Sklearn preprocessing - PolynomialFeatures - How to keep column names/headers of the output array / dataframe

Sklearn preprocessing - PolynomialFeatures - How to keep column names/headers of the output array / dataframe

When using PolynomialFeatures from scikit-learn to generate polynomial features, you can retain the column names or headers of the resulting array or DataFrame by combining PolynomialFeatures with ColumnTransformer and Pipeline. Here's how you can do it:

import numpy as np import pandas as pd from sklearn.compose import ColumnTransformer from sklearn.pipeline import Pipeline from sklearn.preprocessing import PolynomialFeatures # Sample data data = { 'feature1': [1, 2, 3], 'feature2': [4, 5, 6] } df = pd.DataFrame(data) # Define PolynomialFeatures poly = PolynomialFeatures(degree=2, include_bias=False) # Define ColumnTransformer preprocessor = ColumnTransformer( transformers=[('poly', poly, ['feature1', 'feature2'])] ) # Define Pipeline pipeline = Pipeline(steps=[('preprocessor', preprocessor)]) # Fit and transform the data transformed_data = pipeline.fit_transform(df) # Get column names from PolynomialFeatures and ColumnTransformer column_names = preprocessor.named_transformers_['poly'].get_feature_names(df.columns) # Create a DataFrame with the transformed data and column names transformed_df = pd.DataFrame(transformed_data, columns=column_names) print(transformed_df)

In this example:

  • We define a PolynomialFeatures object with the desired degree and include_bias set to False.
  • We use ColumnTransformer to apply the polynomial transformation to specific columns.
  • We define a Pipeline that encapsulates the transformation process.
  • After fitting and transforming the data using the pipeline, we retrieve the column names from the PolynomialFeatures object and the ColumnTransformer using preprocessor.named_transformers_.
  • Finally, we create a DataFrame with the transformed data and column names.

This approach allows you to generate polynomial features while preserving the column names of the original features and the new polynomial features.

Examples

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    • Code:
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