diff --git a/doc/source/user_guide/categorical.rst b/doc/source/user_guide/categorical.rst
index 0105cf99193dd..be26427d11d81 100644
--- a/doc/source/user_guide/categorical.rst
+++ b/doc/source/user_guide/categorical.rst
@@ -439,15 +439,17 @@ Sorting and order
 .. _categorical.sort:
 
 If categorical data is ordered (``s.cat.ordered == True``), then the order of the categories has a
-meaning and certain operations are possible. If the categorical is unordered, ``.min()/.max()`` will raise a ``TypeError``.
+meaning and certain operations are possible. If the categorical is unordered, the data can still be sorted,
+but ``.min()/.max()`` will raise a ``TypeError``.
 
 .. ipython:: python
+    :okexcept:
 
     s = pd.Series(pd.Categorical(["a", "b", "c", "a"], ordered=False))
-    s.sort_values(inplace=True)
-    s = pd.Series(["a", "b", "c", "a"]).astype(CategoricalDtype(ordered=True))
-    s.sort_values(inplace=True)
-    s
+    s.sort_values(ascending=False)
+    s.min()
+    s = s.astype(CategoricalDtype(ordered=True))
+    s.sort_values()
     s.min(), s.max()
 
 You can set categorical data to be ordered by using ``as_ordered()`` or unordered by using ``as_unordered()``. These will by
diff --git a/pandas/core/arrays/categorical.py b/pandas/core/arrays/categorical.py
index 2c3b7c2f2589d..8503ce30bd325 100644
--- a/pandas/core/arrays/categorical.py
+++ b/pandas/core/arrays/categorical.py
@@ -256,7 +256,7 @@ class Categorical(NDArrayBackedExtensionArray, PandasObject, ObjectStringArrayMi
     """
     Represent a categorical variable in classic R / S-plus fashion.
 
-    `Categoricals` can only take on only a limited, and usually fixed, number
+    `Categoricals` can only take on a limited, and usually fixed, number
     of possible values (`categories`). In contrast to statistical categorical
     variables, a `Categorical` might have an order, but numerical operations
     (additions, divisions, ...) are not possible.