DOC: Added examples for union_categoricals

pandas/core/dtypes/concat.py

@@ -242,6 +242,72 @@ def union_categoricals(to_union, sort_categories=False, ignore_order=False):
         - 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
+    --------
+
+    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 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