diff --git a/pandas/core/dtypes/concat.py b/pandas/core/dtypes/concat.py
index ddff78c9d511f..129eddd3cc8d7 100644
--- a/pandas/core/dtypes/concat.py
+++ b/pandas/core/dtypes/concat.py
@@ -242,6 +242,80 @@ def union_categoricals(to_union, sort_categories=False, ignore_order=False):
         - sort_categories=True and Categoricals are ordered
     ValueError
         Empty list of categoricals passed
+
+    Examples
+    --------
+    If you want to combine categoricals that do not necessarily have the same
+    categories, the union_categoricals function will combine a list-like of
+    categoricals. The new categories will be the union of the categories being
+    combined.
+
+    >>> from pandas.api.types import union_categoricals
+
+    >>> 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
+    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 “easy” 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]
+
+    The below 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
+
+    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]
+
+    Notes
+    -----
+    To learn more about categories, please see `this link
+    <http://pandas.pydata.org/pandas-docs/stable/categorical.html#unioning>`.
+
     """
     from pandas import Index, Categorical, CategoricalIndex, Series