BUG: Pyarrow timestamp support for map() function

doc/source/whatsnew/v3.0.0.rst

@@ -844,6 +844,7 @@ Other
 - 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.isin` raising ``TypeError`` when series is large (>10**6) and ``values`` contains NA (:issue:`60678`)
+- Bug in :meth:`Series.map` where mapping with a ``dict`` failed to match keys when the Series used ``timestamp[ns][pyarrow]`` dtype. (:issue:`61231`)
 - Bug in :meth:`Series.mode` where an exception was raised when taking the mode with nullable types with no null values in the series. (:issue:`58926`)
 - 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`)

pandas/core/arrays/arrow/array.py

@@ -1483,6 +1483,8 @@ def to_numpy(
     def map(self, mapper, na_action: Literal["ignore"] | None = None):
         if is_numeric_dtype(self.dtype):
             return map_array(self.to_numpy(), mapper, na_action=na_action)
+        elif self.dtype == "timestamp[ns][pyarrow]":
+            return map_array(self.to_numpy(dtype=object), mapper, na_action=na_action)
         else:
             return super().map(mapper, na_action)
 

pandas/tests/series/methods/test_map.py

@@ -604,3 +604,14 @@ def test_map_kwargs():
     result = Series([2, 4, 5]).map(lambda x, y: x + y, y=2)
     expected = Series([4, 6, 7])
     tm.assert_series_equal(result, expected)
+
+
+def test_map_arrow_timestamp_dict():
+    # GH 61231
+    pytest.importorskip("pyarrow", minversion="10.0.1")
+
+    ser = Series(date_range("2023-01-01", periods=3)).astype("timestamp[ns][pyarrow]")
+    mapper = {ts: i for i, ts in enumerate(ser)}
+    result = ser.map(mapper)
+    expected = Series([0, 1, 2], dtype="int64")
+    tm.assert_series_equal(result, expected)