yong27/COVID19-analysis-0304.ipynb

## COVID19-analysis-0304.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 코로나바이러스감염증-19 감염현황 데이터 분석 (03-04 00:00 현재)\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": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from io import BytesIO\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import requests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "matplotlib 차트 한글폰트 설정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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": 3,
   "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",
    "df['대구'] = df['대구'].str.replace(',', '').astype(float)\n",
    "df['확진자'] = df['확진자'].str.replace(',', '').astype(float)\n",
    "df['검사수'] = df['검사수'].str.replace(',', '').astype(float)\n",
    "df = df.replace(np.nan, 0).sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>전남</th>\n",
       "      <th>경북</th>\n",
       "      <th>경남</th>\n",
       "      <th>제주</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",
       "    </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>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",
       "    </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>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>25.0</td>\n",
       "      <td>4.00</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>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",
       "    </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>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>48.0</td>\n",
       "      <td>2.08</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>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>57.0</td>\n",
       "      <td>1.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             서울   부산   대구   인천   광주   대전   울산   세종   경기   강원  ...   전남   경북  \\\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",
       "             경남   제주  지역  모름  확진자   사망  격리  해제   검사수  양성  비율(%)  \n",
       "2020-01-23  0.0  0.0     1.0  1.0  0.0     0.0  22.0       4.55  \n",
       "2020-01-24  0.0  0.0     1.0  1.0  0.0     0.0  25.0       4.00  \n",
       "2020-01-25  0.0  0.0     0.0  0.0  0.0     0.0   0.0       0.00  \n",
       "2020-01-26  0.0  0.0     1.0  1.0  0.0     0.0  48.0       2.08  \n",
       "2020-01-27  0.0  0.0     1.0  1.0  0.0     0.0  57.0       1.75  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "누적값을 별도 컬럼에 추가합니다."
   ]
  },
  {
   "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>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>전남 누적</th>\n",
       "      <th>경북 누적</th>\n",
       "      <th>경남 누적</th>\n",
       "      <th>제주 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",
       "    </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>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",
       "    </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>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",
       "    </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>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",
       "    </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>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",
       "    </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>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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             서울   부산   대구   인천   광주   대전   울산   세종   경기   강원  ...  전남 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",
       "            경북 누적  경남 누적  제주 누적  지역  모름 누적  확진자 누적  사망 누적  격리  해제 누적  검사수 누적  \\\n",
       "2020-01-23    0.0    0.0    0.0        1.0     1.0    0.0        0.0    22.0   \n",
       "2020-01-24    0.0    0.0    0.0        2.0     2.0    0.0        0.0    47.0   \n",
       "2020-01-25    0.0    0.0    0.0        2.0     2.0    0.0        0.0    47.0   \n",
       "2020-01-26    0.0    0.0    0.0        3.0     3.0    0.0        0.0    95.0   \n",
       "2020-01-27    0.0    0.0    0.0        4.0     4.0    0.0        0.0   152.0   \n",
       "\n",
       "            양성  비율(%) 누적  \n",
       "2020-01-23          4.55  \n",
       "2020-01-24          8.55  \n",
       "2020-01-25          8.55  \n",
       "2020-01-26         10.63  \n",
       "2020-01-27         12.38  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 5,
     "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": "code",
   "execution_count": 6,
   "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'] = 100 * (df['대구 누적'] + df['경북 누적']) / df['확진자 누적']\n",
    "df = df.loc['2020-02-15':]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "차트를 그립니다. 가능한 한장의 차트에 모든 정보를 담을 수 있도록 노력합니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x288 with 4 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.tick_params(axis='y', labelcolor=color)\n",
    "\n",
    "color = 'tab:orange'\n",
    "ax3 = ax1.twinx()\n",
    "ax3.spines[\"right\"].set_position((\"axes\", 1.15))\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\", 1.3))\n",
    "ax4.set_ylabel('누적 대구경북 (%)', color=color)  # we already handled the x-label with ax1\n",
    "ax4.plot(df['누적 대구경북'], color=color)\n",
    "ax4.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('코로나바이러스감염증-19 감염현황 (03-04 00:00 현재)')\n",
    "\n",
    "fig.tight_layout()\n",
    "plt.savefig('covid19-stat-0304.png', dpi=200, format='png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "양성비율 감소시작, 사망율 약간 증가, 대구경북 비율 증가 \n",
    "\n",
    "확진자수와 양성비율 감소로 하강 추세 진입을 기대합니다. "
   ]
  },
  {
   "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
}