Adding upstream version 0.12.0.

Signed-off-by: Daniel Baumann <daniel@debian.org>

stubs/pyarrow/__init__.pyi

@@ -0,0 +1,166 @@
+from __future__ import annotations
+
+from typing import Any, Iterable, Iterator, Literal, Mapping, Sequence, Type, TypeVar
+
+import pandas as pd
+
+from .compute import CastOptions
+
+class DataType: ...
+class Date32Type(DataType): ...
+class Date64Type(DataType): ...
+class TimestampType(DataType): ...
+
+def string() -> DataType: ...
+def null() -> DataType: ...
+def bool_() -> DataType: ...
+def int8() -> DataType: ...
+def int16() -> DataType: ...
+def int32() -> DataType: ...
+def int64() -> DataType: ...
+def uint8() -> DataType: ...
+def uint16() -> DataType: ...
+def uint32() -> DataType: ...
+def uint64() -> DataType: ...
+def float16() -> DataType: ...
+def float32() -> DataType: ...
+def float64() -> DataType: ...
+def date32() -> DataType: ...
+def date64() -> DataType: ...
+def binary(length: int = -1) -> DataType: ...
+def large_binary() -> DataType: ...
+def large_string() -> DataType: ...
+def month_day_nano_interval() -> DataType: ...
+def time32(unit: Literal["s", "ms", "us", "ns"]) -> DataType: ...
+def time64(unit: Literal["s", "ms", "us", "ns"]) -> DataType: ...
+def timestamp(
+    unit: Literal["s", "ms", "us", "ns"], tz: str | None = None
+) -> DataType: ...
+def duration(unit: Literal["s", "ms", "us", "ns"]) -> DataType: ...
+
+class MemoryPool: ...
+class Schema: ...
+class Field: ...
+class NativeFile: ...
+class MonthDayNano: ...
+
+class Scalar:
+    def as_py(self) -> Any: ...
+    @property
+    def type(self) -> DataType: ...
+
+A = TypeVar("A", bound="_PandasConvertible")
+
+class _PandasConvertible:
+    @property
+    def type(self) -> DataType: ...  # noqa: A003
+    def cast(
+        self: A,
+        target_type: DataType | None = None,
+        safe: bool = True,
+        options: CastOptions | None = None,
+    ) -> A: ...
+    def __getitem__(self, index: int) -> Scalar: ...
+    def __iter__(self) -> Any: ...
+    def to_pylist(self) -> list[Any]: ...
+    def fill_null(self: A, fill_value: Any) -> A: ...
+    def drop_null(self: A) -> A: ...
+
+class Array(_PandasConvertible): ...
+class ChunkedArray(_PandasConvertible): ...
+
+class StructArray(Array):
+    def flatten(self, memory_pool: MemoryPool | None = None) -> list[Array]: ...
+
+T = TypeVar("T", bound="_Tabular")
+
+class _Tabular:
+    @classmethod
+    def from_arrays(
+        cls: Type[T],
+        arrays: list[_PandasConvertible],
+        names: list[str] | None = None,
+        schema: Schema | None = None,
+        metadata: Mapping | None = None,
+    ) -> T: ...
+    @classmethod
+    def from_pydict(
+        cls: Type[T],
+        mapping: Mapping,
+        schema: Schema | None = None,
+        metadata: Mapping | None = None,
+    ) -> T: ...
+    def __getitem__(self, index: int) -> _PandasConvertible: ...
+    def __len__(self) -> int: ...
+    @property
+    def column_names(self) -> list[str]: ...
+    @property
+    def columns(self) -> list[_PandasConvertible]: ...
+    @property
+    def num_rows(self) -> int: ...
+    @property
+    def num_columns(self) -> int: ...
+    @property
+    def schema(self) -> Schema: ...
+    def append_column(
+        self: T, field_: str | Field, column: Array | ChunkedArray
+    ) -> T: ...
+    def column(self, i: int | str) -> _PandasConvertible: ...
+    def equals(self: T, other: T, check_metadata: bool = False) -> bool: ...
+    def itercolumns(self) -> Iterator[_PandasConvertible]: ...
+    def rename_columns(self: T, names: list[str]) -> T: ...
+    def select(self: T, columns: Sequence[str | int]) -> T: ...
+    def set_column(
+        self: T, i: int, field_: str | Field, column: Array | ChunkedArray
+    ) -> T: ...
+    def slice(  # noqa: A003
+        self: T,
+        offset: int = 0,
+        length: int | None = None,
+    ) -> T: ...
+    def sort_by(
+        self: T,
+        sorting: str | list[tuple[str, Literal["ascending", "descending"]]],
+        **kwargs: Any,
+    ) -> T: ...
+    def to_pylist(self) -> list[dict[str, Any]]: ...
+
+class RecordBatch(_Tabular): ...
+
+class Table(_Tabular):
+    @classmethod
+    def from_batches(
+        cls,
+        batches: Iterable[RecordBatch],
+        schema: Schema | None = None,
+    ) -> "Table": ...
+    def to_batches(self) -> list[RecordBatch]: ...
+
+def scalar(value: Any, type: DataType) -> Scalar: ...  # noqa: A002
+def array(
+    obj: Iterable,
+    type: DataType | None = None,  # noqa: A002
+    mask: Array | None = None,
+    size: int | None = None,
+    from_pandas: bool | None = None,
+    safe: bool = True,
+    memory_pool: MemoryPool | None = None,
+) -> Array | ChunkedArray: ...
+def concat_arrays(
+    arrays: Iterable[Array], memory_pool: MemoryPool | None = None
+) -> Array: ...
+def nulls(
+    size: int,
+    type: DataType | None = None,  # noqa: A002
+    memory_pool: MemoryPool | None = None,
+) -> Array: ...
+def table(
+    data: pd.DataFrame
+    | Mapping[str, _PandasConvertible | list]
+    | list[_PandasConvertible],
+    names: list[str] | None = None,
+    schema: Schema | None = None,
+    metadata: Mapping | None = None,
+    nthreads: int | None = None,
+) -> Table: ...
+def set_timezone_db_path(path: str) -> None: ...