How to Efficiently Extract Multiple Columns from a Pandas DataFrame using a Custom Function?

Multiple Column Extraction with Pandas Function

This question explores the issue of extracting multiple columns from a pandas DataFrame using a custom function. The return type of the function becomes problematic as it needs to align properly with the desired output.

Initially, the recommended approach was to iterate over the rows using df.iterrows(). However, this method was later found to be significantly slower. Consequently, the author opted for splitting the function into six distinct map(lambda ...) calls to extract the desired columns.

A more efficient approach is to utilize the zip function to assign the outputs of the custom function to multiple columns simultaneously. This method is illustrated using an example where a function named powers is applied to a column of numbers. The function calculates six power values for each number and the results are assigned to six new columns in the DataFrame.

This approach is both elegant and efficient, and it avoids the need for iterating over the rows of the DataFrame. It is a recommended technique for extracting multiple columns from a DataFrame based on a custom function.

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