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

doc/source/whatsnew/v2.2.2.rst

@@ -16,6 +16,7 @@ Fixed regressions
 - :meth:`DataFrame.__dataframe__` was producing incorrect data buffers when the a column's type was a pandas nullable on with missing values (:issue:`56702`)
 - :meth:`DataFrame.__dataframe__` was producing incorrect data buffers when the a column's type was a pyarrow nullable on with missing values (:issue:`57664`)
 - Avoid issuing a spurious ``DeprecationWarning`` when a custom :class:`DataFrame` or :class:`Series` subclass method is called (:issue:`57553`)
+- 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 precision of :func:`to_datetime` with string and ``unit`` input (:issue:`57051`)
 
 .. ---------------------------------------------------------------------------

pandas/_libs/lib.pyi

@@ -54,6 +54,7 @@ def is_timedelta_or_timedelta64_array(
     values: np.ndarray, skipna: bool = True
 ) -> bool: ...
 def is_datetime_with_singletz_array(values: np.ndarray) -> bool: ...
+def is_datetime_naive_array(values: np.ndarray) -> bool: ...
 def is_time_array(values: np.ndarray, skipna: bool = ...): ...
 def is_date_array(values: np.ndarray, skipna: bool = ...): ...
 def is_datetime_array(values: np.ndarray, skipna: bool = ...): ...

pandas/_libs/lib.pyx

@@ -2068,6 +2068,26 @@ def is_datetime_with_singletz_array(values: ndarray) -> bool:
     return True
 
 
+def is_datetime_naive_array(values: ndarray) -> bool:
+    """
+    Check values have are datetime naive.
+    Doesn't check values are datetime-like types.
+    """
+    cdef:
+        Py_ssize_t j, n = len(values)
+        object tz
+
+    if n == 0:
+        return False
+
+    for j in range(n):
+        tz = getattr(values[j], "tzinfo", None)
+        if tz is not None:
+            return False
+
+    return True
+
+
 @cython.internal
 cdef class TimedeltaValidator(TemporalValidator):
     cdef bint is_value_typed(self, object value) except -1:

pandas/core/arrays/datetimes.py

@@ -290,9 +290,15 @@ def _scalar_type(self) -> type[Timestamp]:
 
     @classmethod
     def _from_scalars(cls, scalars, *, dtype: DtypeObj) -> Self:
-        if lib.infer_dtype(scalars, skipna=True) not in ["datetime", "datetime64"]:
-            # TODO: require any NAs be valid-for-DTA
-            # TODO: if dtype is passed, check for tzawareness compat?
+        # TODO: require any NAs be valid-for-DTA
+        # TODO: if dtype is passed, check for tzawareness compat?
+        if not lib.is_datetime64_array(scalars):
+            raise ValueError
+        elif isinstance(
+            dtype, DatetimeTZDtype
+        ) and not lib.is_datetime_with_singletz_array(scalars):
+            raise ValueError
+        elif isinstance(dtype, np.dtype) and not lib.is_datetime_naive_array(scalars):
             raise ValueError
         return cls._from_sequence(scalars, dtype=dtype)
 

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)