[REVIEW] Fix find_common_dtype and values to handle complex dtypes

python/cudf/cudf/core/frame.py

@@ -496,8 +496,8 @@ def get_column_values_na(col):
                     ),
                 ):
                     raise NotImplementedError(
-                        f"{dtype} is not yet supported via "
-                        "`__cuda_array_interface__`"
+                        f"{dtype} is not yet supported to be exported to"
+                        "a cupy array"
                     )
             dtype = find_common_type(dtypes)
 

python/cudf/cudf/tests/test_concat.py

@@ -1872,14 +1872,13 @@ def test_concat_invalid_axis(axis):
 
 
 @pytest.mark.parametrize(
-    "s1,s2,expected",
+    "s1,s2",
     [
-        ([1, 2], [[1, 2], [3, 4]], ["1", "2", "[1, 2]", "[3, 4]"]),
+        ([1, 2], [[1, 2], [3, 4]]),
     ],
 )
-def test_concat_mixed_list_types(s1, s2, expected):
+def test_concat_mixed_list_types_error(s1, s2):
     s1, s2 = gd.Series(s1), gd.Series(s2)
-    expected = pd.Series(expected)
-    actual = gd.concat([s1, s2], ignore_index=True)
 
-    assert_eq(expected, actual, check_dtype=False)
+    with pytest.raises(NotImplementedError):
+        gd.concat([s1, s2], ignore_index=True)

python/cudf/cudf/tests/test_dataframe.py

@@ -10030,6 +10030,7 @@ def test_dataframe_transpose_complex_types(data):
 
     assert_eq(expected, actual)
 
+
 @pytest.mark.parametrize(
     "data",
     [
@@ -10043,6 +10044,7 @@ def test_dataframe_values_complex_types(data):
     with pytest.raises(NotImplementedError):
         gdf.values
 
+
 def test_dataframe_from_arrow_slice():
     table = pa.Table.from_pandas(
         pd.DataFrame.from_dict(
@@ -10055,4 +10057,3 @@ def test_dataframe_from_arrow_slice():
     actual = cudf.DataFrame.from_arrow(table_slice)
 
     assert_eq(expected, actual)
-

python/cudf/cudf/utils/dtypes.py

@@ -580,7 +580,10 @@ def find_common_type(dtypes):
             # common dtype, for example:
             # ListDtype(int64) & ListDtype(int32) common
             # dtype could be ListDtype(int64).
-            return cudf.dtype("O")
+            raise NotImplementedError(
+                "Finding a common type for `ListDtype` is currently "
+                "not supported"
+            )
     if any(cudf.api.types.is_struct_dtype(dtype) for dtype in dtypes):
         if len(dtypes) == 1:
             return dtypes.get(0)