How to Efficiently Fill Missing Values in Pandas DataFrames?

Filling Missing Values in DataFrames: Efficient Approach

In pandas, when working with incomplete datasets, it's often necessary to fill missing values. While iterating through each row is inefficient, fillna offers a convenient solution for filling missing values across columns.

Consider the following DataFrame with missing values in the "Cat1" column:

        Day  Cat1  Cat2
0         1    cat   mouse
1         2    dog   elephant
2         3    cat   giraf
3         4    NaN   ant

To fill the missing value in "Cat1" for the fourth row using values from "Cat2," we can utilize the fillna method as follows:

df['Cat1'].fillna(df['Cat2'])

This approach provides a quick and memory-efficient solution for filling missing values in large datasets. The fillna method takes another column as an argument and uses matching indexes to replace missing values.

The result:

        Day  Cat1  Cat2
0         1    cat   mouse
1         2    dog   elephant
2         3    cat   giraf
3         4    ant   ant

By utilizing this efficient method to fill missing values in pandas, developers can ensure data integrity and enhance the accuracy of their data analysis.

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