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yong27/COVID19-analysis-0303.ipynb

Created March 3, 2020 04:13
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코로나바이러스감염증-19 감염현황 데이터 분석 (03-03 00:00 현재)
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COVID19-analysis-0303.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 코로나바이러스감염증-19 감염현황 데이터 분석 (03-03 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": 10,
"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": 11,
"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": 12,
"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",
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" <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",
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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>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",
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" <td>0.0</td>\n",
" <td>...</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>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",
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" <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": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"누적값을 별도 컬럼에 추가합니다."
]
},
{
"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>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",
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" <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",
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" <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>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",
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" <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": 13,
"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": 14,
"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": 16,
"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 (03-03 00:00 현재)')\n",
"\n",
"plt.savefig('covid19-stat-0303.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
}
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