update

Author: topper-123

doc/source/whatsnew/v1.2.0.rst

@@ -585,7 +585,6 @@ Categorical
 - :meth:`Categorical.fillna` will always return a copy, validate a passed fill value regardless of whether there are any NAs to fill, and disallow an ``NaT`` as a fill value for numeric categories (:issue:`36530`)
 - Bug in :meth:`Categorical.__setitem__` that incorrectly raised when trying to set a tuple value (:issue:`20439`)
 - Bug in :meth:`CategoricalIndex.equals` incorrectly casting non-category entries to ``np.nan`` (:issue:`37667`)
-- Bug in :meth:`Categorical.unique`, where the dtype changed in the unique array if there were unused categories in the original array (:issue:`38140`).
 - Bug in :meth:`CategoricalIndex.where` incorrectly setting non-category entries to ``np.nan`` instead of raising ``TypeError`` (:issue:`37977`)
 - Bug in :meth:`Categorical.to_numpy` and ``np.array(categorical)`` with tz-aware ``datetime64`` categories incorrectly dropping the time zone information instead of casting to object dtype (:issue:`38136`)
 

doc/source/whatsnew/v1.3.0.rst

@@ -205,6 +205,37 @@ Notable bug fixes
 
 These are bug fixes that might have notable behavior changes.
 
+``Categorical.unique`` now always maintains same dtype as original
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+Previously, when calling :meth:`Categorical.unique`, unused categories in the new array
+would be removed, meaning that the dtype of the new array would be different than the
+original, if some categories are not present in the unique array:
+
+As an example of this, given:
+
+.. ipython:: python
+
+        dtype = pd.CategoricalDtype(['bad', 'neutral', 'good'], ordered=True)
+        original = pd.Categorical(['good','good', 'bad', 'bad'], dtype=dtype)
+        unique = original.unique()
+
+*pandas < 1.2.0*:
+
+.. code-block:: ipython
+
+    In [1]: unique
+    ['good', 'bad']
+    Categories (2, object): ['bad' < 'good']
+    In [2]: original.dtype == unique.dtype
+    False
+
+*pandas >= 1.2.0*
+
+.. ipython:: python
+
+        unique
+        original.dtype == unique.dtype
 
 Preserve dtypes in  :meth:`~pandas.DataFrame.combine_first`
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^