Fixes dimension order in xarray.Dataset.to_stacked_array

doc/whats-new.rst

@@ -36,6 +36,9 @@ Deprecations
 Bug fixes
 ~~~~~~~~~
 
+- :py:meth:`~xarray.Dataset.to_stacked_array` now uses dimensions in order of appearance.
+  This fixes the issue where using :py:meth:`~xarray.Dataset.transpose` before :py:meth:`~xarray.Dataset.to_stacked_array`
+  had no effect. (Mentioned in :issue:`9921`)
 
 Documentation
 ~~~~~~~~~~~~~

xarray/core/dataset.py

@@ -5246,7 +5246,13 @@ def to_stacked_array(
         """
         from xarray.structure.concat import concat
 
-        stacking_dims = tuple(dim for dim in self.dims if dim not in sample_dims)
+        # add stacking dims by order of appearance
+        stacking_dims_list: list[Hashable] = []
+        for da in self.data_vars.values():
+            for dim in da.dims:
+                if dim not in sample_dims and dim not in stacking_dims_list:
+                    stacking_dims_list.append(dim)
+        stacking_dims = tuple(stacking_dims_list)
 
         for key, da in self.data_vars.items():
             missing_sample_dims = set(sample_dims) - set(da.dims)

xarray/tests/test_dataset.py

@@ -4098,6 +4098,33 @@ def test_to_stacked_array_preserves_dtype(self) -> None:
             expected_stacked_variable,
         )
 
+    def test_to_stacked_array_transposed(self) -> None:
+        # test that to_stacked_array uses updated dim order after transposition
+        ds = xr.Dataset(
+            data_vars=dict(
+                v1=(["d1", "d2"], np.arange(6).reshape((2, 3))),
+            ),
+            coords=dict(
+                d1=(["d1"], np.arange(2)),
+                d2=(["d2"], np.arange(3)),
+            ),
+        )
+        da = ds.to_stacked_array(
+            new_dim="new_dim",
+            sample_dims=[],
+            variable_dim="variable",
+        )
+        dsT = ds.transpose()
+        daT = dsT.to_stacked_array(
+            new_dim="new_dim",
+            sample_dims=[],
+            variable_dim="variable",
+        )
+        v1 = np.arange(6)
+        v1T = np.arange(6).reshape((2, 3)).T.flatten()
+        np.testing.assert_equal(da.to_numpy(), v1)
+        np.testing.assert_equal(daT.to_numpy(), v1T)
+
     def test_update(self) -> None:
         data = create_test_data(seed=0)
         expected = data.copy()