Fix indexing with datetime64[ns] with pandas=1.1

Fixes pydata#4283

The underlying issue is that calling `.item()` on a NumPy array with
`dtype=datetime64[ns]` returns an _integer_, rather than an `np.datetime64
scalar. This is somewhat baffling but works this way because `.item()`
returns native Python types, but `datetime.datetime` doesn't support
nanosecond precision.

`pandas.Index.get_loc` used to support these integers, but now is more strict.
Hence we get errors.

We can fix this by using `array[()]` to convert 0d arrays into NumPy scalars
instead of calling `array.item()`.

I've added a crude regression test. There may well be a better way to test this
but I haven't figured it out yet.

xarray/core/indexing.py

@@ -173,8 +173,9 @@ def convert_label_indexer(index, label, index_name="", method=None, tolerance=No
             else _asarray_tuplesafe(label)
         )
         if label.ndim == 0:
+            label_value = label[()]
             if isinstance(index, pd.MultiIndex):
-                indexer, new_index = index.get_loc_level(label.item(), level=0)
+                indexer, new_index = index.get_loc_level(label_value, level=0)
             elif isinstance(index, pd.CategoricalIndex):
                 if method is not None:
                     raise ValueError(
@@ -184,11 +185,9 @@ def convert_label_indexer(index, label, index_name="", method=None, tolerance=No
                     raise ValueError(
                         "'tolerance' is not a valid kwarg when indexing using a CategoricalIndex."
                     )
-                indexer = index.get_loc(label.item())
+                indexer = index.get_loc(label_value)
             else:
-                indexer = index.get_loc(
-                    label.item(), method=method, tolerance=tolerance
-                )
+                indexer = index.get_loc(label_value, method=method, tolerance=tolerance)
         elif label.dtype.kind == "b":
             indexer = label
         else:

xarray/tests/test_dataarray.py

@@ -938,7 +938,7 @@ def test_sel_invalid_slice(self):
         with raises_regex(ValueError, "cannot use non-scalar arrays"):
             array.sel(x=slice(array.x))
 
-    def test_sel_dataarray_datetime(self):
+    def test_sel_dataarray_datetime_slice(self):
         # regression test for GH1240
         times = pd.date_range("2000-01-01", freq="D", periods=365)
         array = DataArray(np.arange(365), [("time", times)])
@@ -1078,6 +1078,12 @@ def test_loc(self):
         assert_identical(da[:3, :4], da.loc[["a", "b", "c"], np.arange(4)])
         assert_identical(da[:, :4], da.loc[:, self.ds["y"] < 4])
 
+    def test_loc_datetime64_value(self):
+        # regression test for https://github.com/pydata/xarray/issues/4283
+        t = np.array(["2017-09-05T12", "2017-09-05T15"], dtype="datetime64[ns]")
+        array = DataArray(np.ones(t.shape), dims=("time",), coords=(t,))
+        assert_identical(array.loc[{"time": t[0]}], array[0])
+
     def test_loc_assign(self):
         self.ds["x"] = ("x", np.array(list("abcdefghij")))
         da = self.ds["foo"]

xarray/tests/test_indexing.py

@@ -7,7 +7,13 @@
 from xarray import DataArray, Dataset, Variable
 from xarray.core import indexing, nputils
 
-from . import IndexerMaker, ReturnItem, assert_array_equal, raises_regex
+from . import (
+    IndexerMaker,
+    ReturnItem,
+    assert_array_equal,
+    assert_identical,
+    raises_regex,
+)
 
 B = IndexerMaker(indexing.BasicIndexer)