How to replace the outlier with 3 standard deviation value for all column based on group by column?

Hi I have data frame in which I want to replace/cap the outlier with the 3*standard deviation value for all column with group by for each column. For example:

df = pd.DataFrame({"A":["A", "A", "A", "A", "B","B","B","B","B","B","B","B","B","B","B","B"], "B":[7, 2, 54, 3, 5,23,5,7,7,7,7,7,7,7,6,7], "C":[20, 16, 11, 3, 8,5,5,20,6,6,6,6,6,5,6,6], "D":[14, 3, 32, 2, 6,5,6,20,4,5,4,5,4,5,5,5],                }) feature=['B','C','D']mean = df.groupby('A')[feature].mean()std = df.groupby('A')[feature].std()

now I want to replace outlier for each column in feature with appropriate standard deviation for that group.

Something like below but for each group and each column

for col in feature: for each in df['A'].unique():  m=mean.loc[each,col]  s=std.loc[each,col]  df.loc[each,df[col]< m-3*s,]=m-3*s

Expected output:

I have many column and loop is time consuming. Is there any better way or can it be done with one loop?