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

You can use pandas.DataFrame.fillna with the method='ffill' option. 'ffill' stands for 'forward fill' and will propagate last valid observation forward. The alternative is 'bfill' which works the same way, but backwards.

import pandas as pd

df = pd.DataFrame([[1, 2, 3], [4, None, None], [None, None, 9]])
df = df.fillna(method='ffill')

print(df)
#   0  1  2
#0  1  2  3
#1  4  2  3
#2  4  2  9

There is also a direct synonym function for this, pandas.DataFrame.ffill, to make things simpler.