BUG: Remove special-casing for date objects in DatetimeIndex indexing (GH#62158)

doc/source/whatsnew/v3.0.0.rst

@@ -889,6 +889,8 @@ Datetimelike
 - Bug in constructing arrays with :class:`ArrowDtype` with ``timestamp`` type incorrectly allowing ``Decimal("NaN")`` (:issue:`61773`)
 - Bug in constructing arrays with a timezone-aware :class:`ArrowDtype` from timezone-naive datetime objects incorrectly treating those as UTC times instead of wall times like :class:`DatetimeTZDtype` (:issue:`61775`)
 - Bug in setting scalar values with mismatched resolution into arrays with non-nanosecond ``datetime64``, ``timedelta64`` or :class:`DatetimeTZDtype` incorrectly truncating those scalars (:issue:`56410`)
+- Removed the special casing for sequences of Python ``date`` objects in ``DatetimeIndex.get_indexer`` and related indexing logic.
+  Indexing a ``DatetimeIndex`` with Python ``date`` objects now behaves consistently with other types. (:issue:`62158`)
 
 Timedelta
 ^^^^^^^^^

pandas/core/indexes/base.py

@@ -39,7 +39,6 @@
     no_default,
 )
 from pandas._libs.tslibs import (
-    OutOfBoundsDatetime,
     Timestamp,
     tz_compare,
 )
@@ -6204,11 +6203,6 @@ def _maybe_downcast_for_indexing(self, other: Index) -> tuple[Index, Index]:
                 # standardize on UTC
                 return self.tz_convert("UTC"), other.tz_convert("UTC")
 
-        elif self.inferred_type == "date" and isinstance(other, ABCDatetimeIndex):
-            try:
-                return type(other)(self), other
-            except OutOfBoundsDatetime:
-                return self, other
         elif self.inferred_type == "timedelta" and isinstance(other, ABCTimedeltaIndex):
             # TODO: we dont have tests that get here
             return type(other)(self), other

pandas/tests/frame/indexing/test_indexing.py

@@ -1880,6 +1880,19 @@ def test_add_new_column_infer_string():
     tm.assert_frame_equal(df, expected)
 
 
+def test_datetime_indexer_consistency_pyarrow_date32():
+    # GH#62158
+    ser = Series(["2016-01-01"], dtype="date32[pyarrow]")
+    ser3 = ser.astype("datetime64[ns]")
+    dti = Index(ser3)
+    # All should be consistent
+    assert dti.get_loc(ser[0]) == 0
+    tm.assert_numpy_array_equal(dti.get_indexer(ser.values), np.array([0]))
+    tm.assert_numpy_array_equal(
+        dti.get_indexer(ser.values.astype(object)), np.array([0])
+    )
+
+
 class TestSetitemValidation:
     # This is adapted from pandas/tests/arrays/masked/test_indexing.py
     def _check_setitem_invalid(self, df, invalid, indexer):

pandas/tests/frame/methods/test_asfreq.py

@@ -192,9 +192,10 @@ def test_asfreq_with_date_object_index(self, frame_or_series):
         ts2 = ts.copy()
         ts2.index = [x.date() for x in ts2.index]
 
-        result = ts2.asfreq("4h", method="ffill")
-        expected = ts.asfreq("4h", method="ffill")
-        tm.assert_equal(result, expected)
+        with pytest.raises(
+            TypeError, match="Cannot compare Timestamp with datetime.date"
+        ):
+            ts2.asfreq("4h", method="ffill")
 
     def test_asfreq_with_unsorted_index(self, frame_or_series):
         # GH#39805

pandas/tests/indexes/datetimes/test_indexing.py

@@ -514,10 +514,11 @@ def test_contains_nonunique(self, vals):
 
 class TestGetIndexer:
     def test_get_indexer_date_objs(self):
+        # Behavior for get_indexer with date objects changed in GH#62158.
         rng = date_range("1/1/2000", periods=20)
 
         result = rng.get_indexer(rng.map(lambda x: x.date()))
-        expected = rng.get_indexer(rng)
+        expected = np.full(len(rng), -1, dtype=np.intp)
         tm.assert_numpy_array_equal(result, expected)
 
     def test_get_indexer(self):
@@ -562,17 +563,22 @@ def test_get_indexer(self):
             idx.get_indexer(idx[[0]], method="nearest", tolerance="foo")
 
     @pytest.mark.parametrize(
-        "target",
+        "target, expected",
         [
-            [date(2020, 1, 1), Timestamp("2020-01-02")],
-            [Timestamp("2020-01-01"), date(2020, 1, 2)],
+            (
+                [date(2020, 1, 1), Timestamp("2020-01-02")],
+                np.array([-1, 1], dtype=np.intp),
+            ),
+            (
+                [Timestamp("2020-01-01"), Timestamp(date(2020, 1, 2))],
+                np.array([0, 1], dtype=np.intp),
+            ),
         ],
     )
-    def test_get_indexer_mixed_dtypes(self, target):
+    def test_get_indexer_mixed_dtypes(self, target, expected):
         # https://github.com/pandas-dev/pandas/issues/33741
         values = DatetimeIndex([Timestamp("2020-01-01"), Timestamp("2020-01-02")])
         result = values.get_indexer(target)
-        expected = np.array([0, 1], dtype=np.intp)
         tm.assert_numpy_array_equal(result, expected)
 
     @pytest.mark.parametrize(