remove the deprecated return value of Dataset.update

Author: keewis

xarray/core/dataset.py

@@ -4157,7 +4157,7 @@ def unstack(
                 result = result._unstack_once(dim, fill_value)
         return result
 
-    def update(self, other: "CoercibleMapping") -> "Dataset":
+    def update(self, other: "CoercibleMapping") -> None:
         """Update this dataset's variables with those from another dataset.
 
         Just like :py:meth:`dict.update` this is a in-place operation.
@@ -4173,14 +4173,6 @@ def update(self, other: "CoercibleMapping") -> "Dataset":
             - mapping {var name: (dimension name, array-like)}
             - mapping {var name: (tuple of dimension names, array-like)}
 
-        Returns
-        -------
-        updated : Dataset
-            Updated dataset. Note that since the update is in-place this is the input
-            dataset.
-
-            It is deprecated since version 0.17 and scheduled to be removed in 0.19.
-
         Raises
         ------
         ValueError
@@ -4192,7 +4184,7 @@ def update(self, other: "CoercibleMapping") -> "Dataset":
         Dataset.assign
         """
         merge_result = dataset_update_method(self, other)
-        return self._replace(inplace=True, **merge_result._asdict())
+        self._replace(inplace=True, **merge_result._asdict())
 
     def merge(
         self,

xarray/tests/test_dask.py

@@ -1619,7 +1619,7 @@ def test_more_transforms_pass_lazy_array_equiv(map_da, map_ds):
         assert_equal(xr.broadcast(map_ds.cxy, map_ds.cxy)[0], map_ds.cxy)
         assert_equal(map_ds.map(lambda x: x), map_ds)
         assert_equal(map_ds.set_coords("a").reset_coords("a"), map_ds)
-        assert_equal(map_ds.update({"a": map_ds.a}), map_ds)
+        assert_equal(map_ds.assign({"a": map_ds.a}), map_ds)
 
         # fails because of index error
         # assert_equal(

xarray/tests/test_dataset.py

@@ -3191,13 +3191,13 @@ def test_update(self):
         data = create_test_data(seed=0)
         expected = data.copy()
         var2 = Variable("dim1", np.arange(8))
-        actual = data.update({"var2": var2})
+        actual = data
+        actual.update({"var2": var2})
         expected["var2"] = var2
         assert_identical(expected, actual)
 
         actual = data.copy()
-        actual_result = actual.update(data)
-        assert actual_result is actual
+        actual.update(data)
         assert_identical(expected, actual)
 
         other = Dataset(attrs={"new": "attr"})