Forward or back-fill NA values in pandas DataFrames

Back-filling replaces NaN with the next non-NA value, while forward-filling takes the previous non-NA value. In pandas, this can be be done with fillna(method=bfill|ffill) or with the shortcuts bfill() and ffill()

# Import libraries
import pandas as pd
import numpy as np
from numpy.random import randint

# Set seed
np.random.seed(21)

# Create sample data
df = (
    pd.DataFrame({'group': list('ABCDE'), 
                  'value': [randint(1000, 20000, randint(2, 5)) for i in range(5)]})
    .explode('value')
    .reset_index(drop=True)
)
df.loc[df.sample(frac=0.5).index, 'value'] = np.nan
df
group value
0 A 6327
1 A NaN
2 A NaN
3 B NaN
4 B 13898
5 C NaN
6 C 18224
7 C 2646
8 D NaN
9 D 16613
10 E 17118
11 E NaN
12 E 3352

Back-fill and forward-fill

# Back-fill and forward-fill
(
    df
    .assign(value_bfill=df['value'].bfill(),
            value_ffill=df['value'].ffill())
)
group value value_bfill value_ffill
0 A 6327 6327 6327
1 A NaN 13898 6327
2 A NaN 13898 6327
3 B NaN 13898 6327
4 B 13898 13898 13898
5 C NaN 18224 13898
6 C 18224 18224 18224
7 C 2646 2646 2646
8 D NaN 16613 2646
9 D 16613 16613 16613
10 E 17118 17118 17118
11 E NaN 3352 17118
12 E 3352 3352 3352

Forward- or back-fill within groups

# Backfill and foward-fill within each group
(
    df
    .assign(value_bfill=df.groupby('group').transform(lambda x: x.bfill()),    # Back-fill
            value_ffill=df.groupby('group').transform(lambda x: x.ffill()),    # Forward-fill
            value_filled=lambda x: x['value_bfill'].fillna(x['value_ffill']))  # Get back- or forward-filled value
)
group value value_bfill value_ffill value_filled
0 A 6327 6327.0 6327.0 6327.0
1 A NaN NaN 6327.0 6327.0
2 A NaN NaN 6327.0 6327.0
3 B NaN 13898.0 NaN 13898.0
4 B 13898 13898.0 13898.0 13898.0
5 C NaN 18224.0 NaN 18224.0
6 C 18224 18224.0 18224.0 18224.0
7 C 2646 2646.0 2646.0 2646.0
8 D NaN 16613.0 NaN 16613.0
9 D 16613 16613.0 16613.0 16613.0
10 E 17118 17118.0 17118.0 17118.0
11 E NaN 3352.0 17118.0 3352.0
12 E 3352 3352.0 3352.0 3352.0