BUG: fixed Series.dt methods in ArrowDtype class that were returning incorrect time values. (pandas-dev#57355)

Author: St0rmie

doc/source/whatsnew/v3.0.0.rst

@@ -483,6 +483,7 @@ Other
 - Bug in :meth:`DataFrame.where` where using a non-bool type array in the function would return a ``ValueError`` instead of a ``TypeError`` (:issue:`56330`)
 - Bug in :meth:`Index.sort_values` when passing a key function that turns values into tuples, e.g. ``key=natsort.natsort_key``, would raise ``TypeError`` (:issue:`56081`)
 - Bug in :meth:`Series.diff` allowing non-integer values for the ``periods`` argument. (:issue:`56607`)
+- Bug in :meth:`Series.dt` methods in :class:`ArrowDtype` that were returning incorrect values. (:issue:`57355`)
 - Bug in :meth:`Series.rank` that doesn't preserve missing values for nullable integers when ``na_option='keep'``. (:issue:`56976`)
 - Bug in :meth:`Series.replace` and :meth:`DataFrame.replace` inconsistently replacing matching instances when ``regex=True`` and missing values are present. (:issue:`56599`)
 - Bug in Dataframe Interchange Protocol implementation was returning incorrect results for data buffers' associated dtype, for string and datetime columns (:issue:`54781`)

pandas/core/arrays/arrow/array.py

@@ -2644,7 +2644,11 @@ def _dt_minutes(self) -> Self:
     def _dt_seconds(self) -> Self:
         return type(self)(
             pa.array(
-                self._to_timedeltaarray().seconds, from_pandas=True, type=pa.int32()
+                [
+                    td.components.seconds if td is not NaT else None
+                    for td in self._to_timedeltaarray()
+                ],
+                type=pa.int32(),
             )
         )
 
@@ -2664,8 +2668,10 @@ def _dt_milliseconds(self) -> Self:
     def _dt_microseconds(self) -> Self:
         return type(self)(
             pa.array(
-                self._to_timedeltaarray().microseconds,
-                from_pandas=True,
+                [
+                    td.components.microseconds if td is not NaT else None
+                    for td in self._to_timedeltaarray()
+                ],
                 type=pa.int32(),
             )
         )
@@ -2674,7 +2680,11 @@ def _dt_microseconds(self) -> Self:
     def _dt_nanoseconds(self) -> Self:
         return type(self)(
             pa.array(
-                self._to_timedeltaarray().nanoseconds, from_pandas=True, type=pa.int32()
+                [
+                    td.components.nanoseconds if td is not NaT else None
+                    for td in self._to_timedeltaarray()
+                ],
+                type=pa.int32(),
             )
         )
 

pandas/tests/extension/test_arrow.py

@@ -2905,6 +2905,31 @@ def test_dt_components():
     tm.assert_frame_equal(result, expected)
 
 
+def test_dt_components_large_values():
+    ser = pd.Series(
+        [
+            pd.Timedelta("365 days 23:59:59.999000"),
+            None,
+        ],
+        dtype=ArrowDtype(pa.duration("ns")),
+    )
+    result = ser.dt.components
+    expected = pd.DataFrame(
+        [[365, 23, 59, 59, 999, 0, 0], [None, None, None, None, None, None, None]],
+        columns=[
+            "days",
+            "hours",
+            "minutes",
+            "seconds",
+            "milliseconds",
+            "microseconds",
+            "nanoseconds",
+        ],
+        dtype="int32[pyarrow]",
+    )
+    tm.assert_frame_equal(result, expected)
+
+
 @pytest.mark.parametrize("skipna", [True, False])
 def test_boolean_reduce_series_all_null(all_boolean_reductions, skipna):
     # GH51624