Support pivot with index or column arguments as lists (#17373)

closes #17360

Technically I suppose this was more of an enhancement since the documentation suggested only a single label was supported, but I'll mark as a bug since the error message was not informative.

Authors:
  - Matthew Roeschke (https://github.com/mroeschke)
  - Vyas Ramasubramani (https://github.com/vyasr)

Approvers:
  - Bradley Dice (https://github.com/bdice)

URL: #17373

python/cudf/cudf/core/reshape.py

@@ -961,7 +961,11 @@ def _merge_sorted(
     )
 
 
-def _pivot(col_accessor: ColumnAccessor, index, columns) -> cudf.DataFrame:
+def _pivot(
+    col_accessor: ColumnAccessor,
+    index: cudf.Index | cudf.MultiIndex,
+    columns: cudf.Index | cudf.MultiIndex,
+) -> cudf.DataFrame:
     """
     Reorganize the values of the DataFrame according to the given
     index and columns.
@@ -1012,12 +1016,12 @@ def as_tuple(x):
         level_names=(None,) + columns._column_names,
         verify=False,
     )
-    return cudf.DataFrame._from_data(
-        ca, index=cudf.Index(index_labels, name=index.name)
-    )
+    return cudf.DataFrame._from_data(ca, index=index_labels)
 
 
-def pivot(data, columns=None, index=no_default, values=no_default):
+def pivot(
+    data: cudf.DataFrame, columns=None, index=no_default, values=no_default
+) -> cudf.DataFrame:
     """
     Return reshaped DataFrame organized by the given index and column values.
 
@@ -1027,10 +1031,10 @@ def pivot(data, columns=None, index=no_default, values=no_default):
 
     Parameters
     ----------
-    columns : column name, optional
-        Column used to construct the columns of the result.
-    index : column name, optional
-        Column used to construct the index of the result.
+    columns : scalar or list of scalars, optional
+        Column label(s) used to construct the columns of the result.
+    index : scalar or list of scalars, optional
+        Column label(s) used to construct the index of the result.
     values : column name or list of column names, optional
         Column(s) whose values are rearranged to produce the result.
         If not specified, all remaining columns of the DataFrame
@@ -1069,24 +1073,46 @@ def pivot(data, columns=None, index=no_default, values=no_default):
     """
     values_is_list = True
     if values is no_default:
+        already_selected = set(
+            itertools.chain(
+                [index] if is_scalar(index) else index,
+                [columns] if is_scalar(columns) else columns,
+            )
+        )
         cols_to_select = [
-            col for col in data._column_names if col not in (index, columns)
+            col for col in data._column_names if col not in already_selected
         ]
     elif not isinstance(values, (list, tuple)):
         cols_to_select = [values]
         values_is_list = False
     else:
-        cols_to_select = values
+        cols_to_select = values  # type: ignore[assignment]
     if index is no_default:
-        index = data.index
+        index_data = data.index
     else:
-        index = cudf.Index(data.loc[:, index])
-    columns = cudf.Index(data.loc[:, columns])
+        index_data = data.loc[:, index]
+        if index_data.ndim == 2:
+            index_data = cudf.MultiIndex.from_frame(index_data)
+            if not is_scalar(index) and len(index) == 1:
+                # pandas converts single level MultiIndex to Index
+                index_data = index_data.get_level_values(0)
+        else:
+            index_data = cudf.Index(index_data)
+
+    column_data = data.loc[:, columns]
+    if column_data.ndim == 2:
+        column_data = cudf.MultiIndex.from_frame(column_data)
+    else:
+        column_data = cudf.Index(column_data)
 
     # Create a DataFrame composed of columns from both
     # columns and index
     ca = ColumnAccessor(
-        dict(enumerate(itertools.chain(index._columns, columns._columns))),
+        dict(
+            enumerate(
+                itertools.chain(index_data._columns, column_data._columns)
+            )
+        ),
         verify=False,
     )
     columns_index = cudf.DataFrame._from_data(ca)
@@ -1095,7 +1121,9 @@ def pivot(data, columns=None, index=no_default, values=no_default):
     if len(columns_index) != len(columns_index.drop_duplicates()):
         raise ValueError("Duplicate index-column pairs found. Cannot reshape.")
 
-    result = _pivot(data._data.select_by_label(cols_to_select), index, columns)
+    result = _pivot(
+        data._data.select_by_label(cols_to_select), index_data, column_data
+    )
 
     # MultiIndex to Index
     if not values_is_list:

python/cudf/cudf/tests/test_reshape.py

@@ -835,3 +835,20 @@ def test_crosstab_simple():
     expected = pd.crosstab(a, [b, c], rownames=["a"], colnames=["b", "c"])
     actual = cudf.crosstab(a, [b, c], rownames=["a"], colnames=["b", "c"])
     assert_eq(expected, actual, check_dtype=False)
+
+
+@pytest.mark.parametrize("index", [["ix"], ["ix", "foo"]])
+@pytest.mark.parametrize("columns", [["col"], ["col", "baz"]])
+def test_pivot_list_like_index_columns(index, columns):
+    data = {
+        "bar": ["x", "y", "z", "w"],
+        "col": ["a", "b", "a", "b"],
+        "foo": [1, 2, 3, 4],
+        "ix": [1, 1, 2, 2],
+        "baz": [0, 0, 0, 0],
+    }
+    pd_df = pd.DataFrame(data)
+    cudf_df = cudf.DataFrame(data)
+    result = cudf_df.pivot(columns=columns, index=index)
+    expected = pd_df.pivot(columns=columns, index=index)
+    assert_eq(result, expected)