Monthly Weighted average (in Pandas)

Question

I'm trying to create a new column with Monthly weighted average. I tried a few options but the weighted average calculation is taking total of column Amount . for example in March 2021 because there is just one value, WA should be 10*(110/100), most code calculate 10*(110/total Amount column)

Any idea how to do this?

My Data:

Date	Column 1	Amount	(Expected Output) Monthly WA
01-Jan-2021	20	100	16.78
01-Jan-2021	25	110	16.78
01-Jan-2021	15	120	16.78
01-Jan-2021	30	135	16.78
02-Jan-2021	10	150	16.78
02-Jan-2021	12	160	16.78
02-Jan-2021	11	175	16.78
01-Feb-2021	13	225	20.09
01-Feb-2021	15	220	20.09
01-Feb-2021	17	158	20.09
02-Feb-2021	20	185	20.09
02-Feb-2021	25	170	20.09
02-Feb-2021	35	160	20.09
01-Mar-2021	10	110	10

Here's the code I've tried:

def weight_avg(df, value, weights):
    x = df[value]  
    y = df[weights]  
    return (x*y).sum()/y.sum() <br />

df.groupby(df['Date']).apply(weight_avg, 'Column 1', 'Amount')

Answer 1

如果您的日期列不是日期时间，那么您可以将其转换并按月分组，如下所示：

df.groupby(df['Date'].to_datetime().dt.month).apply(weight_avg, 'Column 1', 'Amount')