How to fill NaN Values with Mean in Pandas?

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

Here are three common ways to use this function:

Method 1: Fill NaN Values in One Column with Mean

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

Method 2: Fill NaN Values in Multiple Columns with Mean

df[['col1', 'col2']] = df[['col1', 'col2']].fillna(df[['col1', 'col2']].mean())

Method 3: Fill NaN Values in All Columns with Mean

df = df.fillna(df.mean())

The following examples show how to use each method in practice 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: Fill NaN Values in One Column with Mean

The following code shows how to fill the NaN values in the rating column with the mean value of the rating column:

#fill NaNs with column mean in 'rating' column
df['rating'] = df['rating'].fillna(df['rating'].mean())

#view updated DataFrame 
df

	rating	points	assists	rebounds
0	85.125	25.0	5.0	11
1	85.000	NaN	7.0	8
2	85.125	14.0	7.0	10
3	88.000	16.0	NaN	6
4	94.000	27.0	5.0	6
5	90.000	20.0	7.0	9
6	76.000	12.0	6.0	6
7	75.000	15.0	9.0	10
8	87.000	14.0	9.0	10
9	86.000	19.0	5.0	7

The mean value in the rating column was 85.125 so each of the NaN values in the rating column were filled with this value.

Example 2: Fill NaN Values in Multiple Columns with Mean

The following code shows how to fill the NaN values in both the rating and points columns with their respective column means:

#fill NaNs with column means in 'rating' and 'points' columns
df[['rating', 'points']] = df[['rating', 'points']].fillna(df[['rating', 'points']].mean())

#view updated DataFrame
df

	rating	points	assists	rebounds
0	85.125	25.0	5.0	11
1	85.000	18.0	7.0	8
2	85.125	14.0	7.0	10
3	88.000	16.0	NaN	6
4	94.000	27.0	5.0	6
5	90.000	20.0	7.0	9
6	76.000	12.0	6.0	6
7	75.000	15.0	9.0	10
8	87.000	14.0	9.0	10
9	86.000	19.0	5.0	7

Example 3: Fill NaN Values in All Columns with Mean

The following code shows how to fill the NaN values in each column with the column means:

#fill NaNs with column means in each column 
df = df.fillna(df.mean())

#view updated DataFrame
df

        rating	points	assists	  rebounds
0	85.125	25.0	5.000000  11
1	85.000	18.0	7.000000  8
2	85.125	14.0	7.000000  10
3	88.000	16.0	6.666667  6
4	94.000	27.0	5.000000  6
5	90.000	20.0	7.000000  9
6	76.000	12.0	6.000000  6
7	75.000	15.0	9.000000  10
8	87.000	14.0	9.000000  10
9	86.000	19.0	5.000000  7

Notice that the NaN values in each column were filled with their column mean.

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