Option to not auto-create index during expand_dims

doc/whats-new.rst

@@ -29,6 +29,9 @@ New Features
   for example, will retain the object.  However, one cannot do operations that are not possible on the `ExtensionArray`
   then, such as broadcasting.
   By `Ilan Gold <https://github.com/ilan-gold>`_.
+- Added the option to avoid automatically creating 1D pandas indexes in :py:meth:`Dataset.expand_dims()`, by passing the new kwarg
+  `create_index=False`. (:pull:`8960`)
+  By `Tom Nicholas <https://github.com/TomNicholas>`_.
 
 Breaking changes
 ~~~~~~~~~~~~~~~~

xarray/core/dataarray.py

@@ -2557,6 +2557,7 @@ def expand_dims(
         self,
         dim: None | Hashable | Sequence[Hashable] | Mapping[Any, Any] = None,
         axis: None | int | Sequence[int] = None,
+        create_index: bool = True,
         **dim_kwargs: Any,
     ) -> Self:
         """Return a new object with an additional axis (or axes) inserted at
@@ -2566,6 +2567,9 @@ def expand_dims(
         If dim is already a scalar coordinate, it will be promoted to a 1D
         coordinate consisting of a single value.
 
+        The automatic creation of indexes to back new 1D coordinate variables
+        controlled by the create_index kwarg.
+
         Parameters
         ----------
         dim : Hashable, sequence of Hashable, dict, or None, optional
@@ -2581,6 +2585,8 @@ def expand_dims(
             multiple axes are inserted. In this case, dim arguments should be
             same length list. If axis=None is passed, all the axes will be
             inserted to the start of the result array.
+        create_index : bool, default is True
+            Whether to create new PandasIndex objects for any new 1D coordinate variables.
         **dim_kwargs : int or sequence or ndarray
             The keywords are arbitrary dimensions being inserted and the values
             are either the lengths of the new dims (if int is given), or their
@@ -2644,7 +2650,7 @@ def expand_dims(
             dim = {dim: 1}
 
         dim = either_dict_or_kwargs(dim, dim_kwargs, "expand_dims")
-        ds = self._to_temp_dataset().expand_dims(dim, axis)
+        ds = self._to_temp_dataset().expand_dims(dim, axis, create_index=create_index)
         return self._from_temp_dataset(ds)
 
     def set_index(

xarray/core/dataset.py

@@ -4497,6 +4497,7 @@ def expand_dims(
         self,
         dim: None | Hashable | Sequence[Hashable] | Mapping[Any, Any] = None,
         axis: None | int | Sequence[int] = None,
+        create_index: bool = True,
         **dim_kwargs: Any,
     ) -> Self:
         """Return a new object with an additional axis (or axes) inserted at
@@ -4506,6 +4507,9 @@ def expand_dims(
         If dim is already a scalar coordinate, it will be promoted to a 1D
         coordinate consisting of a single value.
 
+        The automatic creation of indexes to back new 1D coordinate variables
+        controlled by the create_index kwarg.
+
         Parameters
         ----------
         dim : hashable, sequence of hashable, mapping, or None
@@ -4521,6 +4525,8 @@ def expand_dims(
             multiple axes are inserted. In this case, dim arguments should be
             same length list. If axis=None is passed, all the axes will be
             inserted to the start of the result array.
+        create_index : bool, default is True
+            Whether to create new PandasIndex objects for any new 1D coordinate variables.
         **dim_kwargs : int or sequence or ndarray
             The keywords are arbitrary dimensions being inserted and the values
             are either the lengths of the new dims (if int is given), or their
@@ -4640,9 +4646,14 @@ def expand_dims(
                 # save the coordinates to the variables dict, and set the
                 # value within the dim dict to the length of the iterable
                 # for later use.
-                index = PandasIndex(v, k)
-                indexes[k] = index
-                variables.update(index.create_variables())
+
+                if create_index:
+                    index = PandasIndex(v, k)
+                    indexes[k] = index
+                    name_and_new_1d_var = index.create_variables()
+                else:
+                    name_and_new_1d_var = {k: Variable(data=v, dims=k)}
+                variables.update(name_and_new_1d_var)
                 coord_names.add(k)
                 dim[k] = variables[k].size
             elif isinstance(v, int):
@@ -4678,11 +4689,23 @@ def expand_dims(
                     variables[k] = v.set_dims(dict(all_dims))
             else:
                 if k not in variables:
-                    # If dims includes a label of a non-dimension coordinate,
-                    # it will be promoted to a 1D coordinate with a single value.
-                    index, index_vars = create_default_index_implicit(v.set_dims(k))
-                    indexes[k] = index
-                    variables.update(index_vars)
+                    if k in coord_names and create_index:
+                        # If dims includes a label of a non-dimension coordinate,
+                        # it will be promoted to a 1D coordinate with a single value.
+                        index, index_vars = create_default_index_implicit(v.set_dims(k))
+                        indexes[k] = index
+                        variables.update(index_vars)
+                    else:
+                        if create_index:
+                            warnings.warn(
+                                f"No index created for dimension {k} because variable {k} is not a coordinate. "
+                                f"To create an index for {k}, please first call `.set_coords('{k}')` on this object.",
+                                UserWarning,
+                            )
+
+                        # create 1D variable without creating a new index
+                        new_1d_var = v.set_dims(k)
+                        variables.update({k: new_1d_var})
 
         return self._replace_with_new_dims(
             variables, coord_names=coord_names, indexes=indexes

xarray/tests/test_dataset.py

@@ -3430,6 +3430,52 @@ def test_expand_dims_kwargs_python36plus(self) -> None:
         )
         assert_identical(other_way_expected, other_way)
 
+    @pytest.mark.parametrize("create_index_flag", [True, False])
+    def test_expand_dims_create_index_data_variable(self, create_index_flag):
+        # data variables should not gain an index ever
+        ds = Dataset({"x": 0})
+
+        if create_index_flag:
+            with pytest.warns(UserWarning, match="No index created"):
+                expanded = ds.expand_dims("x", create_index=create_index_flag)
+        else:
+            expanded = ds.expand_dims("x", create_index=create_index_flag)
+
+        # TODO Can't just create the expected dataset directly using constructor because of GH issue 8959
+        expected = Dataset({"x": ("x", [0])}).drop_indexes("x").reset_coords("x")
+
+        assert_identical(expanded, expected, check_default_indexes=False)
+        assert expanded.indexes == {}
+
+    def test_expand_dims_create_index_coordinate_variable(self):
+        # coordinate variables should gain an index only if create_index is True (the default)
+        ds = Dataset(coords={"x": 0})
+        expanded = ds.expand_dims("x")
+        expected = Dataset({"x": ("x", [0])})
+        assert_identical(expanded, expected)
+
+        expanded_no_index = ds.expand_dims("x", create_index=False)
+
+        # TODO Can't just create the expected dataset directly using constructor because of GH issue 8959
+        expected = Dataset(coords={"x": ("x", [0])}).drop_indexes("x")
+
+        assert_identical(expanded_no_index, expected, check_default_indexes=False)
+        assert expanded_no_index.indexes == {}
+
+    def test_expand_dims_create_index_from_iterable(self):
+        ds = Dataset(coords={"x": 0})
+        expanded = ds.expand_dims(x=[0, 1])
+        expected = Dataset({"x": ("x", [0, 1])})
+        assert_identical(expanded, expected)
+
+        expanded_no_index = ds.expand_dims(x=[0, 1], create_index=False)
+
+        # TODO Can't just create the expected dataset directly using constructor because of GH issue 8959
+        expected = Dataset(coords={"x": ("x", [0, 1])}).drop_indexes("x")
+
+        assert_identical(expanded, expected, check_default_indexes=False)
+        assert expanded_no_index.indexes == {}
+
     def test_expand_dims_non_nanosecond_conversion(self) -> None:
         # Regression test for https://github.com/pydata/xarray/issues/7493#issuecomment-1953091000
         with pytest.warns(UserWarning, match="non-nanosecond precision"):