How to fillna by groupby outputs in pandas?

Question

I have a dataframe having 4 columns(A,B,C,D). D has some NaN entries. I want to fill the NaN values by the average value of D having same value of A,B,C.

For example,if the value of A,B,C,D are x,y,z and Nan respectively,then I want the NaN value to be replaced by the average of D for the rows where the value of A,B,C are x,y,z respectively.

Answer 1

df['D'].fillna(df.groupby(['A','B','C'])['D'].transform('mean')) would be faster than apply

In [2400]: df
Out[2400]:
   A  B  C    D
0  1  1  1  1.0
1  1  1  1  NaN
2  1  1  1  3.0
3  3  3  3  5.0

In [2401]: df['D'].fillna(df.groupby(['A','B','C'])['D'].transform('mean'))
Out[2401]:
0    1.0
1    2.0
2    3.0
3    5.0
Name: D, dtype: float64

In [2402]: df['D'] = df['D'].fillna(df.groupby(['A','B','C'])['D'].transform('mean'))

In [2403]: df
Out[2403]:
   A  B  C    D
0  1  1  1  1.0
1  1  1  1  2.0
2  1  1  1  3.0
3  3  3  3  5.0

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Details

In [2396]: df.shape
Out[2396]: (10000, 4)

In [2398]: %timeit df['D'].fillna(df.groupby(['A','B','C'])['D'].transform('mean'))
100 loops, best of 3: 3.44 ms per loop


In [2397]: %timeit df.groupby(['A','B','C'])['D'].apply(lambda x: x.fillna(x.mean()))
100 loops, best of 3: 5.34 ms per loop