Class OneHotEncoder

sklearn/preprocessing/_encoders.py:474–1260

Encode categorical 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 s

class OneHotEncoder(_BaseEncoder):
    """
    Encode categorical 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 (depending on the ``sparse_output``
    parameter).

    By default, the encoder derives the categories based on the unique values
    in each feature. Alternatively, you can also specify the `categories`
    manually.

    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.

    Read more in the :ref:`User Guide <preprocessing_categorical_features>`.
    For a comparison of different encoders, refer to:
    :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder.py`.

    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 ith
          column. The passed categories should not mix strings and numeric
          values within a single feature, and should be sorted in case of
          numeric values.

        The used categories can be found in the ``categories_`` attribute.

        .. versionadded:: 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 per
        feature. This is useful in situations where perfectly collinear
        features cause problems, such as when feeding the resulting data
        into an unregularized linear regression model.

        However, dropping one category breaks the symmetry of the original
        representation 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 one
          category is present, the feature will be dropped entirely.
        - 'if_binary' : drop the first category in each feature with two
          categories. Features with 1 or more than 2 categories are
          left intact.
        - array : ``drop[i]`` is the category in feature ``X[:, i]`` that