By default, :class:`OrdinalEncoder` is lenient towards missing values by
    propagating them.

    >>> import numpy as np
    >>> X = [['Male', 1], ['Female', 3], ['Female', np.nan]]
    >>> enc.fit_transform(X)
    array([[ 1.,  0.],
           [ 0.,  1.],
           [ 0., nan]])

    You can use the parameter `encoded_missing_value` to encode missing values.

    >>> enc.set_params(encoded_missing_value=-1).fit_transform(X)
    array([[ 1.,  0.],
           [ 0.,  1.],
           [ 0., -1.]])

By default, :class:`OrdinalEncoder` will also passthrough missing values that are
indicated by `np.nan`.

    >>> enc = preprocessing.OrdinalEncoder()
    >>> X = [['male'], ['female'], [np.nan], ['female']]
    >>> enc.fit_transform(X)
    array([[ 1.],
           [ 0.],
           [nan],
           [ 0.]])

:class:`OrdinalEncoder` provides a parameter `encoded_missing_value` to encode
the missing values without the need to create a pipeline and using
:class:`~sklearn.impute.SimpleImputer`.

    >>> enc = preprocessing.OrdinalEncoder(encoded_missing_value=-1)
    >>> X = [['male'], ['female'], [np.nan], ['female']]
    >>> enc.fit_transform(X)
    array([[ 1.],
           [ 0.],
           [-1.],
           [ 0.]])

The above processing is equivalent to the following pipeline::

    >>> from sklearn.pipeline import Pipeline
    >>> from sklearn.impute import SimpleImputer
    >>> enc = Pipeline(steps=[
    ...     ("encoder", preprocessing.OrdinalEncoder()),
    ...     ("imputer", SimpleImputer(strategy="constant", fill_value=-1)),
    ... ])
    >>> enc.fit_transform(X)
    array([[ 1.],
           [ 0.],
           [-1.],
           [ 0.]])