서울시 따릉이 API 활용

 

 

 

1. 따릉이 API¶

• https://www.bikeseoul.com/app/station/getStationRealtimeStatus.do

 

In [2]:

import requests
import folium
import json
import pandas as pd
import warnings
warnings.filterwarnings('ignore')

 

 

1-1. 데이터 요청하기¶

 

In [5]:

targetSite = 'https://www.bikeseoul.com/app/station/getStationRealtimeStatus.do'
request  = requests.post(targetSite, data={'stationGrpSeq':'ALL'})
print(request)
# print(request.text)

 

 

<Response [200]>

 

 

1-2. json 데이터 처리하기¶

• json.loads(): json 타입의 문자열 데이터를 파이썬에서 처리할 수 있도록 변환(딕셔너리로 변환)

 

In [7]:

bike_json = json.loads(request.text)
print(bike_json)
print(type(bike_json))

 

 

{'stationImgPath': '/nas_link/spb/attachFiles/file_admin/basePath', 'appUserSessionVO': {'lang': 'LAG_001', 'regDttm': None, 'encPwd': None, 'usrClsCd': None, 'usrDeviceId': None, 'loginId': None, 'mbId': None, 'mpnLostYn': None, 'snsType': None, 'usrSeq': None, 'voucherEndDttm': None, 'voucherSeq': None, 'usrType': None, 'usrMpnNo': None, 'appOsType': None, 'orgType': None, 'snsId': None, 'viewFlg': 'list', 'usrBirthDate': None, 'sexCd': None, 'rentEncPwd': None, 'telecomClsCd': None, 'authCiVal': None, 'authClsCd': None, 'mbTelNo': None, 'mbEmailName': None, 'mbPostNo': None, 'mbAddr1': None, 'mbAddr2': None, 'parentSexCd': None, 'parentBirthDate': None, 'parentMpnNo': None, 'emailSendAgreeYn': None, 'lastLoginDttm': None, 'mbWgt': None, 'langClsCd': None, 'leaveYn': None, 'leaveReasonCd': None, 'leaveDttm': None, 'mbInfoColecAgreeDttm': None, 'mpnLostDttm': None, 'pagingYn': None, 'usrIp': None, 'partyVoucherSeq': None, 'elecVoucherSeq': None, 'requestSeq': None, 'usrDeviceType': None, 'authDiVal': None, 'snsEmail': None, 'tCardYn': None, 'mCardYn': None, 'mainType': None, 'pageSize': 0, 'currentPageNo': 1, 'recordCountPerPage': 0, 'totalRecordCount': 0, 'firstRecordIndex': 0, 'searchValue': None, 'searchParameter': None, 'searchDate': None, 'searchStartDate': None, 'searchEndDate': None, 'mode': None}, 'loginYn': 'N', 'realtimeList': [{'stationName': '102. 망원역 1번출구 앞', 'stationImgFileName': '', 'stationId': 'ST-4', 'stationLongitude': '126.91062927', 'stationLatitude': '37.55564880', 'rackTotCnt': '15', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '5', 'parkingELECBikeCnt': '11', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '103. 망원역 2번출구 앞', 'stationImgFileName': '', 'stationId': 'ST-5', 'stationLongitude': '126.91083527', 'stationLatitude': '37.55495071', 'rackTotCnt': '14', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '2', 'parkingELECBikeCnt': '0', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '104. 합정역 1번출구 앞', 'stationImgFileName': '', 'stationId': 'ST-6', 'stationLongitude': '126.91508484', 'stationLatitude': '37.55073929', 'rackTotCnt': '13', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '3', 'parkingELECBikeCnt': '0', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '105. 합정역 5번출구 앞', 'stationImgFileName': '', 'stationId': 'ST-7', 'stationLongitude': '126.91482544', 'stationLatitude': '37.55000687', 'rackTotCnt': '5', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '0', 'parkingELECBikeCnt': '0', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '106. 합정역 7번출구 앞', 'stationImgFileName': '', 'stationId': 'ST-8', 'stationLongitude': '126.91282654', 'stationLatitude': '37.54864502', 'rackTotCnt': '12', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '0', 'parkingELECBikeCnt': '0', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '107. 신한은행 서교동지점', 'stationImgFileName': '', 'stationId': 'ST-9', 'stationLongitude': '126.91850281', 'stationLatitude': '37.55751038', 'rackTotCnt': '5', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '18', 'parkingELECBikeCnt': '2', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '108. 서교동 사거리', 'stationImgFileName': '', 'stationId': 'ST-10', 'stationLongitude': '126.91861725', 'stationLatitude': '37.55274582', 'rackTotCnt': '12', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '16', 'parkingELECBikeCnt': '0', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '111. 상수역 2번출구 앞', 'stationImgFileName': '', 'stationId': 'ST-15', 'stationLongitude': '126.92353058', 'stationLatitude': '37.54787064', 'rackTotCnt': '10', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '0', 'parkingELECBikeCnt': '0', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '112. 극동방송국 앞', 'stationImgFileName': '', 'stationId': 'ST-16', 'stationLongitude': '126.92320251', 'stationLatitude': '37.54920197', 'rackTotCnt': '10', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '2', 'parkingELECBikeCnt': '0', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '113. 홍대입구역 2번출구 앞', 'stationImgFileName': '', 'stationId': 'ST-18', 'stationLongitude': '126.92382050', 'stationLatitude': '37.55743790', 'rackTotCnt': '25', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '51', 'parkingELECBikeCnt': '11', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '114. 홍대입구역 8번출구 앞', 'stationImgFileName': '', 'stationId': 'ST-20', 'stationLongitude': '126.92442322', 'stationLatitude': '37.55706024', 'rackTotCnt': '15', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '11', 'parkingELECBikeCnt': '1', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '115. 마스타 빌딩 앞', 'stationImgFileName': '', 'stationId': 'ST-12', 'stationLongitude': '126.92711639', 'stationLatitude': '37.55893326', 'rackTotCnt': '15', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '9', 'parkingELECBikeCnt': '3', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '117. 홍은사거리', 'stationImgFileName': '', 'stationId': 'ST-17', 'stationLongitude': '126.94132996', 'stationLatitude': '37.59115982', 'rackTotCnt': '23', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '8', 'parkingELECBikeCnt': '4', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '118. 광흥창역 2번출구 앞', 'stationImgFileName': '', 'stationId': 'ST-19', 'stationLongitude': '126.93176270', 'stationLatitude': '37.54773331', 'rackTotCnt': '9', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '0', 'parkingELECBikeCnt': '0', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '119. 서강나루 공원', 'stationImgFileName': '', 'stationId': 'ST-21', 'stationLongitude': '126.93146515', 'stationLatitude': '37.54520798', 'rackTotCnt': '10', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '0', 'parkingELECBikeCnt': '5', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '120. 신수동 사거리', 'stationImgFileName': '', 'stationId': 'ST-22', 'stationLongitude': '126.93411255', 'stationLatitude': '37.54524231', 'rackTotCnt': '5', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '1', 'parkingELECBikeCnt': '0', 'stationSeCd': 'RAK_002', 'mode': None}, {'stationName': '121. 마포소방서 앞', 'stationImgFileName': '', 'stationId': 'ST-23', 'stationLongitude': '126.93317413', 'stationLatitude': '37.54976654', 'rackTotCnt': '15', 'parkingBikeTotCnt': '0', 'parkingQRBikeCnt': '1', 'parkingELECBikeCnt': '0', 'stationSeCd': 'RAK_002', 'mode': None}
<class 'dict'>

 

 

1-3. 딕셔너리 타입의 데이터를 데이터프레임으로 변환하기¶

• json_normalize(): 딕셔너리 타입의 데이터를 판다스 데이터프레임으로 변환

 

In [9]:

bike_df = pd.json_normalize(bike_json, 'realtimeList')
bike_df

 

Out[9]:

	stationName	stationImgFileName	stationId	stationLongitude	stationLatitude	rackTotCnt	parkingBikeTotCnt	parkingQRBikeCnt	parkingELECBikeCnt	stationSeCd	mode
0	102. 망원역 1번출구 앞		ST-4	126.91062927	37.55564880	15	0	5	11	RAK_002	None
1	103. 망원역 2번출구 앞		ST-5	126.91083527	37.55495071	14	0	2	0	RAK_002	None
2	104. 합정역 1번출구 앞		ST-6	126.91508484	37.55073929	13	0	3	0	RAK_002	None
3	105. 합정역 5번출구 앞		ST-7	126.91482544	37.55000687	5	0	0	0	RAK_002	None
4	106. 합정역 7번출구 앞		ST-8	126.91282654	37.54864502	12	0	0	0	RAK_002	None
...	...	...	...	...	...	...	...	...	...	...	...
2730	6058. 서울도시건축전시관 옆		ST-3297	126.97684479	37.56653976	10	0	0	0	RAK_002	None
2731	6058. 서울도시건축전시관 옆		ST-3297	126.97684479	37.56653976	10	0	0	0	RAK_002	None
2732	6171. 월드빌딩 앞		ST-3276	126.83743286	37.54098129	12	0	4	0	RAK_002	None
2733	6172. 가양5단지아파트		ST-3281	126.85464478	37.56447983	10	0	12	0	RAK_002	None
2734	6173. 서울자동차운전전문학원		ST-3284	126.82106018	37.54613495	10	0	18	0	RAK_002	None

2735 rows × 11 columns

 

In [10]:

bike_df.columns

 

Out[10]:

Index(['stationName', 'stationImgFileName', 'stationId', 'stationLongitude',
       'stationLatitude', 'rackTotCnt', 'parkingBikeTotCnt',
       'parkingQRBikeCnt', 'parkingELECBikeCnt', 'stationSeCd', 'mode'],
      dtype='object')

 

 

• stationName: 대여소 이름
• stationId: 고유한 대여소 번호
• stationLongitude: 대여소 경도
• stationLatitude: 대여소 위도
• rackTotCnt: 주차 가능한 전체 자전거 대수
• parkingBikeTotCnt: 주차된 따릉이 총 대수
• parkingQRBikeCnt: 주차된 따릉이 QR형 총 대수
• parkingELECBikeCnt: 주차된 새싹 따릉이 총 대수

 

In [11]:

bike_df_map = bike_df[['stationName', 'stationId', 'stationLongitude', 'stationLatitude',
                       'rackTotCnt', 'parkingBikeTotCnt', 'parkingQRBikeCnt', 'parkingELECBikeCnt']]
bike_df_map

 

Out[11]:

	stationName	stationId	stationLongitude	stationLatitude	rackTotCnt	parkingBikeTotCnt	parkingQRBikeCnt	parkingELECBikeCnt
0	102. 망원역 1번출구 앞	ST-4	126.91062927	37.55564880	15	0	5	11
1	103. 망원역 2번출구 앞	ST-5	126.91083527	37.55495071	14	0	2	0
2	104. 합정역 1번출구 앞	ST-6	126.91508484	37.55073929	13	0	3	0
3	105. 합정역 5번출구 앞	ST-7	126.91482544	37.55000687	5	0	0	0
4	106. 합정역 7번출구 앞	ST-8	126.91282654	37.54864502	12	0	0	0
...	...	...	...	...	...	...	...	...
2730	6058. 서울도시건축전시관 옆	ST-3297	126.97684479	37.56653976	10	0	0	0
2731	6058. 서울도시건축전시관 옆	ST-3297	126.97684479	37.56653976	10	0	0	0
2732	6171. 월드빌딩 앞	ST-3276	126.83743286	37.54098129	12	0	4	0
2733	6172. 가양5단지아파트	ST-3281	126.85464478	37.56447983	10	0	12	0
2734	6173. 서울자동차운전전문학원	ST-3284	126.82106018	37.54613495	10	0	18	0

2735 rows × 8 columns

 

In [12]:

# 위도, 경도 -> float 변환
bike_df_map['stationLongitude'] = bike_df_map['stationLongitude'].astype(float)
bike_df_map['stationLatitude'] = bike_df_map['stationLatitude'].astype(float)

# 주차할 수 있는 자전거 대수, 주차된 자전거 총 대수, 주차된 QR자전거 총 대수, 주차된 새싹 자전거 총 대수 -> int
bike_df_map['rackTotCnt'] = bike_df_map['rackTotCnt'].astype(int)
bike_df_map['parkingBikeTotCnt'] = bike_df_map['parkingBikeTotCnt'].astype(int)
bike_df_map['parkingQRBikeCnt'] = bike_df_map['parkingQRBikeCnt'].astype(int)
bike_df_map['parkingELECBikeCnt'] = bike_df_map['parkingELECBikeCnt'].astype(int)

# 파생변수 만들기[total]
# 따릉이 + QR + 새싹
bike_df_map['total'] = bike_df_map['parkingBikeTotCnt'] + bike_df_map['parkingQRBikeCnt'] + bike_df_map['parkingELECBikeCnt']

 

In [16]:

bike_df_map.dtypes

 

Out[16]:

stationName            object
stationId              object
stationLongitude      float64
stationLatitude       float64
rackTotCnt              int64
parkingBikeTotCnt       int64
parkingQRBikeCnt        int64
parkingELECBikeCnt      int64
total                   int64
dtype: object

 

In [17]:

bike_df_map.head()

 

Out[17]:

	stationName	stationId	stationLongitude	stationLatitude	rackTotCnt	parkingBikeTotCnt	parkingQRBikeCnt	parkingELECBikeCnt	total
0	102. 망원역 1번출구 앞	ST-4	126.910629	37.555649	15	0	5	11	16
1	103. 망원역 2번출구 앞	ST-5	126.910835	37.554951	14	0	2	0	2
2	104. 합정역 1번출구 앞	ST-6	126.915085	37.550739	13	0	3	0	3
3	105. 합정역 5번출구 앞	ST-7	126.914825	37.550007	5	0	0	0	0
4	106. 합정역 7번출구 앞	ST-8	126.912827	37.548645	12	0	0	0	0

 

In [18]:

bike_df_map.shape

 

Out[18]:

(2735, 9)

 

In [20]:

# {정류소명} 일반:{}대, QR:{}대, 새싹:{}대, 총:{}대
bike_map = folium.Map(location=[bike_df_map['stationLatitude'].mean(),
                                bike_df_map['stationLongitude'].mean()],
                                zoom_start=12)
for index, data in bike_df_map.iterrows():
    popup_str = '{} 일반:{}대, QR:{}대, 새싹:{}대, 총:{}대'.format(
        data['stationName'], data['parkingBikeTotCnt'], data['parkingQRBikeCnt'],
        data['parkingELECBikeCnt'], data['total']
    )
    popup = folium.Popup(popup_str, max_width=600)
    folium.Marker(location=[data['stationLatitude'], data['stationLongitude']],
                  popup=popup).add_to(bike_map)
bike_map