fix pandas dev issues

Author: kmuehlbauer

xarray/coding/times.py

@@ -579,20 +579,23 @@ def _numbers_to_timedelta(
 ) -> np.ndarray:
     """Transform numbers to np.timedelta64."""
     # keep NaT/nan mask
-    nan = np.isnan(flat_num) | (flat_num == np.iinfo(np.int64).min)
+    if flat_num.dtype.kind == "f":
+        nan = np.asarray(np.isnan(flat_num))
+    elif flat_num.dtype.kind == "i":
+        nan = np.asarray(flat_num == np.iinfo(np.int64).min)
 
     # in case we need to change the unit, we fix the numbers here
     # this should be safe, as errors would have been raised above
     ns_time_unit = _NS_PER_TIME_DELTA[time_unit]
     ns_ref_date_unit = _NS_PER_TIME_DELTA[ref_unit]
     if ns_time_unit > ns_ref_date_unit:
-        flat_num *= np.int64(ns_time_unit / ns_ref_date_unit)
+        flat_num = np.asarray(flat_num * np.int64(ns_time_unit / ns_ref_date_unit))
         time_unit = ref_unit
 
     # estimate fitting resolution for floating point values
     # this iterates until all floats are fractionless or time_unit == "ns"
     if flat_num.dtype.kind == "f" and time_unit != "ns":
-        flat_num_dates, new_time_unit = _check_higher_resolution(flat_num, time_unit)  # type: ignore[arg-type]
+        flat_num, new_time_unit = _check_higher_resolution(flat_num, time_unit)
         if time_unit != new_time_unit:
             msg = (
                 f"Can't decode floating point {datatype} to {time_unit!r} without "
@@ -608,7 +611,8 @@ def _numbers_to_timedelta(
     with warnings.catch_warnings():
         warnings.simplefilter("ignore", RuntimeWarning)
         flat_num = flat_num.astype(np.int64)
-    flat_num[nan] = np.iinfo(np.int64).min
+    if nan.any():
+        flat_num[nan] = np.iinfo(np.int64).min
 
     # cast to wanted type
     return flat_num.astype(f"timedelta64[{time_unit}]")

xarray/tests/test_variable.py

@@ -37,6 +37,7 @@
     assert_identical,
     assert_no_warnings,
     has_dask_ge_2024_11_0,
+    has_pandas_3,
     raise_if_dask_computes,
     requires_bottleneck,
     requires_cupy,
@@ -208,8 +209,11 @@ def test_index_0d_datetime(self):
         x = self.cls(["x"], [np.datetime64(d)])
         self._assertIndexedLikeNDArray(x, np.datetime64(d), "datetime64[us]")
 
+        expected_unit = "us" if has_pandas_3 else "ns"
         x = self.cls(["x"], pd.DatetimeIndex([d]))
-        self._assertIndexedLikeNDArray(x, np.datetime64(d), "datetime64[ns]")
+        self._assertIndexedLikeNDArray(
+            x, np.datetime64(d), f"datetime64[{expected_unit}]"
+        )
 
     def test_index_0d_timedelta64(self):
         td = timedelta(hours=1)
@@ -283,7 +287,10 @@ def test_0d_time_data(self):
             (dt64_data.values.astype("datetime64[m]"), "s"),
             (dt64_data.values.astype("datetime64[s]"), "s"),
             (dt64_data.values.astype("datetime64[ps]"), "ns"),
-            (dt64_data.to_pydatetime(), "ns"),
+            (
+                dt64_data.to_pydatetime(),
+                "us" if has_pandas_3 else "ns",
+            ),
         ],
     )
     def test_datetime64_conversion(self, values, unit):
@@ -1071,8 +1078,14 @@ def test_numpy_same_methods(self):
         "values, unit",
         [
             (np.datetime64("2000-01-01"), "s"),
-            (pd.Timestamp("2000-01-01T00"), "ns"),
-            (datetime(2000, 1, 1), "ns"),
+            (
+                pd.Timestamp("2000-01-01T00"),
+                "s" if has_pandas_3 else "ns",
+            ),
+            (
+                datetime(2000, 1, 1),
+                "us" if has_pandas_3 else "ns",
+            ),
             (np.datetime64("2000-01-01T00:00:00.1234567891"), "ns"),
         ],
     )
@@ -1109,8 +1122,9 @@ def test_0d_str(self):
 
     def test_0d_datetime(self):
         v = Variable([], pd.Timestamp("2000-01-01"))
-        assert v.dtype == np.dtype("datetime64[ns]")
-        assert v.values == np.datetime64("2000-01-01", "ns")
+        expected_unit = "s" if has_pandas_3 else "ns"
+        assert v.dtype == np.dtype(f"datetime64[{expected_unit}]")
+        assert v.values == np.datetime64("2000-01-01", expected_unit)
 
     @pytest.mark.parametrize(
         "values, unit", [(pd.to_timedelta("1s"), "ns"), (np.timedelta64(1, "s"), "s")]
@@ -2654,11 +2668,14 @@ def test_datetime(self):
         assert np.dtype("datetime64[ns]") == actual.dtype
         assert expected is source_ndarray(np.asarray(actual))
 
-        expected = np.datetime64("2000-01-01", "ns")
+        expected = np.datetime64(
+            "2000-01-01",
+            "us" if has_pandas_3 else "ns",
+        )
         actual = as_compatible_data(datetime(2000, 1, 1))
         assert np.asarray(expected) == actual
         assert np.ndarray is type(actual)
-        assert np.dtype("datetime64[ns]") == actual.dtype
+        assert expected.dtype == actual.dtype
 
     def test_tz_datetime(self) -> None:
         tz = pytz.timezone("America/New_York")
@@ -2980,8 +2997,14 @@ def test_from_pint_wrapping_dask(self, Var):
         (np.array([np.datetime64("2000-01-01", "ns")]), "ns"),
         (np.array([np.datetime64("2000-01-01", "s")]), "s"),
         (pd.date_range("2000", periods=1), "ns"),
-        (datetime(2000, 1, 1), "ns"),
-        (np.array([datetime(2000, 1, 1)]), "ns"),
+        (
+            datetime(2000, 1, 1),
+            "us" if has_pandas_3 else "ns",
+        ),
+        (
+            np.array([datetime(2000, 1, 1)]),
+            "us" if has_pandas_3 else "ns",
+        ),
         (pd.date_range("2000", periods=1, tz=pytz.timezone("America/New_York")), "ns"),
         (
             pd.Series(