-01-23      0.0      1.0  \n",
       "2020-01-24      0.0      2.0  \n",
       "2020-01-25      0.0      2.0  \n",
       "2020-01-26      0.0      3.0  \n",
       "2020-01-27      0.0      4.0  \n",
       "\n",
       "[5 rows x 52 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for c in df.columns:\n",
    "    df[f'{c} 누적'] = df[c].cumsum()\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "누적 양성비율, 누적 사망율, 누적 대구경북 비율, 누적 완치율을 계산합니다. 확산 시작 직전으로 보이는 2월 18일 부터 표시합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['누적 양성비율'] = 100 * df['확진자 누적'] / df['검사수 누적']\n",
    "df['누적 사망율'] = 100 * df['사망 누적'] / df['확진자 누적']\n",
    "df['누적 대구경북 비율'] = 100 * (df['대구 누적'] + df['경북 누적']) / df['확진자 누적']\n",
    "df['누적 완치율'] = 100 * df['격리  해제 누적'] / df['확진자 누적']\n",
    "df['누적 수도권'] = df['서울 누적'] + df['경기 누적'] + df['인천 누적']\n",
    "df['누적 수도권 비율'] = 100 * df['누적 수도권'] / df['확진자 누적']\n",
    "df = df.loc['2020-02-18':]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "일일 현황을 차트로 표시합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/var/pyenv/versions/3.7.4/envs/jupyter/lib/python3.7/site-packages/pandas/plotting/_matplotlib/converter.py:103: FutureWarning: Using an implicitly registered datetime converter for a matplotlib plotting method. The converter was registered by pandas on import. Future versions of pandas will require you to explicitly register matplotlib converters.\n",
      "\n",
      "To register the converters:\n",
      "\t>>> from pandas.plotting import register_matplotlib_converters\n",
      "\t>>> register_matplotlib_converters()\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(8, 4))\n",
    "\n",
    "color = 'tab:green'\n",
    "ax1.set_xlabel('날짜')\n",
    "ax1.set_ylabel('확진자 수 (대구경북/전국)', color=color)\n",
    "#ax1.set_ylim(0, 1000)\n",
    "plt.bar(x=df.index, height=df['확진자'], color=color, alpha=0.6)\n",
    "plt.bar(x=df.index, height=df['대구']+df['경북'], color=\"tab:blue\", alpha=0.6)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "color = 'tab:red'\n",
    "ax2.set_ylabel('누적 양성비율(%)', color=color)  # we already handled the x-label with ax1\n",
    "ax2.plot(df['누적 양성비율'], color=color)\n",
    "#ax2.set_ylim(0., 4.5)\n",
    "ax2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "color = 'tab:orange'\n",
    "ax3 = ax1.twinx()\n",
    "ax3.spines[\"right\"].set_position((\"axes\", 1.12))\n",
    "ax3.set_ylabel('누적 사망율(%)', color=color)  # we already handled the x-label with ax1\n",
    "ax3.plot(df['누적 사망율'], color=color)\n",
    "ax3.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "color = 'tab:blue'\n",
    "ax4 = ax1.twinx()\n",
    "ax4.spines[\"right\"].set_position((\"axes\", 1.24))\n",
    "ax4.set_ylabel('누적 대구경북 비율(%)', color=color)  # we already handled the x-label with ax1\n",
    "ax4.plot(df['누적 대구경북 비율'], color=color)\n",
    "ax4.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "color = 'tab:brown'\n",
    "ax5 = ax1.twinx()\n",
    "ax5.spines[\"right\"].set_position((\"axes\", 1.35))\n",
    "ax5.set_ylabel('누적 완치율(%)', color=color)  # we already handled the x-label with ax1\n",
    "ax5.plot(df['누적 완치율'], color=color)\n",
    "ax5.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "#plt.xticks(rotation=90)\n",
    "fig.autofmt_xdate()\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.title(f'코로나바이러스감염증-19 감염현황 ({datestr})')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "인구밀도 자료를 가져옵니다. http://www.index.go.kr/potal/main/EachDtlPageDetail.do?idx_cd=1007 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(19, 9)\n"
     ]
    },
    {
     "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>Unnamed: 0</th>\n",
       "      <th>인구</th>\n",
       "      <th>인구밀도</th>\n",
       "      <th>인구.1</th>\n",
       "      <th>인구밀도.1</th>\n",
       "      <th>인구.2</th>\n",
       "      <th>인구밀도.2</th>\n",
       "      <th>인구.3</th>\n",
       "      <th>인구밀도.3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>계</td>\n",
       "      <td>51015</td>\n",
       "      <td>509</td>\n",
       "      <td>51218</td>\n",
       "      <td>510</td>\n",
       "      <td>51362</td>\n",
       "      <td>512</td>\n",
       "      <td>51607</td>\n",
       "      <td>514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>9941</td>\n",
       "      <td>16425</td>\n",
       "      <td>9843</td>\n",
       "      <td>16263</td>\n",
       "      <td>9766</td>\n",
       "      <td>16136</td>\n",
       "      <td>9705</td>\n",
       "      <td>16034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>부산</td>\n",
       "      <td>3452</td>\n",
       "      <td>4484</td>\n",
       "      <td>3447</td>\n",
       "      <td>4477</td>\n",
       "      <td>3424</td>\n",
       "      <td>4447</td>\n",
       "      <td>3400</td>\n",
       "      <td>4416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>대구</td>\n",
       "      <td>2469</td>\n",
       "      <td>2794</td>\n",
       "      <td>2461</td>\n",
       "      <td>2786</td>\n",
       "      <td>2458</td>\n",
       "      <td>2782</td>\n",
       "      <td>2450</td>\n",
       "      <td>2773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>인천</td>\n",
       "      <td>2883</td>\n",
       "      <td>2748</td>\n",
       "      <td>2907</td>\n",
       "      <td>2736</td>\n",
       "      <td>2924</td>\n",
       "      <td>2750</td>\n",
       "      <td>2939</td>\n",
       "      <td>2764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0     인구   인구밀도   인구.1  인구밀도.1   인구.2  인구밀도.2   인구.3  인구밀도.3\n",
       "0          계  51015    509  51218     510  51362     512  51607     514\n",
       "1         서울   9941  16425   9843   16263   9766   16136   9705   16034\n",
       "2         부산   3452   4484   3447    4477   3424    4447   3400    4416\n",
       "3         대구   2469   2794   2461    2786   2458    2782   2450    2773\n",
       "4         인천   2883   2748   2907    2736   2924    2750   2939    2764"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "density = pd.read_excel('data/stats_100701.xlsx', skiprows=2)\n",
    "print(density.shape)\n",
    "density.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', '인구', '인구밀도', '인구.1', '인구밀도.1', '인구.2', '인구밀도.2', '인구.3',\n",
       "       '인구밀도.3'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "density.columns"
   ]
  },
  {
   "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>2015-인구</th>\n",
       "      <th>2015-인구밀도</th>\n",
       "      <th>2016-인구</th>\n",
       "      <th>2016-인구밀도</th>\n",
       "      <th>2017-인구</th>\n",
       "      <th>2017-인구밀도</th>\n",
       "      <th>2018-인구</th>\n",
       "      <th>2018-인구밀도</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>지역</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>서울</th>\n",
       "      <td>9941.0</td>\n",
       "      <td>16425.0</td>\n",
       "      <td>9843.0</td>\n",
       "      <td>16263.0</td>\n",
       "      <td>9766.0</td>\n",
       "      <td>16136.0</td>\n",
       "      <td>9705.0</td>\n",
       "      <td>16034.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>3452.0</td>\n",
       "      <td>4484.0</td>\n",
       "      <td>3447.0</td>\n",
       "      <td>4477.0</td>\n",
       "      <td>3424.0</td>\n",
       "      <td>4447.0</td>\n",
       "      <td>3400.0</td>\n",
       "      <td>4416.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구</th>\n",
       "      <td>2469.0</td>\n",
       "      <td>2794.0</td>\n",
       "      <td>2461.0</td>\n",
       "      <td>2786.0</td>\n",
       "      <td>2458.0</td>\n",
       "      <td>2782.0</td>\n",
       "      <td>2450.0</td>\n",
       "      <td>2773.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>2883.0</td>\n",
       "      <td>2748.0</td>\n",
       "      <td>2907.0</td>\n",
       "      <td>2736.0</td>\n",
       "      <td>2924.0</td>\n",
       "      <td>2750.0</td>\n",
       "      <td>2939.0</td>\n",
       "      <td>2764.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광주</th>\n",
       "      <td>1506.0</td>\n",
       "      <td>3005.0</td>\n",
       "      <td>1502.0</td>\n",
       "      <td>2997.0</td>\n",
       "      <td>1495.0</td>\n",
       "      <td>2984.0</td>\n",
       "      <td>1493.0</td>\n",
       "      <td>2980.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    2015-인구  2015-인구밀도  2016-인구  2016-인구밀도  2017-인구  2017-인구밀도  2018-인구  \\\n",
       "지역                                                                        \n",
       "서울   9941.0    16425.0   9843.0    16263.0   9766.0    16136.0   9705.0   \n",
       "부산   3452.0     4484.0   3447.0     4477.0   3424.0     4447.0   3400.0   \n",
       "대구   2469.0     2794.0   2461.0     2786.0   2458.0     2782.0   2450.0   \n",
       "인천   2883.0     2748.0   2907.0     2736.0   2924.0     2750.0   2939.0   \n",
       "광주   1506.0     3005.0   1502.0     2997.0   1495.0     2984.0   1493.0   \n",
       "\n",
       "    2018-인구밀도  \n",
       "지역             \n",
       "서울    16034.0  \n",
       "부산     4416.0  \n",
       "대구     2773.0  \n",
       "인천     2764.0  \n",
       "광주     2980.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "density.columns = ['지역', '2015-인구', '2015-인구밀도', '2016-인구', '2016-인구밀도', '2017-인구', '2017-인구밀도', \n",
    "                   '2018-인구', '2018-인구밀도']\n",
    "density = density.iloc[1:, :].set_index('지역')\n",
    "density = density.astype(float)\n",
    "density.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x10f1ada50>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "density.iloc[:-1, :][['2018-인구', '2018-인구밀도']].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/var/pyenv/versions/3.7.4/envs/jupyter/lib/python3.7/site-packages/pandas/core/indexing.py:376: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/usr/local/var/pyenv/versions/3.7.4/envs/jupyter/lib/python3.7/site-packages/pandas/core/indexing.py:576: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[item_labels[indexer[info_axis]]] = value\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 62 artists>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df_from_april = df.loc['2020-04-03':]\n",
    "df_from_april.loc[:, '해외유입 누적 전체'] = df_from_april['해외유입 누적'] + 647  # 04-03까지의 해외유입\n",
    "df_from_april.loc[:, '해외유입 비율'] = 100 * df_from_april['해외유입 누적 전체'] / df_from_april['확진자 누적']\n",
    "plt.bar(x=df_from_april.index, height=df_from_april['확진자'])\n",
    "plt.bar(x=df_from_april.index, height=df_from_april['지역발생'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>서울</th>\n",
       "      <th>부산</th>\n",
       "      <th>대구</th>\n",
       "      <th>인천</th>\n",
       "      <th>광주</th>\n",
       "      <th>대전</th>\n",
       "      <th>울산</th>\n",
       "      <th>세종</th>\n",
       "      <th>경기</th>\n",
       "      <th>강원</th>\n",
       "      <th>...</th>\n",
       "      <th>해외유입 누적</th>\n",
       "      <th>지역발생 누적</th>\n",
       "      <th>누적 양성비율</th>\n",
       "      <th>누적 사망율</th>\n",
       "      <th>누적 대구경북 비율</th>\n",
       "      <th>누적 완치율</th>\n",
       "      <th>누적 수도권</th>\n",
       "      <th>누적 수도권 비율</th>\n",
       "      <th>해외유입 누적 전체</th>\n",
       "      <th>해외유입 비율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-04-03</th>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>32.0</td>\n",
       "      <td>10124.0</td>\n",
       "      <td>2.231931</td>\n",
       "      <td>1.742812</td>\n",
       "      <td>79.588421</td>\n",
       "      <td>62.278456</td>\n",
       "      <td>1135.0</td>\n",
       "      <td>11.175660</td>\n",
       "      <td>679.0</td>\n",
       "      <td>6.685703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-04</th>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>...</td>\n",
       "      <td>72.0</td>\n",
       "      <td>10165.0</td>\n",
       "      <td>2.219486</td>\n",
       "      <td>1.787633</td>\n",
       "      <td>79.066133</td>\n",
       "      <td>63.133731</td>\n",
       "      <td>1171.0</td>\n",
       "      <td>11.438898</td>\n",
       "      <td>719.0</td>\n",
       "      <td>7.023542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-05</th>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>88.0</td>\n",
       "      <td>10196.0</td>\n",
       "      <td>2.203066</td>\n",
       "      <td>1.808635</td>\n",
       "      <td>78.850642</td>\n",
       "      <td>64.157915</td>\n",
       "      <td>1191.0</td>\n",
       "      <td>11.581097</td>\n",
       "      <td>735.0</td>\n",
       "      <td>7.147025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-06</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>105.0</td>\n",
       "      <td>10226.0</td>\n",
       "      <td>2.164449</td>\n",
       "      <td>1.858484</td>\n",
       "      <td>78.627432</td>\n",
       "      <td>64.795276</td>\n",
       "      <td>1205.0</td>\n",
       "      <td>11.663924</td>\n",
       "      <td>752.0</td>\n",
       "      <td>7.279063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-04-07</th>\n",
       "      <td>11.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>129.0</td>\n",
       "      <td>10255.0</td>\n",
       "      <td>2.136612</td>\n",
       "      <td>1.926040</td>\n",
       "      <td>78.341680</td>\n",
       "      <td>65.254237</td>\n",
       "      <td>1226.0</td>\n",
       "      <td>11.806626</td>\n",
       "      <td>776.0</td>\n",
       "      <td>7.473035</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
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       "              서울   부산    대구   인천   광주   대전   울산   세종    경기   강원  ...  해외유입 누적  \\\n",
       "2020-04-03  22.0  0.0  27.0  3.0  0.0  0.0  0.0  0.0  23.0  1.0  ...     32.0   \n",
       "2020-04-04  24.0  0.0   7.0  2.0  1.0  1.0  0.0  0.0  10.0  3.0  ...     72.0   \n",
       "2020-04-05  11.0  0.0  13.0  1.0  0.0  2.0  0.0  0.0   8.0  0.0  ...     88.0   \n",
       "2020-04-06   4.0  1.0  13.0  0.0  0.0  0.0  0.0  0.0  10.0  2.0  ...    105.0   \n",
       "2020-04-07  11.0  2.0   9.0  4.0  0.0  0.0  0.0  0.0   6.0  2.0  ...    129.0   \n",
       "\n",
       "            지역발생 누적   누적 양성비율    누적 사망율  누적 대구경북 비율     누적 완치율  누적 수도권  \\\n",
       "2020-04-03  10124.0  2.231931  1.742812   79.588421  62.278456  1135.0   \n",
       "2020-04-04  10165.0  2.219486  1.787633   79.066133  63.133731  1171.0   \n",
       "2020-04-05  10196.0  2.203066  1.808635   78.850642  64.157915  1191.0   \n",
       "2020-04-06  10226.0  2.164449  1.858484   78.627432  64.795276  1205.0   \n",
       "2020-04-07  10255.0  2.136612  1.926040   78.341680  65.254237  1226.0   \n",
       "\n",
       "            누적 수도권 비율  해외유입 누적 전체   해외유입 비율  \n",
       "2020-04-03  11.175660       679.0  6.685703  \n",
       "2020-04-04  11.438898       719.0  7.023542  \n",
       "2020-04-05  11.581097       735.0  7.147025  \n",
       "2020-04-06  11.663924       752.0  7.279063  \n",
       "2020-04-07  11.806626       776.0  7.473035  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_from_april.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>서울</th>\n",
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       "      <th>2020-05-30</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>528.0</td>\n",
       "      <td>10940.0</td>\n",
       "      <td>1.259082</td>\n",
       "      <td>2.354377</td>\n",
       "      <td>72.148587</td>\n",
       "      <td>90.730729</td>\n",
       "      <td>1875.0</td>\n",
       "      <td>16.349843</td>\n",
       "      <td>1175.0</td>\n",
       "      <td>10.245902</td>\n",
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       "    <tr>\n",
       "      <th>2020-05-31</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18.0</td>\n",
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       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>533.0</td>\n",
       "      <td>10970.0</td>\n",
       "      <td>1.248439</td>\n",
       "      <td>2.355907</td>\n",
       "      <td>71.937755</td>\n",
       "      <td>90.602452</td>\n",
       "      <td>1906.0</td>\n",
       "      <td>16.569591</td>\n",
       "      <td>1180.0</td>\n",
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       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>535.0</td>\n",
       "      <td>11006.0</td>\n",
       "      <td>1.227961</td>\n",
       "      <td>2.356815</td>\n",
       "      <td>71.700892</td>\n",
       "      <td>90.512087</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>16.835629</td>\n",
       "      <td>1182.0</td>\n",
       "      <td>10.241747</td>\n",
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       "    <tr>\n",
       "      <th>2020-06-02</th>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>538.0</td>\n",
       "      <td>11052.0</td>\n",
       "      <td>1.211264</td>\n",
       "      <td>2.355479</td>\n",
       "      <td>71.406385</td>\n",
       "      <td>90.310613</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>17.178602</td>\n",
       "      <td>1185.0</td>\n",
       "      <td>10.224331</td>\n",
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       "    <tr>\n",
       "      <th>2020-06-03</th>\n",
       "      <td>14.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>2027.0</td>\n",
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       "      <td>1191.0</td>\n",
       "      <td>10.241637</td>\n",
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       "              서울   부산   대구    인천   광주   대전   울산   세종    경기   강원  ...  해외유입 누적  \\\n",
       "2020-05-30   6.0  1.0  1.0   3.0  0.0  1.0  2.0  0.0  12.0  0.0  ...    528.0   \n",
       "2020-05-31   1.0  0.0  1.0  18.0  0.0  0.0  0.0  0.0  12.0  0.0  ...    533.0   \n",
       "2020-06-01  14.0  0.0  0.0   8.0  0.0  0.0  0.0  0.0  15.0  0.0  ...    535.0   \n",
       "2020-06-02  19.0  0.0  1.0  17.0  0.0  0.0  0.0  0.0  12.0  0.0  ...    538.0   \n",
       "2020-06-03  14.0  0.0  0.0   7.0  0.0  0.0  0.0  0.0  15.0  0.0  ...    544.0   \n",
       "\n",
       "            지역발생 누적   누적 양성비율    누적 사망율  누적 대구경북 비율     누적 완치율  누적 수도권  \\\n",
       "2020-05-30  10940.0  1.259082  2.354377   72.148587  90.730729  1875.0   \n",
       "2020-05-31  10970.0  1.248439  2.355907   71.937755  90.602452  1906.0   \n",
       "2020-06-01  11006.0  1.227961  2.356815   71.700892  90.512087  1943.0   \n",
       "2020-06-02  11052.0  1.211264  2.355479   71.406385  90.310613  1991.0   \n",
       "2020-06-03  11085.0  1.194117  2.347579   71.175510  90.282913  2027.0   \n",
       "\n",
       "            누적 수도권 비율  해외유입 누적 전체    해외유입 비율  \n",
       "2020-05-30  16.349843      1175.0  10.245902  \n",
       "2020-05-31  16.569591      1180.0  10.258194  \n",
       "2020-06-01  16.835629      1182.0  10.241747  \n",
       "2020-06-02  17.178602      1185.0  10.224331  \n",
       "2020-06-03  17.430562      1191.0  10.241637  \n",
       "\n",
       "[5 rows x 60 columns]"
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     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_from_april.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "여러 차트를 종합하여 한장의 그림을 만듭니다. 가능한 한장의 차트에 모든 정보를 담을 수 있도록 노력합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/var/pyenv/versions/3.7.4/envs/jupyter/lib/python3.7/site-packages/pandas/core/indexing.py:961: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n",
      "  return getattr(section, self.name)[new_key]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x1152 with 13 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(9, 16))\n",
    "\n",
    "ax1 = fig.add_subplot(5, 1, 1)\n",
    "color = 'tab:green'\n",
    "ax1.set_ylabel('확진자 수 (명, 대구경북/전국)', color=color)\n",
    "ax1.tick_params(axis='y', labelcolor=color)\n",
    "ax1.set_title(f'코로나바이러스감염증-19 감염현황 ({datestr})')\n",
    "ax1.xaxis.set_minor_locator(mdates.DayLocator())\n",
    "plt.bar(x=df.index, height=df['확진자'], color=color, alpha=0.5)\n",
    "plt.bar(x=df.index, height=df['대구']+df['경북'], color=\"tab:blue\", alpha=0.5)\n",
    "\n",
    "ax1_1 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "color = 'tab:red'\n",
    "ax1_1.set_ylabel('누적 확진자수 (명)', color=color)  # we already handled the x-label with ax1\n",
    "ax1_1.plot(df.index, df['확진자 누적'], color=color)\n",
    "ax1_1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax1_2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "color = 'tab:orange'\n",
    "ax1_2.spines[\"right\"].set_position((\"axes\", 1.20))\n",
    "ax1_2.set_ylabel('누적 양성비율(%)', color=color)  # we already handled the x-label with ax1\n",
    "ax1_2.plot(df.index, df['누적 양성비율'], color=color)\n",
    "ax1_2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax1_3 = ax1.twinx()\n",
    "color = 'tab:brown'\n",
    "ax1_3.spines[\"right\"].set_position((\"axes\", 1.34))\n",
    "ax1_3.set_ylabel('누적 사망율(%)', color=color)  # we already handled the x-label with ax1\n",
    "ax1_3.plot(df['누적 사망율'], color=color)\n",
    "ax1_3.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax1_4 = ax1.twinx()\n",
    "color = 'tab:blue'\n",
    "ax1_4.spines[\"right\"].set_position((\"axes\", 1.48))\n",
    "ax1_4.set_ylabel('누적 완치율(%)', color=color)  # we already handled the x-label with ax1\n",
    "ax1_4.plot(df['누적 완치율'], color=color)\n",
    "ax1_4.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(5, 1, 2)\n",
    "ax2.set_ylabel('확진자 수 (명, 수도권)')\n",
    "ax2.set_title(f'수도권 감염현황 (서울 경기 인천)')\n",
    "ax2.xaxis.set_minor_locator(mdates.DayLocator())\n",
    "plt.bar(x=df.index, height=df['서울']+df['경기']+df['인천'], alpha=0.7)\n",
    "\n",
    "ax2_1 = ax2.twinx()\n",
    "color = 'tab:red'\n",
    "ax2_1.set_ylabel('누적 확진자수 (명, 수도권)', color=color)  # we already handled the x-label with ax1\n",
    "ax2_1.plot(df['누적 수도권'], color=color)\n",
    "ax2_1.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax2_2 = ax2.twinx()\n",
    "color = 'tab:orange'\n",
    "ax2_2.spines[\"right\"].set_position((\"axes\", 1.18))\n",
    "ax2_2.set_ylabel('누적 수도권 비율 (%)', color=color)  # we already handled the x-label with ax1\n",
    "ax2_2.plot(df['누적 수도권 비율'] , color=color)\n",
    "ax2_2.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "ax3 = fig.add_subplot(5, 1, 3)\n",
    "ax3.set_title('지역별 확진자수 누적 현황 (대구경북 제외 7지역)')\n",
    "\n",
    "for acu in ['서울 누적', '경기 누적', '부산 누적', '충남 누적', '경남 누적', '세종 누적', '인천 누적', '검역 누적']:\n",
    "    df[acu].plot(ax=ax3)\n",
    "    ax3.fill_between(df.index, df[acu], alpha=0.07)\n",
    "\n",
    "ax3.legend(loc='upper left', bbox_to_anchor=(1.1, 1))\n",
    "ax3.xaxis.set_minor_locator(mdates.DayLocator())\n",
    "ax3.set_ylabel('확진자 수 (명)')\n",
    "\n",
    "\n",
    "ax4 = fig.add_subplot(5, 1, 4)\n",
    "ax4.set_title('확진자 중 지역발생 (4월 3일 이후)')\n",
    "plt.bar(x=df_from_april.index, height=df_from_april['확진자'])\n",
    "plt.bar(x=df_from_april.index, height=df_from_april['지역발생'])\n",
    "ax4.set_ylabel('확진자 수(명, 지역발생이 오렌지색)')\n",
    "\n",
    "ax4_1 = ax4.twinx()\n",
    "ax4_1.set_ylabel('누적 해외유입 비율 (%)')\n",
    "ax4_1.plot(df_from_april['해외유입 비율'])\n",
    "\n",
    "\n",
    "ax5_1 = fig.add_subplot(5, 2, 9)\n",
    "ax5_1.set_title('확진자 수 (명)')\n",
    "wotk = regions.copy()\n",
    "df[wotk].sum().sort_values().plot(kind='barh', ax=ax5_1)\n",
    "ax5_1.set_xscale('log')\n",
    "ax5_1.set_xlabel('명 (로그스케일)')\n",
    "\n",
    "ax5_2 = fig.add_subplot(5, 2, 10)\n",
    "ax5_2.set_title('인구 백만명당 확진자 수 (명)')\n",
    "confirm_density = df[regions].sum() * 1000 / density.loc[regions, '2018-인구']\n",
    "confirm_density.sort_values().plot(kind='barh', ax=ax5_2)\n",
    "ax5_2.set_xscale('log')\n",
    "ax5_2.set_xlabel('명 (로그스케일)')\n",
    "\n",
    "fig.autofmt_xdate()\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'daily-chart/covid19-stat-{today.strftime(\"%m%d\")}.png', dpi=200, format='png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1 = plt.subplots(figsize=(8, 15))\n",
    "color = 'tab:green'\n",
    "ax1.set_ylabel('확진자수 / 총 검사수')\n",
    "ax1.set_title(f'COVID-19 대한민국 총 검사수 및 확진현황 ({datestr})')\n",
    "ax1.xaxis.set_minor_locator(mdates.DayLocator())\n",
    "ax1.set_ylim([0, 18500])\n",
    "plt.bar(x=df.index, height=df['검사수'], color=color, alpha=0.3)\n",
    "plt.bar(x=df.index, height=df['확진자'], color=\"tab:red\")\n",
    "plt.xticks(rotation=45)\n",
    "fig.autofmt_xdate()\n",
    "plt.tight_layout()\n",
    "plt.savefig(f'daily-chart/covid19-stat-wtests-{today.strftime(\"%m%d\")}.png', dpi=200, format='png')"
   ]
  },
  {
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}