서울특별시 부동산 실거래 API 활용 불러오기

2023년-06월-12일 4 분 소요

1. 서울특별시 부동산 XML 형태 가져오기

1-1. 라이브러리 설치

# 라이브러리 가져오기
import lxml
import requests
from bs4 import BeautifulSoup
import pandas as pd
import time
import math

1-2. 인증키와 URL 가져오기

service_key = ''
url = f'http://openapi.seoul.go.kr:8088/{service_key}/xml/tbLnOpendataRtmsV/1/5/'
print(url)

http://openapi.seoul.go.kr:8088/596a6f71646a616e37305945544979/xml/tbLnOpendataRtmsV/1/5/

1-3. 데이터 요청

req = requests.get(url)
req.content

b'<?xml version="1.0" encoding="UTF-8"?>\n<tbLnOpendataRtmsV>\n<list_total_count>2664624</list_total_count>\n<RESULT>\n<CODE>INFO-000</CODE>\n<MESSAGE>\xec\xa0\x95\xec\x83\x81 \xec\xb2\x98\xeb\xa6\xac\xeb\x90\x98\xec\x97\x88\xec\x8a\xb5\xeb\x8b\x88\xeb\x8b\xa4</MESSAGE>\n</RESULT>\n<row>\n<ACC_YEAR>2023</ACC_YEAR>\n<SGG_CD>11500</SGG_CD>\n<SGG_NM>\xea\xb0\x95\xec\x84\x9c\xea\xb5\xac</SGG_NM>\n<BJDONG_CD>10300</BJDONG_CD>\n<BJDONG_NM>\xed\x99\x94\xea\xb3\xa1\xeb\x8f\x99</BJDONG_NM>\n<LAND_GBN>1</LAND_GBN>\n<LAND_GBN_NM>\xeb\x8c\x80\xec\xa7\x80</LAND_GBN_NM>\n<BONBEON>0851</BONBEON>\n<BUBEON>0033</BUBEON>\n<BLDG_NM>SK\xed\x8c\x8c\xed\x81\xac\xeb\xa6\xac\xec\x9b\x80(851-33)</BLDG_NM>\n<DEAL_YMD>20230609</DEAL_YMD>\n<OBJ_AMT>29500</OBJ_AMT>\n<BLDG_AREA>26.12</BLDG_AREA>\n<TOT_AREA>15.910000</TOT_AREA>\n<FLOOR>3</FLOOR>\n<RIGHT_GBN/>\n<CNTL_YMD/>\n<BUILD_YEAR>2022</BUILD_YEAR>\n<HOUSE_TYPE>\xec\x97\xb0\xeb\xa6\xbd\xeb\x8b\xa4\xec\x84\xb8\xeb\x8c\x80</HOUSE_TYPE>\n<REQ_GBN>\xec\xa7\x81\xea\xb1\xb0\xeb\x9e\x98</REQ_GBN>\n<RDEALER_LAWDNM/>\n</row>\n<row>\n<ACC_YEAR>2023</ACC_YEAR>\n<SGG_CD>11215</SGG_CD>\n<SGG_NM>\xea\xb4\x91\xec\xa7\x84\xea\xb5\xac</SGG_NM>\n<BJDONG_CD>10700</BJDONG_CD>\n<BJDONG_NM>\xed\x99\x94\xec\x96\x91\xeb\x8f\x99</BJDONG_NM>\n<LAND_GBN>1</LAND_GBN>\n<LAND_GBN_NM>\xeb\x8c\x80\xec\xa7\x80</LAND_GBN_NM>\n<BONBEON>0111</BONBEON>\n<BUBEON>0012</BUBEON>\n<BLDG_NM/>\n<DEAL_YMD>20230609</DEAL_YMD>\n<OBJ_AMT>26500</OBJ_AMT>\n<BLDG_AREA>16.88</BLDG_AREA>\n<TOT_AREA>29.900000</TOT_AREA>\n<FLOOR>3</FLOOR>\n<RIGHT_GBN/>\n<CNTL_YMD/>\n<BUILD_YEAR>2021</BUILD_YEAR>\n<HOUSE_TYPE>\xec\x98\xa4\xed\x94\xbc\xec\x8a\xa4\xed\x85\x94</HOUSE_TYPE>\n<REQ_GBN>\xec\xa4\x91\xea\xb0\x9c\xea\xb1\xb0\xeb\x9e\x98</REQ_GBN>\n<RDEALER_LAWDNM>\xec\x84\x9c\xec\x9a\xb8 \xec\xa4\x91\xea\xb5\xac</RDEALER_LAWDNM>\n</row>\n<row>\n<ACC_YEAR>2023</ACC_YEAR>\n<SGG_CD>11500</SGG_CD>\n<SGG_NM>\xea\xb0\x95\xec\x84\x9c\xea\xb5\xac</SGG_NM>\n<BJDONG_CD>10300</BJDONG_CD>\n<BJDONG_NM>\xed\x99\x94\xea\xb3\xa1\xeb\x8f\x99</BJDONG_NM>\n<LAND_GBN>1</LAND_GBN>\n<LAND_GBN_NM>\xeb\x8c\x80\xec\xa7\x80</LAND_GBN_NM>\n<BONBEON>0105</BONBEON>\n<BUBEON>0020</BUBEON>\n<BLDG_NM>(105-20)</BLDG_NM>\n<DEAL_YMD>20230609</DEAL_YMD>\n<OBJ_AMT>24000</OBJ_AMT>\n<BLDG_AREA>62.64</BLDG_AREA>\n<TOT_AREA>36.690000</TOT_AREA>\n<FLOOR>3</FLOOR>\n<RIGHT_GBN/>\n<CNTL_YMD/>\n<BUILD_YEAR>2011</BUILD_YEAR>\n<HOUSE_TYPE>\xec\x97\xb0\xeb\xa6\xbd\xeb\x8b\xa4\xec\x84\xb8\xeb\x8c\x80</HOUSE_TYPE>\n<REQ_GBN>\xec\xa4\x91\xea\xb0\x9c\xea\xb1\xb0\xeb\x9e\x98</REQ_GBN>\n<RDEALER_LAWDNM>\xec\x84\x9c\xec\x9a\xb8 \xea\xb0\x95\xec\x84\x9c\xea\xb5\xac</RDEALER_LAWDNM>\n</row>\n<row>\n<ACC_YEAR>2023</ACC_YEAR>\n<SGG_CD>11500</SGG_CD>\n<SGG_NM>\xea\xb0\x95\xec\x84\x9c\xea\xb5\xac</SGG_NM>\n<BJDONG_CD>10300</BJDONG_CD>\n<BJDONG_NM>\xed\x99\x94\xea\xb3\xa1\xeb\x8f\x99</BJDONG_NM>\n<LAND_GBN>1</LAND_GBN>\n<LAND_GBN_NM>\xeb\x8c\x80\xec\xa7\x80</LAND_GBN_NM>\n<BONBEON>0851</BONBEON>\n<BUBEON>0033</BUBEON>\n<BLDG_NM>SK\xed\x8c\x8c\xed\x81\xac\xeb\xa6\xac\xec\x9b\x80(851-33)</BLDG_NM>\n<DEAL_YMD>20230609</DEAL_YMD>\n<OBJ_AMT>30500</OBJ_AMT>\n<BLDG_AREA>28.54</BLDG_AREA>\n<TOT_AREA>17.060000</TOT_AREA>\n<FLOOR>2</FLOOR>\n<RIGHT_GBN/>\n<CNTL_YMD/>\n<BUILD_YEAR>2022</BUILD_YEAR>\n<HOUSE_TYPE>\xec\x97\xb0\xeb\xa6\xbd\xeb\x8b\xa4\xec\x84\xb8\xeb\x8c\x80</HOUSE_TYPE>\n<REQ_GBN>\xec\xa7\x81\xea\xb1\xb0\xeb\x9e\x98</REQ_GBN>\n<RDEALER_LAWDNM/>\n</row>\n<row>\n<ACC_YEAR>2023</ACC_YEAR>\n<SGG_CD>11470</SGG_CD>\n<SGG_NM>\xec\x96\x91\xec\xb2\x9c\xea\xb5\xac</SGG_NM>\n<BJDONG_CD>10300</BJDONG_CD>\n<BJDONG_NM>\xec\x8b\xa0\xec\x9b\x94\xeb\x8f\x99</BJDONG_NM>\n<LAND_GBN>1</LAND_GBN>\n<LAND_GBN_NM>\xeb\x8c\x80\xec\xa7\x80</LAND_GBN_NM>\n<BONBEON>0410</BONBEON>\n<BUBEON>0026</BUBEON>\n<BLDG_NM>\xed\x9c\xb4\xeb\xa8\xbc\xed\x8c\x8c\xed\x81\xac\xed\x83\x80\xec\x9a\xb4(410-26)</BLDG_NM>\n<DEAL_YMD>20230609</DEAL_YMD>\n<OBJ_AMT>16000</OBJ_AMT>\n<BLDG_AREA>29.2</BLDG_AREA>\n<TOT_AREA>21.960000</TOT_AREA>\n<FLOOR>2</FLOOR>\n<RIGHT_GBN/>\n<CNTL_YMD/>\n<BUILD_YEAR>2011</BUILD_YEAR>\n<HOUSE_TYPE>\xec\x97\xb0\xeb\xa6\xbd\xeb\x8b\xa4\xec\x84\xb8\xeb\x8c\x80</HOUSE_TYPE>\n<REQ_GBN>\xec\xa4\x91\xea\xb0\x9c\xea\xb1\xb0\xeb\x9e\x98</REQ_GBN>\n<RDEALER_LAWDNM>\xec\x84\x9c\xec\x9a\xb8 \xec\x96\x91\xec\xb2\x9c\xea\xb5\xac</RDEALER_LAWDNM>\n</row>\n</tbLnOpendataRtmsV>\n'

soup = BeautifulSoup(req.content, "lxml") # XML 파싱
print(soup)

<?xml version="1.0" encoding="UTF-8"?><html><body><tblnopendatartmsv>
<list_total_count>2664624</list_total_count>
<result>
<code>INFO-000</code>
<message>정상 처리되었습니다</message>
</result>
<row>
<acc_year>2023</acc_year>
<sgg_cd>11500</sgg_cd>
<sgg_nm>강서구</sgg_nm>
<bjdong_cd>10300</bjdong_cd>
<bjdong_nm>화곡동</bjdong_nm>
<land_gbn>1</land_gbn>
<land_gbn_nm>대지</land_gbn_nm>
<bonbeon>0851</bonbeon>
<bubeon>0033</bubeon>
<bldg_nm>SK파크리움(851-33)</bldg_nm>
<deal_ymd>20230609</deal_ymd>
<obj_amt>29500</obj_amt>
<bldg_area>26.12</bldg_area>
<tot_area>15.910000</tot_area>
<floor>3</floor>
<right_gbn></right_gbn>
<cntl_ymd></cntl_ymd>
<build_year>2022</build_year>
<house_type>연립다세대</house_type>
<req_gbn>직거래</req_gbn>
<rdealer_lawdnm></rdealer_lawdnm>
</row>
<row>
<acc_year>2023</acc_year>
<sgg_cd>11215</sgg_cd>
<sgg_nm>광진구</sgg_nm>
<bjdong_cd>10700</bjdong_cd>
<bjdong_nm>화양동</bjdong_nm>
<land_gbn>1</land_gbn>
<land_gbn_nm>대지</land_gbn_nm>
<bonbeon>0111</bonbeon>
<bubeon>0012</bubeon>
<bldg_nm></bldg_nm>
<deal_ymd>20230609</deal_ymd>
<obj_amt>26500</obj_amt>
<bldg_area>16.88</bldg_area>
<tot_area>29.900000</tot_area>
<floor>3</floor>
<right_gbn></right_gbn>
<cntl_ymd></cntl_ymd>
<build_year>2021</build_year>
<house_type>오피스텔</house_type>
<req_gbn>중개거래</req_gbn>
<rdealer_lawdnm>서울 중구</rdealer_lawdnm>
</row>
<row>
<acc_year>2023</acc_year>
<sgg_cd>11500</sgg_cd>
<sgg_nm>강서구</sgg_nm>
<bjdong_cd>10300</bjdong_cd>
<bjdong_nm>화곡동</bjdong_nm>
<land_gbn>1</land_gbn>
<land_gbn_nm>대지</land_gbn_nm>
<bonbeon>0105</bonbeon>
<bubeon>0020</bubeon>
<bldg_nm>(105-20)</bldg_nm>
<deal_ymd>20230609</deal_ymd>
<obj_amt>24000</obj_amt>
<bldg_area>62.64</bldg_area>
<tot_area>36.690000</tot_area>
<floor>3</floor>
<right_gbn></right_gbn>
<cntl_ymd></cntl_ymd>
<build_year>2011</build_year>
<house_type>연립다세대</house_type>
<req_gbn>중개거래</req_gbn>
<rdealer_lawdnm>서울 강서구</rdealer_lawdnm>
</row>
<row>
<acc_year>2023</acc_year>
<sgg_cd>11500</sgg_cd>
<sgg_nm>강서구</sgg_nm>
<bjdong_cd>10300</bjdong_cd>
<bjdong_nm>화곡동</bjdong_nm>
<land_gbn>1</land_gbn>
<land_gbn_nm>대지</land_gbn_nm>
<bonbeon>0851</bonbeon>
<bubeon>0033</bubeon>
<bldg_nm>SK파크리움(851-33)</bldg_nm>
<deal_ymd>20230609</deal_ymd>
<obj_amt>30500</obj_amt>
<bldg_area>28.54</bldg_area>
<tot_area>17.060000</tot_area>
<floor>2</floor>
<right_gbn></right_gbn>
<cntl_ymd></cntl_ymd>
<build_year>2022</build_year>
<house_type>연립다세대</house_type>
<req_gbn>직거래</req_gbn>
<rdealer_lawdnm></rdealer_lawdnm>
</row>
<row>
<acc_year>2023</acc_year>
<sgg_cd>11470</sgg_cd>
<sgg_nm>양천구</sgg_nm>
<bjdong_cd>10300</bjdong_cd>
<bjdong_nm>신월동</bjdong_nm>
<land_gbn>1</land_gbn>
<land_gbn_nm>대지</land_gbn_nm>
<bonbeon>0410</bonbeon>
<bubeon>0026</bubeon>
<bldg_nm>휴먼파크타운(410-26)</bldg_nm>
<deal_ymd>20230609</deal_ymd>
<obj_amt>16000</obj_amt>
<bldg_area>29.2</bldg_area>
<tot_area>21.960000</tot_area>
<floor>2</floor>
<right_gbn></right_gbn>
<cntl_ymd></cntl_ymd>
<build_year>2011</build_year>
<house_type>연립다세대</house_type>
<req_gbn>중개거래</req_gbn>
<rdealer_lawdnm>서울 양천구</rdealer_lawdnm>
</row>
</tblnopendatartmsv>
</body></html>


c:\Users\YONSAI\anaconda4\lib\site-packages\bs4\builder\__init__.py:545: XMLParsedAsHTMLWarning:

It looks like you're parsing an XML document using an HTML parser. If this really is an HTML document (maybe it's XHTML?), you can ignore or filter this warning. If it's XML, you should know that using an XML parser will be more reliable. To parse this document as XML, make sure you have the lxml package installed, and pass the keyword argument `features="xml"` into the BeautifulSoup constructor.

1-4 데이터 가공

years            = soup.find_all('acc_year')         # 접수년월
sgg_cds          = soup.find_all('sgg_cd')           # 자치구코드
sgg_nms          = soup.find_all('sgg_nm')           # 자치구명
bjdong_cds       = soup.find_all('bjdong_cd')        # 법정동코드
bjdong_nms       = soup.find_all('bjdong_nm')        # 법정동명
land_gbns        = soup.find_all('land_gbn')         # 지번구분
land_gbn_nms     = soup.find_all('land_gbn_nm')      # 지번구분명
land_gbn_nms     = soup.find_all('land_gbn_nm')      # 지번구분명
bonbeons         = soup.find_all('bonbeon')          # 본번
bubeons          = soup.find_all('bubeon')           # 부번
bldg_nms         = soup.find_all('bldg_nm')          # 건물명
deal_ymds        = soup.find_all('deal_ymd')         # 계약일
obj_amts         = soup.find_all('obj_amt')          # 물건금액(만원)
bldg_areas       = soup.find_all('bldg_area')        # 건물면적(㎡)
tot_areas        = soup.find_all('tot_area')         # 토지면적(㎡)
floors           = soup.find_all('floor')            # 층
right_gbns       = soup.find_all('right_gbn')        # 권리구분
cntl_ymds        = soup.find_all('cntl_ymd')         # 취소일
build_years      = soup.find_all('build_years')      # 건축년도
house_types      = soup.find_all('house_type')       # 건물용도
req_gbn          = soup.find_all('req_gbn')          # 신고구분
rdealer_lawdnms  = soup.find_all('rdealer_lawdnm')   # 신고한 개업공인중개사 시군구명

# 반복문 활용
year_list           = []
sgg_cd_list         = []
bldg_nm_list        = []
obj_amt_list        = []
house_type_list     = []
rdealer_lawdnm_list = []

for year, sgg_cd, bldg_nm, obj_amt, house_type, rdealer_lawdnm in zip(years, sgg_cds, bldg_nms, obj_amts, house_types, rdealer_lawdnms):
  year_list.append(year.get_text())
  sgg_cd_list.append(sgg_cd.get_text())
  bldg_nm_list.append(bldg_nm.get_text())
  obj_amt_list.append(obj_amt.get_text())
  house_type_list.append(house_type.get_text())
  rdealer_lawdnm_list.append(rdealer_lawdnm.get_text())

df = pd.DataFrame({
    "acc_year": year_list, 
    "sgg_cd": sgg_cd_list, 
    "bldg_nm" : bldg_nm_list,
    "obj_amt": obj_amt_list,
    "house_type" : house_type_list,
    "rdealer_lawdnm": rdealer_lawdnm_list
})

df

	acc_year	sgg_cd	bldg_nm	obj_amt	house_type	rdealer_lawdnm
0	2023	11500	SK파크리움(851-33)	29500	연립다세대
1	2023	11215		26500	오피스텔	서울 중구
2	2023	11500	(105-20)	24000	연립다세대	서울 강서구
3	2023	11500	SK파크리움(851-33)	30500	연립다세대
4	2023	11470	휴먼파크타운(410-26)	16000	연립다세대	서울 양천구

2. JSON 형태 가져오기

service_key = '596a6f71646a616e37305945544979'
url = f'http://openapi.seoul.go.kr:8088/{service_key}/json/tbLnOpendataRtmsV/1/5/'
print(url)

http://openapi.seoul.go.kr:8088/596a6f71646a616e37305945544979/json/tbLnOpendataRtmsV/1/5/

req = requests.get(url)
content = req.json() # HTTP 요청의 본문을 JSON 형식으로 파싱하여 해당 데이터를 변환
content

{'tbLnOpendataRtmsV': {'list_total_count': 2664624,
  'RESULT': {'CODE': 'INFO-000', 'MESSAGE': '정상 처리되었습니다'},
  'row': [{'ACC_YEAR': '2023',
    'SGG_CD': '11500',
    'SGG_NM': '강서구',
    'BJDONG_CD': '10300',
    'BJDONG_NM': '화곡동',
    'LAND_GBN': '1',
    'LAND_GBN_NM': '대지',
    'BONBEON': '0851',
    'BUBEON': '0033',
    'BLDG_NM': 'SK파크리움(851-33)',
    'DEAL_YMD': '20230609',
    'OBJ_AMT': '29500',
    'BLDG_AREA': 26.12,
    'TOT_AREA': 15.91,
    'FLOOR': 3.0,
    'RIGHT_GBN': '',
    'CNTL_YMD': '',
    'BUILD_YEAR': '2022',
    'HOUSE_TYPE': '연립다세대',
    'REQ_GBN': '직거래',
    'RDEALER_LAWDNM': ''},
   {'ACC_YEAR': '2023',
    'SGG_CD': '11215',
    'SGG_NM': '광진구',
    'BJDONG_CD': '10700',
    'BJDONG_NM': '화양동',
    'LAND_GBN': '1',
    'LAND_GBN_NM': '대지',
    'BONBEON': '0111',
    'BUBEON': '0012',
    'BLDG_NM': '',
    'DEAL_YMD': '20230609',
    'OBJ_AMT': '26500',
    'BLDG_AREA': 16.88,
    'TOT_AREA': 29.9,
    'FLOOR': 3.0,
    'RIGHT_GBN': '',
    'CNTL_YMD': '',
    'BUILD_YEAR': '2021',
    'HOUSE_TYPE': '오피스텔',
    'REQ_GBN': '중개거래',
    'RDEALER_LAWDNM': '서울 중구'},
   {'ACC_YEAR': '2023',
    'SGG_CD': '11500',
    'SGG_NM': '강서구',
    'BJDONG_CD': '10300',
    'BJDONG_NM': '화곡동',
    'LAND_GBN': '1',
    'LAND_GBN_NM': '대지',
    'BONBEON': '0105',
    'BUBEON': '0020',
    'BLDG_NM': '(105-20)',
    'DEAL_YMD': '20230609',
    'OBJ_AMT': '24000',
    'BLDG_AREA': 62.64,
    'TOT_AREA': 36.69,
    'FLOOR': 3.0,
    'RIGHT_GBN': '',
    'CNTL_YMD': '',
    'BUILD_YEAR': '2011',
    'HOUSE_TYPE': '연립다세대',
    'REQ_GBN': '중개거래',
    'RDEALER_LAWDNM': '서울 강서구'},
   {'ACC_YEAR': '2023',
    'SGG_CD': '11500',
    'SGG_NM': '강서구',
    'BJDONG_CD': '10300',
    'BJDONG_NM': '화곡동',
    'LAND_GBN': '1',
    'LAND_GBN_NM': '대지',
    'BONBEON': '0851',
    'BUBEON': '0033',
    'BLDG_NM': 'SK파크리움(851-33)',
    'DEAL_YMD': '20230609',
    'OBJ_AMT': '30500',
    'BLDG_AREA': 28.54,
    'TOT_AREA': 17.06,
    'FLOOR': 2.0,
    'RIGHT_GBN': '',
    'CNTL_YMD': '',
    'BUILD_YEAR': '2022',
    'HOUSE_TYPE': '연립다세대',
    'REQ_GBN': '직거래',
    'RDEALER_LAWDNM': ''},
   {'ACC_YEAR': '2023',
    'SGG_CD': '11470',
    'SGG_NM': '양천구',
    'BJDONG_CD': '10300',
    'BJDONG_NM': '신월동',
    'LAND_GBN': '1',
    'LAND_GBN_NM': '대지',
    'BONBEON': '0410',
    'BUBEON': '0026',
    'BLDG_NM': '휴먼파크타운(410-26)',
    'DEAL_YMD': '20230609',
    'OBJ_AMT': '16000',
    'BLDG_AREA': 29.2,
    'TOT_AREA': 21.96,
    'FLOOR': 2.0,
    'RIGHT_GBN': '',
    'CNTL_YMD': '',
    'BUILD_YEAR': '2011',
    'HOUSE_TYPE': '연립다세대',
    'REQ_GBN': '중개거래',
    'RDEALER_LAWDNM': '서울 양천구'}]}}

df = pd.DataFrame(content['tbLnOpendataRtmsV']['row'])
df

	ACC_YEAR	SGG_CD	SGG_NM	BJDONG_CD	BJDONG_NM	LAND_GBN	LAND_GBN_NM	BONBEON	BUBEON	BLDG_NM	...	OBJ_AMT	BLDG_AREA	TOT_AREA	FLOOR	BUILD_YEAR	HOUSE_TYPE	REQ_GBN	RDEALER_LAWDNM
0	2023	11500	강서구	10300	화곡동	1	대지	0851	0033	SK파크리움(851-33)	...	29500	26.12	15.91	3.0	2022	연립다세대	직거래
1	2023	11215	광진구	10700	화양동	1	대지	0111	0012		...	26500	16.88	29.90	3.0	2021	오피스텔	중개거래	서울 중구
2	2023	11500	강서구	10300	화곡동	1	대지	0105	0020	(105-20)	...	24000	62.64	36.69	3.0	2011	연립다세대	중개거래	서울 강서구
3	2023	11500	강서구	10300	화곡동	1	대지	0851	0033	SK파크리움(851-33)	...	30500	28.54	17.06	2.0	2022	연립다세대	직거래
4	2023	11470	양천구	10300	신월동	1	대지	0410	0026	휴먼파크타운(410-26)	...	16000	29.20	21.96	2.0	2011	연립다세대	중개거래	서울 양천구

5 rows × 21 columns

3. 시각화

# 라이브러리 설치
import plotly.express as px

fig = px.bar(df, x = 'BONBEON', y = 'BLDG_AREA') # 건물 면적 시각화

fig.update_layout(
    xaxis_title = '본번',
    yaxis_title = '건물면적(㎡)'
)

fig.show()

import plotly.graph_objects as go

# Figure  생성
fig = go.Figure()

# Bar Trace 추가
fig.add_trace(go.Bar(x = df['BONBEON'], y = df['BLDG_AREA']))

fig.show()

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서울특별시 부동산 실거래 API 활용 불러오기

1. 서울특별시 부동산 XML 형태 가져오기

1-1. 라이브러리 설치

1-2. 인증키와 URL 가져오기

1-3. 데이터 요청

1-4 데이터 가공

2. JSON 형태 가져오기

3. 시각화

공유하기

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참고

Kaggle 타이타닉 데이터 피처 엔지니어링(Feature Engineering)

Kaggle 타이타닉 데이터 EDA 연습

matplotlib 시각화 연습 - 8

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