sklearn.preprocessing.LabelEncoder¶

class sklearn.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.

Attributes

classes_array of shape (n_class,)

Holds the label for each class.

See also

sklearn.preprocessing.OrdinalEncoder

Encode categorical features using an ordinal encoding scheme.

sklearn.preprocessing.OneHotEncoder

Encode categorical features as a one-hot numeric array.

Examples

LabelEncoder can be used to normalize labels.

>>> from sklearn import preprocessing
>>> le = preprocessing.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 = preprocessing.LabelEncoder()
>>> le.fit(["paris", "paris", "tokyo", "amsterdam"])
LabelEncoder()
>>> list(le.classes_)
['amsterdam', 'paris', 'tokyo']
>>> le.transform(["tokyo", "tokyo", "paris"])
array([2, 2, 1]...)
>>> list(le.inverse_transform([2, 2, 1]))
['tokyo', 'tokyo', 'paris']

Methods

fit(self, y)	Fit label encoder
fit_transform(self, y)	Fit label encoder and return encoded labels
get_params(self[, deep])	Get parameters for this estimator.
inverse_transform(self, y)	Transform labels back to original encoding.
set_params(self, \*\*params)	Set the parameters of this estimator.
transform(self, y)	Transform labels to normalized encoding.

__init__(self, /, *args, **kwargs)¶

Initialize self. See help(type(self)) for accurate signature.

fit(self, y)[source]¶

Fit label encoder

Parameters

yarray-like of shape (n_samples,)

Target values.

Returns

selfreturns an instance of self.

fit_transform(self, 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]

get_params(self, deep=True)[source]¶

Get parameters for this estimator.

Parameters

deepboolean, optional

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsmapping of string to any

Parameter names mapped to their values.

inverse_transform(self, y)[source]¶

Transform labels back to original encoding.

Parameters

ynumpy array of shape [n_samples]

Target values.

Returns

ynumpy array of shape [n_samples]

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, y)[source]¶

Transform labels to normalized encoding.

Parameters

yarray-like of shape [n_samples]

Target values.

Returns

yarray-like of shape [n_samples]