Pandas: How to Use fillna() with Specific Columns

You can use the following methods with fillna() to replace NaN values in specific columns of a pandas DataFrame:

Method 1: Use fillna() with One Specific Column

df['col1'] = df['col1'].fillna(0)

Method 2: Use fillna() with Several Specific Columns

df[['col1', 'col2']] = df[['col1', 'col2']].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: Use fillna() with One Specific Column

The following code shows how to use fillna() to replace the NaN values with zeros in just 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

Notice that the NaN values have been replaced only in the “rating” column and every other column remained untouched.

Example 2: Use fillna() with Several Specific Columns

The following code shows how to use fillna() 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

Notice that the NaN values have been replaced in the “rating” and “points” columns but the other columns remain untouched.

Note: You can find the complete documentation for the pandas 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