[금융보안원]2021 금융데이터 경진대회.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import pandas as pd\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import copy "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. 데이터 전처리\n",
    "### 1-1KB 국민카드 : 성별과 연령에 따른 소비자들의 선호 업종 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def agesexfunction(col):\n",
    "    if col in ['01.남성_1.20대이하']:\n",
    "        return \"남성 20대 이하\";\n",
    "    if col in ['01.남성_2.30대']:\n",
    "        return \"남성 30대\";\n",
    "    if col in ['01.남성_3.40대']:\n",
    "        return \"남성 40대\";\n",
    "    if col in ['01.남성_4.50대']:\n",
    "        return \"남성 50대\";\n",
    "    if col in ['01.남성_5.60대']:\n",
    "        return \"남성 60대\"\n",
    "    if col in ['02.여성_1.20대이하']:\n",
    "        return \"여성 20대 이하\";\n",
    "    if col in ['02.여성_2.30대']:\n",
    "        return \"여성 30대\";\n",
    "    if col in ['02.여성_3.40대']:\n",
    "        return \"여성 40대\";\n",
    "    if col in ['02.여성_4.50대']:\n",
    "        return \"여성 50대\";\n",
    "    if col in ['02.여성_5.60대']:\n",
    "        return \"여성 60대\"\n",
    "    else:\n",
    "        return 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def place(x):\n",
    "    if x in ['국/탕/백반류','돼지고기','소고기','기타고기']:\n",
    "        return '한식'\n",
    "    \n",
    "    if x in ['해산물']:\n",
    "        return '일식/수산물'\n",
    "    \n",
    "    if x in ['닭고기']:\n",
    "        return '닭/오리요리'\n",
    "    \n",
    "    if x in ['종합양식']:\n",
    "        return '양식'\n",
    "    \n",
    "    if x in ['면요리']:\n",
    "        return '분식'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def isbefore(value):\n",
    "    if value in ['2019']:\n",
    "        return \"코로나 전\"\n",
    "    if value in ['2020', '2021']:\n",
    "        return \"코로나 후\"\n",
    "    else:\n",
    "        return 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findmin(value):\n",
    "    if value in ['2019']:\n",
    "        return \"코로나 전\"\n",
    "    if value in ['2020', '2021']:\n",
    "        return \"코로나 후\"\n",
    "    else:\n",
    "        return 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv('KB국민카드_금융빅데이터경진대회_데이터_raw2.csv')\n",
    "dfbefore = df1[df1['기준년도']==2019]\n",
    "dfb_group = dfbefore.groupby(by=['성별구분','연령대']).sum()[['매출건수']].reset_index()\n",
    "dfafter = df1[df1['기준년도']==(2020|2021)]\n",
    "dfa_group = dfafter.groupby(by=['성별구분','연령대']).sum()[['매출건수']].reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "is_Seoul = df1['한글시도명'] == '서울특별시'\n",
    "Seoul=df1[is_Seoul]\n",
    "Seoul = Seoul.astype('string')##모든 데이터타입을 string으로\n",
    "Seoul = Seoul.astype({'매출건수': 'int'})\n",
    "Seoul['기준년월']=Seoul['기준년월'].str.zfill(2)\n",
    "Seoul[\"날짜\"] = Seoul[\"기준년도\"].map(str)+\"-\"+Seoul[\"기준년월\"]\n",
    "pie_food = Seoul.groupby(['기준년도','성별구분','연령대','마케팅업종세분류명','마케팅업종세세분류명']).sum(['매출건수'])\n",
    "pie_food = pie_food.reset_index()\n",
    "\n",
    "pie_food=pie_food.astype({'성별구분' : 'string', '연령대' : 'string'})\n",
    "cols=['성별구분', '연령대']\n",
    "pie_food['성별연령'] = pie_food[cols].apply(lambda row: '_'.join(row.values.astype(str)), axis=1)\n",
    "pie_food.drop(columns=['성별구분', '연령대'], inplace=True)\n",
    "\n",
    "pie_food['new성별연령']=pie_food['성별연령'].map(agesexfunction)\n",
    "#TotalForeigner['생활지역권'] = TotalForeigner['행정동코드'].map(myfunction)\n",
    "pie_food.drop(columns=['성별연령'], inplace=True)\n",
    "pie_food = pie_food.astype('string')##모든 데이터타입을 string으로\n",
    "pie_food = pie_food.astype({'매출건수': 'int'})\n",
    "pie_food['중분류명']=pie_food['마케팅업종세분류명'].map(place)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 서로 다른 csv 파일의 요식업종을 일치시키는 함수\n",
    "def place(x):\n",
    "    if x in ['국/탕/백반류','돼지고기','소고기','기타고기']:\n",
    "        return '한식'\n",
    "    \n",
    "    if x in ['해산물']:\n",
    "        return '일식/수산물'\n",
    "    \n",
    "    if x in ['닭고기']:\n",
    "        return '닭/오리요리'\n",
    "    \n",
    "    if x in ['종합양식']:\n",
    "        return '양식'\n",
    "    \n",
    "    if x in ['면요리']:\n",
    "        return '분식'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-2. 소상공인시장진흥공단_상가정보 : 서울시 내의 상가 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "##행정동을 지역생활권으로 바꾸는 함수\n",
    "def myfunction(x):\n",
    "    #종로\n",
    "    if re.search(x,'평창동, 부암동') is not None:\n",
    "        return '평창.부암'\n",
    "    if re.search(x,'종로1.2.3.4가동, 종로5.6가동, 혜화동, 이화동') is not None: #가동인지 그냥 동인지\n",
    "        return '혜화.이화'\n",
    "    if re.search(x,'창신1동 창신2동 창신3동, 숭인1동, 숭인2동부암동') is not None:\n",
    "        return '창신.숭인'\n",
    "    if re.search(x,' 청운효자동, 사직동, 삼청동, 무악동, 교남동, 가회동') is not None:\n",
    "        return '청운효자.사직'\n",
    "    \n",
    "    #중구\n",
    "    if re.search(x,'소공동, 회현동, 명동, 중림동') is not None:\n",
    "        return '소공.회현'\n",
    "    if re.search(x,'신당동, 황학동, 신당5동, 동화동, 청구동, 약수동, 다산동') is not None:\n",
    "        return '신당.황학'\n",
    "    if re.search(x,'광희동, 을지로동, 필동, 장충동') is not None:\n",
    "        return '필동.장충'\n",
    "    \n",
    "    #용산구\n",
    "    if re.search(x,'원효로1동, 원효로2동, 효창동, 용문동, 청파동') is not None:\n",
    "        return '청파.원효'\n",
    "    if re.search(x,'후암동, 용산2가동, 남영동, 이태원2동') is not None:\n",
    "        return '후암.용산'\n",
    "    if re.search(x,'한강로동, 서빙고동, 이촌1동, 이촌2동') is not None:\n",
    "        return '이촌.한강'\n",
    "    if re.search(x,'한남동, 이태원1동, 보광동') is not None:\n",
    "        return '한남'\n",
    "    \n",
    "    \n",
    "    #성동구\n",
    "    if re.search(x,'성수1가1동 성수1가2동, 성수2가1동, 성수2가3동, 송정동') is not None:\n",
    "        return '성수'\n",
    "    if re.search(x,'금호1가동 금호2가동 금호3가동 금호4가동, 옥수동 금호2.3가동') is not None:\n",
    "        return '금호.옥수 '\n",
    "    if re.search(x,'마장동, 사근동, 용답동') is not None:\n",
    "        return '마장.용답'\n",
    "    if re.search(x,'왕십리도선동, 왕십리2동, 행당1동, 행당2동, 응봉동') is not None:\n",
    "        return '왕십리.행당 '\n",
    "    \n",
    "    #강북구\n",
    "    if re.search(x,'번1동 번2동 번3동') is not None:\n",
    "        return '번동'\n",
    "    if re.search(x,'삼각산동, 송천동, 송중동') is not None:\n",
    "        return '삼각산'\n",
    "    if re.search(x,'우이동, 인수동, 수유2동 수유3동') is not None:\n",
    "        return '수유'\n",
    "    if re.search(x,'수유1동, 삼양동, 미아동') is not None:\n",
    "        return '미아'\n",
    "    \n",
    "    #중랑구\n",
    "    if re.search(x,'묵1동 묵2동, 중화1동 중화2동, 상봉1동 상봉2동') is not None:\n",
    "        return '중화.묵동'\n",
    "    if re.search(x,'신내1동 신내2동, 망우본동') is not None:\n",
    "        return '신내.망우'\n",
    "    if re.search(x,'면목2동 면목3동 면목4동 면목5동, 면목7동 면목8동, 면목본동, 망우3동 면목3,8동') is not None:\n",
    "        return '면목'\n",
    "    \n",
    "    #성북구\n",
    "    if re.search(x,' 정릉1동 정릉2동 정릉3동 정릉4동') is not None:\n",
    "        return '정릉'\n",
    "    if re.search(x,'성북동, 삼선동, 동선동, 돈암2동, 안암동, 보문동') is not None:\n",
    "        return '성북.동선'\n",
    "    if re.search(x,'장위1동 장위2동 장위3동, 석관동') is not None:\n",
    "        return '장위.석관'\n",
    "    if re.search(x,'돈암1동, 종암동, 월곡2동') is not None:\n",
    "        return '종암.월곡'\n",
    "    if re.search(x,'길음1동,길음2동 , 월곡1동') is not None:\n",
    "        return '길음'\n",
    "    \n",
    "    #동대문구\n",
    "    if re.search(x,'장안1동 장안2동') is not None:\n",
    "        return '장안'\n",
    "    if re.search(x,'전농1동 전농2동, 답십리1동 답십리2동') is not None:\n",
    "        return '전농.답십리'\n",
    "    if re.search(x,'이문1동-이문2동, 회기동, 휘경1동 휘경2동') is not None:\n",
    "        return '이문.휘경.회기 '\n",
    "    if re.search(x,'청량리동, 제기동, 용신동') is not None:\n",
    "        return '청량리'\n",
    "    \n",
    "    #노원구\n",
    "    if re.search(x,'상계1동, 상계8동, 상계9동') is not None:\n",
    "        return '마들'\n",
    "    if re.search(x,'상계3.4동 상계5동') is not None:\n",
    "        return '상계'\n",
    "    if re.search(x,'상계2동 상계6.7동, 상계10동') is not None:\n",
    "        return '노원'\n",
    "    if re.search(x,'중계본동, 중계1동, 중계4동') is not None:\n",
    "        return '중계'\n",
    "    if re.search(x,'하계1동 하계2동 중계2.3동') is not None:\n",
    "        return '하계'\n",
    "    if re.search(x,'월계1동 월계2동-월계3동') is not None:\n",
    "        return '월계'\n",
    "    if re.search(x,'공릉1동 공릉2동') is not None:\n",
    "        return '공릉'\n",
    "    \n",
    "    #광진구\n",
    "    if re.search(x,'중곡1동 중곡2동 중곡3동 중곡4동') is not None:\n",
    "        return '중곡'\n",
    "    if re.search(x,'구의1동 구의2동 구의3동, 광장동') is not None:\n",
    "        return '구의'\n",
    "    if re.search(x,'군자동, 능동, 화양동') is not None:\n",
    "        return '화양'\n",
    "    if re.search(x,'자양1동 자양2동 자양3동-자양4동') is not None:\n",
    "        return '자양'\n",
    "    \n",
    "    #도봉구\n",
    "    if re.search(x,'도봉1동-도봉2동') is not None:\n",
    "        return '도봉'\n",
    "    if re.search(x,'방학1동 방학2동 방학3동') is not None:\n",
    "        return '방학'\n",
    "    if re.search(x,'쌍문1동-쌍문2동, 쌍문4동') is not None:\n",
    "        return '쌍문'\n",
    "    if re.search(x,'쌍문3동, 창2동-창3동') is not None:\n",
    "        return '초안산'\n",
    "    if re.search(x,'창1동, 창4동-창5동') is not None:\n",
    "        return '창동'\n",
    "    \n",
    "    \n",
    "    #은평구\n",
    "    if re.search(x,'진관동') is not None:\n",
    "        return '진관'\n",
    "    if re.search(x,'갈현1동 갈현2동, 구산동, 역촌동') is not None:\n",
    "        return '연신내'\n",
    "    if re.search(x,'수색동, 증산동, 신사1동 신사2동') is not None:\n",
    "        return '수색'\n",
    "    if re.search(x,'불광1동 불광2동, 대조동') is not None:\n",
    "        return '불광'\n",
    "    if re.search(x,'응암1동 응암2동 응암3동, 녹번동') is not None:\n",
    "        return '응암'\n",
    "    \n",
    "    #서대문구\n",
    "    if re.search(x,'홍은1동홍은2동, 홍제1동 홍제2동 홍제3동') is not None:\n",
    "        return '홍제'\n",
    "    if re.search(x,'남가좌1동남가좌2동, 북가좌1동북가좌2동') is not None:\n",
    "        return '가좌'\n",
    "    if re.search(x,'천연동, 충현동, 북아현동') is not None:\n",
    "        return '충청'\n",
    "    if re.search(x,'연희동, 신촌동') is not None:\n",
    "        return '신촌'\n",
    "    \n",
    "    #마포구\n",
    "    if re.search(x,'상암동') is not None:\n",
    "        return '상암'\n",
    "    if re.search(x,'성산1동 성산2동, 망원1동망원2동') is not None:\n",
    "        return '성산'\n",
    "    if re.search(x,'연남동, 서교동, 합정동, 서강동') is not None:\n",
    "        return '합정.서교'\n",
    "    if re.search(x,'대흥동, 염리동, 아현동, 공덕동') is not None:\n",
    "        return '아현'\n",
    "    if re.search(x,'신수동, 용강동, 도화동') is not None:\n",
    "        return '용강'\n",
    "    \n",
    "    #양천구\n",
    "    if re.search(x,'신월1동, 신월3동, 신월5동') is not None:\n",
    "        return '신월1'\n",
    "    if re.search(x,'신월2동, 신월4동, 신월6동-신월7동') is not None:\n",
    "        return '신월2'\n",
    "    if re.search(x,'신정1동, 신정3동-신정4동') is not None:\n",
    "        return '신정'\n",
    "    if re.search(x,'목2동, 목3동, 목4동') is not None:\n",
    "        return '목동1'\n",
    "    if re.search(x,'목1동, 목5동, 신정2동, 신정6동 신정7동') is not None:\n",
    "        return '목동2'\n",
    "    \n",
    "    #동작구\n",
    "    if re.search(x,'노량진1동 노량진2동, 대방동') is not None:\n",
    "        return '노량진'\n",
    "    if re.search(x,'흑석동') is not None:\n",
    "        return '흑석'\n",
    "    if re.search(x,'신대방1동 신대방2동') is not None:\n",
    "        return '신대방'\n",
    "    if re.search(x,'상도1동 상도2동 상도3동 상도4동') is not None:\n",
    "        return '상도'\n",
    "    if re.search(x,'사당1동 사당2동 사당3동 사당4동 사당5동') is not None:\n",
    "        return '사당'\n",
    "    \n",
    "    #금천구\n",
    "    if re.search(x,'가산동') is not None:\n",
    "        return '가산'\n",
    "    if re.search(x,'독산1동 독산2동 독산3동 독산4동') is not None:\n",
    "        return '독산'\n",
    "    if re.search(x,'시흥1동 시흥2동 시흥3동 시흥4동 시흥5동') is not None:\n",
    "        return '시흥'\n",
    "    \n",
    "    #영등포구\n",
    "    if re.search(x,'여의도동 여의동') is not None:\n",
    "        return '여의도'\n",
    "    if re.search(x,'당산1동, 양평1동, 도림동, 문래동, 영등포동, 영등포본동') is not None:\n",
    "        return '영등포'\n",
    "    if re.search(x,'당산2동, 양평2동') is not None:\n",
    "        return '당산'\n",
    "    if re.search(x,'신길1동,신길2동 신길3동 신길4동 신길5동 신길6동 신길7동') is not None:\n",
    "        return '신길'\n",
    "    if re.search(x,'대림1동 대림2동 대림3동') is not None:\n",
    "        return '대림'\n",
    "    \n",
    "    #구로구\n",
    "    if re.search(x,'고척1동 고척2동, 개봉1동 개봉2동 개봉3동') is not None:\n",
    "        return '고척.개봉'\n",
    "    if re.search(x,'오류1동 오류2동, 수궁동') is not None:\n",
    "        return '오류.수궁'\n",
    "    \n",
    "    ##3동!!!\n",
    "    if re.search(x,'신도림동, 구로1동 구로2동, 구로5동') is not None:\n",
    "        return '구로신도림'\n",
    "    if re.search(x,'가리봉동, 구로2동, 구로4동 구로3동') is not None:\n",
    "        return '구로디지털단지'\n",
    "    \n",
    "    #강서구\n",
    "    if re.search(x,'공항동, 방화1동 방화2동 방화3동') is not None:\n",
    "        return '공항.방화'\n",
    "    if re.search(x,'등촌3동, 가양1동') is not None:\n",
    "        return '마곡'\n",
    "    if re.search(x,'염창동, 등촌1동, 가양2동 가양3동') is not None:\n",
    "        return '염창'\n",
    "    if re.search(x,'우장산동, 발산1동, 화곡3동') is not None:\n",
    "        return '발산'\n",
    "    if re.search(x,'화곡1동 화곡2동, 화곡4동, 화곡8동') is not None:\n",
    "        return '화곡1'\n",
    "    if re.search(x,'등촌2동, 화곡본동, 화곡6동') is not None:\n",
    "        return '화곡2'\n",
    "    \n",
    "    #관악구\n",
    "    if re.search(x,'청림동, 행운동, 낙성대동, 인헌동, 남현동') is not None:\n",
    "        return '낙성대'\n",
    "    if re.search(x,'신사동, 난향동, 조원동, 미성동, 난곡동') is not None:\n",
    "        return '난곡'\n",
    "    if re.search(x,'보라매동, 서원동, 신원동, 신림동') is not None:\n",
    "        return '신림'\n",
    "    if re.search(x,'중앙동, 은천동, 성현동, 청룡동') is not None:\n",
    "        return '봉천'\n",
    "    if re.search(x,'서림동, 대학동, 삼성동') is not None:\n",
    "        return '대학'\n",
    "    \n",
    "    #서초구\n",
    "    if re.search(x,'반포본동, 반포1동 반포2동 반포3동 반포4동, 잠원동') is not None:\n",
    "        return '반포'\n",
    "    if re.search(x,'방배본동, 방배1동 방배2동 방배3동 방배4동') is not None:\n",
    "        return '방배'\n",
    "    if re.search(x,'서초1동 서초2동 서초3동 서초4동') is not None:\n",
    "        return '서초'\n",
    "    if re.search(x,'양재1동 양재2동, 내곡동') is not None:\n",
    "        return '양재'\n",
    "    \n",
    "    #강동구\n",
    "    if re.search(x,'암사1동 암사2동 암사3동') is not None:\n",
    "        return '암사'\n",
    "    if re.search(x,'명일1동 명일2동, 고덕1동 고덕2동') is not None:\n",
    "        return '명일.고덕'\n",
    "    if re.search(x,'천호1동 천호2동 천호3동, 성내1동 성내2동 성내3동') is not None:\n",
    "        return '천호.성내'\n",
    "    if re.search(x,'강일동, 상일동') is not None:\n",
    "        return '강일.상일'\n",
    "    if re.search(x,'둔촌1동 둔촌2동, 길동') is not None:\n",
    "        return '길동.둔촌'\n",
    "    \n",
    "    #강남구\n",
    "    if re.search(x,'신사동, 압구정동, 청담동') is not None:\n",
    "        return '압구정.청담'\n",
    "    if re.search(x,'삼성1동 삼성2동') is not None:\n",
    "        return '삼성'\n",
    "    if re.search(x,'역삼1동 역삼2동, 논현1동 논현2동') is not None:\n",
    "        return '역삼.논현 '\n",
    "    if re.search(x,'도곡1동 도곡2동, 대치1동 대치2동, 대치4동') is not None:\n",
    "        return '대치.도곡'\n",
    "    if re.search(x,'개포1동 개포2동, 개포4동, 일원1동 일원2동') is not None:\n",
    "        return '개포.일원'\n",
    "    if re.search(x,'수서동, 세곡동, 일원본동') is not None:\n",
    "        return '수서.세곡'\n",
    "    \n",
    "    #송파구 - \n",
    "    if re.search(x,'잠실본동, 잠실2동 잠실3동, 잠실7동') is not None:\n",
    "        return '잠실1'\n",
    "    if re.search(x,'잠실4동, 잠실6동, 풍납1동 풍납2동') is not None:\n",
    "        return '잠실2'\n",
    "    if re.search(x,'송파1동 송파2동, 방이1동 방이2동') is not None:\n",
    "        return '송파'\n",
    "    if re.search(x,'오륜동, 오금동, 가락본동, 가락2동') is not None:\n",
    "        return '가락'\n",
    "    if re.search(x,'삼전동, 석촌동, 가락1동') is not None:\n",
    "        return '석촌'\n",
    "    if re.search(x,'문정1동 문정2동, 장지동, 위례동') is not None:\n",
    "        return '문정'    \n",
    "    if re.search(x,'거여1동, 거여2동, 마천1동, 마천2동, 위례동') is not None:\n",
    "        return '거여.마천'\n",
    "    \n",
    "    return x+'dd'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.read_csv('소상공인시장진흥공단_상가(상권)정보_서울_202103.csv')\n",
    "df2=df2[[ '상권업종대분류코드', '상권업종대분류명', '상권업종중분류명',  '상권업종소분류명', '표준산업분류명', \n",
    "        '시군구명','법정동코드', '법정동명', '대지구분명', '행정동명' ]]\n",
    "df2.rename(columns = {'상권업종대분류코드' : '대분류코드','상권업종대분류명' : '대분류명',\n",
    "                     '상권업종중분류명' : '중분류명','상권업종소분류명' : '소분류명',\n",
    "                    '표준산업분류명' : '분류명',},inplace = True)\n",
    "df2 = df2.loc[(df2['대분류명'] == '음식') &(df2['대분류코드']=='Q'),:] # 음식인 것들만\n",
    "df2.reset_index(drop=True, inplace=True) #index정리\n",
    "df2['생활지역권']=df2['행정동명'].map(myfunction)\n",
    "whichregion=copy.deepcopy(df2[['행정동명', '생활지역권']])\n",
    "df2.drop(columns=['대분류코드','대분류명','대지구분명','시군구명','법정동코드','법정동명','행정동명','분류명'],inplace = True)\n",
    "onemenuSelect=copy.deepcopy(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1-3. 서울생활인구 : 예비소비자 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "##행정동 코드를 지역생활권으로 바꾸는 함수\n",
    "def regionfunction(x):\n",
    "    #종로\n",
    "    if x in [1111056000, 1111055000]:\n",
    "        return '평창.부암'\n",
    "    if x in [1111061500, 1111063000, 1111064000, 1111065000]:\n",
    "        return '혜화.이화'\n",
    "    if x in [1111067000, 1111068000, 1111069000, 1111070000, 1111071000]:\n",
    "        return '창신.숭인'\n",
    "    if x in [1111051500, 1111053000, 1111054000, 1111057000, 1111058000, 1111060000]:\n",
    "        return '청운효자.사직'\n",
    "    \n",
    "    #중구\n",
    "    if x in [1114052000, 1114054000, 1114055000, 1114068000]:\n",
    "        return '소공.회현'\n",
    "    if x in [1114061500, 1114062500, 1114063500, 1114064500, 1114065000, 1114066500, 1114067000]:\n",
    "        return '신당.황학'\n",
    "    if x in [1114057000, 1114058000, 1114059000, 1114060500]:\n",
    "        return '필동.장충'\n",
    "    \n",
    "    #용산구\n",
    "    if x in [1117055500, 1117056000, 1117057000, 1117058000, 1117059000]:\n",
    "        return '청파.원효'\n",
    "    if x in [1117051000, 1117052000, 1117053000, 1117066000]:\n",
    "        return '후암.용산'\n",
    "    if x in [1117063000, 1117064000, 1117062500, 1117069000]:\n",
    "        return '이촌.한강'\n",
    "    if x in [1117065000, 1117068500, 1117070000]:\n",
    "        return '한남'\n",
    "    \n",
    "    \n",
    "    #성동구\n",
    "    if x in [1120065000, 1120066000, 1120067000, 1120069000, 1120072000]:\n",
    "        return '성수'\n",
    "    if x in [1120059000, 1120061500, 1120062000, 1120064500]:\n",
    "        return '금호.옥수 '\n",
    "    if x in [1120054000, 1120055000, 1120079000]:\n",
    "        return '마장.용답'\n",
    "    if x in [1120052000, 1120053500, 1120056000, 1120057000, 1120058000]:\n",
    "        return '왕십리.행당 '\n",
    "    \n",
    "    #강북구\n",
    "    if x in [1130559000, 1130560000, 1130560600]: \n",
    "        return '번동'\n",
    "    if x in [1130554500, 1130555500, 1130557500]:\n",
    "        return '삼각산'\n",
    "    if x in [1130562000, 11305635000, 1130564000, 1130566000,1130564500]:  \n",
    "        return '수유'\n",
    "    if x in [1130561000, 1130553400, 1130553500, 1130563000]: \n",
    "        return '미아'\n",
    "    \n",
    "    #중랑구\n",
    "    if x in [1126058000, 1126059000, 1126060000, 1126061000, 1126062000, 1126063000]:\n",
    "        return '중화.묵동'\n",
    "    if x in [1126068000, 1126069000, 1126065500]:\n",
    "        return '신내.망우'\n",
    "    if x in [1126052000, 1126054000, 1126055000, 1126056500, 1126057000, 1126057500, 1126066000]:\n",
    "        return '면목'\n",
    "    \n",
    "    #성북구\n",
    "    if x in [1129062000, 1129063000, 1129064000, 1129065000]:\n",
    "        return '정릉'\n",
    "    if x in [1129052500, 1129055500, 1129057500, 1129059000, 1129060000, 1129061000]:\n",
    "        return '성북.동선'\n",
    "    if x in [1129076000, 1129077000, 1129078000, 1129081000]:\n",
    "        return '장위.석관'\n",
    "    if x in [1129058000, 1129070500, 1129072500]:\n",
    "        return '종암.월곡'\n",
    "    if x in [1129066000, 1129068500, 1129071500]:\n",
    "        return '길음'\n",
    "    \n",
    "    #동대문구\n",
    "    if x in [1123065000, 1123066000]:\n",
    "        return '장안'\n",
    "    if x in [1123056000, 1123057000, 1123060000, 1123061000]:\n",
    "        return '전농.답십리'\n",
    "    if x in [1123071000, 1123072000, 1123073000, 1123074000, 1123075000]:\n",
    "        return '이문.휘경.회기 '\n",
    "    if x in [1123070500, 1123053600, 1123054500]:\n",
    "        return '청량리'\n",
    "    \n",
    "    #노원구\n",
    "    if x in [1135063000, 1135070000, 1135071000]:\n",
    "        return '마들'\n",
    "    if x in [1135066500, 1135067000]:\n",
    "        return '상계'\n",
    "    if x in [1135064000, 1135069500, 1135072000]:\n",
    "        return '노원'\n",
    "    if x in [1135061900, 1135062100, 1135062400]:\n",
    "        return '중계'\n",
    "    if x in [1135061100, 1135061200, 1135062500]:\n",
    "        return '하계'\n",
    "    if x in [1135056000, 1135057000, 1135058000]:\n",
    "        return '월계'\n",
    "    if x in [1135059500, 1135060000]:\n",
    "        return '공릉'\n",
    "    \n",
    "    #광진구\n",
    "    if x in [1121574000, 1121575000, 1121576000, 1121577000]:\n",
    "        return '중곡'\n",
    "    if x in [1121585000, 1121586000, 1121587000, 1121581000]:\n",
    "        return '구의'\n",
    "    if x in [1121571000, 1121573000, 1121578000]:\n",
    "        return '화양'\n",
    "    if x in [1121582000, 1121583000, 1121584000, 1121584700]:\n",
    "        return '자양'\n",
    "    \n",
    "    #도봉구\n",
    "    if x in [1132052100, 1132052200]:\n",
    "        return '도봉'\n",
    "    if x in [1132069000, 1132070000, 1132071000]:\n",
    "        return '방학'\n",
    "    if x in [1132066000, 1132067000, 1132068100]:\n",
    "        return '쌍문'\n",
    "    if x in [1132068000, 1132051200, 1132051300]:\n",
    "        return '초안산'\n",
    "    if x in [1132051100, 1132051400, 1132051500]:\n",
    "        return '창동'\n",
    "    \n",
    "    \n",
    "    #은평구\n",
    "    if x in [1138069000]:\n",
    "        return '진관'\n",
    "    if x in [1138055100, 1138055200, 1138056000, 1138062500]:\n",
    "        return '연신내'\n",
    "    if x in [1138063100, 1138063200, 1138064000, 1138065000]:\n",
    "        return '수색'\n",
    "    if x in [1138057000, 1138052000, 1138053000]:\n",
    "        return '불광'\n",
    "    if x in [1138058000, 1138059000, 1138060000, 1138051000]:\n",
    "        return '응암'\n",
    "    \n",
    "    #서대문구\n",
    "    if x in [1141062000, 1141064000, 1141065500, 1141066000, 1141068500]:\n",
    "        return '홍제'\n",
    "    if x in [1141069000, 1141070000, 1141071000, 1141072000]:\n",
    "        return '가좌'\n",
    "    if x in [1141052000, 1141055500, 1141056500]:\n",
    "        return '충청'\n",
    "    if x in [1141058500, 1141061500]:\n",
    "        return '신촌'\n",
    "    \n",
    "    #마포구\n",
    "    if x in [1144074000]:\n",
    "        return '상암'\n",
    "    if x in [1144072000, 1144073000, 1144069000, 1144070000]:\n",
    "        return '성산'\n",
    "    if x in [1144071000, 1144065500, 1144066000, 1144068000]:\n",
    "        return '합정.서교'\n",
    "    if x in [1144060000, 1144061000, 1144055500, 1144056500]:\n",
    "        return '아현'\n",
    "    if x in [1144063000, 1144058500, 1144059000]:\n",
    "        return '용강'\n",
    "    \n",
    "    #양천구\n",
    "    if x in [1147056000, 1147058000, 1147060000]:\n",
    "        return '신월1'\n",
    "    if x in [1147057000, 1147059000, 1147061000, 1147061100]:\n",
    "        return '신월2'\n",
    "    if x in [1147062000, 1147064000, 1147065000]:\n",
    "        return '신정'\n",
    "    if x in [1147052000, 1147053000, 1147054000]:\n",
    "        return '목동1'\n",
    "    if x in [1147051000, 1147055000, 1147063000, 1147067000, 1147068000]:\n",
    "        return '목동2'\n",
    "    \n",
    "    #동작구\n",
    "    if x in [1159051000, 1159052000, 1159066000]:\n",
    "        return '노량진'\n",
    "    if x in [1159060500]:\n",
    "        return '흑석'\n",
    "    if x in [1159067000, 1159068000]:\n",
    "        return '신대방'\n",
    "    if x in [1159053000, 1159054000, 1159055000, 1159056000]:\n",
    "        return '상도'\n",
    "    if x in [1159062000, 1159063000, 1159064000, 1159065000, 1159065100]:\n",
    "        return '사당'\n",
    "    \n",
    "    #금천구\n",
    "    if x in [1154551000]:\n",
    "        return '가산'\n",
    "    if x in [1154561000, 1154562000, 1154563000, 1154564000]:\n",
    "        return '독산'\n",
    "    if x in [1154567000, 1154568000, 1154569000, 1154570000, 1154571000]:\n",
    "        return '시흥'\n",
    "    \n",
    "    #영등포구\n",
    "    if x in [1156054000]:\n",
    "        return '여의도'\n",
    "    if x in [1156055000, 1156061000, 1156058500, 1156060500, 1156051500, 1156053500]:\n",
    "        return '영등포'\n",
    "    if x in [1156056000, 1156062000]:\n",
    "        return '당산'\n",
    "    if x in [1156063000, 1156065000, 1156066000, 1156067000, 1156068000, 1156069000]:\n",
    "        return '신길'\n",
    "    if x in [1156070000, 1156071000, 1156072000]:\n",
    "        return '대림'\n",
    "    \n",
    "    #구로구\n",
    "    if x in [1153072000, 1153073000, 1153074000, 1153075000, 1153076000]:\n",
    "        return '고척.개봉'\n",
    "    if x in [1153077000, 1153078000, 1153079000]:\n",
    "        return '오류.수궁'\n",
    "    \n",
    "    ##3동!!!\n",
    "    if x in [1153051000, 1153052000, 1153053000, 1153056000]:\n",
    "        return '구로신도림'\n",
    "    if x in [1153054000, 1153055000, 1153059500]:\n",
    "        return '구로디지털단지'\n",
    "    \n",
    "    #강서구\n",
    "    if x in [1150062000, 1150063000, 1150064000, 1150064100]:\n",
    "        return '공항.방화'\n",
    "    if x in [1150053500, 1150060300]:\n",
    "        return '마곡'\n",
    "    if x in [1150051000, 1150052000, 1150060400, 1150060500]:\n",
    "        return '염창'\n",
    "    if x in [1150061100, 1150061500, 1150056000]:\n",
    "        return '발산'\n",
    "    if x in [1150054000, 1150055000, 1150057000,  1150059300]:\n",
    "        return '화곡1'\n",
    "    if x in [1150053000, 1150059000, 1150059100]:\n",
    "        return '화곡2'\n",
    "    \n",
    "    #관악구\n",
    "    if x in [1162054500, 1162057500, 1162058500, 1162062500, 1162063000]:\n",
    "        return '낙성대'\n",
    "    if x in [1162068500, 1162071500, 1162072500, 1162076500, 1162077500]:\n",
    "        return '난곡'\n",
    "    if x in [1162052500, 1162064500, 1162065500, 1162069500]:\n",
    "        return '신림'\n",
    "    if x in [1162059500, 1162060500, 1162061500, 1162056500]:\n",
    "        return '봉천'\n",
    "    if x in [1162066500, 1162073500, 1162074500]:\n",
    "        return '대학'\n",
    "    \n",
    "    #서초구\n",
    "    if x in [1165054000, 1165055000, 1165056000, 1165057000, 1165058000, 1165058100]:\n",
    "        return '반포'\n",
    "    if x in [1165059000, 1165060000, 1165061000, 1165062000, 1165062100]:\n",
    "        return '방배'\n",
    "    if x in [1165051000, 1165052000, 1165053000, 1165053100]:\n",
    "        return '서초'\n",
    "    if x in [1165065100, 1165065200, 1165066000]:\n",
    "        return '양재'\n",
    "    \n",
    "    #강동구\n",
    "    if x in [1174057000, 1174058000, 1174059000]:\n",
    "        return '암사'\n",
    "    if x in [1174053000, 1174054000, 1174055000, 1174056000]:\n",
    "        return '명일.고덕'\n",
    "    if x in [1174060000, 1174061000, 1174062000, 1174064000, 1174065000, 1174066000]:\n",
    "        return '천호.성내'\n",
    "    if x in [1174051500, 1174052000]:\n",
    "        return '강일.상일'\n",
    "    if x in [1174068500, 1174069000, 1174070000]:\n",
    "        return '길동.둔촌'\n",
    "    \n",
    "    #강남구\n",
    "    if x in [1168051000, 1168054500, 1168056500]:\n",
    "        return '압구정.청담'\n",
    "    if x in [1168058000, 1168059000]:\n",
    "        return '삼성'\n",
    "    if x in [1168064000, 1168065000, 1168052100, 1168053100]:\n",
    "        return '역삼.논현 '\n",
    "    if x in [1168065500, 1168065600, 1168060000, 1168061000, 1168063000]:\n",
    "        return '대치.도곡'\n",
    "    if x in [1168066000, 1168067000, 1168069000, 1168073000, 1168074000]:\n",
    "        return '개포.일원'\n",
    "    if x in [1168070000, 1168075000, 1168072000]:\n",
    "        return '수서.세곡'\n",
    "    \n",
    "    #송파구 - \n",
    "    if x in [1171072000, 1171067000, 1171068000, 1171065000]:\n",
    "        return '잠실1'\n",
    "    if x in [1171069000, 1171071000, 1171051000, 1171052000]:\n",
    "        return '잠실2'\n",
    "    if x in [1171058000, 1171059000, 1171056100, 1171056200]:\n",
    "        return '송파'\n",
    "    if x in [1171056600, 1171057000, 1171062000, 1171063200]:\n",
    "        return '가락'\n",
    "    if x in [1171060000, 1171061000, 1171063100]:\n",
    "        return '석촌'\n",
    "    if x in [1171064100, 1171064200, 1171064600, 1171064700]:\n",
    "        return '문정'    \n",
    "    if x in [1171053100, 1171053200, 1171054000, 1171055000, 1171064700]:\n",
    "        return '거여.마천'\n",
    "    \n",
    "    return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "##시간중 정수 자료형이 아닌 데이터를 을 정수형 시간으로 변환하는 함수\n",
    "def number(time):\n",
    "    if type(time) == 'integer': #in [0,1,2,3,4,5,6,7,8,9,11,12,13,14,15,16,17,18,19,20,21,22,23]:\n",
    "        return time\n",
    "    if time =='0': return 0\n",
    "    if time in ['01','1']: return 1\n",
    "    if time in ['02','2']: return 2\n",
    "    if time in ['03','3']: return 3\n",
    "    if time in ['04','4']: return 4\n",
    "    if time in ['05','5']: return 5\n",
    "    if time in ['06','6']: return 6\n",
    "    if time in ['07','7']: return 7\n",
    "    if time in ['08','8']: return 8\n",
    "    if time in ['09','9']: return 9\n",
    "    if time == '10': return 10\n",
    "    if time == '11': return 11\n",
    "    if time == '12': return 12\n",
    "    if time == '13': return 13\n",
    "    if time == '14': return 14\n",
    "    if time == '15': return 15\n",
    "    if time == '16': return 16\n",
    "    if time == '17': return 17\n",
    "    if time == '18': return 18\n",
    "    if time == '19': return 19\n",
    "    if time == '20': return 20\n",
    "    if time == '21': return 21\n",
    "    if time == '22': return 22\n",
    "    if time == '23': return 23\n",
    "    \n",
    "    return 9999\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 2019 생활인구"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2798400 entries, 0 to 2798399\n",
      "Data columns (total 32 columns):\n",
      " #   Column           Dtype  \n",
      "---  ------           -----  \n",
      " 0   기준일ID            string \n",
      " 1   시간대구분            int64  \n",
      " 2   행정동코드            int64  \n",
      " 3   총생활인구수           float64\n",
      " 4   남자0세부터9세생활인구수    float64\n",
      " 5   남자10세부터14세생활인구수  float64\n",
      " 6   남자15세부터19세생활인구수  float64\n",
      " 7   남자20세부터24세생활인구수  float64\n",
      " 8   남자25세부터29세생활인구수  float64\n",
      " 9   남자30세부터34세생활인구수  float64\n",
      " 10  남자35세부터39세생활인구수  float64\n",
      " 11  남자40세부터44세생활인구수  float64\n",
      " 12  남자45세부터49세생활인구수  float64\n",
      " 13  남자50세부터54세생활인구수  float64\n",
      " 14  남자55세부터59세생활인구수  float64\n",
      " 15  남자60세부터64세생활인구수  float64\n",
      " 16  남자65세부터69세생활인구수  float64\n",
      " 17  남자70세이상생활인구수     float64\n",
      " 18  여자0세부터9세생활인구수    float64\n",
      " 19  여자10세부터14세생활인구수  float64\n",
      " 20  여자15세부터19세생활인구수  float64\n",
      " 21  여자20세부터24세생활인구수  float64\n",
      " 22  여자25세부터29세생활인구수  float64\n",
      " 23  여자30세부터34세생활인구수  float64\n",
      " 24  여자35세부터39세생활인구수  float64\n",
      " 25  여자40세부터44세생활인구수  float64\n",
      " 26  여자45세부터49세생활인구수  float64\n",
      " 27  여자50세부터54세생활인구수  float64\n",
      " 28  여자55세부터59세생활인구수  float64\n",
      " 29  여자60세부터64세생활인구수  float64\n",
      " 30  여자65세부터69세생활인구수  float64\n",
      " 31  여자70세이상생활인구수     float64\n",
      "dtypes: float64(29), int64(2), string(1)\n",
      "memory usage: 683.2 MB\n"
     ]
    }
   ],
   "source": [
    "# 2019생활인구\n",
    "import pandas as pd\n",
    "Local201901=pd.read_csv(r'LOCAL_PEOPLE_DONG_201901.csv', error_bad_lines=False)\n",
    "Local201901=Local201901.reset_index()\n",
    "Local201901=Local201901.shift(periods=6,axis=1)\n",
    "Local201901=Local201901.drop(columns=['level_0','level_1','level_2','level_3','level_4','level_5'])\n",
    "\n",
    "Local201902=pd.read_csv(r'LOCAL_PEOPLE_DONG_201902.csv', engine='python', error_bad_lines=False,encoding='UTF8' )\n",
    "Local201902=Local201902.reset_index()\n",
    "Local201902=Local201902.shift(periods=1,axis=1)\n",
    "Local201902=Local201902.drop(columns=['index'])\n",
    "\n",
    "Local201903=pd.read_csv(r'LOCAL_PEOPLE_DONG_201903.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local201903=Local201903.reset_index()\n",
    "Local201903=Local201903.shift(periods=6,axis=1)\n",
    "Local201903=Local201903.drop(columns=['level_0','level_1','level_2','level_3','level_4','level_5'])\n",
    "\n",
    "Local201904=pd.read_csv(r'LOCAL_PEOPLE_DONG_201904.csv',engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local201904=Local201904.reset_index()\n",
    "Local201904=Local201904.shift(periods=1,axis=1)\n",
    "Local201904=Local201904.drop(columns=['index'])\n",
    "\n",
    "Local201905=pd.read_csv(r'LOCAL_PEOPLE_DONG_201905.csv',engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local201905=Local201905.reset_index()\n",
    "Local201905=Local201905.shift(periods=1,axis=1)\n",
    "Local201905=Local201905.drop(columns=['index'])\n",
    "\n",
    "Local201906=pd.read_csv(r'LOCAL_PEOPLE_DONG_201906.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local201906=Local201906.reset_index()\n",
    "Local201906=Local201906.shift(periods=1,axis=1)\n",
    "Local201906=Local201906.drop(columns=['index'])\n",
    "\n",
    "Local201907=pd.read_csv(r'LOCAL_PEOPLE_DONG_201907.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local201907=Local201907.reset_index()\n",
    "Local201907=Local201907.shift(periods=1,axis=1)\n",
    "Local201907=Local201907.drop(columns=['index'])\n",
    "\n",
    "Local201908=pd.read_csv(r'LOCAL_PEOPLE_DONG_201908.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local201908=Local201908.reset_index()\n",
    "Local201908=Local201908.shift(periods=1,axis=1)\n",
    "Local201908=Local201908.drop(columns=['index'])\n",
    "\n",
    "Local201909=pd.read_csv(r'LOCAL_PEOPLE_DONG_201909.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local201909=Local201909.reset_index()\n",
    "Local201909=Local201909.shift(periods=1,axis=1)\n",
    "Local201909=Local201909.drop(columns=['index'])\n",
    "\n",
    "#인코딩 이상하게 된 것 같아요 info 하면 잘 ㅇ안나와서 나중에 다시 추가해야할듯..! S:encoding='cp949'\n",
    "Local201910=pd.read_csv(r'LOCAL_PEOPLE_DONG_201910.csv', engine='python', error_bad_lines=False, encoding='cp949')\n",
    "Local201910=Local201910.reset_index()\n",
    "Local201910=Local201910.shift(periods=1,axis=1)\n",
    "Local201910=Local201910.drop(columns=['index'])\n",
    "\n",
    "Local201911=pd.read_csv(r'LOCAL_PEOPLE_DONG_201911.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local201911=Local201911.reset_index()\n",
    "Local201911=Local201911.shift(periods=1,axis=1)\n",
    "Local201911=Local201911.drop(columns=['index'])\n",
    "\n",
    "Local201912=pd.read_csv(r'LOCAL_PEOPLE_DONG_201912.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local201912=Local201912.reset_index()\n",
    "Local201912=Local201912.shift(periods=1,axis=1)\n",
    "Local201912=Local201912.drop(columns=['index'])\n",
    "\n",
    "Local2019 = pd.concat([Local201903,Local201904,Local201905,Local201906,Local201907,Local201908,Local201909, #Local201910,\n",
    "                      Local201911,Local201912], ignore_index = True)\n",
    "\n",
    "## Local2019 = Local2019[['기준일ID','시간대구분','행정동코드','총생활인구수']]\n",
    "Local2019.행정동코드 = Local2019.행정동코드 * 100\n",
    "Local2019.총생활인구수 = Local2019.총생활인구수 * 10000\n",
    "Local2019 = Local2019.astype({'기준일ID':'string'})\n",
    "#Local2020.총생활인구수\n",
    "Local2019.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2020 생활인구"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2020 생활인구\n",
    "Local202001=pd.read_csv(r'LOCAL_PEOPLE_DONG_202001.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202001=Local202001.reset_index()\n",
    "Local202001=Local202001.shift(periods=6,axis=1)\n",
    "Local202001=Local202001.drop(columns=['level_0','level_1','level_2','level_3','level_4','level_5'])\n",
    "\n",
    "Local202002=pd.read_csv(r'LOCAL_PEOPLE_DONG_202002.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202002=Local202002.reset_index()\n",
    "Local202002=Local202002.shift(periods=1,axis=1)\n",
    "Local202002=Local202002.drop(columns=['index'])\n",
    "\n",
    "Local202003=pd.read_csv(r'LOCAL_PEOPLE_DONG_202003.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202003=Local202003.reset_index()\n",
    "Local202003=Local202003.shift(periods=1,axis=1)\n",
    "Local202003=Local202003.drop(columns=['index'])\n",
    "\n",
    "Local202004=pd.read_csv(r'LOCAL_PEOPLE_DONG_202004.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202004=Local202004.reset_index()\n",
    "Local202004=Local202004.shift(periods=1,axis=1)\n",
    "Local202004=Local202004.drop(columns=['index'])\n",
    "\n",
    "Local202005=pd.read_csv(r'LOCAL_PEOPLE_DONG_202005.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202005=Local202005.reset_index()\n",
    "Local202005=Local202005.shift(periods=1,axis=1)\n",
    "Local202005=Local202005.drop(columns=['index'])\n",
    "\n",
    "Local202006=pd.read_csv(r'LOCAL_PEOPLE_DONG_202006.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202006=Local202006.reset_index()\n",
    "Local202006=Local202006.shift(periods=1,axis=1)\n",
    "Local202006=Local202006.drop(columns=['index'])\n",
    "\n",
    "Local202007=pd.read_csv(r'LOCAL_PEOPLE_DONG_202007.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202007=Local202007.reset_index()\n",
    "Local202007=Local202007.shift(periods=1,axis=1)\n",
    "Local202007=Local202007.drop(columns=['index'])\n",
    "\n",
    "Local202008=pd.read_csv(r'LOCAL_PEOPLE_DONG_202008.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202008=Local202008.reset_index()\n",
    "Local202008=Local202008.shift(periods=1,axis=1)\n",
    "Local202008=Local202008.drop(columns=['index'])\n",
    "\n",
    "Local202009=pd.read_csv(r'LOCAL_PEOPLE_DONG_202009.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202009=Local202009.reset_index()\n",
    "Local202009=Local202009.shift(periods=1,axis=1)\n",
    "Local202009=Local202009.drop(columns=['index'])\n",
    "\n",
    "Local202010=pd.read_csv(r'LOCAL_PEOPLE_DONG_202010.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202010=Local202010.reset_index()\n",
    "Local202010=Local202010.shift(periods=1,axis=1)\n",
    "Local202010=Local202010.drop(columns=['index'])\n",
    "\n",
    "Local202011=pd.read_csv(r'LOCAL_PEOPLE_DONG_202011.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202011=Local202011.reset_index()\n",
    "Local202011=Local202011.shift(periods=1,axis=1)\n",
    "Local202011=Local202011.drop(columns=['index'])\n",
    "\n",
    "Local202012=pd.read_csv(r'LOCAL_PEOPLE_DONG_202012.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202012=Local202012.reset_index()\n",
    "Local202012=Local202012.shift(periods=1,axis=1)\n",
    "Local202012=Local202012.drop(columns=['index'])\n",
    "\n",
    "Local2020 = pd.concat([Local202003,Local202004,Local202005,Local202006,Local202007,Local202008,Local202009,\n",
    "                      Local202010,Local202011,Local202012], ignore_index = True)\n",
    "\n",
    "##Local2020 = Local2020[['기준일ID','시간대구분','행정동코드','총생활인구수']]\n",
    "Local2020.행정동코드 = Local2020.행정동코드 * 100\n",
    "Local2020.총생활인구수 = Local2020.총생활인구수 * 10000\n",
    "Local2020 = Local2020.astype({'총생활인구수':'int64'})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2021 생활인구"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2021 생활인구\n",
    "Local202101=pd.read_csv(r'LOCAL_PEOPLE_DONG_202101.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202101=Local202101.reset_index()\n",
    "Local202101=Local202101.shift(periods=1,axis=1)\n",
    "Local202101=Local202101.drop(columns=['index'])\n",
    "\n",
    "Local202102=pd.read_csv(r'LOCAL_PEOPLE_DONG_202102.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202102=Local202102.reset_index()\n",
    "Local202102=Local202102.shift(periods=1,axis=1)\n",
    "Local202102=Local202102.drop(columns=['index'])\n",
    "\n",
    "Local202103=pd.read_csv(r'LOCAL_PEOPLE_DONG_202103.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202103=Local202103.reset_index()\n",
    "Local202103=Local202103.shift(periods=1,axis=1)\n",
    "Local202103=Local202103.drop(columns=['index'])\n",
    "\n",
    "Local202104=pd.read_csv(r'LOCAL_PEOPLE_DONG_202104.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202104=Local202104.reset_index()\n",
    "Local202104=Local202104.shift(periods=1,axis=1)\n",
    "Local202104=Local202104.drop(columns=['index'])\n",
    "\n",
    "Local202105=pd.read_csv(r'LOCAL_PEOPLE_DONG_202105.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202105=Local202105.reset_index()\n",
    "Local202105=Local202105.shift(periods=1,axis=1)\n",
    "Local202105=Local202105.drop(columns=['index'])\n",
    "\n",
    "Local202106=pd.read_csv(r'LOCAL_PEOPLE_DONG_202106.csv', engine='python', error_bad_lines=False, encoding='UTF8')\n",
    "Local202106=Local202106.reset_index()\n",
    "Local202106=Local202106.shift(periods=1,axis=1)\n",
    "Local202106=Local202106.drop(columns=['index'])\n",
    "\n",
    "Local2021 = pd.concat([Local202103,Local202104,Local202105,Local202106], ignore_index = True)\n",
    "\n",
    "##Local2021 = Local2021[['기준일ID','시간대구분','행정동코드','총생활인구수']]\n",
    "Local2021.행정동코드 = Local2021.행정동코드 * 100\n",
    "Local2021.총생활인구수 = Local2021.총생활인구수 * 10000\n",
    "Local2021 = Local2021.astype({'총생활인구수':'int64'})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "BeforeCoronaLocal = Local2019\n",
    "AfterCoronaLocal = pd.concat([Local2020,Local2021],ignore_index = True)\n",
    "TotalLocal  = pd.concat([AfterCoronaLocal,BeforeCoronaLocal],ignore_index = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ** 코로나 전후 내국인 : TotalLoca(2019-2021), BeforeCoronaLocal(2019), AfterCoronaLoca(2020-2021)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "TotalLocal['생활지역권'] = TotalLocal['행정동코드'].map(regionfunction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "BeforeCoronaLocal['생활지역권'] = BeforeCoronaLocal['행정동코드'].map(regionfunction)\n",
    "AfterCoronaLocal['생활지역권'] = AfterCoronaLocal['행정동코드'].map(regionfunction)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. 가이드 제공(지역생활권, 영업시간, 업종에 따라)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "BeforeCoronaLocalcopy=copy.deepcopy(BeforeCoronaLocal)\n",
    "\n",
    "BeforeCoronaLocalcopy['성별']='남자'\n",
    "import pandas as pd\n",
    "\n",
    "BeforeCoronaLocalMan=pd.melt(BeforeCoronaLocalcopy, id_vars=['생활지역권', '시간대구분','성별'], value_vars=['남자0세부터9세생활인구수', '남자10세부터14세생활인구수', '남자15세부터19세생활인구수','남자20세부터24세생활인구수', '남자25세부터29세생활인구수', '남자30세부터34세생활인구수', '남자35세부터39세생활인구수',\n",
    "    '남자40세부터44세생활인구수', '남자45세부터49세생활인구수', '남자50세부터54세생활인구수','남자55세부터59세생활인구수', \n",
    "    '남자60세부터64세생활인구수', '남자65세부터69세생활인구수', '남자70세이상생활인구수'])\n",
    "BeforeCoronaLocalcopy.drop(['성별'], axis='columns')\n",
    "\n",
    "BeforeCoronaLocalcopy['성별']='여자'\n",
    "BeforeCoronaLocalWoman=pd.melt(BeforeCoronaLocalcopy, id_vars=['생활지역권', '시간대구분','성별'], value_vars=['여자0세부터9세생활인구수', '여자10세부터14세생활인구수', '여자15세부터19세생활인구수','여자20세부터24세생활인구수', '여자25세부터29세생활인구수', '여자30세부터34세생활인구수', '여자35세부터39세생활인구수',\n",
    "    '여자40세부터44세생활인구수', '여자45세부터49세생활인구수', '여자50세부터54세생활인구수','여자55세부터59세생활인구수', \n",
    "    '여자60세부터64세생활인구수', '여자65세부터69세생활인구수', '여자70세이상생활인구수'])\n",
    "AfterCoronaLocalcopy=copy.deepcopy(AfterCoronaLocal)\n",
    "\n",
    "AfterCoronaLocalcopy['성별']='남자'\n",
    "import pandas as pd\n",
    "AfterCoronaLocalMan=pd.melt(AfterCoronaLocalcopy, id_vars=['생활지역권', '시간대구분','성별'], value_vars=['남자0세부터9세생활인구수', '남자10세부터14세생활인구수', '남자15세부터19세생활인구수','남자20세부터24세생활인구수', '남자25세부터29세생활인구수', '남자30세부터34세생활인구수', '남자35세부터39세생활인구수',\n",
    "    '남자40세부터44세생활인구수', '남자45세부터49세생활인구수', '남자50세부터54세생활인구수','남자55세부터59세생활인구수', \n",
    "    '남자60세부터64세생활인구수', '남자65세부터69세생활인구수', '남자70세이상생활인구수'])\n",
    "AfterCoronaLocalcopy.drop(['성별'], axis='columns')\n",
    "\n",
    "AfterCoronaLocalcopy['성별']='여자'\n",
    "AfterCoronaLocalWoman=pd.melt(AfterCoronaLocalcopy, id_vars=['생활지역권', '시간대구분','성별'], value_vars=['여자0세부터9세생활인구수', '여자10세부터14세생활인구수', '여자15세부터19세생활인구수','여자20세부터24세생활인구수', '여자25세부터29세생활인구수', '여자30세부터34세생활인구수', '여자35세부터39세생활인구수',\n",
    "    '여자40세부터44세생활인구수', '여자45세부터49세생활인구수', '여자50세부터54세생활인구수','여자55세부터59세생활인구수', \n",
    "    '여자60세부터64세생활인구수', '여자65세부터69세생활인구수', '여자70세이상생활인구수'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sexagefunction(x):\n",
    "    if x in ['남자0세부터9세생활인구수', '남자10세부터14세생활인구수', '남자15세부터19세생활인구수']:\n",
    "        return \"남자 20대 이하\";\n",
    "    if x in ['남자20세부터24세생활인구수', '남자25세부터29세생활인구수']:\n",
    "        return \"남자 20대\";\n",
    "    if x in ['남자30세부터34세생활인구수', '남자35세부터39세생활인구수']:\n",
    "        return \"남자 30대\";\n",
    "    if x in ['남자40세부터44세생활인구수', '남자45세부터49세생활인구수']:\n",
    "        return \"남자 40대\"\n",
    "    if x in ['남자50세부터54세생활인구수','남자55세부터59세생활인구수']:\n",
    "        return \"남자 50대\"\n",
    "    if x in ['남자60세부터64세생활인구수', '남자65세부터69세생활인구수', '남자70세이상생활인구수']:\n",
    "        return '남자 60대 이상'\n",
    "    if x in ['여자0세부터9세생활인구수', '여자10세부터14세생활인구수', '여자15세부터19세생활인구수']:\n",
    "        return \"여자 20대 이하\"\n",
    "    if x in ['여자20세부터24세생활인구수', '여자25세부터29세생활인구수']:\n",
    "        return \"여자 20대\"\n",
    "    if x in ['여자30세부터34세생활인구수', '여자35세부터39세생활인구수']:\n",
    "        return \"여자 30대\"\n",
    "    if x in ['여자40세부터44세생활인구수', '여자45세부터49세생활인구수']:\n",
    "        return \"여자 40대\"\n",
    "    if x in ['여자50세부터54세생활인구수','여자55세부터59세생활인구수']:\n",
    "        return \"여자 50대\"\n",
    "    if x in ['여자60세부터64세생활인구수', '여자65세부터69세생활인구수', '여자70세이상생활인구수']:\n",
    "        return '여자 60대 이상'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "BeforeCoronaLocalMan['연령대']=BeforeCoronaLocalMan['variable'].map(sexagefunction)\n",
    "BeforeCoronaLocalWoman['연령대']=BeforeCoronaLocalWoman['variable'].map(sexagefunction)\n",
    "BeforeCoronaLocalMan.rename(columns = {'value' : '생활인구수'}, inplace = True)\n",
    "BeforeCoronaLocalWoman.rename(columns = {'value' : '생활인구수'}, inplace = True)\n",
    "AfterCoronaLocalMan['연령대']=AfterCoronaLocalMan['variable'].map(sexagefunction)\n",
    "AfterCoronaLocalWoman['연령대']=AfterCoronaLocalWoman['variable'].map(sexagefunction)\n",
    "AfterCoronaLocalMan.rename(columns = {'value' : '생활인구수'}, inplace = True)\n",
    "AfterCoronaLocalWoman.rename(columns = {'value' : '생활인구수'}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "BeforeCoronaLocalManSex=BeforeCoronaLocalMan.groupby(by=['생활지역권','시간대구분']).sum()[['생활인구수']]\n",
    "BeforeCoronaLocalWomanSex=BeforeCoronaLocalWoman.groupby(by=['생활지역권','시간대구분']).sum()[['생활인구수']]\n",
    "BeforeCoronaLocalManSex.reset_index(inplace=True)\n",
    "BeforeCoronaLocalWomanSex.reset_index(inplace=True)\n",
    "\n",
    "BeforeCoronaLocalManAge=BeforeCoronaLocalMan.groupby(by=['생활지역권','시간대구분', '연령대']).sum()[['생활인구수']]\n",
    "BeforeCoronaLocalWomanAge=BeforeCoronaLocalWoman.groupby(by=['생활지역권','시간대구분','연령대']).sum()[['생활인구수']]\n",
    "BeforeCoronaLocalManAge.reset_index(inplace=True)\n",
    "BeforeCoronaLocalWomanAge.reset_index(inplace=True)\n",
    "\n",
    "\n",
    "AfterCoronaLocalManSex=AfterCoronaLocalMan.groupby(by=['생활지역권','시간대구분']).sum()[['생활인구수']]\n",
    "AfterCoronaLocalWomanSex=AfterCoronaLocalWoman.groupby(by=['생활지역권','시간대구분']).sum()[['생활인구수']]\n",
    "AfterCoronaLocalManSex.reset_index(inplace=True)\n",
    "AfterCoronaLocalWomanSex.reset_index(inplace=True)\n",
    "\n",
    "AfterCoronaLocalManAge=AfterCoronaLocalMan.groupby(by=['생활지역권','시간대구분', '연령대']).sum()[['생활인구수']]\n",
    "AfterCoronaLocalWomanAge=AfterCoronaLocalWoman.groupby(by=['생활지역권','시간대구분','연령대']).sum()[['생활인구수']]\n",
    "AfterCoronaLocalManAge.reset_index(inplace=True)\n",
    "AfterCoronaLocalWomanAge.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 시간을 영업시간으로 변환하는 함수\n",
    "def timefunction(x):\n",
    "    if x in [0, 1, 2, 3, 4, 5]:\n",
    "        return '새벽'\n",
    "    if x in [6, 7, 8, 9, 10, 11]:\n",
    "        return '아침'\n",
    "    if x in [12, 13, 14, 15, 16, 17]:\n",
    "        return '점심'\n",
    "    if x in [18, 19, 20, 21, 22, 23]:\n",
    "        return '저녁'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## - 지역 생활권 상권분석, \n",
    "## - (코로나 전<->후) 특정 생활지역권  성별 연령별 구매력, 전체 업종별 선호도(매출건수),업종의 성별 연령별 업종별 선호도\n",
    "## ipywidgets - 버튼구현(선택한 버튼에 따라 결과가 다르게 표현)\n",
    "## plotly 이용해서 interactive_graph 구현(그래프 위에 마우스를 올리면 관련 정보를 얻을 수 있고 , 범례를 클릭하면 특정 그래프의 요소를 제거 가능)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "##버튼 내용 생성\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import ipywidgets as widgets\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "ALL = 'ALL'\n",
    "def unique_sorted_values_plus_ALL(array):\n",
    "    unique = array.unique().tolist()\n",
    "    unique.sort()\n",
    "    unique.insert(0, ALL)\n",
    "    return unique\n",
    "selectregion=unique_sorted_values_plus_ALL(TotalLocal[\"생활지역권\"])\n",
    "selectregion[0]=\"지역생활권을 선택하세요\"\n",
    "#selectregion\n",
    "\n",
    "selectmenu=[\"업종을 선택하세요\", \"한식\", '일식/수산물', '닭/오리요리', '양식', '분식']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "******************************************************************\n",
      "\n",
      "지역권생활권별 요식업종 가이드\n",
      "\n",
      "1. 지역생활권 이란?\n",
      "서울시 지역을 생활권 단위로 나누어 도시관리 계획을 구체적으로 제시하기 위한 공간 관리 지침\n",
      "\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e29c62bbb8346e7b1490359c14bc3bf",
       "version_major": 2,
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        {
         "alignmentgroup": "True",
         "hovertemplate": "소분류명=꼬치구이전문점<br>상가수=%{x}<br>중분류명=%{y}<extra></extra>",
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        {
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         "hovertemplate": "소분류명=룸살롱/단란주점<br>상가수=%{x}<br>중분류명=%{y}<extra></extra>",
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         "yaxis": "y"
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        {
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         "hovertemplate": "소분류명=민속주점<br>상가수=%{x}<br>중분류명=%{y}<extra></extra>",
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        {
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        {
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        {
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        {
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        {
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        {
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        {
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        {
         "alignmentgroup": "True",
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        {
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        {
         "alignmentgroup": "True",
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        {
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        {
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          "shapedefaults": {
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            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
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           },
           "bgcolor": "#E5ECF6",
           "caxis": {
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          },
          "title": {
           "x": 0.05
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          "xaxis": {
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           "ticks": "",
           "title": {
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        },
        "title": {
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        "xaxis": {
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         "title": {
          "text": "중분류명"
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      "업종위에 마우스를 올리시면가산 지역 생활권의 정확한 상가 개수를 알 수 있습니다.\n"
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        "title": {
         "text": "<b>3-3 [닭/오리요리] 업종 코로나 전후 성별 연령별 업종별 선호도</b>"
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     },
     "metadata": {},
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   ],
   "source": [
    "from IPython.display import clear_output\n",
    "import ipywidgets as widgets\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "import plotly.express as px\n",
    "##지역 생활권 dropdown\n",
    "dropdown_region = widgets.Dropdown(\n",
    "    description='지역생활권',options=selectregion)\n",
    "##영업시간 위젯\n",
    "time_buttons = widgets.ToggleButtons(\n",
    "    options=[('전체', '0-24'),('아침', '6-12'), ('점심', '13-18'), ('저녁', '18-24'),('새벽', '0-6')],\n",
    "    description='영업시간별',\n",
    ")\n",
    "#업종 위젯\n",
    "dropdown_menu = widgets.Dropdown(\n",
    "    description='업종별',\n",
    "    options=selectmenu)\n",
    "# 위젯을 container로 합침.\n",
    "container = widgets.VBox([\n",
    "                widgets.HBox([dropdown_region]),\n",
    "                widgets.HBox([time_buttons]),\n",
    "                widgets.HBox([dropdown_menu]),\n",
    "                ])\n",
    "\n",
    "\n",
    "def response(change):\n",
    "    # 위젯에 변화가 감지되면 실행되는 코드\n",
    "    # 이전 셀 아웃풋은 삭제\n",
    "    clear_output()  \n",
    "    # 위젯 출력\n",
    "    print('******************************************************************\\n')\n",
    "    print('지역권생활권별 요식업종 가이드\\n')\n",
    "    print('1. 지역생활권 이란?')\n",
    "    print('서울시 지역을 생활권 단위로 나누어 도시관리 계획을 구체적으로 제시하기 위한 공간 관리 지침\\n')\n",
    "    display(container)\n",
    "    print('2. '+dropdown_region.value+\"지역 생활권 상권분석\")\n",
    "    istemp=whichregion['생활지역권']==dropdown_region.value\n",
    "    tempregion=whichregion[istemp]\n",
    "    tempregion=tempregion['행정동명'].unique()\n",
    "    print(\"행정구역 : \"+','.join(tempregion))\n",
    "\n",
    "    #print(tempregion)\n",
    "    region=make_subplots(rows=1, cols=1)\n",
    "    #region = px.data.tips()\n",
    "    tempRegion=df2[df2['생활지역권']==dropdown_region.value]\n",
    "    tempRegion=tempRegion.groupby(by=['중분류명','소분류명']).count()[['생활지역권']]\n",
    "    tempRegion=tempRegion.reset_index()\n",
    "    tempRegion['상가수']=tempRegion['생활지역권']\n",
    "    region = px.bar(tempRegion, x=\"상가수\", y=\"중분류명\", color='소분류명', orientation='h',\n",
    "             height=400,\n",
    "             title='<b>2-1 '+dropdown_region.value+'지역 생활권 업종별 상가 분석</b>')\n",
    "    \n",
    "    display(region)\n",
    "    print(\"업종위에 마우스를 올리시면\" +dropdown_region.value+\" 지역 생활권의 정확한 상가 개수를 알 수 있습니다.\")\n",
    "   \n",
    "    \n",
    "    consumersbefore=make_subplots(rows=1, cols=2)\n",
    "    consumersbefore.update_layout(title='<b>2-2-1(코로나 전) '+dropdown_region.value+ ' 생활지역권 시간에 따른 성별 연령별 생활인구</b>')\n",
    "    tempMan=BeforeCoronaLocalManSex[BeforeCoronaLocalManSex[\"생활지역권\"]==dropdown_region.value]\n",
    "    tempWoman=BeforeCoronaLocalWomanSex[BeforeCoronaLocalWomanSex[\"생활지역권\"]==dropdown_region.value]\n",
    "    consumersbefore.add_trace(go.Scatter(x=tempMan['시간대구분'],y=tempMan['생활인구수'], mode='lines+markers', name='남자'), row=1, col=1)\n",
    "    consumersbefore.add_trace(go.Scatter(x=tempWoman['시간대구분'],y=tempWoman['생활인구수'], mode='lines+markers', name='여자'), row=1, col=1)\n",
    "    consumersbefore.add_trace(go.Scatter(x=tempWoman['시간대구분'],y=tempMan['생활인구수']+tempWoman['생활인구수'], mode='lines+markers', name='내국인'), row=1, col=1)\n",
    "    \n",
    "    isDongAge=(BeforeCoronaLocalManAge['생활지역권']==dropdown_region.value)&(BeforeCoronaLocalManAge['연령대']== \"남자 20대 이하\")\n",
    "    tempMan20U=BeforeCoronaLocalManAge[isDongAge]\n",
    "    isDongAge=(BeforeCoronaLocalManAge['생활지역권']==dropdown_region.value)&(BeforeCoronaLocalManAge['연령대']== \"남자 20대\")\n",
    "    tempMan20=BeforeCoronaLocalManAge[isDongAge]\n",
    "    isDongAge=(BeforeCoronaLocalManAge['생활지역권']==dropdown_region.value)&(BeforeCoronaLocalManAge['연령대']== \"남자 30대\")\n",
    "    tempMan30=BeforeCoronaLocalManAge[isDongAge]\n",
    "    isDongAge=(BeforeCoronaLocalManAge['생활지역권']==dropdown_region.value)&(BeforeCoronaLocalManAge['연령대']== \"남자 40대\")\n",
    "    tempMan40=BeforeCoronaLocalManAge[isDongAge]\n",
    "    isDongAge=(BeforeCoronaLocalManAge['생활지역권']==dropdown_region.value)&(BeforeCoronaLocalManAge['연령대']== \"남자 50대\")\n",
    "    tempMan50=BeforeCoronaLocalManAge[isDongAge]\n",
    "    isDongAge=(BeforeCoronaLocalManAge['생활지역권']==dropdown_region.value)&(BeforeCoronaLocalManAge['연령대']== \"남자 60대 이상\")\n",
    "    tempMan60U=BeforeCoronaLocalManAge[isDongAge]\n",
    "\n",
    "    isDongAge=(BeforeCoronaLocalWomanAge['생활지역권']==dropdown_region.value)&(BeforeCoronaLocalWomanAge['연령대']== \"여자 20대 이하\")\n",
    "    tempWoman20U=BeforeCoronaLocalWomanAge[isDongAge]\n",
    "    isDongAge=(BeforeCoronaLocalWomanAge['생활지역권']==dropdown_region.value)&(BeforeCoronaLocalWomanAge['연령대']== \"여자 20대\")\n",
    "    tempWoman20=BeforeCoronaLocalWomanAge[isDongAge]\n",
    "    isDongAge=(BeforeCoronaLocalWomanAge['생활지역권']==dropdown_region.value)&(BeforeCoronaLocalWomanAge['연령대']== \"여자 30대\")\n",
    "    tempWoman30=BeforeCoronaLocalWomanAge[isDongAge]\n",
    "    isDongAge=(BeforeCoronaLocalWomanAge['생활지역권']==dropdown_region.value)&(BeforeCoronaLocalWomanAge['연령대']== \"여자 40대\")\n",
    "    tempWoman40=BeforeCoronaLocalWomanAge[isDongAge]\n",
    "    isDongAge=(BeforeCoronaLocalWomanAge['생활지역권']==dropdown_region.value)&(BeforeCoronaLocalWomanAge['연령대']== \"여자 50대\")\n",
    "    tempWoman50=BeforeCoronaLocalWomanAge[isDongAge]\n",
    "    isDongAge=(BeforeCoronaLocalWomanAge['생활지역권']==dropdown_region.value)&(BeforeCoronaLocalWomanAge['연령대']== \"여자 60대 이상\")\n",
    "    tempWoman60U=BeforeCoronaLocalWomanAge[isDongAge]\n",
    "\n",
    "    consumersbefore.add_trace(go.Scatter(x=tempMan20U['시간대구분'],y=tempMan20U['생활인구수']+tempWoman20U['생활인구수'], mode='lines+markers', name='20 Under'), row=1, col=2)\n",
    "    consumersbefore.add_trace(go.Scatter(x=tempMan20['시간대구분'],y=tempMan20['생활인구수']+tempWoman20['생활인구수'], mode='lines+markers', name='20s'), row=1, col=2)\n",
    "    consumersbefore.add_trace(go.Scatter(x=tempMan30['시간대구분'],y=tempMan30['생활인구수']+tempWoman30['생활인구수'], mode='lines+markers', name='30s'), row=1, col=2)\n",
    "    consumersbefore.add_trace(go.Scatter(x=tempMan40['시간대구분'],y=tempMan40['생활인구수']+tempWoman40['생활인구수'], mode='lines+markers', name='40s'), row=1, col=2)\n",
    "    consumersbefore.add_trace(go.Scatter(x=tempMan50['시간대구분'],y=tempMan50['생활인구수']+tempWoman50['생활인구수'], mode='lines+markers', name='50s'), row=1, col=2)\n",
    "    consumersbefore.add_trace(go.Scatter(x=tempMan60U['시간대구분'],y=tempMan60U['생활인구수']+tempWoman60U['생활인구수'], mode='lines+markers', name='60 Upper'), row=1, col=2)\n",
    "    display(consumersbefore)\n",
    "    \n",
    "    ##################################################################################################################\n",
    "    consumersafter=make_subplots(rows=1, cols=2)\n",
    "    consumersafter.update_layout(title='<b>2-2-2(코로나 후) '+dropdown_region.value+ ' 생활지역권 시간에 따른 성별 연령별 생활인구</b>')\n",
    "    tempMan=AfterCoronaLocalManSex[AfterCoronaLocalManSex[\"생활지역권\"]==dropdown_region.value]\n",
    "    tempWoman=AfterCoronaLocalWomanSex[AfterCoronaLocalWomanSex[\"생활지역권\"]==dropdown_region.value]\n",
    "\n",
    "    consumersafter.add_trace(go.Scatter(x=tempMan['시간대구분'],y=tempMan['생활인구수'], mode='lines+markers', name='남자'), row=1, col=1)\n",
    "    consumersafter.add_trace(go.Scatter(x=tempWoman['시간대구분'],y=tempWoman['생활인구수'], mode='lines+markers', name='여자'), row=1, col=1)\n",
    "    consumersafter.add_trace(go.Scatter(x=tempWoman['시간대구분'],y=tempMan['생활인구수']+tempWoman['생활인구수'], mode='lines+markers', name='내국인'), row=1, col=1)\n",
    "    \n",
    "    isDongAge=(AfterCoronaLocalManAge['생활지역권']==dropdown_region.value)&(AfterCoronaLocalManAge['연령대']== \"남자 20대 이하\")\n",
    "    tempMan20U=AfterCoronaLocalManAge[isDongAge]\n",
    "    isDongAge=(AfterCoronaLocalManAge['생활지역권']==dropdown_region.value)&(AfterCoronaLocalManAge['연령대']== \"남자 20대\")\n",
    "    tempMan20=AfterCoronaLocalManAge[isDongAge]\n",
    "    isDongAge=(AfterCoronaLocalManAge['생활지역권']==dropdown_region.value)&(AfterCoronaLocalManAge['연령대']== \"남자 30대\")\n",
    "    tempMan30=AfterCoronaLocalManAge[isDongAge]\n",
    "    isDongAge=(AfterCoronaLocalManAge['생활지역권']==dropdown_region.value)&(AfterCoronaLocalManAge['연령대']== \"남자 40대\")\n",
    "    tempMan40=AfterCoronaLocalManAge[isDongAge]\n",
    "    isDongAge=(AfterCoronaLocalManAge['생활지역권']==dropdown_region.value)&(AfterCoronaLocalManAge['연령대']== \"남자 50대\")\n",
    "    tempMan50=AfterCoronaLocalManAge[isDongAge]\n",
    "    isDongAge=(AfterCoronaLocalManAge['생활지역권']==dropdown_region.value)&(AfterCoronaLocalManAge['연령대']== \"남자 60대 이상\")\n",
    "    tempMan60U=AfterCoronaLocalManAge[isDongAge]\n",
    "\n",
    "    isDongAge=(AfterCoronaLocalWomanAge['생활지역권']==dropdown_region.value)&(AfterCoronaLocalWomanAge['연령대']== \"여자 20대 이하\")\n",
    "    tempWoman20U=AfterCoronaLocalWomanAge[isDongAge]\n",
    "    isDongAge=(AfterCoronaLocalWomanAge['생활지역권']==dropdown_region.value)&(AfterCoronaLocalWomanAge['연령대']== \"여자 20대\")\n",
    "    tempWoman20=AfterCoronaLocalWomanAge[isDongAge]\n",
    "    isDongAge=(AfterCoronaLocalWomanAge['생활지역권']==dropdown_region.value)&(AfterCoronaLocalWomanAge['연령대']== \"여자 30대\")\n",
    "    tempWoman30=AfterCoronaLocalWomanAge[isDongAge]\n",
    "    isDongAge=(AfterCoronaLocalWomanAge['생활지역권']==dropdown_region.value)&(AfterCoronaLocalWomanAge['연령대']== \"여자 40대\")\n",
    "    tempWoman40=AfterCoronaLocalWomanAge[isDongAge]\n",
    "    isDongAge=(AfterCoronaLocalWomanAge['생활지역권']==dropdown_region.value)&(AfterCoronaLocalWomanAge['연령대']== \"여자 50대\")\n",
    "    tempWoman50=AfterCoronaLocalWomanAge[isDongAge]\n",
    "    isDongAge=(AfterCoronaLocalWomanAge['생활지역권']==dropdown_region.value)&(AfterCoronaLocalWomanAge['연령대']== \"여자 60대 이상\")\n",
    "    tempWoman60U=AfterCoronaLocalWomanAge[isDongAge]\n",
    "\n",
    "    consumersafter.add_trace(go.Scatter(x=tempMan20U['시간대구분'],y=tempMan20U['생활인구수']+tempWoman20U['생활인구수'], mode='lines+markers', name='20 Under'), row=1, col=2)\n",
    "    consumersafter.add_trace(go.Scatter(x=tempMan20['시간대구분'],y=tempMan20['생활인구수']+tempWoman20['생활인구수'], mode='lines+markers', name='20s'), row=1, col=2)\n",
    "    consumersafter.add_trace(go.Scatter(x=tempMan30['시간대구분'],y=tempMan30['생활인구수']+tempWoman30['생활인구수'], mode='lines+markers', name='30s'), row=1, col=2)\n",
    "    consumersafter.add_trace(go.Scatter(x=tempMan40['시간대구분'],y=tempMan40['생활인구수']+tempWoman40['생활인구수'], mode='lines+markers', name='40s'), row=1, col=2)\n",
    "    consumersafter.add_trace(go.Scatter(x=tempMan50['시간대구분'],y=tempMan50['생활인구수']+tempWoman50['생활인구수'], mode='lines+markers', name='50s'), row=1, col=2)\n",
    "    consumersafter.add_trace(go.Scatter(x=tempMan60U['시간대구분'],y=tempMan60U['생활인구수']+tempWoman60U['생활인구수'], mode='lines+markers', name='60 Upper'), row=1, col=2)\n",
    "\n",
    "    display(consumersafter)\n",
    "    \n",
    "############################################################################################\n",
    "    consumersrate = make_subplots(rows=1, cols=2, specs=[[{\"type\": \"pie\"}, {\"type\": \"pie\"}]])\n",
    "    BeforeCoronaLocalManWoman=copy.deepcopy(BeforeCoronaLocalManAge)\n",
    "    BeforeCoronaLocalManWoman=BeforeCoronaLocalManWoman.append(BeforeCoronaLocalWomanAge)\n",
    "    BeforeCoronaLocalManWoman=BeforeCoronaLocalManWoman[BeforeCoronaLocalManWoman['생활지역권']==dropdown_region.value]\n",
    "    BeforeCoronaLocalManWoman['영업시간']=BeforeCoronaLocalManWoman['시간대구분'].map(timefunction)\n",
    "    BeforeCoronaLocalManWoman.drop('시간대구분', axis=1, inplace=True)\n",
    "    BeforeCoronaLocalManWoman=BeforeCoronaLocalManWoman.groupby(['연령대', '영업시간']).sum(['생활인구수'])\n",
    "    BeforeCoronaLocalManWoman.reset_index(inplace=True)\n",
    "    if time_buttons.label!='전체':\n",
    "        BeforeCoronaLocalManWoman=BeforeCoronaLocalManWoman[BeforeCoronaLocalManWoman['영업시간']==time_buttons.label]\n",
    "    consumersrate.add_trace(go.Pie(labels=BeforeCoronaLocalManWoman['연령대'].unique().tolist(), values=BeforeCoronaLocalManWoman['생활인구수'], hole=.3, name=\"코로나 전 생활인구\"), row=1, col=1)\n",
    "\n",
    "    AfterCoronaLocalManWoman=copy.deepcopy(AfterCoronaLocalManAge)\n",
    "    AfterCoronaLocalManWoman=AfterCoronaLocalManWoman.append(AfterCoronaLocalWomanAge)\n",
    "    AfterCoronaLocalManWoman=AfterCoronaLocalManWoman[AfterCoronaLocalManWoman['생활지역권']==dropdown_region.value]\n",
    "    AfterCoronaLocalManWoman['영업시간']=AfterCoronaLocalManWoman['시간대구분'].map(timefunction)\n",
    "    AfterCoronaLocalManWoman.drop('시간대구분', axis=1, inplace=True)\n",
    "    AfterCoronaLocalManWoman=AfterCoronaLocalManWoman.groupby(['연령대', '영업시간']).sum(['생활인구수'])\n",
    "    AfterCoronaLocalManWoman.reset_index(inplace=True)\n",
    "    if time_buttons.label!='전체':\n",
    "        AfterCoronaLocalManWoman=AfterCoronaLocalManWoman[AfterCoronaLocalManWoman['영업시간']==time_buttons.label]\n",
    "    consumersrate.add_trace(go.Pie(labels=BeforeCoronaLocalManWoman['연령대'].unique().tolist(), values=AfterCoronaLocalManWoman['생활인구수'], hole=.3, name=\"코로나 후 생활인구\"), row=1, col=2)\n",
    "    consumersrate.update_layout(title=\"<b>2-2-3[\"+str(time_buttons.label)+\"] 시간 [\"+str(dropdown_region.value)+\"] 생활 지역권 코로나 전후 가락 저녁 생활인구 비율 비교</b>\")\n",
    "    display(consumersrate)\n",
    "###############################################################################################\n",
    "    \n",
    "    paypower=make_subplots(rows=1, cols=2, specs=[[{\"type\": \"bar\"}, {\"type\": \"bar\"}]])\n",
    "    paypower.update_layout(title='<b>3-1(코로나 전<->후) '+dropdown_region.value+ ' 생활지역권  성별 연령별 구매력</b>')\n",
    "\n",
    "    paypower.add_trace(go.Bar(x=dfb_group[dfb_group['연령대'] == '2.30대'].성별구분, y=dfb_group[dfb_group['연령대'] == '2.30대'].매출건수, name='2.30대'), row=1, col=1)\n",
    "    paypower.add_trace(go.Bar(x=dfb_group[dfb_group['연령대'] == '3.40대'].성별구분, y=dfb_group[dfb_group['연령대'] == '3.40대'].매출건수, name='3.40대'), row=1, col=1)\n",
    "    paypower.add_trace(go.Bar(x=dfb_group[dfb_group['연령대'] == '4.50대'].성별구분, y=dfb_group[dfb_group['연령대'] == '4.50대'].매출건수, name='4.50대'), row=1, col=1)\n",
    "    paypower.add_trace(go.Bar(x=dfb_group[dfb_group['연령대'] == '5.60대'].성별구분, y=dfb_group[dfb_group['연령대'] == '5.60대'].매출건수, name='5.60대'), row=1, col=1)\n",
    "    paypower.add_trace(go.Bar(x=dfb_group[dfb_group['연령대'] == '2.30대'].성별구분, y=dfa_group[dfa_group['연령대'] == '2.30대'].매출건수, name='2.30대'), row=1, col=2)\n",
    "    paypower.add_trace(go.Bar(x=dfb_group[dfb_group['연령대'] == '3.40대'].성별구분, y=dfa_group[dfa_group['연령대'] == '3.40대'].매출건수, name='3.40대'), row=1, col=2)\n",
    "    paypower.add_trace(go.Bar(x=dfb_group[dfb_group['연령대'] == '4.50대'].성별구분, y=dfa_group[dfa_group['연령대'] == '4.50대'].매출건수, name='4.50대'), row=1, col=2)\n",
    "    paypower.add_trace(go.Bar(x=dfb_group[dfb_group['연령대'] == '5.60대'].성별구분, y=dfa_group[dfa_group['연령대'] == '5.60대'].매출건수, name='5.60대'), row=1, col=2)\n",
    "    display(paypower)\n",
    "    \n",
    "###################################################################################################\n",
    "    allmenu=go.Figure()\n",
    "    allmenu.update_layout(title=\"<b>3-2 (코로나 전<->후) 전체 업종별 선호도(매출건수)</b>\")\n",
    "    def myfood(foodname):\n",
    "        alldf=pie_food[pie_food['중분류명']==foodname]\n",
    "        ##2019 : 10달 2020-2021 :15달 S=> eoul['날짜'].unique()결과\n",
    "        alldf['코로나 전후']=alldf['기준년도'].map(isbefore)\n",
    "        alldf=alldf.groupby(['코로나 전후']).sum(['매출건수'])\n",
    "        alldf=alldf.reset_index()\n",
    "        alldfxlist=[\"코로나 전\", \"코로나 후\"]\n",
    "        alldfylist=[alldf.loc[0]['매출건수'],alldf.loc[1]['매출건수'] ]\n",
    "        alldfylist[0]/=10\n",
    "        alldfylist[1]/=15\n",
    "        return alldfxlist, alldfylist\n",
    "    tempfood1=myfood('한식')\n",
    "    allmenu.add_trace(go.Scatter(x=tempfood1[0],y=tempfood1[1], mode='lines+markers', name='한식'))\n",
    "    tempfood2=myfood('일식/수산물')\n",
    "    allmenu.add_trace(go.Scatter(x=tempfood2[0],y=tempfood2[1], mode='lines+markers', name='일식/수산물'))\n",
    "    tempfood3=myfood('닭/오리요리')\n",
    "    allmenu.add_trace(go.Scatter(x=tempfood3[0],y=tempfood3[1], mode='lines+markers', name='닭/오리고기'))\n",
    "    tempfood4=myfood('양식')\n",
    "    allmenu.add_trace(go.Scatter(x=tempfood4[0],y=tempfood4[1], mode='lines+markers', name='양식'))\n",
    "    tempfood5=myfood('분식')\n",
    "    allmenu.add_trace(go.Scatter(x=tempfood5[0],y=tempfood5[1], mode='lines+markers', name='분식'))\n",
    "    display(allmenu)\n",
    "    \n",
    "#################################################################################3\n",
    "    menu=make_subplots(rows=1, cols=2, specs=[[{\"type\": \"pie\"}, {\"type\": \"pie\"}]])\n",
    "    menu.update_layout(title=\"<b>3-3 [\"+dropdown_menu.value+\"] 업종 코로나 전후 성별 연령별 업종별 선호도</b>\")\n",
    "    pie_foodbefore=pie_food[pie_food['기준년도']=='2019']\n",
    "    pie_foodbefore=pie_foodbefore[pie_foodbefore['중분류명']==dropdown_menu.value]\n",
    "    pie_foodbefore=pie_foodbefore.groupby(['new성별연령', '중분류명']).sum(['매출건수'])\n",
    "    pie_foodbefore = pie_foodbefore.reset_index()\n",
    "    pie_foodafter=pie_food[pie_food['기준년도']!=\"2019\"]\n",
    "    pie_foodafter=pie_foodafter[pie_foodafter['중분류명']==dropdown_menu.value]\n",
    "    pie_foodafter=pie_foodafter.groupby(['new성별연령', '중분류명']).sum(['매출건수'])\n",
    "    pie_foodafter = pie_foodafter.reset_index()\n",
    "    if(dropdown_menu.value==\"업종을 선택하세요\"):\n",
    "        print('<b>3-3 [선택] 업종 코로나 전후 성별 연령별 업종별 선호도</b>')\n",
    "        print(\"업종을 선택하시면 특정 업종의 성별 연령별 선호도를 볼 수 있습니다\")\n",
    "    else:\n",
    "        menu.add_trace(go.Pie(labels=pie_foodbefore['new성별연령'].unique().tolist(), values=pie_foodbefore['매출건수'], hole=.3, name=\"코로나 전 매출건수 비율\"), row=1, col=1)\n",
    "        menu.add_trace(go.Pie(labels=pie_foodafter['new성별연령'].unique().tolist(), values=pie_foodafter['매출건수'], hole=.3, name=\"코로나 전 매출건수 비율\"), row=1, col=2)\n",
    "        display(menu)\n",
    "    \n",
    "# 위젯 변화를 observe하여 response 함수를 실행\n",
    "dropdown_region.observe(response, names='value')\n",
    "time_buttons.observe(response, names=\"value\")\n",
    "dropdown_menu.observe(response, names=\"value\")\n",
    "print('******************************************************************\\n')\n",
    "print('지역권생활권별 요식업종 가이드\\n')\n",
    "print('1. 지역생활권 이란?')\n",
    "print('서울시 지역을 생활권 단위로 나누어 도시관리 계획을 구체적으로 제시하기 위한 공간 관리 지침\\n')\n",
    "print('**분석하고 싶은 지역생활권을 선택하세요')\n",
    "display(container)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2-4 지역생활권별, 영업시간<->업종별 추천\n",
    "### [성. 연령별 업종별 구매건수 : 성. 연령별:12개  업종5개)\n",
    "#### arraynumpy1 : 연령별 중분류명 매출건수 (닭/오리요리, 분식, 양식, 일식/수산물, 한식)\n",
    "#### arraynumpy2 : 지역생활권별 중분류명 상가수\n",
    "#### arraynumpy3all (dawn, breakfast, afternoon, dinner) : 시계열 결과 만들어진 DF  (지역생활권별 예비소비자 예측 - \n",
    "#### https://github.com/CUAI-CAU/CUAI-FINANCE-TEAM2 \n",
    "#### 시계열분석_전체, 시계열분석_새벽, 시계열 분석_아침, 시계열분석_점심, 시계열분석_저녁) 실행 결과 \n",
    "#### finaldf.csv, dawn_data.csv, breakfast_data.csv, afternoon_data.csv, dinner_data.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "4. 요식업 업종&영업시간 추천\n"
     ]
    },
    {
     "data": {
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       "model_id": "565d4a5a56df4fde96e2c806b48c0a41",
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       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Dropdown(description='지역생활권', index=14, options=('지역생활권을 선택하세요', '가락', '가산', '가좌…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c3aeb5a4537946cd9fa4279466487bd9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(ToggleButtons(description='영업시간별', options=(('전체', '0-24'), ('아침', '6-12'), ('점심…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[길동.둔촌] 지역생활권 [전체] 영업시간 업종추천 : \n",
      "\n",
      "1. 양식\n",
      "2. 일식/수산물\n",
      "3. 한식\n",
      "4. 분식\n",
      "5. 닭/오리요리\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d06ad1a689104b48bde842153bc2eda1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HBox(children=(Dropdown(description='업종별', index=3, options=('업종을 선택하세요', '한식', '일식/수산물', '닭/오리…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[길동.둔촌] 지역생활권 [닭/오리요리] 업종 영업시간 추천 : \n",
      "1. 새벽\n",
      "2. 아침\n",
      "3. 저녁\n",
      "4. 점심\n"
     ]
    }
   ],
   "source": [
    "arraydata1=pie_food[pie_food['기준년도']!=\"2019\"]\n",
    "arraydata1=arraydata1.groupby(by=['new성별연령','중분류명']).sum()[['매출건수']]\n",
    "arraydata1=arraydata1.reset_index()\n",
    "arraydata1=arraydata1.set_index('new성별연령')\n",
    "arraydata1=arraydata1.reset_index()\n",
    "arraydata11=arraydata1['매출건수'].to_numpy()\n",
    "arraynumpy1=arraydata11.reshape((-1, 5))\n",
    "\n",
    "arraydata2=copy.deepcopy(df2)\n",
    "arraydata2=arraydata2.groupby(by=['생활지역권','중분류명']).count()\n",
    "arraydata2=arraydata2.reset_index()\n",
    "arraydata22=arraydata2[(arraydata2['중분류명']=='닭/오리요리')|(arraydata2['중분류명']=='분식')|(arraydata2['중분류명']=='양식')|(arraydata2['중분류명']=='일식/수산물')|(arraydata2['중분류명']=='한식')]\n",
    "arraydata22=arraydata22['소분류명'].to_numpy()\n",
    "arraynumpy2=arraydata22.reshape((-1, 5))\n",
    "\n",
    "arraydata3all=pd.read_csv('finaldf.csv')\n",
    "arraydata3dawn=pd.read_csv('dawn_data.csv')\n",
    "arraydata3breakfast=pd.read_csv('breakfast_data.csv')\n",
    "arraydata3afternoon=pd.read_csv('afternoon_data.csv')\n",
    "arraydata3dinner=pd.read_csv('dinner_data.csv')\n",
    "\n",
    "import numpy\n",
    "region_name=arraydata2['생활지역권'].unique()\n",
    "region_name_List=region_name.tolist()\n",
    "menulist=['닭/오리요리', \"분식\", '양식', '일식/수산물', '한식']\n",
    "\n",
    "\n",
    "dropdown_region2 = widgets.Dropdown(\n",
    "    description='지역생활권',options=selectregion)\n",
    "##영업시간 위젯\n",
    "time_buttons2 = widgets.ToggleButtons(\n",
    "    options=[('전체', '0-24'),('아침', '6-12'), ('점심', '13-18'), ('저녁', '18-24'),('새벽', '0-6')],\n",
    "    description='영업시간별',\n",
    ")\n",
    "#업종 위젯\n",
    "dropdown_menu2 = widgets.Dropdown(\n",
    "    description='업종별',\n",
    "    options=selectmenu)\n",
    "# 위젯을 container로 합침.\n",
    "container1=widgets.VBox([widgets.HBox([dropdown_region2])])\n",
    "container2 = widgets.VBox([widgets.HBox([time_buttons2])])\n",
    "container3 = widgets.VBox([widgets.HBox([dropdown_menu2])])\n",
    "\n",
    "def response2(change):\n",
    "    # 위젯에 변화가 감지되면 실행되는 코드\n",
    "    # 이전 셀 아웃풋은 삭제\n",
    "    clear_output()  \n",
    "    from plotly.subplots import make_subplots\n",
    "    import plotly.graph_objects as go\n",
    "    print(\"\\n4. 요식업 업종&영업시간 추천\")\n",
    "    display(container1)\n",
    "    display(container2)\n",
    "    print(\"\\n[\"+dropdown_region2.value+\"] 지역생활권 [\"+time_buttons2.label+\"] 영업시간 업종추천 : \\n\")\n",
    "    import numpy\n",
    "    region_name=arraydata2['생활지역권'].unique()\n",
    "    region_name_List=region_name.tolist()\n",
    "    ## 성 연령 : 20대 이하 남성, 30대 남성.... 60대, 20대 이사 여성, ... 60대 이상 여성 : 10개\n",
    "    ## 업종(중분류명) : 닭/오리요리, 분식, 양식, 일식/수산물, 한식\n",
    "    ## 지역생활권 : 125개\n",
    "\n",
    "    #### arraynumpy1 : 성. 연령별 중분류명 매출건수 [10*5]\n",
    "    #### arraynumpy2 : 지역생활권별 중분류명 상가수 [125*5]\n",
    "    ### arraynumpy3 : 지역생활권별 성.연령별 예비소비자 [125*10]\n",
    "    \n",
    "    ##arraynumpy3*arraynumpy1/arraynumpy2[(125*10)X(10*5)/(125*5)] \n",
    "    ##내적곱 수행결과 지역생활권별 중분류 매출건수 예측\n",
    "    ##나눗셈 수행 결과=지역생활권별 중분류 개업시 얻을 수 있는 매출건수\n",
    "    \n",
    "    arraydata3A=arraydata3all[arraydata3all['생활지역권']!='목동1']\n",
    "    arraydata33A=arraydata3A.drop(columns='생활지역권')\n",
    "    arraynumpy3A=arraydata33A.to_numpy()\n",
    "    mulA=arraynumpy3A.dot(arraynumpy1)\n",
    "    muldivA=mulA/arraynumpy2\n",
    "    \n",
    "    arraydata3Dawn=arraydata3dawn[arraydata3dawn['생활지역권']!='목동1']\n",
    "    arraydata33Dawn=arraydata3Dawn.drop(columns='생활지역권')\n",
    "    arraynumpy3Dawn=arraydata33Dawn.to_numpy()\n",
    "    mulDawn=arraynumpy3Dawn.dot(arraynumpy1)\n",
    "    muldivDawn=mulDawn/arraynumpy2\n",
    "    \n",
    "    arraydata3B=arraydata3breakfast[arraydata3breakfast['생활지역권']!='목동1']\n",
    "    arraydata33B=arraydata3B.drop(columns='생활지역권')\n",
    "    arraynumpy3B=arraydata33B.to_numpy()\n",
    "    mulB=arraynumpy3B.dot(arraynumpy1)\n",
    "    muldivB=mulB/arraynumpy2\n",
    "    \n",
    "    arraydata3L=arraydata3afternoon[arraydata3afternoon['생활지역권']!='목동1']\n",
    "    arraydata33L=arraydata3L.drop(columns='생활지역권')\n",
    "    arraynumpy3L=arraydata33L.to_numpy()\n",
    "    mulL=arraynumpy3L.dot(arraynumpy1)\n",
    "    muldivL=mulL/arraynumpy2\n",
    "    \n",
    "    arraydata3D=arraydata3dinner[arraydata3dinner['생활지역권']!='목동1']\n",
    "    arraydata33D=arraydata3D.drop(columns='생활지역권')\n",
    "    arraynumpy3D=arraydata33D.to_numpy()\n",
    "    mulD=arraynumpy3D.dot(arraynumpy1)\n",
    "    muldivD=mulD/arraynumpy2\n",
    "    \n",
    "    if time_buttons2.label==\"전체\":\n",
    "        selectRegionTime=muldivA\n",
    "    if time_buttons2.label==\"새벽\":\n",
    "        selectRegionTime=muldivDawn\n",
    "    if time_buttons2.label==\"아침\":\n",
    "        selectRegionTime=muldivB\n",
    "    if time_buttons2.label==\"점심\":\n",
    "        selectRegionTime=muldivL\n",
    "    if time_buttons2.label==\"저녁\":\n",
    "        selectRegionTime=muldivD\n",
    "\n",
    "    region_index=region_name_List.index(str(dropdown_region2.value))\n",
    "    templist=selectRegionTime[region_index].argsort()\n",
    "    nummenu=len(menulist)\n",
    "    while nummenu!=0:\n",
    "        print(str(5-nummenu+1)+\". \"+str(menulist[templist[nummenu-1]]))\n",
    "        nummenu-=1\n",
    "    \n",
    "#################################################################################################################\n",
    "    display(container3)\n",
    "    if dropdown_menu2.value==\"업종을 선택하세요\":\n",
    "        print(\"\\n업종을 선택하시면 영업시간을 추천 받으실 수 있습니다.\")\n",
    "    else:\n",
    "        print(\"\\n[\"+dropdown_region2.value+\"] 지역생활권 [\"+dropdown_menu2.value+\"] 업종 영업시간 추천 : \")\n",
    "        menu_index=menulist.index(str(dropdown_menu2.value))\n",
    "        recommendTime=list()\n",
    "        recommendTime.append(muldivDawn[region_index][menu_index])\n",
    "        recommendTime.append(muldivB[region_index][menu_index])\n",
    "        recommendTime.append(muldivL[region_index][menu_index])\n",
    "        recommendTime.append(muldivD[region_index][menu_index])\n",
    "        templist=numpy.array(recommendTime).argsort()\n",
    "        timelist=[\"새벽\", \"아침\", \"점심\", \"저녁\"]\n",
    "        nummenu=len(timelist)\n",
    "        while nummenu!=0:\n",
    "            print(str(4-nummenu+1)+\". \"+str(timelist[templist[nummenu-1]]))\n",
    "            nummenu-=1\n",
    "    \n",
    "dropdown_region2.observe(response2, names='value')\n",
    "time_buttons2.observe(response2, names=\"value\")\n",
    "dropdown_menu2.observe(response2, names=\"value\")\n",
    "print(\"**********************************************\")\n",
    "print(\"\\n4. 요식업 지역생활권별 업종&영업시간 추천\\n\")\n",
    "print('지역생활권을 선택하세요')\n",
    "dropdown_region2.value='지역생활권을 선택하세요'\n",
    "time_buttons2.value='0-24'\n",
    "dropdown_menu2.value='업종을 선택하세요'\n",
    "display(container1)"
   ]
  }
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