fix: Make compatible with latest cuDF release

narwhals/_pandas_like/dataframe.py

@@ -15,6 +15,7 @@
 from narwhals._pandas_like.utils import create_compliant_series
 from narwhals._pandas_like.utils import horizontal_concat
 from narwhals._pandas_like.utils import native_to_narwhals_dtype
+from narwhals._pandas_like.utils import pivot_table
 from narwhals._pandas_like.utils import rename
 from narwhals._pandas_like.utils import select_columns_by_name
 from narwhals._pandas_like.utils import validate_dataframe_comparand
@@ -853,58 +854,67 @@ def pivot(
 
         if isinstance(on, str):
             on = [on]
+
+        if isinstance(values, str):
+            values = [values]
         if isinstance(index, str):
             index = [index]
 
+        if index is None:
+            index = [c for c in self.columns if c not in {*on, *values}]  # type: ignore[misc]
+
         if values is None:
-            values_ = [c for c in self.columns if c not in {*on, *index}]  # type: ignore[misc]
-        elif isinstance(values, str):  # pragma: no cover
-            values_ = [values]
-        else:
-            values_ = values
+            values = [c for c in self.columns if c not in {*on, *index}]
 
         if aggregate_function is None:
-            result = frame.pivot(columns=on, index=index, values=values_)
-
+            result = frame.pivot(columns=on, index=index, values=values)
         elif aggregate_function == "len":
             result = (
-                frame.groupby([*on, *index])  # type: ignore[misc]
-                .agg({v: "size" for v in values_})
+                frame.groupby([*on, *index])
+                .agg({v: "size" for v in values})
                 .reset_index()
-                .pivot(columns=on, index=index, values=values_)
+                .pivot(columns=on, index=index, values=values)
             )
         else:
-            result = frame.pivot_table(
-                values=values_,
+            result = pivot_table(
+                df=self,
+                values=values,
                 index=index,
                 columns=on,
-                aggfunc=aggregate_function,
-                margins=False,
-                observed=True,
+                aggregate_function=aggregate_function,
             )
 
         # Put columns in the right order
-        if sort_columns:
+        if sort_columns and self._implementation is Implementation.CUDF:
+            uniques = {
+                col: sorted(self._native_frame[col].unique().to_arrow().to_pylist())
+                for col in on
+            }
+        elif sort_columns:
             uniques = {
                 col: sorted(self._native_frame[col].unique().tolist()) for col in on
             }
+        elif self._implementation is Implementation.CUDF:
+            uniques = {
+                col: self._native_frame[col].unique().to_arrow().to_pylist() for col in on
+            }
         else:
             uniques = {col: self._native_frame[col].unique().tolist() for col in on}
-        all_lists = [values_, *list(uniques.values())]
+        all_lists = [values, *list(uniques.values())]
         ordered_cols = list(product(*all_lists))
         result = result.loc[:, ordered_cols]
         columns = result.columns.tolist()
 
         n_on = len(on)
         if n_on == 1:
             new_columns = [
-                separator.join(col).strip() if len(values_) > 1 else col[-1]
+                separator.join(col).strip() if len(values) > 1 else col[-1]
                 for col in columns
             ]
         else:
             new_columns = [
                 separator.join([col[0], '{"' + '","'.join(col[-n_on:]) + '"}'])
-                if len(values_) > 1
+                if len(values) > 1
                 else '{"' + '","'.join(col[-n_on:]) + '"}'
                 for col in columns
             ]

narwhals/_pandas_like/group_by.py

@@ -126,7 +126,7 @@ def __iter__(self) -> Iterator[tuple[Any, PandasLikeDataFrame]]:
         if (
             self._df._implementation is Implementation.PANDAS
             and self._df._backend_version < (2, 2)
-        ) or (self._df._implementation is Implementation.CUDF):  # pragma: no cover
+        ):  # pragma: no cover
             for key in indices:
                 yield (key, self._from_native_frame(self._grouped.get_group(key)))
         else:

narwhals/_pandas_like/utils.py

@@ -23,6 +23,7 @@
 T = TypeVar("T")
 
 if TYPE_CHECKING:
+    from narwhals._pandas_like.dataframe import PandasLikeDataFrame
     from narwhals._pandas_like.expr import PandasLikeExpr
     from narwhals._pandas_like.series import PandasLikeSeries
     from narwhals.dtypes import DType
@@ -614,7 +615,7 @@ def narwhals_to_native_dtype(  # noqa: PLR0915
                     )
                 )
             )
-        else:
+        else:  # pragma: no cover
             msg = (
                 "Converting to List dtype is not supported for implementation "
                 f"{implementation} and version {version}."
@@ -770,3 +771,38 @@ def select_columns_by_name(
         raise ColumnNotFoundError.from_missing_and_available_column_names(
             missing_columns, available_columns
         ) from e
+
+
+def pivot_table(
+    df: PandasLikeDataFrame,
+    values: list[str],
+    index: list[str],
+    columns: list[str],
+    aggregate_function: str | None,
+) -> Any:
+    dtypes = import_dtypes_module(df._version)
+    if df._implementation is Implementation.CUDF:
+        if any(
+            x == dtypes.Categorical
+            for x in df.select(*[*values, *index, *columns]).schema.values()
+        ):
+            msg = "`pivot` with Categoricals is not implemented for cuDF backend"
+            raise NotImplementedError(msg)
+        # cuDF doesn't support `observed` argument
+        result = df._native_frame.pivot_table(
+            values=values,
+            index=index,
+            columns=columns,
+            aggfunc=aggregate_function,
+            margins=False,
+        )
+    else:
+        result = df._native_frame.pivot_table(
+            values=values,
+            index=index,
+            columns=columns,
+            aggfunc=aggregate_function,
+            margins=False,
+            observed=True,
+        )
+    return result

narwhals/dataframe.py

@@ -2924,6 +2924,10 @@ def pivot(
             │ 2   ┆ 4     ┆ 1     ┆ 0     ┆ 4     │
             └─────┴───────┴───────┴───────┴───────┘
         """
+        if values is None and index is None:
+            msg = "At least one of `values` and `index` must be passed"
+            raise ValueError(msg)
+
         return self._from_compliant_dataframe(
             self._compliant_frame.pivot(
                 on=on,

pyproject.toml

@@ -31,7 +31,7 @@ exclude = [
 ]
 
 [project.optional-dependencies]
-cudf = ["cudf>=23.08.00"]
+cudf = ["cudf>=24.12.0"]
 modin = ["modin"]
 pandas = ["pandas>=0.25.3"]
 polars = ["polars>=0.20.3"]
@@ -182,7 +182,7 @@ omit = [
 exclude_also = [
   "if sys.version_info() <",
   "if (:?self._)?implementation is Implementation.MODIN",
-  "if (:?self._)?implementation is Implementation.CUDF",
+  "if .*implementation is Implementation.CUDF",
   'request.applymarker\(pytest.mark.xfail',
   '\w+._backend_version < ',
   'backend_version <',

tests/expr_and_series/over_test.py

@@ -138,7 +138,7 @@ def test_over_cummin(request: pytest.FixtureRequest, constructor: Constructor) -
 
 
 def test_over_cumprod(request: pytest.FixtureRequest, constructor: Constructor) -> None:
-    if "pyarrow_table" in str(constructor) or "dask_lazy_p2" in str(constructor):
+    if any(x in str(constructor) for x in ("pyarrow_table", "dask_lazy_p2", "cudf")):
         request.applymarker(pytest.mark.xfail)
 
     if "pandas_pyarrow" in str(constructor) and PANDAS_VERSION < (2, 1):

tests/frame/pivot_test.py

@@ -9,6 +9,7 @@
 import narwhals.stable.v1 as nw
 from tests.utils import PANDAS_VERSION
 from tests.utils import POLARS_VERSION
+from tests.utils import ConstructorEager
 from tests.utils import assert_equal_data
 
 data = {
@@ -115,14 +116,14 @@
 )
 @pytest.mark.parametrize(("on", "index"), [("col", "ix"), (["col"], ["ix"])])
 def test_pivot(
-    constructor_eager: Any,
+    constructor_eager: ConstructorEager,
     agg_func: str,
     expected: dict[str, list[Any]],
     on: str | list[str],
     index: str | list[str],
     request: pytest.FixtureRequest,
 ) -> None:
-    if any(x in str(constructor_eager) for x in ("pyarrow_table", "modin", "cudf")):
+    if any(x in str(constructor_eager) for x in ("pyarrow_table", "modin")):
         request.applymarker(pytest.mark.xfail)
     if ("polars" in str(constructor_eager) and POLARS_VERSION < (1, 0)) or (
         "pandas" in str(constructor_eager) and PANDAS_VERSION < (1, 1)
@@ -149,7 +150,7 @@ def test_pivot(
     ],
 )
 def test_pivot_no_agg(
-    request: Any, constructor_eager: Any, data_: Any, context: Any
+    request: Any, constructor_eager: ConstructorEager, data_: Any, context: Any
 ) -> None:
     if any(x in str(constructor_eager) for x in ("pyarrow_table", "modin")):
         request.applymarker(pytest.mark.xfail)
@@ -177,9 +178,12 @@ def test_pivot_no_agg(
     ],
 )
 def test_pivot_sort_columns(
-    request: Any, constructor_eager: Any, sort_columns: Any, expected: list[str]
+    request: Any,
+    constructor_eager: ConstructorEager,
+    sort_columns: Any,
+    expected: list[str],
 ) -> None:
-    if any(x in str(constructor_eager) for x in ("pyarrow_table", "modin", "cudf")):
+    if any(x in str(constructor_eager) for x in ("pyarrow_table", "modin")):
         request.applymarker(pytest.mark.xfail)
     if ("polars" in str(constructor_eager) and POLARS_VERSION < (1, 0)) or (
         "pandas" in str(constructor_eager) and PANDAS_VERSION < (1, 1)
@@ -227,9 +231,9 @@ def test_pivot_sort_columns(
     ],
 )
 def test_pivot_names_out(
-    request: Any, constructor_eager: Any, kwargs: Any, expected: list[str]
+    request: Any, constructor_eager: ConstructorEager, kwargs: Any, expected: list[str]
 ) -> None:
-    if any(x in str(constructor_eager) for x in ("pyarrow_table", "modin", "cudf")):
+    if any(x in str(constructor_eager) for x in ("pyarrow_table", "modin")):
         request.applymarker(pytest.mark.xfail)
     if ("polars" in str(constructor_eager) and POLARS_VERSION < (1, 0)) or (
         "pandas" in str(constructor_eager) and PANDAS_VERSION < (1, 1)
@@ -243,3 +247,30 @@ def test_pivot_names_out(
         df.pivot(aggregate_function="min", index="ix", **kwargs).collect_schema().names()
     )
     assert result == expected
+
+
+def test_pivot_no_index_no_values(constructor_eager: ConstructorEager) -> None:
+    df = nw.from_native(constructor_eager(data_no_dups), eager_only=True)
+    with pytest.raises(ValueError, match="At least one of `values` and `index` must"):
+        df.pivot(on="col")
+
+
+def test_pivot_no_index(
+    constructor_eager: ConstructorEager, request: pytest.FixtureRequest
+) -> None:
+    if any(x in str(constructor_eager) for x in ("pyarrow_table", "modin")):
+        request.applymarker(pytest.mark.xfail)
+    if ("polars" in str(constructor_eager) and POLARS_VERSION < (1, 0)) or (
+        "pandas" in str(constructor_eager) and PANDAS_VERSION < (1, 1)
+    ):
+        # not implemented
+        request.applymarker(pytest.mark.xfail)
+    df = nw.from_native(constructor_eager(data_no_dups), eager_only=True)
+    result = df.pivot(on="col", values="foo").sort("ix", "bar")
+    expected = {
+        "ix": [1, 1, 2, 2],
+        "bar": ["x", "y", "w", "z"],
+        "a": [1.0, float("nan"), float("nan"), 3.0],
+        "b": [float("nan"), 2.0, 4.0, float("nan")],
+    }
+    assert_equal_data(result, expected)

tests/frame/unpivot_test.py

@@ -58,12 +58,7 @@ def test_unpivot_var_value_names(
     constructor: Constructor,
     variable_name: str | None,
     value_name: str | None,
-    request: pytest.FixtureRequest,
 ) -> None:
-    if variable_name == "" and "cudf" in str(constructor):
-        # https://github.com/rapidsai/cudf/issues/16972
-        request.applymarker(pytest.mark.xfail)
-
     df = nw.from_native(constructor(data))
     result = df.unpivot(
         on=["b", "c"], index=["a"], variable_name=variable_name, value_name=value_name

tests/group_by_test.py

@@ -75,7 +75,12 @@ def test_invalid_group_by() -> None:
         )
 
 
-def test_group_by_iter(constructor_eager: ConstructorEager) -> None:
+def test_group_by_iter(
+    constructor_eager: ConstructorEager, request: pytest.FixtureRequest
+) -> None:
+    if "cudf" in str(constructor_eager):
+        # https://github.com/rapidsai/cudf/issues/17650
+        request.applymarker(pytest.mark.xfail)
     df = nw.from_native(constructor_eager(data), eager_only=True)
     expected_keys = [(1,), (3,)]
     keys = []
@@ -117,8 +122,6 @@ def test_group_by_depth_1_agg(
     expected: dict[str, list[int | float]],
     request: pytest.FixtureRequest,
 ) -> None:
-    if "cudf" in str(constructor) and attr == "n_unique":
-        request.applymarker(pytest.mark.xfail)
     if "pandas_pyarrow" in str(constructor) and attr == "var" and PANDAS_VERSION < (2, 1):
         # Known issue with variance calculation in pandas 2.0.x with pyarrow backend in groupby operations"
         request.applymarker(pytest.mark.xfail)
@@ -140,13 +143,7 @@ def test_group_by_median(constructor: Constructor) -> None:
     assert_equal_data(result, expected)
 
 
-def test_group_by_n_unique_w_missing(
-    constructor: Constructor, request: pytest.FixtureRequest
-) -> None:
-    if "cudf" in str(constructor):
-        # Issue in cuDF https://github.com/rapidsai/cudf/issues/16861
-        request.applymarker(pytest.mark.xfail)
-
+def test_group_by_n_unique_w_missing(constructor: Constructor) -> None:
     data = {"a": [1, 1, 2], "b": [4, None, 5], "c": [None, None, 7], "d": [1, 1, 3]}
     result = (
         nw.from_native(constructor(data))
@@ -294,6 +291,9 @@ def test_key_with_nulls_iter(
     if PANDAS_VERSION < (1, 3) and "pandas_constructor" in str(constructor_eager):
         # bug in old pandas
         request.applymarker(pytest.mark.xfail)
+    if "cudf" in str(constructor_eager):
+        # https://github.com/rapidsai/cudf/issues/17650
+        request.applymarker(pytest.mark.xfail)
     data = {"b": ["4", "5", None, "7"], "a": [1, 2, 3, 4], "c": ["4", "3", None, None]}
     result = dict(
         nw.from_native(constructor_eager(data), eager_only=True)
@@ -369,10 +369,10 @@ def test_group_by_shift_raises(
 def test_double_same_aggregation(
     constructor: Constructor, request: pytest.FixtureRequest
 ) -> None:
-    if "dask" in str(constructor) or "modin" in str(constructor):
+    if any(x in str(constructor) for x in ("dask", "modin", "cudf")):
         # bugged in dask https://github.com/dask/dask/issues/11612
         # and modin lol https://github.com/modin-project/modin/issues/7414
-        # At least cudf gets it right
+        # and cudf https://github.com/rapidsai/cudf/issues/17649
         request.applymarker(pytest.mark.xfail)
     if "pandas" in str(constructor) and PANDAS_VERSION < (1,):
         request.applymarker(pytest.mark.xfail)