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/tslibs/vectorized.pyi

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"""
For cython types that cannot be represented precisely, closest-available
python equivalents are used, and the precise types kept as adjacent comments.
"""
from datetime import tzinfo
import numpy as np
from pandas._libs.tslibs.dtypes import Resolution
from pandas._libs.tslibs.offsets import BaseOffset
from pandas._typing import npt
def dt64arr_to_periodarr(
stamps: npt.NDArray[np.int64], # const int64_t[:]
freq: int,
tz: tzinfo | None,
) -> npt.NDArray[np.int64]: ... # np.ndarray[np.int64, ndim=1]
def is_date_array_normalized(
stamps: npt.NDArray[np.int64], # const int64_t[:]
tz: tzinfo | None = ...,
) -> bool: ...
def normalize_i8_timestamps(
stamps: npt.NDArray[np.int64], # const int64_t[:]
tz: tzinfo | None,
) -> npt.NDArray[np.int64]: ...
def get_resolution(
stamps: npt.NDArray[np.int64], # const int64_t[:]
tz: tzinfo | None = ...,
) -> Resolution: ...
def ints_to_pydatetime(
arr: npt.NDArray[np.int64], # const int64_t[:}]
tz: tzinfo | None = ...,
freq: str | BaseOffset | None = ...,
fold: bool = ...,
box: str = ...,
) -> npt.NDArray[np.object_]: ...