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• API Reference
• sklearn.preprocessing
• LabelEncoder

LabelEncoder# classsklearn.preprocessing.LabelEncoder[source]#

Encode target labels with value between 0 and n_classes-1.

This transformer should be used to encode target values, i.e. y, and not the input X.

Read more in the User Guide.

Added in version 0.12.

Attributes: classes_ndarray of shape (n_classes,)

Holds the label for each class.

See also

OrdinalEncoder

Encode categorical features using an ordinal encoding scheme.

OneHotEncoder

Encode categorical features as a one-hot numeric array.

Examples

LabelEncoder can be used to normalize labels.

>>> fromsklearn.preprocessingimport LabelEncoder >>> le = LabelEncoder() >>> le.fit([1, 2, 2, 6]) LabelEncoder() >>> le.classes_ array([1, 2, 6]) >>> le.transform([1, 1, 2, 6]) array([0, 0, 1, 2]...) >>> le.inverse_transform([0, 0, 1, 2]) array([1, 1, 2, 6])

It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels.

>>> le = LabelEncoder() >>> le.fit(["paris", "paris", "tokyo", "amsterdam"]) LabelEncoder() >>> list(le.classes_) [np.str_('amsterdam'), np.str_('paris'), np.str_('tokyo')] >>> le.transform(["tokyo", "tokyo", "paris"]) array([2, 2, 1]...) >>> list(le.inverse_transform([2, 2, 1])) [np.str_('tokyo'), np.str_('tokyo'), np.str_('paris')] fit(y)[source]#

Fit label encoder.

Parameters: yarray-like of shape (n_samples,)

Target values.

Returns: selfreturns an instance of self.

Fitted label encoder.

fit_transform(y)[source]#

Fit label encoder and return encoded labels.

Parameters: yarray-like of shape (n_samples,)

Target values.

Returns: yarray-like of shape (n_samples,)

Encoded labels.

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(y)[source]#

Transform labels back to original encoding.

Parameters: yarray-like of shape (n_samples,)

Target values.

Returns: y_originalndarray of shape (n_samples,)

Original encoding.

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

Set output container.

See 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 it’s possible to update each component of a nested object.

Parameters: **paramsdict

Estimator parameters.

Returns: selfestimator instance

Estimator instance.

transform(y)[source]#

Transform labels to normalized encoding.

Parameters: yarray-like of shape (n_samples,)

Target values.

Returns: yarray-like of shape (n_samples,)

Labels as normalized encodings.

On this page

• LabelEncoder
  • fit
  • fit_transform
  • get_metadata_routing
  • get_params
  • inverse_transform
  • set_output
  • set_params
  • transform

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