Pandas: filling missing values by mean in each group

Question

This should be straightforward, but the closest thing I've found is this post: pandas: Filling missing values within a group, and I still can't solve my problem....

Suppose I have the following dataframe

df = pd.DataFrame({'value': [1, np.nan, np.nan, 2, 3, 1, 3, np.nan, 3], 'name': ['A','A', 'B','B','B','B', 'C','C','C']})

  name  value
0    A      1
1    A    NaN
2    B    NaN
3    B      2
4    B      3
5    B      1
6    C      3
7    C    NaN
8    C      3

and I'd like to fill in "NaN" with mean value in each "name" group, i.e.

      name  value
0    A      1
1    A      1
2    B      2
3    B      2
4    B      3
5    B      1
6    C      3
7    C      3
8    C      3

I'm not sure where to go after:

grouped = df.groupby('name').mean()

Thanks a bunch.

Answer 1

One way would be to use transform:

>>> df
  name  value
0    A      1
1    A    NaN
2    B    NaN
3    B      2
4    B      3
5    B      1
6    C      3
7    C    NaN
8    C      3
>>> df["value"] = df.groupby("name").transform(lambda x: x.fillna(x.mean()))
>>> df
  name  value
0    A      1
1    A      1
2    B      2
3    B      2
4    B      3
5    B      1
6    C      3
7    C      3
8    C      3