有没有更快的方法来做 Pandas groupby 累积平均值？

Question

我正在尝试在 Python 中创建一个查找参考 table 来计算玩家之前（按 datetime）的比赛得分 cumulative mean，按场地分组.但是，根据我的特定需要，玩家之前应该在相关场地至少玩过 2 次才能进行 'Venue Preference' cumulative mean 计算。

df 格式如下所示：

DateTime	Player	Venue	Score
2021-09-25 17:15:00	Tim	Stadium A	20
2021-09-27 10:00:00	Blake	Stadium B	30

我现有的代码可以完美运行，但不幸的是速度很慢，如下所示：

import numpy as np
import pandas as pd

VenueSum = pd.DataFrame(df.groupby(['DateTime', 'Player', 'Venue'])['Score'].sum().reset_index(name = 'Sum'))
VenueSum['Cumulative Sum'] = VenueSum.sort_values('DateTime').groupby(['Player', 'Venue'])['Sum'].cumsum()
VenueCount = pd.DataFrame(df.groupby(['DateTime', 'Player', 'Venue'])['Score'].count().reset_index(name = 'Count'))
VenueCount['Cumulative Count'] = VenueCount.sort_values('DateTime').groupby(['Player', 'Venue'])['Count'].cumsum()
VenueLookup = VenueSum.merge(VenueCount, how = 'outer', on = ['DateTime', 'Player', 'Venue'])
VenueLookup['Venue Preference'] = np.where(VenueLookup['Cumulative Count'] >= 2, VenueLookup['Cumulative Sum'] / VenueLookup['Cumulative Count'], np.nan)
VenueLookup = VenueLookup.drop(['Sum', 'Cumulative Sum', 'Count', 'Cumulative Count'], axis = 1)

我确信有一种方法可以一步计算 cumulative mean，而无需首先计算 cumulative sum 和 cumulative count，但不幸的是我无法让它工作。

Answer 1

IIUC 首先按 sum 和 size 聚合删除 2 groupby，然后按两列累计和：

df1 = df.groupby(['DateTime', 'Player', 'Venue'])['Score'].agg(['sum','count'])
df1 = df1.groupby(['Player', 'Venue'])[['sum', 'count']].cumsum().reset_index()
df1['Venue Preference'] = np.where(df1['count'] >= 2, df1['sum'] / df1['count'], np.nan)
df1 = df1.drop(['sum', 'count'], axis=1)
print (df1)
              DateTime Player      Venue  Venue Preference
0  2021-09-25 17:15:00    Tim  Stadium A               NaN
1  2021-09-27 10:00:00  Blake  Stadium B               NaN

有没有更快的方法来做 Pandas groupby 累积平均值？

Is there a faster method to do a Pandas groupby cumulative mean?

python

performance

cumulative-sum

pandas

pandas-groupby