I have a dataframe in pandas with several columns I want to forward fill the values for. At the moment I\'m doing:
columns = [\'a\', \'b\', \'c\']
for column in
In addition to the answer from Alexander, you can use the following if you want to conserve bottom rows with NaNs
:
df2 = pd.DataFrame({
'A': [1, None, None, None, None],
'B': [1, 2, None, None, None],
'C': [1, None, 3, None, None],
'D': [1, None, None, 4, None]})
df2
A B C D
0 1 1 1 1
1 NaN 2 NaN NaN
2 NaN NaN 3 NaN
3 NaN NaN NaN 4
4 NaN NaN NaN NaN
pd.concat([df2.apply(lambda series: series.loc[:series.last_valid_index()].ffill()),
df2.loc[df2.last_valid_index()+1:]])
A B C D
0 1.0 1.0 1.0 1.0
1 NaN 2.0 1.0 1.0
2 NaN NaN 3.0 1.0
3 NaN NaN NaN 4.0
4 NaN NaN NaN NaN