6. seoul-bike-01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 서울특별시 공공자전거 따릉이 대여이력 분석\n",
    "* [miningful/seoulbike: 따릉이 따세권EDA](https://github.com/miningful/seoulbike)에서 흥미를 얻어 분석을 시작하게 됨\n",
    "\n",
    "## 데이터 출처 :\n",
    "* 서울특별시 공공자전거 대여이력 정보\n",
    "http://data.seoul.go.kr/dataList/datasetView.do?infId=OA-15182&srvType=F&serviceKind=1&currentPageNo=1\n",
    "\n",
    "* `(이달의주요공개정보)_서울_공공자전거(따릉이)_이용현황 자료(161021).xlsx (1.24MB)`\n",
    "https://opengov.seoul.go.kr/anspruch/10045476\n",
    "\n",
    "\n",
    "## 관련자료 \n",
    "* [(석간) 서울시 따릉이 회원 62만 돌파 출 퇴근시간 38% 집중](http://spp.seoul.go.kr/main/news/news_report.jsp#view/253821)\n",
    "* [공공자전거 따릉이 인기 따라 '따세권' 형성…소외지역도 :: 공감언론 뉴시스통신사 ::](http://www.newsis.com/view/?id=NISX20180706_0000356247&cID=10201&pID=10200)\n",
    "* [서울특별시 빅데이터 캠퍼스 > 분석결과/사례공유 > 서울시 분석사례 > 서울시 자전거 이동경로 분석](https://bigdata.seoul.go.kr/noti/selectNoti.do?r_id=P430&bbs_seq=229&sch_type=&sch_text=&currentPage=1)\n",
    "* [따릉이 편리하게 이용하기 위한 14가지 | 서울시 정보소통광장(정보공개)](https://opengov.seoul.go.kr/mediahub/15085803)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: plotnine\n",
      "Version: 0.8.0\n",
      "Summary: A grammar of graphics for python\n",
      "Home-page: https://github.com/has2k1/plotnine\n",
      "Author: None\n",
      "Author-email: None\n",
      "License: GPL-2\n",
      "Location: c:\\programdata\\anaconda3\\lib\\site-packages\n",
      "Requires: mizani, descartes, patsy, scipy, statsmodels, numpy, pandas, matplotlib\n",
      "Required-by: \n"
     ]
    }
   ],
   "source": [
    "#plotnine 설치\n",
    "!pip show plotnine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Name: folium\n",
      "Version: 0.12.1\n",
      "Summary: Make beautiful maps with Leaflet.js & Python\n",
      "Home-page: https://github.com/python-visualization/folium\n",
      "Author: Rob Story\n",
      "Author-email: wrobstory@gmail.com\n",
      "License: MIT\n",
      "Location: c:\\programdata\\anaconda3\\lib\\site-packages\n",
      "Requires: requests, branca, jinja2, numpy\n",
      "Required-by: \n"
     ]
    }
   ],
   "source": [
    "#folium 설치\n",
    "!pip show folium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 노트북이 간결해 보이도록 경고 문구는 출력되지 않도록 함\n",
    "# 프로그래밍에 익숙하다면 경고 문구도 출력해서 보도록 한다. \n",
    "# 하지만 익숙하지 않다면 당황하지 않기 위해 경고 메시지는 찍히지 않도록 한다.\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 사용할 도구 가져오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install plotnine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from plotnine import *\n",
    "\n",
    "\n",
    "# 그래프가 노트북 안에 보이게 하기 위해\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 불러오기\n",
    "* 서울시정보소통광장에서 다운로드 받은 csv파일과 크롤링해온 대여소 정보가 있다.\n",
    "* 이 노트북에서는 전체 데이터 중 일부 데이터만을 사용해서 특정 시기의 데이터만 분석해 보고 점점 더 큰 데이터를 불러와 보도록 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " C 드라이브의 볼륨에는 이름이 없습니다.\n",
      " 볼륨 일련 번호: 860D-053A\n",
      "\n",
      " C:\\mypython\\data 디렉터리\n",
      "\n",
      "2021-08-16  오후 05:39    <DIR>          .\n",
      "2021-08-16  오후 05:39    <DIR>          ..\n",
      "2021-08-16  오후 04:50           192,100 bike_rent_station.csv\n",
      "2021-08-16  오후 04:50        73,556,846 서울특별시 공공자전거 대여이력 정보_2018년_2분기_4.csv\n",
      "               2개 파일          73,748,946 바이트\n",
      "               2개 디렉터리  18,264,436,736 바이트 남음\n"
     ]
    }
   ],
   "source": [
    "%ls data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(586304, 11)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/서울특별시 공공자전거 대여이력 정보_2018년_2분기_4.csv', encoding='cp949',engine='python')\n",
    "# 2018년 2분기까지의 데이터를 보고 싶다면 아래 파일을 불러온다.\n",
    "# df = pd.read_csv('data/seoul-bike-2018.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 미리보기 및 요약하기\n",
    "* head와 tail을 통해 데이터 미리보기를 해볼 수 있다.\n",
    "* head는 데이터의 윗부분을 tail은 데이터의 하단부를 미리보기 해준다. \n",
    "* 괄호안에 숫자를 넣어주면 해당 숫자만큼의 데이터를 가져온다. 기본값은 5이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>'자전거번호'</th>\n",
       "      <th>'대여일시'</th>\n",
       "      <th>'대여대여소번호'</th>\n",
       "      <th>'대여대여소명'</th>\n",
       "      <th>'대여거치대'</th>\n",
       "      <th>'반납일시'</th>\n",
       "      <th>'반납대여소번호'</th>\n",
       "      <th>'반납대여소명'</th>\n",
       "      <th>'반납거치대'</th>\n",
       "      <th>'이용시간(분)'</th>\n",
       "      <th>'이용거리(M)'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'SPB-11906'</td>\n",
       "      <td>'2018-06-16 00:00:07'</td>\n",
       "      <td>'932'</td>\n",
       "      <td>' 예일여중'</td>\n",
       "      <td>1</td>\n",
       "      <td>'2018-06-16 00:16:41'</td>\n",
       "      <td>'934'</td>\n",
       "      <td>' 신사동 성당'</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>2160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>'SPB-16954'</td>\n",
       "      <td>'2018-06-16 00:00:07'</td>\n",
       "      <td>'272'</td>\n",
       "      <td>' 당산육갑문'</td>\n",
       "      <td>16</td>\n",
       "      <td>'2018-06-16 02:00:36'</td>\n",
       "      <td>'1827'</td>\n",
       "      <td>' 독산보도육교 앞 자전거 보관소'</td>\n",
       "      <td>9</td>\n",
       "      <td>120</td>\n",
       "      <td>18100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>'SPB-09676'</td>\n",
       "      <td>'2018-06-16 00:00:11'</td>\n",
       "      <td>'1036'</td>\n",
       "      <td>' 고덕동 주양쇼핑'</td>\n",
       "      <td>7</td>\n",
       "      <td>'2018-06-16 00:24:05'</td>\n",
       "      <td>'1038'</td>\n",
       "      <td>' 강일 다솜 어린이 공원'</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>3350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'SPB-07143'</td>\n",
       "      <td>'2018-06-16 00:00:15'</td>\n",
       "      <td>'1726'</td>\n",
       "      <td>' 삼익세라믹아파트교차로'</td>\n",
       "      <td>7</td>\n",
       "      <td>'2018-06-16 00:15:31'</td>\n",
       "      <td>'1706'</td>\n",
       "      <td>' 기업은행 앞'</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>'SPB-13007'</td>\n",
       "      <td>'2018-06-16 00:00:16'</td>\n",
       "      <td>'2058'</td>\n",
       "      <td>' 노량진동 맥도널드앞'</td>\n",
       "      <td>10</td>\n",
       "      <td>'2018-06-16 00:14:25'</td>\n",
       "      <td>'258'</td>\n",
       "      <td>' 신길역3번출구'</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>2920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       '자전거번호'                 '대여일시' '대여대여소번호'        '대여대여소명'  '대여거치대'  \\\n",
       "0  'SPB-11906'  '2018-06-16 00:00:07'     '932'         ' 예일여중'        1   \n",
       "1  'SPB-16954'  '2018-06-16 00:00:07'     '272'        ' 당산육갑문'       16   \n",
       "2  'SPB-09676'  '2018-06-16 00:00:11'    '1036'     ' 고덕동 주양쇼핑'        7   \n",
       "3  'SPB-07143'  '2018-06-16 00:00:15'    '1726'  ' 삼익세라믹아파트교차로'        7   \n",
       "4  'SPB-13007'  '2018-06-16 00:00:16'    '2058'   ' 노량진동 맥도널드앞'       10   \n",
       "\n",
       "                  '반납일시' '반납대여소번호'             '반납대여소명'  '반납거치대'  '이용시간(분)'  \\\n",
       "0  '2018-06-16 00:16:41'     '934'            ' 신사동 성당'       11         15   \n",
       "1  '2018-06-16 02:00:36'    '1827'  ' 독산보도육교 앞 자전거 보관소'        9        120   \n",
       "2  '2018-06-16 00:24:05'    '1038'      ' 강일 다솜 어린이 공원'        9         23   \n",
       "3  '2018-06-16 00:15:31'    '1706'            ' 기업은행 앞'        9         14   \n",
       "4  '2018-06-16 00:14:25'     '258'           ' 신길역3번출구'        8         13   \n",
       "\n",
       "   '이용거리(M)'  \n",
       "0       2160  \n",
       "1      18100  \n",
       "2       3350  \n",
       "3       1480  \n",
       "4       2920  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>'자전거번호'</th>\n",
       "      <th>'대여일시'</th>\n",
       "      <th>'대여대여소번호'</th>\n",
       "      <th>'대여대여소명'</th>\n",
       "      <th>'대여거치대'</th>\n",
       "      <th>'반납일시'</th>\n",
       "      <th>'반납대여소번호'</th>\n",
       "      <th>'반납대여소명'</th>\n",
       "      <th>'반납거치대'</th>\n",
       "      <th>'이용시간(분)'</th>\n",
       "      <th>'이용거리(M)'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>586299</th>\n",
       "      <td>'SPB-15985'</td>\n",
       "      <td>'2018-06-30 23:59:55'</td>\n",
       "      <td>'505'</td>\n",
       "      <td>' 자양사거리 광진아크로텔 앞'</td>\n",
       "      <td>9</td>\n",
       "      <td>'2018-07-01 00:09:38'</td>\n",
       "      <td>'593'</td>\n",
       "      <td>'자양중앙나들목'</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>1230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586300</th>\n",
       "      <td>'SPB-15081'</td>\n",
       "      <td>'2018-06-30 23:59:55'</td>\n",
       "      <td>'1328'</td>\n",
       "      <td>' 월곡역 3번출구'</td>\n",
       "      <td>7</td>\n",
       "      <td>'2018-07-01 00:18:02'</td>\n",
       "      <td>'647'</td>\n",
       "      <td>' 신이문역 1번출구'</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586301</th>\n",
       "      <td>'SPB-13677'</td>\n",
       "      <td>'2018-06-30 23:59:57'</td>\n",
       "      <td>'2376'</td>\n",
       "      <td>' 수서역 6번출구 앞'</td>\n",
       "      <td>2</td>\n",
       "      <td>'2018-07-01 00:20:38'</td>\n",
       "      <td>'2387'</td>\n",
       "      <td>' 래미안강남힐즈 사거리'</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586302</th>\n",
       "      <td>'SPB-06577'</td>\n",
       "      <td>'2018-06-30 23:59:57'</td>\n",
       "      <td>'1251'</td>\n",
       "      <td>' 석촌역 2번출구'</td>\n",
       "      <td>2</td>\n",
       "      <td>'2018-07-01 00:57:29'</td>\n",
       "      <td>'1245'</td>\n",
       "      <td>' 문정 법조단지9'</td>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>6640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586303</th>\n",
       "      <td>'SPB-03842'</td>\n",
       "      <td>'2018-06-30 23:59:58'</td>\n",
       "      <td>'2169'</td>\n",
       "      <td>' 봉천역 2번출구'</td>\n",
       "      <td>6</td>\n",
       "      <td>'2018-07-01 00:08:05'</td>\n",
       "      <td>'2112'</td>\n",
       "      <td>' 서울대입구역 마에스트로'</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            '자전거번호'                 '대여일시' '대여대여소번호'           '대여대여소명'  \\\n",
       "586299  'SPB-15985'  '2018-06-30 23:59:55'     '505'  ' 자양사거리 광진아크로텔 앞'   \n",
       "586300  'SPB-15081'  '2018-06-30 23:59:55'    '1328'        ' 월곡역 3번출구'   \n",
       "586301  'SPB-13677'  '2018-06-30 23:59:57'    '2376'      ' 수서역 6번출구 앞'   \n",
       "586302  'SPB-06577'  '2018-06-30 23:59:57'    '1251'        ' 석촌역 2번출구'   \n",
       "586303  'SPB-03842'  '2018-06-30 23:59:58'    '2169'        ' 봉천역 2번출구'   \n",
       "\n",
       "        '대여거치대'                 '반납일시' '반납대여소번호'         '반납대여소명'  '반납거치대'  \\\n",
       "586299        9  '2018-07-01 00:09:38'     '593'        '자양중앙나들목'        5   \n",
       "586300        7  '2018-07-01 00:18:02'     '647'     ' 신이문역 1번출구'        7   \n",
       "586301        2  '2018-07-01 00:20:38'    '2387'   ' 래미안강남힐즈 사거리'        5   \n",
       "586302        2  '2018-07-01 00:57:29'    '1245'      ' 문정 법조단지9'        4   \n",
       "586303        6  '2018-07-01 00:08:05'    '2112'  ' 서울대입구역 마에스트로'        7   \n",
       "\n",
       "        '이용시간(분)'  '이용거리(M)'  \n",
       "586299          9       1230  \n",
       "586300         17       3600  \n",
       "586301         20       2590  \n",
       "586302         57       6640  \n",
       "586303          7        910  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* info를 사용하면 해당 데이터의 요약정보를 볼 수 있다.\n",
    "* 데이터의 갯수와 데이터의 타입, 크기 등을 볼 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 586304 entries, 0 to 586303\n",
      "Data columns (total 11 columns):\n",
      " #   Column     Non-Null Count   Dtype \n",
      "---  ------     --------------   ----- \n",
      " 0   '자전거번호'    586304 non-null  object\n",
      " 1   '대여일시'     586304 non-null  object\n",
      " 2   '대여대여소번호'  586304 non-null  object\n",
      " 3   '대여대여소명'   586304 non-null  object\n",
      " 4   '대여거치대'    586304 non-null  int64 \n",
      " 5   '반납일시'     586304 non-null  object\n",
      " 6   '반납대여소번호'  586304 non-null  object\n",
      " 7   '반납대여소명'   586304 non-null  object\n",
      " 8   '반납거치대'    586304 non-null  int64 \n",
      " 9   '이용시간(분)'  586304 non-null  int64 \n",
      " 10  '이용거리(M)'  586304 non-null  int64 \n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 49.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>'대여거치대'</th>\n",
       "      <th>'반납거치대'</th>\n",
       "      <th>'이용시간(분)'</th>\n",
       "      <th>'이용거리(M)'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>586304.000000</td>\n",
       "      <td>586304.000000</td>\n",
       "      <td>586304.000000</td>\n",
       "      <td>586304.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.981460</td>\n",
       "      <td>6.939248</td>\n",
       "      <td>28.922530</td>\n",
       "      <td>4712.563602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.217671</td>\n",
       "      <td>5.196975</td>\n",
       "      <td>30.866556</td>\n",
       "      <td>9003.052657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1320.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>2530.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>5290.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>4320.000000</td>\n",
       "      <td>255070.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             '대여거치대'        '반납거치대'      '이용시간(분)'      '이용거리(M)'\n",
       "count  586304.000000  586304.000000  586304.000000  586304.000000\n",
       "mean        6.981460       6.939248      28.922530    4712.563602\n",
       "std         5.217671       5.196975      30.866556    9003.052657\n",
       "min         1.000000       1.000000       2.000000       0.000000\n",
       "25%         3.000000       3.000000       9.000000    1320.000000\n",
       "50%         6.000000       6.000000      18.000000    2530.000000\n",
       "75%        10.000000      10.000000      40.000000    5290.000000\n",
       "max        40.000000      40.000000    4320.000000  255070.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>'자전거번호'</th>\n",
       "      <th>'대여일시'</th>\n",
       "      <th>'대여대여소번호'</th>\n",
       "      <th>'대여대여소명'</th>\n",
       "      <th>'반납일시'</th>\n",
       "      <th>'반납대여소번호'</th>\n",
       "      <th>'반납대여소명'</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>586304</td>\n",
       "      <td>586304</td>\n",
       "      <td>586304</td>\n",
       "      <td>586304</td>\n",
       "      <td>586304</td>\n",
       "      <td>586304</td>\n",
       "      <td>586304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>12970</td>\n",
       "      <td>426750</td>\n",
       "      <td>1267</td>\n",
       "      <td>1267</td>\n",
       "      <td>427833</td>\n",
       "      <td>1269</td>\n",
       "      <td>1269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>'SPB-18238'</td>\n",
       "      <td>'2018-06-18 19:42:44'</td>\n",
       "      <td>'207'</td>\n",
       "      <td>' 여의나루역 1번출구 앞'</td>\n",
       "      <td>'2018-06-19 19:53:32'</td>\n",
       "      <td>'113'</td>\n",
       "      <td>' 홍대입구역 2번출구 앞'</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>139</td>\n",
       "      <td>42</td>\n",
       "      <td>4073</td>\n",
       "      <td>4073</td>\n",
       "      <td>18</td>\n",
       "      <td>3896</td>\n",
       "      <td>3896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            '자전거번호'                 '대여일시' '대여대여소번호'         '대여대여소명'  \\\n",
       "count        586304                 586304    586304           586304   \n",
       "unique        12970                 426750      1267             1267   \n",
       "top     'SPB-18238'  '2018-06-18 19:42:44'     '207'  ' 여의나루역 1번출구 앞'   \n",
       "freq            139                     42      4073             4073   \n",
       "\n",
       "                       '반납일시' '반납대여소번호'         '반납대여소명'  \n",
       "count                  586304    586304           586304  \n",
       "unique                 427833      1269             1269  \n",
       "top     '2018-06-19 19:53:32'     '113'  ' 홍대입구역 2번출구 앞'  \n",
       "freq                       18      3896             3896  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=np.object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 분석을 위한 전처리\n",
    "* 위에서 데이터를 미리보기 했을 때 컬럼과 데이터에 불필요한 홑따옴표가 포함되어 있다.\n",
    "* 전처리를 통해 제거해 준다.\n",
    "* object 타입의 데이터에는 모두 홑따옴표가 들어가 있다. 따라서 object 데이터 타입이라면 홑따옴표를 제거해 주도록 한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 컬럼에 있는 '를 제거한다.\n",
    "df.columns = df.columns.str.strip(\"'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>자전거번호</th>\n",
       "      <th>대여일시</th>\n",
       "      <th>대여대여소번호</th>\n",
       "      <th>대여대여소명</th>\n",
       "      <th>대여거치대</th>\n",
       "      <th>반납일시</th>\n",
       "      <th>반납대여소번호</th>\n",
       "      <th>반납대여소명</th>\n",
       "      <th>반납거치대</th>\n",
       "      <th>이용시간(분)</th>\n",
       "      <th>이용거리(M)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'SPB-11906'</td>\n",
       "      <td>'2018-06-16 00:00:07'</td>\n",
       "      <td>'932'</td>\n",
       "      <td>' 예일여중'</td>\n",
       "      <td>1</td>\n",
       "      <td>'2018-06-16 00:16:41'</td>\n",
       "      <td>'934'</td>\n",
       "      <td>' 신사동 성당'</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>2160</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         자전거번호                   대여일시 대여대여소번호   대여대여소명  대여거치대  \\\n",
       "0  'SPB-11906'  '2018-06-16 00:00:07'   '932'  ' 예일여중'      1   \n",
       "\n",
       "                    반납일시 반납대여소번호     반납대여소명  반납거치대  이용시간(분)  이용거리(M)  \n",
       "0  '2018-06-16 00:16:41'   '934'  ' 신사동 성당'     11       15     2160  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 불필요하게 '가 들어가 있다. 스트링 타입으로 되어있는 데이터에서 '을 제거하자.\n",
    "df = df.apply(lambda x: x.str.strip(\"'\") if x.dtype == np.dtype('object') else x)\n",
    "\n",
    "# 다음의 방법으로 제거할 수도 있다.\n",
    "# df.iloc[:, :4] = df.iloc[:, :4].apply(lambda x: x.str.strip(\"'\"))\n",
    "# df.iloc[:, 5:8] = df.iloc[:, 5:8].apply(lambda x: x.str.strip(\"'\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>자전거번호</th>\n",
       "      <th>대여일시</th>\n",
       "      <th>대여대여소번호</th>\n",
       "      <th>대여대여소명</th>\n",
       "      <th>대여거치대</th>\n",
       "      <th>반납일시</th>\n",
       "      <th>반납대여소번호</th>\n",
       "      <th>반납대여소명</th>\n",
       "      <th>반납거치대</th>\n",
       "      <th>이용시간(분)</th>\n",
       "      <th>이용거리(M)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SPB-11906</td>\n",
       "      <td>2018-06-16 00:00:07</td>\n",
       "      <td>932</td>\n",
       "      <td>예일여중</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-06-16 00:16:41</td>\n",
       "      <td>934</td>\n",
       "      <td>신사동 성당</td>\n",
       "      <td>11</td>\n",
       "      <td>15</td>\n",
       "      <td>2160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SPB-16954</td>\n",
       "      <td>2018-06-16 00:00:07</td>\n",
       "      <td>272</td>\n",
       "      <td>당산육갑문</td>\n",
       "      <td>16</td>\n",
       "      <td>2018-06-16 02:00:36</td>\n",
       "      <td>1827</td>\n",
       "      <td>독산보도육교 앞 자전거 보관소</td>\n",
       "      <td>9</td>\n",
       "      <td>120</td>\n",
       "      <td>18100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SPB-09676</td>\n",
       "      <td>2018-06-16 00:00:11</td>\n",
       "      <td>1036</td>\n",
       "      <td>고덕동 주양쇼핑</td>\n",
       "      <td>7</td>\n",
       "      <td>2018-06-16 00:24:05</td>\n",
       "      <td>1038</td>\n",
       "      <td>강일 다솜 어린이 공원</td>\n",
       "      <td>9</td>\n",
       "      <td>23</td>\n",
       "      <td>3350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SPB-07143</td>\n",
       "      <td>2018-06-16 00:00:15</td>\n",
       "      <td>1726</td>\n",
       "      <td>삼익세라믹아파트교차로</td>\n",
       "      <td>7</td>\n",
       "      <td>2018-06-16 00:15:31</td>\n",
       "      <td>1706</td>\n",
       "      <td>기업은행 앞</td>\n",
       "      <td>9</td>\n",
       "      <td>14</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SPB-13007</td>\n",
       "      <td>2018-06-16 00:00:16</td>\n",
       "      <td>2058</td>\n",
       "      <td>노량진동 맥도널드앞</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-06-16 00:14:25</td>\n",
       "      <td>258</td>\n",
       "      <td>신길역3번출구</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "      <td>2920</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       자전거번호                 대여일시 대여대여소번호        대여대여소명  대여거치대  \\\n",
       "0  SPB-11906  2018-06-16 00:00:07     932          예일여중      1   \n",
       "1  SPB-16954  2018-06-16 00:00:07     272         당산육갑문     16   \n",
       "2  SPB-09676  2018-06-16 00:00:11    1036      고덕동 주양쇼핑      7   \n",
       "3  SPB-07143  2018-06-16 00:00:15    1726   삼익세라믹아파트교차로      7   \n",
       "4  SPB-13007  2018-06-16 00:00:16    2058    노량진동 맥도널드앞     10   \n",
       "\n",
       "                  반납일시 반납대여소번호             반납대여소명  반납거치대  이용시간(분)  이용거리(M)  \n",
       "0  2018-06-16 00:16:41     934             신사동 성당     11       15     2160  \n",
       "1  2018-06-16 02:00:36    1827   독산보도육교 앞 자전거 보관소      9      120    18100  \n",
       "2  2018-06-16 00:24:05    1038       강일 다솜 어린이 공원      9       23     3350  \n",
       "3  2018-06-16 00:15:31    1706             기업은행 앞      9       14     1480  \n",
       "4  2018-06-16 00:14:25     258            신길역3번출구      8       13     2920  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>자전거번호</th>\n",
       "      <th>대여일시</th>\n",
       "      <th>대여대여소번호</th>\n",
       "      <th>대여대여소명</th>\n",
       "      <th>대여거치대</th>\n",
       "      <th>반납일시</th>\n",
       "      <th>반납대여소번호</th>\n",
       "      <th>반납대여소명</th>\n",
       "      <th>반납거치대</th>\n",
       "      <th>이용시간(분)</th>\n",
       "      <th>이용거리(M)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>586299</th>\n",
       "      <td>SPB-15985</td>\n",
       "      <td>2018-06-30 23:59:55</td>\n",
       "      <td>505</td>\n",
       "      <td>자양사거리 광진아크로텔 앞</td>\n",
       "      <td>9</td>\n",
       "      <td>2018-07-01 00:09:38</td>\n",
       "      <td>593</td>\n",
       "      <td>자양중앙나들목</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>1230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586300</th>\n",
       "      <td>SPB-15081</td>\n",
       "      <td>2018-06-30 23:59:55</td>\n",
       "      <td>1328</td>\n",
       "      <td>월곡역 3번출구</td>\n",
       "      <td>7</td>\n",
       "      <td>2018-07-01 00:18:02</td>\n",
       "      <td>647</td>\n",
       "      <td>신이문역 1번출구</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>3600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586301</th>\n",
       "      <td>SPB-13677</td>\n",
       "      <td>2018-06-30 23:59:57</td>\n",
       "      <td>2376</td>\n",
       "      <td>수서역 6번출구 앞</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-07-01 00:20:38</td>\n",
       "      <td>2387</td>\n",
       "      <td>래미안강남힐즈 사거리</td>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "      <td>2590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586302</th>\n",
       "      <td>SPB-06577</td>\n",
       "      <td>2018-06-30 23:59:57</td>\n",
       "      <td>1251</td>\n",
       "      <td>석촌역 2번출구</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-07-01 00:57:29</td>\n",
       "      <td>1245</td>\n",
       "      <td>문정 법조단지9</td>\n",
       "      <td>4</td>\n",
       "      <td>57</td>\n",
       "      <td>6640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586303</th>\n",
       "      <td>SPB-03842</td>\n",
       "      <td>2018-06-30 23:59:58</td>\n",
       "      <td>2169</td>\n",
       "      <td>봉천역 2번출구</td>\n",
       "      <td>6</td>\n",
       "      <td>2018-07-01 00:08:05</td>\n",
       "      <td>2112</td>\n",
       "      <td>서울대입구역 마에스트로</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            자전거번호                 대여일시 대여대여소번호           대여대여소명  대여거치대  \\\n",
       "586299  SPB-15985  2018-06-30 23:59:55     505   자양사거리 광진아크로텔 앞      9   \n",
       "586300  SPB-15081  2018-06-30 23:59:55    1328         월곡역 3번출구      7   \n",
       "586301  SPB-13677  2018-06-30 23:59:57    2376       수서역 6번출구 앞      2   \n",
       "586302  SPB-06577  2018-06-30 23:59:57    1251         석촌역 2번출구      2   \n",
       "586303  SPB-03842  2018-06-30 23:59:58    2169         봉천역 2번출구      6   \n",
       "\n",
       "                       반납일시 반납대여소번호         반납대여소명  반납거치대  이용시간(분)  이용거리(M)  \n",
       "586299  2018-07-01 00:09:38     593        자양중앙나들목      5        9     1230  \n",
       "586300  2018-07-01 00:18:02     647      신이문역 1번출구      7       17     3600  \n",
       "586301  2018-07-01 00:20:38    2387    래미안강남힐즈 사거리      5       20     2590  \n",
       "586302  2018-07-01 00:57:29    1245       문정 법조단지9      4       57     6640  \n",
       "586303  2018-07-01 00:08:05    2112   서울대입구역 마에스트로      7        7      910  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 586304 entries, 0 to 586303\n",
      "Data columns (total 11 columns):\n",
      " #   Column   Non-Null Count   Dtype \n",
      "---  ------   --------------   ----- \n",
      " 0   자전거번호    586304 non-null  object\n",
      " 1   대여일시     586304 non-null  object\n",
      " 2   대여대여소번호  586304 non-null  object\n",
      " 3   대여대여소명   586304 non-null  object\n",
      " 4   대여거치대    586304 non-null  int64 \n",
      " 5   반납일시     586304 non-null  object\n",
      " 6   반납대여소번호  586304 non-null  object\n",
      " 7   반납대여소명   586304 non-null  object\n",
      " 8   반납거치대    586304 non-null  int64 \n",
      " 9   이용시간(분)  586304 non-null  int64 \n",
      " 10  이용거리(M)  586304 non-null  int64 \n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 49.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>자전거번호</th>\n",
       "      <th>대여일시</th>\n",
       "      <th>대여대여소번호</th>\n",
       "      <th>대여대여소명</th>\n",
       "      <th>반납일시</th>\n",
       "      <th>반납대여소번호</th>\n",
       "      <th>반납대여소명</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>586304</td>\n",
       "      <td>586304</td>\n",
       "      <td>586304</td>\n",
       "      <td>586304</td>\n",
       "      <td>586304</td>\n",
       "      <td>586304</td>\n",
       "      <td>586304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>12970</td>\n",
       "      <td>426750</td>\n",
       "      <td>1267</td>\n",
       "      <td>1267</td>\n",
       "      <td>427833</td>\n",
       "      <td>1269</td>\n",
       "      <td>1269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>SPB-18238</td>\n",
       "      <td>2018-06-18 18:56:44</td>\n",
       "      <td>207</td>\n",
       "      <td>여의나루역 1번출구 앞</td>\n",
       "      <td>2018-06-19 19:53:32</td>\n",
       "      <td>113</td>\n",
       "      <td>홍대입구역 2번출구 앞</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>139</td>\n",
       "      <td>42</td>\n",
       "      <td>4073</td>\n",
       "      <td>4073</td>\n",
       "      <td>18</td>\n",
       "      <td>3896</td>\n",
       "      <td>3896</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            자전거번호                 대여일시 대여대여소번호         대여대여소명  \\\n",
       "count      586304               586304  586304         586304   \n",
       "unique      12970               426750    1267           1267   \n",
       "top     SPB-18238  2018-06-18 18:56:44     207   여의나루역 1번출구 앞   \n",
       "freq          139                   42    4073           4073   \n",
       "\n",
       "                       반납일시 반납대여소번호         반납대여소명  \n",
       "count                586304  586304         586304  \n",
       "unique               427833    1269           1269  \n",
       "top     2018-06-19 19:53:32     113   홍대입구역 2번출구 앞  \n",
       "freq                     18    3896           3896  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=np.object)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 결측치 보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "자전거번호      0\n",
       "대여일시       0\n",
       "대여대여소번호    0\n",
       "대여대여소명     0\n",
       "대여거치대      0\n",
       "반납일시       0\n",
       "반납대여소번호    0\n",
       "반납대여소명     0\n",
       "반납거치대      0\n",
       "이용시간(분)    0\n",
       "이용거리(M)    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 결측치 시각화 하기\n",
    "* missingno는 별도로 설치해야 하는 시각화 도구 입니다. 결측치를 시각화해서 볼 수 있습니다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install missingno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 결측치 보기\n",
    "#import missingno as msno\n",
    "\n",
    "#msno.matrix(df, figsize=(12,5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 대여소별 대여|반납 현황"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df['대여대여소번호'].unique().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('# 대여소 수')\n",
    "# print('대여 대여소: ', df['대여대여소번호'].unique().shape[0])\n",
    "# print('반납 대여소: ', df['반납대여소번호'].unique().shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# df['대여대여소번호'].value_counts().tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 대여 대여소번호에 상암센터 정비실과 위트콤 같은 숫자가 아닌 곳들이 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 여의나루역 1번출구 앞               4073\n",
       " 뚝섬유원지역 1번출구 앞              3369\n",
       " 홍대입구역 2번출구 앞               3174\n",
       " 봉림교 교통섬                    2709\n",
       " 롯데월드타워(잠실역2번출구 쪽)          2637\n",
       " 고속터미널역 8-1번, 8-2번 출구 사이    2453\n",
       " 마포구민체육센터 앞                 2178\n",
       " 신도림역 1번 출구 앞               2022\n",
       " 안암로터리 버스정류장 앞              1987\n",
       " 잠실새내역 5번 출구 뒤              1841\n",
       "Name: 대여대여소명, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 대여량이 많은 상위 대여소\n",
    "df['대여대여소명'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 홍대입구역 2번출구 앞               3896\n",
       " 뚝섬유원지역 1번출구 앞              3814\n",
       " 여의나루역 1번출구 앞               3730\n",
       " 봉림교 교통섬                    2762\n",
       " 롯데월드타워(잠실역2번출구 쪽)          2624\n",
       " 고속터미널역 8-1번, 8-2번 출구 사이    2495\n",
       " 마포구민체육센터 앞                 2454\n",
       " 안암로터리 버스정류장 앞              2110\n",
       " 신도림역 1번 출구 앞               1961\n",
       " CJ 드림시티                    1932\n",
       "Name: 반납대여소명, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 반납량이 많은 상위 대여소\n",
    "df['반납대여소명'].value_counts().head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 대여반납이 많은 상위 대여소인 홍대입구역, 여의나루역, 뚝섬유원지, 롯데월드타워는 모두 강이나 호수를 인근에 두고 있다. \n",
    "2. 성대입구나 안암로터리 버스정류장은 대학가 인근이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "대여대여소명\n",
       "  강동구청 앞          3823.703704\n",
       "  둔촌역 3번 출입구      3733.403042\n",
       " (구)신한은행 중랑교지점    4562.977346\n",
       " (구)합정동 주민센터      4876.831169\n",
       " 3호선 매봉역 3번출구앞    5489.920319\n",
       "                     ...     \n",
       "이수역 4번 출구         4728.075370\n",
       "자양중앙나들목           4192.175503\n",
       "중랑센터              4281.914894\n",
       "청계산입구역 1번출구       2695.479705\n",
       "하나은행 방배동지점 앞      6159.078341\n",
       "Name: 이용거리(M), Length: 1267, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['대여대여소명'])['이용거리(M)'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>대여대여소명</th>\n",
       "      <th>이용거리(M)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>동작역 5번출구 동작주차공원</td>\n",
       "      <td>10454.240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>독산보도육교 앞 자전거 보관소</td>\n",
       "      <td>10401.840607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>810</th>\n",
       "      <td>신한은행 시흥대로금융센터지점</td>\n",
       "      <td>9857.738095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>달맞이공원</td>\n",
       "      <td>9639.504950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1166</th>\n",
       "      <td>한내근린공원 남측</td>\n",
       "      <td>9419.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1197</th>\n",
       "      <td>현대자동차서비스 앞</td>\n",
       "      <td>9364.282178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>망원초록길 입구</td>\n",
       "      <td>9160.294118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1194</th>\n",
       "      <td>현대고등학교 건너편</td>\n",
       "      <td>9047.614350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>흑석역 1번출구</td>\n",
       "      <td>8938.909300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>청계천 생태교실 앞</td>\n",
       "      <td>8923.918819</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 대여대여소명       이용거리(M)\n",
       "353     동작역 5번출구 동작주차공원  10454.240000\n",
       "330    독산보도육교 앞 자전거 보관소  10401.840607\n",
       "810     신한은행 시흥대로금융센터지점   9857.738095\n",
       "280               달맞이공원   9639.504950\n",
       "1166          한내근린공원 남측   9419.090909\n",
       "1197         현대자동차서비스 앞   9364.282178\n",
       "419            망원초록길 입구   9160.294118\n",
       "1194         현대고등학교 건너편   9047.614350\n",
       "1231           흑석역 1번출구   8938.909300\n",
       "1113         청계천 생태교실 앞   8923.918819"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 평균 이용거리가 긴 상위 대여소 \n",
    "# df.groupby(['대여대여소명'])['이용거리(M)'].mean() 의 결과는 Series객체이지만 \n",
    "# reset_index()를 하면 DataFrame이 된다.\n",
    "df.groupby(['대여대여소명'])['이용거리(M)'].mean().reset_index()\\\n",
    ".sort_values(by='이용거리(M)', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>대여대여소명</th>\n",
       "      <th>이용거리(M)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>상암센터 정비실</td>\n",
       "      <td>256.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>국군복지단</td>\n",
       "      <td>1828.244275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CJ오쇼핑앞</td>\n",
       "      <td>1832.727273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>건국대학교 과학관(이과대) 앞</td>\n",
       "      <td>2143.065327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>937</th>\n",
       "      <td>원남동사거리</td>\n",
       "      <td>2234.752294</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 대여대여소명      이용거리(M)\n",
       "1249           상암센터 정비실   256.250000\n",
       "188               국군복지단  1828.244275\n",
       "7                CJ오쇼핑앞  1832.727273\n",
       "100    건국대학교 과학관(이과대) 앞  2143.065327\n",
       "937              원남동사거리  2234.752294"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 평균 이용거리가 짧은 대여소\n",
    "df.groupby(['대여대여소명'])['이용거리(M)'].mean().reset_index()\\\n",
    ".sort_values(by='이용거리(M)', ascending=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>대여대여소명</th>\n",
       "      <th>이용시간(분)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>달맞이공원</td>\n",
       "      <td>59.782178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>353</th>\n",
       "      <td>동작역 5번출구 동작주차공원</td>\n",
       "      <td>59.484000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>개화동상사마을종점 버스정류장</td>\n",
       "      <td>55.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>649</th>\n",
       "      <td>서울숲 남문 버스정류소 옆</td>\n",
       "      <td>55.495074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>648</th>\n",
       "      <td>서울숲 관리사무소</td>\n",
       "      <td>55.492228</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               대여대여소명    이용시간(분)\n",
       "280             달맞이공원  59.782178\n",
       "353   동작역 5번출구 동작주차공원  59.484000\n",
       "93    개화동상사마을종점 버스정류장  55.500000\n",
       "649    서울숲 남문 버스정류소 옆  55.495074\n",
       "648         서울숲 관리사무소  55.492228"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 평균 이용시간이 많은 상위 대여소\n",
    "df.groupby(['대여대여소명'])['이용시간(분)'].mean().reset_index()\\\n",
    ".sort_values(by='이용시간(분)', ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "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>대여대여소명</th>\n",
       "      <th>이용시간(분)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>CJ오쇼핑앞</td>\n",
       "      <td>11.416667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>상암센터 정비실</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>국군복지단</td>\n",
       "      <td>12.022901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1089</th>\n",
       "      <td>참새공원(백곡경노당)</td>\n",
       "      <td>12.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>삼일초등학교 인근</td>\n",
       "      <td>14.460274</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            대여대여소명    이용시간(분)\n",
       "7           CJ오쇼핑앞  11.416667\n",
       "1249      상암센터 정비실  12.000000\n",
       "188          국군복지단  12.022901\n",
       "1089   참새공원(백곡경노당)  12.666667\n",
       "575      삼일초등학교 인근  14.460274"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 평균 이용시간이 적은 상위 대여소\n",
    "df.groupby(['대여대여소명'])['이용시간(분)'].mean().reset_index()\\\n",
    ".sort_values(by='이용시간(분)', ascending=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 시계열 데이터 보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "자전거번호      object\n",
       "대여일시       object\n",
       "대여대여소번호    object\n",
       "대여대여소명     object\n",
       "대여거치대       int64\n",
       "반납일시       object\n",
       "반납대여소번호    object\n",
       "반납대여소명     object\n",
       "반납거치대       int64\n",
       "이용시간(분)     int64\n",
       "이용거리(M)     int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "자전거번호              object\n",
       "대여일시       datetime64[ns]\n",
       "대여대여소번호            object\n",
       "대여대여소명             object\n",
       "대여거치대               int64\n",
       "반납일시       datetime64[ns]\n",
       "반납대여소번호            object\n",
       "반납대여소명             object\n",
       "반납거치대               int64\n",
       "이용시간(분)             int64\n",
       "이용거리(M)             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['대여일시'] = pd.to_datetime(df['대여일시'])\n",
    "df['반납일시'] = pd.to_datetime(df['반납일시'])\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['자전거번호', '대여일시', '대여대여소번호', '대여대여소명', '대여거치대', '반납일시', '반납대여소번호',\n",
       "       '반납대여소명', '반납거치대', '이용시간(분)', '이용거리(M)', '대여년', '대여월', '대여일', '대여시',\n",
       "       '대여분', '대여요일'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['대여년'] = df['대여일시'].dt.year\n",
    "df['대여월'] = df['대여일시'].dt.month\n",
    "df['대여일'] = df['대여일시'].dt.day\n",
    "df['대여시'] = df['대여일시'].dt.hour\n",
    "df['대여분'] = df['대여일시'].dt.minute\n",
    "df['대여요일'] = df['대여일시'].dt.dayofweek\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['자전거번호', '대여일시', '대여대여소번호', '대여대여소명', '대여거치대', '반납일시', '반납대여소번호',\n",
       "       '반납대여소명', '반납거치대', '이용시간(분)', '이용거리(M)', '대여년', '대여월', '대여일', '대여시',\n",
       "       '대여분', '대여요일', '반납년', '반납월', '반납일', '반납시', '반납분', '반납요일'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['반납년'] = df['반납일시'].dt.year\n",
    "df['반납월'] = df['반납일시'].dt.month\n",
    "df['반납일'] = df['반납일시'].dt.day\n",
    "df['반납시'] = df['반납일시'].dt.hour\n",
    "df['반납분'] = df['반납일시'].dt.minute\n",
    "df['반납요일'] = df['반납일시'].dt.dayofweek\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 일자별 대여|반납 현황\n",
    "* 날짜에 따라 대여|반납 횟수를 확인한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>47024</td>\n",
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       "      <td>46839</td>\n",
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       "      <th>3</th>\n",
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       "      <td>46107</td>\n",
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       "      <td>46105</td>\n",
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       "      <td>46038</td>\n",
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       "      <td>42313</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27</td>\n",
       "      <td>40698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24</td>\n",
       "      <td>39164</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18</td>\n",
       "      <td>35828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28</td>\n",
       "      <td>27453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30</td>\n",
       "      <td>26669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>26</td>\n",
       "      <td>6312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index    대여일\n",
       "0      21  47319\n",
       "1      22  47024\n",
       "2      19  46839\n",
       "3      20  46107\n",
       "4      25  46105\n",
       "5      16  46038\n",
       "6      23  45274\n",
       "7      17  43161\n",
       "8      29  42313\n",
       "9      27  40698\n",
       "10     24  39164\n",
       "11     18  35828\n",
       "12     28  27453\n",
       "13     30  26669\n",
       "14     26   6312"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['대여일'].value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>46276</td>\n",
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       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>46085</td>\n",
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       "      <th>5</th>\n",
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       "      <td>45695</td>\n",
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       "      <th>6</th>\n",
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       "      <td>29</td>\n",
       "      <td>41498</td>\n",
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       "      <td>40450</td>\n",
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       "      <th>10</th>\n",
       "      <td>24</td>\n",
       "      <td>38962</td>\n",
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       "      <th>11</th>\n",
       "      <td>18</td>\n",
       "      <td>35781</td>\n",
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       "      <th>12</th>\n",
       "      <td>28</td>\n",
       "      <td>27540</td>\n",
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       "      <th>13</th>\n",
       "      <td>30</td>\n",
       "      <td>27299</td>\n",
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       "      <th>14</th>\n",
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       "      <th>17</th>\n",
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       "</div>"
      ],
      "text/plain": [
       "    index    반납일\n",
       "0      21  47232\n",
       "1      19  46878\n",
       "2      22  46670\n",
       "3      25  46276\n",
       "4      20  46085\n",
       "5      23  45695\n",
       "6      16  44701\n",
       "7      17  43669\n",
       "8      29  41498\n",
       "9      27  40450\n",
       "10     24  38962\n",
       "11     18  35781\n",
       "12     28  27540\n",
       "13     30  27299\n",
       "14     26   7028\n",
       "15      1    538\n",
       "16      3      1\n",
       "17      2      1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['반납일'].value_counts().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15, 3)\n",
      "(18, 3)\n",
      "(33, 3)\n"
     ]
    },
    {
     "data": {
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       "      <th>day</th>\n",
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       "      <th>5</th>\n",
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       "      <th>2</th>\n",
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       "      <td>46839</td>\n",
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      "text/plain": [
       "    day  count 대여반납\n",
       "5    16  46038   대여\n",
       "7    17  43161   대여\n",
       "11   18  35828   대여\n",
       "2    19  46839   대여\n",
       "3    20  46107   대여"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_day_1 = df['대여일'].value_counts().reset_index()\n",
    "df_day_1.columns = ['day', 'count']\n",
    "\n",
    "df_day_1 = df_day_1.sort_values('day')\n",
    "df_day_1['대여반납'] = '대여'\n",
    "#print(df_day_1)\n",
    "print(df_day_1.shape)\n",
    "\n",
    "df_day_2 = df['반납일'].value_counts().reset_index()\n",
    "df_day_2.columns = ['day', 'count']\n",
    "df_day_2 = df_day_2.sort_values('day')\n",
    "df_day_2['대여반납'] = '반납'\n",
    "#print(df_day_2)\n",
    "print(df_day_2.shape)\n",
    "\n",
    "df_day = pd.concat([df_day_1, df_day_2])\n",
    "print(df_day.shape)\n",
    "df_day.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
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       "      <th>4</th>\n",
       "      <td>25</td>\n",
       "      <td>46105</td>\n",
       "      <td>대여</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>26</td>\n",
       "      <td>6312</td>\n",
       "      <td>대여</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27</td>\n",
       "      <td>40698</td>\n",
       "      <td>대여</td>\n",
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       "      <th>12</th>\n",
       "      <td>28</td>\n",
       "      <td>27453</td>\n",
       "      <td>대여</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>29</td>\n",
       "      <td>42313</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30</td>\n",
       "      <td>26669</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1</td>\n",
       "      <td>538</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>16</td>\n",
       "      <td>44701</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>17</td>\n",
       "      <td>43669</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18</td>\n",
       "      <td>35781</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>46878</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>46085</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>47232</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>22</td>\n",
       "      <td>46670</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23</td>\n",
       "      <td>45695</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24</td>\n",
       "      <td>38962</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>25</td>\n",
       "      <td>46276</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>26</td>\n",
       "      <td>7028</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>27</td>\n",
       "      <td>40450</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28</td>\n",
       "      <td>27540</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>29</td>\n",
       "      <td>41498</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30</td>\n",
       "      <td>27299</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    day  count 대여반납\n",
       "5    16  46038   대여\n",
       "7    17  43161   대여\n",
       "11   18  35828   대여\n",
       "2    19  46839   대여\n",
       "3    20  46107   대여\n",
       "0    21  47319   대여\n",
       "1    22  47024   대여\n",
       "6    23  45274   대여\n",
       "10   24  39164   대여\n",
       "4    25  46105   대여\n",
       "14   26   6312   대여\n",
       "9    27  40698   대여\n",
       "12   28  27453   대여\n",
       "8    29  42313   대여\n",
       "13   30  26669   대여\n",
       "15    1    538   반납\n",
       "17    2      1   반납\n",
       "16    3      1   반납\n",
       "6    16  44701   반납\n",
       "7    17  43669   반납\n",
       "11   18  35781   반납\n",
       "1    19  46878   반납\n",
       "4    20  46085   반납\n",
       "0    21  47232   반납\n",
       "2    22  46670   반납\n",
       "5    23  45695   반납\n",
       "10   24  38962   반납\n",
       "3    25  46276   반납\n",
       "14   26   7028   반납\n",
       "9    27  40450   반납\n",
       "12   28  27540   반납\n",
       "8    29  41498   반납\n",
       "13   30  27299   반납"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_day"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.font_manager as fm\n",
    "\n",
    "font_path = 'C:/Windows/Fonts/malgun.ttf'\n",
    "fontprop = fm.FontProperties(fname=font_path).get_name()\n",
    "matplotlib.rc('font', family = fontprop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "ggplot2의 문법을 구성하는 기본 요소는 아래와 같습니다.\n",
    "\n",
    "* 데이터 프레임(data frame)\n",
    "* 색상, 크기 같은 외적 요소(aes)\n",
    "* 점, 선, 모양 같은 기하학적 요소(geoms)\n",
    "* 통계적 처리 방법(stats)\n",
    "* aes에서 사용할 스케일(scale)\n",
    "* geom_bar()에서 아무 것도 설정해 주지 않으면 histogram/도수분포표가 됩니다.\n",
    "* stat 요소를 “identity” (데이터 프레임의 값을 그대로 사용해서 그래프를 그리라는 뜻)으로 설정해 주는 것입니다.\n",
    "* position을 설정하지 않으면 stack bar가 된다.\n",
    "* 막대 모양을 바꿔주려면 geom_bar에서 position 요소를 “dodge”로 설정\n",
    "* coord_flip()는 가로형 막대 그래프"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (140328180465)>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(df_day) \n",
    "       + aes(x='day', y='count', fill='대여반납') \n",
    "       + geom_bar(stat='identity',position='dodge')\n",
    "       + coord_flip()\n",
    "       + theme(text=element_text(family=fontprop),figure_size=(12,6))\n",
    "       + labs(x='대여/반납일', y='대여/반납수', title='일별 자전거 대여/반납수')\n",
    "      )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (140327961456)>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(df_day)\n",
    " + aes(x='day', y='count', fill='대여반납')\n",
    " + geom_bar(stat='identity', position='dodge')\n",
    " + labs(x='대여일', y='대여수', title='일별 자전거 대여수')\n",
    " + theme(text=element_text(family=fontprop),\n",
    "        figure_size=(12,6))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 일부 날짜의 데이터만 있다.\n",
    "* 2018년 6월 데이터만 봤을 때 26일에는 대여와 반납모두 적다.\n",
    "* 26일의 일기예보를 보면 장맛비가 내린것을 확인해 볼 수 있다. https://www.youtube.com/watch?v=cYHC8iwfFyU"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 요일별 대여|반납 현황\n",
    "* 대여|반납별로 어떤 요일에 주로 대여반납이 되는지 시각화해보기 위해 Tidy Data를 만든다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(7, 3)\n",
      "(7, 3)\n",
      "(14, 3)\n"
     ]
    },
    {
     "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>dayofweek</th>\n",
       "      <th>count</th>\n",
       "      <th>대여반납</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>81933</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>53151</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>86805</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>74772</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>89337</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>117981</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>82325</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>82058</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>53907</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>86535</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>74772</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>88168</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>117695</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>83169</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dayofweek   count 대여반납\n",
       "4          0   81933   대여\n",
       "6          1   53151   대여\n",
       "2          2   86805   대여\n",
       "5          3   74772   대여\n",
       "1          4   89337   대여\n",
       "0          5  117981   대여\n",
       "3          6   82325   대여\n",
       "4          0   82058   반납\n",
       "6          1   53907   반납\n",
       "2          2   86535   반납\n",
       "5          3   74772   반납\n",
       "1          4   88168   반납\n",
       "0          5  117695   반납\n",
       "3          6   83169   반납"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dow_1 = df['대여요일'].value_counts().reset_index()\n",
    "df_dow_1.columns = ['dayofweek', 'count']\n",
    "df_dow_1 = df_dow_1.sort_values('dayofweek')\n",
    "df_dow_1['대여반납'] = '대여'\n",
    "print(df_dow_1.shape)\n",
    "\n",
    "df_dow_2 = df['반납요일'].value_counts().reset_index()\n",
    "df_dow_2.columns = ['dayofweek', 'count']\n",
    "df_dow_2 = df_dow_2.sort_values('dayofweek')\n",
    "df_dow_2['대여반납'] = '반납'\n",
    "print(df_dow_2.shape)\n",
    "\n",
    "df_dow = pd.concat([df_dow_1, df_dow_2])\n",
    "print(df_dow.shape)\n",
    "df_dow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>dayofweek</th>\n",
       "      <th>count</th>\n",
       "      <th>대여반납</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>81933</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>53151</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>86805</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dayofweek  count 대여반납\n",
       "4          0  81933   대여\n",
       "6          1  53151   대여\n",
       "2          2  86805   대여"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dow.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>dayofweek</th>\n",
       "      <th>count</th>\n",
       "      <th>대여반납</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>88168</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>117695</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>83169</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dayofweek   count 대여반납\n",
       "1          4   88168   반납\n",
       "0          5  117695   반납\n",
       "3          6   83169   반납"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dow.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "weekday_map= {0:'MON', 1:'TUE', 2:'WED', 3:'THU',\n",
    "              4:'FRI', 5:'SAT', 6:'SUN'}\n",
    "df_dow['label'] = df_dow['dayofweek'].replace(weekday_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>dayofweek</th>\n",
       "      <th>count</th>\n",
       "      <th>대여반납</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>86535</td>\n",
       "      <td>반납</td>\n",
       "      <td>WED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>74772</td>\n",
       "      <td>반납</td>\n",
       "      <td>THU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>88168</td>\n",
       "      <td>반납</td>\n",
       "      <td>FRI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>117695</td>\n",
       "      <td>반납</td>\n",
       "      <td>SAT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>83169</td>\n",
       "      <td>반납</td>\n",
       "      <td>SUN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   dayofweek   count 대여반납 label\n",
       "2          2   86535   반납   WED\n",
       "5          3   74772   반납   THU\n",
       "1          4   88168   반납   FRI\n",
       "0          5  117695   반납   SAT\n",
       "3          6   83169   반납   SUN"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dow.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (140327707441)>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(df_dow)\n",
    " + aes(x='dayofweek', y='count', fill='대여반납')\n",
    " + geom_bar(stat='identity', position='dodge')\n",
    " + geom_point()\n",
    " + geom_line(color='blue')\n",
    " + labs(x='대여요일', y='대여수', title='요일별 자전거 대여수')\n",
    " + theme(text=element_text(family=fontprop),\n",
    "        figure_size=(12,6))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 시간대별 대여|반납량"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>hour</th>\n",
       "      <th>count</th>\n",
       "      <th>대여반납</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0</td>\n",
       "      <td>19837</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>1</td>\n",
       "      <td>15358</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>12255</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>7462</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4</td>\n",
       "      <td>4392</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hour  count 대여반납\n",
       "13     0  19837   대여\n",
       "17     1  15358   대여\n",
       "19     2  12255   대여\n",
       "21     3   7462   대여\n",
       "23     4   4392   대여"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hour_1 = df['대여시'].value_counts().reset_index()\n",
    "df_hour_1.columns = ['hour', 'count']\n",
    "df_hour_1 = df_hour_1.sort_values('hour')\n",
    "df_hour_1['대여반납'] = '대여'\n",
    "df_hour_1.head()\n",
    "\n",
    "df_hour_2 = df['반납시'].value_counts().reset_index()\n",
    "df_hour_2.columns = ['hour', 'count']\n",
    "df_hour_2 = df_hour_2.sort_values('hour')\n",
    "df_hour_2['대여반납'] = '반납'\n",
    "df_hour_2.head()\n",
    "\n",
    "df_hour = pd.concat([df_hour_1, df_hour_2])\n",
    "df_hour.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "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>hour</th>\n",
       "      <th>count</th>\n",
       "      <th>대여반납</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>46836</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>45713</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>45628</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>44417</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>23</td>\n",
       "      <td>34927</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   hour  count 대여반납\n",
       "1    19  46836   반납\n",
       "2    20  45713   반납\n",
       "3    21  45628   반납\n",
       "4    22  44417   반납\n",
       "5    23  34927   반납"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_hour.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (140327654129)>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(df_hour)\n",
    " + aes(x='hour', y='count', fill='대여반납')\n",
    " + geom_bar(stat='identity', position='dodge')\n",
    " + geom_point()\n",
    " + geom_line(color='blue')\n",
    " + labs(x='시간', y='대여/반납 수', title='시간대별 자전거 대여/반납수')\n",
    " + theme(text=element_text(family=fontprop),\n",
    "        figure_size=(12,6))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 새벽시간대로 갈 수록 대여/반납 수가 줄어든다.\n",
    "* 출근시간대에는 대여가 반납보다 많다.\n",
    "* 오전에는 8시대에 대여/반납이 많으며, 오후시간대에는 18시에 대여/반납이 가장 많다.\n",
    "* 오전 11시부터 20시까지는 대여가 반납보다 많다.(대여>반납)\n",
    "* 21시 이후부터 오전 4시까지는 대여보다 반납이 많다.(대여<반납)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60, 3)\n",
      "(60, 3)\n",
      "(120, 3)\n"
     ]
    }
   ],
   "source": [
    "df_minute_1 = df['대여분'].value_counts().reset_index()\n",
    "df_minute_1.columns = ['minute', 'count']\n",
    "df_minute_1 = df_minute_1.sort_values('minute')\n",
    "df_minute_1['대여반납'] = '대여'\n",
    "print(df_minute_1.shape)\n",
    "\n",
    "df_minute_2 = df['반납분'].value_counts().reset_index()\n",
    "df_minute_2.columns = ['minute', 'count']\n",
    "df_minute_2 = df_minute_2.sort_values('minute')\n",
    "df_minute_2['대여반납'] = '반납'\n",
    "print(df_minute_2.shape)\n",
    "\n",
    "df_minute = pd.concat([df_minute_1, df_minute_2])\n",
    "print(df_minute.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>minute</th>\n",
       "      <th>count</th>\n",
       "      <th>대여반납</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>0</td>\n",
       "      <td>9326</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>1</td>\n",
       "      <td>9601</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2</td>\n",
       "      <td>9758</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>9748</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>4</td>\n",
       "      <td>10037</td>\n",
       "      <td>대여</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    minute  count 대여반납\n",
       "57       0   9326   대여\n",
       "41       1   9601   대여\n",
       "24       2   9758   대여\n",
       "28       3   9748   대여\n",
       "11       4  10037   대여"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_minute.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>minute</th>\n",
       "      <th>count</th>\n",
       "      <th>대여반납</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55</td>\n",
       "      <td>10369</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>56</td>\n",
       "      <td>10095</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>57</td>\n",
       "      <td>10094</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>58</td>\n",
       "      <td>9882</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>59</td>\n",
       "      <td>9887</td>\n",
       "      <td>반납</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    minute  count 대여반납\n",
       "3       55  10369   반납\n",
       "10      56  10095   반납\n",
       "11      57  10094   반납\n",
       "19      58   9882   반납\n",
       "18      59   9887   반납"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_minute.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (140327624171)>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(ggplot(df_minute)\n",
    " + aes(x='minute', y='count', fill='대여반납')\n",
    " + geom_bar(stat='identity', position='dodge')\n",
    " + geom_line(color='blue')\n",
    " + labs(x='분', y='대여/반납 수', title='시간대별(분) 자전거 대여/반납수')\n",
    " + theme(text=element_text(family=fontprop),\n",
    "        figure_size=(12,6))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 대여/반납이 거의 비슷하게 이루어지고 있지만 대여는 0~20분 사이에 더 많이 일어나며 반납은 40~60분 사이에 더 많이 일어난다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 대여와 반납은 같은 곳에서 이루어지고 있을까?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 같은대여반납소라는 컬럼을 새로 만들고 대여반납이 같은 곳에서 이루어지고 있는지 여부를 추가한다.\n",
    "df['같은대여반납소'] = df['대여대여소명'] == df['반납대여소명']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7.856020784242644\n",
      "같은 곳에서 대여반납 하는 것보다 다른 곳에서 대여반납 하는 것이 7.86배 많다.\n"
     ]
    },
    {
     "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>일치여부</th>\n",
       "      <th>대여반납수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>520100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>True</td>\n",
       "      <td>66204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    일치여부   대여반납수\n",
       "0  False  520100\n",
       "1   True   66204"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_location_diff = df['같은대여반납소'].value_counts().reset_index()\n",
    "df_location_diff.columns = ['일치여부', '대여반납수']\n",
    "# 같은|다른 반납소별로 대여반납수가 몇 배나 차이가 나는지 확인한다.\n",
    "location_diff_ratio = df_location_diff['대여반납수'][0]/df_location_diff['대여반납수'][1]\n",
    "print(location_diff_ratio)\n",
    "print('같은 곳에서 대여반납 하는 것보다 다른 곳에서 대여반납 하는 것이 {0:.2f}배 많다.'.format(location_diff_ratio))\n",
    "df_location_diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    520100\n",
       "1     66204\n",
       "Name: 대여반납수, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_location_diff['대여반납수']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (140327241210)>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 대여반납수로 fill을 넣어주어 그래프에 색상표현이 되도록 한다.\n",
    "(ggplot(df_location_diff)\n",
    " + geom_col(mapping=aes(x='일치여부', y='대여반납수', fill='대여반납수'))\n",
    " + labs(title='대여반납소 일치여부')\n",
    " + theme(text=element_text(family=fontprop))\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "같은 곳에서 대여반납을 하는 것보다 다른 곳으로의 대여반납이 훨씬 많은 것을 확인할 수 있다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>자전거번호</th>\n",
       "      <th>대여일시</th>\n",
       "      <th>대여대여소번호</th>\n",
       "      <th>대여대여소명</th>\n",
       "      <th>대여거치대</th>\n",
       "      <th>반납일시</th>\n",
       "      <th>반납대여소번호</th>\n",
       "      <th>반납대여소명</th>\n",
       "      <th>반납거치대</th>\n",
       "      <th>이용시간(분)</th>\n",
       "      <th>...</th>\n",
       "      <th>대여시</th>\n",
       "      <th>대여분</th>\n",
       "      <th>대여요일</th>\n",
       "      <th>반납년</th>\n",
       "      <th>반납월</th>\n",
       "      <th>반납일</th>\n",
       "      <th>반납시</th>\n",
       "      <th>반납분</th>\n",
       "      <th>반납요일</th>\n",
       "      <th>같은대여반납소</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>SPB-09000</td>\n",
       "      <td>2018-06-16 00:01:22</td>\n",
       "      <td>2025</td>\n",
       "      <td>흑석역 1번출구</td>\n",
       "      <td>13</td>\n",
       "      <td>2018-06-16 01:10:42</td>\n",
       "      <td>2025</td>\n",
       "      <td>흑석역 1번출구</td>\n",
       "      <td>9</td>\n",
       "      <td>67</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>SPB-08794</td>\n",
       "      <td>2018-06-16 00:01:32</td>\n",
       "      <td>2025</td>\n",
       "      <td>흑석역 1번출구</td>\n",
       "      <td>10</td>\n",
       "      <td>2018-06-16 01:10:42</td>\n",
       "      <td>2025</td>\n",
       "      <td>흑석역 1번출구</td>\n",
       "      <td>15</td>\n",
       "      <td>64</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>SPB-02178</td>\n",
       "      <td>2018-06-16 00:01:47</td>\n",
       "      <td>1308</td>\n",
       "      <td>안암로터리 버스정류장 앞</td>\n",
       "      <td>3</td>\n",
       "      <td>2018-06-16 01:52:14</td>\n",
       "      <td>1308</td>\n",
       "      <td>안암로터리 버스정류장 앞</td>\n",
       "      <td>4</td>\n",
       "      <td>110</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>52</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>SPB-06487</td>\n",
       "      <td>2018-06-16 00:01:50</td>\n",
       "      <td>421</td>\n",
       "      <td>마포구청 앞</td>\n",
       "      <td>6</td>\n",
       "      <td>2018-06-16 00:04:24</td>\n",
       "      <td>421</td>\n",
       "      <td>마포구청 앞</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>SPB-09293</td>\n",
       "      <td>2018-06-16 00:01:56</td>\n",
       "      <td>235</td>\n",
       "      <td>신길동 우리은행 옆</td>\n",
       "      <td>5</td>\n",
       "      <td>2018-06-16 00:05:02</td>\n",
       "      <td>235</td>\n",
       "      <td>신길동 우리은행 옆</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        자전거번호                대여일시 대여대여소번호          대여대여소명  대여거치대  \\\n",
       "27  SPB-09000 2018-06-16 00:01:22    2025        흑석역 1번출구     13   \n",
       "34  SPB-08794 2018-06-16 00:01:32    2025        흑석역 1번출구     10   \n",
       "40  SPB-02178 2018-06-16 00:01:47    1308   안암로터리 버스정류장 앞      3   \n",
       "42  SPB-06487 2018-06-16 00:01:50     421          마포구청 앞      6   \n",
       "47  SPB-09293 2018-06-16 00:01:56     235      신길동 우리은행 옆      5   \n",
       "\n",
       "                  반납일시 반납대여소번호          반납대여소명  반납거치대  이용시간(분)  ...  대여시  대여분  \\\n",
       "27 2018-06-16 01:10:42    2025        흑석역 1번출구      9       67  ...    0    1   \n",
       "34 2018-06-16 01:10:42    2025        흑석역 1번출구     15       64  ...    0    1   \n",
       "40 2018-06-16 01:52:14    1308   안암로터리 버스정류장 앞      4      110  ...    0    1   \n",
       "42 2018-06-16 00:04:24     421          마포구청 앞      7        2  ...    0    1   \n",
       "47 2018-06-16 00:05:02     235      신길동 우리은행 옆      5        3  ...    0    1   \n",
       "\n",
       "    대여요일   반납년  반납월  반납일  반납시  반납분  반납요일  같은대여반납소  \n",
       "27     5  2018    6   16    1   10     5     True  \n",
       "34     5  2018    6   16    1   10     5     True  \n",
       "40     5  2018    6   16    1   52     5     True  \n",
       "42     5  2018    6   16    0    4     5     True  \n",
       "47     5  2018    6   16    0    5     5     True  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_same_loc = df.loc[df['같은대여반납소'] == True]\n",
    "df_same_loc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 여의나루역 1번출구 앞               1046\n",
       " 뚝섬유원지역 1번출구 앞               900\n",
       " 서울숲 관리사무소                   665\n",
       " 봉림교 교통섬                     543\n",
       " 마포구민체육센터 앞                  541\n",
       " 고속터미널역 8-1번, 8-2번 출구 사이     485\n",
       " 옥수역 3번출구                    457\n",
       " 몽촌토성역 1번출구 옆                390\n",
       " 노들역 1번출구                    372\n",
       " 당산육갑문                       336\n",
       " 양천향교역 7번출구앞                 326\n",
       " 월드컵공원                       321\n",
       " CJ 드림시티                     311\n",
       " 홍대입구역 2번출구 앞                302\n",
       " 청계천 생태교실 앞                  292\n",
       " 홍은사거리                       288\n",
       " 마포구청 앞                      288\n",
       " 황금내근린공원                     280\n",
       " 잠실새내역 5번 출구 뒤               279\n",
       " 흑석역 1번출구                    274\n",
       " 보문2교                        266\n",
       " 외국어대 정문 앞                   262\n",
       " 한신16차아파트 119동 앞             260\n",
       " 새절역 2번출구                    252\n",
       " 현대고등학교 건너편                  246\n",
       " 서울숲역 4번 출구 옆                214\n",
       " 도림천 신화교                     212\n",
       " 도봉구청 옆(중랑천변)                211\n",
       " 독산보도육교 앞 자전거 보관소            210\n",
       " 안암로터리 버스정류장 앞               204\n",
       "Name: 대여대여소명, dtype: int64"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 같은 곳에서 대여반납이 이루어지는 상위 대여소(대여|반납 건 수)\n",
    "df_same_loc['대여대여소명'].value_counts().head(30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 같은 곳에서 대여반납이 이루어지는 대여소들은 한강주변이나 공원 주변에 위치한 것을 확인해 볼 수 있다."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 같은|다른 대여소에서 대여가 되는 자전거의 평균 이용시간\n",
    "### 같은 대여반납소"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "같은곳에서 대여, 반납이 이루어지는 자전거의 평균 이용시간 :  53.18429400036251\n"
     ]
    },
    {
     "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>대여대여소명</th>\n",
       "      <th>반납대여소명</th>\n",
       "      <th>이용시간(분)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>강동구청 앞</td>\n",
       "      <td>강동구청 앞</td>\n",
       "      <td>57.882353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>둔촌역 3번 출입구</td>\n",
       "      <td>둔촌역 3번 출입구</td>\n",
       "      <td>46.810127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(구)신한은행 중랑교지점</td>\n",
       "      <td>(구)신한은행 중랑교지점</td>\n",
       "      <td>47.545455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(구)합정동 주민센터</td>\n",
       "      <td>(구)합정동 주민센터</td>\n",
       "      <td>62.639344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3호선 매봉역 3번출구앞</td>\n",
       "      <td>3호선 매봉역 3번출구앞</td>\n",
       "      <td>48.227273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           대여대여소명          반납대여소명    이용시간(분)\n",
       "0          강동구청 앞          강동구청 앞  57.882353\n",
       "1      둔촌역 3번 출입구      둔촌역 3번 출입구  46.810127\n",
       "2   (구)신한은행 중랑교지점   (구)신한은행 중랑교지점  47.545455\n",
       "3     (구)합정동 주민센터     (구)합정동 주민센터  62.639344\n",
       "4   3호선 매봉역 3번출구앞   3호선 매봉역 3번출구앞  48.227273"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "same_loc_mean_time = df_same_loc['이용시간(분)'].mean()\n",
    "print('같은곳에서 대여, 반납이 이루어지는 자전거의 평균 이용시간 : ', same_loc_mean_time)\n",
    "df_same_mean = df_same_loc.groupby(['대여대여소명', '반납대여소명'])['이용시간(분)'].mean().reset_index()\n",
    "df_same_mean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>대여대여소명</th>\n",
       "      <th>반납대여소명</th>\n",
       "      <th>이용횟수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>강동구청 앞</td>\n",
       "      <td>강동구청 앞</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>둔촌역 3번 출입구</td>\n",
       "      <td>둔촌역 3번 출입구</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(구)신한은행 중랑교지점</td>\n",
       "      <td>(구)신한은행 중랑교지점</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(구)합정동 주민센터</td>\n",
       "      <td>(구)합정동 주민센터</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3호선 매봉역 3번출구앞</td>\n",
       "      <td>3호선 매봉역 3번출구앞</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           대여대여소명          반납대여소명  이용횟수\n",
       "0          강동구청 앞          강동구청 앞    34\n",
       "1      둔촌역 3번 출입구      둔촌역 3번 출입구    79\n",
       "2   (구)신한은행 중랑교지점   (구)신한은행 중랑교지점    44\n",
       "3     (구)합정동 주민센터     (구)합정동 주민센터    61\n",
       "4   3호선 매봉역 3번출구앞   3호선 매봉역 3번출구앞    66"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_same_count = df_same_loc.groupby(['대여대여소명', '반납대여소명']).size().reset_index()\n",
    "df_same_count.columns = ['대여대여소명', '반납대여소명', '이용횟수']\n",
    "df_same_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>대여대여소명</th>\n",
       "      <th>반납대여소명</th>\n",
       "      <th>이용시간(분)</th>\n",
       "      <th>이용횟수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>강동구청 앞</td>\n",
       "      <td>강동구청 앞</td>\n",
       "      <td>57.882353</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>둔촌역 3번 출입구</td>\n",
       "      <td>둔촌역 3번 출입구</td>\n",
       "      <td>46.810127</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(구)신한은행 중랑교지점</td>\n",
       "      <td>(구)신한은행 중랑교지점</td>\n",
       "      <td>47.545455</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(구)합정동 주민센터</td>\n",
       "      <td>(구)합정동 주민센터</td>\n",
       "      <td>62.639344</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3호선 매봉역 3번출구앞</td>\n",
       "      <td>3호선 매봉역 3번출구앞</td>\n",
       "      <td>48.227273</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           대여대여소명          반납대여소명    이용시간(분)  이용횟수\n",
       "0          강동구청 앞          강동구청 앞  57.882353    34\n",
       "1      둔촌역 3번 출입구      둔촌역 3번 출입구  46.810127    79\n",
       "2   (구)신한은행 중랑교지점   (구)신한은행 중랑교지점  47.545455    44\n",
       "3     (구)합정동 주민센터     (구)합정동 주민센터  62.639344    61\n",
       "4   3호선 매봉역 3번출구앞   3호선 매봉역 3번출구앞  48.227273    66"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_same_loc2 = df_same_mean.merge(df_same_count, left_on=['대여대여소명', '반납대여소명'],\\\n",
    "                                  right_on=['대여대여소명', '반납대여소명'])\n",
    "df_same_loc2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\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>대여대여소명</th>\n",
       "      <th>반납대여소명</th>\n",
       "      <th>이용시간(분)</th>\n",
       "      <th>이용횟수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>구로1동우체국 앞</td>\n",
       "      <td>구로1동우체국 앞</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>278</th>\n",
       "      <td>달맞이공원</td>\n",
       "      <td>달맞이공원</td>\n",
       "      <td>122.066667</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>434</th>\n",
       "      <td>명지대학교 학생회관</td>\n",
       "      <td>명지대학교 학생회관</td>\n",
       "      <td>121.285714</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>856</th>\n",
       "      <td>역삼동 sk뷰 501동앞</td>\n",
       "      <td>역삼동 sk뷰 501동앞</td>\n",
       "      <td>114.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>강남세브란스교차로  래미안그레이튼 104동 앞</td>\n",
       "      <td>강남세브란스교차로  래미안그레이튼 104동 앞</td>\n",
       "      <td>99.727273</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>공항시장역 4번출구</td>\n",
       "      <td>공항시장역 4번출구</td>\n",
       "      <td>95.545455</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>833</th>\n",
       "      <td>양원역 2번출구</td>\n",
       "      <td>양원역 2번출구</td>\n",
       "      <td>94.333333</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>서대문구청</td>\n",
       "      <td>서대문구청</td>\n",
       "      <td>92.023810</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>금호역 1번출구 앞</td>\n",
       "      <td>금호역 1번출구 앞</td>\n",
       "      <td>91.777778</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>235</th>\n",
       "      <td>낙성대역 1번출구</td>\n",
       "      <td>낙성대역 1번출구</td>\n",
       "      <td>89.562500</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         대여대여소명                      반납대여소명     이용시간(분)  이용횟수\n",
       "167                   구로1동우체국 앞                   구로1동우체국 앞  143.500000     2\n",
       "278                       달맞이공원                       달맞이공원  122.066667    15\n",
       "434                  명지대학교 학생회관                  명지대학교 학생회관  121.285714     7\n",
       "856               역삼동 sk뷰 501동앞               역삼동 sk뷰 501동앞  114.000000     1\n",
       "66    강남세브란스교차로  래미안그레이튼 104동 앞   강남세브란스교차로  래미안그레이튼 104동 앞   99.727273    11\n",
       "131                  공항시장역 4번출구                  공항시장역 4번출구   95.545455    11\n",
       "833                    양원역 2번출구                    양원역 2번출구   94.333333     3\n",
       "618                       서대문구청                       서대문구청   92.023810    42\n",
       "221                  금호역 1번출구 앞                  금호역 1번출구 앞   91.777778     9\n",
       "235                   낙성대역 1번출구                   낙성대역 1번출구   89.562500    16"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_same_loc2.sort_values(by='이용시간(분)', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>대여대여소명</th>\n",
       "      <th>반납대여소명</th>\n",
       "      <th>이용시간(분)</th>\n",
       "      <th>이용횟수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>850</th>\n",
       "      <td>여의나루역 1번출구 앞</td>\n",
       "      <td>여의나루역 1번출구 앞</td>\n",
       "      <td>66.167304</td>\n",
       "      <td>1046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>369</th>\n",
       "      <td>뚝섬유원지역 1번출구 앞</td>\n",
       "      <td>뚝섬유원지역 1번출구 앞</td>\n",
       "      <td>56.593333</td>\n",
       "      <td>900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>646</th>\n",
       "      <td>서울숲 관리사무소</td>\n",
       "      <td>서울숲 관리사무소</td>\n",
       "      <td>57.178947</td>\n",
       "      <td>665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>518</th>\n",
       "      <td>봉림교 교통섬</td>\n",
       "      <td>봉림교 교통섬</td>\n",
       "      <td>58.836096</td>\n",
       "      <td>543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>마포구민체육센터 앞</td>\n",
       "      <td>마포구민체육센터 앞</td>\n",
       "      <td>57.975970</td>\n",
       "      <td>541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>고속터미널역 8-1번, 8-2번 출구 사이</td>\n",
       "      <td>고속터미널역 8-1번, 8-2번 출구 사이</td>\n",
       "      <td>72.138144</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>905</th>\n",
       "      <td>옥수역 3번출구</td>\n",
       "      <td>옥수역 3번출구</td>\n",
       "      <td>55.855580</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445</th>\n",
       "      <td>몽촌토성역 1번출구 옆</td>\n",
       "      <td>몽촌토성역 1번출구 옆</td>\n",
       "      <td>58.420513</td>\n",
       "      <td>390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>노들역 1번출구</td>\n",
       "      <td>노들역 1번출구</td>\n",
       "      <td>63.424731</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>당산육갑문</td>\n",
       "      <td>당산육갑문</td>\n",
       "      <td>66.157738</td>\n",
       "      <td>336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       대여대여소명                    반납대여소명    이용시간(분)  이용횟수\n",
       "850              여의나루역 1번출구 앞              여의나루역 1번출구 앞  66.167304  1046\n",
       "369             뚝섬유원지역 1번출구 앞             뚝섬유원지역 1번출구 앞  56.593333   900\n",
       "646                 서울숲 관리사무소                 서울숲 관리사무소  57.178947   665\n",
       "518                   봉림교 교통섬                   봉림교 교통섬  58.836096   543\n",
       "404                마포구민체육센터 앞                마포구민체육센터 앞  57.975970   541\n",
       "118   고속터미널역 8-1번, 8-2번 출구 사이   고속터미널역 8-1번, 8-2번 출구 사이  72.138144   485\n",
       "905                  옥수역 3번출구                  옥수역 3번출구  55.855580   457\n",
       "445              몽촌토성역 1번출구 옆              몽촌토성역 1번출구 옆  58.420513   390\n",
       "253                  노들역 1번출구                  노들역 1번출구  63.424731   372\n",
       "287                     당산육갑문                     당산육갑문  66.157738   336"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 이용횟수가 많은 곳의 평균 이용시간\n",
    "df_same_loc2.sort_values(by='이용횟수', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_same_loc2['이용횟수'].plot.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 다른 대여|반납소"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(520100, 24)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_diff_loc = df.loc[df['같은대여반납소'] == False]\n",
    "df_diff_loc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "다른곳에서 대여, 반납이 이루어지는 자전거의 평균 이용시간 :  25.834228033070563\n"
     ]
    },
    {
     "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>대여대여소명</th>\n",
       "      <th>반납대여소명</th>\n",
       "      <th>이용시간(분)</th>\n",
       "      <th>이용횟수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>94100</th>\n",
       "      <td>천왕역 4번출구 뒤</td>\n",
       "      <td>신정3동 현장민원실 앞</td>\n",
       "      <td>740.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72879</th>\n",
       "      <td>여의도고교 앞</td>\n",
       "      <td>보라매역 8번출구</td>\n",
       "      <td>651.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59814</th>\n",
       "      <td>성수역3번출구</td>\n",
       "      <td>논현역 7번출구</td>\n",
       "      <td>553.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70466</th>\n",
       "      <td>압구정파출소 앞</td>\n",
       "      <td>전농사거리 교통섬</td>\n",
       "      <td>506.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66230</th>\n",
       "      <td>신설동역8번출구</td>\n",
       "      <td>청계천 박물관 앞</td>\n",
       "      <td>504.000000</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41979</th>\n",
       "      <td>방화역 4번출구앞</td>\n",
       "      <td>황금내근린공원</td>\n",
       "      <td>479.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1077</th>\n",
       "      <td>DMC역 2번출구 옆</td>\n",
       "      <td>홍은동 정원여중 입구</td>\n",
       "      <td>440.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84463</th>\n",
       "      <td>이태원지하보도</td>\n",
       "      <td>마포구청역</td>\n",
       "      <td>437.000000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84329</th>\n",
       "      <td>이태원관광특구입구</td>\n",
       "      <td>국립현대미술관</td>\n",
       "      <td>405.666667</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15423</th>\n",
       "      <td>구로역 광장</td>\n",
       "      <td>유한양행앞</td>\n",
       "      <td>405.000000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             대여대여소명         반납대여소명     이용시간(분)  이용횟수\n",
       "94100    천왕역 4번출구 뒤   신정3동 현장민원실 앞  740.000000     1\n",
       "72879       여의도고교 앞      보라매역 8번출구  651.000000     1\n",
       "59814       성수역3번출구       논현역 7번출구  553.000000     1\n",
       "70466      압구정파출소 앞      전농사거리 교통섬  506.000000     1\n",
       "66230      신설동역8번출구      청계천 박물관 앞  504.000000     3\n",
       "41979     방화역 4번출구앞        황금내근린공원  479.000000     1\n",
       "1077    DMC역 2번출구 옆    홍은동 정원여중 입구  440.000000     2\n",
       "84463       이태원지하보도         마포구청역   437.000000     2\n",
       "84329     이태원관광특구입구        국립현대미술관  405.666667     3\n",
       "15423        구로역 광장          유한양행앞  405.000000     1"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diff_loc_mean_time = df_diff_loc['이용시간(분)'].mean()\n",
    "print('다른곳에서 대여, 반납이 이루어지는 자전거의 평균 이용시간 : ', df_diff_loc['이용시간(분)'].mean())\n",
    "df_diff_mean = df_diff_loc.groupby(['대여대여소명', '반납대여소명'])['이용시간(분)'].mean().reset_index()\n",
    "df_diff_count = df_diff_loc.groupby(['대여대여소명', '반납대여소명']).size().reset_index()\n",
    "df_diff_count.columns = ['대여대여소명', '반납대여소명', '이용횟수']\n",
    "df_diff_loc2 = df_diff_mean.merge(df_diff_count, left_on=['대여대여소명', '반납대여소명'], right_on=['대여대여소명', '반납대여소명'])\n",
    "df_diff_loc2.sort_values(by='이용시간(분)', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>대여대여소명</th>\n",
       "      <th>반납대여소명</th>\n",
       "      <th>이용시간(분)</th>\n",
       "      <th>이용횟수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99349</th>\n",
       "      <td>하늘채코오롱아파트 건너편</td>\n",
       "      <td>홍대입구역 2번출구 앞</td>\n",
       "      <td>10.240938</td>\n",
       "      <td>469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104360</th>\n",
       "      <td>홍대입구역 2번출구 앞</td>\n",
       "      <td>하늘채코오롱아파트 건너편</td>\n",
       "      <td>14.943734</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86527</th>\n",
       "      <td>장승배기역 5번출구</td>\n",
       "      <td>노량진역 5번출구</td>\n",
       "      <td>8.200000</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43695</th>\n",
       "      <td>봉림교 교통섬</td>\n",
       "      <td>도림천 신화교</td>\n",
       "      <td>17.679856</td>\n",
       "      <td>278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26686</th>\n",
       "      <td>도림천 신화교</td>\n",
       "      <td>봉림교 교통섬</td>\n",
       "      <td>14.248175</td>\n",
       "      <td>274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108597</th>\n",
       "      <td>청계산입구역 1번출구</td>\n",
       "      <td>언남초등학교 앞</td>\n",
       "      <td>8.184426</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43745</th>\n",
       "      <td>봉림교 교통섬</td>\n",
       "      <td>서울신성초등학교</td>\n",
       "      <td>21.479339</td>\n",
       "      <td>242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72270</th>\n",
       "      <td>언남초등학교 앞</td>\n",
       "      <td>청계산입구역 1번출구</td>\n",
       "      <td>6.260504</td>\n",
       "      <td>238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7959</th>\n",
       "      <td>건국대학교 (입학정보관)</td>\n",
       "      <td>건국대학교 학생회관</td>\n",
       "      <td>12.218341</td>\n",
       "      <td>229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85076</th>\n",
       "      <td>자양나들목</td>\n",
       "      <td>뚝섬유원지역 1번출구 앞</td>\n",
       "      <td>8.204651</td>\n",
       "      <td>215</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                대여대여소명          반납대여소명    이용시간(분)  이용횟수\n",
       "99349    하늘채코오롱아파트 건너편    홍대입구역 2번출구 앞  10.240938   469\n",
       "104360    홍대입구역 2번출구 앞   하늘채코오롱아파트 건너편  14.943734   391\n",
       "86527       장승배기역 5번출구       노량진역 5번출구   8.200000   285\n",
       "43695          봉림교 교통섬         도림천 신화교  17.679856   278\n",
       "26686          도림천 신화교         봉림교 교통섬  14.248175   274\n",
       "108597     청계산입구역 1번출구        언남초등학교 앞   8.184426   244\n",
       "43745          봉림교 교통섬        서울신성초등학교  21.479339   242\n",
       "72270         언남초등학교 앞     청계산입구역 1번출구   6.260504   238\n",
       "7959     건국대학교 (입학정보관)      건국대학교 학생회관  12.218341   229\n",
       "85076            자양나들목   뚝섬유원지역 1번출구 앞   8.204651   215"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_diff_loc2.sort_values(by='이용횟수', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "같은 곳에서 대여반납이 이루어지는 자전거의 이용시간(분)이 다른 대여소의 대여반납 이용시간보다 27.35 분 많다.\n"
     ]
    }
   ],
   "source": [
    "rent_mean_time = same_loc_mean_time - diff_loc_mean_time\n",
    "print('같은 곳에서 대여반납이 이루어지는 자전거의 이용시간(분)이 다른 대여소의 대여반납 이용시간보다 {:,.2f} 분 많다.'.format(rent_mean_time))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 이용횟수가 많은 대여소라고 해서 평균이용시간이 평균보다 높지는 않다.\n",
    "* 하늘채코오롱아파트 건너편<->홍대입구역 2번출구 앞은 이용횟수가 가장 높은 대여반납소다.\n",
    "* 관련기사\n",
    "    * ['따릉이' 타고 가장 많이 다닌 경로는? - 통플러스](http://news.tongplus.com/site/data/html_dir/2018/10/24/2018102402585.html)\n",
    "    * [[ONE SHOT] 따릉이, ‘이곳’서 가장 많이 빌리고 ‘여기’서 가장 많이 탄다](https://news.joins.com/article/23003297)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 해당 기간동안 가장 많이 이용된 자전거는 몇 회 대여 되었을까?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 586304 entries, 0 to 586303\n",
      "Data columns (total 24 columns):\n",
      " #   Column   Non-Null Count   Dtype         \n",
      "---  ------   --------------   -----         \n",
      " 0   자전거번호    586304 non-null  object        \n",
      " 1   대여일시     586304 non-null  datetime64[ns]\n",
      " 2   대여대여소번호  586304 non-null  object        \n",
      " 3   대여대여소명   586304 non-null  object        \n",
      " 4   대여거치대    586304 non-null  int64         \n",
      " 5   반납일시     586304 non-null  datetime64[ns]\n",
      " 6   반납대여소번호  586304 non-null  object        \n",
      " 7   반납대여소명   586304 non-null  object        \n",
      " 8   반납거치대    586304 non-null  int64         \n",
      " 9   이용시간(분)  586304 non-null  int64         \n",
      " 10  이용거리(M)  586304 non-null  int64         \n",
      " 11  대여년      586304 non-null  int64         \n",
      " 12  대여월      586304 non-null  int64         \n",
      " 13  대여일      586304 non-null  int64         \n",
      " 14  대여시      586304 non-null  int64         \n",
      " 15  대여분      586304 non-null  int64         \n",
      " 16  대여요일     586304 non-null  int64         \n",
      " 17  반납년      586304 non-null  int64         \n",
      " 18  반납월      586304 non-null  int64         \n",
      " 19  반납일      586304 non-null  int64         \n",
      " 20  반납시      586304 non-null  int64         \n",
      " 21  반납분      586304 non-null  int64         \n",
      " 22  반납요일     586304 non-null  int64         \n",
      " 23  같은대여반납소  586304 non-null  bool          \n",
      "dtypes: bool(1), datetime64[ns](2), int64(16), object(5)\n",
      "memory usage: 103.4+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count        586304\n",
       "unique        12970\n",
       "top       SPB-18238\n",
       "freq            139\n",
       "Name: 자전거번호, dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info()\n",
    "df['자전거번호'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "대여된 자전거 수 : 12970\n",
      "가장 많이 대여된 자전거 번호 : SPB-18238\n",
      "가장 많이 대여된 자전거의 대여횟수 : 139\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "count        586304\n",
       "unique        12970\n",
       "top       SPB-18238\n",
       "freq            139\n",
       "Name: 자전거번호, dtype: object"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_describe = df['자전거번호'].describe()\n",
    "print('대여된 자전거 수 :', bike_describe[1]) #unique\n",
    "print('가장 많이 대여된 자전거 번호 :', bike_describe[2]) #top\n",
    "print('가장 많이 대여된 자전거의 대여횟수 :', bike_describe[3]) #freq\n",
    "bike_describe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "해당 기간동안 자전거 하나당 평균 대여 수: 45.20462606013878\n",
      "자전거 하나당 가장 많이 대여된 횟수: 139\n",
      "자전거 하나당 가장 적게 대여된 횟수: 1\n"
     ]
    },
    {
     "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>자전거번호</th>\n",
       "      <th>대여수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SPB-18238</td>\n",
       "      <td>139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SPB-14689</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SPB-16288</td>\n",
       "      <td>126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SPB-16176</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SPB-18073</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       자전거번호  대여수\n",
       "0  SPB-18238  139\n",
       "1  SPB-14689  128\n",
       "2  SPB-16288  126\n",
       "3  SPB-16176  125\n",
       "4  SPB-18073  121"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_rent_counts = df['자전거번호'].value_counts().reset_index()\n",
    "bike_rent_counts.columns = ['자전거번호', '대여수']\n",
    "print('해당 기간동안 자전거 하나당 평균 대여 수:', bike_rent_counts['대여수'].mean())\n",
    "print('자전거 하나당 가장 많이 대여된 횟수:', bike_rent_counts['대여수'].max())\n",
    "print('자전거 하나당 가장 적게 대여된 횟수:', bike_rent_counts['대여수'].min())\n",
    "bike_rent_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>자전거번호</th>\n",
       "      <th>대여수</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12965</th>\n",
       "      <td>SPB-12070</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12966</th>\n",
       "      <td>SPB-08351</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12967</th>\n",
       "      <td>SPB-03992</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12968</th>\n",
       "      <td>SPB-16483</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12969</th>\n",
       "      <td>SPB-02168</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           자전거번호  대여수\n",
       "12965  SPB-12070    1\n",
       "12966  SPB-08351    1\n",
       "12967  SPB-03992    1\n",
       "12968  SPB-16483    1\n",
       "12969  SPB-02168    1"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_rent_counts.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bike_rent_counts.plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "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>자전거번호</th>\n",
       "      <th>대여일시</th>\n",
       "      <th>대여대여소번호</th>\n",
       "      <th>대여대여소명</th>\n",
       "      <th>대여거치대</th>\n",
       "      <th>반납일시</th>\n",
       "      <th>반납대여소번호</th>\n",
       "      <th>반납대여소명</th>\n",
       "      <th>반납거치대</th>\n",
       "      <th>이용시간(분)</th>\n",
       "      <th>...</th>\n",
       "      <th>대여시</th>\n",
       "      <th>대여분</th>\n",
       "      <th>대여요일</th>\n",
       "      <th>반납년</th>\n",
       "      <th>반납월</th>\n",
       "      <th>반납일</th>\n",
       "      <th>반납시</th>\n",
       "      <th>반납분</th>\n",
       "      <th>반납요일</th>\n",
       "      <th>같은대여반납소</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13282</th>\n",
       "      <td>SPB-18238</td>\n",
       "      <td>2018-06-16 13:01:05</td>\n",
       "      <td>2140</td>\n",
       "      <td>신림1교 교차로</td>\n",
       "      <td>6</td>\n",
       "      <td>2018-06-16 13:46:40</td>\n",
       "      <td>1975</td>\n",
       "      <td>대림역 1번 출입구 밑</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>46</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14861</th>\n",
       "      <td>SPB-18238</td>\n",
       "      <td>2018-06-16 13:47:21</td>\n",
       "      <td>1975</td>\n",
       "      <td>대림역 1번 출입구 밑</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-06-16 14:17:53</td>\n",
       "      <td>2140</td>\n",
       "      <td>신림1교 교차로</td>\n",
       "      <td>6</td>\n",
       "      <td>30</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>47</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18639</th>\n",
       "      <td>SPB-18238</td>\n",
       "      <td>2018-06-16 15:25:52</td>\n",
       "      <td>2140</td>\n",
       "      <td>신림1교 교차로</td>\n",
       "      <td>6</td>\n",
       "      <td>2018-06-16 16:16:09</td>\n",
       "      <td>2140</td>\n",
       "      <td>신림1교 교차로</td>\n",
       "      <td>2</td>\n",
       "      <td>49</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22887</th>\n",
       "      <td>SPB-18238</td>\n",
       "      <td>2018-06-16 17:01:22</td>\n",
       "      <td>2140</td>\n",
       "      <td>신림1교 교차로</td>\n",
       "      <td>2</td>\n",
       "      <td>2018-06-16 18:39:24</td>\n",
       "      <td>2140</td>\n",
       "      <td>신림1교 교차로</td>\n",
       "      <td>1</td>\n",
       "      <td>97</td>\n",
       "      <td>...</td>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>18</td>\n",
       "      <td>39</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28700</th>\n",
       "      <td>SPB-18238</td>\n",
       "      <td>2018-06-16 18:43:39</td>\n",
       "      <td>2140</td>\n",
       "      <td>신림1교 교차로</td>\n",
       "      <td>1</td>\n",
       "      <td>2018-06-16 19:21:39</td>\n",
       "      <td>2171</td>\n",
       "      <td>서울대입구역 5번출구</td>\n",
       "      <td>12</td>\n",
       "      <td>37</td>\n",
       "      <td>...</td>\n",
       "      <td>18</td>\n",
       "      <td>43</td>\n",
       "      <td>5</td>\n",
       "      <td>2018</td>\n",
       "      <td>6</td>\n",
       "      <td>16</td>\n",
       "      <td>19</td>\n",
       "      <td>21</td>\n",
       "      <td>5</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           자전거번호                대여일시 대여대여소번호         대여대여소명  대여거치대  \\\n",
       "13282  SPB-18238 2018-06-16 13:01:05    2140       신림1교 교차로      6   \n",
       "14861  SPB-18238 2018-06-16 13:47:21    1975   대림역 1번 출입구 밑      4   \n",
       "18639  SPB-18238 2018-06-16 15:25:52    2140       신림1교 교차로      6   \n",
       "22887  SPB-18238 2018-06-16 17:01:22    2140       신림1교 교차로      2   \n",
       "28700  SPB-18238 2018-06-16 18:43:39    2140       신림1교 교차로      1   \n",
       "\n",
       "                     반납일시 반납대여소번호         반납대여소명  반납거치대  이용시간(분)  ...  대여시  \\\n",
       "13282 2018-06-16 13:46:40    1975   대림역 1번 출입구 밑      4       45  ...   13   \n",
       "14861 2018-06-16 14:17:53    2140       신림1교 교차로      6       30  ...   13   \n",
       "18639 2018-06-16 16:16:09    2140       신림1교 교차로      2       49  ...   15   \n",
       "22887 2018-06-16 18:39:24    2140       신림1교 교차로      1       97  ...   17   \n",
       "28700 2018-06-16 19:21:39    2171    서울대입구역 5번출구     12       37  ...   18   \n",
       "\n",
       "       대여분  대여요일   반납년  반납월  반납일  반납시  반납분  반납요일  같은대여반납소  \n",
       "13282    1     5  2018    6   16   13   46     5    False  \n",
       "14861   47     5  2018    6   16   14   17     5    False  \n",
       "18639   25     5  2018    6   16   16   16     5     True  \n",
       "22887    1     5  2018    6   16   18   39     5     True  \n",
       "28700   43     5  2018    6   16   19   21     5    False  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 가장 많이 대여된 자전거의 대여이력\n",
    "most_bike_df = df.loc[df['자전거번호'] == bike_describe[2]]\n",
    "most_bike_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 같은 자전거가 동일 시간대에 여러번 대여된 이력을 확인 할 수 있다. 좀 더 확인이 필요하다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1122    57\n",
       "2140     4\n",
       "623      4\n",
       "2139     3\n",
       "589      2\n",
       "Name: 대여대여소번호, dtype: int64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_bike_df['대여대여소번호'].value_counts().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-06-18 18:58:02    22\n",
       "2018-06-18 18:49:46    18\n",
       "2018-06-18 18:55:24    15\n",
       "2018-06-18 19:03:29     1\n",
       "2018-06-18 17:58:03     1\n",
       "Name: 대여일시, dtype: int64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_bike_df[most_bike_df['대여대여소번호'] == '1122']['대여일시'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2018-06-18 18:59:27    3\n",
       "2018-06-18 18:59:36    3\n",
       "2018-06-18 18:55:45    3\n",
       "2018-06-18 18:55:58    3\n",
       "2018-06-18 18:56:12    3\n",
       "2018-06-18 18:56:24    3\n",
       "2018-06-18 18:58:21    3\n",
       "2018-06-18 18:58:36    3\n",
       "2018-06-18 18:59:07    3\n",
       "2018-06-18 18:53:48    3\n",
       "2018-06-18 18:55:32    3\n",
       "2018-06-18 19:00:01    3\n",
       "2018-06-18 18:52:28    3\n",
       "2018-06-18 18:52:53    3\n",
       "2018-06-18 18:53:01    3\n",
       "2018-06-18 18:51:10    2\n",
       "2018-06-18 18:58:52    2\n",
       "2018-06-18 18:53:29    2\n",
       "2018-06-18 18:58:11    1\n",
       "2018-06-18 18:58:51    1\n",
       "2018-06-18 18:51:09    1\n",
       "2018-06-18 18:49:05    1\n",
       "2018-06-18 18:53:28    1\n",
       "2018-06-18 20:09:26    1\n",
       "Name: 반납일시, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_bike_df[most_bike_df['대여대여소번호'] == '1122']['반납일시'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 황금내근린공원            57\n",
       " 신림1교 교차로            4\n",
       " 서울시립대 정문 앞          4\n",
       " 서울신성초등학교            3\n",
       " 광진광장 교통섬            2\n",
       "                    ..\n",
       " 신림동걷고싶은문화의거리입구      1\n",
       " 영동대교 북단             1\n",
       " KB국민은행 염창역 지점 앞     1\n",
       " 사루비아 빌딩 앞           1\n",
       " 북성초교                1\n",
       "Name: 대여대여소명, Length: 67, dtype: int64"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "most_bike_df['대여대여소명'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' 신림1교 교차로', ' 대림역 1번 출입구 밑', ' 서울대입구역 5번출구', ' 봉림교 교통섬',\n",
       "       ' 서울신성초등학교', ' 구로디지털단지역 앞', ' 대림역4번출구', ' 삼부르네상스파크빌', ' 가산동 주민센터',\n",
       "       ' 가리봉동주민센터', ' 신림역 5번출구', ' 신림동걷고싶은문화의거리입구', ' 대방역6번출구', ' 산업은행 앞',\n",
       "       ' IFC몰', ' 황금내근린공원', ' 서빙고동 주민센터 앞', ' 북가좌1동 주민센터 ', ' 가좌역 4번출구 앞',\n",
       "       ' 상명대입구', ' 청와대앞길', ' 종로구청 옆', ' 삼각산동 주민센터', ' 정릉현대힐스테이트 1차 후문',\n",
       "       ' 돈암초교 입구', ' 동성중학교 앞', ' 돈암성당 옆', ' 맥스타일 앞', ' 포르투갈 대사관 앞',\n",
       "       ' 종로3가역 2번출구 뒤', ' 청계8가 사거리', ' 안암로터리 버스정류장 앞', ' 서울시립대 정문 앞',\n",
       "       ' 청량리 기업은행 앞', ' 용답역 1번 출구', ' 옥수역 3번출구', ' 청계천 생태교실 앞', ' 베르가모앞',\n",
       "       ' 합정역 7번출구 앞', ' (구)합정동 주민센터', ' 마포구민체육센터 앞', ' 사루비아 빌딩 앞',\n",
       "       ' KB국민은행 염창역 지점 앞', ' 마포역 1번출구 뒤', ' 신촌역(2호선) 7번출구 앞',\n",
       "       ' 광흥창역 2번출구 앞', ' 서강대 정문 건너편', ' 북성초교', ' 충정로역 7번출구 뒤',\n",
       "       ' 종각역 5번출구', ' 세운스퀘어 앞', ' 동대문역 8번 출구', ' 신설동역 10번출구 앞',\n",
       "       ' 도선동 주민센터 앞', ' 신설동역8번출구', ' 영동대교 북단', ' 성수역3번출구',\n",
       "       ' 성수2가1동 공영주차장 인근', ' 광진광장 교통섬', ' 광진메디칼 앞', ' 어린이대공원역6번출구',\n",
       "       ' 동원사거리', ' 세종사이버대학교', ' 군자역 7번출구뒤', ' 군자지하보도 앞', ' 성수아카데미타워 앞',\n",
       "       ' 홈플러스 면목동점'], dtype=object)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rent_station = most_bike_df['대여대여소명'].unique()\n",
    "rent_station"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['신림1교 교차로',\n",
       " '대림역 1번 출입구 밑',\n",
       " '서울대입구역 5번출구',\n",
       " '봉림교 교통섬',\n",
       " '서울신성초등학교',\n",
       " '구로디지털단지역 앞',\n",
       " '대림역4번출구',\n",
       " '삼부르네상스파크빌',\n",
       " '가산동 주민센터',\n",
       " '가리봉동주민센터',\n",
       " '신림역 5번출구',\n",
       " '신림동걷고싶은문화의거리입구',\n",
       " '대방역6번출구',\n",
       " '산업은행 앞',\n",
       " 'IFC몰',\n",
       " '황금내근린공원',\n",
       " '서빙고동 주민센터 앞',\n",
       " '북가좌1동 주민센터',\n",
       " '가좌역 4번출구 앞',\n",
       " '상명대입구',\n",
       " '청와대앞길',\n",
       " '종로구청 옆',\n",
       " '삼각산동 주민센터',\n",
       " '정릉현대힐스테이트 1차 후문',\n",
       " '돈암초교 입구',\n",
       " '동성중학교 앞',\n",
       " '돈암성당 옆',\n",
       " '맥스타일 앞',\n",
       " '포르투갈 대사관 앞',\n",
       " '종로3가역 2번출구 뒤',\n",
       " '청계8가 사거리',\n",
       " '안암로터리 버스정류장 앞',\n",
       " '서울시립대 정문 앞',\n",
       " '청량리 기업은행 앞',\n",
       " '용답역 1번 출구',\n",
       " '옥수역 3번출구',\n",
       " '청계천 생태교실 앞',\n",
       " '베르가모앞',\n",
       " '합정역 7번출구 앞',\n",
       " '(구)합정동 주민센터',\n",
       " '마포구민체육센터 앞',\n",
       " '사루비아 빌딩 앞',\n",
       " 'KB국민은행 염창역 지점 앞',\n",
       " '마포역 1번출구 뒤',\n",
       " '신촌역(2호선) 7번출구 앞',\n",
       " '광흥창역 2번출구 앞',\n",
       " '서강대 정문 건너편',\n",
       " '북성초교',\n",
       " '충정로역 7번출구 뒤',\n",
       " '종각역 5번출구',\n",
       " '세운스퀘어 앞',\n",
       " '동대문역 8번 출구',\n",
       " '신설동역 10번출구 앞',\n",
       " '도선동 주민센터 앞',\n",
       " '신설동역8번출구',\n",
       " '영동대교 북단',\n",
       " '성수역3번출구',\n",
       " '성수2가1동 공영주차장 인근',\n",
       " '광진광장 교통섬',\n",
       " '광진메디칼 앞',\n",
       " '어린이대공원역6번출구',\n",
       " '동원사거리',\n",
       " '세종사이버대학교',\n",
       " '군자역 7번출구뒤',\n",
       " '군자지하보도 앞',\n",
       " '성수아카데미타워 앞',\n",
       " '홈플러스 면목동점']"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 대여소명 앞에 들어간 공백을 제거합니다.\n",
    "bike_move = []\n",
    "for s in rent_station:\n",
    "    bike_move.append(s.strip())\n",
    "    \n",
    "bike_move    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1463, 5)\n"
     ]
    },
    {
     "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",
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       "    }\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>대여소</th>\n",
       "      <th>상태</th>\n",
       "      <th>주소</th>\n",
       "      <th>위도</th>\n",
       "      <th>경도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>광진교 남단 사거리(디지털프라자앞)</td>\n",
       "      <td>운영중</td>\n",
       "      <td>서울특별시 강동구 구천면로 171 376 남단사거리</td>\n",
       "      <td>37.541805</td>\n",
       "      <td>127.124718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>해공공원(천호동)</td>\n",
       "      <td>운영중</td>\n",
       "      <td>서울특별시 강동구 올림픽로 702 265</td>\n",
       "      <td>37.545219</td>\n",
       "      <td>127.125916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>해공도서관앞</td>\n",
       "      <td>운영중</td>\n",
       "      <td>서울특별시 강동구 올림픽로 702 367</td>\n",
       "      <td>37.543915</td>\n",
       "      <td>127.125458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>삼성광나루아파트 버스정류장</td>\n",
       "      <td>운영중</td>\n",
       "      <td>서울특별시 강동구 상암로3길 77 암사동 534</td>\n",
       "      <td>37.553349</td>\n",
       "      <td>127.128860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>롯데캐슬 115동앞</td>\n",
       "      <td>운영중</td>\n",
       "      <td>서울특별시 강동구 양재대로 1665 명일동 304</td>\n",
       "      <td>37.554920</td>\n",
       "      <td>127.142799</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                   대여소   상태                            주소         위도  \\\n",
       "0  광진교 남단 사거리(디지털프라자앞)  운영중  서울특별시 강동구 구천면로 171 376 남단사거리  37.541805   \n",
       "1            해공공원(천호동)  운영중        서울특별시 강동구 올림픽로 702 265  37.545219   \n",
       "2               해공도서관앞  운영중        서울특별시 강동구 올림픽로 702 367  37.543915   \n",
       "3       삼성광나루아파트 버스정류장  운영중    서울특별시 강동구 상암로3길 77 암사동 534  37.553349   \n",
       "4           롯데캐슬 115동앞  운영중   서울특별시 강동구 양재대로 1665 명일동 304  37.554920   \n",
       "\n",
       "           경도  \n",
       "0  127.124718  \n",
       "1  127.125916  \n",
       "2  127.125458  \n",
       "3  127.128860  \n",
       "4  127.142799  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations = pd.read_csv('data/bike_rent_station.csv')\n",
    "print(stations.shape)\n",
    "stations.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(67, 1)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>대여소</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>신림1교 교차로</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>대림역 1번 출입구 밑</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울대입구역 5번출구</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>봉림교 교통섬</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울신성초등학교</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            대여소\n",
       "0      신림1교 교차로\n",
       "1  대림역 1번 출입구 밑\n",
       "2   서울대입구역 5번출구\n",
       "3       봉림교 교통섬\n",
       "4      서울신성초등학교"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_move_df = pd.DataFrame(bike_move)\n",
    "bike_move_df.columns = ['대여소']\n",
    "print(bike_move_df.shape)\n",
    "bike_move_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(67, 5)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_move_df = bike_move_df.merge(stations, left_on='대여소', right_on='대여소', how='left')\n",
    "bike_move_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>대여소</th>\n",
       "      <th>상태</th>\n",
       "      <th>주소</th>\n",
       "      <th>위도</th>\n",
       "      <th>경도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>신림1교 교차로</td>\n",
       "      <td>운영중</td>\n",
       "      <td>서울특별시 관악구 신림로 270 관악구 신림동 808-263</td>\n",
       "      <td>37.478428</td>\n",
       "      <td>126.931862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>대림역 1번 출입구 밑</td>\n",
       "      <td>운영중</td>\n",
       "      <td>서울특별시 구로구 도림천로 351 대림역 1번 출입구 밑</td>\n",
       "      <td>37.492580</td>\n",
       "      <td>126.894737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울대입구역 5번출구</td>\n",
       "      <td>운영중</td>\n",
       "      <td>서울특별시 관악구 남부순환로 1817 서울대입구역 5번출구</td>\n",
       "      <td>37.481548</td>\n",
       "      <td>126.952003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>봉림교 교통섬</td>\n",
       "      <td>운영중</td>\n",
       "      <td>서울특별시 관악구 관천로 52 관악구 신림동 1433-150</td>\n",
       "      <td>37.484230</td>\n",
       "      <td>126.926392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울신성초등학교</td>\n",
       "      <td>운영중</td>\n",
       "      <td>서울특별시 관악구 신림로 114 서울신성초등학교</td>\n",
       "      <td>37.471165</td>\n",
       "      <td>126.937019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            대여소   상태                                 주소         위도          경도\n",
       "0      신림1교 교차로  운영중  서울특별시 관악구 신림로 270 관악구 신림동 808-263  37.478428  126.931862\n",
       "1  대림역 1번 출입구 밑  운영중    서울특별시 구로구 도림천로 351 대림역 1번 출입구 밑  37.492580  126.894737\n",
       "2   서울대입구역 5번출구  운영중   서울특별시 관악구 남부순환로 1817 서울대입구역 5번출구  37.481548  126.952003\n",
       "3       봉림교 교통섬  운영중  서울특별시 관악구 관천로 52 관악구 신림동 1433-150  37.484230  126.926392\n",
       "4      서울신성초등학교  운영중         서울특별시 관악구 신림로 114 서울신성초등학교  37.471165  126.937019"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_move_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "대여소    0\n",
       "상태     2\n",
       "주소     2\n",
       "위도     2\n",
       "경도     2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bike_move_df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 가장 많이 대여된 자전거가 거쳐간 대여소"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>대여소</th>\n",
       "      <th>상태</th>\n",
       "      <th>주소</th>\n",
       "      <th>위도</th>\n",
       "      <th>경도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>정릉현대힐스테이트 1차 후문</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>군자역 7번출구뒤</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                대여소   상태   주소  위도  경도\n",
       "23  정릉현대힐스테이트 1차 후문  NaN  NaN NaN NaN\n",
       "63        군자역 7번출구뒤  NaN  NaN NaN NaN"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 정보가 없는 대여소\n",
    "bike_move_df.loc[bike_move_df['경도'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><span style=\"color:#565656\">Make this Notebook Trusted to load map: File -> Trust Notebook</span><iframe src=\"about:blank\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" 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      ],
      "text/plain": [
       "<folium.folium.Map at 0x20ad54e1100>"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 지도 표현을 위해 folium 을 import 한다.\n",
    "# 데이터 프레임에서 위도와 경도의 평균값을 구한다. 이 값으로 지도의 중심이 될 좌표를 정한다.\n",
    "# zoom_start는 지도를 얼마나 확대해서 볼것인지다.\n",
    "# tiles는 지도의 스타일이다.\n",
    "import folium \n",
    "\n",
    "geo_df = bike_move_df\n",
    "map = folium.Map(location=[geo_df['위도'].mean(), geo_df['경도'].mean()], zoom_start=12, tiles='Stamen Toner')\n",
    "\n",
    "for n in geo_df.index:\n",
    "    if geo_df.loc[n, '위도'] > 0 and geo_df.loc[n, '경도'] > 0 :\n",
    "        loc_name = geo_df.loc[n, '대여소']\n",
    "\n",
    "        icon_color = 'red'\n",
    "        folium.Circle(\n",
    "            location=[geo_df.loc[n, '위도'], geo_df.loc[n, '경도']],\n",
    "            popup=loc_name,\n",
    "            radius=50,\n",
    "            color=icon_color,\n",
    "            fill=True,\n",
    "            fill_color=icon_color\n",
    "        ).add_to(map)\n",
    "    \n",
    "map"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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