Fixes

python/cudf/cudf/_lib/groupby.pyx

@@ -136,7 +136,10 @@ cdef class GroupBy:
         return (
             offsets,
             columns_from_pylibcudf_table(grouped_keys),
-            columns_from_pylibcudf_table(grouped_values),
+            (
+                columns_from_pylibcudf_table(grouped_values)
+                if grouped_values is not None else []
+            ),
         )
 
     def aggregate(self, values, aggregations):

python/cudf/cudf/core/groupby/groupby.py

@@ -790,7 +790,7 @@ def _head_tail(self, n, *, take_head: bool, preserve_order: bool):
             # Can't use _mimic_pandas_order because we need to
             # subsample the gather map from the full input ordering,
             # rather than permuting the gather map of the output.
-            _, (ordering,), _ = self._groupby.groups(
+            _, _, (ordering,) = self._groupby.groups(
                 [as_column(range(0, len(self.obj)))]
             )
             # Invert permutation from original order to groups on the
@@ -2583,7 +2583,7 @@ def _mimic_pandas_order(
         # result coming back from libcudf has null_count few rows than
         # the input, so we must produce an ordering from the full
         # input range.
-        _, (ordering,), _ = self._groupby.groups(
+        _, _, (ordering,) = self._groupby.groups(
             [as_column(range(0, len(self.obj)))]
         )
         if self._dropna and any(

python/cudf/cudf/tests/test_groupby.py

@@ -3763,4 +3763,4 @@ def test_groupby_internal_groups_empty(gdf):
     # .groups() method with an empty list:
     gb = gdf.groupby("y")._groupby
     _, _, grouped_vals = gb.groups([])
-    assert grouped_vals is None
+    assert grouped_vals == []