From 54b16b94cc7b5e300960a2aedec1f477e505b992 Mon Sep 17 00:00:00 2001
From: Bradley Dice <bdice@bradleydice.com>
Date: Wed, 26 Jan 2022 16:42:56 -0600
Subject: [PATCH] Run pyupgrade 2.31.0, only changes to typing.

---
 python/cudf/cudf/api/types.py                 |  2 +-
 python/cudf/cudf/core/_base_index.py          |  2 +-
 python/cudf/cudf/core/buffer.py               |  4 +-
 python/cudf/cudf/core/column/categorical.py   | 50 +++++------
 python/cudf/cudf/core/column/column.py        | 86 +++++++++----------
 python/cudf/cudf/core/column/datetime.py      | 19 ++--
 python/cudf/cudf/core/column/methods.py       |  2 +-
 python/cudf/cudf/core/column/numerical.py     | 26 +++---
 .../cudf/cudf/core/column/numerical_base.py   |  4 +-
 python/cudf/cudf/core/column/string.py        | 46 +++++-----
 python/cudf/cudf/core/column/struct.py        |  2 +-
 python/cudf/cudf/core/column/timedelta.py     | 28 +++---
 python/cudf/cudf/core/column_accessor.py      | 16 ++--
 python/cudf/cudf/core/dataframe.py            |  6 +-
 python/cudf/cudf/core/frame.py                | 58 ++++++-------
 python/cudf/cudf/core/index.py                | 22 +++--
 python/cudf/cudf/core/indexed_frame.py        |  6 +-
 python/cudf/cudf/core/join/_join_helpers.py   |  4 +-
 python/cudf/cudf/core/multiindex.py           | 14 +--
 python/cudf/cudf/core/series.py               |  8 +-
 python/cudf/cudf/core/single_column_frame.py  | 14 +--
 python/cudf/cudf/core/subword_tokenizer.py    |  2 +-
 python/cudf/cudf/testing/testing.py           |  2 +-
 23 files changed, 203 insertions(+), 220 deletions(-)

diff --git a/python/cudf/cudf/api/types.py b/python/cudf/cudf/api/types.py
index 6d5387591cb..050a71d83f0 100644
--- a/python/cudf/cudf/api/types.py
+++ b/python/cudf/cudf/api/types.py
@@ -200,7 +200,7 @@ def wrapped_func(obj):
 
 
 def _union_categoricals(
-    to_union: List[Union[cudf.Series, cudf.CategoricalIndex]],
+    to_union: list[cudf.Series | cudf.CategoricalIndex],
     sort_categories: bool = False,
     ignore_order: bool = False,
 ):
diff --git a/python/cudf/cudf/core/_base_index.py b/python/cudf/cudf/core/_base_index.py
index 5c5ccaf94c0..a70681a06d3 100644
--- a/python/cudf/cudf/core/_base_index.py
+++ b/python/cudf/cudf/core/_base_index.py
@@ -39,7 +39,7 @@ class BaseIndex(Serializable):
     """Base class for all cudf Index types."""
 
     dtype: DtypeObj
-    _accessors: Set[Any] = set()
+    _accessors: set[Any] = set()
     _data: ColumnAccessor
 
     def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
diff --git a/python/cudf/cudf/core/buffer.py b/python/cudf/cudf/core/buffer.py
index 0658927975f..c5b4198581f 100644
--- a/python/cudf/cudf/core/buffer.py
+++ b/python/cudf/cudf/core/buffer.py
@@ -38,7 +38,7 @@ class Buffer(Serializable):
     _owner: Any
 
     def __init__(
-        self, data: Any = None, size: Optional[int] = None, owner: Any = None
+        self, data: Any = None, size: int | None = None, owner: Any = None
     ):
 
         if isinstance(data, Buffer):
@@ -117,7 +117,7 @@ def _init_from_array_like(self, data, owner):
                 f"Cannot construct Buffer from {data.__class__.__name__}"
             )
 
-    def serialize(self) -> Tuple[dict, list]:
+    def serialize(self) -> tuple[dict, list]:
         header = {}  # type: Dict[Any, Any]
         header["type-serialized"] = pickle.dumps(type(self))
         header["constructor-kwargs"] = {}
diff --git a/python/cudf/cudf/core/column/categorical.py b/python/cudf/cudf/core/column/categorical.py
index de06e62cbb1..bafcc19ab1d 100644
--- a/python/cudf/cudf/core/column/categorical.py
+++ b/python/cudf/cudf/core/column/categorical.py
@@ -110,14 +110,14 @@ def __init__(self, parent: SeriesOrSingleColumnIndex):
         super().__init__(parent=parent)
 
     @property
-    def categories(self) -> "cudf.core.index.BaseIndex":
+    def categories(self) -> cudf.core.index.BaseIndex:
         """
         The categories of this categorical.
         """
         return cudf.core.index.as_index(self._column.categories)
 
     @property
-    def codes(self) -> "cudf.Series":
+    def codes(self) -> cudf.Series:
         """
         Return Series of codes as well as the index.
         """
@@ -129,13 +129,13 @@ def codes(self) -> "cudf.Series":
         return cudf.Series(self._column.codes, index=index)
 
     @property
-    def ordered(self) -> Optional[bool]:
+    def ordered(self) -> bool | None:
         """
         Whether the categories have an ordered relationship.
         """
         return self._column.ordered
 
-    def as_ordered(self, inplace: bool = False) -> Optional[SeriesOrIndex]:
+    def as_ordered(self, inplace: bool = False) -> SeriesOrIndex | None:
         """
         Set the Categorical to be ordered.
 
@@ -192,7 +192,7 @@ def as_ordered(self, inplace: bool = False) -> Optional[SeriesOrIndex]:
             self._column.as_ordered(), inplace=inplace
         )
 
-    def as_unordered(self, inplace: bool = False) -> Optional[SeriesOrIndex]:
+    def as_unordered(self, inplace: bool = False) -> SeriesOrIndex | None:
         """
         Set the Categorical to be unordered.
 
@@ -262,7 +262,7 @@ def as_unordered(self, inplace: bool = False) -> Optional[SeriesOrIndex]:
 
     def add_categories(
         self, new_categories: Any, inplace: bool = False
-    ) -> Optional[SeriesOrIndex]:
+    ) -> SeriesOrIndex | None:
         """
         Add new categories.
 
@@ -347,7 +347,7 @@ def add_categories(
 
     def remove_categories(
         self, removals: Any, inplace: bool = False,
-    ) -> Optional[SeriesOrIndex]:
+    ) -> SeriesOrIndex | None:
         """
         Remove the specified categories.
 
@@ -441,7 +441,7 @@ def set_categories(
         ordered: bool = False,
         rename: bool = False,
         inplace: bool = False,
-    ) -> Optional[SeriesOrIndex]:
+    ) -> SeriesOrIndex | None:
         """
         Set the categories to the specified new_categories.
 
@@ -535,7 +535,7 @@ def reorder_categories(
         new_categories: Any,
         ordered: bool = False,
         inplace: bool = False,
-    ) -> Optional[SeriesOrIndex]:
+    ) -> SeriesOrIndex | None:
         """
         Reorder categories as specified in new_categories.
 
@@ -624,8 +624,8 @@ class CategoricalColumn(column.ColumnBase):
     """
 
     dtype: cudf.core.dtypes.CategoricalDtype
-    _codes: Optional[NumericalColumn]
-    _children: Tuple[NumericalColumn]
+    _codes: NumericalColumn | None
+    _children: tuple[NumericalColumn]
 
     def __init__(
         self,
@@ -634,7 +634,7 @@ def __init__(
         size: int = None,
         offset: int = 0,
         null_count: int = None,
-        children: Tuple["column.ColumnBase", ...] = (),
+        children: tuple[column.ColumnBase, ...] = (),
     ):
 
         if size is None:
@@ -671,8 +671,8 @@ def __contains__(self, item: ScalarLike) -> bool:
             return False
         return self._encode(item) in self.as_numerical
 
-    def serialize(self) -> Tuple[dict, list]:
-        header: Dict[Any, Any] = {}
+    def serialize(self) -> tuple[dict, list]:
+        header: dict[Any, Any] = {}
         frames = []
         header["type-serialized"] = pickle.dumps(type(self))
         header["dtype"], dtype_frames = self.dtype.serialize()
@@ -729,23 +729,23 @@ def set_base_data(self, value):
 
     def _process_values_for_isin(
         self, values: Sequence
-    ) -> Tuple[ColumnBase, ColumnBase]:
+    ) -> tuple[ColumnBase, ColumnBase]:
         lhs = self
         # We need to convert values to same type as self,
         # hence passing dtype=self.dtype
         rhs = cudf.core.column.as_column(values, dtype=self.dtype)
         return lhs, rhs
 
-    def set_base_mask(self, value: Optional[Buffer]):
+    def set_base_mask(self, value: Buffer | None):
         super().set_base_mask(value)
         self._codes = None
 
-    def set_base_children(self, value: Tuple[ColumnBase, ...]):
+    def set_base_children(self, value: tuple[ColumnBase, ...]):
         super().set_base_children(value)
         self._codes = None
 
     @property
-    def children(self) -> Tuple[NumericalColumn]:
+    def children(self) -> tuple[NumericalColumn]:
         if self._children is None:
             codes_column = self.base_children[0]
 
@@ -788,7 +788,7 @@ def codes(self) -> NumericalColumn:
         return cast(cudf.core.column.NumericalColumn, self._codes)
 
     @property
-    def ordered(self) -> Optional[bool]:
+    def ordered(self) -> bool | None:
         return self.dtype.ordered
 
     @ordered.setter
@@ -842,7 +842,7 @@ def _fill(
         begin: int,
         end: int,
         inplace: bool = False,
-    ) -> "column.ColumnBase":
+    ) -> column.ColumnBase:
         if end <= begin or begin >= self.size:
             return self if inplace else self.copy()
 
@@ -858,7 +858,7 @@ def _fill(
 
     def slice(
         self, start: int, stop: int, stride: int = None
-    ) -> "column.ColumnBase":
+    ) -> column.ColumnBase:
         codes = self.codes.slice(start, stop, stride)
         return cudf.core.column.build_categorical_column(
             categories=self.categories,
@@ -909,7 +909,7 @@ def normalize_binop_value(self, other: ScalarLike) -> CategoricalColumn:
 
     def sort_by_values(
         self, ascending: bool = True, na_position="last"
-    ) -> Tuple[CategoricalColumn, NumericalColumn]:
+    ) -> tuple[CategoricalColumn, NumericalColumn]:
         codes, inds = self.as_numerical.sort_by_values(ascending, na_position)
         col = column.build_categorical_column(
             categories=self.dtype.categories._values,
@@ -991,7 +991,7 @@ def values(self):
         """
         raise NotImplementedError("cudf.Categorical is not yet implemented")
 
-    def clip(self, lo: ScalarLike, hi: ScalarLike) -> "column.ColumnBase":
+    def clip(self, lo: ScalarLike, hi: ScalarLike) -> column.ColumnBase:
         return (
             self.astype(self.categories.dtype).clip(lo, hi).astype(self.dtype)
         )
@@ -1329,7 +1329,7 @@ def memory_usage(self) -> int:
 
     def _mimic_inplace(
         self, other_col: ColumnBase, inplace: bool = False
-    ) -> Optional[ColumnBase]:
+    ) -> ColumnBase | None:
         out = super()._mimic_inplace(other_col, inplace=inplace)
         if inplace and isinstance(other_col, CategoricalColumn):
             self._codes = other_col._codes
@@ -1572,7 +1572,7 @@ def as_unordered(self):
 
 
 def _create_empty_categorical_column(
-    categorical_column: CategoricalColumn, dtype: "CategoricalDtype"
+    categorical_column: CategoricalColumn, dtype: CategoricalDtype
 ) -> CategoricalColumn:
     return column.build_categorical_column(
         categories=column.as_column(dtype.categories),
diff --git a/python/cudf/cudf/core/column/column.py b/python/cudf/cudf/core/column/column.py
index 7999fa9039b..ed19264b228 100644
--- a/python/cudf/cudf/core/column/column.py
+++ b/python/cudf/cudf/core/column/column.py
@@ -83,7 +83,7 @@
 
 
 class ColumnBase(Column, Serializable):
-    def as_frame(self) -> "cudf.core.frame.Frame":
+    def as_frame(self) -> cudf.core.frame.Frame:
         """
         Converts a Column to Frame
         """
@@ -92,14 +92,14 @@ def as_frame(self) -> "cudf.core.frame.Frame":
         )
 
     @property
-    def data_array_view(self) -> "cuda.devicearray.DeviceNDArray":
+    def data_array_view(self) -> cuda.devicearray.DeviceNDArray:
         """
         View the data as a device array object
         """
         return cuda.as_cuda_array(self.data).view(self.dtype)
 
     @property
-    def mask_array_view(self) -> "cuda.devicearray.DeviceNDArray":
+    def mask_array_view(self) -> cuda.devicearray.DeviceNDArray:
         """
         View the mask as a device array
         """
@@ -115,7 +115,7 @@ def __repr__(self):
             f"dtype: {self.dtype}"
         )
 
-    def to_pandas(self, index: pd.Index = None, **kwargs) -> "pd.Series":
+    def to_pandas(self, index: pd.Index = None, **kwargs) -> pd.Series:
         """Convert object to pandas type.
 
         The default implementation falls back to PyArrow for the conversion.
@@ -134,7 +134,7 @@ def __iter__(self):
         cudf.utils.utils.raise_iteration_error(obj=self)
 
     @property
-    def values_host(self) -> "np.ndarray":
+    def values_host(self) -> np.ndarray:
         """
         Return a numpy representation of the Column.
         """
@@ -147,7 +147,7 @@ def values_host(self) -> "np.ndarray":
         return self.data_array_view.copy_to_host()
 
     @property
-    def values(self) -> "cupy.ndarray":
+    def values(self) -> cupy.ndarray:
         """
         Return a CuPy representation of the Column.
         """
@@ -319,7 +319,7 @@ def _default_na_value(self) -> Any:
 
     # TODO: This method is deprecated and can be removed when the associated
     # Frame methods are removed.
-    def to_gpu_array(self, fillna=None) -> "cuda.devicearray.DeviceNDArray":
+    def to_gpu_array(self, fillna=None) -> cuda.devicearray.DeviceNDArray:
         """Get a dense numba device array for the data.
 
         Parameters
@@ -365,7 +365,7 @@ def _fill(
         begin: int,
         end: int,
         inplace: bool = False,
-    ) -> Optional[ColumnBase]:
+    ) -> ColumnBase | None:
         if end <= begin or begin >= self.size:
             return self if inplace else self.copy()
 
@@ -517,7 +517,7 @@ def slice(self, start: int, stop: int, stride: int = None) -> ColumnBase:
             )
             return self.take(gather_map)
 
-    def __getitem__(self, arg) -> Union[ScalarLike, ColumnBase]:
+    def __getitem__(self, arg) -> ScalarLike | ColumnBase:
         if _is_scalar_or_zero_d_array(arg):
             return self.element_indexing(int(arg))
         elif isinstance(arg, slice):
@@ -677,7 +677,7 @@ def append(self, other: ColumnBase) -> ColumnBase:
 
     def quantile(
         self,
-        q: Union[float, Sequence[float]],
+        q: float | Sequence[float],
         interpolation: builtins.str,
         exact: bool,
     ) -> ColumnBase:
@@ -740,7 +740,7 @@ def isin(self, values: Sequence) -> ColumnBase:
 
     def _process_values_for_isin(
         self, values: Sequence
-    ) -> Tuple[ColumnBase, ColumnBase]:
+    ) -> tuple[ColumnBase, ColumnBase]:
         """
         Helper function for `isin` which pre-process `values` based on `self`.
         """
@@ -752,7 +752,7 @@ def _process_values_for_isin(
             rhs = rhs.astype(lhs.dtype)
         return lhs, rhs
 
-    def _isin_earlystop(self, rhs: ColumnBase) -> Union[ColumnBase, None]:
+    def _isin_earlystop(self, rhs: ColumnBase) -> ColumnBase | None:
         """
         Helper function for `isin` which determines possibility of
         early-stopping or not.
@@ -847,7 +847,7 @@ def sort_by_values(
         self: ColumnBase,
         ascending: bool = True,
         na_position: builtins.str = "last",
-    ) -> Tuple[ColumnBase, "cudf.core.column.NumericalColumn"]:
+    ) -> tuple[ColumnBase, cudf.core.column.NumericalColumn]:
         col_inds = self.as_frame()._get_sorted_inds(
             ascending=ascending, na_position=na_position
         )
@@ -960,47 +960,47 @@ def as_categorical_column(self, dtype, **kwargs) -> ColumnBase:
 
     def as_numerical_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.NumericalColumn":
+    ) -> cudf.core.column.NumericalColumn:
         raise NotImplementedError
 
     def as_datetime_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.DatetimeColumn":
+    ) -> cudf.core.column.DatetimeColumn:
         raise NotImplementedError
 
     def as_interval_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.IntervalColumn":
+    ) -> cudf.core.column.IntervalColumn:
         raise NotImplementedError
 
     def as_timedelta_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.TimeDeltaColumn":
+    ) -> cudf.core.column.TimeDeltaColumn:
         raise NotImplementedError
 
     def as_string_column(
         self, dtype: Dtype, format=None, **kwargs
-    ) -> "cudf.core.column.StringColumn":
+    ) -> cudf.core.column.StringColumn:
         raise NotImplementedError
 
     def as_decimal_column(
         self, dtype: Dtype, **kwargs
-    ) -> Union["cudf.core.column.decimal.DecimalBaseColumn"]:
+    ) -> cudf.core.column.decimal.DecimalBaseColumn:
         raise NotImplementedError
 
     def as_decimal128_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.Decimal128Column":
+    ) -> cudf.core.column.Decimal128Column:
         raise NotImplementedError
 
     def as_decimal64_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.Decimal64Column":
+    ) -> cudf.core.column.Decimal64Column:
         raise NotImplementedError
 
     def as_decimal32_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.Decimal32Column":
+    ) -> cudf.core.column.Decimal32Column:
         raise NotImplementedError
 
     def apply_boolean_mask(self, mask) -> ColumnBase:
@@ -1110,8 +1110,8 @@ def unique(self) -> ColumnBase:
 
         return drop_duplicates([self], keep="first")[0]
 
-    def serialize(self) -> Tuple[dict, list]:
-        header: Dict[Any, Any] = {}
+    def serialize(self) -> tuple[dict, list]:
+        header: dict[Any, Any] = {}
         frames = []
         header["type-serialized"] = pickle.dumps(type(self))
         header["dtype"] = self.dtype.str
@@ -1155,7 +1155,7 @@ def binary_operator(
 
     def normalize_binop_value(
         self, other: ScalarLike
-    ) -> Union[ColumnBase, ScalarLike]:
+    ) -> ColumnBase | ScalarLike:
         raise NotImplementedError
 
     def _minmax(self, skipna: bool = None):
@@ -1217,7 +1217,7 @@ def nans_to_nulls(self: T) -> T:
 
     def _process_for_reduction(
         self, skipna: bool = None, min_count: int = 0
-    ) -> Union[ColumnBase, ScalarLike]:
+    ) -> ColumnBase | ScalarLike:
         skipna = True if skipna is None else skipna
 
         if skipna:
@@ -1347,14 +1347,14 @@ def column_empty(
 
 
 def build_column(
-    data: Union[Buffer, None],
+    data: Buffer | None,
     dtype: Dtype,
     *,
     size: int = None,
     mask: Buffer = None,
     offset: int = 0,
     null_count: int = None,
-    children: Tuple[ColumnBase, ...] = (),
+    children: tuple[ColumnBase, ...] = (),
 ) -> ColumnBase:
     """
     Build a Column of the appropriate type from the given parameters
@@ -1516,7 +1516,7 @@ def build_categorical_column(
     offset: int = 0,
     null_count: int = None,
     ordered: bool = None,
-) -> "cudf.core.column.CategoricalColumn":
+) -> cudf.core.column.CategoricalColumn:
     """
     Build a CategoricalColumn
 
@@ -1606,7 +1606,7 @@ def build_list_column(
     size: int = None,
     offset: int = 0,
     null_count: int = None,
-) -> "cudf.core.column.ListColumn":
+) -> cudf.core.column.ListColumn:
     """
     Build a ListColumn
 
@@ -1638,13 +1638,13 @@ def build_list_column(
 
 def build_struct_column(
     names: Sequence[str],
-    children: Tuple[ColumnBase, ...],
-    dtype: Optional[Dtype] = None,
+    children: tuple[ColumnBase, ...],
+    dtype: Dtype | None = None,
     mask: Buffer = None,
     size: int = None,
     offset: int = 0,
     null_count: int = None,
-) -> "cudf.core.column.StructColumn":
+) -> cudf.core.column.StructColumn:
     """
     Build a StructColumn
 
@@ -2177,8 +2177,8 @@ def as_column(
 
 
 def _construct_array(
-    arbitrary: Any, dtype: Optional[Dtype]
-) -> Union[np.ndarray, cupy.ndarray]:
+    arbitrary: Any, dtype: Dtype | None
+) -> np.ndarray | cupy.ndarray:
     """
     Construct a CuPy or NumPy array from `arbitrary`
     """
@@ -2212,7 +2212,7 @@ def _data_from_cuda_array_interface_desc(obj) -> Buffer:
     return data
 
 
-def _mask_from_cuda_array_interface_desc(obj) -> Union[Buffer, None]:
+def _mask_from_cuda_array_interface_desc(obj) -> Buffer | None:
     desc = obj.__cuda_array_interface__
     mask = desc.get("mask", None)
 
@@ -2235,7 +2235,7 @@ def _mask_from_cuda_array_interface_desc(obj) -> Union[Buffer, None]:
     return mask
 
 
-def serialize_columns(columns) -> Tuple[List[dict], List]:
+def serialize_columns(columns) -> tuple[list[dict], list]:
     """
     Return the headers and frames resulting
     from serializing a list of Column
@@ -2250,7 +2250,7 @@ def serialize_columns(columns) -> Tuple[List[dict], List]:
     frames : list
         list of frames
     """
-    headers: List[Dict[Any, Any]] = []
+    headers: list[dict[Any, Any]] = []
     frames = []
 
     if len(columns) > 0:
@@ -2262,7 +2262,7 @@ def serialize_columns(columns) -> Tuple[List[dict], List]:
     return headers, frames
 
 
-def deserialize_columns(headers: List[dict], frames: List) -> List[ColumnBase]:
+def deserialize_columns(headers: list[dict], frames: list) -> list[ColumnBase]:
     """
     Construct a list of Columns from a list of headers
     and frames.
@@ -2281,9 +2281,9 @@ def deserialize_columns(headers: List[dict], frames: List) -> List[ColumnBase]:
 
 
 def arange(
-    start: Union[int, float],
-    stop: Union[int, float] = None,
-    step: Union[int, float] = 1,
+    start: int | float,
+    stop: int | float = None,
+    step: int | float = 1,
     dtype=None,
 ) -> ColumnBase:
     """
@@ -2372,7 +2372,7 @@ def full(size: int, fill_value: ScalarLike, dtype: Dtype = None) -> ColumnBase:
     return ColumnBase.from_scalar(cudf.Scalar(fill_value, dtype), size)
 
 
-def concat_columns(objs: "MutableSequence[ColumnBase]") -> ColumnBase:
+def concat_columns(objs: MutableSequence[ColumnBase]) -> ColumnBase:
     """Concatenate a sequence of columns."""
     if len(objs) == 0:
         dtype = cudf.dtype(None)
diff --git a/python/cudf/cudf/core/column/datetime.py b/python/cudf/cudf/core/column/datetime.py
index b763790986a..75b50f7e12e 100644
--- a/python/cudf/cudf/core/column/datetime.py
+++ b/python/cudf/cudf/core/column/datetime.py
@@ -193,7 +193,7 @@ def day_of_year(self) -> ColumnBase:
 
     def to_pandas(
         self, index: pd.Index = None, nullable: bool = False, **kwargs
-    ) -> "cudf.Series":
+    ) -> cudf.Series:
         # Workaround until following issue is fixed:
         # https://issues.apache.org/jira/browse/ARROW-9772
 
@@ -264,7 +264,7 @@ def normalize_binop_value(self, other: DatetimeLikeScalar) -> ScalarLike:
             raise TypeError(f"cannot normalize {type(other)}")
 
     @property
-    def as_numerical(self) -> "cudf.core.column.NumericalColumn":
+    def as_numerical(self) -> cudf.core.column.NumericalColumn:
         return cast(
             "cudf.core.column.NumericalColumn",
             column.build_column(
@@ -311,21 +311,21 @@ def as_datetime_column(self, dtype: Dtype, **kwargs) -> DatetimeColumn:
 
     def as_timedelta_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.TimeDeltaColumn":
+    ) -> cudf.core.column.TimeDeltaColumn:
         raise TypeError(
             f"cannot astype a datetimelike from {self.dtype} to {dtype}"
         )
 
     def as_numerical_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.NumericalColumn":
+    ) -> cudf.core.column.NumericalColumn:
         return cast(
             "cudf.core.column.NumericalColumn", self.as_numerical.astype(dtype)
         )
 
     def as_string_column(
         self, dtype: Dtype, format=None, **kwargs
-    ) -> "cudf.core.column.StringColumn":
+    ) -> cudf.core.column.StringColumn:
         if format is None:
             format = _dtype_to_format_conversion.get(
                 self.dtype.name, "%Y-%m-%d %H:%M:%S"
@@ -370,7 +370,7 @@ def median(self, skipna: bool = None) -> pd.Timestamp:
         )
 
     def quantile(
-        self, q: Union[float, Sequence[float]], interpolation: str, exact: bool
+        self, q: float | Sequence[float], interpolation: str, exact: bool
     ) -> ColumnBase:
         result = self.as_numerical.quantile(
             q=q, interpolation=interpolation, exact=exact
@@ -380,14 +380,11 @@ def quantile(
         return result.astype(self.dtype)
 
     def binary_operator(
-        self,
-        op: str,
-        rhs: Union[ColumnBase, "cudf.Scalar"],
-        reflect: bool = False,
+        self, op: str, rhs: ColumnBase | cudf.Scalar, reflect: bool = False,
     ) -> ColumnBase:
         if isinstance(rhs, cudf.DateOffset):
             return rhs._datetime_binop(self, op, reflect=reflect)
-        lhs: Union[ScalarLike, ColumnBase] = self
+        lhs: ScalarLike | ColumnBase = self
         if op in ("eq", "ne", "lt", "gt", "le", "ge", "NULL_EQUALS"):
             out_dtype = cudf.dtype(np.bool_)  # type: Dtype
         elif op == "add" and pd.api.types.is_timedelta64_dtype(rhs.dtype):
diff --git a/python/cudf/cudf/core/column/methods.py b/python/cudf/cudf/core/column/methods.py
index 9bea94cfecb..6f0ffae3343 100644
--- a/python/cudf/cudf/core/column/methods.py
+++ b/python/cudf/cudf/core/column/methods.py
@@ -51,7 +51,7 @@ def _return_or_inplace(
         inplace: bool = False,
         expand: bool = False,
         retain_index: bool = True,
-    ) -> Optional[ParentType]:
+    ) -> ParentType | None:
         ...
 
     def _return_or_inplace(
diff --git a/python/cudf/cudf/core/column/numerical.py b/python/cudf/cudf/core/column/numerical.py
index a7481ce62a3..730e223f111 100644
--- a/python/cudf/cudf/core/column/numerical.py
+++ b/python/cudf/cudf/core/column/numerical.py
@@ -61,7 +61,7 @@ class NumericalColumn(NumericalBaseColumn):
     mask : Buffer, optional
     """
 
-    _nan_count: Optional[int]
+    _nan_count: int | None
 
     def __init__(
         self,
@@ -142,7 +142,7 @@ def __cuda_array_interface__(self) -> Mapping[str, Any]:
 
         return output
 
-    def unary_operator(self, unaryop: Union[str, Callable]) -> ColumnBase:
+    def unary_operator(self, unaryop: str | Callable) -> ColumnBase:
         if callable(unaryop):
             return libcudf.transform.transform(self, unaryop)
 
@@ -179,7 +179,7 @@ def binary_operator(
                 msg = "{!r} operator not supported between {} and {}"
                 raise TypeError(msg.format(binop, type(self), type(rhs)))
             if isinstance(rhs, cudf.core.column.Decimal128Column):
-                lhs: Union[ScalarLike, ColumnBase] = self.as_decimal_column(
+                lhs: ScalarLike | ColumnBase = self.as_decimal_column(
                     Decimal128Dtype(Decimal128Dtype.MAX_PRECISION, 0)
                 )
                 return lhs.binary_operator(binop, rhs)
@@ -226,7 +226,7 @@ def nans_to_nulls(self: NumericalColumn) -> NumericalColumn:
 
     def normalize_binop_value(
         self, other: ScalarLike
-    ) -> Union[ColumnBase, ScalarLike]:
+    ) -> ColumnBase | ScalarLike:
         if other is None:
             return other
         if isinstance(other, cudf.Scalar):
@@ -259,7 +259,7 @@ def normalize_binop_value(
         else:
             raise TypeError(f"cannot broadcast {type(other)}")
 
-    def int2ip(self) -> "cudf.core.column.StringColumn":
+    def int2ip(self) -> cudf.core.column.StringColumn:
         if self.dtype != cudf.dtype("int64"):
             raise TypeError("Only int64 type can be converted to ip")
 
@@ -267,7 +267,7 @@ def int2ip(self) -> "cudf.core.column.StringColumn":
 
     def as_string_column(
         self, dtype: Dtype, format=None, **kwargs
-    ) -> "cudf.core.column.StringColumn":
+    ) -> cudf.core.column.StringColumn:
         if len(self) > 0:
             return string._numeric_to_str_typecast_functions[
                 cudf.dtype(self.dtype)
@@ -279,7 +279,7 @@ def as_string_column(
 
     def as_datetime_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.DatetimeColumn":
+    ) -> cudf.core.column.DatetimeColumn:
         return cast(
             "cudf.core.column.DatetimeColumn",
             build_column(
@@ -293,7 +293,7 @@ def as_datetime_column(
 
     def as_timedelta_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.TimeDeltaColumn":
+    ) -> cudf.core.column.TimeDeltaColumn:
         return cast(
             "cudf.core.column.TimeDeltaColumn",
             build_column(
@@ -307,7 +307,7 @@ def as_timedelta_column(
 
     def as_decimal_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.DecimalBaseColumn":
+    ) -> cudf.core.column.DecimalBaseColumn:
         return libcudf.unary.cast(self, dtype)
 
     def as_numerical_column(self, dtype: Dtype, **kwargs) -> NumericalColumn:
@@ -327,7 +327,7 @@ def nan_count(self) -> int:
 
     def _process_values_for_isin(
         self, values: Sequence
-    ) -> Tuple[ColumnBase, ColumnBase]:
+    ) -> tuple[ColumnBase, ColumnBase]:
         lhs = cast("cudf.core.column.ColumnBase", self)
         rhs = as_column(values, nan_as_null=False)
 
@@ -346,7 +346,7 @@ def _can_return_nan(self, skipna: bool = None) -> bool:
 
     def _process_for_reduction(
         self, skipna: bool = None, min_count: int = 0
-    ) -> Union[ColumnBase, ScalarLike]:
+    ) -> ColumnBase | ScalarLike:
         skipna = True if skipna is None else skipna
 
         if self._can_return_nan(skipna=skipna):
@@ -642,7 +642,7 @@ def _with_type_metadata(self: ColumnBase, dtype: Dtype) -> ColumnBase:
 
     def to_pandas(
         self, index: pd.Index = None, nullable: bool = False, **kwargs
-    ) -> "pd.Series":
+    ) -> pd.Series:
         if nullable and self.dtype in np_dtypes_to_pandas_dtypes:
             pandas_nullable_dtype = np_dtypes_to_pandas_dtypes[self.dtype]
             arrow_array = self.to_arrow()
@@ -670,7 +670,7 @@ def _reduction_result_dtype(self, reduction_op: str) -> Dtype:
 
 
 def _normalize_find_and_replace_input(
-    input_column_dtype: DtypeObj, col_to_normalize: Union[ColumnBase, list]
+    input_column_dtype: DtypeObj, col_to_normalize: ColumnBase | list
 ) -> ColumnBase:
     normalized_column = column.as_column(
         col_to_normalize,
diff --git a/python/cudf/cudf/core/column/numerical_base.py b/python/cudf/cudf/core/column/numerical_base.py
index 1f84cb88e37..e5918578fbf 100644
--- a/python/cudf/cudf/core/column/numerical_base.py
+++ b/python/cudf/cudf/core/column/numerical_base.py
@@ -129,7 +129,7 @@ def skew(self, skipna: bool = None) -> ScalarLike:
         return skew
 
     def quantile(
-        self, q: Union[float, Sequence[float]], interpolation: str, exact: bool
+        self, q: float | Sequence[float], interpolation: str, exact: bool
     ) -> NumericalBaseColumn:
         if isinstance(q, Number) or cudf.api.types.is_list_like(q):
             np_array_q = np.asarray(q)
@@ -158,7 +158,7 @@ def median(self, skipna: bool = None) -> NumericalBaseColumn:
         return self.quantile(0.5, interpolation="linear", exact=True)
 
     def _numeric_quantile(
-        self, q: Union[float, Sequence[float]], interpolation: str, exact: bool
+        self, q: float | Sequence[float], interpolation: str, exact: bool
     ) -> NumericalBaseColumn:
         quant = [float(q)] if not isinstance(q, (Sequence, np.ndarray)) else q
         # get sorted indices and exclude nulls
diff --git a/python/cudf/cudf/core/column/string.py b/python/cudf/cudf/core/column/string.py
index 7f62d8fe03f..7a00c7401d2 100644
--- a/python/cudf/cudf/core/column/string.py
+++ b/python/cudf/cudf/core/column/string.py
@@ -257,7 +257,7 @@ def cat(self, sep: str = None, na_rep: str = None) -> str:
     @overload
     def cat(
         self, others, sep: str = None, na_rep: str = None
-    ) -> Union[SeriesOrIndex, "cudf.core.column.string.StringColumn"]:
+    ) -> SeriesOrIndex | cudf.core.column.string.StringColumn:
         ...
 
     def cat(self, others=None, sep=None, na_rep=None):
@@ -630,7 +630,7 @@ def extract(
 
     def contains(
         self,
-        pat: Union[str, Sequence],
+        pat: str | Sequence,
         case: bool = True,
         flags: int = 0,
         na=np.nan,
@@ -771,7 +771,7 @@ def contains(
             )
         return self._return_or_inplace(result_col)
 
-    def repeat(self, repeats: Union[int, Sequence],) -> SeriesOrIndex:
+    def repeat(self, repeats: int | Sequence,) -> SeriesOrIndex:
         """
         Duplicate each string in the Series or Index.
         Equivalent to `str.repeat()
@@ -826,8 +826,8 @@ def repeat(self, repeats: Union[int, Sequence],) -> SeriesOrIndex:
 
     def replace(
         self,
-        pat: Union[str, Sequence],
-        repl: Union[str, Sequence],
+        pat: str | Sequence,
+        repl: str | Sequence,
         n: int = -1,
         case=None,
         flags: int = 0,
@@ -1988,7 +1988,7 @@ def filter_alphanum(
         )
 
     def slice_from(
-        self, starts: "cudf.Series", stops: "cudf.Series"
+        self, starts: cudf.Series, stops: cudf.Series
     ) -> SeriesOrIndex:
         """
         Return substring of each string using positions for each string.
@@ -3608,7 +3608,7 @@ def endswith(self, pat: str) -> SeriesOrIndex:
 
         return self._return_or_inplace(result_col)
 
-    def startswith(self, pat: Union[str, Sequence]) -> SeriesOrIndex:
+    def startswith(self, pat: str | Sequence) -> SeriesOrIndex:
         """
         Test if the start of each string element matches a pattern.
 
@@ -4285,7 +4285,7 @@ def tokenize(self, delimiter: str = " ") -> SeriesOrIndex:
             )
 
     def detokenize(
-        self, indices: "cudf.Series", separator: str = " "
+        self, indices: cudf.Series, separator: str = " "
     ) -> SeriesOrIndex:
         """
         Combines tokens into strings by concatenating them in the order
@@ -4958,9 +4958,9 @@ class StringColumn(column.ColumnBase):
         respectively
     """
 
-    _start_offset: Optional[int]
-    _end_offset: Optional[int]
-    _cached_sizeof: Optional[int]
+    _start_offset: int | None
+    _end_offset: int | None
+    _cached_sizeof: int | None
 
     def __init__(
         self,
@@ -4968,7 +4968,7 @@ def __init__(
         size: int = None,  # TODO: make non-optional
         offset: int = 0,
         null_count: int = None,
-        children: Tuple["column.ColumnBase", ...] = (),
+        children: tuple[column.ColumnBase, ...] = (),
     ):
         dtype = cudf.dtype("object")
 
@@ -5123,7 +5123,7 @@ def __contains__(self, item: ScalarLike) -> bool:
 
     def as_numerical_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.NumericalColumn":
+    ) -> cudf.core.column.NumericalColumn:
         out_dtype = cudf.dtype(dtype)
         string_col = self
         if out_dtype.kind in {"i", "u"}:
@@ -5165,7 +5165,7 @@ def _as_datetime_or_timedelta_column(self, dtype, format):
 
     def as_datetime_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.DatetimeColumn":
+    ) -> cudf.core.column.DatetimeColumn:
         out_dtype = cudf.dtype(dtype)
 
         # infer on host from the first not na element
@@ -5189,14 +5189,14 @@ def as_datetime_column(
 
     def as_timedelta_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.TimeDeltaColumn":
+    ) -> cudf.core.column.TimeDeltaColumn:
         out_dtype = cudf.dtype(dtype)
         format = "%D days %H:%M:%S"
         return self._as_datetime_or_timedelta_column(out_dtype, format)
 
     def as_decimal_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.DecimalBaseColumn":
+    ) -> cudf.core.column.DecimalBaseColumn:
         return libstrings.to_decimal(self, dtype)
 
     def as_string_column(
@@ -5240,7 +5240,7 @@ def to_array(self, fillna: bool = None) -> np.ndarray:
 
     def to_pandas(
         self, index: pd.Index = None, nullable: bool = False, **kwargs
-    ) -> "pd.Series":
+    ) -> pd.Series:
         if nullable:
             pandas_array = pd.StringDtype().__from_arrow__(self.to_arrow())
             pd_series = pd.Series(pandas_array, copy=False)
@@ -5251,8 +5251,8 @@ def to_pandas(
             pd_series.index = index
         return pd_series
 
-    def serialize(self) -> Tuple[dict, list]:
-        header: Dict[Any, Any] = {"null_count": self.null_count}
+    def serialize(self) -> tuple[dict, list]:
+        header: dict[Any, Any] = {"null_count": self.null_count}
         header["type-serialized"] = pickle.dumps(type(self))
         header["size"] = self.size
 
@@ -5366,7 +5366,7 @@ def fillna(
         else:
             return super().fillna(method=method)
 
-    def _find_first_and_last(self, value: ScalarLike) -> Tuple[int, int]:
+    def _find_first_and_last(self, value: ScalarLike) -> tuple[int, int]:
         found_indices = libcudf.search.contains(
             self, column.as_column([value], dtype=self.dtype)
         )
@@ -5383,7 +5383,7 @@ def find_first_value(
     def find_last_value(self, value: ScalarLike, closest: bool = False) -> int:
         return self._find_first_and_last(value)[1]
 
-    def normalize_binop_value(self, other) -> "column.ColumnBase":
+    def normalize_binop_value(self, other) -> column.ColumnBase:
         # fastpath: gpu scalar
         if isinstance(other, cudf.Scalar) and other.dtype == "object":
             return column.as_column(other, length=len(self))
@@ -5407,7 +5407,7 @@ def _default_na_value(self) -> ScalarLike:
 
     def binary_operator(
         self, op: builtins.str, rhs, reflect: bool = False
-    ) -> "column.ColumnBase":
+    ) -> column.ColumnBase:
         lhs = self
         if reflect:
             lhs, rhs = rhs, lhs
@@ -5431,7 +5431,7 @@ def binary_operator(
         )
 
     @copy_docstring(column.ColumnBase.view)
-    def view(self, dtype) -> "cudf.core.column.ColumnBase":
+    def view(self, dtype) -> cudf.core.column.ColumnBase:
         if self.null_count > 0:
             raise ValueError(
                 "Can not produce a view of a string column with nulls"
diff --git a/python/cudf/cudf/core/column/struct.py b/python/cudf/cudf/core/column/struct.py
index f0d02a706e2..c344ab3739d 100644
--- a/python/cudf/cudf/core/column/struct.py
+++ b/python/cudf/cudf/core/column/struct.py
@@ -81,7 +81,7 @@ def to_arrow(self):
             pa_type, len(self), buffers, children=children
         )
 
-    def to_pandas(self, index: pd.Index = None, **kwargs) -> "pd.Series":
+    def to_pandas(self, index: pd.Index = None, **kwargs) -> pd.Series:
         # We cannot go via Arrow's `to_pandas` because of the following issue:
         # https://issues.apache.org/jira/browse/ARROW-12680
 
diff --git a/python/cudf/cudf/core/column/timedelta.py b/python/cudf/cudf/core/column/timedelta.py
index 4b7a3bcc197..da485a144bc 100644
--- a/python/cudf/cudf/core/column/timedelta.py
+++ b/python/cudf/cudf/core/column/timedelta.py
@@ -133,7 +133,7 @@ def to_pandas(
 
     def _binary_op_floordiv(
         self, rhs: BinaryOperand
-    ) -> Tuple["column.ColumnBase", BinaryOperand, DtypeObj]:
+    ) -> tuple[column.ColumnBase, BinaryOperand, DtypeObj]:
         lhs = self  # type: column.ColumnBase
         if pd.api.types.is_timedelta64_dtype(rhs.dtype):
             common_dtype = determine_out_dtype(self.dtype, rhs.dtype)
@@ -203,7 +203,7 @@ def _binary_op_lt_gt_le_ge(self, rhs: BinaryOperand) -> DtypeObj:
 
     def _binary_op_truediv(
         self, rhs: BinaryOperand
-    ) -> Tuple["column.ColumnBase", BinaryOperand, DtypeObj]:
+    ) -> tuple[column.ColumnBase, BinaryOperand, DtypeObj]:
         lhs = self  # type: column.ColumnBase
         if pd.api.types.is_timedelta64_dtype(rhs.dtype):
             common_dtype = determine_out_dtype(self.dtype, rhs.dtype)
@@ -229,7 +229,7 @@ def _binary_op_truediv(
 
     def binary_operator(
         self, op: str, rhs: BinaryOperand, reflect: bool = False
-    ) -> "column.ColumnBase":
+    ) -> column.ColumnBase:
         lhs, rhs = self, rhs
 
         if op in ("eq", "ne"):
@@ -292,7 +292,7 @@ def normalize_binop_value(self, other) -> BinaryOperand:
             raise TypeError(f"cannot normalize {type(other)}")
 
     @property
-    def as_numerical(self) -> "cudf.core.column.NumericalColumn":
+    def as_numerical(self) -> cudf.core.column.NumericalColumn:
         return cast(
             "cudf.core.column.NumericalColumn",
             column.build_column(
@@ -334,21 +334,21 @@ def fillna(
 
     def as_numerical_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.NumericalColumn":
+    ) -> cudf.core.column.NumericalColumn:
         return cast(
             "cudf.core.column.NumericalColumn", self.as_numerical.astype(dtype)
         )
 
     def as_datetime_column(
         self, dtype: Dtype, **kwargs
-    ) -> "cudf.core.column.DatetimeColumn":
+    ) -> cudf.core.column.DatetimeColumn:
         raise TypeError(
             f"cannot astype a timedelta from {self.dtype} to {dtype}"
         )
 
     def as_string_column(
         self, dtype: Dtype, format=None, **kwargs
-    ) -> "cudf.core.column.StringColumn":
+    ) -> cudf.core.column.StringColumn:
         if format is None:
             format = _dtype_to_format_conversion.get(
                 self.dtype.name, "%D days %H:%M:%S"
@@ -384,8 +384,8 @@ def isin(self, values: Sequence) -> ColumnBase:
         return cudf.core.tools.datetimes._isin_datetimelike(self, values)
 
     def quantile(
-        self, q: Union[float, Sequence[float]], interpolation: str, exact: bool
-    ) -> "column.ColumnBase":
+        self, q: float | Sequence[float], interpolation: str, exact: bool
+    ) -> column.ColumnBase:
         result = self.as_numerical.quantile(
             q=q, interpolation=interpolation, exact=exact
         )
@@ -411,7 +411,7 @@ def std(
             unit=self.time_unit,
         )
 
-    def components(self, index=None) -> "cudf.DataFrame":
+    def components(self, index=None) -> cudf.DataFrame:
         """
         Return a Dataframe of the components of the Timedeltas.
 
@@ -505,7 +505,7 @@ def components(self, index=None) -> "cudf.DataFrame":
         )
 
     @property
-    def days(self) -> "cudf.core.column.NumericalColumn":
+    def days(self) -> cudf.core.column.NumericalColumn:
         """
         Number of days for each element.
 
@@ -518,7 +518,7 @@ def days(self) -> "cudf.core.column.NumericalColumn":
         )
 
     @property
-    def seconds(self) -> "cudf.core.column.NumericalColumn":
+    def seconds(self) -> cudf.core.column.NumericalColumn:
         """
         Number of seconds (>= 0 and less than 1 day).
 
@@ -541,7 +541,7 @@ def seconds(self) -> "cudf.core.column.NumericalColumn":
         )
 
     @property
-    def microseconds(self) -> "cudf.core.column.NumericalColumn":
+    def microseconds(self) -> cudf.core.column.NumericalColumn:
         """
         Number of microseconds (>= 0 and less than 1 second).
 
@@ -561,7 +561,7 @@ def microseconds(self) -> "cudf.core.column.NumericalColumn":
         )
 
     @property
-    def nanoseconds(self) -> "cudf.core.column.NumericalColumn":
+    def nanoseconds(self) -> cudf.core.column.NumericalColumn:
         """
         Return the number of nanoseconds (n), where 0 <= n < 1 microsecond.
 
diff --git a/python/cudf/cudf/core/column_accessor.py b/python/cudf/cudf/core/column_accessor.py
index c2ea9d756f7..d836dc5b2db 100644
--- a/python/cudf/cudf/core/column_accessor.py
+++ b/python/cudf/cudf/core/column_accessor.py
@@ -94,13 +94,13 @@ class ColumnAccessor(MutableMapping):
         may be passe.
     """
 
-    _data: "Dict[Any, ColumnBase]"
+    _data: dict[Any, ColumnBase]
     multiindex: bool
-    _level_names: Tuple[Any, ...]
+    _level_names: tuple[Any, ...]
 
     def __init__(
         self,
-        data: Union[MutableMapping, ColumnAccessor] = None,
+        data: MutableMapping | ColumnAccessor = None,
         multiindex: bool = False,
         level_names=None,
     ):
@@ -137,7 +137,7 @@ def __init__(
     @classmethod
     def _create_unsafe(
         cls,
-        data: Dict[Any, ColumnBase],
+        data: dict[Any, ColumnBase],
         multiindex: bool = False,
         level_names=None,
     ) -> ColumnAccessor:
@@ -177,7 +177,7 @@ def __repr__(self) -> str:
         return f"{type_info}\n{column_info}"
 
     @property
-    def level_names(self) -> Tuple[Any, ...]:
+    def level_names(self) -> tuple[Any, ...]:
         if self._level_names is None or len(self._level_names) == 0:
             return tuple((None,) * max(1, self.nlevels))
         else:
@@ -206,11 +206,11 @@ def nrows(self) -> int:
             return len(next(iter(self.values())))
 
     @cached_property
-    def names(self) -> Tuple[Any, ...]:
+    def names(self) -> tuple[Any, ...]:
         return tuple(self.keys())
 
     @cached_property
-    def columns(self) -> Tuple[ColumnBase, ...]:
+    def columns(self) -> tuple[ColumnBase, ...]:
         return tuple(self.values())
 
     @cached_property
@@ -463,7 +463,7 @@ def _pad_key(self, key: Any, pad_value="") -> Any:
         return key + (pad_value,) * (self.nlevels - len(key))
 
     def rename_levels(
-        self, mapper: Union[Mapping[Any, Any], Callable], level: Optional[int]
+        self, mapper: Mapping[Any, Any] | Callable, level: int | None
     ) -> ColumnAccessor:
         """
         Rename the specified levels of the given ColumnAccessor
diff --git a/python/cudf/cudf/core/dataframe.py b/python/cudf/cudf/core/dataframe.py
index bd08ac385c7..336b659d115 100644
--- a/python/cudf/cudf/core/dataframe.py
+++ b/python/cudf/cudf/core/dataframe.py
@@ -503,7 +503,7 @@ class DataFrame(IndexedFrame, Serializable, GetAttrGetItemMixin):
     """
 
     _PROTECTED_KEYS = frozenset(("_data", "_index"))
-    _accessors: Set[Any] = set()
+    _accessors: set[Any] = set()
     _loc_indexer_type = _DataFrameLocIndexer
     _iloc_indexer_type = _DataFrameIlocIndexer
 
@@ -822,7 +822,7 @@ def _init_from_dict_like(
     def _from_data(
         cls,
         data: MutableMapping,
-        index: Optional[BaseIndex] = None,
+        index: BaseIndex | None = None,
         columns: Any = None,
     ) -> DataFrame:
         out = super()._from_data(data, index)
@@ -6433,7 +6433,7 @@ def _setitem_with_dataframe(
     input_df: DataFrame,
     replace_df: DataFrame,
     input_cols: Any = None,
-    mask: Optional[cudf.core.column.ColumnBase] = None,
+    mask: cudf.core.column.ColumnBase | None = None,
     ignore_index: bool = False,
 ):
     """
diff --git a/python/cudf/cudf/core/frame.py b/python/cudf/cudf/core/frame.py
index 69dc5389e7a..9d86aa30b16 100644
--- a/python/cudf/cudf/core/frame.py
+++ b/python/cudf/cudf/core/frame.py
@@ -65,11 +65,11 @@ class Frame:
         A Frame representing the (optional) index columns.
     """
 
-    _data: "ColumnAccessor"
+    _data: ColumnAccessor
     # TODO: Once all dependence on Frame having an index is removed, this
     # attribute should be moved to IndexedFrame.
-    _index: Optional[cudf.core.index.BaseIndex]
-    _names: Optional[List]
+    _index: cudf.core.index.BaseIndex | None
+    _names: list | None
 
     def __init__(self, data=None, index=None):
         if data is None:
@@ -90,11 +90,11 @@ def _num_rows(self) -> int:
         return len(self._data.columns[0])
 
     @property
-    def _column_names(self) -> List[Any]:  # TODO: List[str]?
+    def _column_names(self) -> list[Any]:  # TODO: List[str]?
         return self._data.names
 
     @property
-    def _index_names(self) -> List[Any]:  # TODO: List[str]?
+    def _index_names(self) -> list[Any]:  # TODO: List[str]?
         # TODO: Temporarily suppressing mypy warnings to avoid introducing bugs
         # by returning an empty list where one is not expected.
         return (
@@ -104,7 +104,7 @@ def _index_names(self) -> List[Any]:  # TODO: List[str]?
         )
 
     @property
-    def _columns(self) -> List[Any]:  # TODO: List[Column]?
+    def _columns(self) -> list[Any]:  # TODO: List[Column]?
         return self._data.columns
 
     def serialize(self):
@@ -126,7 +126,7 @@ def deserialize(cls, header, frames):
     def _from_data(
         cls,
         data: MutableMapping,
-        index: Optional[cudf.core.index.BaseIndex] = None,
+        index: cudf.core.index.BaseIndex | None = None,
     ):
         obj = cls.__new__(cls)
         Frame.__init__(obj, data, index)
@@ -135,9 +135,9 @@ def _from_data(
     @classmethod
     def _from_columns(
         cls,
-        columns: List[ColumnBase],
-        column_names: List[str],
-        index_names: Optional[List[str]] = None,
+        columns: list[ColumnBase],
+        column_names: list[str],
+        index_names: list[str] | None = None,
     ):
         """Construct a `Frame` object from a list of columns.
 
@@ -165,9 +165,9 @@ def _from_columns(
 
     def _from_columns_like_self(
         self,
-        columns: List[ColumnBase],
-        column_names: List[str],
-        index_names: Optional[List[str]] = None,
+        columns: list[ColumnBase],
+        column_names: list[str],
+        index_names: list[str] | None = None,
     ):
         """Construct a `Frame` from a list of columns with metadata from self.
 
@@ -181,7 +181,7 @@ def _from_columns_like_self(
 
     def _mimic_inplace(
         self: T, result: Frame, inplace: bool = False
-    ) -> Optional[Frame]:
+    ) -> Frame | None:
         if inplace:
             for col in self._data:
                 if col in result._data:
@@ -616,9 +616,9 @@ def _to_array(
         self,
         get_column_values: Callable,
         make_empty_matrix: Callable,
-        dtype: Union[Dtype, None] = None,
+        dtype: Dtype | None = None,
         na_value=None,
-    ) -> Union[cupy.ndarray, np.ndarray]:
+    ) -> cupy.ndarray | np.ndarray:
         # Internal function to implement to_cupy and to_numpy, which are nearly
         # identical except for the attribute they access to generate values.
 
@@ -650,10 +650,7 @@ def get_column_values_na(col):
         return matrix
 
     def to_cupy(
-        self,
-        dtype: Union[Dtype, None] = None,
-        copy: bool = False,
-        na_value=None,
+        self, dtype: Dtype | None = None, copy: bool = False, na_value=None,
     ) -> cupy.ndarray:
         """Convert the Frame to a CuPy array.
 
@@ -684,10 +681,7 @@ def to_cupy(
         )
 
     def to_numpy(
-        self,
-        dtype: Union[Dtype, None] = None,
-        copy: bool = True,
-        na_value=None,
+        self, dtype: Dtype | None = None, copy: bool = True, na_value=None,
     ) -> np.ndarray:
         """Convert the Frame to a NumPy array.
 
@@ -3440,7 +3434,7 @@ def _binaryop(
     @classmethod
     def _colwise_binop(
         cls,
-        operands: Dict[Optional[str], Tuple[ColumnBase, Any, bool, Any]],
+        operands: dict[str | None, tuple[ColumnBase, Any, bool, Any]],
         fn: str,
     ):
         """Implement binary ops between two frame-like objects.
@@ -6438,8 +6432,8 @@ def ge(self, other, axis="columns", level=None, fill_value=None):
 
 
 def _get_replacement_values_for_columns(
-    to_replace: Any, value: Any, columns_dtype_map: Dict[Any, Any]
-) -> Tuple[Dict[Any, bool], Dict[Any, Any], Dict[Any, Any]]:
+    to_replace: Any, value: Any, columns_dtype_map: dict[Any, Any]
+) -> tuple[dict[Any, bool], dict[Any, Any], dict[Any, Any]]:
     """
     Returns a per column mapping for the values to be replaced, new
     values to be replaced with and if all the values are empty.
@@ -6464,9 +6458,9 @@ def _get_replacement_values_for_columns(
         A dict mapping of all columns and the corresponding values
         to be replaced with.
     """
-    to_replace_columns: Dict[Any, Any] = {}
-    values_columns: Dict[Any, Any] = {}
-    all_na_columns: Dict[Any, Any] = {}
+    to_replace_columns: dict[Any, Any] = {}
+    values_columns: dict[Any, Any] = {}
+    all_na_columns: dict[Any, Any] = {}
 
     if is_scalar(to_replace) and is_scalar(value):
         to_replace_columns = {col: [to_replace] for col in columns_dtype_map}
@@ -6603,8 +6597,8 @@ def _is_series(obj):
 
 def _drop_rows_by_labels(
     obj: DataFrameOrSeries,
-    labels: Union[ColumnLike, abc.Iterable, str],
-    level: Union[int, str],
+    labels: ColumnLike | abc.Iterable | str,
+    level: int | str,
     errors: str,
 ) -> DataFrameOrSeries:
     """Remove rows specified by `labels`. If `errors=True`, an error is raised
diff --git a/python/cudf/cudf/core/index.py b/python/cudf/cudf/core/index.py
index 88371666ce6..983525e4ea2 100644
--- a/python/cudf/cudf/core/index.py
+++ b/python/cudf/cudf/core/index.py
@@ -60,10 +60,8 @@
 
 
 def _lexsorted_equal_range(
-    idx: Union[GenericIndex, cudf.MultiIndex],
-    key_as_table: Frame,
-    is_sorted: bool,
-) -> Tuple[int, int, Optional[ColumnBase]]:
+    idx: GenericIndex | cudf.MultiIndex, key_as_table: Frame, is_sorted: bool,
+) -> tuple[int, int, ColumnBase | None]:
     """Get equal range for key in lexicographically sorted index. If index
     is not sorted when called, a sort will take place and `sort_inds` is
     returned. Otherwise `None` is returned in that position.
@@ -94,8 +92,8 @@ def _index_from_data(data: MutableMapping, name: Any = None):
 
         if isinstance(values, NumericalColumn):
             try:
-                index_class_type: Type[
-                    Union[GenericIndex, cudf.MultiIndex]
+                index_class_type: type[
+                    GenericIndex | cudf.MultiIndex
                 ] = _dtype_to_index[values.dtype.type]
             except KeyError:
                 index_class_type = GenericIndex
@@ -115,7 +113,7 @@ def _index_from_data(data: MutableMapping, name: Any = None):
 
 
 def _index_from_columns(
-    columns: List[cudf.core.column.ColumnBase], name: Any = None
+    columns: list[cudf.core.column.ColumnBase], name: Any = None
 ):
     """Construct an index from ``columns``, with levels named 0, 1, 2..."""
     return _index_from_data(dict(zip(range(len(columns)), columns)), name=name)
@@ -1213,7 +1211,7 @@ class NumericIndex(GenericIndex):
     """
 
     # Subclasses must define the dtype they are associated with.
-    _dtype: Union[None, Type[np.number]] = None
+    _dtype: None | type[np.number] = None
 
     def __init__(self, data=None, dtype=None, copy=False, name=None):
 
@@ -2246,7 +2244,7 @@ def is_categorical(self):
 
 def interval_range(
     start=None, end=None, periods=None, freq=None, name=None, closed="right",
-) -> "IntervalIndex":
+) -> IntervalIndex:
     """
     Returns a fixed frequency IntervalIndex.
 
@@ -2593,7 +2591,7 @@ def as_index(arbitrary, nan_as_null=None, **kwargs) -> BaseIndex:
     )
 
 
-_dtype_to_index: Dict[Any, Type[NumericIndex]] = {
+_dtype_to_index: dict[Any, type[NumericIndex]] = {
     np.int8: Int8Index,
     np.int16: Int16Index,
     np.int32: Int32Index,
@@ -2704,7 +2702,7 @@ def from_arrow(cls, obj):
             return cudf.MultiIndex.from_arrow(obj)
 
 
-def _concat_range_index(indexes: List[RangeIndex]) -> BaseIndex:
+def _concat_range_index(indexes: list[RangeIndex]) -> BaseIndex:
     """
     An internal Utility function to concat RangeIndex objects.
     """
@@ -2744,7 +2742,7 @@ def _concat_range_index(indexes: List[RangeIndex]) -> BaseIndex:
     return RangeIndex(start, stop, step)
 
 
-def _extended_gcd(a: int, b: int) -> Tuple[int, int, int]:
+def _extended_gcd(a: int, b: int) -> tuple[int, int, int]:
     """
     Extended Euclidean algorithms to solve Bezout's identity:
        a*x + b*y = gcd(x, y)
diff --git a/python/cudf/cudf/core/indexed_frame.py b/python/cudf/cudf/core/indexed_frame.py
index 72878078593..784be09494c 100644
--- a/python/cudf/cudf/core/indexed_frame.py
+++ b/python/cudf/cudf/core/indexed_frame.py
@@ -135,8 +135,8 @@ class IndexedFrame(Frame):
     """
 
     # mypy can't handle bound type variables as class members
-    _loc_indexer_type: Type[_LocIndexerClass]  # type: ignore
-    _iloc_indexer_type: Type[_IlocIndexerClass]  # type: ignore
+    _loc_indexer_type: type[_LocIndexerClass]  # type: ignore
+    _iloc_indexer_type: type[_IlocIndexerClass]  # type: ignore
     _index: cudf.core.index.BaseIndex
 
     def __init__(self, data=None, index=None):
@@ -1486,7 +1486,7 @@ def _reset_index(self, level, drop, col_level=0, col_fill=""):
 
     def _first_or_last(
         self, offset, idx: int, op: Callable, side: str, slice_func: Callable
-    ) -> "IndexedFrame":
+    ) -> IndexedFrame:
         """Shared code path for ``first`` and ``last``."""
         if not isinstance(self._index, cudf.core.index.DatetimeIndex):
             raise TypeError("'first' only supports a DatetimeIndex index.")
diff --git a/python/cudf/cudf/core/join/_join_helpers.py b/python/cudf/cudf/core/join/_join_helpers.py
index ead0cd566d9..7589ed27484 100644
--- a/python/cudf/cudf/core/join/_join_helpers.py
+++ b/python/cudf/cudf/core/join/_join_helpers.py
@@ -57,7 +57,7 @@ def set(self, obj: Frame, value: ColumnBase, validate=False):
 
 def _match_join_keys(
     lcol: ColumnBase, rcol: ColumnBase, how: str
-) -> Tuple[ColumnBase, ColumnBase]:
+) -> tuple[ColumnBase, ColumnBase]:
     # Casts lcol and rcol to a common dtype for use as join keys. If no casting
     # is necessary, they are returned as is.
 
@@ -118,7 +118,7 @@ def _match_join_keys(
 
 def _match_categorical_dtypes_both(
     lcol: CategoricalColumn, rcol: CategoricalColumn, how: str
-) -> Tuple[ColumnBase, ColumnBase]:
+) -> tuple[ColumnBase, ColumnBase]:
     ltype, rtype = lcol.dtype, rcol.dtype
 
     # when both are ordered and both have the same categories,
diff --git a/python/cudf/cudf/core/multiindex.py b/python/cudf/cudf/core/multiindex.py
index 3796b596ad6..0c573d4efed 100644
--- a/python/cudf/cudf/core/multiindex.py
+++ b/python/cudf/cudf/core/multiindex.py
@@ -255,7 +255,7 @@ def set_names(self, names, level=None, inplace=False):
     def _from_data(
         cls,
         data: MutableMapping,
-        index: Optional[cudf.core.index.BaseIndex] = None,
+        index: cudf.core.index.BaseIndex | None = None,
         name: Any = None,
     ) -> MultiIndex:
         assert index is None
@@ -781,9 +781,9 @@ def _index_and_downcast(self, result, index, index_key):
     def _get_row_major(
         self,
         df: DataFrameOrSeries,
-        row_tuple: Union[
-            numbers.Number, slice, Tuple[Any, ...], List[Tuple[Any, ...]]
-        ],
+        row_tuple: (
+            numbers.Number | slice | tuple[Any, ...] | list[tuple[Any, ...]]
+        ),
     ) -> DataFrameOrSeries:
         if pd.api.types.is_bool_dtype(
             list(row_tuple) if isinstance(row_tuple, tuple) else row_tuple
@@ -805,9 +805,9 @@ def _get_row_major(
 
     def _validate_indexer(
         self,
-        indexer: Union[
-            numbers.Number, slice, Tuple[Any, ...], List[Tuple[Any, ...]]
-        ],
+        indexer: (
+            numbers.Number | slice | tuple[Any, ...] | list[tuple[Any, ...]]
+        ),
     ):
         if isinstance(indexer, numbers.Number):
             return
diff --git a/python/cudf/cudf/core/series.py b/python/cudf/cudf/core/series.py
index 7f00162099a..e2d350f0f5b 100644
--- a/python/cudf/cudf/core/series.py
+++ b/python/cudf/cudf/core/series.py
@@ -156,7 +156,7 @@ class _SeriesLocIndexer(_FrameIndexer):
     Label-based selection
     """
 
-    def __getitem__(self, arg: Any) -> Union[ScalarLike, DataFrameOrSeries]:
+    def __getitem__(self, arg: Any) -> ScalarLike | DataFrameOrSeries:
         if isinstance(arg, pd.MultiIndex):
             arg = cudf.from_pandas(arg)
 
@@ -281,7 +281,7 @@ class Series(SingleColumnFrame, IndexedFrame, Serializable):
         If ``False``, leaves ``np.nan`` values as is.
     """
 
-    _accessors: Set[Any] = set()
+    _accessors: set[Any] = set()
     _loc_indexer_type = _SeriesLocIndexer
     _iloc_indexer_type = _SeriesIlocIndexer
 
@@ -450,7 +450,7 @@ def __init__(
     def _from_data(
         cls,
         data: MutableMapping,
-        index: Optional[BaseIndex] = None,
+        index: BaseIndex | None = None,
         name: Any = None,
     ) -> Series:
         """
@@ -1049,7 +1049,7 @@ def __array_function__(self, func, types, args, kwargs):
             kwargs,
         )
 
-    def map(self, arg, na_action=None) -> "Series":
+    def map(self, arg, na_action=None) -> Series:
         """
         Map values of Series according to input correspondence.
 
diff --git a/python/cudf/cudf/core/single_column_frame.py b/python/cudf/cudf/core/single_column_frame.py
index 7793a2fdf29..2d98c66e726 100644
--- a/python/cudf/cudf/core/single_column_frame.py
+++ b/python/cudf/cudf/core/single_column_frame.py
@@ -57,7 +57,7 @@ def _scan(self, op, axis=None, *args, **kwargs):
     def _from_data(
         cls,
         data: MutableMapping,
-        index: Optional[cudf.core.index.BaseIndex] = None,
+        index: cudf.core.index.BaseIndex | None = None,
         name: Any = None,
     ):
 
@@ -118,18 +118,12 @@ def values_host(self):  # noqa: D102
         return self._column.values_host
 
     def to_cupy(
-        self,
-        dtype: Union[Dtype, None] = None,
-        copy: bool = True,
-        na_value=None,
+        self, dtype: Dtype | None = None, copy: bool = True, na_value=None,
     ) -> cupy.ndarray:  # noqa: D102
         return super().to_cupy(dtype, copy, na_value).flatten()
 
     def to_numpy(
-        self,
-        dtype: Union[Dtype, None] = None,
-        copy: bool = True,
-        na_value=None,
+        self, dtype: Dtype | None = None, copy: bool = True, na_value=None,
     ) -> np.ndarray:  # noqa: D102
         return super().to_numpy(dtype, copy, na_value).flatten()
 
@@ -294,7 +288,7 @@ def _make_operands_for_binop(
         reflect: bool = False,
         *args,
         **kwargs,
-    ) -> Dict[Optional[str], Tuple[ColumnBase, Any, bool, Any]]:
+    ) -> dict[str | None, tuple[ColumnBase, Any, bool, Any]]:
         """Generate the dictionary of operands used for a binary operation.
 
         Parameters
diff --git a/python/cudf/cudf/core/subword_tokenizer.py b/python/cudf/cudf/core/subword_tokenizer.py
index 782b74ef4a6..cb72faac004 100644
--- a/python/cudf/cudf/core/subword_tokenizer.py
+++ b/python/cudf/cudf/core/subword_tokenizer.py
@@ -61,7 +61,7 @@ def __call__(
         max_num_rows: int,
         add_special_tokens: bool = True,
         padding: str = "max_length",
-        truncation: Union[bool, str] = False,
+        truncation: bool | str = False,
         stride: int = 0,
         return_tensors: str = "cp",
         return_token_type_ids: bool = False,
diff --git a/python/cudf/cudf/testing/testing.py b/python/cudf/cudf/testing/testing.py
index 59c291eea0b..79d08cca2a2 100644
--- a/python/cudf/cudf/testing/testing.py
+++ b/python/cudf/cudf/testing/testing.py
@@ -239,7 +239,7 @@ def assert_index_equal(
     right,
     exact="equiv",
     check_names: bool = True,
-    check_less_precise: Union[bool, int] = False,
+    check_less_precise: bool | int = False,
     check_exact: bool = True,
     check_categorical: bool = True,
     check_order: bool = True,