You can use the following syntax to replace NaN values in a column of a pandas DataFrame with the values from another column:

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

This particular syntax will replace any NaN values in **col1** with the corresponding values in **col2**.

The following example shows how to use this syntax in practice.

**Example: Replace Missing Values with Another Column**

Suppose we have the following pandas DataFrame with some missing values:

import numpy as np import pandas as pd #create DataFrame with some NaN values df = pd.DataFrame({'team1': ['Mavs', np.nan, 'Nets', 'Hawks', np.nan, 'Jazz'], 'team2': ['Spurs', 'Lakers', 'Kings', 'Celtics', 'Heat', 'Magic']}) #view DataFrame df team1 team2 0 Mavs Spurs 1 NaN Lakers 2 Nets Kings 3 Hawks Celtics 4 NaN Heat 5 Jazz Magic

Notice that there are two NaN values in the **team1** column.

We can use the **fillna()** function to fill the NaN values in the **team1 **column with the corresponding value in the **team2 **column:

#fill NaNs in team1 column with corresponding values in team2 column df['team1'] = df['team1'].fillna(df['team2']) #view updated DataFrame df team1 team2 0 Mavs Spurs 1 Lakers Lakers 2 Nets Kings 3 Hawks Celtics 4 Heat Heat 5 Jazz Magic

Notice that both of the NaN values in the **team1** column were replaced with the corresponding values in the **team2** column.

**Note**: 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