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# Filling missing values using another column values in Pandas DataFrame

schedule Aug 10, 2023
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local_offer
PythonPandas
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Consider the following DataFrame:

``` import pandas as pdimport numpy as npdf = pd.DataFrame({"A":[1,np.nan,3,4],"B":[5,6,7,8]})df A B0 1.0 51 NaN 62 3.0 73 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 B0 1.0 51 6.0 62 3.0 73 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 False1 True2 False3 FalseName: A, dtype: bool ```

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

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