Merge branch 'main' into perf/cache-array-keys

doc/whats-new.rst

@@ -69,6 +69,10 @@ Internal Changes
 ~~~~~~~~~~~~~~~~
 - Move non-CF related ``ensure_dtype_not_object`` from conventions to backends (:pull:`9828`).
   By `Kai Mühlbauer <https://github.com/kmuehlbauer>`_.
+- Move handling of scalar datetimes into ``_possibly_convert_objects``
+  within ``as_compatible_data``. This is consistent with how lists of these objects
+  will be converted (:pull:`9900`).
+  By `Kai Mühlbauer <https://github.com/kmuehlbauer>`_.
 
 .. _whats-new.2024.11.0:
 

xarray/core/variable.py

@@ -6,7 +6,6 @@
 import numbers
 import warnings
 from collections.abc import Callable, Hashable, Mapping, Sequence
-from datetime import timedelta
 from functools import partial
 from types import EllipsisType
 from typing import TYPE_CHECKING, Any, NoReturn, cast
@@ -232,10 +231,16 @@ def _as_nanosecond_precision(data):
 
 def _possibly_convert_objects(values):
     """Convert arrays of datetime.datetime and datetime.timedelta objects into
-    datetime64 and timedelta64, according to the pandas convention. For the time
-    being, convert any non-nanosecond precision DatetimeIndex or TimedeltaIndex
-    objects to nanosecond precision.  While pandas is relaxing this in version
-    2.0.0, in xarray we will need to make sure we are ready to handle
+    datetime64 and timedelta64, according to the pandas convention.
+
+    * datetime.datetime
+    * datetime.timedelta
+    * pd.Timestamp
+    * pd.Timedelta
+
+    For the time being, convert any non-nanosecond precision DatetimeIndex or
+    TimedeltaIndex objects to nanosecond precision.  While pandas is relaxing this
+    in version 2.0.0, in xarray we will need to make sure we are ready to handle
     non-nanosecond precision datetimes or timedeltas in our code before allowing
     such values to pass through unchanged.  Converting to nanosecond precision
     through pandas.Series objects ensures that datetimes and timedeltas are
@@ -305,13 +310,6 @@ def convert_non_numpy_type(data):
     if isinstance(data, tuple):
         data = utils.to_0d_object_array(data)
 
-    if isinstance(data, pd.Timestamp):
-        # TODO: convert, handle datetime objects, too
-        data = np.datetime64(data.value, "ns")
-
-    if isinstance(data, timedelta):
-        data = np.timedelta64(getattr(data, "value", data), "ns")
-
     # we don't want nested self-described arrays
     if isinstance(data, pd.Series | pd.DataFrame):
         pandas_data = data.values

xarray/tests/test_plot.py

@@ -90,27 +90,27 @@ def text_in_fig() -> set[str]:
     """
     Return the set of all text in the figure
     """
-    return {t.get_text() for t in plt.gcf().findobj(mpl.text.Text)}  # type: ignore[attr-defined] # mpl error?
+    return {t.get_text() for t in plt.gcf().findobj(mpl.text.Text)}
 
 
 def find_possible_colorbars() -> list[mpl.collections.QuadMesh]:
     # nb. this function also matches meshes from pcolormesh
-    return plt.gcf().findobj(mpl.collections.QuadMesh)  # type: ignore[return-value] # mpl error?
+    return plt.gcf().findobj(mpl.collections.QuadMesh)
 
 
 def substring_in_axes(substring: str, ax: mpl.axes.Axes) -> bool:
     """
     Return True if a substring is found anywhere in an axes
     """
-    alltxt: set[str] = {t.get_text() for t in ax.findobj(mpl.text.Text)}  # type: ignore[attr-defined] # mpl error?
+    alltxt: set[str] = {t.get_text() for t in ax.findobj(mpl.text.Text)}
     return any(substring in txt for txt in alltxt)
 
 
 def substring_not_in_axes(substring: str, ax: mpl.axes.Axes) -> bool:
     """
     Return True if a substring is not found anywhere in an axes
     """
-    alltxt: set[str] = {t.get_text() for t in ax.findobj(mpl.text.Text)}  # type: ignore[attr-defined] # mpl error?
+    alltxt: set[str] = {t.get_text() for t in ax.findobj(mpl.text.Text)}
     check = [(substring not in txt) for txt in alltxt]
     return all(check)
 
@@ -122,7 +122,7 @@ def property_in_axes_text(
     Return True if the specified text in an axes
     has the property assigned to property_str
     """
-    alltxt: list[mpl.text.Text] = ax.findobj(mpl.text.Text)  # type: ignore[assignment]
+    alltxt: list[mpl.text.Text] = ax.findobj(mpl.text.Text)
     return all(
         plt.getp(t, property) == property_str
         for t in alltxt