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