跨多列滚动平均匹配案例 [英] Rolling average matching cases across multiple columns

问题描述

如果有人问过这个问题，我很抱歉，但我找不到其他类似的问题.

I'm sorry if this has been asked but I can't find another question like this.

我在 Pandas 中有一个这样的数据框:

I have a data frame in Pandas like this:

Home   Away   Home_Score   Away_Score
MIL    NYC    1            2
ATL    NYC    1            3
NYC    PHX    2            1
HOU    NYC    1            6

我想计算每支球队的移动平均数，但问题是我想为他们的所有比赛(包括主客场比赛)计算.

I want to calculate the moving average for each team, but the catch is that I want to do it for all of their games, both home and away combined.

因此，对于NYC"大小为 3 的移动平均窗口，第 1 行的答案应该是 (2+3+2)/3，然后第 2 行的答案应该是 (3+2+6)/3，等等.

So for a moving average window of size 3 for 'NYC' the answer should be (2+3+2)/3 for row 1 and then (3+2+6)/3 for row 2, etc.

推荐答案

你可以用exploid stack 把两列转换成一列和groupby:

You can exploid stack to convert the two columns into one and groupby:

(df[['Home_Score','Away_Score']]
   .stack()
   .groupby(df[['Home','Away']].stack().values)
   .rolling(3).mean()
   .reset_index(level=0, drop=True)
   .unstack()
   .add_prefix('Avg_')
)

输出:

   Avg_Away_Score  Avg_Home_Score
0             NaN             NaN
1             NaN             NaN
2             NaN        2.333333
3        3.666667             NaN

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