假设我有一个如下数据框
ema_features = df[['team']].copy()
for feature_name in df[['score','yards']]:
span=10
feature_ema = (df.groupby('team')[feature_name].rolling(window=span, min_periods=span).mean()[:span])
rest = df[feature_name][span:]
x = pd.concat([feature_ema, rest]).ewm(span=span, adjust=False).mean()
ema_features[feature_name] = x
我正在尝试使用此帖子(Does Pandas calculate ewm wrong?)上针对“得分”和“码”列的手动方法计算ewm,但我注意到我的跨度无法按预期的方式用于每个分组球队。到目前为止,这就是我的代码了
ema_features
team score yards
0 team1 NaN NaN
1 team1 NaN NaN
2 team1 NaN NaN
3 team1 NaN NaN
4 team1 NaN NaN
5 team1 NaN NaN
6 team1 NaN NaN
7 team1 NaN NaN
8 team1 NaN NaN
9 team1 NaN NaN
10 team1 6.500000 65.000000
11 team1 7.500000 75.000000
12 team1 8.500000 85.000000
13 team1 9.500000 95.000000
14 team2 7.954545 79.545455
15 team2 6.871901 68.719008
16 team2 6.167919 61.679189
17 team2 5.773752 57.737518
18 team2 5.633070 56.330696
19 team2 5.699784 56.997843
20 team2 5.936187 59.361871
21 team2 6.311426 63.114258
22 team2 6.800257 68.002575
23 team2 7.382029 73.820289
24 team2 8.039842 80.398418
25 team2 8.759871 87.598706
26 team2 9.530803 95.308032
27 team2 10.343384 103.433844
其输出如下
ema_features
team score yards
0 team1 NaN NaN
1 team1 NaN NaN
2 team1 NaN NaN
3 team1 NaN NaN
4 team1 NaN NaN
5 team1 NaN NaN
6 team1 NaN NaN
7 team1 NaN NaN
8 team1 NaN NaN
9 team1 NaN NaN
10 team1 6.500000 65.000000
11 team1 7.500000 75.000000
12 team1 8.500000 85.000000
13 team1 9.500000 95.000000
14 team2 NaN NaN
15 team2 NaN NaN
16 team2 NaN NaN
17 team2 NaN NaN
18 team2 NaN NaN
19 team2 NaN NaN
20 team2 NaN NaN
21 team2 NaN NaN
22 team2 NaN NaN
23 team2 6.500000 65.000000
24 team2 7.500000 75.000000
25 team2 8.500000 85.000000
26 team2 9.500000 95.000000
我的问题是,如何使我的跨度也适用于第2队?而不是上面的输出,其中第2团队的ewm是与第1团队一起计算的。我希望每个团队的ewm相互独立地计算,这需要应用正确的跨度然后进行计算,就像我在下面期望的那样。
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