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Forward and backward filling of missing values of DataFrame columns in Pandas?

Forward and backward filling of missing values:

import pandas as pd

df = pd.DataFrame([[10, 30, 40], [], [15, 8, 12],
                   [15, 14, 1, 8], [7, 8], [5, 4, 1]],
                  columns=['Apple', 'Orange', 'Banana', 'Pear'],
                  index=['Basket1', 'Basket2', 'Basket3', 'Basket4',
                         'Basket5', 'Basket6'])

print("\n------ DataFrame with NaN -----\n")
print(df)

print("\n------ DataFrame with Forward Filling -----\n")
print(df.ffill())

print("\n------ DataFrame with Forward Filling -----\n")
print(df.bfill())

C:\pandas>python example.py
 
------ DataFrame with NaN -----
 
         Apple  Orange  Banana  Pear
Basket1   10.0    30.0    40.0   NaN
Basket2    NaN     NaN     NaN   NaN
Basket3   15.0     8.0    12.0   NaN
Basket4   15.0    14.0     1.0   8.0
Basket5    7.0     8.0     NaN   NaN
Basket6    5.0     4.0     1.0   NaN
 
------ DataFrame with Forward Filling -----
 
         Apple  Orange  Banana  Pear
Basket1   10.0    30.0    40.0   NaN
Basket2   10.0    30.0    40.0   NaN
Basket3   15.0     8.0    12.0   NaN
Basket4   15.0    14.0     1.0   8.0
Basket5    7.0     8.0     1.0   8.0
Basket6    5.0     4.0     1.0   8.0
 
------ DataFrame with Forward Filling -----
 
         Apple  Orange  Banana  Pear
Basket1   10.0    30.0    40.0   8.0
Basket2   15.0     8.0    12.0   8.0
Basket3   15.0     8.0    12.0   8.0
Basket4   15.0    14.0     1.0   8.0
Basket5    7.0     8.0     1.0   NaN
Basket6    5.0     4.0     1.0   NaN
 
C:\pandas>

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