Imputing categorical variables

Categorical variables usually contain strings as values, instead of numbers. We replace missing data in categorical variables with the most frequent category, or with a different string. Frequent categories are estimated using the train set and then used to impute values in the train, test, and future datasets. Thus, we need to learn and store these values, which we can do using scikit-learn and feature-engine’s out-of-the-box transformers. In this recipe, we will replace missing data in categorical variables with the most frequent category, or with an arbitrary string.

How to do it...

To begin, let’s make a few imports and prepare the data:

1. Let’s import pandas and the required functions and classes from scikit-learn and feature-engine:

   import pandas as pd
   from sklearn.model_selection import train_test_split
   from sklearn.impute import SimpleImputer
   from sklearn.compose import ColumnTransformer
   from feature_engine.imputation...