`sklearn.preprocessing`.OneHotEncoder¶

class sklearn.preprocessing.OneHotEncoder(*, categories='auto', drop=None, sparse=True, dtype=<class 'numpy.float64'>, handle_unknown='error')[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(['gender', 'group'])
array(['gender_Female', 'gender_Male', 'group_1', 'group_2', 'group_3'],
  dtype=object)

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.]])

Methods

`fit`(X[, y])	Fit OneHotEncoder to X.
`fit_transform`(X[, y])	Fit OneHotEncoder to X, then transform X.
`get_feature_names`([input_features])	Return feature names for output features.
`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

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]¶

Return feature names for output features.

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_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.

In case unknown categories are encountered (all zeros in the one-hot encoding), None is used to represent this 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