Fill in missing pandas data with previous non-miss

问题:

I am dealing with pandas DataFrames like this:

   id    x
0   1   10
1   1   20
2   2  100
3   2  200
4   1  NaN
5   2  NaN
6   1  300
7   1  NaN

I would like to replace each NAN 'x' with the previous non-NAN 'x' from a row with the same 'id' value:

   id    x
0   1   10
1   1   20
2   2  100
3   2  200
4   1   20
5   2  200
6   1  300
7   1  300

Is there some slick way to do this without manually looping over rows?

回答1:

You could perform a groupby/forward-fill operation on each group:

import numpy as np
import pandas as pd

df = pd.DataFrame({'id': [1,1,2,2,1,2,1,1], 'x':[10,20,100,200,np.nan,np.nan,300,np.nan]})
df['x'] = df.groupby(['id'])['x'].ffill()
print(df)

yields

   id      x
0   1   10.0
1   1   20.0
2   2  100.0
3   2  200.0
4   1   20.0
5   2  200.0
6   1  300.0
7   1  300.0

回答2:

df
   id   val
0   1   23.0
1   1   NaN
2   1   NaN
3   2   NaN
4   2   34.0
5   2   NaN
6   3   2.0
7   3   NaN
8   3   NaN

df.sort_values(['id','val']).groupby('id').ffill()

    id  val
0   1   23.0
1   1   23.0
2   1   23.0
4   2   34.0
3   2   34.0
5   2   34.0
6   3   2.0
7   3   2.0
8   3   2.0

use sort_values, groupby and ffill so that if you have Nan value for the first value or set of first values they also get filled.