def union_categoricals(to_union, sort_categories=False, ignore_order=False):
    """
    Combine list-like of Categorical-like, unioning categories. All
    categories must have the same dtype.

    .. versionadded:: 0.19.0

    Parameters
    ----------
    to_union : list-like of Categorical, CategoricalIndex,
               or Series with dtype='category'
    sort_categories : boolean, default False
        If true, resulting categories will be lexsorted, otherwise
        they will be ordered as they appear in the data.
    ignore_order : boolean, default False
        If true, the ordered attribute of the Categoricals will be ignored.
        Results in an unordered categorical.

        .. versionadded:: 0.20.0

    Returns
    -------
    result : Categorical

    Raises
    ------
    TypeError
        - all inputs do not have the same dtype
        - all inputs do not have the same ordered property
        - all inputs are ordered and their categories are not identical
        - sort_categories=True and Categoricals are ordered
    ValueError
        Empty list of categoricals passed

    Notes
    -----

    To learn more about categories, see `link
    <http://pandas.pydata.org/pandas-docs/stable/categorical.html#unioning>`__

    Examples
    --------

    >>> from pandas.api.types import union_categoricals

    If you want to combine categoricals that do not necessarily have
    the same categories, `union_categoricals` will combine a list-like
    of categoricals. The new categories will be the union of the
    categories being combined.

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

    By default, the resulting categories will be ordered as they appear
    in the `categories` of the data. If you want the categories to be
    lexsorted, use `sort_categories=True` argument.

    >>> union_categoricals([a, b], sort_categories=True)
    [b, c, a, b]
    Categories (3, object): [a, b, c]

    `union_categoricals` also works with the case of combining two
    categoricals of the same categories and order information (e.g. what
    you could also `append` for).

    >>> a = pd.Categorical(["a", "b"], ordered=True)
    >>> b = pd.Categorical(["a", "b", "a"], ordered=True)
    >>> union_categoricals([a, b])
    [a, b, a, b, a]
    Categories (2, object): [a < b]

    Raises `TypeError` because the categories are ordered and not identical.

    >>> a = pd.Categorical(["a", "b"], ordered=True)
    >>> b = pd.Categorical(["a", "b", "c"], ordered=True)
    >>> union_categoricals([a, b])
    TypeError: to union ordered Categoricals, all categories must be the same

    New in version 0.20.0

    Ordered categoricals with different categories or orderings can be
    combined by using the `ignore_ordered=True` argument.

    >>> a = pd.Categorical(["a", "b", "c"], ordered=True)
    >>> b = pd.Categorical(["c", "b", "a"], ordered=True)
    >>> union_categoricals([a, b], ignore_order=True)
    [a, b, c, c, b, a]
    Categories (3, object): [a, b, c]

    `union_categoricals` also works with a `CategoricalIndex`, or `Series`
    containing categorical data, but note that the resulting array will
    always be a plain `Categorical`

    >>> a = pd.Series(["b", "c"], dtype='category')
    >>> b = pd.Series(["a", "b"], dtype='category')
    >>> union_categoricals([a, b])
    [b, c, a, b]
    Categories (3, object): [b, c, a]
    """
    from pandas import Index, Categorical, CategoricalIndex, Series
    from pandas.core.arrays.categorical import _recode_for_categories

    if len(to_union) == 0:
        raise ValueError('No Categoricals to union')

    def _maybe_unwrap(x):
        if isinstance(x, (CategoricalIndex, Series)):
            return x.values
        elif isinstance(x, Categorical):
            return x
        else:
            raise TypeError("all components to combine must be Categorical")

    to_union = [_maybe_unwrap(x) for x in to_union]
    first = to_union[0]

    if not all(is_dtype_equal(other.categories.dtype, first.categories.dtype)
               for other in to_union[1:]):
        raise TypeError("dtype of categories must be the same")

    ordered = False
    if all(first.is_dtype_equal(other) for other in to_union[1:]):
        # identical categories - fastpath
        categories = first.categories
        ordered = first.ordered

        if all(first.categories.equals(other.categories)
               for other in to_union[1:]):
            new_codes = np.concatenate([c.codes for c in to_union])
        else:
            codes = [first.codes] + [_recode_for_categories(other.codes,
                                                            other.categories,
                                                            first.categories)
                                     for other in to_union[1:]]
            new_codes = np.concatenate(codes)

        if sort_categories and not ignore_order and ordered:
            raise TypeError("Cannot use sort_categories=True with "
                            "ordered Categoricals")

        if sort_categories and not categories.is_monotonic_increasing:
            categories = categories.sort_values()
            indexer = categories.get_indexer(first.categories)

            from pandas.core.algorithms import take_1d
            new_codes = take_1d(indexer, new_codes, fill_value=-1)
    elif ignore_order or all(not c.ordered for c in to_union):
        # different categories - union and recode
        cats = first.categories.append([c.categories for c in to_union[1:]])
        categories = Index(cats.unique())
        if sort_categories:
            categories = categories.sort_values()

        new_codes = [_recode_for_categories(c.codes, c.categories, categories)
                     for c in to_union]
        new_codes = np.concatenate(new_codes)
    else:
        # ordered - to show a proper error message
        if all(c.ordered for c in to_union):
            msg = ("to union ordered Categoricals, "
                   "all categories must be the same")
            raise TypeError(msg)
        else:
            raise TypeError('Categorical.ordered must be the same')

    if ignore_order:
        ordered = False

    return Categorical(new_codes, categories=categories, ordered=ordered,
                       fastpath=True)
Combine list-like of Categorical-like, unioning categories. All
    categories must have the same dtype.

    .. versionadded:: 0.19.0

    Parameters
    ----------
    to_union : list-like of Categorical, CategoricalIndex,
               or Series with dtype='category'
    sort_categories : boolean, default False
        If true, resulting categories will be lexsorted, otherwise
        they will be ordered as they appear in the data.
    ignore_order : boolean, default False
        If true, the ordered attribute of the Categoricals will be ignored.
        Results in an unordered categorical.

        .. versionadded:: 0.20.0

    Returns
    -------
    result : Categorical

    Raises
    ------
    TypeError
        - all inputs do not have the same dtype
        - all inputs do not have the same ordered property
        - all inputs are ordered and their categories are not identical
        - sort_categories=True and Categoricals are ordered
    ValueError
        Empty list of categoricals passed

    Notes
    -----

    To learn more about categories, see `link
    <http://pandas.pydata.org/pandas-docs/stable/categorical.html#unioning>`__

    Examples
    --------

    >>> from pandas.api.types import union_categoricals

    If you want to combine categoricals that do not necessarily have
    the same categories, `union_categoricals` will combine a list-like
    of categoricals. The new categories will be the union of the
    categories being combined.

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

    By default, the resulting categories will be ordered as they appear
    in the `categories` of the data. If you want the categories to be
    lexsorted, use `sort_categories=True` argument.

    >>> union_categoricals([a, b], sort_categories=True)
    [b, c, a, b]
    Categories (3, object): [a, b, c]

    `union_categoricals` also works with the case of combining two
    categoricals of the same categories and order information (e.g. what
    you could also `append` for).

    >>> a = pd.Categorical(["a", "b"], ordered=True)
    >>> b = pd.Categorical(["a", "b", "a"], ordered=True)
    >>> union_categoricals([a, b])
    [a, b, a, b, a]
    Categories (2, object): [a < b]

    Raises `TypeError` because the categories are ordered and not identical.

    >>> a = pd.Categorical(["a", "b"], ordered=True)
    >>> b = pd.Categorical(["a", "b", "c"], ordered=True)
    >>> union_categoricals([a, b])
    TypeError: to union ordered Categoricals, all categories must be the same

    New in version 0.20.0

    Ordered categoricals with different categories or orderings can be
    combined by using the `ignore_ordered=True` argument.

    >>> a = pd.Categorical(["a", "b", "c"], ordered=True)
    >>> b = pd.Categorical(["c", "b", "a"], ordered=True)
    >>> union_categoricals([a, b], ignore_order=True)
    [a, b, c, c, b, a]
    Categories (3, object): [a, b, c]

    `union_categoricals` also works with a `CategoricalIndex`, or `Series`
    containing categorical data, but note that the resulting array will
    always be a plain `Categorical`

    >>> a = pd.Series(["b", "c"], dtype='category')
    >>> b = pd.Series(["a", "b"], dtype='category')
    >>> union_categoricals([a, b])
    [b, c, a, b]
    Categories (3, object): [b, c, a]