Final doc fixups

Author: TomAugspurger

doc/source/categorical.rst

@@ -207,8 +207,10 @@ All instances of ``CategoricalDtype`` compare equal to the string ``'category'``
 .. warning::
 
    Since ``dtype='category'`` is essentially ``CategoricalDtype(None, False)``,
-   and since all instances ``CategoricalDtype`` compare equal to ``'`category'``,
-   all instances of ``CategoricalDtype`` compare equal to a ``CategoricalDtype(None)``
+   and since all instances ``CategoricalDtype`` compare equal to ``'category'``,
+   all instances of ``CategoricalDtype`` compare equal to a
+   ``CategoricalDtype(None, False)``, regardless of ``categories`` or
+   ``ordered``.
 
 Description
 -----------

doc/source/whatsnew/v0.21.0.txt

@@ -100,7 +100,8 @@ Setting a list-like data structure into a new attribute now raise a ``UserWarnin
 expanded to include the ``categories`` and ``ordered`` attributes. A
 ``CategoricalDtype`` can be used to specify the set of categories and
 orderedness of an array, independent of the data themselves. This can be useful,
-e.g., when converting string data to a ``Categorical`` (:issue:`14711`, :issue:`15078`):
+e.g., when converting string data to a ``Categorical`` (:issue:`14711`,
+:issue:`15078`, :issue:`16015`):
 
 .. ipython:: python
 

pandas/tests/test_categorical.py

@@ -199,7 +199,7 @@ def test_is_equal_dtype(self):
         assert not c1.is_dtype_equal(c1.astype(object))
         assert c1.is_dtype_equal(CategoricalIndex(c1))
         assert (c1.is_dtype_equal(
-            CategoricalIndex(c1, categories=list('cab'))))  # XXX: changed
+            CategoricalIndex(c1, categories=list('cab'))))
         assert not c1.is_dtype_equal(CategoricalIndex(c1, ordered=True))
 
     def test_constructor(self):
@@ -4250,7 +4250,7 @@ def test_categorical_index_preserver(self):
 
         # wrong catgories
         df3 = DataFrame({'A': a,
-                         'B': pd.Categorical(b, categories=list('abe'))  # XXX
+                         'B': pd.Categorical(b, categories=list('abe'))
                          }).set_index('B')
         pytest.raises(TypeError, lambda: pd.concat([df2, df3]))