`py_predpurchase.function_preprocessing`

Module Contents

Functions

numerical_categorical_preprocess(X_train, X_test, ...)

Applies preprocessing transformations to the data, including scaling, encoding, and passing through features as specified.

py_predpurchase.function_preprocessing.numerical_categorical_preprocess(X_train, X_test, y_train, y_test, numeric_features, categorical_features)[source]

Applies preprocessing transformations to the data, including scaling, encoding, and passing through features as specified. This function requires target data to be provided and includes it in the output DataFrames.

Parameters:

X_trainDataFrame: Training feature data.
X_testDataFrame: Testing feature data.
y_trainDataFrame or Series: Training target data.
y_testDataFrame or Series: Testing target data.
numeric_featureslist: Names of numeric features to scale.
categorical_featureslist: Names of categorical features to encode.

Returns:

Tuple: Contains preprocessed training and testing DataFrames including target data, and transformed column names.

Examples:

Assume you want to transform the following features and your data set has already been split into train and test

>>> numeric_features = ['feature1', 'feature2']
>>> categorical_features = ['feature3', 'feature4']
>>> train_transformed, test_transformed, transformed_columns = numerical_categorical_preprocess(
        X_train, X_test, y_train, y_test, numeric_features, categorical_features)

The function will transform feature1,2,3,4 accordingly, carrying out scaling and one-hot encoding and storing the preprocessed data in ‘train_transformed’ and ‘test_transformed’. Column names will also be stored in ‘transformed_columns’.