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GH-49103: [Python][Annotations] Add internal type system stubs (_types, error, _stubs_typing) #48622
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GH-49103: [Python][Annotations] Add internal type system stubs (_types, error, _stubs_typing) #48622
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0bd8440
check stubs are included at wheel build time
rok b865f34
Add internal types and helpers
rok 37d87fb
Update python/pyarrow-stubs/pyarrow/_stubs_typing.pyi
rok 43ff40f
post rebase changes
rok 07c1398
linting and formatting
rok 0acbc9d
add pyi check to pre-commit
rok 83cab21
ruff format
rok 8e55077
Apply suggestions from code review
rok eae20d1
review feedback
rok 789934a
Decimals should be covariant
rok 7729137
fix editable install collisions
rok ddd48c0
review feedback
rok 6f2a03a
move placeholder
rok ed2c72b
post rebase
rok f0272fe
Apply suggestions from code review
rok 50077b6
Update python/pyarrow-stubs/pyarrow/_types.pyi
rok 94fcdf4
update
rok 9701c2f
lint
rok 8982d1e
more covariant types
rok 604b09b
Apply suggestions from code review
rok 3e9e864
lint
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,138 @@ | ||
| # Licensed to the Apache Software Foundation (ASF) under one | ||
| # or more contributor license agreements. See the NOTICE file | ||
| # distributed with this work for additional information | ||
| # regarding copyright ownership. The ASF licenses this file | ||
| # to you under the Apache License, Version 2.0 (the | ||
| # "License"); you may not use this file except in compliance | ||
| # with the License. You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, | ||
| # software distributed under the License is distributed on an | ||
| # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY | ||
| # KIND, either express or implied. See the License for the | ||
| # specific language governing permissions and limitations | ||
| # under the License. | ||
|
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| import datetime as dt | ||
|
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||
| from _typeshed import ( | ||
| SupportsDunderGE, | ||
| SupportsDunderGT, | ||
| SupportsDunderLE, | ||
| SupportsDunderLT, | ||
| ) | ||
| from collections.abc import Collection, Container, Iterator, Sequence, Sized | ||
| from decimal import Decimal | ||
| from typing import Any, Literal, Protocol, TypeAlias, TypeVar | ||
| from typing_extensions import TypeAliasType | ||
|
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| import numpy as np | ||
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| from numpy.typing import NDArray | ||
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| from pyarrow import lib | ||
| from pyarrow.lib import ChunkedArray | ||
|
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| ArrayLike: TypeAlias = Any | ||
| ScalarLike: TypeAlias = Any | ||
| Order: TypeAlias = Literal["ascending", "descending"] | ||
| JoinType: TypeAlias = Literal[ | ||
| "left semi", | ||
| "right semi", | ||
| "left anti", | ||
| "right anti", | ||
| "inner", | ||
| "left outer", | ||
| "right outer", | ||
| "full outer", | ||
| ] | ||
| Compression: TypeAlias = Literal[ | ||
| "gzip", "bz2", "brotli", "lz4", "lz4_frame", "lz4_raw", "zstd", "snappy" | ||
| ] | ||
| NullEncoding: TypeAlias = Literal["mask", "encode"] | ||
| NullSelectionBehavior: TypeAlias = Literal["drop", "emit_null"] | ||
| TimeUnit: TypeAlias = Literal["s", "ms", "us", "ns"] | ||
|
|
||
| IntegerType: TypeAlias = ( | ||
| lib.Int8Type | ||
| | lib.Int16Type | ||
| | lib.Int32Type | ||
| | lib.Int64Type | ||
| | lib.UInt8Type | ||
| | lib.UInt16Type | ||
| | lib.UInt32Type | ||
| | lib.UInt64Type | ||
| ) | ||
| PyScalar: TypeAlias = ( | ||
| bool | ||
| | int | ||
| | float | ||
| | Decimal | ||
| | str | ||
| | bytes | ||
| | dt.date | ||
| | dt.datetime | ||
| | dt.time | ||
| | dt.timedelta | ||
| ) | ||
| NumpyScalar: TypeAlias = np.generic[Any] | ||
|
|
||
| _PyScalarT_co = TypeVar("_PyScalarT_co", bound=PyScalar, covariant=True) | ||
| _NumpyScalarT_co = TypeVar("_NumpyScalarT_co", bound=NumpyScalar, covariant=True) | ||
| _DataTypeT_co = TypeVar("_DataTypeT_co", bound=lib.DataType, covariant=True) | ||
| IntoArray = TypeAliasType( | ||
| "IntoArray", | ||
| Sequence[_PyScalarT_co | None] | ||
| | NDArray[_NumpyScalarT_co] | ||
| | lib.Array[lib.Scalar[_DataTypeT_co]] | ||
| | ChunkedArray[Any], | ||
| type_params=(_PyScalarT_co, _NumpyScalarT_co, _DataTypeT_co), | ||
| ) | ||
|
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| Mask: TypeAlias = IntoArray[bool, np.bool_, lib.BoolType] | ||
| Indices: TypeAlias = IntoArray[int, np.integer[Any], IntegerType] | ||
|
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| _T = TypeVar("_T") | ||
| _V = TypeVar("_V", covariant=True) | ||
|
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| SingleOrList: TypeAlias = list[_T] | _T | ||
|
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| class SupportsDunderEQ(Protocol): | ||
| def __eq__(self, other: object, /) -> bool: ... | ||
|
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| FilterTuple: TypeAlias = ( | ||
| tuple[str, Literal["=", "==", "!="], SupportsDunderEQ] | ||
| | tuple[str, Literal["<"], SupportsDunderLT[Any]] | ||
| | tuple[str, Literal[">"], SupportsDunderGT[Any]] | ||
| | tuple[str, Literal["<="], SupportsDunderLE[Any]] | ||
| | tuple[str, Literal[">="], SupportsDunderGE[Any]] | ||
| | tuple[str, Literal["in", "not in"], Collection[Any]] | ||
| | tuple[str, str, Any] # Allow general str for operator to avoid type errors | ||
| ) | ||
|
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||
| class Buffer(Protocol): ... | ||
| class SupportsPyBuffer(Protocol): ... | ||
|
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| class SupportsArrowStream(Protocol): | ||
| def __arrow_c_stream__(self, requested_schema: Any = None, /) -> Any: ... | ||
|
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| class SupportsPyArrowArray(Protocol): | ||
| def __arrow_array__(self, type: Any = None, /) -> Any: ... | ||
|
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| class SupportsArrowArray(Protocol): | ||
| def __arrow_c_array__(self, requested_schema: Any = None, /) -> Any: ... | ||
|
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| class SupportsArrowDeviceArray(Protocol): | ||
| def __arrow_c_device_array__( | ||
| self, requested_schema: Any = None, /, **kwargs: Any | ||
| ) -> Any: ... | ||
|
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| class SupportsArrowSchema(Protocol): | ||
| def __arrow_c_schema__(self) -> Any: ... | ||
|
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| class NullableCollection(Sized, Container[Any], Protocol[_V]): | ||
| def __iter__(self) -> Iterator[_V] | Iterator[_V | None]: ... | ||
| def __len__(self) -> int: ... | ||
| def __contains__(self, item: Any, /) -> bool: ... |
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(Just for reference)
This is what our
pre-commitlooks like, since we haveruffandflake8in there - the answer to (#48622 (comment)) is probably something similar 🙂There was a problem hiding this comment.
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Will revisit seperately!