dtoolkit.transformer.OneHotEncoder#

class dtoolkit.transformer.OneHotEncoder(*, sparse: bool = False, sparse_output: bool = False, categories_with_parent: bool = False, categories='auto', drop=None, dtype=<class 'numpy.float64'>, handle_unknown: ~typing.Literal['error', 'ignore', 'infrequent_if_exist'] = 'error', min_frequency: int | float = None, max_categories: int = None)[source]#

Encode categorical features as a one-hot numeric array.

Parameters

categories_with_parentbool, default False: Returned column would hook parent labels if True else would be categories.
sparsebool, default False: Will return sparse matrix if True else will return an array.
Other parameters: See sklearn.preprocessing.OneHotEncoder.

Notes

Different to sklearn.preprocessing.OneHotEncoder. The result would return a DataFrame which uses categories as columns.

Examples

Given a dataset with two features, we let the encoder find the unique values per feature and transform the data to a binary one-hot encoding.

DataFrame in, DataFrame out with categories as columns.

>>> from dtoolkit.transformer import OneHotEncoder
>>> import pandas as pd
>>> X = [['Male', 1], ['Female', 3], ['Female', 2]]
>>> df = pd.DataFrame(X, columns=['gender', 'number'])
>>> df
    gender  number
0    Male       1
1  Female       3
2  Female       2
>>> enc = OneHotEncoder()
>>> enc.fit_transform(df)
   Female  Male    1    2    3
0     0.0   1.0  1.0  0.0  0.0
1     1.0   0.0  0.0  0.0  1.0
2     1.0   0.0  0.0  1.0  0.0

The encoded data also could hook parent labels.

>>> enc = OneHotEncoder(categories_with_parent=True)
>>> enc.fit_transform(df)
   gender_Female  gender_Male  number_1  number_2  number_3
0            0.0          1.0       1.0       0.0       0.0
1            1.0          0.0       0.0       0.0       1.0
2            1.0          0.0       0.0       1.0       0.0

Attributes

infrequent_categories_: Infrequent categories for each feature.

Methods

`fit`(X[, y])	Fit OneHotEncoder to X.
`fit_transform`(X[, y])	Fit to data, then transform it.
`get_feature_names_out`([input_features])	Get output feature names for transformation.
`get_params`([deep])	Get parameters for this estimator.
`inverse_transform`(X)	Convert the data back to the original representation.
`set_output`(*[, transform])	Set output container.
`set_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Transform X using one-hot encoding.