Implement sort_remaining for sort_index

python/cudf/cudf/core/dataframe.py

@@ -36,7 +36,7 @@
 from pandas.core.dtypes.common import is_float, is_integer
 from pandas.io.formats import console
 from pandas.io.formats.printing import pprint_thing
-from typing_extensions import assert_never
+from typing_extensions import Self, assert_never
 
 import cudf
 import cudf.core.common
@@ -1830,25 +1830,27 @@ def _repr_latex_(self):
         return self._get_renderable_dataframe().to_pandas()._repr_latex_()
 
     @_cudf_nvtx_annotate
-    def _get_columns_by_label(self, labels, downcast=False):
+    def _get_columns_by_label(
+        self, labels, *, downcast=False
+    ) -> Self | Series:
         """
         Return columns of dataframe by `labels`
 
         If downcast is True, try and downcast from a DataFrame to a Series
         """
-        new_data = super()._get_columns_by_label(labels, downcast)
+        ca = self._data.select_by_label(labels)
         if downcast:
             if is_scalar(labels):
                 nlevels = 1
             elif isinstance(labels, tuple):
                 nlevels = len(labels)
             if self._data.multiindex is False or nlevels == self._data.nlevels:
                 out = self._constructor_sliced._from_data(
-                    new_data, index=self.index, name=labels
+                    ca, index=self.index, name=labels
                 )
                 return out
         out = self.__class__._from_data(
-            new_data, index=self.index, columns=new_data.to_pandas_index()
+            ca, index=self.index, columns=ca.to_pandas_index()
         )
         return out
 

python/cudf/cudf/core/frame.py

@@ -362,12 +362,12 @@ def equals(self, other):
         )
 
     @_cudf_nvtx_annotate
-    def _get_columns_by_label(self, labels, downcast=False):
+    def _get_columns_by_label(self, labels, *, downcast=False) -> Self:
         """
         Returns columns of the Frame specified by `labels`
 
         """
-        return self._data.select_by_label(labels)
+        return self.__class__._from_data(self._data.select_by_label(labels))
 
     @property
     @_cudf_nvtx_annotate

python/cudf/cudf/core/indexed_frame.py

@@ -1530,7 +1530,9 @@ def sort_index(
         na_position : {'first', 'last'}, default 'last'
             Puts NaNs at the beginning if first; last puts NaNs at the end.
         sort_remaining : bool, default True
-            Not yet supported
+            When sorting a multiindex on a subset of its levels,
+            should entries be lexsorted by the remaining
+            (non-specified) levels as well?
         ignore_index : bool, default False
             if True, index will be replaced with RangeIndex.
         key : callable, optional
@@ -1596,11 +1598,6 @@ def sort_index(
         if kind is not None:
             raise NotImplementedError("kind is not yet supported")
 
-        if not sort_remaining:
-            raise NotImplementedError(
-                "sort_remaining == False is not yet supported"
-            )
-
         if key is not None:
             raise NotImplementedError("key is not yet supported.")
 
@@ -1613,16 +1610,22 @@ def sort_index(
                 if level is not None:
                     # Pandas doesn't handle na_position in case of MultiIndex.
                     na_position = "first" if ascending is True else "last"
-                    labels = [
-                        idx._get_level_label(lvl)
-                        for lvl in (level if is_list_like(level) else (level,))
-                    ]
-                    # Explicitly construct a Frame rather than using type(self)
-                    # to avoid constructing a SingleColumnFrame (e.g. Series).
-                    idx = Frame._from_data(idx._data.select_by_label(labels))
+                    if not is_list_like(level):
+                        level = [level]
+                    by = list(map(idx._get_level_label, level))
+                    if sort_remaining:
+                        handled = set(by)
+                        by.extend(
+                            filter(
+                                lambda n: n not in handled,
+                                self.index._data.names,
+                            )
+                        )
+                else:
+                    by = list(idx._data.names)
 
                 inds = idx._get_sorted_inds(
-                    ascending=ascending, na_position=na_position
+                    by=by, ascending=ascending, na_position=na_position
                 )
                 out = self._gather(
                     GatherMap.from_column_unchecked(

python/cudf/cudf/core/series.py

@@ -797,17 +797,17 @@ def deserialize(cls, header, frames):
 
         return obj
 
-    def _get_columns_by_label(self, labels, downcast=False):
+    def _get_columns_by_label(self, labels, *, downcast=False) -> Self:
         """Return the column specified by `labels`
 
         For cudf.Series, either the column, or an empty series is returned.
         Parameter `downcast` does not have effects.
         """
-        new_data = super()._get_columns_by_label(labels, downcast)
+        ca = self._data.select_by_label(labels)
 
         return (
-            self.__class__._from_data(data=new_data, index=self.index)
-            if len(new_data) > 0
+            self.__class__._from_data(data=ca, index=self.index)
+            if len(ca) > 0
             else self.__class__(dtype=self.dtype, name=self.name)
         )
 

python/cudf/cudf/tests/test_multiindex.py

@@ -1897,3 +1897,26 @@ def test_multiindex_empty_slice_pandas_compatibility():
     with cudf.option_context("mode.pandas_compatible", True):
         actual = cudf.from_pandas(expected)
     assert_eq(expected, actual, exact=False)
+
+
+@pytest.mark.parametrize(
+    "levels",
+    itertools.chain.from_iterable(
+        itertools.permutations(range(3), n) for n in range(1, 4)
+    ),
+    ids=str,
+)
+def test_multiindex_sort_index_partial(levels):
+    df = pd.DataFrame(
+        {
+            "a": [3, 3, 3, 1, 1, 1, 2, 2],
+            "b": [4, 2, 7, -1, 11, -2, 7, 7],
+            "c": [4, 4, 2, 3, 3, 3, 1, 1],
+            "val": [1, 2, 3, 4, 5, 6, 7, 8],
+        }
+    ).set_index(["a", "b", "c"])
+    cdf = cudf.from_pandas(df)
+
+    expect = df.sort_index(level=levels, sort_remaining=True)
+    got = cdf.sort_index(level=levels, sort_remaining=True)
+    assert_eq(expect, got)