국토교통 빅데이터 온라인 해커톤 경진대회_필사.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 국토교통 빅데이터 온라인 해커톤 경진대회 필사\n",
    "- 대회 정보 https://www.dacon.io/competitions/official/235622/overview/description\n",
    "\n",
    "- 주제 및 배경\n",
    "    - 국토교통 데이터와 코로나 데이터 등을 융합분석하여, 국민의 안전한 이동을 위한 새로운 통찰과 창의적 아이디어 도출\n",
    "    - 국토교통 데이터 활용 저변을 넓히고 국민적 관심도 제고\n",
    "    - 교통빅데이터 플랫폼을 통한 데이터 이용 활성화"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 포스트 코로나, 고령자의 안전한 이동을 위한 서울시 자치구 및 지하철역 분석\n",
    "\n",
    "링크: https://dacon.io/competitions/official/235622/codeshare/1603#reply_area"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 주제\n",
    "### 포스트 코로나, 고령자의 안전한 이동을 위한 서울시 자치구 및 지하철역 분석"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 목차\n",
    "### 1. 코로나가 서울시 대중교통에 미친 영향\n",
    "- 코로나 전후 서울시 대중교통 이용 증감률 비교\n",
    "\n",
    "### 2. 코로나 데이터 분석을 통한 고령층의 코로나 치명성 검증\n",
    "- 연령별 누적 확진자 수 및 누적 사망자 수\n",
    "- 연령별 치사율 비교\n",
    "- 연령별 코로나 평균 치료일 수\n",
    "\n",
    "### 3. 고령층 지하철 이용 현황\n",
    "- 서울시 대중교통 이용인원 중 고령자 비율\n",
    "- 고령층의 서울시 지하철 이용인원 비교\n",
    "\n",
    "### 4. 유동인구와 코로나 감염과의 상관관계 분석\n",
    "- 가설 검증: 유동인구 수가 많은 지역에 코로나 감염자 수도 많을 것이다.\n",
    "- 서울시 자치구별 전체 유동인구와 고령 유동인구 상관관계 분석\n",
    "- 시간대별 최대 고령 유동인구를 기록한 자치구 추출\n",
    "- 서울시 자치구별 유동인구와 코로나 확진자 수 상관관계 분석\n",
    "\n",
    "### 5. 유동인구와 지하철 이용인원간의 상관관계 분석\n",
    "- 서울시 자치구별 유동인구와 지하철 이용률 상관관계 분석\n",
    "\n",
    "### 6. 유동인구, 지하철 이용인원 모두 많은 구를 선정하고 해당 자치구의 지하철역 추출\n",
    "- 2019, 2020년 자치구별 고령자의 유동인구 및 지하철 이용인원 비교\n",
    "- 서울시 자치구별 고령자의 지하철역 이용 현황 분석\n",
    "- 서울시 자치구별 집단감염 확진자 수 분석\n",
    "- 서울시 자치구별 고령자의 지하철 이용이 많은 자치구 분석\n",
    "\n",
    "### 7. 제언: 향후 활용 방안\n",
    "- 태그리스(Tagless): 우대자용 게이트 시범운행 구 지정"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 활용 데이터\n",
    "[DACON 제공 데이터]\n",
    "- DS4C COVID-19 데이터\n",
    "- KT 행정동별 유동인구 데이터\n",
    "- 한국교통안전공단 교통카드 데이터\n",
    "\n",
    "[외부 데이터]\n",
    "- SKT 서울시 유동인구\n",
    "- 서울시 노인여가 복지시설 자치구별 통계\n",
    "\n",
    "### 데이터 기간 통일\n",
    "- 2019년 1월~5월, 2020년 1월~5월\n",
    "\n",
    "### 고령자: 60대 이상으로 정의\n",
    "- 도로교통법 상 고령자: 도로교통법 제 11조 고령자(노인) 65세 이상\n",
    "- 코로나확진자데이터(PatientInfo.csv)가 10세 단위로 연령이 구분되어 있음\n",
    "- 따라서 60대 이상을 고령자로 설정\n",
    "\n",
    "### 주제 선정 이유1. 대중교통 중 지하철 선택\n",
    "\n",
    "1) 2019년 대비 2020 대중교통(버스, 지하철) 이용인원 증감률 확인\n",
    "- 코로나 전(2019), 후(2020) 버스 이용 증감률 -20.89%\n",
    "- 코로나 전(2019), 후(2020) 지하철 이용 증감률 -23.74%\n",
    "- 버스보다 지하철 감소폭이 더 컸음\n",
    "\n",
    "2) 2월부터 5월까지 지하철 이용 감소폭이 버스 이용 감소폭보다 더 큼\n",
    "\n",
    "### 주제 선정 이유2. 고령자가 다른 연령층에 비해 코로나에 훨씬 더 취햑함\n",
    "- 누적 확진자 수는 20대 및 50대가 많은 반면 누적 사망자 수는 60대 이상이 대부분임\n",
    "- 2020.06.30 기준 고령층 사망자가 92.9%를 차지함\n",
    "- 치사율(Infection Fatality Rate = 사망자수/확진자수 * 100): 3월 중순 대구, 경북 지역의 신천지 교회발 집단 감염 사태 이후로 80대의 치사율이 치솟음\n",
    "\n",
    "### 주제 선정 이유3. 지하철 이용 인원 중 고령층이 차지하는 비율 높음\n",
    "- 일반인을 제외하면 특정 이용자 유형 중 고령층(15%)의 지하철 사용 비율이 눈에 띔"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 필요한 모듈 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
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    }
   ],
   "source": [
    "# 필요 라이브러리 실행\n",
    "# folium 설치\n",
    "\n",
    "!pip install folium"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "!pip install plotly"
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      "  Building wheel for cufflinks (setup.py): finished with status 'done'\n",
      "  Created wheel for cufflinks: filename=cufflinks-0.17.3-py3-none-any.whl size=68729 sha256=974b968ed5da554879aeb58e98b686c68dcbf5cece720f22879c5e030f7cb71f\n",
      "  Stored in directory: c:\\users\\tende\\appdata\\local\\pip\\cache\\wheels\\6b\\76\\62\\6da97734911ffcbdd559fd1a3f28526321f0ae699182a23866\n",
      "Successfully built cufflinks\n",
      "Installing collected packages: colorlover, cufflinks\n",
      "Successfully installed colorlover-0.3.0 cufflinks-0.17.3\n"
     ]
    }
   ],
   "source": [
    "!pip install cufflinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting googlemaps\n",
      "  Downloading googlemaps-4.4.5.tar.gz (31 kB)\n",
      "Requirement already satisfied: requests<3.0,>=2.20.0 in c:\\users\\tende\\anaconda3\\lib\\site-packages (from googlemaps) (2.24.0)\n",
      "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in c:\\users\\tende\\anaconda3\\lib\\site-packages (from requests<3.0,>=2.20.0->googlemaps) (1.25.9)\n",
      "Requirement already satisfied: chardet<4,>=3.0.2 in c:\\users\\tende\\anaconda3\\lib\\site-packages (from requests<3.0,>=2.20.0->googlemaps) (3.0.4)\n",
      "Requirement already satisfied: idna<3,>=2.5 in c:\\users\\tende\\anaconda3\\lib\\site-packages (from requests<3.0,>=2.20.0->googlemaps) (2.10)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\tende\\anaconda3\\lib\\site-packages (from requests<3.0,>=2.20.0->googlemaps) (2020.6.20)\n",
      "Building wheels for collected packages: googlemaps\n",
      "  Building wheel for googlemaps (setup.py): started\n",
      "  Building wheel for googlemaps (setup.py): finished with status 'done'\n",
      "  Created wheel for googlemaps: filename=googlemaps-4.4.5-py3-none-any.whl size=37989 sha256=379a6d1b09e1aa31a5e08913016da9ca99c76fe2ef5c90f34a3c019639344564\n",
      "  Stored in directory: c:\\users\\tende\\appdata\\local\\pip\\cache\\wheels\\ba\\e0\\b5\\c1a0de4000d5c565052ded4d5e77bf185674de0f52aaa37d81\n",
      "Successfully built googlemaps\n",
      "Installing collected packages: googlemaps\n",
      "Successfully installed googlemaps-4.4.5\n"
     ]
    }
   ],
   "source": [
    "!pip install googlemaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "        <script type=\"text/javascript\">\n",
       "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
       "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
       "        if (typeof require !== 'undefined') {\n",
       "        require.undef(\"plotly\");\n",
       "        requirejs.config({\n",
       "            paths: {\n",
       "                'plotly': ['https://cdn.plot.ly/plotly-2.2.0.min']\n",
       "            }\n",
       "        });\n",
       "        require(['plotly'], function(Plotly) {\n",
       "            window._Plotly = Plotly;\n",
       "        });\n",
       "        }\n",
       "        </script>\n",
       "        "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.12.1\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import json\n",
    "import folium\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline \n",
    "import seaborn as sns\n",
    "\n",
    "#plotly\n",
    "import plotly\n",
    "import plotly.express as px\n",
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "from plotly import tools\n",
    "\n",
    "# for offline ploting\n",
    "import plotly.offline as offline \n",
    "\n",
    "# cufflinks\n",
    "import cufflinks as cf\n",
    "cf.go_offline()\n",
    "\n",
    "# for folium plugins(지도 및 마커 시각화)\n",
    "import os \n",
    "import folium\n",
    "from folium import plugins \n",
    "print(folium.__version__)\n",
    "\n",
    "# googlemap\n",
    "import googlemaps\n",
    "\n",
    "# json file load\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# matplotlib 폰트 변경(한글 사용)\n",
    "import platform\n",
    "\n",
    "from matplotlib import font_manager, rc\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "if platform.system() == 'Darwin':\n",
    "    rc('font', family = 'AppleGothic')\n",
    "    \n",
    "elif platform.system() == 'Windows':\n",
    "    path = 'C:/Windows/Fonts/NanumBarunGothic.ttf'\n",
    "    font_name = font_manager.FontProperties(fname=path).get_name()\n",
    "    rc('font', family=font_name)\n",
    "\n",
    "else:\n",
    "    print('Unknown system')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 한국교통안전공단 교통카드 데이터 전처리 (폰데이터..)\n",
    "### 대중교통 이용인원 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 첫 번째 40,000,000 rows\n",
    "\n",
    "df_card_1 = pd.read_csv('../data/DM_PUBTRF_USESTF_T.dat', skiprows = range(0, 0), nrows=4e7, sep='|', header = None,)\n",
    "columns = ['년도','년월','운행 일자','요일 구분','이용자 유형 코드','정산사 ID',\n",
    "           '정산 지역 코드','교통수단 구분 코드','노선 ID','정류장 ID','시도 코드',\n",
    "          '시군구 코드', '이용 지역 코드', '이용 인원']\n",
    "df_card_1.columns = columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_card_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 두 번째 40,000,000 rows 분할해서 불러오기\n",
    "df_card_2 = pd.read_csv('../data/DM_PUBTRF_USESTF_T.dat', skiprows = range(0, 40000001), nrows=4e7, sep='|', header=None)\n",
    "df_card_2.columns = columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_card_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 세 번째 40,000,000 rows 분할해서 불러오기\n",
    "df_card_3 = pd.read_csv('../data/DM_PUBTRF_USESTF_T.dat', skiprows = range(0, 80000001), nrows=4e7, sep='|', header=None, )\n",
    "df_card_3.columns = columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_card_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 네 번째 40,000,000 rows 분할해서 불러오기\n",
    "df_card_4 = pd.read_csv('../data/DM_PUBTRF_USESTF_T.dat', skiprows = range(0, 120000001), nrows=3e7, sep='|', header=None, )\n",
    "df_card_4.columns = columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 이용자 유형 데이터(DW_USER_TYPE.dat) - 경로자 코드 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "user_type = pd.read_csv('../data/DW_USER_TYPE.dat', encoding = 'utf-8', sep='|', header = None)\n",
    "\n",
    "# columns name 정해주고 인덱스 행 설정\n",
    "user_type.columns = ['code', 'user_type']\n",
    "user_type.set_index('code')\n",
    "\n",
    "# 이용자 유형 코드 확인 결과: 경로자 코드(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 지역 코드 데이터 확인(DD_AREA.dat) - 시군구 코드 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 불러오기\n",
    "area = pd.read_csv('../data/DD_AREA.dat',encoding='utf-8',sep='|')\n",
    "\n",
    "# 컬럼명 변경\n",
    "area.columns = ['지역구분','시도 코드','시군구 코드','이용지역 코드','시도','시군구','읍면동']\n",
    "\n",
    "# 구별 지역코드 추출\n",
    "# '시도'열이 서울특별시인 행만 추출\n",
    "area_code_seoul = area[area['시도']=='서울특별시']\n",
    "\n",
    "# 구만 나오는 행 추출\n",
    "area_code_seoul = area_code_seoul[area_code_seoul['시군구']==area_code_seoul['읍면동']]\n",
    "\n",
    "# 필요하지 않는 열 제거 - 지역구분, 이용지역 코드, 읍면동\n",
    "area_code_seoul.drop(['지역구분','이용지역 코드','읍면동'], axis=1, inplace=True)\n",
    "\n",
    "# 인덱스 정리\n",
    "# 필요한 지역코드 데이터 추출 완료\n",
    "area_code_seoul.reset_index(drop=True, inplace=True)\n",
    "area_code_seoul.head()\n",
    "\n",
    "# 시도 코드 확인 결과 : 서울특별시(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 필요한 조건의 행 추출하기\n",
    "# -이용자 유형 코드: 경로(4)\n",
    "# -시도 코드: 서울특별시(11)\n",
    "# -교통수단 구분 코드: 지하철(T)\n",
    "\n",
    "senior_card_1 = df_card_1[(df_card_1['이용자 유형 코드']==4) & \n",
    "                          (df_card_1['교통수단 구분 코드']=='T') & (df_card_1['시도 코드']==11)]\n",
    "senior_card_2 = df_card_2[(df_card_2['이용자 유형 코드']==4) & \n",
    "                          (df_card_2['교통수단 구분 코드']=='T') & (df_card_2['시도 코드']==11)]\n",
    "senior_card_3 = df_card_3[(df_card_3['이용자 유형 코드']==4) & \n",
    "                          (df_card_3['교통수단 구분 코드']=='T') & (df_card_3['시도 코드']==11)]\n",
    "senior_card_4 = df_card_4[(df_card_4['이용자 유형 코드']==4) & \n",
    "                          (df_card_4['교통수단 구분 코드']=='T') & (df_card_4['시도 코드']==11)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 병합\n",
    "senior_card_sub = pd.concat([senior_card_1, senior_card_2, senior_card_3, senior_card_4])\n",
    "\n",
    "# 열 정리: '년도', '년월', '시도 코드', '시군구 코드', '이용 인원'\n",
    "senior_card_sub.drop(['이용자 유형 코드','정산사 ID','정산 지역 코드',\n",
    "                      '교통수단 구분 코드','노선 ID','정류장 ID','이용 지역 코드'], axis=1, inplace=True)\n",
    "\n",
    "# 인덱스 정리\n",
    "senior_card_sub.reset_index(drop=True, inplace=True)\n",
    "senior_card_sub.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 지하철이용인원 데이터에 지역코드 데이터 merge()\n",
    "# 사용 데이터: 지하철 이용인원 데이터(senior_card_sub), 지역코드 데이터(DD.AREA.dat)\n",
    "\n",
    "# mearge data\n",
    "senior_card_sub = pd.merge(area_code_seoul, senior_card_sub, how='outer', on=['시도 코드', '시군구 코드'])\n",
    "\n",
    "# 시군구코드 == 11000 인 행 제거(서울특별시 | 서울특별시 : 11000)\n",
    "senior_card_sub = senior_card_sub[senior_card_sub['시군구 코드'] != 11000]\n",
    "senior_card_sub"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 코로나가 서울시 대중교통에 미친 영향\n",
    "### 버스 vs 지하철 코로나 전후(2019, 2020) 증감률 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전 연령 버스 이용 증감률 계산하기 위한 새 변수 생성: 서울특별시(11), 버스(B)\n",
    "allage_bus_1 = df_card_1[(df_card_1['교통수단 구분 코드']=='B') & \n",
    "                         (df_card_1['시도 코드']==11)][['년도','년월','이용자 유형 코드','시군구 코드','이용 인원']]\n",
    "allage_bus_2 = df_card_2[(df_card_2['교통수단 구분 코드']=='B') & \n",
    "                         (df_card_2['시도 코드']==11)][['년도','년월','이용자 유형 코드','시군구 코드','이용 인원']]\n",
    "allage_bus_3 = df_card_3[(df_card_3['교통수단 구분 코드']=='B') & \n",
    "                         (df_card_3['시도 코드']==11)][['년도','년월','이용자 유형 코드','시군구 코드','이용 인원']]\n",
    "allage_bus_4 = df_card_4[(df_card_4['교통수단 구분 코드']=='B') & \n",
    "                         (df_card_4['시도 코드']==11)][['년도','년월','이용자 유형 코드','시군구 코드','이용 인원']]\n",
    "\n",
    "# 전 연령 지하철 이용 증감률 계산하기 위한 새 변수 생성:서울특별시(11), 지하철(T)\n",
    "allage_card_1 = df_card_1[(df_card_1['교통수단 구분 코드']=='T') & \n",
    "                          (df_card_1['시도 코드']==11)][['년도','년월','이용자 유형 코드','시군구 코드','이용 인원']]\n",
    "allage_card_2 = df_card_2[(df_card_2['교통수단 구분 코드']=='T') & \n",
    "                          (df_card_2['시도 코드']==11)][['년도','년월','이용자 유형 코드','시군구 코드','이용 인원']]\n",
    "allage_card_3 = df_card_3[(df_card_3['교통수단 구분 코드']=='T') & \n",
    "                          (df_card_3['시도 코드']==11)][['년도','년월','이용자 유형 코드','시군구 코드','이용 인원']]\n",
    "allage_card_4 = df_card_4[(df_card_4['교통수단 구분 코드']=='T') & \n",
    "                          (df_card_4['시도 코드']==11)][['년도','년월','이용자 유형 코드','시군구 코드','이용 인원']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 병합 : allage_card_bus, allage_card_sub\n",
    "allage_card_bus= pd.concat([allage_bus_1, allage_bus_2, allage_bus_3, allage_bus_4])\n",
    "allage_card_sub= pd.concat([allage_card_1, allage_card_2, allage_card_3, allage_card_4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전 연령 년도별 버스 이용 인원 합계\n",
    "allage_bus_total = allage_card_bus.pivot_table('이용 인원','년도',aggfunc='sum')\n",
    "allage_bus_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 버스 증감률 계산(2020-2019)/2019*100\n",
    "allage_bus_total.reset_index(drop=True,inplace=True)\n",
    "allbusupdown=round((allage_bus_total['이용 인원'][1]-allage_bus_total['이용 인원'][0])/allage_bus_total['이용 인원'][0]*100,2)\n",
    "allbusupdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전 연령 년도별 지하철 이용 인원 합계\n",
    "allage_card_sub1= allage_card_sub.pivot_table('이용 인원','년도',aggfunc='sum')\n",
    "allage_card_sub1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#지하철 증갑률 계산(2020-2019)/2019*100\n",
    "allage_card_sub1.reset_index(drop=True,inplace=True)\n",
    "allsubupdown=round((allage_card_sub1['이용 인원'][1]-allage_card_sub1['이용 인원'][0])/allage_card_sub1['이용 인원'][0]*100,2)\n",
    "allsubupdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 코로나 전,후 월 별 전연령 버스 이용 증감률 구하기\n",
    "allage_bus_sub_updown=allage_card_bus.pivot_table('이용 인원','년월','년도', aggfunc='sum')\n",
    "allage_bus_sub_updown "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#버스 2019년 대비 2020년 증감률 구하기\n",
    "b2019 = [0, 0, 0, 0, 0]\n",
    "b19 = [58266908.0,50201719.0,60822158.0,60653528.0,62107587.0]\n",
    "b29 = [54546466.0,47107718.0,40586588.0,42281059.0,46510338.0]\n",
    "\n",
    "for i, (b19i, b29i) in enumerate(zip(b19, b29)):\n",
    "    b2019[i] = round((b29i - b19i)/b19i*100,2)\n",
    "\n",
    "b2019"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 코로나 전,후(2019, 2020) 대중교통 이용 증감률 시각화"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 화면 분할해서 그래프 생성\n",
    "plt.figure(figsize = (15, 8))\n",
    "plt.subplots_adjust(hspace = 0.5, wspace = 0.4)\n",
    "gridshape = (4, 3)\n",
    "\n",
    "# 코로나 전, 후 년도 별 대중교통 이용 증감률: 막대 그래프\n",
    "x1 = ['버스', '지하철']\n",
    "y = [-20.89,-23.74,]\n",
    "\n",
    "loc = (0, 0)\n",
    "plt.subplot2grid(gridshape, loc, colspan=2)\n",
    "plt.bar(x1, y,color = '#A52A2A')\n",
    "\n",
    "xlabel = x1\n",
    "plt.ylabel('증감률')\n",
    "plt.title('코로나 전, 후 대중교통 년도 별 증감률 비교')\n",
    "plt.xticks(x1, xlabel)\n",
    "plt.yticks(sorted(y))\n",
    "\n",
    "#코로나 전,후 월 별 대중교통 이용 증감률: 꺾은선 그래프\n",
    "x=['1월','2월','3월','4월','5월']\n",
    "\n",
    "loc=(0,2)\n",
    "plt.subplot2grid(gridshape, loc, rowspan=2)\n",
    "plt.plot(x,b2019,color='#9AFE2E',marker='o',label='버스') #b2019 2019년 대비 2020년 버스 증감률 \n",
    "plt.plot(x,s2019,color='#086A87', marker='o',label='지하철') #s2019 2019년 대비 2020년 지하철 증감률 \n",
    "\n",
    "plt.legend(loc='upper right', bbox_to_anchor=(1.0, 1.0), fontsize='x-large')\n",
    "plt.title(\"코로나 전, 후 대중교통 월 별 증감률 비교\")\n",
    "plt.xlabel(\"월\")\n",
    "plt.ylabel(\"총 이용 인원\")\n",
    "\n",
    "for x, y in enumerate( list(b2019) ):\n",
    "    txt = \"%d%%\" % y\n",
    "    plt.text(x, y, txt, fontsize=14, color='#A52A2A', \n",
    "                    horizontalalignment='center', verticalalignment='bottom')\n",
    "for x, y in enumerate( list(s2019) ):\n",
    "    txt = \"%d%%\" % y\n",
    "    plt.text(x, y, txt, fontsize=14, color='#A52A2A', \n",
    "                    horizontalalignment='center', verticalalignment='bottom')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결과 분석\n",
    "- 신천지 사태로 코로나가 지역 확산 되었던 3월에 전년 대비 대중교통 이용률이 급격하게 줄어들음(버스:33% 감소, 지하철:37% 감소)\n",
    "- 2월부터 전년 대비 지하철 이용 감소폭이 버스 이용 감소폭보다 컸음\n",
    "- 코로나 전(2019),후(2020) 버스 이용 증감률 -20.89%\n",
    "- 코로나 전(2019),후(2020) 지하철 이용 증감률 -23.74%\n",
    "- 전체적으로 버스보다 지하철 이용량이 더 줄어들음."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 코로나 데이터 분석을 통한 고령층의 코로나 치명성 검증"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2020-03-02</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "         date  time  age  confirmed  deceased\n",
       "0  2020-03-02     0   0s         32         0\n",
       "1  2020-03-02     0  10s        169         0\n",
       "2  2020-03-02     0  20s       1235         0\n",
       "3  2020-03-02     0  30s        506         1\n",
       "4  2020-03-02     0  40s        633         1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid_TimeAge = pd.read_csv('data/TimeAge.csv')\n",
    "\n",
    "covid_TimeAge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>날짜</th>\n",
       "      <th>환자의 나이</th>\n",
       "      <th>누적 확진자 수</th>\n",
       "      <th>누적 사망자 수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-03-02</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>10s</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>20s</td>\n",
       "      <td>1235</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>30s</td>\n",
       "      <td>506</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>40s</td>\n",
       "      <td>633</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           날짜 환자의 나이  누적 확진자 수  누적 사망자 수\n",
       "0  2020-03-02     0s        32         0\n",
       "1  2020-03-02    10s       169         0\n",
       "2  2020-03-02    20s      1235         0\n",
       "3  2020-03-02    30s       506         1\n",
       "4  2020-03-02    40s       633         1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 칼럼명 변경: 테이블 정의서 참고\n",
    "covid_TimeAge.columns = ['날짜','시간','환자의 나이','누적 확진자 수','누적 사망자 수']\n",
    "\n",
    "# 불필요 컬럼 제거\n",
    "covid_TimeAge.drop(['시간'], axis = 1, inplace=True)\n",
    "covid_TimeAge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>날짜</th>\n",
       "      <th>환자의 나이</th>\n",
       "      <th>누적 확진자 수</th>\n",
       "      <th>누적 사망자 수</th>\n",
       "      <th>나이_int</th>\n",
       "      <th>연령층</th>\n",
       "      <th>누적 확진자 수 비율</th>\n",
       "      <th>누적 사망자 수 비율</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1080</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>0s</td>\n",
       "      <td>193</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>미성년자</td>\n",
       "      <td>1.51</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>10s</td>\n",
       "      <td>708</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>미성년자</td>\n",
       "      <td>5.53</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>20s</td>\n",
       "      <td>3362</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>청년층</td>\n",
       "      <td>26.27</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1083</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>30s</td>\n",
       "      <td>1496</td>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>청년층</td>\n",
       "      <td>11.69</td>\n",
       "      <td>0.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1084</th>\n",
       "      <td>2020-06-30</td>\n",
       "      <td>40s</td>\n",
       "      <td>1681</td>\n",
       "      <td>3</td>\n",
       "      <td>40</td>\n",
       "      <td>중년층</td>\n",
       "      <td>13.13</td>\n",
       "      <td>1.06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              날짜 환자의 나이  누적 확진자 수  누적 사망자 수  나이_int   연령층  누적 확진자 수 비율  \\\n",
       "1080  2020-06-30     0s       193         0       0  미성년자         1.51   \n",
       "1081  2020-06-30    10s       708         0      10  미성년자         5.53   \n",
       "1082  2020-06-30    20s      3362         0      20   청년층        26.27   \n",
       "1083  2020-06-30    30s      1496         2      30   청년층        11.69   \n",
       "1084  2020-06-30    40s      1681         3      40   중년층        13.13   \n",
       "\n",
       "      누적 사망자 수 비율  \n",
       "1080         0.00  \n",
       "1081         0.00  \n",
       "1082         0.00  \n",
       "1083         0.71  \n",
       "1084         1.06  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 마지막 집계일(2020.06.30)만 추출\n",
    "total_0630 = covid_TimeAge[covid_TimeAge['날짜']=='2020-06-30']\n",
    "\n",
    "# 연령대별 치료일수 비교\n",
    "# 나이 중 숫자만 비교하기 위해 추출\n",
    "total_0630['나이_int'] = total_0630['환자의 나이'].apply(lambda x : x[:-1]).astype('int')\n",
    "\n",
    "# Age_Group 추가\n",
    "def age(x):\n",
    "    if x <= 10: return '미성년자'\n",
    "    elif x<=30: return '청년층'\n",
    "    elif x<=50: return '중년층'\n",
    "    else: return '고령층'\n",
    "\n",
    "total_0630['연령층'] = total_0630['나이_int'].apply(age)\n",
    "\n",
    "# 칼럼 추가\n",
    "total_0630['누적 확진자 수 비율'] = round(total_0630['누적 확진자 수']/(total_0630['누적 확진자 수'].sum())*100, 2)\n",
    "total_0630['누적 사망자 수 비율'] = round(total_0630['누적 사망자 수']/(total_0630['누적 사망자 수'].sum())*100, 2)\n",
    "\n",
    "total_0630.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 연령별 누적 확진자 수 및 누적 사망자 수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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    "# line charts: 연령별 누적 확진자 수 추이 그래프\n",
    "fig = px.bar(covid_TimeAge, x='날짜', y='누적 확진자 수',\n",
    "            color = '환자의 나이', color_discrete_sequence = px.colors.qualitative.Pastel)\n",
    "\n",
    "fig.update_layout(title='<b>연령별 누적 확진자 수 추이</b>', plot_bgcolor='#FFFFFF', yaxis_gridcolor = '#D5D5D5')\n",
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   "source": [
    "# pie chart: 연령별 확진자 비중\n",
    "fig = px.pie(total_0630, names='연령층' ,values='누적 확진자 수', \n",
    "             title='<b>연령별 확진자 수(2020.06.30)</b>',\n",
    "             color_discrete_sequence = px.colors.colorbrewer.Set2)\n",
    "                \n",
    "fig.update_traces(textinfo='percent+label', textfont_size=13, hole=.3)\n",
    "fig.show()"
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   "source": [
    "# line charts: 연령별 누적 확진자 수 추이 그래프\n",
    "fig = px.bar(covid_TimeAge, x='날짜', y='누적 사망자 수',\n",
    "            color = '환자의 나이', color_discrete_sequence = px.colors.qualitative.Pastel)\n",
    "\n",
    "fig.update_layout(title='<b>연령별 누적 사망자 수 추이</b>',\n",
    "                 plot_bgcolor='#FFFFFF', yaxis_gridcolor = '#D5D5D5')\n",
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    "fig.show()"
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   ],
   "source": [
    "# pie chart : 연령별 사망자 비중\n",
    "fig = px.pie(total_0630, names='연령층' ,values='누적 사망자 수',\n",
    "             title='<b>연령별 사망자 수 (2020.06.30)</b>',\n",
    "             color_discrete_sequence = px.colors.colorbrewer.Set2)\n",
    "                \n",
    "fig.update_traces(textinfo='percent+label', textfont_size=13, hole=.3)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결과 분석\n",
    "- 2020년 6월 30일을 기준으로 연령별 확진자 수는 청년층(38%, 4,858명), 중년층(31%, 3,967명), 고령층(24%, 3,074명), 미성년자(7%, 901명) 순\n",
    "- 반면, 2020년 6월 30일을 기준으로 연령별 사망자 수를 보았을 때 고령층이 전체 사망자(282명) 중 92.9%(262명)으로 압도적으로 많음\n",
    "- 고령층은 다른 연령군보다 코로나가 생사에 치명적"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 연령별 치사율 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 치사율 컬럼 추가(소수점 둘째자리까지 표기)\n",
    "covid_TimeAge['치사율'] = round(covid_TimeAge['누적 사망자 수']/covid_TimeAge['누적 확진자 수']*100, 2)"
   ]
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   "source": [
    "# 치사율 그래프\n",
    "# 데이터 전처리; 불필요한 칼럼 제외 및 순서 변경\n",
    "covid_ifr = covid_TimeAge[['날짜','환자의 나이','치사율']]\n",
    "\n",
    "# 연령별 치사율 추이 데이터 프레임 생성\n",
    "covid_ifr = covid_ifr.pivot_table('치사율', '날짜', '환자의 나이')\n",
    "\n",
    "# line charts\n",
    "layout = go.Layout(\n",
    "    title=\"<b>Covid-19 IFR(Infection Fatality Rate) by age</b>\",    \n",
    "    plot_bgcolor=\"#FFFFFF\",\n",
    "    hovermode=\"x\",\n",
    "    xaxis=dict(title=\"날짜\", \n",
    "               linecolor=\"#A6A6A6\",\n",
    "               showspikes=True,\n",
    "               spikethickness=2,\n",
    "               spikedash=\"dot\",\n",
    "               spikecolor=\"#8C8C8C\",\n",
    "               spikemode=\"across\"),\n",
    "    yaxis=dict(title=\"IFR\",\n",
    "               gridcolor = '#D5D5D5',\n",
    "               linecolor=\"#A6A6A6\"))\n",
    "\n",
    "covid_ifr.iplot(layout=layout, width=3, color = px.colors.colorbrewer.Spectral)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결과 분석\n",
    "- 시간의 흐름에 따라, 연령이 높아짐에 따라 치사율이 높아짐\n",
    "- 3, 4월의 대구, 경북 지역의 신천지 교회발 집단 감염 확산 이후로 80대 치사율이 치솟음\n",
    "- 5, 6월에 접어들면서 코로나의 확산속도 및 감염추세가 완화되면서 각 연령별 치사율도 유지 및 감소 추세지만 여전히 고령자의 치사율은 높은 상태로 유지되는 편\n",
    "- 코로나가 다시 집단감염으로 확산하게 된다면 고연령층의 치사율은 다시 높아질 가능성 존재"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 연령별 코로나 평균 치료일 수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>나이</th>\n",
       "      <th>확진날짜</th>\n",
       "      <th>퇴원날짜</th>\n",
       "      <th>Age</th>\n",
       "      <th>연령층</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50s</td>\n",
       "      <td>2020-01-23</td>\n",
       "      <td>2020-02-05</td>\n",
       "      <td>50</td>\n",
       "      <td>중년층</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30s</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-03-02</td>\n",
       "      <td>30</td>\n",
       "      <td>청년층</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50s</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-02-19</td>\n",
       "      <td>50</td>\n",
       "      <td>중년층</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20s</td>\n",
       "      <td>2020-01-30</td>\n",
       "      <td>2020-02-15</td>\n",
       "      <td>20</td>\n",
       "      <td>청년층</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20s</td>\n",
       "      <td>2020-01-31</td>\n",
       "      <td>2020-02-24</td>\n",
       "      <td>20</td>\n",
       "      <td>청년층</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    나이        확진날짜        퇴원날짜  Age  연령층\n",
       "0  50s  2020-01-23  2020-02-05   50  중년층\n",
       "1  30s  2020-01-30  2020-03-02   30  청년층\n",
       "2  50s  2020-01-30  2020-02-19   50  중년층\n",
       "3  20s  2020-01-30  2020-02-15   20  청년층\n",
       "4  20s  2020-01-31  2020-02-24   20  청년층"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 불러오기 : covid_PatientInfo.csv\n",
    "covid_PatientInfo = pd.read_csv('data/PatientInfo.csv')\n",
    "\n",
    "# 컬럼명 변경 : 테이블 정의서 참고\n",
    "covid_PatientInfo.columns = ['ID','성별','나이','국가','지방','도시',\n",
    "                             '감염경로','감염시킨 사람의 ID','사람들과의 접촉 수',\n",
    "                             '증상 발현 날짜','확진날짜','퇴원날짜','사망날짜','격리상태']\n",
    "\n",
    "# 필요 칼럼만 추출하여 변수에 저장\n",
    "covid_hospital = covid_PatientInfo[['나이', '확진날짜', '퇴원날짜']]\n",
    "\n",
    "# 결측치 제거\n",
    "covid_hospital = covid_hospital.dropna(axis=0)\n",
    "\n",
    "# Age 열 추가\n",
    "covid_hospital['Age'] = covid_hospital['나이'].apply(lambda x : x[:-1]).astype('int')\n",
    "# 연령층 열 추가\n",
    "def age(x):\n",
    "    if x <= 10 : return '미성년자'\n",
    "    elif x <= 30: return '청년층'\n",
    "    elif x <= 50 : return '중년층'\n",
    "    else: return '고령층'\n",
    "        \n",
    "covid_hospital['연령층'] = covid_hospital['Age'].apply(age)\n",
    "\n",
    "covid_hospital.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 결측치 제거\n",
    "covid_hospital = covid_hospital.dropna(axis=0)\n",
    "\n",
    "# 계산하기 위해 날짜 변환\n",
    "covid_hospital['확진날짜'] = pd.to_datetime(covid_hospital['확진날짜'])\n",
    "covid_hospital['퇴원날짜'] = pd.to_datetime(covid_hospital['퇴원날짜'])\n",
    "\n",
    "# 치료일수 계산(퇴원날짜-확진날짜)\n",
    "covid_hospital['치료일수'] = covid_hospital['퇴원날짜'] - covid_hospital['확진날짜']\n",
    "\n",
    "# 확진날짜 5월달까지만\n",
    "covid_hospital = covid_hospital[covid_hospital['확진날짜']<='2020-05-30']\n",
    "\n",
    "# 치료날짜 숫자로 변환\n",
    "covid_hospital['치료일수'] = round(pd.to_numeric(covid_hospital['치료일수'].dt.days, downcast='integer'), 2)\n",
    "\n",
    "# 시각화\n",
    "# 연령층별 치료일수 평균 도출\n",
    "age_group_period = covid_hospital = covid_hospital[['치료일수', '연령층']].groupby(by='연령층').mean()\n",
    "\n",
    "# 연령순으로 변경\n",
    "age_group_period = age_group_period.reindex(index = ['미성년자', '청년층', '중년층', '고령층'])\n",
    "\n",
    "# 인덱스 정리\n",
    "age_group_period = age_group_period.reset_index()"
   ]
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          "고령층"
         ],
         "categoryorder": "array",
         "domain": [
          0,
          1
         ],
         "title": {
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        },
        "yaxis": {
         "anchor": "x",
         "domain": [
          0,
          1
         ],
         "title": {
          "text": "치료일수"
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       }
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 연령별 평균 치료일수 그래프\n",
    "fig = px.bar(age_group_period, x='연령층', y='치료일수',\n",
    "            color = '연령층', color_discrete_sequence = px.colors.colorbrewer.Set2)\n",
    "\n",
    "fig.update_layout(title = '<b><연령층별 평균 치료일수/b>', plot_bgcolor='#FFFFFF')\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결과 분석\n",
    "- 미성년자(0-10)세, 청년층(20-30대), 중년층(40-50대), 고령층(60세 이상)으로 연령대를 구분하여 평균 치료일수를 계산\n",
    "    - 중년층(23.85일), 청년층(23.41일), 미성년자(21.68일) 순\n",
    "    - 고령층이 압도적으로 치료일수(약 30일, 29.96일)가 길었음\n",
    "    - 결론적으로, 연령이 높을수록 치료일수도 길어진다는 사실 알 수 있었음"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 고령층 지하철 이용현황\n",
    "### 서울시 대중교통 이용인원 중 고령자 비율"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전연령 년도 년월 이용인원 나와 있는 파일(allage_card_sub) 확인\n",
    "allage_card_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pivot_table로 불러올 시 이용자 유형 코드에 0(숫자)이 붙어서 인덱스가 붙는 오류 확인\n",
    "allage_card_sub_total = allage_card_sub.pivot_table('이용 인원','이용자 유형 코드',aggfunc='sum')\n",
    "allage_card_sub_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#01 같이 0(숫자)이 붙은 인덱스 다시 한자리수로 변경\n",
    "allage_card_sub_total.rename(index={'01':1,'03':3,'04':4,'05':5,'06':6,'07':7},inplace=True)\n",
    "allage_card_sub_total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#다시 피벗테이블로 이용자 유형 코드 별 총 이용 인원 합산\n",
    "allage_card_sub_total1 = allage_card_sub_total.groupby(by='이용자 유형 코드').sum()\n",
    "allage_card_sub_total1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 이용자 유형 별 대중교통 이용 파이 그래프 그리기\n",
    "labels = ['일반인','어린이','청소년','경로','장애인','국가유공자','외국인']\n",
    "values=allage_card_sub_total1['이용 인원']\n",
    "\n",
    "fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3,textinfo='label+percent',\n",
    "                             insidetextorientation='radial')])\n",
    "fig.update_layout(title_text=\"이용자 유형 별 지하철 이용 비율\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결과 분석\n",
    "- 일반인을 제외하면 전체 이용자 유형 중 경로자 지하철 이용률이 제일 높았음"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 고령층의 서울시 지하철 이용인원 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "senior_card_sub"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 그래프를 그릴 때 필요한 열만 추출\n",
    "senior_sub_pop = senior_card_sub[['년도','년월','시군구','이용 인원']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "senior_sub_pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 데이터 타입 확인\n",
    "senior_sub_pop.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# '년월' 형 변환\n",
    "# - 변환 예시 : 201901 -> 1, 202001 -> 1\n",
    "# - 데이터 타입 int -> 문자 형태로 변화시키기    \n",
    "    \n",
    "senior_sub_pop['년월'] = [str(value) for value in senior_sub_pop['년월']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# '년월' 열 분리 후 '월' 열 추가\n",
    "month = []\n",
    "\n",
    "for m in senior_sub_pop['년월']:\n",
    "    month.append(m[-1])\n",
    "    \n",
    "senior_sub_pop['월'] = month\n",
    "senior_sub_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 열 정리 - '년월' 열 삭제, 순서 재배열\n",
    "# 열 순서 변경\n",
    "senior_sub_pop = senior_sub_pop[['년도','월','시군구','이용 인원']]\n",
    "senior_sub_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 그래프 그리기 위한 피봇테이블 생성\n",
    "senior_sub_pop2 = senior_sub_pop.pivot_table('이용 인원', ['년도','월'], aggfunc='sum')\n",
    "senior_sub_pop2.reset_index(inplace=True)\n",
    "senior_sub_pop2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 묶음 세로 막대그래프 - seaborn\n",
    "\n",
    "sns.barplot(x='월', y='이용 인원', hue='년도', data=senior_sub_pop2, dodge=True, palette='Set2') # default : dodge=True\n",
    "plt.rcParams['figure.figsize'] = [13,8]\n",
    "plt.title('고령자 지하철 이용인원 비교', fontsize=15)\n",
    "\n",
    "plt.legend(fontsize=15, loc=3)\n",
    "\n",
    "for x, y in enumerate(list(senior_sub_pop2['이용 인원'])):\n",
    "    t = 0\n",
    "    if x > 4:\n",
    "        x = x - 5 \n",
    "        t = 0.4   \n",
    "    txt = \"%d\" % y\n",
    "    plt.text(x-0.2 + t, y, txt, fontsize=13, color='#ff0000', \n",
    "                    horizontalalignment='center', verticalalignment='bottom')\n",
    "    \n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 코로나 관련 주요 이슈\n",
    "- 1월 말 첫 확진자 발생\n",
    "- 2월 후반 대구 신천지 교회 발 대규모 감염자 발생, 지역사회 불안 확산\n",
    "- 3월 22일 ~4월 19일 강력한 사회적 거리두기 시행\n",
    "- 5월 6일부터 '생활 속 거리두기' 시행"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결과 해석\n",
    "- 2019년 대비 2020년 1~5월 모두 고령층 지하철 이용인원이 감소 추세\n",
    "- 코로나 확산세가 가장 심했던 3월의 이용인원이 가장 적음\n",
    "- 강력한 사회적 거리두기의 영향으로 지하철 이용인원 감소세는 4월까지 계속 이어짐.\n",
    "- 5월부터는 생활 속 거리두기가 시행되고 황금 연휴가 겹치면서 전년 대비 감소비중이 3~4월에 비해 적음"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 유동인구와 코로나 감염과의 상관관계 분석\n",
    "### 가설 검증: 유동인구 수가 많은 지역에 코로나 감염자 수도 많을 것이다\n",
    "### 서울시 자치구별 전체 유동인구와 고령 유동인구 상관관계 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>행정동 코드</th>\n",
       "      <th>동명</th>\n",
       "      <th>시</th>\n",
       "      <th>시군구</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11110515.0</td>\n",
       "      <td>청운효자동</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>종로구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11110530.0</td>\n",
       "      <td>사직동</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>종로구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11110540.0</td>\n",
       "      <td>삼청동</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>종로구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11110550.0</td>\n",
       "      <td>부암동</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>종로구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11110560.0</td>\n",
       "      <td>평창동</td>\n",
       "      <td>서울특별시</td>\n",
       "      <td>종로구</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       행정동 코드     동명      시  시군구\n",
       "0  11110515.0  청운효자동  서울특별시  종로구\n",
       "1  11110530.0    사직동  서울특별시  종로구\n",
       "2  11110540.0    삼청동  서울특별시  종로구\n",
       "3  11110550.0    부암동  서울특별시  종로구\n",
       "4  11110560.0    평창동  서울특별시  종로구"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 행정동 코드 파일 불러오기 : adstrd_master.csv\n",
    "# 변수명 : loc\n",
    "loc = pd.read_csv('data/adstrd_master.csv')\n",
    "\n",
    "# 컬럼명 변경\n",
    "loc.columns = ['행정동 코드', '동명', '시', '시군구']\n",
    "loc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File data/fpopl_2019.csv does not exist: 'data/fpopl_2019.csv'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-218f47531d08>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# 2019년 1~3월 유동인구 파일(KT) 불러오기\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[1;31m# 변수명 : pop2019_kt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mpop2019_kt\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'data/fpopl_2019.csv'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;31m# 컬럼명 변경\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    674\u001b[0m         )\n\u001b[0;32m    675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 676\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    447\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 448\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    449\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1112\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1113\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1114\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1115\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1889\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1891\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1892\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1893\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File data/fpopl_2019.csv does not exist: 'data/fpopl_2019.csv'"
     ]
    }
   ],
   "source": [
    "# 2019년 1~3월 유동인구 파일(KT) 불러오기\n",
    "# 변수명 : pop2019_kt\n",
    "pop2019_kt = pd.read_csv('data/fpopl_2019.csv')\n",
    "\n",
    "# 컬럼명 변경\n",
    "pop2019_kt.columns = ['일자', '시간대', '성별', '나이대', '행정동 코드', '유동인구 수']\n",
    "\n",
    "# 결과 확인\n",
    "pop2019_kt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2019년 4, 5월 유동인구 파일(SKT) 불러오기\n",
    "skt_4 = pd.read_csv('../data/FLT_SEOUL_04MONTH.csv')\n",
    "skt_5 = pd.read_csv('../data/FLT_SEOUL_05MONTH.csv')\n",
    "\n",
    "# 4,5월 데이터 병합 : pop2019_skt\n",
    "pop2019_skt = pd.concat([skt_4, skt_5])\n",
    "\n",
    "# 컬럼명 변경\n",
    "pop2019_skt.columns = ['일자', '시간대', '나이대', '성별', '시', '시군구', '유동인구 수']\n",
    "\n",
    "# pop2019_skt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2020년 1~3월 유동인구 파일 불러오기\n",
    "# 변수명 : pop2020\n",
    "pop2020_kt = pd.read_csv('../data/fpopl_2020.csv')\n",
    "\n",
    "# 컬럼명 변경\n",
    "pop2020_kt.columns = ['일자', '시간대', '성별', '나이대', '행정동 코드', '유동인구 수']\n",
    "\n",
    "# pop2020_kt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2020년 4, 5월 유동인구 파일(SKT) 불러오기\n",
    "skt_4 = pd.read_csv('../data/Floating_Population_2004.csv')\n",
    "skt_5 = pd.read_csv('../data/Floating_Population_2005.csv')\n",
    "\n",
    "# 4,5월 데이터 병합 : pop2020_skt\n",
    "pop2020_skt = pd.concat([skt_4, skt_5])\n",
    "\n",
    "# 컬럼명 변경\n",
    "pop2020_skt.columns = ['일자', '시간대', '나이대', '성별', '시', '시군구', '유동인구 수']\n",
    "\n",
    "# pop2020_skt.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2019년 전연령 유동인구 데이터 전처리 (1~5월)\n",
    "\n",
    "# 행정동 코드를 기준으로 2019 유동인구 데이터에 '동명', '시', '구' 열 추가 후 병합\n",
    "pop2019_kt = pd.merge(pop2019_kt, loc, on='행정동 코드')\n",
    "\n",
    "# '시' == 서울특별시 조건 충족 열 추출 : pop2019_seoul\n",
    "pop2019_seoul = pop2019_kt[pop2019_kt['시']=='서울특별시']\n",
    "\n",
    "# kt와 skt 유동인구 데이터 병합을 위해 필요한 열만 저장\n",
    "pop2019_all_kt = pop2019_kt[['일자','시군구','유동인구 수']]\n",
    "pop2019_all_skt = pop2019_skt[['일자','시군구','유동인구 수']]\n",
    "\n",
    "# pop2019_kt, pop2019_skt 데이터를 행 병합 : pop2019\n",
    "pop2019 = pd.concat([pop2019_all_kt,pop2019_all_skt])\n",
    "\n",
    "pop2019.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 구별로 전연령 유동인구 수 합산 : pop2019_all_gu\n",
    "pop2019_all_gu = pop2019.groupby(pop2019.시군구)[['유동인구 수']].sum()\n",
    "\n",
    "# 전연령 유동인구 수 기준으로 내림차순 정렬\n",
    "pop2019_all_gu.sort_values('유동인구 수',ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2019년 구별로 60대 이상 고령자 유동인구 수 추출\n",
    "\n",
    "# pop2019_seoul에서 나이대가 60대 이상인 행 추출 \n",
    "# 데이터 프레임 생성 : pop60, pop65, pop70\n",
    "pop60 = pop2019_seoul[pop2019_seoul['나이대']=='age_60']\n",
    "pop65 = pop2019_seoul[pop2019_seoul['나이대']=='age_65']\n",
    "pop70 = pop2019_seoul[pop2019_seoul['나이대']=='age_70']\n",
    "\n",
    "# 데이터 병합 : pop2019_old_kt\n",
    "pop2019_old_kt = pd.concat([pop60,pop65,pop70])\n",
    "\n",
    "# pop2019_skt에서 나이대가 60대 이상인 행 추출\n",
    "# 데이터 프레임 생성 : pop60, pop70\n",
    "pop60 = pop2019_skt[pop2019_skt['나이대']==60]\n",
    "pop70 = pop2019_skt[pop2019_skt['나이대']==70]\n",
    "\n",
    "# 데이터 병합 : pop2019_old_skt\n",
    "pop2019_old_skt = pd.concat([pop60,pop70])\n",
    "\n",
    "# pop2019_old_kt, pop2019_old_skt 데이터 병합하기 위해 필요한 열만 추출\n",
    "pop2019_old_kt2 = pop2019_old_kt[['일자','시군구','유동인구 수']]\n",
    "pop2019_old_skt2 = pop2019_old_skt[['일자','시군구','유동인구 수']]\n",
    "\n",
    "# pop2019_kt, pop2019_skt 데이터를 행 병합 : pop2019_old\n",
    "pop2019_old = pd.concat([pop2019_old_kt2,pop2019_old_skt2])\n",
    "\n",
    "# pop2019_old.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 구별로 고령자 유동인구 수 합산 : pop2019_old_gu \n",
    "pop2019_old_gu = pop2019_old.groupby(pop2019_old.시군구)[['유동인구 수']].sum()\n",
    "\n",
    "# 유동인구 수 기준으로 내림차순 정렬\n",
    "# pop2019_old_gu.sort_values('유동인구 수',ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전연령 유동인구와 고령자 유동인구 데이터 열로 병합\n",
    "# 인덱스 해제\n",
    "pop2019_all_gu.reset_index(inplace=True)\n",
    "pop2019_old_gu.reset_index(inplace=True)\n",
    "\n",
    "# 컬럼명 변경\n",
    "pop2019_all_gu.rename(columns = {'유동인구 수':'전연령_유동인구_2019'}, inplace=True)\n",
    "pop2019_old_gu.rename(columns = {'유동인구 수':'고령_유동인구_2019'}, inplace=True)\n",
    "\n",
    "# 구를 기준으로 열 병합 : pop2019__gu\n",
    "pop2019_gu = pd.merge(pop2019_all_gu, pop2019_old_gu, on='시군구', left_index=True)\n",
    "\n",
    "pop2019_gu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2020년 전연령 유동인구 데이터 전처리 (1~5월)\n",
    "\n",
    "# 행정동 코드를 기준으로 2020 유동인구 데이터에 '동명', '시', '구' 열 추가 후 재할당\n",
    "pop2020_kt = pd.merge(pop2020_kt, loc, on='행정동 코드')\n",
    "\n",
    "# '시'가 서울특별시인 곳만 추출  : pop2020_seoul\n",
    "pop2020_seoul = pop2020_kt[pop2020_kt['시']=='서울특별시']\n",
    "\n",
    "## kt와 skt 유동인구 데이터 병합을 위해 필요한 열만 저장\n",
    "pop2020_all_kt = pop2020_seoul[['일자','시군구','유동인구 수']]\n",
    "pop2020_all_skt = pop2020_skt[['일자','시군구','유동인구 수']]\n",
    "\n",
    "# pop2020_kt, pop2020_skt 데이터를 행 병합 : pop2020\n",
    "pop2020 = pd.concat([pop2020_all_kt,pop2020_all_skt])\n",
    "\n",
    "# pop2020.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 구별로 전연령 유동인구 수 합산 후 pop2020_all_gu 로 저장\n",
    "pop2020_all_gu = pop2020.groupby(pop2020.시군구)[['유동인구 수']].sum()\n",
    "\n",
    "# 전연령 유동인구 수 기준으로 내림차순 정렬\n",
    "pop2020_all_gu.sort_values('유동인구 수',ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2020년 구별로 60대 이상 고령자 유동인구 수 추출\n",
    "\n",
    "# pop2020_seoul에서 나이대가 60대 이상인 행만 추출\n",
    "# 데이터 프레임 생성 : pop60, pop65, pop70\n",
    "pop60 = pop2020_seoul[pop2020_seoul['나이대']=='age_60']\n",
    "pop65 = pop2020_seoul[pop2020_seoul['나이대']=='age_65']\n",
    "pop70 = pop2020_seoul[pop2020_seoul['나이대']=='age_70']\n",
    "\n",
    "# 데이터 병합 : pop2020_old_kt\n",
    "pop2020_old_kt = pd.concat([pop60,pop65,pop70])\n",
    "\n",
    "# pop2020_skt에서 나이대가 60대 이상인 행만 추출\n",
    "pop60 = pop2020_skt[pop2020_skt['나이대']==60]\n",
    "pop70 = pop2020_skt[pop2020_skt['나이대']==70]\n",
    "\n",
    "# 데이터 병합 : pop2020_old_skt\n",
    "pop2020_old_skt = pd.concat([pop60,pop70])\n",
    "\n",
    "# pop2020_old_kt, pop2020_old_skt 데이터 병합하기 위해 필요한 열만 추출\n",
    "pop2020_old_kt2 = pop2020_old_kt[['일자','시군구','유동인구 수']]\n",
    "pop2020_old_skt2 = pop2020_old_skt[['일자','시군구','유동인구 수']]\n",
    "\n",
    "# pop2020_kt, pop2020_skt 데이터를 행 병합 : pop2020_old\n",
    "pop2020_old = pd.concat([pop2020_old_kt2,pop2020_old_skt2])\n",
    "\n",
    "# pop2020_old.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 구별로 고령자 유동인구 수 합산 : pop2020_old_gu\n",
    "pop2020_old_gu = pop2020_old.groupby(pop2020_old.시군구)[['유동인구 수']].sum()\n",
    "\n",
    "# 고령자 유동인구 수 기준으로 내림차순 정렬\n",
    "# pop2020_old_gu.sort_values('유동인구 수',ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 전연령 유동인구와 고령자 유동인구 데이터 열로 병합\n",
    "# 인덱스 해제\n",
    "pop2020_all_gu.reset_index(inplace=True)\n",
    "pop2020_old_gu.reset_index(inplace=True)\n",
    "\n",
    "# 컬럼명 변경\n",
    "pop2020_all_gu.rename(columns = {'유동인구 수':'전연령_유동인구_2020'}, inplace=True)\n",
    "pop2020_old_gu.rename(columns = {'유동인구 수':'고령_유동인구_2020'}, inplace=True)\n",
    "\n",
    "# 구를 기준으로 열 병합 후 pop2020_gu로 저장\n",
    "pop2020_gu = pd.merge(pop2020_all_gu, pop2020_old_gu, on='시군구', left_index=True)\n",
    "\n",
    "pop2020_gu.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 사용할 데이터셋 : pop2019_gu , pop2020_gu\n",
    "# pop2019_gu.head()\n",
    "\n",
    "# 2019년\n",
    "# 전연령 유동인구 수와 60대 이상 고령자 유동인구 수 상관관계 분석\n",
    "sns.lmplot(x='고령_유동인구_2019', y='전연령_유동인구_2019',\n",
    "          data=pop2019_gu, height=8)\n",
    "plt.title(\"2019년 전연령 유동인구 수와 고령자 유동인구 수 상관관계\", size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 구체적인 상관계수 확인\n",
    "np.corrcoef(pop2019_gu['고령_유동인구_2019'], pop2019_gu['전연령_유동인구_2019'])\n",
    "# 결과 : 높은 상관관계 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2. 2020년 1~5월 자치구별 전체 유동인구와 고령 유동인구 수 상관관계 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### 2020년\n",
    "# 전연령 유동인구 수와 60대 이상 고령자 유동인구 수 상관관계 분석\n",
    "sns.lmplot(x='고령_유동인구_2020', y='전연령_유동인구_2020',\n",
    "          data=pop2020_gu, height=8)\n",
    "plt.title(\"2020년 전연령 유동인구 수와 고령자 유동인구 수 상관관계\", size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 구체적인 상관계수 확인\n",
    "np.corrcoef(pop2020_gu['고령_유동인구_2020'], pop2020_gu['전연령_유동인구_2020'])\n",
    "# 결과 : 높은 상관관계 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결과 분석\n",
    "- 코로나 데이터에 결측치가 다수 존재. 확진자의 연령 구분이 어려움\n",
    "- 전체 확진자 수를 분석하는 대신 전체 유동인구와 60대 이상 고령 유동인구의 상관관계를 분석\n",
    "- 2019년, 2020년 1~5월 자치구별 유동인구 수를 전연령, 60대 이상 고령자로 나누어 둘의 상관관계를 분석\n",
    "- 2019년의 상관계수가 0.892 , 2020년의 상관계수가 0.896로, 매우 높은 상관관계가 있음을 확인\n",
    "\n",
    "\" 전체 유동인구가 많은 지역일수록 60대 이상의 고령 유동인구도 많다 \""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 시간대별 최대 고령 유동인구를 기록한 자치구 추출"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2019년 시간대별 고령 유동인구 수 최대 자치구 \n",
    "# 사용할 데이터셋 : pop2019_old_kt, pop2019_old_skt\n",
    "\n",
    "# 필요한 열만 추출 후 변수에 저장\n",
    "pop2019_old_kt3 = pop2019_old_kt[['일자','시간대','시군구','유동인구 수']]\n",
    "pop2019_old_skt3 = pop2019_old_skt[['일자','시간대','시군구','유동인구 수']]\n",
    "\n",
    "# pop2019_old_kt3, pop2019_old_skt3 병합 : pop2019_old_time\n",
    "pop2019_old_time = pd.concat([pop2019_old_kt3,pop2019_old_skt3])\n",
    "\n",
    "# 고령 유동인구 데이터를 시간대별, 자치구별로 합산 후 재할당\n",
    "pop2019_old_time = pop2019_old_time.groupby(['시군구','시간대']).agg({'유동인구 수':np.sum})\n",
    "\n",
    "# 분석을 위해 인덱스 해제\n",
    "pop2019_old_time.reset_index(inplace=True)\n",
    "# pop2019_old_time.head()\n",
    "\n",
    "# 2019년 시간대별 최대 유동인구 추출 후 변수에 재할당\n",
    "pop2019_old_time_max = pop2019_old_time.groupby(pop2019_old_time.시간대)[['유동인구 수']].max()\n",
    "pop2019_old_time_max.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 시간, max에 해당되는 '시군구' 찾아서 반환하는 함수 : get_code() \n",
    "def get_code(data, cnt, index):\n",
    "    mask = (data['유동인구 수']==cnt) & (data['시간대'] == index)\n",
    "    return data.loc[mask, '시군구'].values\n",
    "\n",
    "# 시간대별 고령 인구 최대 유동량에 해당하는 시군구 열 추가 함수 : add_col() \n",
    "def add_col(data, time_data) :\n",
    "    adstrd_nm_list = []\n",
    "    # index(시간)와 max 전달하고 'adstrd_code' 받아서 리스트에 추가\n",
    "    for i, cnt in enumerate(time_data['유동인구 수'], 0):\n",
    "        adstrd_nm_list.append(get_code(data, cnt, time_data.index[i]).tolist()[0])\n",
    "        # 'adstrd_code' 리스트 값을 '시군구' 열로 추가    \n",
    "    time_data['시군구'] = adstrd_nm_list\n",
    "    \n",
    "# 함수 적용\n",
    "add_col(pop2019_old_time, pop2019_old_time_max)\n",
    "\n",
    "pop2019_old_time_max.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2020년 시간대별 고령 유동인구 수 최대 자치구 \n",
    "\n",
    "# 사용할 데이터셋 : pop2020_old_kt, pop2020_old_skt\n",
    "\n",
    "# 필요한 열만 추출 : pop2020_old_kt3, pop2020_old_skt3\n",
    "pop2020_old_kt3 = pop2020_old_kt[['일자','시간대','시군구','유동인구 수']]\n",
    "pop2020_old_skt3 = pop2020_old_skt[['일자','시간대','시군구','유동인구 수']]\n",
    "\n",
    "# 데이터 병합 : pop2020_old_time\n",
    "pop2020_old_time = pd.concat([pop2020_old_kt3,pop2020_old_skt3])\n",
    "\n",
    "pop2020_old_time.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 고령 유동인구 데이터를 시간대별, 자치구별로 합산 후 재할당\n",
    "pop2020_old_time = pop2020_old_time.groupby(['시군구','시간대']).agg({'유동인구 수':np.sum})\n",
    "\n",
    "# 분석을 위해 인덱스 해제\n",
    "pop2020_old_time.reset_index(inplace=True)\n",
    "\n",
    "# 2020년 시간대별 최대 유동인구 추출 후 변수에 재할당\n",
    "pop2020_old_time_max = pop2020_old_time.groupby(pop2020_old_time.시간대)[['유동인구 수']].max()\n",
    "\n",
    "# 함수 적용\n",
    "add_col(pop2020_old_time, pop2020_old_time_max)\n",
    "\n",
    "pop2020_old_time_max.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2019, 2020 시간대별 최대 고령 유동인구가 기록된 자치구 그래프 그리기\n",
    "x = pop2020_old_time_max.index\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.bar(x,-pop2020_old_time_max['유동인구 수']/10000, facecolor='#9999FF', edgecolor='white')\n",
    "plt.bar(x,pop2019_old_time_max['유동인구 수']/10000, facecolor='#ff9999', edgecolor='white')\n",
    "plt.xticks(x, pop2020_old_time_max.index)\n",
    "\n",
    "# 막대에 시군구 표시\n",
    "n = 0\n",
    "for x,y in zip(pop2019_old_time_max.index, pop2019_old_time_max['유동인구 수']/10000):\n",
    "    plt.text(x-0.1, y/1.5, pop2019_old_time_max['시군구'][n], rotation=90)\n",
    "    n += 1\n",
    "\n",
    "n = 0\n",
    "for x,y in zip(pop2020_old_time_max.index, pop2020_old_time_max['유동인구 수']/10000):\n",
    "    plt.text(x-0.1, -y/1.5, pop2020_old_time_max['시군구'][n], rotation=90)\n",
    "    n += 1\n",
    "\n",
    "plt.xlabel('시간대(1시간 단위)')\n",
    "plt.ylabel('최대 유동인구 수(만 명)')\n",
    "plt.title('2019년, 2020년 시간대별 최대 고령 유동인구 (1~5월)', size=15)\n",
    "plt.legend(['2020년','2019년'],loc=1,prop={'size': 15})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결과 분석\n",
    "- 2019, 2020년 모두 송파구와 강남구에서 가장 높은 고령 유동인구가 기록됨\n",
    "- 2020년도 기준, 개인 생활시간인 0시에서 7시까지, 17시에서 23시까지 송파구에서 가장 많은 고령 유동인구가 측정됨\n",
    "- 주요 활동시간인 9시에서 16시까지 강남구에서 가장 많은 고령 유동인구가 측정됨\n",
    "\n",
    "\" 송파, 강남 모두 유동인구 수가 높은 자치구지만, 송파구는 거주지의 성격을 띠고 강남구는 상업지의 성격을 띠고 있음 \""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 서울시 자치구별 유동인구와 코로나 확진자 수 상관관계 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>나이</th>\n",
       "      <th>지방</th>\n",
       "      <th>도시</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50s</td>\n",
       "      <td>서울</td>\n",
       "      <td>강서구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30s</td>\n",
       "      <td>서울</td>\n",
       "      <td>중랑구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50s</td>\n",
       "      <td>서울</td>\n",
       "      <td>종로구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20s</td>\n",
       "      <td>서울</td>\n",
       "      <td>마포구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20s</td>\n",
       "      <td>서울</td>\n",
       "      <td>성북구</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    나이  지방   도시\n",
       "0  50s  서울  강서구\n",
       "1  30s  서울  중랑구\n",
       "2  50s  서울  종로구\n",
       "3  20s  서울  마포구\n",
       "4  20s  서울  성북구"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# covid_PatientInfo\n",
    "# 서울만 추출\n",
    "covid_seoul = covid_PatientInfo[covid_PatientInfo['지방']=='서울']\n",
    "\n",
    "# 확진날짜 5월달 까지만 추출\n",
    "covid_seoul = covid_seoul[covid_seoul['확진날짜']<='2020-05-30']\n",
    "\n",
    "# 필요 컬럼만 추출\n",
    "covid_seoul = covid_seoul[['나이','지방','도시']]\n",
    "\n",
    "# 서울 자치구 기타로 된 데이터는 제외\n",
    "covid_seoul = covid_seoul[covid_seoul['도시'] !='기타']\n",
    "\n",
    "# 데이터 확인\n",
    "covid_seoul.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>시군구</th>\n",
       "      <th>전연령</th>\n",
       "      <th>60세이상</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>강남구</td>\n",
       "      <td>74</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>관악구</td>\n",
       "      <td>59</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>송파구</td>\n",
       "      <td>48</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>강서구</td>\n",
       "      <td>45</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서초구</td>\n",
       "      <td>41</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   시군구  전연령  60세이상\n",
       "0  강남구   74    5.0\n",
       "1  관악구   59    6.0\n",
       "2  송파구   48    3.0\n",
       "3  강서구   45    2.0\n",
       "4  서초구   41    2.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 자치구별 전연령 확진자 수 \n",
    "covid_all = covid_seoul['도시'].value_counts()\n",
    "covid_all = pd.DataFrame(covid_all)\n",
    "\n",
    "# 자치구별 60세 이상 확진자 수\n",
    "# 결측치 제거\n",
    "covid_seoul.dropna(axis=0, inplace=True)\n",
    "\n",
    "# 나이 중 숫자만 비교하기 위해 추출\n",
    "covid_seoul['나이_int'] = covid_seoul['나이'].apply(lambda x : x[:-1]).astype('int')\n",
    "\n",
    "# 연령층 열 추가\n",
    "def age(x):\n",
    "    if x <= 10 : return '미성년자'\n",
    "    elif x <= 30: return '청년층'\n",
    "    elif x <= 50 : return '중년층'\n",
    "    else: return '고령층'\n",
    "        \n",
    "covid_seoul['연령층'] = covid_seoul['나이_int'].apply(age)\n",
    "\n",
    "# 고령층(60세 이상)만 추출\n",
    "covid_over_60 = covid_seoul[covid_seoul['연령층']=='고령층']\n",
    "\n",
    "# 자치구 기준으로 확진자 수 합치기\n",
    "covid_over_60 = covid_over_60['도시'].value_counts()\n",
    "covid_over_60 = pd.DataFrame(covid_over_60)\n",
    "\n",
    "# 서울시 자치구별 전연령 및 60세 이상 확진자 수\n",
    "# 데이터 병합\n",
    "covid_all_vs_60 = pd.concat([covid_all, covid_over_60], axis=1)\n",
    "covid_all_vs_60.columns = ['전연령','60세이상']\n",
    "\n",
    "# 결측치 0으로 변경\n",
    "covid_all_vs_60 = covid_all_vs_60.fillna(0)\n",
    "\n",
    "# 컬럼 추가\n",
    "covid_all_vs_60 = covid_all_vs_60.reset_index()\n",
    "covid_all_vs_60.rename(columns ={'index':'시군구'}, inplace=True)\n",
    "\n",
    "# 동구 구 > 동작구로 변경\n",
    "covid_all_vs_60['시군구'] = covid_all_vs_60['시군구'].replace('동구 구', '동작구')\n",
    "\n",
    "# 데이터 확인\n",
    "covid_all_vs_60.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'pop2019_gu' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-25-5104420ce424>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;31m# pop2019_gu와 covid_all_vs_60 병합 후 covid_num으로 저장\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mcovid_num\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpop2019_gu\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcovid_all_vs_60\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'시군구'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# covid_num 컬럼명 변경\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mcovid_num\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m'시군구'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'전연령_유동인구_2019'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'고령_유동인구_2019'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'전연령 확진자 수'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'고령 확진자 수'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'pop2019_gu' is not defined"
     ]
    }
   ],
   "source": [
    "# pop2019_gu와 covid_all_vs_60 병합 후 covid_num으로 저장\n",
    "covid_num = pd.merge(pop2019_gu, covid_all_vs_60, on='시군구')\n",
    "\n",
    "# covid_num 컬럼명 변경\n",
    "covid_num.columns = ['시군구', '전연령_유동인구_2019', '고령_유동인구_2019','전연령 확진자 수','고령 확진자 수']\n",
    "# covid_num.head()\n",
    "\n",
    "# 구를 기준으로 covid_num, pop2020_gu 병합\n",
    "pop19_20 = pd.merge(covid_num, pop2020_gu, on='시군구')\n",
    "\n",
    "pop19_20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 유동인구 수 증감률 열 추가\n",
    "# 열 이름 : 전연령 증감률, 고령 증감률\n",
    "pop19_20['전연령 증감률'] = round((pop19_20['전연령_유동인구_2020'] - pop19_20['전연령_유동인구_2019']) / pop19_20['전연령_유동인구_2019'] * 100, 2)\n",
    "pop19_20['고령 증감률'] = round((pop19_20['고령_유동인구_2020'] - pop19_20['고령_유동인구_2019']) / pop19_20['고령_유동인구_2019'] * 100, 2)\n",
    "\n",
    "# 열 순서 변경\n",
    "pop19_20 = pop19_20[['시군구', '전연령_유동인구_2019', '고령_유동인구_2019', '전연령_유동인구_2020', \n",
    "                     '고령_유동인구_2020','전연령 증감률', '고령 증감률', '전연령 확진자 수', '고령 확진자 수']]\n",
    "pop19_20.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 사용할 데이터셋 : pop19_20\n",
    "pop19_20.sort_values('전연령 확진자 수', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 도시=기타 제외한 누적 확진자 수와 2019년 고령 유동인구 상관관계 분석\n",
    "sns.lmplot(x='전연령 확진자 수', y='전연령_유동인구_2019',\n",
    "          data=pop19_20, height=8, scatter_kws={'color': 'red'}, line_kws={'color': 'red'})\n",
    "plt.title(\"2019년 유동인구 수와 2020년 확진자 수 상관관계(1~5월)\", size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 구체적인 상관계수 확인\n",
    "np.corrcoef(pop19_20['전연령 확진자 수'], pop19_20['전연령_유동인구_2019'])\n",
    "# 결과 : 높은 상관관계 존재"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 자치구별 1~5월 고령 유동인구 수와 확진자 수 상관관계 분석\n",
    "sns.lmplot(x='전연령 확진자 수', y='고령_유동인구_2019',\n",
    "          data=pop19_20, height=8, scatter_kws={'color': 'red'}, line_kws={'color': 'red'})\n",
    "plt.title(\"2019년 고령 유동인구 수와 2020년 확진자 수 상관관계(1~5월)\", size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 구체적인 상관관계 분석\n",
    "np.corrcoef(pop19_20['전연령 확진자 수'], pop19_20['고령_유동인구_2019'])\n",
    "# 결과 : 높은 상관관계 존재"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결과 분석\n",
    "- 자치구별 2019년 전체 유동인구, 고령 유동인구와 2020년 코로나 확진자 수 간의 상관관계를 분석\n",
    "- 전체 유동인구와의 상관계수가 0.777, 고령 유동인구와 상관계수는 0.746\n",
    "\n",
    "\" 유동인구가 많은 지역일수록 확진자 수도 많다는 사실을 확인. 가설이 검증됨 \""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 위 데이터를 산점도로 표현\n",
    "fig = px.scatter(data_frame=pop19_20, x='전연령 확진자 수', y='고령_유동인구_2019', color='시군구',\n",
    "                title = \"자치구별 2019년 고령 유동인구 수와 2020년 확진자 수 산점도 (1~5월)\")\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결과 분석\n",
    "- 그래프 상에서 우측 상위 자치구를 주목\n",
    "- 확진자 수도 많으면서 유동인구도 많은 지역\n",
    "\n",
    "\" 강남구, 관악구, 송파구, 강서구, 서초구 = 코로나 감염 위험 지역으로 상정 \""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. 유동인구와 지하철 이용인원간의 상관관계 분석\n",
    "### 서울시 자치구별 유동인구와 지하철 이용률 상관관계 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 서울시 자치구별 연도별 지하철 이용인원 추출\n",
    "subway=senior_card_sub.pivot_table('이용 인원','시군구','년도',aggfunc='sum')\n",
    "subway=subway.sort_values([2019],ascending=[False])\n",
    "\n",
    "# pop19_20에서 고령 유동인구수만 추출하여 pop19_20_old로 저장\n",
    "pop19_20_old = pop19_20[['시군구','고령_유동인구_2019', '고령_유동인구_2020']]\n",
    "pop19_20_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# subway, pop19_20_old 병합하여 gu_old로 저장\n",
    "gu_old = pd.merge(subway, pop19_20_old, on = '시군구')\n",
    "\n",
    "# 칼럼명 변경\n",
    "gu_old.columns = ['시군구','지하철_이용량_2019', '지하철_이용량_2020',\n",
    "                  '고령_유동인구_2019', '고령_유동인구_2020']\n",
    "# 결과 확인\n",
    "gu_old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 지역구별 고령자의 2019년 유동인구 수와 지하철 이용량 상관관계 그래프 작성\n",
    "sns.lmplot(x='고령_유동인구_2019', y='지하철_이용량_2019',\n",
    "          data=gu_old, height=8, scatter_kws={'color': 'green'}, line_kws={'color': 'green'})\n",
    "plt.title('2019년 자치구별 고령 유동인구와 지하철 이용량 상관관계 (1~5월)', size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 구체적인 상관계수 확인\n",
    "np.corrcoef(gu_old['고령_유동인구_2019'], gu_old['지하철_이용량_2019'])\n",
    "# 결과 : 높은 상관관계가 존재"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 지역구별 고령자의 2020년 유동인구 수와 지하철 이용량 상관관계 그래프 작성\n",
    "sns.lmplot(x='고령_유동인구_2020', y='지하철_이용량_2020',\n",
    "          data=gu_old, height=8, scatter_kws={'color': 'green'}, line_kws={'color': 'green'})\n",
    "plt.title('2020년 자치구별 고령 유동인구와 지하철 이용량 상관관계 (1~5월)', size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 구체적인 상관관계 확인\n",
    "np.corrcoef(gu_old['고령_유동인구_2020'], gu_old['지하철_이용량_2020'])\n",
    "# 결과 : 높은 상관관계가 존재"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. 유동인구, 지하철 이용인원 모두 많은 구를 선정하고 해당 자치구의 지하철역 추출\n",
    "### 2019년, 2020년 자치구별 고령자의 유동인구 및 지하철 이용인원 비교"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2019년 고령 유동인구수가 많은 상위 5개 구 그래프 작성\n",
    "# 사용할 데이터셋 : pop19_20\n",
    "pop19_20.sort_values(by='고령_유동인구_2019',ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2019년 60세 이상 고령 유동인구 수 상위 5개 구 (1~5월)\n",
    "x = range(0,5)\n",
    "\n",
    "pop19_20.sort_values(by='고령_유동인구_2019',ascending=False).head(5)['고령_유동인구_2019'].plot(kind='barh', color='yellowgreen', figsize=(8,6))\n",
    "plt.yticks(x, pop19_20.sort_values(by='고령_유동인구_2019',ascending=False).시군구)\n",
    "\n",
    "plt.xlabel('유동인구 수(명)')\n",
    "plt.ylabel('자치구')\n",
    "plt.title('2019년 60세 이상 고령 유동인구 수 상위 5개 구 (1~5월)', size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2020년 60세 이상 고령 유동인구 수 상위 5개 구 (1~5월)\n",
    "x = range(0,5)\n",
    "\n",
    "pop19_20.sort_values(by='고령_유동인구_2020',ascending=False).head(5)['고령_유동인구_2020'].plot(kind='barh', color='orange', figsize=(8,6))\n",
    "plt.yticks(x, pop19_20.sort_values(by='고령_유동인구_2020',ascending=False).시군구)\n",
    "\n",
    "plt.xlabel('유동인구 수(명)')\n",
    "plt.ylabel('자치구')\n",
    "plt.title('2020년 60세 이상 고령 유동인구 수 상위 5개 구 (1~5월)', size=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결과 분석\n",
    "- 전체적인 이용인원의 변화 거의 없음.\n",
    "- 유동인구가 많은 상위 5개 구의 순위는 약간 바뀌었지만 2019년의 top5 구가 2020년에도 그대로 유지"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 서울시 자치구별 고령자의 지하철역 이용 현황 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 지하철 이용인원 상위 10개, 하위 5개 구( 2019, 2020년 비교)\n",
    "sub_year_sum = senior_card_sub.pivot_table('이용 인원','시군구','년도', aggfunc='sum')\n",
    "# sub_year_sum\n",
    "\n",
    "senior_pop_large_19 = pd.DataFrame(sub_year_sum[2019].sort_values(ascending=False).head())\n",
    "senior_pop_large_19.reset_index(inplace=True)\n",
    "senior_pop_large_19.rename(columns = {2019:'이용인원'}, inplace=True)\n",
    "# senior_pop_large_19\n",
    "\n",
    "senior_pop_large_20 = pd.DataFrame(sub_year_sum[2020].sort_values(ascending=False).head())\n",
    "senior_pop_large_20.reset_index(inplace=True)\n",
    "senior_pop_large_20.rename(columns = {2020:'이용인원'}, inplace=True)\n",
    "# senior_pop_large_20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 구별 고령자의 지하철 이용인원 – 2019년, 2020년 비교\n",
    "np.random.seed(0)\n",
    "\n",
    "plt.figure(figsize=(15,10))\n",
    "\n",
    "plt.subplot(221)\n",
    "sns.barplot(x='이용인원', y='시군구', data=senior_pop_large_19, palette='Reds_d')\n",
    "plt.title(\"2019년 고령자 지하철 이용인원 top5\", fontsize=21)\n",
    "\n",
    "plt.subplot(222)\n",
    "sns.barplot(x='이용인원', y='시군구', data=senior_pop_large_20, palette='Blues_d')\n",
    "plt.title(\"2020년 고령자 지하철 이용인원 top5\", fontsize=21)\n",
    "\n",
    "# 플롯 간의 간격을 자동으로 맞춤\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 결과 해석\n",
    "- 전체적인 이용인원은 모두 줄었지만, 고령자 지하철 이용인원이 많은 구 top5 순위는 변화 없음\n",
    "- 송파구, 강남구, 노원구, 강서구, 은평구 순\n",
    "\n",
    "### 유동인구와 지하철 이용인원 모두 top5인 구 확인\n",
    "- 송파구, 강남구, 노원구, 강서구"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 서울시 자치구별 집단감염 확진자 수 분석"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지방</th>\n",
       "      <th>도시</th>\n",
       "      <th>감염 위치</th>\n",
       "      <th>누적 확진자 수</th>\n",
       "      <th>위도</th>\n",
       "      <th>경도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울</td>\n",
       "      <td>용산구</td>\n",
       "      <td>Itaewon Clubs</td>\n",
       "      <td>139.0</td>\n",
       "      <td>37.538621</td>\n",
       "      <td>126.992652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>관악구</td>\n",
       "      <td>Richway</td>\n",
       "      <td>119.0</td>\n",
       "      <td>37.482080</td>\n",
       "      <td>126.901384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>구로구</td>\n",
       "      <td>Guro-gu Call Center</td>\n",
       "      <td>95.0</td>\n",
       "      <td>37.508163</td>\n",
       "      <td>126.884387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>양천구</td>\n",
       "      <td>Yangcheon Table Tennis Club</td>\n",
       "      <td>43.0</td>\n",
       "      <td>37.546061</td>\n",
       "      <td>126.874209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>도봉구</td>\n",
       "      <td>Day Care Center</td>\n",
       "      <td>43.0</td>\n",
       "      <td>37.679422</td>\n",
       "      <td>127.044374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   지방   도시                        감염 위치  누적 확진자 수         위도          경도\n",
       "0  서울  용산구                Itaewon Clubs     139.0  37.538621  126.992652\n",
       "1  서울  관악구                      Richway     119.0  37.482080  126.901384\n",
       "2  서울  구로구          Guro-gu Call Center      95.0  37.508163  126.884387\n",
       "3  서울  양천구  Yangcheon Table Tennis Club      43.0  37.546061  126.874209\n",
       "4  서울  도봉구              Day Care Center      43.0  37.679422  127.044374"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 코로나 집단감염 데이터 전처리 : Case.csv\n",
    "# 'Case.csv' 데이터 불러오기\n",
    "covid_infection = pd.read_csv('data/Case.csv', sep=',')\n",
    "\n",
    "# 칼렴 이름 변경\n",
    "columns = ['감염 사례의 ID', '지방', '도시', '집단감염 여부', '감염 위치',\n",
    "           '누적 확진자 수' ,'위도', '경도'] \n",
    "\n",
    "covid_infection.columns = columns\n",
    "\n",
    "# 수도권만 추출\n",
    "covid_infection = covid_infection.loc[(covid_infection['지방'] == '서울')]\n",
    "\n",
    "# 위경도 값 없는 결측값 제외\n",
    "covid_infection = covid_infection[covid_infection['위도'] != '-']\n",
    "\n",
    "# 불필요한 열 삭제\n",
    "covid_infection.drop(['감염 사례의 ID', '집단감염 여부'], \n",
    "                     axis=1,inplace=True)\n",
    "\n",
    "# 형변환\n",
    "covid_infection = covid_infection.astype({'누적 확진자 수': 'float'},)\n",
    "covid_infection['위도'] = pd.to_numeric(covid_infection['위도'])\n",
    "covid_infection['경도'] = pd.to_numeric(covid_infection['경도'])\n",
    "\n",
    "covid_infection.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# googlemaps : 구글 키 설정\n",
    "gmaps_key = \"AIzaSyDT-R4SNhLWD9rOiU9OqKzR1eIqxbPwbGI\" # 코드 실행 시 구글키 입력 \n",
    "gmaps = googlemaps.Client(key=gmaps_key)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>감염 사례의 ID</th>\n",
       "      <th>지방</th>\n",
       "      <th>도시</th>\n",
       "      <th>집단감염 여부</th>\n",
       "      <th>감염 위치</th>\n",
       "      <th>누적 확진자 수</th>\n",
       "      <th>위도</th>\n",
       "      <th>경도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1000025</td>\n",
       "      <td>서울</td>\n",
       "      <td>강남구</td>\n",
       "      <td>True</td>\n",
       "      <td>Gangnam Dongin Church</td>\n",
       "      <td>1</td>\n",
       "      <td>37.522331</td>\n",
       "      <td>127.057388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1000029</td>\n",
       "      <td>서울</td>\n",
       "      <td>강남구</td>\n",
       "      <td>True</td>\n",
       "      <td>Samsung Fire &amp; Marine Insurance</td>\n",
       "      <td>4</td>\n",
       "      <td>37.498279</td>\n",
       "      <td>127.030139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1000014</td>\n",
       "      <td>서울</td>\n",
       "      <td>강남구</td>\n",
       "      <td>True</td>\n",
       "      <td>Samsung Medical Center</td>\n",
       "      <td>7</td>\n",
       "      <td>37.48825</td>\n",
       "      <td>127.08559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1000018</td>\n",
       "      <td>서울</td>\n",
       "      <td>강남구</td>\n",
       "      <td>True</td>\n",
       "      <td>Gangnam Yeoksam-dong gathering</td>\n",
       "      <td>6</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1000030</td>\n",
       "      <td>서울</td>\n",
       "      <td>강서구</td>\n",
       "      <td>True</td>\n",
       "      <td>SJ Investment Call Center</td>\n",
       "      <td>0</td>\n",
       "      <td>37.559649</td>\n",
       "      <td>126.835102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    감염 사례의 ID  지방   도시  집단감염 여부                            감염 위치  누적 확진자 수  \\\n",
       "24    1000025  서울  강남구     True            Gangnam Dongin Church         1   \n",
       "28    1000029  서울  강남구     True  Samsung Fire & Marine Insurance         4   \n",
       "13    1000014  서울  강남구     True           Samsung Medical Center         7   \n",
       "17    1000018  서울  강남구     True   Gangnam Yeoksam-dong gathering         6   \n",
       "29    1000030  서울  강서구     True        SJ Investment Call Center         0   \n",
       "\n",
       "           위도          경도  \n",
       "24  37.522331  127.057388  \n",
       "28  37.498279  127.030139  \n",
       "13   37.48825   127.08559  \n",
       "17          -           -  \n",
       "29  37.559649  126.835102  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 파일 불러와서 저장 : covid_group_map, covid_group_map_mk, \n",
    "covid_group_map = pd.read_csv('data/Case.csv', sep=',')\n",
    "covid_group_map_mk = pd.read_csv('data/Case.csv', sep=',')\n",
    "\n",
    "# 칼렴 이름 변경\n",
    "columns = ['감염 사례의 ID', '지방', '도시', '집단감염 여부', '감염 위치',\n",
    "           '누적 확진자 수' ,'위도', '경도'] \n",
    "covid_group_map.columns = columns\n",
    "covid_group_map_mk.columns = columns\n",
    "\n",
    "# covid_infection 데이터 복사 : covid_group_map, covid_group_map_mk\n",
    "covid_group_map = covid_group_map[(covid_group_map['지방'] == '서울') \n",
    "                                    & (covid_group_map['도시'] != '-') \n",
    "                                    & (covid_group_map['도시'] != '타도시')].sort_values(by='도시')\n",
    "covid_group_map_mk = covid_group_map_mk[(covid_group_map_mk['지방'] == '서울') \n",
    "                                    & (covid_group_map_mk['도시'] != '-') \n",
    "                                    & (covid_group_map_mk['도시'] != '타도시')].sort_values(by='도시')\n",
    "\n",
    "covid_group_map.head()\n",
    "covid_group_map_mk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>감염 사례의 ID</th>\n",
       "      <th>지방</th>\n",
       "      <th>도시</th>\n",
       "      <th>집단감염 여부</th>\n",
       "      <th>감염 위치</th>\n",
       "      <th>누적 확진자 수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>1000025</td>\n",
       "      <td>서울</td>\n",
       "      <td>강남구</td>\n",
       "      <td>True</td>\n",
       "      <td>Gangnam Dongin Church</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1000029</td>\n",
       "      <td>서울</td>\n",
       "      <td>강남구</td>\n",
       "      <td>True</td>\n",
       "      <td>Samsung Fire &amp; Marine Insurance</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1000014</td>\n",
       "      <td>서울</td>\n",
       "      <td>강남구</td>\n",
       "      <td>True</td>\n",
       "      <td>Samsung Medical Center</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1000018</td>\n",
       "      <td>서울</td>\n",
       "      <td>강남구</td>\n",
       "      <td>True</td>\n",
       "      <td>Gangnam Yeoksam-dong gathering</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>1000030</td>\n",
       "      <td>서울</td>\n",
       "      <td>강서구</td>\n",
       "      <td>True</td>\n",
       "      <td>SJ Investment Call Center</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    감염 사례의 ID  지방   도시  집단감염 여부                            감염 위치  누적 확진자 수\n",
       "24    1000025  서울  강남구     True            Gangnam Dongin Church         1\n",
       "28    1000029  서울  강남구     True  Samsung Fire & Marine Insurance         4\n",
       "13    1000014  서울  강남구     True           Samsung Medical Center         7\n",
       "17    1000018  서울  강남구     True   Gangnam Yeoksam-dong gathering         6\n",
       "29    1000030  서울  강서구     True        SJ Investment Call Center         0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 감염 장소별 누적 확진자 수 도출\n",
    "covid_group_map.drop(['위도', '경도'], axis=1, inplace=True)\n",
    "covid_group_map.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>감염 사례의 ID</th>\n",
       "      <th>지방</th>\n",
       "      <th>집단감염 여부</th>\n",
       "      <th>감염 위치</th>\n",
       "      <th>누적 확진자 수</th>\n",
       "      <th>위도</th>\n",
       "      <th>경도</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>도시</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>1000025</td>\n",
       "      <td>서울</td>\n",
       "      <td>True</td>\n",
       "      <td>Gangnam Dongin Church</td>\n",
       "      <td>1</td>\n",
       "      <td>37.522331</td>\n",
       "      <td>127.057388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>1000029</td>\n",
       "      <td>서울</td>\n",
       "      <td>True</td>\n",
       "      <td>Samsung Fire &amp; Marine Insurance</td>\n",
       "      <td>4</td>\n",
       "      <td>37.498279</td>\n",
       "      <td>127.030139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강남구</th>\n",
       "      <td>1000014</td>\n",
       "      <td>서울</td>\n",
       "      <td>True</td>\n",
       "      <td>Samsung Medical Center</td>\n",
       "      <td>7</td>\n",
       "      <td>37.48825</td>\n",
       "      <td>127.08559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강서구</th>\n",
       "      <td>1000030</td>\n",
       "      <td>서울</td>\n",
       "      <td>True</td>\n",
       "      <td>SJ Investment Call Center</td>\n",
       "      <td>0</td>\n",
       "      <td>37.559649</td>\n",
       "      <td>126.835102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>관악구</th>\n",
       "      <td>1000002</td>\n",
       "      <td>서울</td>\n",
       "      <td>True</td>\n",
       "      <td>Richway</td>\n",
       "      <td>119</td>\n",
       "      <td>37.48208</td>\n",
       "      <td>126.901384</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     감염 사례의 ID  지방  집단감염 여부                            감염 위치  누적 확진자 수  \\\n",
       "도시                                                                       \n",
       "강남구    1000025  서울     True            Gangnam Dongin Church         1   \n",
       "강남구    1000029  서울     True  Samsung Fire & Marine Insurance         4   \n",
       "강남구    1000014  서울     True           Samsung Medical Center         7   \n",
       "강서구    1000030  서울     True        SJ Investment Call Center         0   \n",
       "관악구    1000002  서울     True                          Richway       119   \n",
       "\n",
       "            위도          경도  \n",
       "도시                          \n",
       "강남구  37.522331  127.057388  \n",
       "강남구  37.498279  127.030139  \n",
       "강남구   37.48825   127.08559  \n",
       "강서구  37.559649  126.835102  \n",
       "관악구   37.48208  126.901384  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 집단감염 지역 누적 확진자 수 합\n",
    "# 지도 데이터로 활용 : covid_group_map_sum\n",
    "covid_group_map_sum = covid_group_map.groupby(by=['도시']).sum()\n",
    "covid_group_map_sum.drop(['감염 사례의 ID', '집단감염 여부'], axis=1, inplace=True)\n",
    "covid_group_map_sum.sort_values(by='누적 확진자 수', ascending=False)\n",
    "# covid_group_map_sum.head()\n",
    "\n",
    "# 마커 데이터로 활용 : covid_group_map_mk\n",
    "covid_group_map_mk = covid_group_map_mk.loc[(covid_group_map_mk['위도'] != '-')]\n",
    "covid_group_map_mk.set_index('도시', inplace=True)\n",
    "covid_group_map_mk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'data/02. skorea_municipalities_geo_simple.json'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-34-8129f15b4f28>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mgeo_path\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# 한글 지명 때문에 utf-8로 인코딩해서 사용\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mgeo_str\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgeo_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mencoding\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'utf-8'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[0mgeo_str\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'data/02. skorea_municipalities_geo_simple.json'"
     ]
    }
   ],
   "source": [
    "# 서울시 구별 경계선을 그리기 위한 json 파일\n",
    "geo_path = 'data/02. skorea_municipalities_geo_simple.json'\n",
    "geo_path\n",
    "# 한글 지명 때문에 utf-8로 인코딩해서 사용\n",
    "geo_str = json.load(open(geo_path, encoding='utf-8'))\n",
    "geo_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 자치구별 집단감염 확진자 인원 분포 : covid_infection_sum_map\n",
    "covid_group_sum_map = folium.Map(location = [37.5502, 126.982],\n",
    "                                 zoom_start = 11,\n",
    "                                 tiles='cartodbpositron')\n",
    "\n",
    "plugins.Fullscreen(position='topright', \n",
    "                   title='Expand me', \n",
    "                   title_cancel='Exit me', \n",
    "                   force_separate_button=True).add_to(covid_group_sum_map) \n",
    "\n",
    "\n",
    "folium.Choropleth(geo_data = geo_str,\n",
    "                  data = covid_group_map_sum['누적 확진자 수'],\n",
    "                  columns = [covid_group_map_sum.index, \n",
    "                             covid_group_map_sum['누적 확진자 수']],\n",
    "                  fill_color=\"OrRd\", #PiYG YlGnBu, BuPu, YlOrBr, PuBuGn\n",
    "                  key_on='feature.id',\n",
    "                  legend_name='집단감염으로 인한 확진자 수').add_to(covid_group_sum_map)\n",
    "\n",
    "\n",
    "for n in range(len(covid_group_map_mk.index)) :\n",
    "        folium.Marker([covid_group_map_mk['위도'][n], covid_group_map_mk['경도'][n]],\n",
    "                      icon=folium.Icon(color='black'),\n",
    "                      tooltip = covid_group_map_mk['감염 위치'][n] \n",
    "                      + \" (\" + str(covid_group_map_mk['누적 확진자 수'][n]) + \"명)\"\n",
    "                     ).add_to(covid_group_sum_map)\n",
    "covid_group_sum_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 서울시 자치구별 고령자의 지하철 이용이 많은 자치구 분석\n",
    "전처리1 : 정류장 데이터 및 이용현황 데이터 가공\n",
    "- 서울 지하철역(정류장) 데이터('stationIDs.csv') 가공\n",
    "- 60대 이상 노인 서울 지하철 이용현황 데이터('senior_card_sub.csv') 가공\n",
    "\n",
    "전처리2 : Choropleth 시각화 위한 지도 및 마커 데이터 만들기\n",
    "- 지도 데이터 변수 만들기: 지하철역 및 법정동 주소 추출('seoul_station_20_tot')\n",
    "- 마커 데이터 변수 만들기(seoul_station_2020_mk)\n",
    "\n",
    "시각화 : Choropleth를 활용한 역별 이용인원을 나타낸 단계구분도 구성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 정류장 데이터 및 이용현황 데이터 가공\n",
    "# 사용할 데이터셋 : stationIDs.csv, senior_card_sub.csv\n",
    "# 정류장 데이터 불러오기\n",
    "stationid=pd.read_csv('../data/가공/stationIDs.csv',\n",
    "                        encoding='euc-kr',sep=',', index_col=0, header=0)\n",
    "# stationid.head()\n",
    "\n",
    "# '정류장 ID' 열 중복값 제거 : final_stationid\n",
    "final_stationid = stationid.drop_duplicates('정류장 ID')\n",
    "\n",
    "# '정류장 ID' 데이터의 불필요한 열 삭제 : 시도코드 제거\n",
    "del final_stationid['시도 코드']\n",
    "final_stationid.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 이용현황 데이터 불러오기\n",
    "senior_card = pd.read_csv('data/가공/senior_card_sub_1.csv', \n",
    "                           encoding='euc-kr', sep=',',header=0)\n",
    "senior_card\n",
    "\n",
    "# 고령자 지하철 데이터의 불필요한 열 삭제\n",
    "# 년도, 년월, 운행일자, 정류장ID, 시군구코드, 이용지역코드, 이용인원\n",
    "senior_card.drop(['요일 구분', '이용자 유형 코드', '정산사 ID', '정산 지역 코드', \n",
    "                  '교통수단 구분 코드', '노선 ID', '시도 코드'], axis = 1, inplace = True)\n",
    "senior_card.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# senior_card, final_stationid 병합 : senior_station_total\n",
    "senior_station_total = pd.merge(senior_card, final_stationid, how='outer', on='정류장 ID')\n",
    "\n",
    "# 열 이름 변경 : 시군구 코드, 지하철 역명\n",
    "senior_station_total = senior_station_total.rename(columns={'시군구 코드_x' : '시군구 코드', '정류장 명칭' : '역명' })\n",
    "\n",
    "# 필요없는 열 제거 : 시군구 코드_y, 정류장 ID, 년월, 운행일자\n",
    "senior_station_total.drop(['시군구 코드_y', '정류장 ID', '년월', '운행 일자'], axis=1, inplace=True)\n",
    "                          \n",
    "# 결측값 제거 : NaN 있는 것은 전부 제거\n",
    "senior_station_total = senior_station_total.dropna(how='any')\n",
    "\n",
    "# 지하철 역명 라인 뺴고 역명만 도출\n",
    "station = []\n",
    "for a in range(len(senior_station_total['역명'])) : \n",
    "    station.append(senior_station_total['역명'][a].split('[')[0])\n",
    "# print(station)\n",
    "senior_station_total['역명'] = station\n",
    "\n",
    "# senior_station_total 데이터형변환\n",
    "senior_station_total = senior_station_total.astype({'년도' : 'int', '시군구 코드': 'int', \n",
    "                                                    '이용 지역 코드' : 'int', '이용 인원' : 'int'})\n",
    "senior_station_total.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choropleth 시각화 위한 지도 및 마커 데이터 만들기\n",
    "# 지도 데이터 변수 만들기 : seoul_station_20_tot\n",
    "# 이용지역코드 데이터('DD_AREA.dat') 불러오기\n",
    "seoul_area = pd.read_csv('../data/DD_AREA.dat', header=None, sep='|')\n",
    "\n",
    "# 서울 지역만 추출\n",
    "seoul_area = seoul_area[(seoul_area[1] == 11)]\n",
    "\n",
    "# 불필요한 열 삭제\n",
    "seoul_area.drop([0,1,4], axis=1, inplace=True)\n",
    "\n",
    "# 열 이름 변경\n",
    "seoul_area = seoul_area.rename(columns= {2 : '시군구 코드', 3 : '이용 지역 코드', 5 : '구', 6 : '동'})\n",
    "\n",
    "# senior_card , final_stationid 병합 : seoul_station\n",
    "seoul_station = pd.merge(seoul_area, senior_station_total, how='outer', on= '이용 지역 코드')\n",
    "\n",
    "# 불필요한 열 삭제 \n",
    "seoul_station.drop(['시군구 코드_x','시군구 코드_y'], axis=1, inplace=True)\n",
    "\n",
    "# 결측값 제거 : NaN\n",
    "seoul_station = seoul_station.dropna(how='any')\n",
    "seoul_station.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2020년 데이터 추출 : seoul_station_20_tot\n",
    "seoul_station_20_tot = seoul_station[(seoul_station['년도'] == 2020)]\n",
    "\n",
    "# 년도, 이용 지역 코드 삭제\n",
    "del seoul_station_20_tot['이용 지역 코드']\n",
    "del seoul_station_20_tot['년도']\n",
    "\n",
    "# 년도 별 합계 구하기\n",
    "seoul_station_20_tot = seoul_station_20_tot.groupby(by='구').sum()\n",
    "\n",
    "# 이용 인원 기준으로 내림차순 정렬\n",
    "seoul_station_20_tot.sort_values(by='이용 인원', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 마커 데이터 변수 만들기 : seoul_station_2020_mk\n",
    "\n",
    "# 위경도 데이터 만들기\n",
    "# 이용 지역 코드 중복값 제거\n",
    "seoul_station_geo = seoul_station.drop_duplicates('이용 지역 코드')\n",
    "\n",
    "# geocode로 위경도 불러오기\n",
    "station_lat = [] # 위도\n",
    "station_lng = [] # 경도\n",
    "\n",
    "for name in seoul_station_geo['동'] : \n",
    "    tmp = gmaps.geocode(name, language='ko')\n",
    "    tmp_loc = tmp[0].get('geometry')\n",
    "    station_lat.append(tmp_loc['location']['lat'])\n",
    "    station_lng.append(tmp_loc['location']['lng'])\n",
    "    \n",
    "# 경도, 위도 열 추가\n",
    "seoul_station_geo['lat'] = station_lat\n",
    "seoul_station_geo['lng'] = station_lng\n",
    "seoul_station_geo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 역별 이용인원 데이터 변수 재정의 : 역별 이용 인원 합계 도출\n",
    "# - 변수 재정의 : seoul_station_2020_a, seoul_station_2020_b\n",
    "# 이용인원 년도별로 구분\n",
    "seoul_station_2020 = seoul_station.loc[seoul_station['년도'] == 2020]\n",
    "\n",
    "# 이용 지역 코드 + 역명 : seoul_station_2020_a\n",
    "seoul_station_2020_a = seoul_station.loc[seoul_station['년도'] == 2020]\n",
    "\n",
    "# 이용 지역 코드 + 이용 인원 : seoul_station_2020_b\n",
    "seoul_station_2020_b = seoul_station.loc[seoul_station['년도'] == 2020]\n",
    "seoul_station_2020_b.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 이용 지역 코드 + 역명 : seoul_station_2020_a \n",
    "# 불필요한 열 삭제\n",
    "del seoul_station_2020_a['이용 인원']\n",
    "del seoul_station_2020_a['년도']\n",
    "\n",
    "# 중복값 제거\n",
    "seoul_station_2020_a = seoul_station_2020_a.drop_duplicates('이용 지역 코드')\n",
    "\n",
    "# '이용 지역 코드' 인덱싱\n",
    "seoul_station_2020_a = seoul_station_2020_a.set_index('이용 지역 코드')\n",
    "seoul_station_2020_a.head()\n",
    "\n",
    "# 이용 지역 코드 + 이용 인원 : seoul_station_2020_b \n",
    "seoul_station_2020_b = seoul_station_2020.groupby(by=['이용 지역 코드']).sum()\n",
    "\n",
    "# 불필요한 열 삭제\n",
    "del seoul_station_2020_b['년도']\n",
    "seoul_station_2020_b.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# seoul_station_2020_a + seoul_station_2020_b 데이터 병합 : seoul_station_2020_mk \n",
    "seoul_station_2020_mk = pd.merge(seoul_station_2020_a, seoul_station_2020_b, \n",
    "                                  how='outer', on=['이용 지역 코드'])\n",
    "# 경도, 위도 열 추가\n",
    "seoul_station_2020_mk['lat'] = station_lat\n",
    "seoul_station_2020_mk['lng'] = station_lng\n",
    "\n",
    "# '구'로 인덱싱\n",
    "seoul_station_2020_mk = seoul_station_2020_mk.set_index('구')\n",
    "seoul_station_2020_mk.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 역별 이용인원을 나타낸 단계구분도 만들기\n",
    "\n",
    "# 역별 이용 인원 많은 순으로 단계구분도\n",
    "seoul_station_2020_map = folium.Map(location = [37.5502, 126.982],\n",
    "                                 zoom_start = 10.5,\n",
    "                                 tiles='cartodbpositron')\n",
    "\n",
    "# 전체 화면 불러오기\n",
    "plugins.Fullscreen(position='topright', \n",
    "                   title='Expand me', \n",
    "                   title_cancel='Exit me', \n",
    "                   force_separate_button=True).add_to(seoul_station_2020_map) \n",
    "\n",
    "# 자도\n",
    "folium.Choropleth(geo_data = geo_str,\n",
    "                  data = seoul_station_20_tot['이용 인원'],\n",
    "                  columns = [seoul_station_20_tot.index, '이용 인원'],\n",
    "                  fill_color=\"PuBuGn\",#YlOrB, PuBuGn\n",
    "                  key_on='feature.id').add_to(seoul_station_2020_map)\n",
    "\n",
    "# 마커\n",
    "for n in range(len(seoul_station_2020_mk.index)) : \n",
    "    folium.CircleMarker(location = [seoul_station_2020_mk['lat'][n], \n",
    "                                    seoul_station_2020_mk['lng'][n]],\n",
    "                        radius = seoul_station_2020_mk['이용 인원'][n]*0.00005, \n",
    "                        color='ffffgg', fill_color='ffffgg',\n",
    "                        popup = [seoul_station_2020_mk['역명'][n], \n",
    "                                 seoul_station_2020_mk['이용 인원'][n]]\n",
    "                 ).add_to(seoul_station_2020_map)    \n",
    "    \n",
    "seoul_station_2020_map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. 제언: 향후 활용 방안\n",
    "### 태그리스(Tagless) : 우대자용 게이트 시범운행 구 지정\n",
    "\n",
    "#### 태그리스(Tagless) 게이트(출처: 서울교통공사)\n",
    "- 2023년 서울지하철 1~8호선 전 역에 교통카드를 찍지 않고 개찰구를 통과만 해도 요금이 자동 결제되는‘100% 비접촉식 게이트’도입 예정\n",
    "- 2021년 우대자용 게이트에 시범 구축-> 2022년 게이트 개소 당 1개 통로 구축(556개소)-> 2023년 전체 게이트 확대(약 3340 통로)\n",
    "\n",
    "#### 정부 정책 활용 방안: 고령자 유동인구, 지하철 이용량 많은 구 시범 운행 거점으로 제시\n",
    "- 고령자 유동인구가 많고 고령자 지하철 이용량이 많았던 4구(강남,송파,노원,강서구)에 우선적으로 설치\n",
    "\n",
    "1) 노원구,강서구: 이용량이 많은 역 중심\n",
    "- 고령자 지하철 이용량이 전체 역 중 1,3위를 차지하는 당고개역(노원구), 우장산역(강서구)와 같이 자치구 내 지하철 이용이 편중된 역 우선 설치\n",
    "\n",
    "2) 강남구,송파구: 전체 지하철 역 균등하게 설치\n",
    "- 고령자는 강남구, 송파구 내 모든 지하철역 비슷하게 이용,\n",
    "- 송파&강남구는 고령자 지하철 이용량이 1,2위였던만큼 자치구 내 모든 역에 전체적으로 설치\n",
    "\n",
    "#### 정부 정책 활용 방안: 태그리스 게이트 사용 교육\n",
    "- 태그리스 게이트 사용시 어플리케이션,블루투스 사용법에 대한 숙지 필요\n",
    "- 하지만 고령층 모바일 서비스 이용에 취약\n",
    "- 앞선 4개 구 노인여가시설 활용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 앞서 게이트를 설치한 4구(강남,송파,노원,강서구)의 노인 여가시설 보유량 확인\n",
    "\n",
    "# 서울특별시 노인여가시설 현황 파일 불러오기\n",
    "senior_center = pd.read_excel('data/서울시 노인여가 복지시설 (구별) 통계.xlsx', encoding='euc-kr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 필요없는 행, 열 정리\n",
    "#- 노인복지관 종사자수 열 삭제\n",
    "#- 열 이름과 중복된 행 삭제\n",
    "senior_center.drop(['기간','노인복지관.1'], axis=1, inplace=True)\n",
    "senior_center.drop([0,1], inplace=True)\n",
    "senior_center.set_index('자치구', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 시설합계를 기준으로 정렬\n",
    "senior_center.sort_values('시설합계', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 그래프 시각화\n",
    "senior_center[['노인복지관','경로당','노인교실']].plot(kind='barh', stacked=True, rot=0, figsize=(8,8))\n",
    "plt.title(\"서울시 노인여가시설 개수\", fontsize=18)\n",
    "# plt.box(False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 정부 정책 활용 방안: 태그리스 게이트 사용 교육\n",
    "- 노원구, 강서구, 강남구, 송파구는 지하철 이용인원과 유동인구가 상위권인 동시에\n",
    "- 서울시 노인여가시설 개수 역시 상위 10위권 내에 포함\n",
    "- 특히 노원, 강서구는 노인여가시설 개수로 서울권 내 1,2위임.\n",
    "- 따라서 이 4개 구의 노인여가시설과 연계하여 테그리스 게이트 설치 및 사용 교육 실시 권장"
   ]
  }
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