How to replace NaNs by preceding values in pandas DataFrame?

Question

Suppose I have a DataFrame with some NaNs:

>>> import pandas as pd
>>> df = pd.DataFrame([[1, 2, 3], [4, None, None], [None, N         


        

Answer 1

One thing that I noticed when trying this solution is that if you have N/A at the start or the end of the array, ffill and bfill don't quite work. You need both.

In [224]: df = pd.DataFrame([None, 1, 2, 3, None, 4, 5, 6, None])

In [225]: df.ffill()
Out[225]:
     0
0  NaN
1  1.0
...
7  6.0
8  6.0

In [226]: df.bfill()
Out[226]:
     0
0  1.0
1  1.0
...
7  6.0
8  NaN

In [227]: df.bfill().ffill()
Out[227]:
     0
0  1.0
1  1.0
...
7  6.0
8  6.0