BUG: .cat changing dtype inplace

doc/source/whatsnew/v1.3.4.rst

@@ -16,6 +16,8 @@ Fixed regressions
 ~~~~~~~~~~~~~~~~~
 - Fixed regression in :meth:`merge` with integer and ``NaN`` keys failing with ``outer`` merge (:issue:`43550`)
 - Fixed performance regression in :meth:`MultiIndex.equals` (:issue:`43549`)
+- Fixed regression in :meth:`Series.cat.reorder_categories` failing to update the categories on the ``Series`` (:issue:`43232`)
+- Fixed regression in :meth:`Series.cat.categories` setter failing to update the categories on the ``Series`` (:issue:`43334`)
 -
 
 .. ---------------------------------------------------------------------------

pandas/core/arrays/categorical.py

@@ -52,6 +52,7 @@
     cache_readonly,
     deprecate_kwarg,
 )
+from pandas.util._exceptions import find_stack_level
 from pandas.util._validators import validate_bool_kwarg
 
 from pandas.core.dtypes.cast import (
@@ -1116,10 +1117,10 @@ def reorder_categories(self, new_categories, ordered=None, inplace=no_default):
             warn(
                 "The `inplace` parameter in pandas.Categorical."
                 "reorder_categories is deprecated and will be removed in "
-                "a future version. Removing unused categories will always "
+                "a future version. Reordering categories will always "
                 "return a new Categorical object.",
                 FutureWarning,
-                stacklevel=2,
+                stacklevel=find_stack_level(),
             )
         else:
             inplace = False

pandas/core/internals/blocks.py

@@ -336,7 +336,6 @@ def getitem_block_columns(self, slicer, new_mgr_locs: BlockPlacement) -> Block:
     def shape(self) -> Shape:
         return self.values.shape
 
-    @final
     @cache_readonly
     def dtype(self) -> DtypeObj:
         return self.values.dtype
@@ -1881,6 +1880,12 @@ class CategoricalBlock(ExtensionBlock):
     # this Block type is kept for backwards-compatibility
     __slots__ = ()
 
+    # GH#43232, GH#43334 self.values.dtype can be changed inplace until 2.0,
+    #  so this cannot be cached
+    @property
+    def dtype(self) -> DtypeObj:
+        return self.values.dtype
+
 
 # -----------------------------------------------------------------
 # Constructor Helpers

pandas/tests/series/accessors/test_cat_accessor.py

@@ -249,3 +249,43 @@ def test_dt_accessor_api_for_categorical(self):
         with pytest.raises(AttributeError, match=msg):
             invalid.dt
         assert not hasattr(invalid, "str")
+
+    def test_reorder_categories_updates_dtype(self):
+        # GH#43232
+        ser = Series(["a", "b", "c"], dtype="category")
+        orig_dtype = ser.dtype
+
+        # Need to construct this before calling reorder_categories inplace
+        expected = ser.cat.reorder_categories(["c", "b", "a"])
+
+        with tm.assert_produces_warning(FutureWarning, match="`inplace` parameter"):
+            ser.cat.reorder_categories(["c", "b", "a"], inplace=True)
+
+        assert not orig_dtype.categories.equals(ser.dtype.categories)
+        assert not orig_dtype.categories.equals(expected.dtype.categories)
+        assert ser.dtype == expected.dtype
+        assert ser.dtype.categories.equals(expected.dtype.categories)
+
+        tm.assert_series_equal(ser, expected)
+
+    def test_set_categories_setitem(self):
+        # GH#43334
+
+        df = DataFrame({"Survived": [1, 0, 1], "Sex": [0, 1, 1]}, dtype="category")
+
+        # change the dtype in-place
+        df["Survived"].cat.categories = ["No", "Yes"]
+        df["Sex"].cat.categories = ["female", "male"]
+
+        # values should not be coerced to NaN
+        assert list(df["Sex"]) == ["female", "male", "male"]
+        assert list(df["Survived"]) == ["Yes", "No", "Yes"]
+
+        df["Sex"] = Categorical(df["Sex"], categories=["female", "male"], ordered=False)
+        df["Survived"] = Categorical(
+            df["Survived"], categories=["No", "Yes"], ordered=False
+        )
+
+        # values should not be coerced to NaN
+        assert list(df["Sex"]) == ["female", "male", "male"]
+        assert list(df["Survived"]) == ["Yes", "No", "Yes"]