r guide
    <https://pandas.pydata.org/pandas-docs/stable/user_guide/categorical.html>`__
    for more.

    Examples
    --------
    >>> pd.Categorical([1, 2, 3, 1, 2, 3])
    [1, 2, 3, 1, 2, 3]
    Categories (3, int64): [1, 2, 3]

    >>> pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c'])
    ['a', 'b', 'c', 'a', 'b', 'c']
    Categories (3, object): ['a', 'b', 'c']

    Missing values are not included as a category.

    >>> c = pd.Categorical([1, 2, 3, 1, 2, 3, np.nan])
    >>> c
    [1, 2, 3, 1, 2, 3, NaN]
    Categories (3, int64): [1, 2, 3]

    However, their presence is indicated in the `codes` attribute
    by code `-1`.

    >>> c.codes
    array([ 0,  1,  2,  0,  1,  2, -1], dtype=int8)

    Ordered `Categoricals` can be sorted according to the custom order
    of the categories and can have a min and max value.

    >>> c = pd.Categorical(['a', 'b', 'c', 'a', 'b', 'c'], ordered=True,
    ...                    categories=['c', 'b', 'a'])
    >>> c
    ['a', 'b', 'c', 'a', 'b', 'c']
    Categories (3, object): ['c' < 'b' < 'a']
    >>> c.min()
    'c'
    ič