`sklearn.preprocessing`.OneHotEncoder¶

class sklearn.preprocessing.OneHotEncoder(*, categories='auto', drop=None, sparse=True, dtype=<class 'numpy.float64'>, handle_unknown='error', min_frequency=None, max_categories=None)[source]¶

Encode categorical features as a one-hot numeric array.

The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The features are encoded using a one-hot (aka ‘one-of-K’ or ‘dummy’) encoding scheme. This creates a binary column for each category and returns a sparse matrix or dense array (depending on the sparse parameter)

By default, the encoder derives the categories based on the unique values in each feature. Alternatively, you can also specify the categories manually.

This encoding is needed for feeding categorical data to many scikit-learn estimators, notably linear models and SVMs with the standard kernels.

Note: a one-hot encoding of y labels should use a LabelBinarizer instead.

See also

OrdinalEncoder: Performs an ordinal (integer) encoding of the categorical features.
sklearn.feature_extraction.DictVectorizer: Performs a one-hot encoding of dictionary items (also handles string-valued features).
sklearn.feature_extraction.FeatureHasher: Performs an approximate one-hot encoding of dictionary items or strings.
LabelBinarizer: Binarizes labels in a one-vs-all fashion.
MultiLabelBinarizer: Transforms between iterable of iterables and a multilabel format, e.g. a (samples x classes) binary matrix indicating the presence of a class label.

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.

>>> from sklearn.preprocessing import OneHotEncoder

One can discard categories not seen during fit:

>>> enc = OneHotEncoder(handle_unknown='ignore')
>>> X = [['Male', 1], ['Female', 3], ['Female', 2]]
>>> enc.fit(X)
OneHotEncoder(handle_unknown='ignore')
>>> enc.categories_
[array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]
>>> enc.transform([['Female', 1], ['Male', 4]]).toarray()
array([[1., 0., 1., 0., 0.],
       [0., 1., 0., 0., 0.]])
>>> enc.inverse_transform([[0, 1, 1, 0, 0], [0, 0, 0, 1, 0]])
array([['Male', 1],
       [None, 2]], dtype=object)
>>> enc.get_feature_names_out(['gender', 'group'])
array(['gender_Female', 'gender_Male', 'group_1', 'group_2', 'group_3'], ...)

One can always drop the first column for each feature:

>>> drop_enc = OneHotEncoder(drop='first').fit(X)
>>> drop_enc.categories_
[array(['Female', 'Male'], dtype=object), array([1, 2, 3], dtype=object)]
>>> drop_enc.transform([['Female', 1], ['Male', 2]]).toarray()
array([[0., 0., 0.],
       [1., 1., 0.]])

Or drop a column for feature only having 2 categories:

>>> drop_binary_enc = OneHotEncoder(drop='if_binary').fit(X)
>>> drop_binary_enc.transform([['Female', 1], ['Male', 2]]).toarray()
array([[0., 1., 0., 0.],
       [1., 0., 1., 0.]])

Infrequent categories are enabled by setting max_categories or min_frequency.

>>> import numpy as np
>>> X = np.array([["a"] * 5 + ["b"] * 20 + ["c"] * 10 + ["d"] * 3], dtype=object).T
>>> ohe = OneHotEncoder(max_categories=3, sparse=False).fit(X)
>>> ohe.infrequent_categories_
[array(['a', 'd'], dtype=object)]
>>> ohe.transform([["a"], ["b"]])
array([[0., 0., 1.],
       [1., 0., 0.]])

Methods

`fit`(X[, y])	Fit OneHotEncoder to X.
`fit_transform`(X[, y])	Fit OneHotEncoder to X, then transform X.
`get_feature_names`([input_features])	DEPRECATED: get_feature_names is deprecated in 1.0 and will be removed in 1.2.
`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_params`(**params)	Set the parameters of this estimator.
`transform`(X)	Transform X using one-hot encoding.

fit(X, y=None)[source]¶

Fit OneHotEncoder to X.

Parameters:

Xarray-like of shape (n_samples, n_features): The data to determine the categories of each feature.
yNone: Ignored. This parameter exists only for compatibility with Pipeline.

Returns:

self: Fitted encoder.

fit_transform(X, y=None)[source]¶

Fit OneHotEncoder to X, then transform X.

Equivalent to fit(X).transform(X) but more convenient.

Parameters:

Xarray-like of shape (n_samples, n_features): The data to encode.
yNone: Ignored. This parameter exists only for compatibility with Pipeline.

Returns:

X_out{ndarray, sparse matrix} of shape (n_samples, n_encoded_features): Transformed input. If sparse=True, a sparse matrix will be returned.

get_feature_names(input_features=None)[source]¶

DEPRECATED: get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.

Return feature names for output features.

For a given input feature, if there is an infrequent category, the most ‘infrequent_sklearn’ will be used as a feature name.

Parameters:

input_featureslist of str of shape (n_features,): String names for input features if available. By default, “x0”, “x1”, … “xn_features” is used.

Returns:

output_feature_namesndarray of shape (n_output_features,): Array of feature names.

get_feature_names_out(input_features=None)[source]¶

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].
If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:

feature_names_outndarray of str objects: Transformed feature names.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

property infrequent_categories_¶: Infrequent categories for each feature.

inverse_transform(X)[source]¶

Convert the data back to the original representation.

When unknown categories are encountered (all zeros in the one-hot encoding), None is used to represent this category. If the feature with the unknown category has a dropped category, the dropped category will be its inverse.

For a given input feature, if there is an infrequent category, ‘infrequent_sklearn’ will be used to represent the infrequent category.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_encoded_features): The transformed data.

Returns:

X_trndarray of shape (n_samples, n_features): Inverse transformed array.

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: