Backport PR pandas-dev#52677: BUG: tz_localize with ArrowDtype

Author: mroeschke

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

@@ -2109,12 +2109,19 @@ 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._data.cast(new_type))
-        pa_tz = str(tz)
-        return type(self)(
-            self._data.cast(pa.timestamp(self.dtype.pyarrow_dtype.unit, pa_tz))
-        )
+            result = self._data.cast(pa.timestamp(self.dtype.pyarrow_dtype.unit))
+        else:
+            result = pc.assume_timezone(
+                self._data, str(tz), ambiguous=ambiguous, nonexistent=nonexistent_pa
+            )
+        return type(self)(result)

pandas/tests/extension/test_arrow.py

@@ -2397,16 +2397,56 @@ 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)