Pandas: How to Fill NaN Values with Values from Another Column?

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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 .