rm, the resulting one-hot encoded columns for this feature
      will be all zeros. In the inverse transform, an unknown category
      will be denoted as None.
    - 'infrequent_if_exist' : When an unknown category is encountered
      during transform, the resulting one-hot encoded columns for this
      feature will map to the infrequent category if it exists. The
      infrequent category will be mapped to the last position in the
      encoding. During inverse transform, an unknown category will be
      mapped to the category denoted `'infrequent'` if it exists. If the
      `'infrequent'` category does not exist, then :meth:`transform` and
      :meth:`inverse_transform` will handle an unknown category as with
      `handle_unknown='ignore'`. Infrequent categories exist based on
      `min_frequency` and `max_categories`. Read more in the
      :ref:`User Guide <encoder_infrequent_categories>`.
    - 'warn' : When an unknown category is encountered during transform
      a warning is issued, and the encoding then proceeds as described for
      `handle_unknown="infrequent_if_exist"`.

    .. versionchanged:: 1.1
        `'infrequent_if_exist'` was added to automatically handle unknown
        categories and infrequent categories.

    .. versionadded:: 1.6
       The option `"warn"` was added in 1.6.

min_frequency : int or float, default=None
    Specifies the minimum frequency below which a category will be
    considered infrequent.

    - If `int`, categories with a smaller cardinality will be considered
      infrequent.

    - If `float`, categories with a smaller cardinality than
      `min_frequency * n_samples`  will be considered infrequent.

    .. versionadded:: 1.1
        Read more in the :ref:`User Guide <encoder_infrequent_categories>`.

max_categories : int, default=None
    Specifies an upper limit to the number of output features for each input
    feature when considering infrequent categories. If there are infrequent
    categories, `max_categories` includes the category representing the
    infrequent categories along with the frequent categories. If `None`,
    there is no limit to the number of output features.

    .. versionadded:: 1.1
        Read more in the :ref:`User Guide <encoder_infrequent_categories>`.

feature_name_combiner : "concat" or callable, default="concat"
    Callable with signature `def callable(input_feature, category)` that returns a
    string. This is used to create feature names to be returned by
    :meth:`get_feature_names_out`.

    `"concat"` concatenates encoded feature name and category with
    `feature + "_" + str(category)`.E.g. feature X with values 1, 6, 7 create
    feature names `X_1, X_6, X_7`.

    .. versionadded:: 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 output
    of ``transform``). This includes the category specified in ``drop``
    (if any).

drop_idx_ : array of shape (n_features,)
    - ``drop_idx_[i]`` is the index in ``categories_[i]`` of the category
      to be dropped for each feature.
    - ``drop_idx_[i] = None`` if no category is to be dropped from the
      feature with index ``i``, e.g. when `drop='if_binary'` and the
      feature isn't binary.
    - ``drop_idx_ = None`` if all the transformed features will be
      retained.

    If infrequent categories are enabled by setting `min_frequency` or
    `max_categories` to a non-default value and `drop_idx[i]` corresponds
    to a infrequent category, then the entire infrequent category is
    dropped.

    .. versionchanged:: 0.23
       Added the possibility to contain `None` values.

infrequent_categories_ : list of ndarray
    Defined only if infrequent categories are enabled by setting
    `min_frequency` or `max_categories` to a non-default value.
    `infrequent_categories_[i]` are the infrequent categories for feature
    `i`. If the feature `i` has no infrequent categories
    `infrequent_categories_[i]` is None.

    .. versionadded:: 1.1

n_features_in_ : int
    Number of features seen during :term:`fit`.

    .. versionadded:: 1.0

feature_names_in_ : ndarray of shape (`n_features_in_`,)
    Names of features seen during :term:`fit`. Defined only when `X`
    has feature names that are all strings.

    .. versionadded:: 1.0

feature_name_combiner : callable or None
    Callable with signature `def callable(input_feature, category)` that returns a
    string. This is used to create feature names to be returned by
    :meth:`get_feature_names_out`.

    .. versionadded:: 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 unique
values per feature and transform the data to a binary one-hot encoding.

>>> from sklearn.preprocessing import OneHotEncoder

One can discard categories not seen during `fit`:

>>> 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.

>>> def custom_combiner(feature, category):
...     return str(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 setting `max_categories` or `min_frequency`.

>>> import numpy as np
>>> 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.]])
rB