Python Feature Engineering Cookbook

How it works...

In this recipe, we added missing value indicators to categorical and numerical variables in the Credit Approval Data Set using NumPy, scikit-learn, and Feature-engine. To add missing indicators using NumPy, we used the where() method, which created a new vector after scanning all the observations in a variable, assigning the value of 1 if there was a missing observation or 0 otherwise. We captured the indicators in columns with the name of the original variable, plus _NA.

To add a missing indicator with Feature-engine, we created an instance of AddNaNBinaryImputer() and fitted it to the train set. Then, we used the transform() method to add missing indicators to the train and test sets. Finally, to add missing indicators with ...