sklearn.preprocessing.OneHotEncoder

class sklearn.preprocessing.OneHotEncoder(*, categories='auto', drop=None, sparse='deprecated', sparse_output=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_output 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.

Read more in the User Guide.

Parameters:
categories‘auto’ or a list of array-like, default=’auto’

Categories (unique values) per feature:

  • ‘auto’ : Determine categories automatically from the training data.

  • list : categories[i] holds the categories expected in the ith column. The passed categories should not mix strings and numeric values within a single feature, and should be sorted in case of numeric values.

The used categories can be found in the categories_ attribute.

New in version 0.20.

drop{‘first’, ‘if_binary’} or an array-like of shape (n_features,), default=None

Specifies a methodology to use to drop one of the categories per feature. This is useful in situations where perfectly collinear features cause problems, such as when feeding the resulting data into an unregularized linear regression model.

However, dropping one category breaks the symmetry of the original representation and can therefore induce a bias in downstream models, for instance for penalized linear classification or regression models.

  • None : retain all features (the default).

  • ‘first’ : drop the first category in each feature. If only one category is present, the feature will be dropped entirely.

  • ‘if_binary’ : drop the first category in each feature with two categories. Features with 1 or more than 2 categories are left intact.

  • array : drop[i] is the category in feature X[:, i] that should be dropped.

New in version 0.21: The parameter drop was added in 0.21.

Changed in version 0.23: The option drop='if_binary' was added in 0.23.

Changed in version 1.1: Support for dropping infrequent categories.

sparsebool, default=True

Will return sparse matrix if set True else will return an array.

Deprecated since version 1.2: sparse is deprecated in 1.2 and will be removed in 1.4. Use sparse_output instead.

sparse_outputbool, default=True

Will return sparse matrix if set True else will return an array.

New in version 1.2: sparse was renamed to sparse_output

dtypenumber type, default=float

Desired dtype of output.

handle_unknown{‘error’, ‘ignore’, ‘infrequent_if_exist’}, default=’error’

Specifies the way unknown categories are handled during transform.

  • ‘error’ : Raise an error if an unknown category is present during transform.

  • ‘ignore’ : When an unknown category is encountered during transform, the resulting one-hot encoded columns for this feature will be all zeros. In the inverse transform, an unknown category will be denoted as None.

  • ‘infrequent_if_exist’ : When an unknown category is encountered during transform, the resulting one-hot encoded columns for this feature will map to the infrequent category if it exists. The infrequent category will be mapped to the last position in the encoding. During inverse transform, an unknown category will be mapped to the category denoted 'infrequent' if it exists. If the 'infrequent' category does not exist, then transform and inverse_transform will handle an unknown category as with handle_unknown='ignore'. Infrequent categories exist based on min_frequency and max_categories. Read more in the User Guide.

Changed in version 1.1: 'infrequent_if_exist' was added to automatically handle unknown categories and infrequent categories.

min_frequencyint or float, default=None

Specifies the minimum frequency below which a category will be considered infrequent.

  • If int, categories with a smaller cardinality will be considered infrequent.

  • If float, categories with a smaller cardinality than min_frequency * n_samples will be considered infrequent.

New in version 1.1: Read more in the User Guide.

max_categoriesint, default=None

Specifies an upper limit to the number of output features for each input feature when considering infrequent categories. If there are infrequent categories, max_categories includes the category representing the infrequent categories along with the frequent categories. If None, there is no limit to the number of output features.

New in version 1.1: Read more in the User Guide.

Attributes:
categories_list of arrays

The categories of each feature determined during fitting (in order of the features in X and corresponding with the output of transform). This includes the category specified in drop (if any).

drop_idx_array of shape (n_features,)
  • drop_idx_[i] is the index in categories_[i] of the category to be dropped for each feature.

  • drop_idx_[i] = None if no category is to be dropped from the feature with index i, e.g. when drop='if_binary' and the feature isn’t binary.

  • drop_idx_ = None if all the transformed features will be retained.

If infrequent categories are enabled by setting min_frequency or max_categories to a non-default value and drop_idx[i] corresponds to a infrequent category, then the entire infrequent category is dropped.

Changed in version 0.23: Added the possibility to contain None values.

infrequent_categories_list of ndarray

Infrequent categories for each feature.

n_features_in_int

Number of features seen during fit.

New in version 1.0.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen during fit. Defined only when X has feature names that are all strings.

New in version 1.0.

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_output=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 to data, then transform it.

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, **fit_params)[source]

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters:
Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns:
X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

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:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

transform(X)[source]

Transform X using one-hot encoding.

If there are infrequent categories for a feature, the infrequent categories will be grouped into a single category.

Parameters:
Xarray-like of shape (n_samples, n_features)

The data to encode.

Returns:
X_out{ndarray, sparse matrix} of shape (n_samples, n_encoded_features)

Transformed input. If sparse_output=True, a sparse matrix will be returned.

Examples using sklearn.preprocessing.OneHotEncoder

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