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What is the Efficient way to fill missing values using groupby

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What is the Efficient way to fill missing values using groupby

Groupby
Python
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Goutamp777

The efficient way to fill missing values using groupby is to use the fillna() method along with the groupby() method. The fillna() method is used to fill the missing values with a specified value or method. The groupby() method is used to group the data by a specific column or columns.


For example, let's say we have a dataset with missing values in the "Age" column, and we want to fill the missing values with the mean age of each group based on the "Gender" column. We can use the following code:


df['Age'] = df.groupby('Gender')['Age'].fillna(df['Age'].mean())

This code will group the data by the "Gender" column and fill the missing values in the "Age" column with the mean age of each group. This is an efficient way to fill missing values using groupby because it only calculates the mean age of each group once and applies it to all the missing values in that group.

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