Address more warnings

Author: spencerkclark

xarray/tests/test_backends.py

@@ -529,7 +529,7 @@ def test_roundtrip_string_encoded_characters(self) -> None:
             assert actual["x"].encoding["_Encoding"] == "ascii"
 
     def test_roundtrip_numpy_datetime_data(self) -> None:
-        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"])
+        times = pd.to_datetime(["2000-01-01", "2000-01-02", "NaT"], unit="ns")
         expected = Dataset({"t": ("t", times), "t0": times[0]})
         kwargs = {"encoding": {"t0": {"units": "days since 1950-01-01"}}}
         with self.roundtrip(expected, save_kwargs=kwargs) as actual:

xarray/tests/test_dataarray.py

@@ -3647,7 +3647,9 @@ def test_to_and_from_dict_with_time_dim(self) -> None:
         t = pd.date_range("20130101", periods=10)
         lat = [77.7, 83.2, 76]
         da = DataArray(x, {"t": t, "lat": lat}, dims=["t", "lat"])
-        roundtripped = DataArray.from_dict(da.to_dict())
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", message="Converting non-nanosecond")
+            roundtripped = DataArray.from_dict(da.to_dict())
         assert_identical(da, roundtripped)
 
     def test_to_and_from_dict_with_nan_nat(self) -> None:
@@ -3657,7 +3659,9 @@ def test_to_and_from_dict_with_nan_nat(self) -> None:
         t[2] = np.nan
         lat = [77.7, 83.2, 76]
         da = DataArray(y, {"t": t, "lat": lat}, dims=["t", "lat"])
-        roundtripped = DataArray.from_dict(da.to_dict())
+        with warnings.catch_warnings():
+            warnings.filterwarnings("ignore", message="Converting non-nanosecond")
+            roundtripped = DataArray.from_dict(da.to_dict())
         assert_identical(da, roundtripped)
 
     def test_to_dict_with_numpy_attrs(self) -> None:

xarray/tests/test_groupby.py

@@ -139,8 +139,7 @@ def test_groupby_da_datetime() -> None:
     times = pd.date_range("2000-01-01", periods=4)
     foo = xr.DataArray([1, 2, 3, 4], coords=dict(time=times), dims="time")
     # create test index
-    dd = times.to_pydatetime()
-    reference_dates = [dd[0], dd[2]]
+    reference_dates = [times[0], times[2]]
     labels = reference_dates[0:1] * 2 + reference_dates[1:2] * 2
     ind = xr.DataArray(
         labels, coords=dict(time=times), dims="time", name="reference_date"
@@ -1881,7 +1880,7 @@ def test_resample_first(self) -> None:
         array = Dataset({"time": times})["time"]
         actual = array.resample(time="1D").last()
         expected_times = pd.to_datetime(
-            ["2000-01-01T18", "2000-01-02T18", "2000-01-03T06"]
+            ["2000-01-01T18", "2000-01-02T18", "2000-01-03T06"], unit="ns"
         )
         expected = DataArray(expected_times, [("time", times[::4])], name="time")
         assert_identical(expected, actual)

xarray/tests/test_plot.py

@@ -5,7 +5,7 @@
 import math
 from collections.abc import Generator, Hashable
 from copy import copy
-from datetime import date, datetime, timedelta
+from datetime import date, timedelta
 from typing import Any, Callable, Literal
 
 import numpy as np
@@ -2912,9 +2912,8 @@ def setUp(self) -> None:
         """
         month = np.arange(1, 13, 1)
         data = np.sin(2 * np.pi * month / 12.0)
-
-        darray = DataArray(data, dims=["time"])
-        darray.coords["time"] = np.array([datetime(2017, m, 1) for m in month])
+        times = pd.date_range(start="2017-01-01", freq="ME", periods=12)
+        darray = DataArray(data, dims=["time"], coords=[times])
 
         self.darray = darray
 

xarray/tests/test_variable.py

@@ -40,6 +40,7 @@
     requires_bottleneck,
     requires_cupy,
     requires_dask,
+    requires_pandas_3,
     requires_pint,
     requires_sparse,
     source_ndarray,
@@ -256,7 +257,9 @@ def test_index_and_concat_datetime(self):
         # regression test for #125
         date_range = pd.date_range("2011-09-01", periods=10)
         for dates in [date_range, date_range.values, date_range.to_pydatetime()]:
-            expected = self.cls("t", dates)
+            with warnings.catch_warnings():
+                warnings.filterwarnings("ignore", message="Converting non-nanosecond")
+                expected = self.cls("t", dates)
             for times in [
                 [expected[i] for i in range(10)],
                 [expected[i : (i + 1)] for i in range(10)],
@@ -2942,8 +2945,8 @@ def test_from_pint_wrapping_dask(self, Var):
         (np.array([np.datetime64("2000-01-01", "ns")]), False),
         (np.array([np.datetime64("2000-01-01", "s")]), True),
         (pd.date_range("2000", periods=1), False),
-        (datetime(2000, 1, 1), False),
-        (np.array([datetime(2000, 1, 1)]), False),
+        pytest.param(datetime(2000, 1, 1), True, marks=requires_pandas_3),
+        pytest.param(np.array([datetime(2000, 1, 1)]), True, marks=requires_pandas_3),
         (pd.date_range("2000", periods=1, tz=pytz.timezone("America/New_York")), False),
         (
             pd.Series(