P粉7757887232023-08-17 10:20:21
Try using pd.read_csv
:
url = "https://gladys.geog.ucl.ac.uk/bikesapi/load.php?scheme=saopaulo" df = pd.read_csv(url) print(df.head())
Output result:
#id timestamp|gmt_local_diff_sec|gmt_servertime_diff_sec name lat lon bikes spaces installed locked temporary total_docks givesbonus_acceptspedelecs_fbbattlevel pedelecs 0 1 1692123219|10800|-3600 1 - Largo da Batata -23.566831 -46.693741 43 37 True False False 83 NaN 10 1 3 1692123219|10800|-3600 3 - CPTM Pinheiros -23.566478 -46.701258 6 7 True False False 15 NaN 3 2 4 1692123219|10800|-3600 4 - Rua Diogo Moreira -23.569145 -46.692003 2 20 True False False 23 NaN 2 3 5 1692123219|10800|-3600 5 - Chicão Vive -23.569894 -46.697897 4 7 True False False 11 NaN 1 4 6 1692123219|10800|-3600 6 - Rua Manduri -23.572137 -46.690107 10 7 True False False 19 NaN 0