woops almost duplicate of pandas-dev#49015

Author: jbrockmendel

pandas/core/arrays/datetimelike.py

@@ -1276,7 +1276,6 @@ def _add_timedeltalike_scalar(self, other):
         # PeriodArray overrides, so we only get here with DTA/TDA
         self = cast("DatetimeArray | TimedeltaArray", self)
         other = Timedelta(other)
-
         self, other = self._ensure_matching_resos(other)
         return self._add_timedeltalike(other)
 

pandas/core/construction.py

@@ -45,7 +45,6 @@
     maybe_convert_platform,
     maybe_infer_to_datetimelike,
     maybe_upcast,
-    sanitize_to_nanoseconds,
 )
 from pandas.core.dtypes.common import (
     is_datetime64_ns_dtype,
@@ -325,13 +324,6 @@ def array(
 
     data = extract_array(data, extract_numpy=True)
 
-    if isinstance(data, ExtensionArray) and (
-        dtype is None or is_dtype_equal(dtype, data.dtype)
-    ):
-        if copy:
-            return data.copy()
-        return data
-
     # this returns None for not-found dtypes.
     if isinstance(dtype, str):
         dtype = registry.find(dtype) or dtype
@@ -789,7 +781,9 @@ def _try_cast(
         if is_ndarray:
             arr = cast(np.ndarray, arr)
             if arr.dtype != object:
-                return sanitize_to_nanoseconds(arr, copy=copy)
+                if copy:
+                    return arr.copy()
+                return arr
 
             out = maybe_infer_to_datetimelike(arr)
             if out is arr and copy:

pandas/core/dtypes/cast.py

@@ -1423,12 +1423,7 @@ def maybe_cast_to_datetime(
             return astype_nansafe(value, dtype)  # type: ignore[arg-type]
 
     elif isinstance(value, np.ndarray):
-        if value.dtype.kind in ["M", "m"]:
-            # catch a datetime/timedelta that is not of ns variety
-            # and no coercion specified
-            value = sanitize_to_nanoseconds(value)
-
-        elif value.dtype == _dtype_obj:
+        if value.dtype == _dtype_obj:
             value = maybe_infer_to_datetimelike(value)
 
     elif isinstance(value, list):

pandas/core/groupby/grouper.py

@@ -26,7 +26,6 @@
 from pandas.util._decorators import cache_readonly
 from pandas.util._exceptions import find_stack_level
 
-from pandas.core.dtypes.cast import sanitize_to_nanoseconds
 from pandas.core.dtypes.common import (
     is_categorical_dtype,
     is_list_like,
@@ -558,9 +557,12 @@ def __init__(
                 raise AssertionError(errmsg)
 
         if isinstance(self.grouping_vector, np.ndarray):
-            # if we have a date/time-like grouper, make sure that we have
-            # Timestamps like
-            self.grouping_vector = sanitize_to_nanoseconds(self.grouping_vector)
+            if self.grouping_vector.dtype.kind in ["m", "M"]:
+                # if we have a date/time-like grouper, make sure that we have
+                # Timestamps like
+                # TODO 2022-10-08 we only have one test that gets here and
+                #  values are already in nanoseconds in that case.
+                self.grouping_vector = Series(self.grouping_vector).to_numpy()
 
     def __repr__(self) -> str:
         return f"Grouping({self.name})"

pandas/tests/scalar/timedelta/test_timedelta.py

@@ -183,8 +183,7 @@ def test_truediv_timedeltalike(self, td):
         assert (2.5 * td) / td == 2.5
 
         other = Timedelta(td.value)
-
-        msg = "Cannot cast 106752 days 00:00:00 to unit='ns' without overflow"
+        msg = "Cannot cast 106752 days 00:00:00 to unit='ns' without overflow."
         with pytest.raises(OutOfBoundsTimedelta, match=msg):
             td / other
 

pandas/tests/tseries/offsets/test_offsets.py

@@ -19,6 +19,7 @@
 
 from pandas._libs.tslibs import (
     NaT,
+    Timedelta,
     Timestamp,
     conversion,
     timezones,
@@ -566,6 +567,12 @@ def test_add_dt64_ndarray_non_nano(self, offset_types, unit, request):
             expected = dti._data + off
             result = dta + off
 
+        exp_unit = unit
+        if isinstance(off, Tick) and off._reso > dta._reso:
+            # cast to higher reso like we would with Timedelta scalar
+            exp_unit = Timedelta(off)._unit
+        expected = expected._as_unit(exp_unit)
+
         if len(w):
             # PerformanceWarning was issued bc _apply_array raised, so we
             #  fell back to object dtype, for which the code path does
@@ -576,14 +583,7 @@ def test_add_dt64_ndarray_non_nano(self, offset_types, unit, request):
             )
             request.node.add_marker(mark)
 
-            # result.dtype should match M8[{unit}], while expected.dtype should
-            #  always be in nanos
-            #  (this is for cases when offset_types is not itself Nano)
-            assert result.dtype != expected.dtype
-
-        tm.assert_numpy_array_equal(
-            result._ndarray, expected._ndarray.astype(result.dtype)
-        )
+        tm.assert_numpy_array_equal(result._ndarray, expected._ndarray)
 
 
 class TestDateOffset(Base):