Forward- or back-fill NA values in 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