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Address additional upstream deprecation warnings #11296

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Address additional upstream deprecation warnings #11296

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26cf1d3
Address NumPy deprecation warning
spencerkclark Apr 11, 2026
7dacd5c
Address lingering generic np.timedelta64("NaT") warning; defensively
spencerkclark Apr 11, 2026
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2 changes: 1 addition & 1 deletion xarray/coding/times.py
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Original file line number Diff line number Diff line change
Expand Up @@ -830,7 +830,7 @@ def cftime_to_nptime(
f"standard calendar. Reason: {e}."
) from e
else:
dt = np.datetime64("NaT")
dt = np.datetime64("NaT", time_unit)
new.append(dt)
return np.asarray(new).reshape(times.shape)

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2 changes: 1 addition & 1 deletion xarray/core/variable.py
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Expand Up @@ -238,7 +238,7 @@ def _possibly_convert_objects(values):
and values.dtype.kind == "O"
and Version(pd.__version__) >= Version("3.0.0dev0")
):
result.dtype = values.dtype
result = result.view(values.dtype)
return result


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2 changes: 1 addition & 1 deletion xarray/tests/test_accessor_dt.py
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Expand Up @@ -266,7 +266,7 @@ def test_seasons(self) -> None:
dates = xr.date_range(
start="2000/01/01", freq="ME", periods=12, use_cftime=False
)
dates = dates.append(pd.Index([np.datetime64("NaT")]))
dates = dates.append(pd.Index([np.datetime64("NaT", "us")]))
dates = xr.DataArray(dates)
seasons = xr.DataArray(
[
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6 changes: 5 additions & 1 deletion xarray/tests/test_coding_times.py
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Expand Up @@ -1391,7 +1391,11 @@ def test_roundtrip_datetime64_nanosecond_precision(
) -> None:
# test for GH7817
time = np.datetime64(timestr, format)
times = [np.datetime64("1970-01-01T00:00:00", format), np.datetime64("NaT"), time]
times = [
np.datetime64("1970-01-01T00:00:00", format),
np.datetime64("NaT", format),
time,
]

if use_encoding:
encoding = dict(dtype=dtype, _FillValue=fill_value)
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2 changes: 1 addition & 1 deletion xarray/tests/test_dataset.py
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Expand Up @@ -1335,7 +1335,7 @@ def test_chunk_by_frequency(self, freq: str, calendar: str, add_gap: bool) -> No
).to_numpy(copy=True)
if add_gap:
# introduce an empty bin
time[31 : 31 + ΔN] = np.datetime64("NaT")
time[31 : 31 + ΔN] = np.datetime64("NaT", "us")
time = time[~np.isnat(time)]
else:
time = time[:N]
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4 changes: 2 additions & 2 deletions xarray/tests/test_duck_array_ops.py
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Expand Up @@ -723,11 +723,11 @@ def test_argmin_max_error():
["array", "expected"],
[
(
np.array([np.datetime64("2000-01-01"), np.datetime64("NaT")]),
np.array([np.datetime64("2000-01-01"), np.datetime64("NaT", "D")]),
np.array([False, True]),
),
(
np.array([np.timedelta64(1, "h"), np.timedelta64("NaT")]),
np.array([np.timedelta64(1, "h"), np.timedelta64("NaT", "h")]),
np.array([False, True]),
),
(
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6 changes: 3 additions & 3 deletions xarray/tests/test_groupby.py
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Expand Up @@ -726,7 +726,7 @@ def test_groupby_drops_nans(shuffle: bool, chunk: Literal[False] | dict) -> None
[
np.datetime64("2001-01-01"),
np.datetime64("2001-01-01"),
np.datetime64("NaT"),
np.datetime64("NaT", "D"),
],
)
expected5 = xr.DataArray(3, [("t", [np.datetime64("2001-01-01")])])
Expand Down Expand Up @@ -2018,7 +2018,7 @@ def test_resample_first_last(self, use_cftime) -> None:
# missing periods, GH10169
actual = array.isel(time=[0, 1, 2, 3, 8, 9]).resample(time="1D").last()
expected = DataArray(
np.array([times[3], np.datetime64("NaT"), times[9]]),
np.array([times[3], np.datetime64("NaT", "us"), times[9]]),
dims="time",
coords={"time": times[::4]},
name="time",
Expand Down Expand Up @@ -3836,7 +3836,7 @@ def test_mean_datetime_edge_cases():
# Test with NaT values
dates_with_nat = pd.date_range("2021-01-01", periods=4, freq="D")
dates_with_nat_array = dates_with_nat.values.copy()
dates_with_nat_array[1] = np.datetime64("NaT")
dates_with_nat_array[1] = np.datetime64("NaT", "us")

ds = xr.Dataset(
{
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