OneHotEncoder#

class sklearn.preprocessing.OneHotEncoder(*, categories='auto', drop=None, sparse_output=True, dtype=<class 'numpy.float64'>, handle_unknown='error', min_frequency=None, max_categories=None, feature_name_combiner='concat')[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. For a comparison of different encoders, refer to: Comparing Target Encoder with Other Encoders.

Parameters:

categoriesauto 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.

Added 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.

When max_categories or min_frequency is configured to group infrequent categories, the dropping behavior is handled after the grouping.

Added 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.

sparse_outputbool, default=True

When True, it returns a SciPy sparse matrix/array in Compressed Sparse Row (CSR) format.

Added in version 1.2: sparse was renamed to sparse_output

dtypenumber type, default=np.float64

Desired dtype of output.

handle_unknown{error, ignore, infrequent_if_exist, warn}, 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.
warn : When an unknown category is encountered during transform a warning is issued, and the encoding then proceeds as described for handle_unknown="infrequent_if_exist".

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

Added in version 1.6: The option "warn" was added in 1.6.

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.

Added 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.

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

feature_name_combinerconcat or callable, default=concat

Callable with signature def callable(input_feature, category) that returns a string. This is used to create feature names to be returned by get_feature_names_out.

"concat" concatenates encoded feature name and category with feature + "_" + str(category).E.g. feature X with values 1, 6, 7 create feature names X_1, X_6, X_7.

Added in version 1.3.

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 isnt 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 an 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.

Added 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.

Added in version 1.0.

feature_name_combinercallable or None

Callable with signature def callable(input_feature, category) that returns a string. This is used to create feature names to be returned by get_feature_names_out.

Added in version 1.3.

See also

OrdinalEncoder: Performs an ordinal (integer) encoding of the categorical features.
TargetEncoder: Encodes categorical features using the target.
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.]])

One can change the way feature names are created.

>>> def custom_combiner(feature, category):
...     return str(feature) + "_" + type(category).__name__ + "_" + str(category)
>>> custom_fnames_enc = OneHotEncoder(feature_name_combiner=custom_combiner).fit(X)
>>> custom_fnames_enc.get_feature_names_out()
array(['x0_str_Female', 'x0_str_Male', 'x1_int_1', 'x1_int_2', 'x1_int_3'],
      dtype=object)

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.]])

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. Pass only if the estimator accepts additional params in its fit method.

Returns:

X_newndarray array of shape (n_samples, n_features_new): Transformed array.

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_metadata_routing()[source]#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRequest: A MetadataRequest encapsulating routing information.

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.

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

set_output(*, transform=None)[source]#

Set output container.

Refer to the user guide for more details and Introducing the set_output API for an example on how to use the API.

Parameters:

transform{default, pandas, polars}, default=None

Configure output of transform and fit_transform.

"default": Default output format of a transformer
"pandas": DataFrame output
"polars": Polars output
None: Transform configuration is unchanged

Added in version 1.4: "polars" option was added.

Returns:

selfestimator instance: Estimator instance.

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 its 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 sparse_output=True (default), it returns a SciPy sparse in CSR format.

If there are infrequent categories for a feature, set by specifying max_categories or min_frequency, the infrequent categories are 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.