对不起，我的英语不好。
这是我的数据框的简化版本：
d = {'League': {5697: 'Premier League', 5695: 'Premier League', 5694: 'Premier League', 5693: 'Premier League', 5692: 'Premier League', 5691: 'Premier League', 5696: 'Premier League', 5689: 'Premier League', 5688: 'Premier League', 5690: 'Premier League', 5684: 'Premier League', 5680: 'Premier League', 5681: 'Premier League', 5682: 'Premier League', 5686: 'Premier League', 5685: 'Premier League', 5687: 'Premier League', 5683: 'Premier League', 5678: 'Premier League', 5679: 'Premier League', 5677: 'Premier League', 5674: 'Premier League', 5676: 'Premier League', 5675: 'Premier League', 5673: 'Premier League', 5670: 'Premier League', 5672: 'Premier League', 5671: 'Premier League', 5665: 'Premier League', 5667: 'Premier League', 5666: 'Premier League', 5669: 'Premier League', 5664: 'Premier League', 5663: 'Premier League', 5662: 'Premier League', 5668: 'Premier League', 5661: 'Premier League', 5660: 'Premier League', 5654: 'Premier League', 5653: 'Premier League', 5655: 'Premier League', 5656: 'Premier League', 5657: 'Premier League', 5658: 'Premier League', 5659: 'Premier League', 5652: 'Premier League', 5651: 'Premier League', 5650: 'Premier League', 5649: 'Premier League', 5642: 'Premier League'}, 'Date': {5697: '2003-08-16 13:30', 5695: '2003-08-16 16:00', 5694: '2003-08-16 16:00', 5693: '2003-08-16 16:00', 5692: '2003-08-16 16:00', 5691: '2003-08-16 16:00', 5696: '2003-08-16 16:00', 5689: '2003-08-17 15:00', 5688: '2003-08-17 15:00', 5690: '2003-08-17 17:05', 5684: '2003-08-23 13:00', 5680: '2003-08-23 16:00', 5681: '2003-08-23 16:00', 5682: '2003-08-23 16:00', 5686: '2003-08-23 16:00', 5685: '2003-08-23 16:00', 5687: '2003-08-23 16:00', 5683: '2003-08-23 16:00', 5678: '2003-08-24 15:00', 5679: '2003-08-24 17:05', 5677: '2003-08-25 21:00', 5674: '2003-08-26 20:45', 5676: '2003-08-26 20:45', 5675: '2003-08-26 20:45', 5673: '2003-08-26 21:00', 5670: '2003-08-27 20:45', 5672: '2003-08-27 21:00', 5671: '2003-08-27 21:00', 5665: '2003-08-30 13:30', 5667: '2003-08-30 16:00', 5666: '2003-08-30 16:00', 5669: '2003-08-30 16:00', 5664: '2003-08-30 16:00', 5663: '2003-08-30 16:00', 5662: '2003-08-30 16:00', 5668: '2003-08-30 16:00', 5661: '2003-08-31 15:00', 5660: '2003-08-31 17:05', 5654: '2003-09-13 16:00', 5653: '2003-09-13 16:00', 5655: '2003-09-13 16:00', 5656: '2003-09-13 16:00', 5657: '2003-09-13 16:00', 5658: '2003-09-13 16:00', 5659: '2003-09-13 16:00', 5652: '2003-09-14 15:00', 5651: '2003-09-14 17:05', 5650: '2003-09-15 21:00', 5649: '2003-09-20 13:30', 5642: '2003-09-20 16:00'}, 'HomeTeam': {5697: 'Portsmouth', 5695: 'Leicester', 5694: 'Fulham', 5693: 'Blackburn', 5692: 'Birmingham', 5691: 'Arsenal', 5696: 'Manchester United', 5689: 'Leeds', 5688: 'Charlton', 5690: 'Liverpool', 5684: 'Newcastle', 5680: 'Bolton', 5681: 'Chelsea', 5682: 'Everton', 5686: 'Tottenham', 5685: 'Southampton', 5687: 'Wolves', 5683: 'Manchester City', 5678: 'Aston Villa', 5679: 'Middlesbrough', 5677: 'Blackburn', 5674: 'Leeds', 5676: 'Portsmouth', 5675: 'Leicester', 5673: 'Charlton', 5670: 'Arsenal', 5672: 'Manchester United', 5671: 'Liverpool', 5665: 'Everton', 5667: 'Newcastle', 5666: 'Middlesbrough', 5669: 'Wolves', 5664: 'Chelsea', 5663: 'Bolton', 5662: 'Aston Villa', 5668: 'Tottenham', 5661: 'Southampton', 5660: 'Manchester City', 5654: 'Blackburn', 5653: 'Arsenal', 5655: 'Bolton', 5656: 'Charlton', 5657: 'Chelsea', 5658: 'Everton', 5659: 'Southampton', 5652: 'Manchester City', 5651: 'Birmingham', 5650: 'Leicester', 5649: 'Wolves', 5642: 'Aston Villa'}, 'AwayTeam': {5697: 'Aston Villa', 5695: 'Southampton', 5694: 'Middlesbrough', 5693: 'Wolves', 5692: 'Tottenham', 5691: 'Everton', 5696: 'Bolton', 5689: 'Newcastle', 5688: 'Manchester City', 5690: 'Chelsea', 5684: 'Manchester United', 5680: 'Blackburn', 5681: 'Leicester', 5682: 'Fulham', 5686: 'Leeds', 5685: 'Birmingham', 5687: 'Charlton', 5683: 'Portsmouth', 5678: 'Liverpool', 5679: 'Arsenal', 5677: 'Manchester City', 5674: 'Southampton', 5676: 'Bolton', 5675: 'Middlesbrough', 5673: 'Everton', 5670: 'Aston Villa', 5672: 'Wolves', 5671: 'Tottenham', 5665: 'Liverpool', 5667: 'Birmingham', 5666: 'Leeds', 5669: 'Portsmouth', 5664: 'Blackburn', 5663: 'Charlton', 5662: 'Leicester', 5668: 'Fulham', 5661: 'Manchester United', 5660: 'Arsenal', 5654: 'Liverpool', 5653: 'Portsmouth', 5655: 'Middlesbrough', 5656: 'Manchester United', 5657: 'Tottenham', 5658: 'Newcastle', 5659: 'Wolves', 5652: 'Aston Villa', 5651: 'Fulham', 5650: 'Leeds', 5649: 'Chelsea', 5642: 'Charlton'}, 'GTOT': {5697: 3, 5695: 4, 5694: 5, 5693: 6, 5692: 1, 5691: 3, 5696: 4, 5689: 4, 5688: 3, 5690: 3, 5684: 3, 5680: 4, 5681: 3, 5682: 4, 5686: 3, 5685: 0, 5687: 4, 5683: 2, 5678: 0, 5679: 4, 5677: 5, 5674: 0, 5676: 4, 5675: 0, 5673: 4, 5670: 2, 5672: 1, 5671: 0, 5665: 3, 5667: 1, 5666: 5, 5669: 0, 5664: 4, 5663: 0, 5662: 4, 5668: 3, 5661: 1, 5660: 3, 5654: 4, 5653: 2, 5655: 2, 5656: 2, 5657: 6, 5658: 4, 5659: 2, 5652: 5, 5651: 4, 5650: 4, 5649: 5, 5642: 3}}
df = pd.DataFrame.from_dict(d)
其中GTOT是比赛中的总进球数。
对于比赛中的每支球队，我想找出GTOT为次要1的连续比赛次数，并将其添加到列表中。
我使用itertuples在数据帧上进行迭代，但是对于大量的比赛（> 50000）和团队（> 100）来说，这非常慢。
这是我函数的简化版本：
def calculates_overdue():
lstTeams = [team for team in df.HomeTeam.unique()]
lstTeams.sort()
lstOverdue = []
for team in lstTeams:
overdue = 0
mask = (df.HomeTeam == team) | (df.AwayTeam == team)
for row in df[mask].itertuples():
if row.GTOT < 1:
lstOverdue.append((row.League, team, overdue, row.Date))
overdue = 0
else:
overdue += 1
return lstOverdue
print(calculates_overdue())
结果：
[('Premier League', 'Aston Villa', 4, '2003-08-24 15:00'), ('Premier League', 'Birmingham', 2, '2003-08-23 16:00'), ('Premier League', 'Bolton', 1, '2003-08-30 16:00'), ('Premier League', 'Charlton', 2, '2003-08-30 16:00'), ('Premier League', 'Leeds', 2, '2003-08-26 20:45'), ('Premier League', 'Leicester', 2, '2003-08-26 20:45'), ('Premier League', 'Liverpool', 2, '2003-08-27 21:00'), ('Premier League', 'Liverpool', 0, '2003-08-24 15:00'), ('Premier League', 'Middlesbrough', 2, '2003-08-26 20:45'), ('Premier League', 'Portsmouth', 1, '2003-08-30 16:00'), ('Premier League', 'Southampton', 2, '2003-08-26 20:45'), ('Premier League', 'Southampton', 0, '2003-08-23 16:00'), ('Premier League', 'Tottenham', 2, '2003-08-27 21:00'), ('Premier League', 'Wolves', 2, '2003-08-30 16:00')]
我知道遍历行是不好的。如何加快这些循环？是否可以向量化此功能？
最佳答案
使用简单的python调用并删除一些熊猫调用。
def testTime():
df = pd.DataFrame.from_dict(d)
#print(df)
#print(d.keys())
lstOverdue = []
lstTeams = [team for team in df.HomeTeam.unique()]
for team in lstTeams:
mask = (df.HomeTeam == team) | (df.AwayTeam == team)
overdue = 0
for row in df[mask].itertuples():
if row.GTOT < 1:
lstOverdue.append((row.League, team, overdue, row.Date))
overdue = 0
else:
overdue += 1
def testTime2():
df = pd.DataFrame.from_dict(d)
lstOverdue = []
for team in df.HomeTeam.unique():
overdue = 0
for row in df.itertuples():
if row.HomeTeam == team or team == row.AwayTeam:
if row.GTOT < 1:
lstOverdue.append((row.League, team, overdue, row.Date))
overdue = 0
else:
overdue += 1
if __name__ == '__main__':
import timeit
print(timeit.timeit("testTime2()", setup="from __main__ import testTime2", number=1000))
print(timeit.timeit("testTime()", setup="from __main__ import testTime", number=1000))
请参见函数testTime2，在运行此代码后，我观察到系统使用您提供的数据在系统上运行了大约一半的时间，但它在系统上也会有所不同。
$ python3.6 test.py
21.352469262998056
39.04840800600141
$ python3.6 test.py
22.977724283999123
39.568580347000534
$ python3.6 test.py
21.838805349998438
39.85868542999742
可以将其进一步减少为数据行数，但是需要进行如下预处理。
在转换过程中将字典转换为
list的namedtuple个，并创建HomeTown集。处理列表类似于功能testTime2处理数据帧。