Class LabelEncoder

sklearn/preprocessing/_label.py:40–181

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 :ref:`User Guide `. .. versionadded:: 0.12 Attributes ---------- classes_ : n

class LabelEncoder(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None):
    """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 :ref:`User Guide <preprocessing_targets>`.

    .. versionadded:: 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.

    >>> from sklearn.preprocessing import 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')]
    """

    def fit(self, y):
        """Fit label encoder.

        Parameters
        ----------
        y : array-like of shape (n_samples,)
            Target values.