Python Pandas DataFrame填充缺失值

Question

I have a dataframe like this with some NaNs:

df:

      0      1
1  11.0  111.0
2  12.0  112.0
3  13.0  113.0
4   NaN  114.0
4  15.0    NaN
5  16.0  116.0
6  17.0  117.0
7  18.0  118.0

So what should I do to it to get the following:

      0      1
1  11.0  111.0
2  12.0  112.0
3  13.0  113.0
4  15.0  114.0
4  15.0  114.0
5  16.0  116.0
6  17.0  117.0
7  18.0  118.0

So that the NaN values in the index 4 are filled with index 4 values from other rows which are not NaN?

Answer 1

you can group by index and ffill() + bfill() inside each group:

In [165]: df.groupby(level=0).apply(lambda x: x.ffill().bfill())
Out[165]:
      0      1
1  11.0  111.0
2  12.0  112.0
3  13.0  113.0
4  15.0  114.0
4  15.0  114.0
5  16.0  116.0
6  17.0  117.0
7  18.0  118.0