BUG: tz_localize with ArrowDtype

doc/source/whatsnew/v2.0.1.rst

@@ -33,6 +33,7 @@ Bug fixes
 - Bug in :meth:`ArrowDtype.__from_arrow__` not respecting if dtype is explicitly given (:issue:`52533`)
 - Bug in :meth:`DataFrame.max` and related casting different :class:`Timestamp` resolutions always to nanoseconds (:issue:`52524`)
 - Bug in :meth:`Series.describe` not returning :class:`ArrowDtype` with ``pyarrow.float64`` type with numeric data (:issue:`52427`)
+- Bug in :meth:`Series.dt.tz_localize` incorrectly localizing timestamps with :class:`ArrowDtype` (:issue:`52677`)
 - Fixed bug in :func:`merge` when merging with ``ArrowDtype`` one one and a NumPy dtype on the other side (:issue:`52406`)
 - Fixed segfault in :meth:`Series.to_numpy` with ``null[pyarrow]`` dtype (:issue:`52443`)
 

pandas/core/arrays/arrow/array.py

@@ -2208,12 +2208,19 @@ def _dt_tz_localize(
  ):
  if ambiguous != "raise":
  raise NotImplementedError(f"{ambiguous=} is not supported")
- if nonexistent != "raise":
+ nonexistent_pa = {
+ "raise": "raise",
+ "shift_backward": "earliest",
+ "shift_forward": "latest",
+ }.get(
+ nonexistent, None # type: ignore[arg-type]
+ )
+ if nonexistent_pa is None:
  raise NotImplementedError(f"{nonexistent=} is not supported")
  if tz is None:
- new_type = pa.timestamp(self.dtype.pyarrow_dtype.unit)
-  return type(self)(self._pa_array.cast(new_type))
- pa_tz = str(tz)
- return type(self)(
- self._pa_array.cast(pa.timestamp(self.dtype.pyarrow_dtype.unit, pa_tz))
- )
+ result = self._pa_array.cast(pa.timestamp(self.dtype.pyarrow_dtype.unit))
+ else:
+  result = pc.assume_timezone(
+  self._pa_array, str(tz), ambiguous=ambiguous, nonexistent=nonexistent_pa
+ )
+ return type(self)(result)

pandas/tests/extension/test_arrow.py

@@ -2392,16 +2392,56 @@ def test_dt_tz_localize_none():
 
 
 @pytest.mark.parametrize("unit", ["us", "ns"])
-def test_dt_tz_localize(unit):
+def test_dt_tz_localize(unit, request):
+ if is_platform_windows() and is_ci_environment():
+ request.node.add_marker(
+ pytest.mark.xfail(
+ raises=pa.ArrowInvalid,
+ reason=(
+ "TODO: Set ARROW_TIMEZONE_DATABASE environment variable "
+ "on CI to path to the tzdata for pyarrow."
+ ),
+ )
+ )
  ser = pd.Series(
  [datetime(year=2023, month=1, day=2, hour=3), None],
  dtype=ArrowDtype(pa.timestamp(unit)),
  )
  result = ser.dt.tz_localize("US/Pacific")
- expected = pd.Series(
- [datetime(year=2023, month=1, day=2, hour=3), None],
- dtype=ArrowDtype(pa.timestamp(unit, "US/Pacific")),
+ exp_data = pa.array(
+ [datetime(year=2023, month=1, day=2, hour=3), None], type=pa.timestamp(unit)
+ )
+ exp_data = pa.compute.assume_timezone(exp_data, "US/Pacific")
+ expected = pd.Series(ArrowExtensionArray(exp_data))
+ tm.assert_series_equal(result, expected)
+
+
+@pytest.mark.parametrize(
+ "nonexistent, exp_date",
+ [
+ ["shift_forward", datetime(year=2023, month=3, day=12, hour=3)],
+ ["shift_backward", pd.Timestamp("2023-03-12 01:59:59.999999999")],
+ ],
+)
+def test_dt_tz_localize_nonexistent(nonexistent, exp_date, request):
+ if is_platform_windows() and is_ci_environment():
+ request.node.add_marker(
+ pytest.mark.xfail(
+ raises=pa.ArrowInvalid,
+ reason=(
+ "TODO: Set ARROW_TIMEZONE_DATABASE environment variable "
+ "on CI to path to the tzdata for pyarrow."
+ ),
+ )
+ )
+ ser = pd.Series(
+ [datetime(year=2023, month=3, day=12, hour=2, minute=30), None],
+ dtype=ArrowDtype(pa.timestamp("ns")),
  )
+ result = ser.dt.tz_localize("US/Pacific", nonexistent=nonexistent)
+ exp_data = pa.array([exp_date, None], type=pa.timestamp("ns"))
+ exp_data = pa.compute.assume_timezone(exp_data, "US/Pacific")
+ expected = pd.Series(ArrowExtensionArray(exp_data))
  tm.assert_series_equal(result, expected)