25 April, 2019

The “fillna” function in Pandas can replace missing values with a given constant value.

Example:

import pandas as pd

import numpy as np

df = pd.DataFrame([[np.nan], [2], [np.nan], [0]])

df

This is the dataframe with few missing values.

Output:

	0
0	NaN
1	2.0
2	NaN
3	0.0

Example:

Replace the missing values by value ‘47’

df.fillna(47)

Output:

	0
0	47.0
1	2.0
2	47.0
3	0.0

You can also replace a missing value with the next (or previous) value in the data frame!

Example:

Replace missing value with previous value

df.fillna(method=”ffill”)

Output:

	0
0	NaN
1	2.0
2	2.0
3	0.0

Note that the first value cannot be replaced because nothing is preceding it.

You can also use the value of the next row to fill a missing value.

Example:

Replace missing value with next available value

df.fillna(method=”bfill”)

Output:

	0
0	2.0
1	2.0
2	0.0
3	0.0