Explain bfill and ffill.

Pandas - Interview Questions

While reindexing NaN can be introduced .bfill and .ffill are used to handle NaN

bfill : Fills the value from ahead value into the previous NaN value

ffill : Fills the value from behind value into the missing NaN value

Reindexing without using any method(bfill or ffill)

import pandas as pd
import numpy as np

df1 = pd.DataFrame(np.random.randn(4, 3), columns=['col1', 'col2', 'col3'])
df2 = pd.DataFrame(np.random.randn(2, 3), columns=['col1', 'col2', 'col3'])

print(df2.reindex_like(df1))

Output :

col1 col2 col3

0 -0.641715 1.031070 -0.208415

1 -1.560385 -0.584403 0.291666

2 NaN NaN NaN

3 NaN NaN NaN

Reindexing with using methods(bfill or ffill)

import pandas as pd
import numpy as np

df1 = pd.DataFrame(np.random.randn(4, 3), columns=['col1', 'col2', 'col3'])
df2 = pd.DataFrame(np.random.randn(2, 3), columns=['col1', 'col2', 'col3'])

print(df2.reindex_like(df1, method='ffill'))

Output :

col1 col2 col3

0 1.332612 -0.479218 -1.016999
1 -1.091319 -0.844934 -0.492755
2 -1.091319 -0.844934 -0.492755
3 -1.091319 -0.844934 -0.492755

import pandas as pd
import numpy as np

df1 = pd.DataFrame(np.random.randn(4, 3), columns=['col1', 'col2', 'col3'])
df2 = pd.DataFrame(np.random.randn(2, 3), columns=['col1', 'col2', 'col3'])

print(df2.reindex_like(df1, method='bfill'))

Output :

col1 col2 col3

0 0.526663 -0.450748 0.791112
1 -1.805287 0.641050 1.864871
2 NaN NaN NaN
3 NaN NaN NaN