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 Median**

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

**Method 2: Fill NaN Values in Multiple Columns with Median**

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

**Method 3: Fill NaN Values in All Columns with Median**

df = df.fillna(df.median())

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 Median**

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

#fill NaNs with column mean in 'rating' column df['rating'] = df['rating'].fillna(df['rating'].median()) #view updated DataFrame df rating points assists rebounds 0 86.5 25.0 5.0 11 1 85.0 NaN 7.0 8 2 86.5 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

The median value in the **rating** column was **86.5 **so each of the NaN values in the **rating** column were filled with this value.

**Example 2: ****Fill NaN Values in Multiple Columns with Median**

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

#fill NaNs with column means in 'rating' and 'points' columns df[['rating', 'points']] = df[['rating', 'points']].fillna(df[['rating', 'points']].median()) #view updated DataFrame df rating points assists rebounds 0 86.5 25.0 5.0 11 1 85.0 16.0 7.0 8 2 86.5 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

The NaN values in both the **ratings** and **points** columns were filled with their respective column medians.

**Example 3: Fill NaN Values in All Columns with Median**

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

#fill NaNs with column means in each column df = df.fillna(df.median()) #view updated DataFrame df rating points assists rebounds 0 86.5 25.0 5.0 11 1 85.0 16.0 7.0 8 2 86.5 14.0 7.0 10 3 88.0 16.0 7.0 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

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

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