[Marko prcaic]提供了以下答案(Sportsipy API请求)
from sportsreference.nba.schedule import Schedule
# MIL removed from league list as it is used to initiate league_schedule
league = ['CHO','LAL','LAC','SAC','ATL','MIA','DAL','POR',
'HOU','NOP','PHO','WAS','MEM','BOS','DEN','TOR','SAS',
'PHI','BRK','UTA','IND','OKC','ORL','MIN','DET',
'NYK','CLE','CHI','GSW']
league_schedule = Schedule('MIL', year="2020").dataframe
for team in league:
league_schedule = league_schedule.append(Schedule(team , year="2020").dataframe)
这完美地满足了我的需求——返回的数据框架产生了除了联赛球队本身之外的所有相关信息。我们可以获得game_time等内容，但却无法获得计划所针对的团队。结果是:
boxscore_index date datetime ... streak time wins
201910240HOU 201910240HOU Thu, Oct 24, 2019 2019-10-24 ... W 1 8:00p 1
201910260MIL 201910260MIL Sat, Oct 26, 2019 2019-10-26 ... L 1 5:00p 1
201910280MIL 201910280MIL Mon, Oct 28, 2019 2019-10-28 ... W 1 8:00p 2
201910300BOS 201910300BOS Wed, Oct 30, 2019 2019-10-30 ... L 1 7:30p 2
201911010ORL 201911010ORL Fri, Nov 1, 2019 2019-11-01 ... W 1 7:00p 3
但是我想要的是一个额外的列，声明与MIL相关的所有列观测值的MIL，与CHO相关的所有列观测值的CHO，等等。生成的DataFrame如下所示
boxscore_index team date datetime ... streak time wins
201910240HOU 201910240HOU MIL Thu, Oct 24, 2019 2019-10-24 ... W 1 8:00p 1
201910260MIL 201910260MIL MIL Sat, Oct 26, 2019 2019-10-26 ... L 1 5:00p 1
201910280MIL 201910280MIL MIL Mon, Oct 28, 2019 2019-10-28 ... W 1 8:00p 2
201910300BOS 201910300BOS MIL Wed, Oct 30, 2019 2019-10-30 ... L 1 7:30p 2
201911010ORL 201911010ORL MIL Fri, Nov 1, 2019 2019-11-01 ... W 1 7:00p 3
再多做一点工作和一点帮助就可以了:
from sportsreference.nba.schedule import Schedule
import pandas as pd
league = ['MIL','CHO','LAL','LAC','SAC','ATL','MIA','DAL',
'POR','HOU','NOP','PHO','WAS','MEM','BOS','DEN',
'TOR','SAS','PHI','BRK','UTA','IND','OKC','ORL',
'MIN','DET','NYK','CLE','CHI','GSW']
df = pd.DataFrame([])
for team in league:
df_ = Schedule(team, year='2020').dataframe
df_['team'] = team
df = df.append(df_)
这将产生:
boxscore_index date datetime ... time wins team
201910240HOU 201910240HOU Thu, Oct 24, 2019 2019-10-24 ... 8:00p 1 MIL
201910260MIL 201910260MIL Sat, Oct 26, 2019 2019-10-26 ... 5:00p 1 MIL
201910280MIL 201910280MIL Mon, Oct 28, 2019 2019-10-28 ... 8:00p 2 MIL
201910300BOS 201910300BOS Wed, Oct 30, 2019 2019-10-30 ... 7:30p 2 MIL
201911010ORL 201911010ORL Fri, Nov 1, 2019 2019-11-01 ... 7:00p 3 MIL