Python 按winsorized均值分组

正常分组平均值很容易：

df.groupby(['col_a','col_b']).mean()[col_i_want]

然而，如果我想应用winsorized平均值（默认限制为0.05和0.95），这相当于剪切数据集，然后执行平均值，那么突然似乎没有简单的方法可以做到这一点？我必须：

winsorized_mean = []
col_i_want = 'col_c'
for entry in df['col_a'].unique():
    for entry2 in df['col_b'].unique():
        sub_df = df[(df['col_a'] == entry) & (df['col_b'] == entry2)]
        m = sub_df[col_to_groupby].clip(lower=0.05,upper=0.95).mean()
        winsorized_mean.append([entry,entry2,m])

是否有一个我不知道的功能可以自动执行此操作

您可以使用：

---

必须将其编辑为

grouped=df.groupby（[col_a，col_b]）[col_i_want]

才能使其正常工作，但这太棒了！谢谢

import pandas as pd
from scipy.stats import trim_mean

# label 'a' will exhibit different means depending on trimming
label = ['a'] * 20 + ['b'] * 80 + ['c'] * 400 + ['a'] * 100

data = list(range(100)) + list(range(500, 1000))

df = pd.DataFrame({'label': label, 'data': data})

grouped = df.groupby('label')

# trim 5% off both ends
print(grouped.apply(stats.trim_mean, .05))

# trim 10% off both ends
print(grouped.apply(stats.trim_mean, .1))