Pandas Groupby計算ewm無法正常工作

Question

假設我有一個如下數據框

import pandas as pd

data = {'team': ['team1','team1','team1','team1','team1','team1','team1','team1','team1','team1','team1','team1','team1','team1',
              'team2','team2','team2','team2','team2','team2','team2','team2','team2','team2','team2','team2','team2','team2',],
     'score': [1,2,3,4,5,6,7,8,9,10,11,12,13,14,1,2,3,4,5,6,7,8,9,10,11,12,13,14],
     'yards': [10,20,30,40,50,60,70,80,90,100,110,120,130,140,10,20,30,40,50,60,70,80,90,100,110,120,130,140]}

df = pd.DataFrame.from_dict(data)

我正在嘗試使用此職位上的此手動方法（ “熊貓計算ewm錯誤嗎？” ） 來計算ewm ，但是我注意到我的跨度不能按每個分組的團隊使用。 到目前為止，這就是我的代碼

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

輸出如下

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   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

我的問題是，如何使我的跨度也適用於第2隊？ 而不是上面的輸出，其中團隊2的ewm是與團隊1一起計算的。我希望每個團隊的ewm相互獨立地計算，這需要應用正確的跨度然后進行計算，就像我在下面期望的那樣。

   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

Answer 1

您可以嘗試通過自定義函數使用GroupBy.apply 。 因此，調整您的for循環，嘗試執行以下操作：

def team_ema(team, span=10):
    feature_ema = team.rolling(window=span, min_periods=span).mean()[:span]
    rest = team[span:]
    return pd.concat([feature_ema, rest]).ewm(span=span, adjust=False).mean()

df.groupby('team').apply(team_ema)