Feature Engineering Made Easy

Custom category imputer

First, we will utilize the scikit-learn TransformerMixin base class to create our own custom categorical imputer. This transformer (and all other custom transformers in this chapter) will work as an element in a pipeline with a fit and transform method.

The following code block will become very familiar throughout this chapter, so we will go over each line in detail:

from sklearn.base import TransformerMixin

class CustomCategoryImputer(TransformerMixin):
    def __init__(self, cols=None):
        self.cols = cols
        
    def transform(self, df):
        X = df.copy()
        for col in self.cols:
            X[col].fillna(X[col].value_counts().index[0], inplace=True)
        return X
    
    def fit(self, *_):
        return self

There is a lot happening in this code block, so let's break ...