OneHotEncoder #

classsklearn.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 orstrings, 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 andreturns a sparse matrix or dense array (depending on thesparse_outputparameter).

By default, the encoder derives the categories based on the unique valuesin each feature. Alternatively, you can also specify thecategoriesmanually.

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

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

Read more in theUser Guide.For a comparison of different encoders, refer to:Comparing Target Encoder with Other Encoders.

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 ithcolumn. The passed categories should not mix strings and numericvalues within a single feature, and should be sorted in case ofnumeric values.

The used categories can be found in thecategories_ 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 perfeature. This is useful in situations where perfectly collinearfeatures cause problems, such as when feeding the resulting datainto an unregularized linear regression model.

However, dropping one category breaks the symmetry of the originalrepresentation 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 onecategory is present, the feature will be dropped entirely.
‘if_binary’ : drop the first category in each feature with twocategories. Features with 1 or more than 2 categories areleft intact.
array :drop[i] is the category in featureX[:,i] thatshould be dropped.

Whenmax_categories ormin_frequency is configured to groupinfrequent categories, the dropping behavior is handled after thegrouping.

Added in version 0.21:The parameterdrop was added in 0.21.

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

Changed in version 1.1:Support for dropping infrequent categories.

sparse_outputbool, default=True

WhenTrue, it returns ascipy.sparse.csr_matrix,i.e. a sparse matrix in “Compressed Sparse Row” (CSR) format.

Added in version 1.2:sparse was renamed tosparse_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 duringtransform.

‘error’ : Raise an error if an unknown category is present during transform.
‘ignore’ : When an unknown category is encountered duringtransform, the resulting one-hot encoded columns for this featurewill be all zeros. In the inverse transform, an unknown categorywill be denoted as None.
‘infrequent_if_exist’ : When an unknown category is encounteredduring transform, the resulting one-hot encoded columns for thisfeature will map to the infrequent category if it exists. Theinfrequent category will be mapped to the last position in theencoding. During inverse transform, an unknown category will bemapped to the category denoted'infrequent' if it exists. If the'infrequent' category does not exist, thentransform andinverse_transform will handle an unknown category as withhandle_unknown='ignore'. Infrequent categories exist based onmin_frequency andmax_categories. Read more in theUser Guide.
‘warn’ : When an unknown category is encountered during transforma warning is issued, and the encoding then proceeds as described forhandle_unknown="infrequent_if_exist".

Changed in version 1.1:'infrequent_if_exist' was added to automatically handle unknowncategories 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 beconsidered infrequent.

Ifint, categories with a smaller cardinality will be consideredinfrequent.
Iffloat, categories with a smaller cardinality thanmin_frequency*n_samples will be considered infrequent.

Added in version 1.1:Read more in theUser Guide.

max_categoriesint, default=None

Specifies an upper limit to the number of output features for each inputfeature when considering infrequent categories. If there are infrequentcategories,max_categories includes the category representing theinfrequent categories along with the frequent categories. IfNone,there is no limit to the number of output features.

Added in version 1.1:Read more in theUser Guide.

feature_name_combiner“concat” or callable, default=”concat”

Callable with signaturedefcallable(input_feature,category) that returns astring. This is used to create feature names to be returned byget_feature_names_out.

"concat" concatenates encoded feature name and category withfeature+"_"+str(category).E.g. feature X with values 1, 6, 7 createfeature namesX_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 outputoftransform). This includes the category specified indrop(if any).

drop_idx_array of shape (n_features,)

drop_idx_[i] is the index incategories_[i] of the categoryto be dropped for each feature.
drop_idx_[i]=None if no category is to be dropped from thefeature with indexi, e.g. whendrop='if_binary' and thefeature isn’t binary.
drop_idx_=None if all the transformed features will beretained.

If infrequent categories are enabled by settingmin_frequency ormax_categories to a non-default value anddrop_idx[i] correspondsto a infrequent category, then the entire infrequent category isdropped.

Changed in version 0.23:Added the possibility to containNone values.

infrequent_categories_list of ndarray

Infrequent categories for each feature.

n_features_in_int

Number of features seen duringfit.

Added in version 1.0.

feature_names_in_ndarray of shape (n_features_in_,)

Names of features seen duringfit. Defined only whenXhas feature names that are all strings.

Added in version 1.0.

feature_name_combinercallable or None

Callable with signaturedefcallable(input_feature,category) that returns astring. This is used to create feature names to be returned byget_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 uniquevalues per feature and transform the data to a binary one-hot encoding.

>>>fromsklearn.preprocessingimportOneHotEncoder

One can discard categories not seen duringfit:

>>>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.

>>>defcustom_combiner(feature,category):...returnstr(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 settingmax_categories ormin_frequency.

>>>importnumpyasnp>>>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 withPipeline.

Returns:

self: Fitted encoder.

fit_transform(X,y=None,**fit_params)[source]#

Fit to data, then transform it.

Fits transformer toX andy with optional parametersfit_paramsand returns a transformed version ofX.

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_out(input_features=None)[source]#

Get output feature names for transformation.

Parameters:

input_featuresarray-like of str or None, default=None

Input features.

Ifinput_features isNone, thenfeature_names_in_ isused as feature names in. Iffeature_names_in_ is not defined,then the following input feature names are generated:["x0","x1",...,"x(n_features_in_-1)"].
Ifinput_features is an array-like, theninput_features mustmatchfeature_names_in_ iffeature_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 checkUser Guide on how the routingmechanism works.

Returns:

routingMetadataRequest: AMetadataRequest encapsulatingrouting information.

get_params(deep=True)[source]#

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator andcontained 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 theone-hot encoding),None is used to represent this category. If thefeature with the unknown category has a dropped category, the droppedcategory 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.

SeeIntroducing the set_output APIfor an example on how to use the API.

Parameters:

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

Configure output oftransform andfit_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 asPipeline). The latter haveparameters of the form<component>__<parameter> so that it’spossible 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.

Ifsparse_output=True (default), it returns an instance ofscipy.sparse._csr.csr_matrix (CSR format).

If there are infrequent categories for a feature, set by specifyingmax_categories ormin_frequency, the infrequent categories aregrouped 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. Ifsparse_output=True, a sparse matrix will bereturned.