A PyQT GUI application for converting InfoLease report outputs into Excel files. Handles parsing and summarizing. Learns where files are meant to be store and compiles monthly and yearly summaries.
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InfoLeaseExtract/venv/Lib/site-packages/pandas/_libs/ops.pyi

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1.2 KiB

from typing import (
Any,
Callable,
Iterable,
Literal,
overload,
)
import numpy as np
from pandas._typing import npt
_BinOp = Callable[[Any, Any], Any]
_BoolOp = 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: ...
@overload
def maybe_convert_bool(
arr: npt.NDArray[np.object_],
true_values: Iterable = ...,
false_values: Iterable = ...,
convert_to_masked_nullable: Literal[False] = ...,
) -> tuple[np.ndarray, None]: ...
@overload
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]: ...