Filling NaN on conditions

Question

I have the following input data:

df = pd.DataFrame({"ID" : [1, 1, 1, 2, 2, 2, 2],
                  "length" : [0.7, 0.7, 0.7, 0.8, 0.6, 0.6, 0.7],
                  "height" : [7, 9, np.nan, 4, 8, np.nan, 5]})
df

    ID  length  height
0   1   0.7     7
1   1   0.7     9
2   1   0.7     np.nan
3   2   0.8     4
4   2   0.6     8
5   2   0.6     np.nan
6   2   0.7     5

I want to be able to fill the NaN if a group of "ID" all have the same "length", fill with the maximum "height" in that group of "ID", else fill with the "height" that correspond to the maximum length in that group.

Required Output:

    ID  length  height
0   1   0.7     7
1   1   0.7     9
2   1   0.7     9
3   2   0.8     4
4   2   0.6     8
5   2   0.6     4
6   2   0.7     5

Thanks.

Asked By: ukanafun

||

Source

Answer 1

You could try with sort_value then we use groupby find the last

#last will find the last not NaN value

df.height.fillna(df.sort_values(['length','height']).groupby(['ID'])['height'].transform('last'),inplace=True)
df
Out[296]: 
   ID  length  height
0   1     0.7     7.0
1   1     0.7     9.0
2   1     0.7     9.0
3   2     0.8     4.0
4   2     0.6     8.0
5   2     0.6     4.0
6   2     0.7     5.0

Answered By: BENY

Filling NaN on conditions

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