Forward Fill All Except Last Value In Python Pandas Dataframe

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 columns: df[column].fi

Solution 1:

You can use last_valid_index in a lambda function to just ffill up to that point.

df = pd.DataFrame({
    'A': [1, None, None, None], 
    'B': [1, 2, None, None], 
    'C': [1, None, 3, None], 
    'D': [1, None, None, 4]})

>>> df
    A   B   C   D
0   1   1   1   1
1 NaN   2 NaN NaN
2 NaN NaN   3 NaN
3 NaN NaN NaN   4

>>> df.apply(lambda series: series.loc[:series.last_valid_index()].ffill())
    A   B   C  D
0   1   1   1  1
1 NaN   2   1  1
2 NaN NaN   3  1
3 NaN NaN NaN  4

Solution 2:

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