8000 Add some dtypes (#179) · MarcoGorelli/dataframe-api@5dd4173 · GitHub
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MarcoGorelliMarcoGorelli
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Add some dtypes (data-apis#179)
* add some dtypes * remove DType import (just use the DType class from the same file) * bool8 and bool1 * bool1, bool8 -> bool
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-13
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3 files changed

+76
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‎spec/API_specification/dataframe_api/__init__.py

Lines changed: 52 additions & 2 deletions
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@@ -9,8 +9,6 @@
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from .dataframe_object import *
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from .groupby_object import *
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from ._types import DType
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__all__ = [
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"__dataframe_api_version",
@@ -19,6 +17,18 @@
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"dataframe_from_dict",
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"is_null",
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"null",
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"DType",
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"Int64",
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"Int32",
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"Int16",
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"Int8",
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"UInt64",
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"UInt32",
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"UInt16",
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"UInt8",
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"Float64",
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"Float32",
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"Bool",
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]
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@@ -129,3 +139,43 @@ def is_null(value: object, /) -> bool:
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implements the dataframe API standard, False otherwise.
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"""
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##########
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# Dtypes #
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##########
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class DType:
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"""Base class for all dtypes."""
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class Int64(DType):
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"""Integer type with 64 bits of precision."""
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class Int32(DType):
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"""Integer type with 32 bits of precision."""
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class Int16(DType):
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"""Integer type with 16 bits of precision."""
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class Int8(DType):
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"""Integer type with 8 bits of precision."""
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class UInt64(DType):
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"""Unsigned integer type with 64 bits of precision."""
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class UInt32(DType):
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"""Unsigned integer type with 32 bits of precision."""
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class UInt16(DType):
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"""Unsigned integer type with 16 bits of precision."""
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class UInt8(DType):
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"""Unsigned integer type with 8 bits of precision."""
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class Float64(DType):
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"""Floating point type with 64 bits of precision."""
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class Float32(DType):
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"""Floating point type with 32 bits of precision."""
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class Bool(DType):
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"""Boolean type with 8 bits of precision."""

‎spec/API_specification/dataframe_api/column_object.py

Lines changed: 12 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -3,7 +3,8 @@
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from typing import NoReturn, Sequence, TYPE_CHECKING
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if TYPE_CHECKING:
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from ._types import Scalar, DType
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from ._types import Scalar
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from . import DType
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__all__ = ['Column']
@@ -52,7 +53,7 @@ def get_rows(self, indices: Column[int]) -> Column:
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"""
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...
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def get_value(self, row_number: int) -> DType:
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def get_value(self, row_number: int) -> Scalar:
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"""
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Select the value at a row number, similar to `ndarray.__getitem__(<int>)`.
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@@ -63,7 +64,7 @@ def get_value(self, row_number: int) -> DType:
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Returns
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-------
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dtype
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Scalar
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Depends on the dtype of the Column, and may vary
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across implementations.
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"""
@@ -369,56 +370,56 @@ def all(self, *, skip_nulls: bool = True) -> bool:
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If column is not boolean.
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"""
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def min(self, *, skip_nulls: bool = True) -> DType:
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def min(self, *, skip_nulls: bool = True) -> Scalar:
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"""
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Reduction returns a scalar. Any data type that supports comparisons
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must be supported. The returned value has the same dtype as the column.
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"""
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def max(self, *, skip_nulls: bool = True) -> DType:
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def max(self, *, skip_nulls: bool = True) -> Scalar:
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"""
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Reduction returns a scalar. Any data type that supports comparisons
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must be supported. The returned value has the same dtype as the column.
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"""
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def sum(self, *, skip_nulls: bool = True) -> DType:
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def sum(self, *, skip_nulls: bool = True) -> Scalar:
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"""
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Reduction returns a scalar. Must be supported for numerical and
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datetime data types. The returned value has the same dtype as the
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column.
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"""
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def prod(self, *, skip_nulls: bool = True) -> DType:
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def prod(self, *, skip_nulls: bool = True) -> Scalar:
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"""
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Reduction returns a scalar. Must be supported for numerical data types.
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The returned value has the same dtype as the column.
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"""
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def median(self, *, skip_nulls: bool = True) -> DType:
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def median(self, *, skip_nulls: bool = True) -> Scalar:
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"""
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Reduction returns a scalar. Must be supported for numerical and
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datetime data types. Returns a float for numerical data types, and
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datetime (with the appropriate timedelta format string) for datetime
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dtypes.
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"""
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def mean(self, *, skip_nulls: bool = True) -> DType:
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def mean(self, *, skip_nulls: bool = True) -> Scalar:
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"""
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Reduction returns a scalar. Must be supported for numerical and
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datetime data types. Returns a float for numerical data types, and
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datetime (with the appropriate timedelta format string) for datetime
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dtypes.
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"""
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def std(self, *, skip_nulls: bool = True) -> DType:
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def std(self, *, skip_nulls: bool = True) -> Scalar:
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"""
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Reduction returns a scalar. Must be supported for numerical and
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datetime data types. Returns a float for numerical data types, and
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datetime (with the appropriate timedelta format string) for datetime
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dtypes.
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"""
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def var(self, *, skip_nulls: bool = True) -> DType:
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def var(self, *, skip_nulls: bool = True) -> Scalar:
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"""
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Reduction returns a scalar. Must be supported for numerical and
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datetime data types. Returns a float for numerical data types, and

‎spec/API_specification/index.rst

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Original file line numberDiff line numberDiff line change
@@ -16,6 +16,18 @@ of objects and functions in the top-level namespace. The latter are:
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__dataframe_api_version__
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is_null
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null
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DType
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Int64
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Int32
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Int16
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Int8
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UInt64
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UInt32
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UInt16
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UInt8
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Float64
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Float32
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Bool
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The ``DataFrame``, ``Column`` and ``GroupBy`` objects have the following
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methods and attributes:

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