Looking ahead, when you invoke the .fit_transform(X) method on a Pipeline object, it applies each transformer to the entire set of features in X. However, this behavior may not always be desired.

For instance, you might not want to encode numerical values or you may need to apply different transformers to specific columns — such as using OrdinalEncoder for ordinal features and OneHotEncoder for nominal features.

The ColumnTransformer resolves this issue by allowing each column to be treated separately. To create a ColumnTransformer, you can utilize the make_column_transformer function from the sklearn.compose module.

The function takes as arguments tuples with the transformer and the list of columns to which this transformer should be applied.

For example, we can create a ColumnTransformer that applies the OrdinalEncoder only to the 'education' column and the OneHotEncoder only to the 'gender' column.

ct = make_column_transformer(
   (OrdinalEncoder(), ['education']),
   (OneHotEncoder(), ['gender']), remainder='passthrough'
)

For example, we will use an exams.csv file containing nominal columns ('gender', 'race/ethnicity', 'lunch', 'test preparation course'). It also contains an ordinal column, 'parental level of education'.

With the help of ColumnTransformer, we can simultaneously transform nominal data using OneHotEncoder and ordinal data using OrdinalEncoder in a single step.

"As you might expect, the ColumnTransformer is a transformer, so it includes all the necessary methods for a transformer, such as .fit(), .fit_transform(), and .transform().