`sklearn.preprocessing`.OneHotEncoder¶

class sklearn.preprocessing.OneHotEncoder(n_values=None, categorical_features=None, categories=None, drop=None, sparse=True, dtype=<class ‘numpy.float64’>, handle_unknown=’error’)[source]¶

Encode categorical integer 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.

By default, the encoder derives the categories based on the unique values in each feature. Alternatively, you can also specify the categories manually. The OneHotEncoder previously assumed that the input features take on values in the range [0, max(values)). This behaviour is deprecated.

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

sklearn.preprocessing.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.
sklearn.preprocessing.LabelBinarizer: binarizes labels in a one-vs-all fashion.
sklearn.preprocessing.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
>>> enc = OneHotEncoder(handle_unknown='ignore')
>>> X = [['Male', 1], ['Female', 3], ['Female', 2]]
>>> enc.fit(X)
... 
... 
OneHotEncoder(categorical_features=None, categories=None, drop=None,
   dtype=<... 'numpy.float64'>, handle_unknown='ignore',
   n_values=None, sparse=True)

>>> 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()
array(['x0_Female', 'x0_Male', 'x1_1', 'x1_2', 'x1_3'], dtype=object)
>>> 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.]])

Methods

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

__init__(self, n_values=None, categorical_features=None, categories=None, drop=None, sparse=True, dtype=<class ‘numpy.float64’>, handle_unknown=’error’)[source]¶

fit(self, X, y=None)[source]¶

Fit OneHotEncoder to X.

Parameters:	X : array-like, shape [n_samples, n_features] The data to determine the categories of each feature.
Returns:	self

fit_transform(self, X, y=None)[source]¶

Fit OneHotEncoder to X, then transform X.

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

Parameters:	X : array-like, shape [n_samples, n_features] The data to encode.
Returns:	X_out : sparse matrix if sparse=True else a 2-d array Transformed input.

get_feature_names(self, input_features=None)[source]¶

Return feature names for output features.

Parameters:	input_features : list of string, length n_features, optional String names for input features if available. By default, “x0”, “x1”, … “xn_features” is used.
Returns:	output_feature_names : array of string, length n_output_features

get_params(self, deep=True)[source]¶

Get parameters for this estimator.

Parameters:	deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:	params : mapping of string to any Parameter names mapped to their values.

inverse_transform(self, X)[source]¶

Convert the back data 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 or sparse matrix, shape [n_samples, n_encoded_features] The transformed data.
Returns:	X_tr : array-like, shape [n_samples, n_features] Inverse transformed array.

set_params(self, **params)[source]¶

Set the parameters of this estimator.

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

Returns:	self

transform(self, X)[source]¶

Transform X using one-hot encoding.

Parameters:	X : array-like, shape [n_samples, n_features] The data to encode.
Returns:	X_out : sparse matrix if sparse=True else a 2-d array Transformed input.

Examples using `sklearn.preprocessing.OneHotEncoder`¶

Column Transformer with Mixed Types

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sklearn.preprocessing.OneHotEncoder¶

Examples using sklearn.preprocessing.OneHotEncoder¶

`sklearn.preprocessing`.OneHotEncoder¶

Examples using `sklearn.preprocessing.OneHotEncoder`¶