Feature Engineering Made Easy

Custom quantitative imputer

We will use the same structure as our custom category imputer. The main difference here is that we will utilize scikit-learn's Imputer class to actually make the transformation on our columns:

# Lets make an imputer that can apply a strategy to select columns by name
from sklearn.preprocessing import Imputerclass CustomQuantitativeImputer(TransformerMixin):
    def __init__(self, cols=None, strategy='mean'):
        self.cols = cols
        self.strategy = strategy
        
    def transform(self, df):
        X = df.copy()
        impute = Imputer(strategy=self.strategy)
        for col in self.cols:
            X[col] = impute.fit_transform(X[[col]])
        return X
    
    def fit(self, *_):
        return self

For our CustomQuantitativeImputer, we have added a strategy parameter that will allow us ...