fix doc strings

Author: topper-123

doc/source/whatsnew/v1.2.0.rst

@@ -523,7 +523,7 @@ 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 changes in the unique array if there are unused categories in the original array (:issue:`38140`).
+- 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`)
 -
 

pandas/core/arrays/categorical.py

@@ -2042,7 +2042,7 @@ def unique(self):
 
         .. versionchanged:: 1.2.0
 
-            Previously unused categories were dropped.
+            Previously, unused categories were dropped from the new categories.
 
         Returns
         -------

pandas/core/groupby/categorical.py

@@ -72,6 +72,8 @@ def recode_for_groupby(
 
     # sort=False should order groups in as-encountered order (GH-8868)
     cat = c.unique()
+
+    # See GH-38140 for block below
     # exclude nan from indexer for categories
     take_codes = cat.codes[cat.codes != -1]
     if cat.ordered:

pandas/core/series.py

@@ -1820,15 +1820,12 @@ def unique(self):
         ['2016-01-01 00:00:00-05:00']
         Length: 1, dtype: datetime64[ns, US/Eastern]
 
-        An unordered Categorical will return categories in the order of
-        appearance.
+        An Categorical will return categories in the order of
+        appearance and with the same dtype.
 
         >>> pd.Series(pd.Categorical(list('baabc'))).unique()
         ['b', 'a', 'c']
-        Categories (3, object): ['b', 'a', 'c']
-
-        An ordered Categorical preserves the category ordering.
-
+        Categories (3, object): ['a', 'b', 'c']
         >>> pd.Series(pd.Categorical(list('baabc'), categories=list('abc'),
         ...                          ordered=True)).unique()
         ['b', 'a', 'c']