Rename dtype typevar

spec/API_specification/dataframe_api/__init__.py

@@ -3,12 +3,15 @@
 """
 from __future__ import annotations
 
-from typing import Mapping, Sequence, Any
+from typing import Mapping, Sequence, Any, TYPE_CHECKING
 
 from .column_object import *
 from .dataframe_object import DataFrame
 from .groupby_object import *
-from ._types import DType
+from .dtypes import *
+
+if TYPE_CHECKING:
+    from ._types import DType
 
 __all__ = [
     "__dataframe_api_version__",
@@ -60,7 +63,7 @@ def concat(dataframes: Sequence[DataFrame]) -> DataFrame:
     """
     ...
 
-def column_from_sequence(sequence: Sequence[Any], *, dtype: Any) -> Column[Any]:
+def column_from_sequence(sequence: Sequence[Any], *, dtype: DType) -> Column[Any]:
     """
     Construct Column from sequence of elements.
 
@@ -140,40 +143,3 @@ def is_null(value: object, /) -> bool:
         implements the dataframe API standard, False otherwise.
 
     """
-
-##########
-# Dtypes #
-##########
-
-class Int64:
-    """Integer type with 64 bits of precision."""
-
-class Int32:
-    """Integer type with 32 bits of precision."""
-
-class Int16:
-    """Integer type with 16 bits of precision."""
-
-class Int8:
-    """Integer type with 8 bits of precision."""
-
-class UInt64:
-    """Unsigned integer type with 64 bits of precision."""
-
-class UInt32:
-    """Unsigned integer type with 32 bits of precision."""
-
-class UInt16:
-    """Unsigned integer type with 16 bits of precision."""
-
-class UInt8:
-    """Unsigned integer type with 8 bits of precision."""
-
-class Float64:
-    """Floating point type with 64 bits of precision."""
-
-class Float32:
-    """Floating point type with 32 bits of precision."""
-
-class Bool:
-    """Boolean type with 8 bits of precision."""

spec/API_specification/dataframe_api/_types.py

@@ -17,16 +17,34 @@
     TypeVar,
     Union,
     Protocol,
+    TYPE_CHECKING,
 )
 from enum import Enum
 
+if TYPE_CHECKING:
+    from .dtypes import (
+        Bool,
+        Float64,
+        Float32,
+        Int64,
+        Int32,
+        Int16,
+        Int8,
+        UInt64,
+        UInt32,
+        UInt16,
+        UInt8,
+    )
+
+    DType = Union[Bool, Float64, Float32, Int64, Int32, Int16, Int8, UInt64, UInt32, UInt16, UInt8]
+
 # Type alias: Mypy needs Any, but for readability we need to make clear this
 # is a Python scalar (i.e., an instance of `bool`, `int`, `float`, `str`, etc.)
 Scalar = Any
 
 array = TypeVar("array")
 device = TypeVar("device")
-DType = TypeVar("DType")
+DTypeT = TypeVar("DTypeT")
 SupportsDLPack = TypeVar("SupportsDLPack")
 SupportsBufferProtocol = TypeVar("SupportsBufferProtocol")
 PyCapsule = TypeVar("PyCapsule")
@@ -60,7 +78,7 @@ def __len__(self, /) -> int:
     "Sequence",
     "array",
     "device",
-    "DType",
+    "DTypeT",
     "ellipsis",
     "Enum",
 ]

spec/API_specification/dataframe_api/column_object.py

@@ -2,7 +2,7 @@
 
 from typing import Any,NoReturn, Sequence, TYPE_CHECKING, Literal, Generic
 
-from ._types import DType
+from ._types import DTypeT
 
 if TYPE_CHECKING:
     from . import Bool, null
@@ -12,7 +12,7 @@
 __all__ = ['Column']
 
 
-class Column(Generic[DType]):
+class Column(Generic[DTypeT]):
     """
     Column object
 
@@ -81,7 +81,7 @@ def dtype(self) -> Any:
         Return data type of column.
         """
 
-    def get_rows(self: Column[DType], indices: Column[Any]) -> Column[DType]:
+    def get_rows(self: Column[DTypeT], indices: Column[Any]) -> Column[DTypeT]:
         """
         Select a subset of rows, similar to `ndarray.take`.
 
@@ -157,7 +157,7 @@ def __eq__(self, other: Column[Any] | Scalar) -> Column[Bool]:  # type: ignore[o
         Column
         """
 
-    def __ne__(self: Column[DType], other: Column[DType] | Scalar) -> Column[Bool]:  # type: ignore[override]
+    def __ne__(self: Column[DTypeT], other: Column[DTypeT] | Scalar) -> Column[Bool]:  # type: ignore[override]
         """
         Compare for non-equality.
 
@@ -175,7 +175,7 @@ def __ne__(self: Column[DType], other: Column[DType] | Scalar) -> Column[Bool]:
         Column
         """
 
-    def __ge__(self: Column[DType], other: Column[DType] | Scalar) -> Column[Bool]:
+    def __ge__(self: Column[DTypeT], other: Column[DTypeT] | Scalar) -> Column[Bool]:
         """
         Compare for "greater than or equal to" `other`.
 
@@ -191,7 +191,7 @@ def __ge__(self: Column[DType], other: Column[DType] | Scalar) -> Column[Bool]:
         Column
         """
 
-    def __gt__(self: Column[DType], other: Column[DType] | Scalar) -> Column[Bool]:
+    def __gt__(self: Column[DTypeT], other: Column[DTypeT] | Scalar) -> Column[Bool]:
         """
         Compare for "greater than" `other`.
 
@@ -207,7 +207,7 @@ def __gt__(self: Column[DType], other: Column[DType] | Scalar) -> Column[Bool]:
         Column
         """
 
-    def __le__(self: Column[DType], other: Column[DType] | Scalar) -> Column[Bool]:
+    def __le__(self: Column[DTypeT], other: Column[DTypeT] | Scalar) -> Column[Bool]:
         """
         Compare for "less than or equal to" `other`.
 
@@ -223,7 +223,7 @@ def __le__(self: Column[DType], other: Column[DType] | Scalar) -> Column[Bool]:
         Column
         """
 
-    def __lt__(self: Column[DType], other: Column[DType] | Scalar) -> Column[Bool]:
+    def __lt__(self: Column[DTypeT], other: Column[DTypeT] | Scalar) -> Column[Bool]:
         """
         Compare for "less than" `other`.
 
@@ -527,26 +527,26 @@ def var(self, *, correction: int | float = 1, skip_nulls: bool = True) -> Scalar
             Whether to skip null values.
         """
 
-    def cumulative_max(self: Column[DType]) -> Column[DType]:
+    def cumulative_max(self: Column[DTypeT]) -> Column[DTypeT]:
         """
         Reduction returns a Column. Any data type that supports comparisons
         must be supported. The returned value has the same dtype as the column.
         """
 
-    def cumulative_min(self: Column[DType]) -> Column[DType]:
+    def cumulative_min(self: Column[DTypeT]) -> Column[DTypeT]:
         """
         Reduction returns a Column. Any data type that supports comparisons
         must be supported. The returned value has the same dtype as the column.
         """
 
-    def cumulative_sum(self: Column[DType]) -> Column[DType]:
+    def cumulative_sum(self: Column[DTypeT]) -> Column[DTypeT]:
         """
         Reduction returns a Column. Must be supported for numerical and
         datetime data types. The returned value has the same dtype as the
         column.
         """
 
-    def cumulative_prod(self: Column[DType]) -> Column[DType]:
+    def cumulative_prod(self: Column[DTypeT]) -> Column[DTypeT]:
         """
         Reduction returns a Column. Must be supported for numerical and
         datetime data types. The returned value has the same dtype as the
@@ -591,7 +591,7 @@ def is_nan(self) -> Column[Bool]:
         In particular, does not check for `np.timedelta64('NaT')`.
         """
 
-    def is_in(self: Column[DType], values: Column[DType]) -> Column[Bool]:
+    def is_in(self: Column[DTypeT], values: Column[DTypeT]) -> Column[Bool]:
         """
         Indicate whether the value at each row matches any value in `values`.
 
@@ -630,7 +630,7 @@ def unique_indices(self, *, skip_nulls: bool = True) -> Column[Any]:
         """
         ...
 
-    def fill_nan(self: Column[DType], value: float | 'null', /) -> Column[DType]:
+    def fill_nan(self: Column[DTypeT], value: float | 'null', /) -> Column[DTypeT]:
         """
         Fill floating point ``nan`` values with the given fill value.
 
@@ -644,7 +644,7 @@ def fill_nan(self: Column[DType], value: float | 'null', /) -> Column[DType]:
         """
         ...
 
-    def fill_null(self: Column[DType], value: Scalar, /) -> Column[DType]:
+    def fill_null(self: Column[DTypeT], value: Scalar, /) -> Column[DTypeT]:
         """
         Fill null values with the given fill value.
 

spec/API_specification/dataframe_api/dtypes.py

@@ -0,0 +1,36 @@
+##########
+# Dtypes #
+##########
+
+class Int64:
+    """Integer type with 64 bits of precision."""
+
+class Int32:
+    """Integer type with 32 bits of precision."""
+
+class Int16:
+    """Integer type with 16 bits of precision."""
+
+class Int8:
+    """Integer type with 8 bits of precision."""
+
+class UInt64:
+    """Unsigned integer type with 64 bits of precision."""
+
+class UInt32:
+    """Unsigned integer type with 32 bits of precision."""
+
+class UInt16:
+    """Unsigned integer type with 16 bits of precision."""
+
+class UInt8:
+    """Unsigned integer type with 8 bits of precision."""
+
+class Float64:
+    """Floating point type with 64 bits of precision."""
+
+class Float32:
+    """Floating point type with 32 bits of precision."""
+
+class Bool:
+    """Boolean type with 8 bits of precision."""

spec/conf.py

@@ -75,7 +75,7 @@
     ('py:class', 'array'),
     ('py:class', 'DataFrame'),
     ('py:class', 'device'),
-    ('py:class', 'DType'),
+    ('py:class', 'DTypeT'),
     ('py:class', 'NestedSequence'),
     ('py:class', 'collections.abc.Sequence'),
     ('py:class', 'PyCapsule'),