How to Use Pandas fillna() to Replace NaN Values

You can use the fillna() function to replace NaN values in a pandas DataFrame.

This function uses the following basic syntax:

#replace NaN values in one column
df['col1'] = df['col1'].fillna(0)

#replace NaN values in multiple columns
df[['col1', 'col2']] = df[['col1', 'col2']].fillna(0) 

#replace NaN values in all columns
df = df.fillna(0)

This tutorial explains how to use this function with the following pandas DataFrame:

import numpy as np
import pandas as pd

#create DataFrame with some NaN values
df = pd.DataFrame({'rating': [np.nan, 85, np.nan, 88, 94, 90, 76, 75, 87, 86],
                   'points': [25, np.nan, 14, 16, 27, 20, 12, 15, 14, 19],
                   'assists': [5, 7, 7, np.nan, 5, 7, 6, 9, 9, 5],
                   'rebounds': [11, 8, 10, 6, 6, 9, 6, 10, 10, 7]})

#view DataFrame
df

        rating	points	assists	rebounds
0	NaN	25.0	5.0	11
1	85.0	NaN	7.0	8
2	NaN	14.0	7.0	10
3	88.0	16.0	NaN	6
4	94.0	27.0	5.0	6
5	90.0	20.0	7.0	9
6	76.0	12.0	6.0	6
7	75.0	15.0	9.0	10
8	87.0	14.0	9.0	10
9	86.0	19.0	5.0	7

Example 1: Replace NaN Values in One Column

The following code shows how to replace the NaN values with zeros in the “rating” column:

#replace NaNs with zeros in 'rating' column
df['rating'] = df['rating'].fillna(0)

#view DataFrame 
df

	rating	points	assists	rebounds
0	0.0	25.0	5.0	11
1	85.0	NaN	7.0	8
2	0.0	14.0	7.0	10
3	88.0	16.0	NaN	6
4	94.0	27.0	5.0	6
5	90.0	20.0	7.0	9
6	76.0	12.0	6.0	6
7	75.0	15.0	9.0	10
8	87.0	14.0	9.0	10
9	86.0	19.0	5.0	7

Example 2: Replace NaN Values in Multiple Columns

The following code shows how to replace the NaN values with zeros in both the “rating” and “points” columns:

#replace NaNs with zeros in 'rating' and 'points' columns
df[['rating', 'points']] = df[['rating', 'points']].fillna(0)

#view DataFrame
df

	rating	points	assists	rebounds
0	0.0	25.0	5.0	11
1	85.0	0.0	7.0	8
2	0.0	14.0	7.0	10
3	88.0	16.0	NaN	6
4	94.0	27.0	5.0	6
5	90.0	20.0	7.0	9
6	76.0	12.0	6.0	6
7	75.0	15.0	9.0	10
8	87.0	14.0	9.0	10
9	86.0	19.0	5.0	7

Example 3: Replace NaN Values in All Columns

The following code shows how to replace the NaN values in every column with zeros:

#replace NaNs with zeros in all columns 
df = df.fillna(0)

#view DataFrame
df

        rating	points	assists	rebounds
0	0.0	25.0	5.0	11
1	85.0	0.0	7.0	8
2	0.0	14.0	7.0	10
3	88.0	16.0	0.0	6
4	94.0	27.0	5.0	6
5	90.0	20.0	7.0	9
6	76.0	12.0	6.0	6
7	75.0	15.0	9.0	10
8	87.0	14.0	9.0	10
9	86.0	19.0	5.0	7

You can find the complete online documentation for the fillna() function here.

Additional Resources

The following tutorials explain how to perform other common operations in pandas:

How to Count Missing Values in Pandas
How to Drop Rows with NaN Values in Pandas
How to Drop Rows that Contain a Specific Value in Pandas