wrangle_for_forecast.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python38364bitdatavenvfe97dd12d13941c2ad0777a7539abb51",
   "display_name": "Python 3.8.3 64-bit ('data': venv)",
   "language": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os import fdopen, remove, walk\n",
    "import glob\n",
    "from tempfile import mkstemp\n",
    "import shutil\n",
    "from shutil import move, copymode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import date"
   ]
  },
  {
   "source": [
    "### Load Dataset"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATASET_PATH = \"/Users/noopy/covid19_unknown_spread/dataset\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# get newest file in the dataset folder\n",
    "import os, heapq\n",
    "def newst_files_in_tree(rootfolder, count=1, extension=\".csv\"):\n",
    "    return heapq.nlargest(count,\n",
    "        (os.path.join(dirname, filename)\n",
    "        for dirname, dirnames, filenames in os.walk(rootfolder)\n",
    "        for filename in filenames\n",
    "        if filename.endswith(extension)),\n",
    "        key=lambda fn: os.stat(fn).st_mtime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "newst_csv = newst_files_in_tree(DATASET_PATH)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          연번     환자     확진일   거주지 여행력             접촉력 퇴원현황\n",
       "9510    7720  31235  11.23.   강남구   -       감염경로 조사 중  NaN\n",
       "16290    940  11696   6.05.   양천구   -     양천구 운동시설 관련   퇴원\n",
       "4714   12616  44180  12.14.   강서구   -       강서구 교회 관련  NaN\n",
       "15589   1641  14510    8.6.   용산구  미국        해외 접촉 추정   퇴원\n",
       "16533    697  10946   5.10.   강서구   -       이태원 클럽 관련   퇴원\n",
       "12765   4465  21545   9.08.   은평구   -        관악구 가족모임   퇴원\n",
       "8789    8441  33003  11.27.  서대문구   -     중구 제조업공장 관련  NaN\n",
       "14512   2718  16958   8.21.   도봉구   -   성북구 사랑제일교회 관련   퇴원\n",
       "16764    466  10081   3.31.   관악구  미국        해외 접촉 추정   퇴원\n",
       "11016   6214  27120  11.05.   관악구   -       서초구 빌딩 관련   퇴원\n",
       "15907   1323  12865   7.01.   동작구   -            확인 중   퇴원\n",
       "14837   2393  16237   8.19.   은평구   -            확인 중   퇴원\n",
       "10313   6917  29113  11.17.   성북구   -       기타 확진자 접촉   퇴원\n",
       "7461    9769  36678  12.04.   성동구   -  성동구 소재 의료기관 관련  NaN\n",
       "15442   1788  14901   8.14.   광진구   -       기타 확진자 접촉   퇴원"
      ],
      "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>연번</th>\n      <th>환자</th>\n      <th>확진일</th>\n      <th>거주지</th>\n      <th>여행력</th>\n      <th>접촉력</th>\n      <th>퇴원현황</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>9510</th>\n      <td>7720</td>\n      <td>31235</td>\n      <td>11.23.</td>\n      <td>강남구</td>\n      <td>-</td>\n      <td>감염경로 조사 중</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>16290</th>\n      <td>940</td>\n      <td>11696</td>\n      <td>6.05.</td>\n      <td>양천구</td>\n      <td>-</td>\n      <td>양천구 운동시설 관련</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>4714</th>\n      <td>12616</td>\n      <td>44180</td>\n      <td>12.14.</td>\n      <td>강서구</td>\n      <td>-</td>\n      <td>강서구 교회 관련</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>15589</th>\n      <td>1641</td>\n      <td>14510</td>\n      <td>8.6.</td>\n      <td>용산구</td>\n      <td>미국</td>\n      <td>해외 접촉 추정</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>16533</th>\n      <td>697</td>\n      <td>10946</td>\n      <td>5.10.</td>\n      <td>강서구</td>\n      <td>-</td>\n      <td>이태원 클럽 관련</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>12765</th>\n      <td>4465</td>\n      <td>21545</td>\n      <td>9.08.</td>\n      <td>은평구</td>\n      <td>-</td>\n      <td>관악구 가족모임</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>8789</th>\n      <td>8441</td>\n      <td>33003</td>\n      <td>11.27.</td>\n      <td>서대문구</td>\n      <td>-</td>\n      <td>중구 제조업공장 관련</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>14512</th>\n      <td>2718</td>\n      <td>16958</td>\n      <td>8.21.</td>\n      <td>도봉구</td>\n      <td>-</td>\n      <td>성북구 사랑제일교회 관련</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>16764</th>\n      <td>466</td>\n      <td>10081</td>\n      <td>3.31.</td>\n      <td>관악구</td>\n      <td>미국</td>\n      <td>해외 접촉 추정</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>11016</th>\n      <td>6214</td>\n      <td>27120</td>\n      <td>11.05.</td>\n      <td>관악구</td>\n      <td>-</td>\n      <td>서초구 빌딩 관련</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>15907</th>\n      <td>1323</td>\n      <td>12865</td>\n      <td>7.01.</td>\n      <td>동작구</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>14837</th>\n      <td>2393</td>\n      <td>16237</td>\n      <td>8.19.</td>\n      <td>은평구</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>10313</th>\n      <td>6917</td>\n      <td>29113</td>\n      <td>11.17.</td>\n      <td>성북구</td>\n      <td>-</td>\n      <td>기타 확진자 접촉</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>7461</th>\n      <td>9769</td>\n      <td>36678</td>\n      <td>12.04.</td>\n      <td>성동구</td>\n      <td>-</td>\n      <td>성동구 소재 의료기관 관련</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>15442</th>\n      <td>1788</td>\n      <td>14901</td>\n      <td>8.14.</td>\n      <td>광진구</td>\n      <td>-</td>\n      <td>기타 확진자 접촉</td>\n      <td>퇴원</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "df_temp = pd.read_csv(newst_csv, encoding=\"utf-8\")\n",
    "df_temp.sample(15)"
   ]
  },
  {
   "source": [
    "### Preprocessing for unknown contact record patients & known contact patients"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array(['감염경로 조사중', '감염경로 확인중', '감염경로 조사 중', '확인 중', '확인중'], dtype=object)"
      ]
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "# get two arrays of unknown route for contagion (미확인경로 확진자)\n",
    "search_condition = (df_temp[\"접촉력\"].str.contains(\"확인\") | df_temp[\"접촉력\"].str.contains(\"조사\"))\n",
    "array_unknown = df_temp.loc[search_condition,\"접촉력\"].unique()\n",
    "array_unknown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array(['확인 중'], dtype=object)"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "# overwrite whatever cell value that contains \"확인\" as \"확인 중\"\n",
    "for item in array_unknown:\n",
    "    df_temp.loc[df_temp[\"접촉력\"].str.contains(item),\"접촉력\"] = \"확인 중\"\n",
    "\n",
    "# check whether those two arrays are combined\n",
    "df_temp.loc[df_temp[\"접촉력\"].str.contains(\"확인\"),\"접촉력\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# remove unnecessary labelings\n",
    "df_temp[\"접촉력\"] = df_temp[\"접촉력\"].str.replace(\"관련\", \"\")\n",
    "df_temp[\"접촉력\"] = df_temp[\"접촉력\"].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fix typos\n",
    "df_temp[\"접촉력\"] = df_temp[\"접촉력\"].str.replace(\"확잔지\", \"확진자\")\n",
    "df_temp[\"접촉력\"] = df_temp[\"접촉력\"].str.replace(\"접촐\", \"접촉\")\n",
    "df_temp[\"접촉력\"] = df_temp[\"접촉력\"].str.replace(\"]\", \"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "number of known contact paths: 476\n['동부구치소' '기타 확진자 접촉' '확인 중' '영등포구 소재 의료기관' '구로구 소재 요양병원' '타시도 확진자 접촉'\n '송파구 소재 장애인 생활시설' '용산구 소재 건설현장' '양천구 소재 요양시설' '강남구 소재 공기업' '강서구 교회'\n '금천구 소재 교회' '해외 유입' '중구 콜센터' '강동구 지인모임' '강남구 개인사저 경비팀' '종로구 소재 요양시설'\n '중구 소재 콜센터(한전SCS)' '타시도 확진다 접촉' '마포구 소재 보험회사' '해외유입' '노원구 병원'\n '동대문구 소재 교회' '중구 소재 직장' '강남구 소재 콜센터Ⅱ' '중구 소재 직장 (농협중앙회)' '동부 구치소'\n '성북구 종교시설(장암교회)' '중구 콜센터Ⅱ' '노원구 병원 (강북연세병원)' '종로구 소재 파고다타운' '-' '기타'\n '동대문구 지혜병원' '송파구 소재 병원(12월)' '중구 소재 직장(농협중앙회)' '서울동부구치소' '중구 콜센터 Ⅱ'\n '중랑구 소재 병원' '서초구 소재 병원' '용산구 음식점' '중구 소재 시장(남대문시장)' '서초구 사우나Ⅱ' '동작구 사우나'\n '동대문구 지헤병원' '구로구 소재 요양벙원' '마포구 홍대세교회' '광진구 소재 병원' '송파구 체육시설'\n '고려대학교 밴드동아리' '마포구 소재 노인복지시설' '성동구 소재 병원' '강남구 소재 마사지샵'\n '강서구 소재 댄스교습시설 연관 병원' '종로구 소재 음식점' '제주여행(퇴직교사)' '서초구 건설회사' '영등포구 소재 콜센터'\n '중랑구 실내체육시설Ⅱ' '강남구 어학원 Ⅰ' '구로구 소재 보험회사' '구로구 소재 고등학교' '영등포구 학원'\n '송파구 소재 교정시설' '노원구 소재 회사' '강북구 소재 교회' '아산병원' '여의도 순복음교회' '동대문구 체육시설'\n '강서구 댄스교습 시설' '강서구 댄스교습 연관 병원' '구로구 고등학교' '노원구 체육시설' '은평구 소재 역사'\n '마포구 홈쇼핑회사' '노원구 의료기관' '강서구 소재 병원(11월)' '성동구 병원' '소규모 지인모임' '마포구 빌딩'\n '노원구 소재 교회 ' '송파구 사우나' '중랑구 실내 체육시설 Ⅱ' '저축 금융컨설팅' '동창 운동 모임'\n '성북구 소재 공연연습실' '송파구 탁구클럽' '영등포구 소재 교회' '강남구 음식점Ⅱ' '노원구청' '마포구 홍대새교회'\n '어플소모임' '연세대 학생' '용산구 소재 음식점 등' '강남구 연기학원' '중구 콜센터Ⅰ' '관악구 소재 음식점'\n '구로구소재 고등학교' '기타확진자 접촉' '성북구 소재 의료기관' '서초구 사우나' '강남구 소재 요양병원' '강남구 소재 병원'\n '성동구 소재 의료기관' '수도권산악회(11월)' '동작구 최비야' '제주도 퇴직교사 모임' '동작구 임용단기고시'\n '강남구 어학원 Ⅱ' '강남구 소재 콜센터' '동작구 임용단기학원' '송파구 소재 의료기관' '중구 소재 콜센터'\n '노원구 소재 교회' '동대문구 고등학교' '춘천 연탄봉사모임' '동작구 교회 기도처' '영등포구 회사' '도봉구 청련사'\n '송파구 잠언의료기(강남구 CJ텔레닉스)' '성동구 소재 PC방' '강남구 콜센터' '중구 소재 판매시설' '강서구 경로당'\n '중구 성00' '성동구 체육시설' '강서구 가족' '노원구 가족' '강서구 소재 병원Ⅲ' '중구 제조업공장' '서대문구 요양시설'\n '삼성서울병원' '지인-가족전파' '중구 소재 보람상조회사' '용산구 국군복지단' '중구 소재 상조회사' '어플 소모임'\n '동작구 모조카페' '수도권 지인모임(11월)' '중구 교회' '중랑구 실내체육시설' '동대문구 고등학교(마포구 홍대 새교회)'\n '강남구 헬스장' '성북구 가족' '송파구 잠언의료기(강남구CJ텔레닉스)' '성동구 금호노인요양원' '동대문구 에이스희망케어센터'\n '서울대병원 발생' '도봉구 의류업작업장' '중랑구 체육시설' '서초구 소재 교회/교육원' '서울시청(11월)'\n '동대문구 고등학교(마포구 홍대새교회 )' '서초구 어린이집' '동작구 가족(11월)' '송파구 지인강원여행모임' '성동구 시장'\n '영등포구 증권회사' '서초구 소재 교회,교육원' '수도권 미술대학원/아이스하키' '강서구 지인모임(11월)' '영등포구 직장'\n '도봉구 소재 종교시설' '도봉구 의류업 작업장' '송파구 잠언의료기[강남구CJ텔레닉스' '송파구 소재 병원'\n '강동구 소재 건설현장' '강서구 사우나' '생일파티모임' '타시도 확진자 접촉[충남아산전기공사업체' '서울음악 교습'\n '강서구 보험회사' '노원구 재가요양서비스' '송파구 시장' '강남구 역삼역' '강남구 럭키사우나'\n '기타 확진자 접촉[강남구제약회사' '영등포구 가족[송파구소재건설현장)' '관악구 삼모스포렉스Ⅱ' '용인시 동문골프 모임'\n '용산구 소재 의류업체' '서초구 빌딩' '강서구 직장' '송파구 잠언의료기(CJ텔레닉스)' '종로구 녹음실' '송파구 소재 교회'\n '동대문구 한국산업인력공단' '언론인' '제주도 지인모임' '구로구 가족' '구로구 가족[부천무용학원'\n '영등포구 가족 (송파구소재 건설현장' '은평구 방문교사' '기타 확진자 접촉[경기도SRC재활병원' '강남서초 지인모임'\n '도봉구 가족' '수도권 지인모임 Ⅱ' '영등포구 가족' '서대문구 소재 장례식장' '부천무용학원[구로구 가족'\n '강남구 CJ텔레닉스[잠언의료기' '수도권 지인모임' '도봉구 예마루데이케어센터' '도봉구 다나병원' '강남구 CJ텔레닉스'\n '송파구 잠언의료기' '강동구 행복데이케어센터' '중랑구 이마트 상봉점' '해외 접촉 추정' '영등포구 방문판매업체'\n '강남구 해냄세무회계' '동작구 가족' '관악구 식당' '영등포구 소모임' '송파구 서민국수' '중구 소재 빌딩'\n '마포구 서울디자인 고등학교' '타시도 확진자 접촉[강원강릉시지인모임' '강남승광빌딩' '관악구 사랑나무 어린이집'\n '관악구 큰믿음유신감리교회' '동대문구 성경모임(발산대우주어린이집)' '강북구 북서울 꿈의교회' '강남구 소재 대우디오빌플러스'\n '영등포구 LG트윈타워' '부산시 방문주사' '서초구 소재 카페' '관악구 지인모임' '동작구 부동산회사' '관악구 휘트니스'\n '강남구 삼성화재' '양천구 양천경찰서' '서초구 LG 유플러스' '고양시부부' '관악구 가족'\n '고양시부부(마포구어린이집&서초구우체국)' '관악구가족 (가가호호데이케어센터)' '관악구 삼모스포렉스' '신도림 역사'\n '강남구 디와이디벨로먼트' '동대문구 성경모임' '송파구 우리교회' '중랑구 서울의료원' '종로구 서울대병원'\n '성동구 덕수고 야구부' '프로그래머 지인모임' '강남구 신도벤처타워' '강남구 K보건산업' '건축설명회' '종로구청'\n '강남구 통신판매업' '도봉구 소재 아파트' '영등포 소재 증권회사' '서대문구 세브란스병원' '영등포구 소재 마트'\n '강남구 소재 금융회사' '동작구 요양시설' '9월 송파구 지인모임' '은평구 수색성당' '서울지방국세청' '서대문구 지인모임'\n '영등포 지인모임' '관악구 에바다' '광진구 혜민병원' '8.15서울도심집회' '도봉구 건설현장' '도봉구 운동시설'\n '송파구 쿠팡' '노원구 빛가온교회' '서초구 장애인교육시설' '강동구 BF모바일' '영등포구 일련정종 서울포교소'\n '동작구 진흥글로벌' '구로구 아파트' 'KT가좌지사' '성북구 사랑제일교회' '노원구 기도모임' '관악구 가족모임'\n '영등포구 국회출입기자' '은평구 헤어콕' '송파구 소재병원' '극단 산' '영등포구 권능교회' '강동구 소재 병원' '다래경매'\n '성북구 체대입시' '성북구 요양시설' '중구소재 은행' '중랑구 소재 체육시설' '동작구 소재 서울신학교' '동작구 닥터큐'\n '용인시 우리제일교회' '노원구 손해보험' '구로구 보성운수' '강서구 서울대효요양병원' '강북구 일가족' '강서구 보안회사'\n '롯데리아 종사자 모임' '강남구 소재 아파트' '제주 게스트하우스' '성북구 벧엘장로교회' '관악구 김혜근의원'\n '동대문구 sk탁구클럽' '동작구 스터디카페' '군인권센터' '중구 보험회사(현대해상)' '강서구 병원' '종로구 혜화경찰서'\n '양천구 되새김교회' '현대커머셜' '영등포 IFC몰 오케스트로' '고양시 반석교회(케네디상가)' '롯데 자산개발' '골드트레인'\n '한양대병원' '강동구 어린이집' '은평구 성경공부모임' '관악구 요양병원' '고대 안암병원' '마포구 푸본생명콜센터'\n '성동구 가족' '양천구 되새김 교회' '중구 통일상가' '고양시 반석교회  (케네디상가)' '강남구 판매업소(골드트레인)'\n '양천구 확진자 접촉' '영등포구 확진자 접촉' '강남구 확진자 접촉' '강남구 판매업소' '광진구 확진자 접촉'\n '고양시 반석교회 (케네디상가)' '구로구 확진자 접촉' '종로구 확진자 접촉' '성북구 확진자 접촉' '서초구 확진자 접촉'\n '마포구 확진자 접촉' '#14483 접촉' '#14488 접촉' '강남구 커피전문점' '선교회 소모임' '도봉구 확진자 접촉'\n '송파구 교회' '강서구 확진자 접촉' '송파구 확진자 접촉' '종로구 지인모임 신명투자' '강서구 요양시설'\n '강남구 사무실 K빌딩' '송파구 지인모임' '관악구 확진자 접촉' '중구 확진자 접촉' '강북구 확진자 접촉' '관악구 사무실'\n '은평구 확진자 접촉' '강남구 사무실' '강남역삼동 금융회사' '성동구 확진자 접촉' '강남구 이동통신 종사자'\n '강남구 역삼동 모임' '용산구 확진자 접촉' '왕성교회' '중랑구 일가족' '관악구 일가족' '대전 꿈꾸는 교회' '리치웨이'\n '동작구 확진자 접촉' '금천구 확진자 접촉' '요양시설' '강동구 확진자 접촉' '시청역' '대자연코리아' '의왕 물류센터'\n '금천구 도정기 회사' '양천구 운동시설' '대전 다단계' '오렌지라이프' '수도권 개척교회' '타시도 확진자 접촉자'\n '연아나뉴스클래스' '한국대학생선교회' '해외접촉 추정' 'kb 생명보험' '부천시 쿠팡' '이태원 클럽' '용인 형제'\n '삼성화재' '서초구 가족' '안양군포 목회자' '영등포 학원' '강남구 동인교회' '원어성경연구회' '#11196 접촉'\n '#10054 접촉' '#9800 접촉' '#10044 접촉' '#9641 접촉' '구로구 교회' '#10407 접촉'\n '#10015 접촉' '#9364 접촉' '#10377 접촉' '#9287 접촉' '#10142 접촉' '#9912 접촉'\n '#9765 접촉' '#10115 접촉' '타시도 확진자 접촉(추정)' '#8063 접촉' '#10084 접촉' '#8601 접촉'\n '#8717 접촉' '#9946 접촉' '콜센터직원 접촉' '#8271 접촉' '#8669 접촉' '#9734 접촉'\n '#9621 접촉' '#9572 접촉' '#9525 접촉' '#7914 접촉' '#8791 접촉' '#9219 접촉'\n '#8057 접촉' '#8079 접촉' '#8646 접촉' '#9020 접촉' '#8245 접촉' '#8709 접촉 추정'\n '#8881 접촉 추정' '구로구 콜센터' '#8486 접촉' '동대문구' '#8196 접촉' '#8138 접촉'\n '구로구 콜센터 접촉' '#8044 접촉' '해외 접촉' '#7923 접촉' '신천지 추정' '#7573 접축 추정'\n '#6959 접촉(추정)' '#7280 접촉(추정)' '#7500 접촉(추정)' '#6727 첩촉' '대구 거주'\n '#6832 접촉자' '#6921접촉자' '#935 접촉자' '#6937 접촉자' '성동구 아파트' '대구 방문'\n '부동산 중개 법인' '여의도 건설현장' '대구 확진자 접촉' '명성교회' '#794 접촉(추정)' '#2621번 접촉'\n '#1247 접촉(추정)' '부동산 중개업' '쇼핑몰' '은평구 병원' '경찰병원' '서울재생병원' '신천지대구교회'\n '#794 접촉자' '대구 방문(명성교회)' '#780 접촉(추정)' '#56 접촉' '#136 접촉' '대구 거주자'\n '#6 접촉(추정)' '#30 접촉' '#29 접촉' '#83 접촉' '#6 접촉' '#5 접촉' '#3 접촉']\n"
     ]
    }
   ],
   "source": [
    "infection_paths = df_temp[\"접촉력\"].unique()\n",
    "print(\"number of known contact paths:\", len(infection_paths))\n",
    "print(infection_paths)"
   ]
  },
  {
   "source": [
    "### Preprocessing dates"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      연번     환자     확진일 거주지 여행력    접촉력 퇴원현황\n",
       "0  17430  56828  12.26.  기타   -  동부구치소  NaN\n",
       "1  17429  56827  12.26.  기타   -  동부구치소  NaN\n",
       "2  17428  56826  12.26.  기타   -  동부구치소  NaN\n",
       "3  17427  56806  12.26.  기타   -  동부구치소  NaN\n",
       "4  17426  56817  12.26.  기타   -  동부구치소  NaN"
      ],
      "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>연번</th>\n      <th>환자</th>\n      <th>확진일</th>\n      <th>거주지</th>\n      <th>여행력</th>\n      <th>접촉력</th>\n      <th>퇴원현황</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>17430</td>\n      <td>56828</td>\n      <td>12.26.</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>17429</td>\n      <td>56827</td>\n      <td>12.26.</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>17428</td>\n      <td>56826</td>\n      <td>12.26.</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>17427</td>\n      <td>56806</td>\n      <td>12.26.</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17426</td>\n      <td>56817</td>\n      <td>12.26.</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 65
    }
   ],
   "source": [
    "df_date = df_temp.sort_values([\"연번\"], ascending=False)\n",
    "df_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      연번     환자    확진일 거주지 여행력    접촉력 퇴원현황\n",
       "0  17430  56828  12-26  기타   -  동부구치소  NaN\n",
       "1  17429  56827  12-26  기타   -  동부구치소  NaN\n",
       "2  17428  56826  12-26  기타   -  동부구치소  NaN\n",
       "3  17427  56806  12-26  기타   -  동부구치소  NaN\n",
       "4  17426  56817  12-26  기타   -  동부구치소  NaN"
      ],
      "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>연번</th>\n      <th>환자</th>\n      <th>확진일</th>\n      <th>거주지</th>\n      <th>여행력</th>\n      <th>접촉력</th>\n      <th>퇴원현황</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>17430</td>\n      <td>56828</td>\n      <td>12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>17429</td>\n      <td>56827</td>\n      <td>12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>17428</td>\n      <td>56826</td>\n      <td>12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>17427</td>\n      <td>56806</td>\n      <td>12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17426</td>\n      <td>56817</td>\n      <td>12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 66
    }
   ],
   "source": [
    "df_date[\"확진일\"] = df_date[\"확진일\"].str.replace(\".\", \"-\")\n",
    "df_date[\"확진일\"] = df_date[\"확진일\"].str[:-1]\n",
    "df_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      연번     환자         확진일 거주지 여행력    접촉력 퇴원현황\n",
       "0  17430  56828  2020-12-26  기타   -  동부구치소  NaN\n",
       "1  17429  56827  2020-12-26  기타   -  동부구치소  NaN\n",
       "2  17428  56826  2020-12-26  기타   -  동부구치소  NaN\n",
       "3  17427  56806  2020-12-26  기타   -  동부구치소  NaN\n",
       "4  17426  56817  2020-12-26  기타   -  동부구치소  NaN"
      ],
      "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>연번</th>\n      <th>환자</th>\n      <th>확진일</th>\n      <th>거주지</th>\n      <th>여행력</th>\n      <th>접촉력</th>\n      <th>퇴원현황</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>17430</td>\n      <td>56828</td>\n      <td>2020-12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>17429</td>\n      <td>56827</td>\n      <td>2020-12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>17428</td>\n      <td>56826</td>\n      <td>2020-12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>17427</td>\n      <td>56806</td>\n      <td>2020-12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17426</td>\n      <td>56817</td>\n      <td>2020-12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 67
    }
   ],
   "source": [
    "# change dates according to datetime format\n",
    "df_date[\"확진일\"] = \"2020-0\" + df_date[\"확진일\"]\n",
    "df_date[\"확진일\"] = df_date[\"확진일\"].str.replace(\"010\",\"10\")\n",
    "df_date[\"확진일\"] = df_date[\"확진일\"].str.replace(\"011\",\"11\")\n",
    "df_date[\"확진일\"] = df_date[\"확진일\"].str.replace(\"012\",\"12\")\n",
    "df_date[\"확진일\"] = df_date[\"확진일\"].str.replace(\"00\",\"0\")\n",
    "df_date.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          연번     환자         확진일  거주지 여행력         접촉력 퇴원현황\n",
       "14674   2556  16609  2020-08-20  광진구   -  타시도 확진자 접촉   퇴원\n",
       "8201    9029  34904  2020-12-01  관악구   -   기타 확진자 접촉   퇴원\n",
       "5242   12088  42578  2020-12-12  성북구   -      성동구 병원   퇴원\n",
       "2375   15055  50849  2020-12-21  강남구  미국        해외유입  NaN\n",
       "391    17039  55374  2020-12-25  양천구   -   기타 확진자 접촉  NaN"
      ],
      "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>연번</th>\n      <th>환자</th>\n      <th>확진일</th>\n      <th>거주지</th>\n      <th>여행력</th>\n      <th>접촉력</th>\n      <th>퇴원현황</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>14674</th>\n      <td>2556</td>\n      <td>16609</td>\n      <td>2020-08-20</td>\n      <td>광진구</td>\n      <td>-</td>\n      <td>타시도 확진자 접촉</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>8201</th>\n      <td>9029</td>\n      <td>34904</td>\n      <td>2020-12-01</td>\n      <td>관악구</td>\n      <td>-</td>\n      <td>기타 확진자 접촉</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>5242</th>\n      <td>12088</td>\n      <td>42578</td>\n      <td>2020-12-12</td>\n      <td>성북구</td>\n      <td>-</td>\n      <td>성동구 병원</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>2375</th>\n      <td>15055</td>\n      <td>50849</td>\n      <td>2020-12-21</td>\n      <td>강남구</td>\n      <td>미국</td>\n      <td>해외유입</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>391</th>\n      <td>17039</td>\n      <td>55374</td>\n      <td>2020-12-25</td>\n      <td>양천구</td>\n      <td>-</td>\n      <td>기타 확진자 접촉</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 69
    }
   ],
   "source": [
    "df = df_date.copy()\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      연번     환자         확진일 거주지 여행력    접촉력 퇴원현황\n",
       "0  17430  56828  2020-12-26  기타   -  동부구치소  NaN\n",
       "1  17429  56827  2020-12-26  기타   -  동부구치소  NaN\n",
       "2  17428  56826  2020-12-26  기타   -  동부구치소  NaN\n",
       "3  17427  56806  2020-12-26  기타   -  동부구치소  NaN\n",
       "4  17426  56817  2020-12-26  기타   -  동부구치소  NaN"
      ],
      "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>연번</th>\n      <th>환자</th>\n      <th>확진일</th>\n      <th>거주지</th>\n      <th>여행력</th>\n      <th>접촉력</th>\n      <th>퇴원현황</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>17430</td>\n      <td>56828</td>\n      <td>2020-12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>17429</td>\n      <td>56827</td>\n      <td>2020-12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>17428</td>\n      <td>56826</td>\n      <td>2020-12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>17427</td>\n      <td>56806</td>\n      <td>2020-12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>17426</td>\n      <td>56817</td>\n      <td>2020-12-26</td>\n      <td>기타</td>\n      <td>-</td>\n      <td>동부구치소</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 70
    }
   ],
   "source": [
    "df.loc[(df[\"확진일\"].str.len() < 10), '확진일'] = df.loc[(df[\"확진일\"].str.len() < 10), '확진일'].str.replace(\"-0\", \"-\")\n",
    "df.loc[(df[\"확진일\"].str.len() < 10), '확진일'] = df.loc[(df[\"확진일\"].str.len() < 10), '확진일'].str.replace(\"-\", \"-0\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          연번     환자         확진일   거주지 여행력         접촉력 퇴원현황\n",
       "8409    8821  34366  2020-11-30   강남구   -   기타 확진자 접촉  NaN\n",
       "3757   13573  46675  2020-12-17  동대문구   -        확인 중  NaN\n",
       "11973   5257  23748  2020-09-29   서초구   -        확인 중   퇴원\n",
       "16624    606  10526  2020-04-12   성동구  미국    해외 접촉 추정   퇴원\n",
       "14776   2454  21531  2020-08-19   타시도   -     고대 안암병원   퇴원\n",
       "10731   6499  27918  2020-11-11   도봉구   -   용산구 국군복지단   퇴원\n",
       "1255   16175  54381  2020-12-24   강남구   -   기타 확진자 접촉  NaN\n",
       "3786   13544  46660  2020-12-17   양천구   -   기타 확진자 접촉  NaN\n",
       "12695   4535  21804  2020-09-10   서초구   -        확인 중   퇴원\n",
       "12648   4582  21876  2020-09-10   서초구   -   기타 확진자 접촉   퇴원\n",
       "17185     45   1378  2020-02-25   송파구   -    #794 접촉자   퇴원\n",
       "5699   11531  40920  2020-12-11  영등포구   -        확인 중  NaN\n",
       "10862   6368  27507  2020-11-08   은평구   -  타시도 확진자 접촉   퇴원\n",
       "14456   2774  17290  2020-08-22   중랑구   -  타시도 확진자 접촉   퇴원\n",
       "4477   12853  45182  2020-12-15   은평구   -   기타 확진자 접촉  NaN"
      ],
      "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>연번</th>\n      <th>환자</th>\n      <th>확진일</th>\n      <th>거주지</th>\n      <th>여행력</th>\n      <th>접촉력</th>\n      <th>퇴원현황</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>8409</th>\n      <td>8821</td>\n      <td>34366</td>\n      <td>2020-11-30</td>\n      <td>강남구</td>\n      <td>-</td>\n      <td>기타 확진자 접촉</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3757</th>\n      <td>13573</td>\n      <td>46675</td>\n      <td>2020-12-17</td>\n      <td>동대문구</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>11973</th>\n      <td>5257</td>\n      <td>23748</td>\n      <td>2020-09-29</td>\n      <td>서초구</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>16624</th>\n      <td>606</td>\n      <td>10526</td>\n      <td>2020-04-12</td>\n      <td>성동구</td>\n      <td>미국</td>\n      <td>해외 접촉 추정</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>14776</th>\n      <td>2454</td>\n      <td>21531</td>\n      <td>2020-08-19</td>\n      <td>타시도</td>\n      <td>-</td>\n      <td>고대 안암병원</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>10731</th>\n      <td>6499</td>\n      <td>27918</td>\n      <td>2020-11-11</td>\n      <td>도봉구</td>\n      <td>-</td>\n      <td>용산구 국군복지단</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>1255</th>\n      <td>16175</td>\n      <td>54381</td>\n      <td>2020-12-24</td>\n      <td>강남구</td>\n      <td>-</td>\n      <td>기타 확진자 접촉</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>3786</th>\n      <td>13544</td>\n      <td>46660</td>\n      <td>2020-12-17</td>\n      <td>양천구</td>\n      <td>-</td>\n      <td>기타 확진자 접촉</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>12695</th>\n      <td>4535</td>\n      <td>21804</td>\n      <td>2020-09-10</td>\n      <td>서초구</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>12648</th>\n      <td>4582</td>\n      <td>21876</td>\n      <td>2020-09-10</td>\n      <td>서초구</td>\n      <td>-</td>\n      <td>기타 확진자 접촉</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>17185</th>\n      <td>45</td>\n      <td>1378</td>\n      <td>2020-02-25</td>\n      <td>송파구</td>\n      <td>-</td>\n      <td>#794 접촉자</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>5699</th>\n      <td>11531</td>\n      <td>40920</td>\n      <td>2020-12-11</td>\n      <td>영등포구</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>10862</th>\n      <td>6368</td>\n      <td>27507</td>\n      <td>2020-11-08</td>\n      <td>은평구</td>\n      <td>-</td>\n      <td>타시도 확진자 접촉</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>14456</th>\n      <td>2774</td>\n      <td>17290</td>\n      <td>2020-08-22</td>\n      <td>중랑구</td>\n      <td>-</td>\n      <td>타시도 확진자 접촉</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>4477</th>\n      <td>12853</td>\n      <td>45182</td>\n      <td>2020-12-15</td>\n      <td>은평구</td>\n      <td>-</td>\n      <td>기타 확진자 접촉</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 71
    }
   ],
   "source": [
    "df.sample(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"./dataset_predict/df_wrangle.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         date  new_confirmed\n",
       "0  2020-12-24            552\n",
       "1  2020-12-19            472\n",
       "2  2020-12-25            467\n",
       "3  2020-12-16            423\n",
       "4  2020-12-17            397"
      ],
      "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>date</th>\n      <th>new_confirmed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2020-12-24</td>\n      <td>552</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2020-12-19</td>\n      <td>472</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2020-12-25</td>\n      <td>467</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2020-12-16</td>\n      <td>423</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2020-12-17</td>\n      <td>397</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 73
    }
   ],
   "source": [
    "temp = df[\"확진일\"].value_counts().rename_axis('date').reset_index(name='new_confirmed')\n",
    "temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           date  new_confirmed\n",
       "294  2020-01-24              1\n",
       "274  2020-01-30              3\n",
       "278  2020-01-31              3\n",
       "306  2020-02-02              1\n",
       "290  2020-02-05              2"
      ],
      "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>date</th>\n      <th>new_confirmed</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>294</th>\n      <td>2020-01-24</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>274</th>\n      <td>2020-01-30</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>278</th>\n      <td>2020-01-31</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>306</th>\n      <td>2020-02-02</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>290</th>\n      <td>2020-02-05</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 74
    }
   ],
   "source": [
    "df_predict = temp.sort_values(by=\"date\")\n",
    "df_predict.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### stretch the dataframe as timeseries dataset with interval of 1 day "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predict[\"date\"] = pd.to_datetime(df_predict[\"date\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 date  new_confirmed\n",
       "date                                \n",
       "2020-01-24 2020-01-24              1\n",
       "2020-01-30 2020-01-30              3\n",
       "2020-01-31 2020-01-31              3\n",
       "2020-02-02 2020-02-02              1\n",
       "2020-02-05 2020-02-05              2"
      ],
      "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>date</th>\n      <th>new_confirmed</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-24</th>\n      <td>2020-01-24</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2020-01-30</th>\n      <td>2020-01-30</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2020-01-31</th>\n      <td>2020-01-31</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>2020-02-02</th>\n      <td>2020-02-02</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2020-02-05</th>\n      <td>2020-02-05</td>\n      <td>2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 76
    }
   ],
   "source": [
    "df_predict.index = pd.DatetimeIndex(df_predict[\"date\"])\n",
    "df_predict.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 date  new_confirmed\n",
       "date                                \n",
       "2020-01-24 2020-01-24            1.0\n",
       "2020-01-25        NaT            NaN\n",
       "2020-01-26        NaT            NaN\n",
       "2020-01-27        NaT            NaN\n",
       "2020-01-28        NaT            NaN"
      ],
      "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>date</th>\n      <th>new_confirmed</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-24</th>\n      <td>2020-01-24</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2020-01-25</th>\n      <td>NaT</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2020-01-26</th>\n      <td>NaT</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2020-01-27</th>\n      <td>NaT</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2020-01-28</th>\n      <td>NaT</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 77
    }
   ],
   "source": [
    "df_predict = df_predict.resample('D').max()\n",
    "df_predict.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 date  new_confirmed\n",
       "date                                \n",
       "2020-01-24 2020-01-24            1.0\n",
       "2020-01-25 2020-01-25            NaN\n",
       "2020-01-26 2020-01-26            NaN\n",
       "2020-01-27 2020-01-27            NaN\n",
       "2020-01-28 2020-01-28            NaN"
      ],
      "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>date</th>\n      <th>new_confirmed</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-24</th>\n      <td>2020-01-24</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2020-01-25</th>\n      <td>2020-01-25</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2020-01-26</th>\n      <td>2020-01-26</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2020-01-27</th>\n      <td>2020-01-27</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>2020-01-28</th>\n      <td>2020-01-28</td>\n      <td>NaN</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 78
    }
   ],
   "source": [
    "df_predict[\"date\"] = df_predict.index\n",
    "df_predict.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                 date  new_confirmed\n",
       "date                                \n",
       "2020-01-24 2020-01-24            1.0\n",
       "2020-01-25 2020-01-25            0.0\n",
       "2020-01-26 2020-01-26            0.0\n",
       "2020-01-27 2020-01-27            0.0\n",
       "2020-01-28 2020-01-28            0.0\n",
       "2020-01-29 2020-01-29            0.0\n",
       "2020-01-30 2020-01-30            3.0\n",
       "2020-01-31 2020-01-31            3.0\n",
       "2020-02-01 2020-02-01            0.0\n",
       "2020-02-02 2020-02-02            1.0"
      ],
      "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>date</th>\n      <th>new_confirmed</th>\n    </tr>\n    <tr>\n      <th>date</th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2020-01-24</th>\n      <td>2020-01-24</td>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>2020-01-25</th>\n      <td>2020-01-25</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2020-01-26</th>\n      <td>2020-01-26</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2020-01-27</th>\n      <td>2020-01-27</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2020-01-28</th>\n      <td>2020-01-28</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2020-01-29</th>\n      <td>2020-01-29</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2020-01-30</th>\n      <td>2020-01-30</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2020-01-31</th>\n      <td>2020-01-31</td>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>2020-02-01</th>\n      <td>2020-02-01</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2020-02-02</th>\n      <td>2020-02-02</td>\n      <td>1.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 79
    }
   ],
   "source": [
    "df_predict = df_predict.fillna(0)\n",
    "df_predict[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predict.to_csv(\"dataset_predict/df_predict.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_predict =  pd.read_csv(\"dataset_predict/df_predict.csv\", encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get untracked patients information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          연번     환자         확진일   거주지 여행력   접촉력 퇴원현황\n",
       "7      17423  56843  2020-12-26   타시도   -  확인 중  NaN\n",
       "8      17422  56842  2020-12-26   타시도   -  확인 중  NaN\n",
       "14     17416  56716  2020-12-26   강서구   -  확인 중  NaN\n",
       "16     17414  56269  2020-12-26  영등포구   -  확인 중  NaN\n",
       "22     17408  56872  2020-12-26   강서구   -  확인 중  NaN\n",
       "...      ...    ...         ...   ...  ..   ...  ...\n",
       "17174     56   1768  2020-02-25   은평구   -  확인 중   퇴원\n",
       "17188     42   1370  2020-02-25   송파구   -  확인 중   퇴원\n",
       "17191     39    924  2020-02-25   금천구   -  확인 중   퇴원\n",
       "17192     38    907  2020-02-25   타시도   -  확인 중   퇴원\n",
       "17195     35    870  2020-02-25   은평구   -  확인 중   퇴원\n",
       "\n",
       "[3838 rows x 7 columns]"
      ],
      "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>연번</th>\n      <th>환자</th>\n      <th>확진일</th>\n      <th>거주지</th>\n      <th>여행력</th>\n      <th>접촉력</th>\n      <th>퇴원현황</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>7</th>\n      <td>17423</td>\n      <td>56843</td>\n      <td>2020-12-26</td>\n      <td>타시도</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>17422</td>\n      <td>56842</td>\n      <td>2020-12-26</td>\n      <td>타시도</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>17416</td>\n      <td>56716</td>\n      <td>2020-12-26</td>\n      <td>강서구</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>17414</td>\n      <td>56269</td>\n      <td>2020-12-26</td>\n      <td>영등포구</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>17408</td>\n      <td>56872</td>\n      <td>2020-12-26</td>\n      <td>강서구</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>17174</th>\n      <td>56</td>\n      <td>1768</td>\n      <td>2020-02-25</td>\n      <td>은평구</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>17188</th>\n      <td>42</td>\n      <td>1370</td>\n      <td>2020-02-25</td>\n      <td>송파구</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>17191</th>\n      <td>39</td>\n      <td>924</td>\n      <td>2020-02-25</td>\n      <td>금천구</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>17192</th>\n      <td>38</td>\n      <td>907</td>\n      <td>2020-02-25</td>\n      <td>타시도</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>퇴원</td>\n    </tr>\n    <tr>\n      <th>17195</th>\n      <td>35</td>\n      <td>870</td>\n      <td>2020-02-25</td>\n      <td>은평구</td>\n      <td>-</td>\n      <td>확인 중</td>\n      <td>퇴원</td>\n    </tr>\n  </tbody>\n</table>\n<p>3838 rows × 7 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 82
    }
   ],
   "source": [
    "df_untracked = df[df[\"접촉력\"]==\"확인 중\"]\n",
    "df_untracked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           date  new_untracked\n",
       "0    2020-02-25              6\n",
       "1    2020-02-26              1\n",
       "2    2020-02-27              1\n",
       "3    2020-02-28              2\n",
       "4    2020-03-02              2\n",
       "..          ...            ...\n",
       "232  2020-12-22            161\n",
       "233  2020-12-23            147\n",
       "234  2020-12-24            100\n",
       "235  2020-12-25            150\n",
       "236  2020-12-26            120\n",
       "\n",
       "[237 rows x 2 columns]"
      ],
      "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>date</th>\n      <th>new_untracked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2020-02-25</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2020-02-26</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2020-02-27</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2020-02-28</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2020-03-02</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>232</th>\n      <td>2020-12-22</td>\n      <td>161</td>\n    </tr>\n    <tr>\n      <th>233</th>\n      <td>2020-12-23</td>\n      <td>147</td>\n    </tr>\n    <tr>\n      <th>234</th>\n      <td>2020-12-24</td>\n      <td>100</td>\n    </tr>\n    <tr>\n      <th>235</th>\n      <td>2020-12-25</td>\n      <td>150</td>\n    </tr>\n    <tr>\n      <th>236</th>\n      <td>2020-12-26</td>\n      <td>120</td>\n    </tr>\n  </tbody>\n</table>\n<p>237 rows × 2 columns</p>\n</div>"
     },
     "metadata": {},
     "execution_count": 83
    }
   ],
   "source": [
    "df_untracked_temp = df_untracked.groupby(by=[\"확진일\"]).size()\n",
    "df_untracked_temp = df_untracked_temp.rename_axis('date').reset_index(name='new_untracked')\n",
    "df_untracked_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_untracked_no = df_untracked_temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "          date  new_confirmed  new_untracked\n",
       "0   2020-01-24            1.0            0.0\n",
       "1   2020-01-25            0.0            0.0\n",
       "2   2020-01-26            0.0            0.0\n",
       "3   2020-01-27            0.0            0.0\n",
       "4   2020-01-28            0.0            0.0\n",
       "5   2020-01-29            0.0            0.0\n",
       "6   2020-01-30            3.0            0.0\n",
       "7   2020-01-31            3.0            0.0\n",
       "8   2020-02-01            0.0            0.0\n",
       "9   2020-02-02            1.0            0.0\n",
       "10  2020-02-03            0.0            0.0\n",
       "11  2020-02-04            0.0            0.0\n",
       "12  2020-02-05            2.0            0.0\n",
       "13  2020-02-06            1.0            0.0\n",
       "14  2020-02-07            1.0            0.0"
      ],
      "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>date</th>\n      <th>new_confirmed</th>\n      <th>new_untracked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2020-01-24</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2020-01-25</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2020-01-26</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2020-01-27</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2020-01-28</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2020-01-29</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2020-01-30</td>\n      <td>3.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2020-01-31</td>\n      <td>3.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2020-02-01</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2020-02-02</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>2020-02-03</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>2020-02-04</td>\n      <td>0.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>2020-02-05</td>\n      <td>2.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>2020-02-06</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>2020-02-07</td>\n      <td>1.0</td>\n      <td>0.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 85
    }
   ],
   "source": [
    "df_merged = pd.merge(df_predict, df_untracked_no, how=\"left\", on=\"date\")\n",
    "df_merged = df_merged.fillna(0)\n",
    "df_merged.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           date  new_confirmed  new_untracked\n",
       "323  2020-12-12          299.0           81.0\n",
       "324  2020-12-13          219.0           53.0\n",
       "325  2020-12-14          251.0           58.0\n",
       "326  2020-12-15          378.0          149.0\n",
       "327  2020-12-16          423.0          128.0\n",
       "328  2020-12-17          397.0          126.0\n",
       "329  2020-12-18          284.0          122.0\n",
       "330  2020-12-19          472.0           85.0\n",
       "331  2020-12-20          328.0          115.0\n",
       "332  2020-12-21          317.0           88.0\n",
       "333  2020-12-22          375.0          161.0\n",
       "334  2020-12-23          319.0          147.0\n",
       "335  2020-12-24          552.0          100.0\n",
       "336  2020-12-25          467.0          150.0\n",
       "337  2020-12-26          361.0          120.0"
      ],
      "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>date</th>\n      <th>new_confirmed</th>\n      <th>new_untracked</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>323</th>\n      <td>2020-12-12</td>\n      <td>299.0</td>\n      <td>81.0</td>\n    </tr>\n    <tr>\n      <th>324</th>\n      <td>2020-12-13</td>\n      <td>219.0</td>\n      <td>53.0</td>\n    </tr>\n    <tr>\n      <th>325</th>\n      <td>2020-12-14</td>\n      <td>251.0</td>\n      <td>58.0</td>\n    </tr>\n    <tr>\n      <th>326</th>\n      <td>2020-12-15</td>\n      <td>378.0</td>\n      <td>149.0</td>\n    </tr>\n    <tr>\n      <th>327</th>\n      <td>2020-12-16</td>\n      <td>423.0</td>\n      <td>128.0</td>\n    </tr>\n    <tr>\n      <th>328</th>\n      <td>2020-12-17</td>\n      <td>397.0</td>\n      <td>126.0</td>\n    </tr>\n    <tr>\n      <th>329</th>\n      <td>2020-12-18</td>\n      <td>284.0</td>\n      <td>122.0</td>\n    </tr>\n    <tr>\n      <th>330</th>\n      <td>2020-12-19</td>\n      <td>472.0</td>\n      <td>85.0</td>\n    </tr>\n    <tr>\n      <th>331</th>\n      <td>2020-12-20</td>\n      <td>328.0</td>\n      <td>115.0</td>\n    </tr>\n    <tr>\n      <th>332</th>\n      <td>2020-12-21</td>\n      <td>317.0</td>\n      <td>88.0</td>\n    </tr>\n    <tr>\n      <th>333</th>\n      <td>2020-12-22</td>\n      <td>375.0</td>\n      <td>161.0</td>\n    </tr>\n    <tr>\n      <th>334</th>\n      <td>2020-12-23</td>\n      <td>319.0</td>\n      <td>147.0</td>\n    </tr>\n    <tr>\n      <th>335</th>\n      <td>2020-12-24</td>\n      <td>552.0</td>\n      <td>100.0</td>\n    </tr>\n    <tr>\n      <th>336</th>\n      <td>2020-12-25</td>\n      <td>467.0</td>\n      <td>150.0</td>\n    </tr>\n    <tr>\n      <th>337</th>\n      <td>2020-12-26</td>\n      <td>361.0</td>\n      <td>120.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 86
    }
   ],
   "source": [
    "df_merged.tail(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[1, 1, 1, 1, 1, 1, 2, 4, 4, 4]\n[3265, 3347, 3455, 3562, 3692, 3761, 3842, 3945, 4120, 4238]\n"
     ]
    }
   ],
   "source": [
    "# Guestimate number of infection contact paths, based on basic reproduction number as 1.5\n",
    "list_infection_paths_no = []\n",
    "R0 = 1.5\n",
    "for index, row in df_merged.iterrows():\n",
    "    filter_end_date = row[\"date\"]\n",
    "    # print(filter_end_date)\n",
    "    df_filtered = df.loc[df['확진일'] <= filter_end_date]\n",
    "    \n",
    "    # 확진자 접촉력이 밝혀진 경우\n",
    "    infection_paths_known_no = len(df_filtered[\"접촉력\"].unique())\n",
    "    \n",
    "    # \"기타 확진자 접촉\", \"타시도 확진자 접촉\" 항목 485건 / R0\n",
    "    etc_contact_no = len(df_filtered[df_filtered[\"접촉력\"].str.contains(\"기타 확진자 접촉\")])/R0\n",
    "    state_contact_no = len(df_filtered[df_filtered[\"접촉력\"].str.contains(\"타시도 확진자 접촉\")])/R0\n",
    "\n",
    "    infection_paths_no = infection_paths_known_no + etc_contact_no + state_contact_no\n",
    "    list_infection_paths_no.append(int(infection_paths_no))\n",
    "print(list_infection_paths_no[:10])\n",
    "print(list_infection_paths_no[-10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         date  new_confirmed  new_untracked  no_paths\n",
       "0  2020-01-24              1              0         1\n",
       "1  2020-01-25              0              0         1\n",
       "2  2020-01-26              0              0         1\n",
       "3  2020-01-27              0              0         1\n",
       "4  2020-01-28              0              0         1"
      ],
      "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>date</th>\n      <th>new_confirmed</th>\n      <th>new_untracked</th>\n      <th>no_paths</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2020-01-24</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2020-01-25</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2020-01-26</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2020-01-27</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2020-01-28</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 91
    }
   ],
   "source": [
    "df_merged[\"no_paths\"] = list_infection_paths_no\n",
    "df_merged[\"new_confirmed\"] = df_merged[\"new_confirmed\"].apply(int)\n",
    "df_merged[\"new_untracked\"] = df_merged[\"new_untracked\"].apply(int)\n",
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           date  new_confirmed  new_untracked  no_paths\n",
       "333  2020-12-22            375            161      3761\n",
       "334  2020-12-23            319            147      3842\n",
       "335  2020-12-24            552            100      3945\n",
       "336  2020-12-25            467            150      4120\n",
       "337  2020-12-26            361            120      4238"
      ],
      "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>date</th>\n      <th>new_confirmed</th>\n      <th>new_untracked</th>\n      <th>no_paths</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>333</th>\n      <td>2020-12-22</td>\n      <td>375</td>\n      <td>161</td>\n      <td>3761</td>\n    </tr>\n    <tr>\n      <th>334</th>\n      <td>2020-12-23</td>\n      <td>319</td>\n      <td>147</td>\n      <td>3842</td>\n    </tr>\n    <tr>\n      <th>335</th>\n      <td>2020-12-24</td>\n      <td>552</td>\n      <td>100</td>\n      <td>3945</td>\n    </tr>\n    <tr>\n      <th>336</th>\n      <td>2020-12-25</td>\n      <td>467</td>\n      <td>150</td>\n      <td>4120</td>\n    </tr>\n    <tr>\n      <th>337</th>\n      <td>2020-12-26</td>\n      <td>361</td>\n      <td>120</td>\n      <td>4238</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 92
    }
   ],
   "source": [
    "df_merged.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           date  new_confirmed  new_untracked  no_paths  new_no_paths\n",
       "328  2020-12-17            397            126      3265         143.0\n",
       "329  2020-12-18            284            122      3347          82.0\n",
       "330  2020-12-19            472             85      3455         108.0\n",
       "331  2020-12-20            328            115      3562         107.0\n",
       "332  2020-12-21            317             88      3692         130.0\n",
       "333  2020-12-22            375            161      3761          69.0\n",
       "334  2020-12-23            319            147      3842          81.0\n",
       "335  2020-12-24            552            100      3945         103.0\n",
       "336  2020-12-25            467            150      4120         175.0\n",
       "337  2020-12-26            361            120      4238         118.0"
      ],
      "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>date</th>\n      <th>new_confirmed</th>\n      <th>new_untracked</th>\n      <th>no_paths</th>\n      <th>new_no_paths</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>328</th>\n      <td>2020-12-17</td>\n      <td>397</td>\n      <td>126</td>\n      <td>3265</td>\n      <td>143.0</td>\n    </tr>\n    <tr>\n      <th>329</th>\n      <td>2020-12-18</td>\n      <td>284</td>\n      <td>122</td>\n      <td>3347</td>\n      <td>82.0</td>\n    </tr>\n    <tr>\n      <th>330</th>\n      <td>2020-12-19</td>\n      <td>472</td>\n      <td>85</td>\n      <td>3455</td>\n      <td>108.0</td>\n    </tr>\n    <tr>\n      <th>331</th>\n      <td>2020-12-20</td>\n      <td>328</td>\n      <td>115</td>\n      <td>3562</td>\n      <td>107.0</td>\n    </tr>\n    <tr>\n      <th>332</th>\n      <td>2020-12-21</td>\n      <td>317</td>\n      <td>88</td>\n      <td>3692</td>\n      <td>130.0</td>\n    </tr>\n    <tr>\n      <th>333</th>\n      <td>2020-12-22</td>\n      <td>375</td>\n      <td>161</td>\n      <td>3761</td>\n      <td>69.0</td>\n    </tr>\n    <tr>\n      <th>334</th>\n      <td>2020-12-23</td>\n      <td>319</td>\n      <td>147</td>\n      <td>3842</td>\n      <td>81.0</td>\n    </tr>\n    <tr>\n      <th>335</th>\n      <td>2020-12-24</td>\n      <td>552</td>\n      <td>100</td>\n      <td>3945</td>\n      <td>103.0</td>\n    </tr>\n    <tr>\n      <th>336</th>\n      <td>2020-12-25</td>\n      <td>467</td>\n      <td>150</td>\n      <td>4120</td>\n      <td>175.0</td>\n    </tr>\n    <tr>\n      <th>337</th>\n      <td>2020-12-26</td>\n      <td>361</td>\n      <td>120</td>\n      <td>4238</td>\n      <td>118.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 93
    }
   ],
   "source": [
    "df_merged[\"new_no_paths\"] = df_merged[\"no_paths\"].diff()\n",
    "df_merged.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged = df_merged[[\"date\", \"new_confirmed\", \"new_untracked\", \"new_no_paths\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_merged.to_csv(\"dataset_predict/df_predict.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label holiday as column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[{'sc': '2000-01-01', 'lc': '1999-11-25', 'w': 7, 'h': True, 'ht': '신정'},\n",
       " {'sc': '2000-01-02', 'lc': '1999-11-26', 'w': 1, 'h': False, 'ht': ''},\n",
       " {'sc': '2000-01-03', 'lc': '1999-11-27', 'w': 2, 'h': False, 'ht': ''},\n",
       " {'sc': '2000-01-04', 'lc': '1999-11-28', 'w': 3, 'h': False, 'ht': ''},\n",
       " {'sc': '2000-01-05', 'lc': '1999-11-29', 'w': 4, 'h': False, 'ht': ''},\n",
       " {'sc': '2000-01-06', 'lc': '1999-11-30', 'w': 5, 'h': False, 'ht': ''},\n",
       " {'sc': '2000-01-07', 'lc': '1999-12-01', 'w': 6, 'h': False, 'ht': ''},\n",
       " {'sc': '2000-01-08', 'lc': '1999-12-02', 'w': 7, 'h': False, 'ht': ''},\n",
       " {'sc': '2000-01-09', 'lc': '1999-12-03', 'w': 1, 'h': False, 'ht': ''},\n",
       " {'sc': '2000-01-10', 'lc': '1999-12-04', 'w': 2, 'h': False, 'ht': ''}]"
      ]
     },
     "metadata": {},
     "execution_count": 96
    }
   ],
   "source": [
    "import json\n",
    "\n",
    "list_calendar=[]\n",
    "\n",
    "calendar_file = \"./korean-calendar/korean-calendar.json\"\n",
    "# read file\n",
    "with open(calendar_file, 'r') as myfile:\n",
    "    data=myfile.readlines()\n",
    "\n",
    "data = [x.strip() for x in data] \n",
    "\n",
    "for i in data:\n",
    "    calendar_data = i[43:]\n",
    "    calendar_item = \"{\"+calendar_data\n",
    "    calendar_item_json = json.loads(calendar_item)\n",
    "    list_calendar.append(calendar_item_json)\n",
    "list_calendar[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           sc  w      h\n",
       "0  2000-01-01  7   True\n",
       "1  2000-01-02  1  False\n",
       "2  2000-01-03  2  False\n",
       "3  2000-01-04  3  False\n",
       "4  2000-01-05  4  False"
      ],
      "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>sc</th>\n      <th>w</th>\n      <th>h</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2000-01-01</td>\n      <td>7</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2000-01-02</td>\n      <td>1</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2000-01-03</td>\n      <td>2</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2000-01-04</td>\n      <td>3</td>\n      <td>False</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2000-01-05</td>\n      <td>4</td>\n      <td>False</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 97
    }
   ],
   "source": [
    "df_calendar = pd.DataFrame(list_calendar)\n",
    "df_calendar = df_calendar[[\"sc\", \"w\", \"h\"]]\n",
    "df_calendar.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# notate sunday, friday, saturday as weekend: which is 1\n",
    "def label_weekend(row):\n",
    "    # sunday\n",
    "    if row['w'] == 1:\n",
    "        return 1\n",
    "    # friday\n",
    "    if row['w'] == 6:\n",
    "        return 1\n",
    "    # saturday\n",
    "    if row['w'] == 7:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           sc  w      h  is_weekend\n",
       "0  2000-01-01  7   True           1\n",
       "1  2000-01-02  1  False           1\n",
       "2  2000-01-03  2  False           0\n",
       "3  2000-01-04  3  False           0\n",
       "4  2000-01-05  4  False           0\n",
       "5  2000-01-06  5  False           0\n",
       "6  2000-01-07  6  False           1\n",
       "7  2000-01-08  7  False           1\n",
       "8  2000-01-09  1  False           1\n",
       "9  2000-01-10  2  False           0"
      ],
      "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>sc</th>\n      <th>w</th>\n      <th>h</th>\n      <th>is_weekend</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2000-01-01</td>\n      <td>7</td>\n      <td>True</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2000-01-02</td>\n      <td>1</td>\n      <td>False</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2000-01-03</td>\n      <td>2</td>\n      <td>False</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2000-01-04</td>\n      <td>3</td>\n      <td>False</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2000-01-05</td>\n      <td>4</td>\n      <td>False</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2000-01-06</td>\n      <td>5</td>\n      <td>False</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2000-01-07</td>\n      <td>6</td>\n      <td>False</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2000-01-08</td>\n      <td>7</td>\n      <td>False</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2000-01-09</td>\n      <td>1</td>\n      <td>False</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2000-01-10</td>\n      <td>2</td>\n      <td>False</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 99
    }
   ],
   "source": [
    "df_calendar[\"is_weekend\"] = df_calendar.apply(lambda row: label_weekend(row), axis=1)\n",
    "df_calendar.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename dataframe name\n",
    "df_calendar.rename(columns = {'sc':'date', 'h':'is_holiday'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "         date  w is_holiday  is_weekend\n",
       "0  2000-01-01  7          1           1\n",
       "1  2000-01-02  1          0           1\n",
       "2  2000-01-03  2          0           0\n",
       "3  2000-01-04  3          0           0\n",
       "4  2000-01-05  4          0           0\n",
       "5  2000-01-06  5          0           0\n",
       "6  2000-01-07  6          0           1\n",
       "7  2000-01-08  7          0           1\n",
       "8  2000-01-09  1          0           1\n",
       "9  2000-01-10  2          0           0"
      ],
      "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>date</th>\n      <th>w</th>\n      <th>is_holiday</th>\n      <th>is_weekend</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2000-01-01</td>\n      <td>7</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2000-01-02</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2000-01-03</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2000-01-04</td>\n      <td>3</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2000-01-05</td>\n      <td>4</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>2000-01-06</td>\n      <td>5</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>2000-01-07</td>\n      <td>6</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>2000-01-08</td>\n      <td>7</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>2000-01-09</td>\n      <td>1</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>2000-01-10</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 101
    }
   ],
   "source": [
    "# if it is holiday, notate as 1. If not, notate as 0\n",
    "df_calendar.loc[df_calendar[\"is_holiday\"]==True, \"is_holiday\"] = 1\n",
    "df_calendar.loc[df_calendar[\"is_holiday\"]==False, \"is_holiday\"] = 0\n",
    "df_calendar[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_calendar.to_csv(\"dataset_predict/df_calendar.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_calendar_subset = df_calendar[[\"date\", \"is_holiday\", \"is_weekend\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           date  new_confirmed  new_untracked  new_no_paths is_holiday  \\\n",
       "333  2020-12-22            375            161          69.0          0   \n",
       "334  2020-12-23            319            147          81.0          0   \n",
       "335  2020-12-24            552            100         103.0          0   \n",
       "336  2020-12-25            467            150         175.0          1   \n",
       "337  2020-12-26            361            120         118.0          0   \n",
       "\n",
       "     is_weekend  \n",
       "333           0  \n",
       "334           0  \n",
       "335           0  \n",
       "336           1  \n",
       "337           1  "
      ],
      "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>date</th>\n      <th>new_confirmed</th>\n      <th>new_untracked</th>\n      <th>new_no_paths</th>\n      <th>is_holiday</th>\n      <th>is_weekend</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>333</th>\n      <td>2020-12-22</td>\n      <td>375</td>\n      <td>161</td>\n      <td>69.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>334</th>\n      <td>2020-12-23</td>\n      <td>319</td>\n      <td>147</td>\n      <td>81.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>335</th>\n      <td>2020-12-24</td>\n      <td>552</td>\n      <td>100</td>\n      <td>103.0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>336</th>\n      <td>2020-12-25</td>\n      <td>467</td>\n      <td>150</td>\n      <td>175.0</td>\n      <td>1</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>337</th>\n      <td>2020-12-26</td>\n      <td>361</td>\n      <td>120</td>\n      <td>118.0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 104
    }
   ],
   "source": [
    "df_train = pd.merge(df_merged, df_calendar_subset, how=\"left\", on=\"date\")\n",
    "df_train.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add Government Social Distancing Policy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           date  new_confirmed  new_untracked  new_no_paths is_holiday  \\\n",
       "328  2020-12-17            397            126         143.0          0   \n",
       "329  2020-12-18            284            122          82.0          0   \n",
       "330  2020-12-19            472             85         108.0          0   \n",
       "331  2020-12-20            328            115         107.0          0   \n",
       "332  2020-12-21            317             88         130.0          0   \n",
       "333  2020-12-22            375            161          69.0          0   \n",
       "334  2020-12-23            319            147          81.0          0   \n",
       "335  2020-12-24            552            100         103.0          0   \n",
       "336  2020-12-25            467            150         175.0          1   \n",
       "337  2020-12-26            361            120         118.0          0   \n",
       "\n",
       "     is_weekend  gov_policy  \n",
       "328           0           0  \n",
       "329           1           0  \n",
       "330           1           0  \n",
       "331           1           0  \n",
       "332           0           0  \n",
       "333           0           0  \n",
       "334           0           0  \n",
       "335           0           0  \n",
       "336           1           0  \n",
       "337           1           0  "
      ],
      "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>date</th>\n      <th>new_confirmed</th>\n      <th>new_untracked</th>\n      <th>new_no_paths</th>\n      <th>is_holiday</th>\n      <th>is_weekend</th>\n      <th>gov_policy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>328</th>\n      <td>2020-12-17</td>\n      <td>397</td>\n      <td>126</td>\n      <td>143.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>329</th>\n      <td>2020-12-18</td>\n      <td>284</td>\n      <td>122</td>\n      <td>82.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>330</th>\n      <td>2020-12-19</td>\n      <td>472</td>\n      <td>85</td>\n      <td>108.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>331</th>\n      <td>2020-12-20</td>\n      <td>328</td>\n      <td>115</td>\n      <td>107.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>332</th>\n      <td>2020-12-21</td>\n      <td>317</td>\n      <td>88</td>\n      <td>130.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>333</th>\n      <td>2020-12-22</td>\n      <td>375</td>\n      <td>161</td>\n      <td>69.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>334</th>\n      <td>2020-12-23</td>\n      <td>319</td>\n      <td>147</td>\n      <td>81.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>335</th>\n      <td>2020-12-24</td>\n      <td>552</td>\n      <td>100</td>\n      <td>103.0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>336</th>\n      <td>2020-12-25</td>\n      <td>467</td>\n      <td>150</td>\n      <td>175.0</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>337</th>\n      <td>2020-12-26</td>\n      <td>361</td>\n      <td>120</td>\n      <td>118.0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 105
    }
   ],
   "source": [
    "df_train[\"gov_policy\"] = 0\n",
    "df_train[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 서울의 사회적 거리두기 2단계는 8월 16일부터 조치됨\n",
    "# 서울에는 8월 19일 0시 기준으로 새로운 조치들이 추가되며, 흔히 얘기하는 2.5단계가 됨. 이후에 기간 연장을 해서 9.7(월)0시 ~ 9.13(일)24시까지 2.5단계가 시행됨\n",
    "# 서울에서 12월 8일 0시부 사회적 거리두기 2.5단계 실시\n",
    "\n",
    "df_train.loc[(df_train[\"date\"]>=\"2020-08-16\") & (df_train[\"date\"]<=\"2020-08-18\"),\"gov_policy\"] = 2\n",
    "df_train.loc[(df_train[\"date\"]>=\"2020-08-19\") & (df_train[\"date\"]<=\"2020-09-13\"),\"gov_policy\"] = 3\n",
    "df_train.loc[(df_train[\"date\"]>=\"2020-09-14\"),\"gov_policy\"] = 2\n",
    "df_train.loc[(df_train[\"date\"]>=\"2020-12-08\"),\"gov_policy\"] = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.to_csv(\"./dataset_predict/df_train.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "               new_confirmed  new_untracked  new_no_paths  is_weekend  \\\n",
       "new_confirmed       1.000000       0.928044      0.953681   -0.007812   \n",
       "new_untracked       0.928044       1.000000      0.920340   -0.025984   \n",
       "new_no_paths        0.953681       0.920340      1.000000    0.008092   \n",
       "is_weekend         -0.007812      -0.025984      0.008092    1.000000   \n",
       "gov_policy          0.656880       0.601399      0.612106   -0.001707   \n",
       "\n",
       "               gov_policy  \n",
       "new_confirmed    0.656880  \n",
       "new_untracked    0.601399  \n",
       "new_no_paths     0.612106  \n",
       "is_weekend      -0.001707  \n",
       "gov_policy       1.000000  "
      ],
      "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>new_confirmed</th>\n      <th>new_untracked</th>\n      <th>new_no_paths</th>\n      <th>is_weekend</th>\n      <th>gov_policy</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>new_confirmed</th>\n      <td>1.000000</td>\n      <td>0.928044</td>\n      <td>0.953681</td>\n      <td>-0.007812</td>\n      <td>0.656880</td>\n    </tr>\n    <tr>\n      <th>new_untracked</th>\n      <td>0.928044</td>\n      <td>1.000000</td>\n      <td>0.920340</td>\n      <td>-0.025984</td>\n      <td>0.601399</td>\n    </tr>\n    <tr>\n      <th>new_no_paths</th>\n      <td>0.953681</td>\n      <td>0.920340</td>\n      <td>1.000000</td>\n      <td>0.008092</td>\n      <td>0.612106</td>\n    </tr>\n    <tr>\n      <th>is_weekend</th>\n      <td>-0.007812</td>\n      <td>-0.025984</td>\n      <td>0.008092</td>\n      <td>1.000000</td>\n      <td>-0.001707</td>\n    </tr>\n    <tr>\n      <th>gov_policy</th>\n      <td>0.656880</td>\n      <td>0.601399</td>\n      <td>0.612106</td>\n      <td>-0.001707</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 108
    }
   ],
   "source": [
    "df_train.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "           date  new_confirmed  new_untracked  new_no_paths is_holiday  \\\n",
       "38   2020-03-02              6              2           0.0          0   \n",
       "41   2020-03-05              1              1           0.0          0   \n",
       "45   2020-03-09             26              3           1.0          0   \n",
       "47   2020-03-11             17              4           0.0          0   \n",
       "52   2020-03-16             13              5           1.0          0   \n",
       "..          ...            ...            ...           ...        ...   \n",
       "329  2020-12-18            284            122          82.0          0   \n",
       "331  2020-12-20            328            115         107.0          0   \n",
       "333  2020-12-22            375            161          69.0          0   \n",
       "334  2020-12-23            319            147          81.0          0   \n",
       "337  2020-12-26            361            120         118.0          0   \n",
       "\n",
       "     is_weekend  gov_policy  \n",
       "38            0           0  \n",
       "41            0           0  \n",
       "45            0           0  \n",
       "47            0           0  \n",
       "52            0           0  \n",
       "..          ...         ...  \n",
       "329           1           3  \n",
       "331           1           3  \n",
       "333           0           3  \n",
       "334           0           3  \n",
       "337           1           3  \n",
       "\n",
       "[109 rows x 7 columns]"
      ],
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     },
     "metadata": {},
     "execution_count": 121
    }
   ],
   "source": [
    "df_train[df_train[\"new_untracked\"] > df_train[\"new_no_paths\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 111
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 전체 그래프\n",
    "df_train[[\"new_confirmed\", \"new_untracked\", \"new_no_paths\"]].plot(figsize=(16,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 112
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1152x648 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "itaewon_crisis = df_train[(df_train[\"date\"]>=\"2020-05-06\") & (df_train[\"date\"]<=\"2020-06-25\")]\n",
    "itaewon_crisis[[\"new_confirmed\", \"new_untracked\", \"new_no_paths\"]].plot(figsize=(16,9))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 113
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 서울시 사랑제일교회 광복절 집회 이후의 데이터\n",
    "jkh_crisis = df_train[(df_train[\"date\"]>=\"2020-08-14\") & (df_train[\"date\"]<=\"2020-08-30\") ]\n",
    "jkh_crisis[[\"new_confirmed\", \"new_untracked\", \"new_no_paths\"]].plot(figsize=(16,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 114
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1152x648 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 최근 2주 데이터\n",
    "recent_two_weeks_data = df_train.tail(14)\n",
    "recent_two_weeks_data[[\"new_confirmed\", \"new_untracked\", \"new_no_paths\"]].plot(figsize=(16,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "metadata": {},
     "execution_count": 115
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 1152x648 with 1 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# 확인된 감염경로 개수와 경로 미확인 감염자 트렌드\n",
    "unknown_and_paths_graph = df_train[[\"new_untracked\", \"new_no_paths\"]][-40:].plot(figsize=(16,9))\n",
    "unknown_and_paths_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig = unknown_and_paths_graph.get_figure()\n",
    "fig.savefig('./img/unknown_and_paths_graph.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}