Add typing to single_column_frame

python/cudf/cudf/api/types.py

@@ -8,7 +8,7 @@
 from collections import abc
 from functools import wraps
 from inspect import isclass
-from typing import List, Union
+from typing import List, Union, cast
 
 import cupy as cp
 import numpy as np
@@ -238,7 +238,10 @@ def _union_categoricals(
         raise TypeError("ignore_order is not yet implemented")
 
     result_col = cudf.core.column.CategoricalColumn._concat(
-        [obj._column for obj in to_union]
+        [
+            cast(cudf.core.column.CategoricalColumn, obj._column)
+            for obj in to_union
+        ]
     )
     if sort_categories:
         sorted_categories = result_col.categories.sort_values(ascending=True)

python/cudf/cudf/core/column/column.py

@@ -212,7 +212,7 @@ def to_pandas(
             return pd.Index(pa_array.to_pandas())
 
     @property
-    def values_host(self) -> "np.ndarray":
+    def values_host(self) -> np.ndarray:
         """
         Return a numpy representation of the Column.
         """
@@ -226,7 +226,7 @@ def values_host(self) -> "np.ndarray":
             return self.data_array_view(mode="read").copy_to_host()
 
     @property
-    def values(self) -> "cupy.ndarray":
+    def values(self) -> cupy.ndarray:
         """
         Return a CuPy representation of the Column.
         """

python/cudf/cudf/core/single_column_frame.py

@@ -7,9 +7,11 @@
 
 import cupy
 import numpy
+import pyarrow as pa
+from typing_extensions import Self
 
 import cudf
-from cudf._typing import Dtype, NotImplementedType, ScalarLike
+from cudf._typing import NotImplementedType, ScalarLike
 from cudf.api.extensions import no_default
 from cudf.api.types import (
     _is_scalar_or_zero_d_array,
@@ -27,8 +29,8 @@
 class SingleColumnFrame(Frame, NotIterable):
     """A one-dimensional frame.
 
-    Frames with only a single column share certain logic that is encoded in
-    this class.
+    Frames with only a single column (Index or Series)
+    share certain logic that is encoded in this class.
     """
 
     _SUPPORT_AXIS_LOOKUP = {
@@ -47,7 +49,7 @@ def _reduce(
         if axis not in (None, 0, no_default):
             raise NotImplementedError("axis parameter is not implemented yet")
 
-        if numeric_only and not is_numeric_dtype(self._column):
+        if numeric_only and not is_numeric_dtype(self.dtype):
             raise TypeError(
                 f"Series.{op} does not allow numeric_only={numeric_only} "
                 "with non-numeric dtypes."
@@ -68,7 +70,7 @@ def _scan(self, op, axis=None, *args, **kwargs):
     @_cudf_nvtx_annotate
     def name(self):
         """Get the name of this object."""
-        return next(iter(self._data.names))
+        return next(iter(self._column_names))
 
     @name.setter  # type: ignore
     @_cudf_nvtx_annotate
@@ -83,7 +85,7 @@ def ndim(self) -> int:  # noqa: D401
 
     @property  # type: ignore
     @_cudf_nvtx_annotate
-    def shape(self):
+    def shape(self) -> tuple[int]:
         """Get a tuple representing the dimensionality of the Index."""
         return (len(self),)
 
@@ -95,45 +97,27 @@ def __bool__(self):
 
     @property  # type: ignore
     @_cudf_nvtx_annotate
-    def _num_columns(self):
+    def _num_columns(self) -> int:
         return 1
 
     @property  # type: ignore
     @_cudf_nvtx_annotate
-    def _column(self):
-        return self._data[self.name]
+    def _column(self) -> ColumnBase:
+        return next(iter(self._columns))
 
     @property  # type: ignore
     @_cudf_nvtx_annotate
-    def values(self):  # noqa: D102
+    def values(self) -> cupy.ndarray:  # noqa: D102
         return self._column.values
 
     @property  # type: ignore
     @_cudf_nvtx_annotate
-    def values_host(self):  # noqa: D102
+    def values_host(self) -> numpy.ndarray:  # noqa: D102
         return self._column.values_host
 
-    @_cudf_nvtx_annotate
-    def to_cupy(
-        self,
-        dtype: Union[Dtype, None] = None,
-        copy: bool = True,
-        na_value=None,
-    ) -> cupy.ndarray:  # noqa: D102
-        return super().to_cupy(dtype, copy, na_value).flatten()
-
-    @_cudf_nvtx_annotate
-    def to_numpy(
-        self,
-        dtype: Union[Dtype, None] = None,
-        copy: bool = True,
-        na_value=None,
-    ) -> numpy.ndarray:  # noqa: D102
-        return super().to_numpy(dtype, copy, na_value).flatten()
-
     @classmethod
     @_cudf_nvtx_annotate
-    def from_arrow(cls, array):
+    def from_arrow(cls, array) -> Self:
         """Create from PyArrow Array/ChunkedArray.
 
         Parameters
@@ -164,7 +148,7 @@ def from_arrow(cls, array):
         return cls(ColumnBase.from_arrow(array))
 
     @_cudf_nvtx_annotate
-    def to_arrow(self):
+    def to_arrow(self) -> pa.Array:
         """
         Convert to a PyArrow Array.
 
@@ -196,7 +180,7 @@ def to_arrow(self):
 
     @property  # type: ignore
     @_cudf_nvtx_annotate
-    def is_unique(self):
+    def is_unique(self) -> bool:
         """Return boolean if values in the object are unique.
 
         Returns
@@ -207,7 +191,7 @@ def is_unique(self):
 
     @property  # type: ignore
     @_cudf_nvtx_annotate
-    def is_monotonic_increasing(self):
+    def is_monotonic_increasing(self) -> bool:
         """Return boolean if values in the object are monotonically increasing.
 
         Returns
@@ -218,7 +202,7 @@ def is_monotonic_increasing(self):
 
     @property  # type: ignore
     @_cudf_nvtx_annotate
-    def is_monotonic_decreasing(self):
+    def is_monotonic_decreasing(self) -> bool:
         """Return boolean if values in the object are monotonically decreasing.
 
         Returns
@@ -243,7 +227,9 @@ def __cuda_array_interface__(self):
             )
 
     @_cudf_nvtx_annotate
-    def factorize(self, sort=False, use_na_sentinel=True):
+    def factorize(
+        self, sort: bool = False, use_na_sentinel: bool = True
+    ) -> tuple[cupy.ndarray, cudf.Index]:
         """Encode the input values as integer labels.
 
         Parameters
@@ -335,7 +321,7 @@ def _make_operands_for_binop(
         return {result_name: (self._column, other, reflect, fill_value)}
 
     @_cudf_nvtx_annotate
-    def nunique(self, dropna: bool = True):
+    def nunique(self, dropna: bool = True) -> int:
         """
         Return count of unique values for the column.