Buckets:
| from collections.abc import ( | |
| Callable, | |
| Iterable, | |
| ) | |
| from typing import ( | |
| Any, | |
| Literal, | |
| TypeAlias, | |
| overload, | |
| ) | |
| import numpy as np | |
| from pandas._typing import npt | |
| _BinOp: TypeAlias = Callable[[Any, Any], Any] | |
| _BoolOp: TypeAlias = Callable[[Any, Any], bool] | |
| def scalar_compare( | |
| values: np.ndarray, # object[:] | |
| val: object, | |
| op: _BoolOp, # {operator.eq, operator.ne, ...} | |
| ) -> npt.NDArray[np.bool_]: ... | |
| def vec_compare( | |
| left: npt.NDArray[np.object_], | |
| right: npt.NDArray[np.object_], | |
| op: _BoolOp, # {operator.eq, operator.ne, ...} | |
| ) -> npt.NDArray[np.bool_]: ... | |
| def scalar_binop( | |
| values: np.ndarray, # object[:] | |
| val: object, | |
| op: _BinOp, # binary operator | |
| ) -> np.ndarray: ... | |
| def vec_binop( | |
| left: np.ndarray, # object[:] | |
| right: np.ndarray, # object[:] | |
| op: _BinOp, # binary operator | |
| ) -> np.ndarray: ... | |
| def maybe_convert_bool( | |
| arr: npt.NDArray[np.object_], | |
| true_values: Iterable | None = None, | |
| false_values: Iterable | None = None, | |
| convert_to_masked_nullable: Literal[False] = ..., | |
| ) -> tuple[np.ndarray, None]: ... | |
| def maybe_convert_bool( | |
| arr: npt.NDArray[np.object_], | |
| true_values: Iterable = ..., | |
| false_values: Iterable = ..., | |
| *, | |
| convert_to_masked_nullable: Literal[True], | |
| ) -> tuple[np.ndarray, np.ndarray]: ... | |
Xet Storage Details
- Size:
- 1.35 kB
- Xet hash:
- 5032eab98224fbee87c9da67a3f94010639f100dbba8d945020b49413137e658
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.