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I have the following dataframe:

df = pd.DataFrame([[np.nan, 2, 20, 4],
                   [3, 1, np.nan, 1],
                   [3, 1, 15, 1],
                   [np.nan, 1, np.nan, 1],
                   [10, 1, 30, 4],
                   [50, 2, 35, 4],
                   [10, 1, 37, 4],
                   [40, 2, 30, 1]],
                  columns=list("ABCD"))

I want to fill the Nan values with their group means. Towards that purpose, I run the following:

df_mean=df.groupby(["B","D"]).mean()
df_mean

        A     C
B   D       
1   1   3.0   15.0
    4   10.0  33.5
2   1   40.0  30.0
    4   50.0  27.5

Is there a way to fill the dataframe df with the values computed in df_mean?

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This is more of a programming question than a data science question and would therefore be better suited for stackoverflow stackexchange, but the following code should do what you're looking for:

df[["A", "C"]] = (
    df
    # create groups
    .groupby(["B", "D"])
    # transform the groups by filling na values with the group mean
    .transform(lambda x: x.fillna(x.mean()))
)
A B C D
50 2 20 4
3 1 15 1
3 1 15 1
3 1 15 1
10 1 30 4
50 2 35 4
10 1 37 4
40 2 30 1
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  • $\begingroup$ I must say it's not the first time one of your answers surprises me... Many thanks. It will take a bit for me to understand exactly how this is working. =D $\endgroup$ Jan 13 at 10:46

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