REGR: Index.map adding back tz to tz-agnostic result

doc/source/whatsnew/v2.2.1.rst

@@ -36,6 +36,7 @@ Fixed regressions
 - Fixed regression in :meth:`DataFrameGroupBy.idxmin`, :meth:`DataFrameGroupBy.idxmax`, :meth:`SeriesGroupBy.idxmin`, :meth:`SeriesGroupBy.idxmax` where values containing the minimum or maximum value for the dtype could produce incorrect results (:issue:`57040`)
 - Fixed regression in :meth:`ExtensionArray.to_numpy` raising for non-numeric masked dtypes (:issue:`56991`)
 - Fixed regression in :meth:`Index.join` raising ``TypeError`` when joining an empty index to a non-empty index containing mixed dtype values (:issue:`57048`)
+- Fixed regression in :meth:`Index.map` that would not change the dtype when the provided mapping would change data from tz-aware to tz-agnostic or tz-agnostic to tz-aware (:issue:`57192`)
 - Fixed regression in :meth:`Series.pct_change` raising a ``ValueError`` for an empty :class:`Series` (:issue:`57056`)
 - Fixed regression in :meth:`Series.to_numpy` when dtype is given as float and the data contains NaNs (:issue:`57121`)
 

pandas/core/indexes/base.py

@@ -6124,14 +6124,16 @@ def map(self, mapper, na_action: Literal["ignore"] | None = None):
             # empty
             dtype = self.dtype
 
-        # e.g. if we are floating and new_values is all ints, then we
-        #  don't want to cast back to floating.  But if we are UInt64
-        #  and new_values is all ints, we want to try.
-        same_dtype = lib.infer_dtype(new_values, skipna=False) == self.inferred_type
-        if same_dtype:
-            new_values = maybe_cast_pointwise_result(
-                new_values, self.dtype, same_dtype=same_dtype
-            )
+        if self.inferred_type != "datetime64":
+            # e.g. if we are floating and new_values is all ints, then we
+            #  don't want to cast back to floating.  But if we are UInt64
+            #  and new_values is all ints, we want to try.
+            # GH#57192 - we skip datetime64 because inference from values is reliable
+            same_dtype = lib.infer_dtype(new_values, skipna=False) == self.inferred_type
+            if same_dtype:
+                new_values = maybe_cast_pointwise_result(
+                    new_values, self.dtype, same_dtype=same_dtype
+                )
 
         return Index._with_infer(new_values, dtype=dtype, copy=False, name=self.name)
 

pandas/tests/indexes/datetimes/methods/test_map.py

@@ -45,3 +45,16 @@ def test_index_map(self, name):
         )
         exp_index = MultiIndex.from_product(((2018,), range(1, 7)), names=[name, name])
         tm.assert_index_equal(index, exp_index)
+
+    @pytest.mark.parametrize("input_tz", ["UTC", None])
+    @pytest.mark.parametrize("output_tz", ["UTC", None])
+    def test_mapping_tz_to_tz_agnostic(self, input_tz, output_tz):
+        # GH#57192
+        index = date_range("2018-01-01", periods=6, freq="ME", tz=input_tz)
+        expected = date_range("2018-01-01", periods=6, freq="ME", tz=output_tz)
+        if input_tz == "UTC" and output_tz == "UTC":
+            method = "tz_convert"
+        else:
+            method = "tz_localize"
+        result = index.map(lambda x: getattr(x, method)(output_tz))
+        tm.assert_index_equal(result, expected)