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Filling missing values using another column values in Pandas DataFrame

Pandas
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Handling Missing Values
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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tocTable of Contents
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Consider the following DataFrame:

import pandas as pd
import numpy as np

df = pd.DataFrame({"A":[1,np.nan,3,4],"B":[5,6,7,8]})
df
A B
0 1.0 5
1 NaN 6
2 3.0 7
3 4.0 8

Solution

To fill the missing values in column A using values in column B:

df.loc[df["A"].isnull(), "A"] = df["B"]
df
A B
0 1.0 5
1 6.0 6
2 3.0 7
3 4.0 8

Explanation

Here, we first obtain a boolean mask that indicates the rows with missing values in column A:

df["A"].isnull()
0 False
1 True
2 False
3 False
Name: A, dtype: bool

We then use the loc property to assign new values to the rows with values True.

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Published by Isshin Inada
Edited by 0 others
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