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62 lines
1.9 KiB
62 lines
1.9 KiB
from datetime import datetime
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import numpy as np
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from pandas._libs.tslibs.offsets import BaseOffset
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from pandas._typing import npt
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class DateParseError(ValueError): ...
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def parse_datetime_string(
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date_string: str,
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dayfirst: bool = ...,
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yearfirst: bool = ...,
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**kwargs,
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) -> datetime: ...
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def parse_time_string(
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arg: str,
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freq: BaseOffset | str | None = ...,
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dayfirst: bool | None = ...,
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yearfirst: bool | None = ...,
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) -> tuple[datetime, str]: ...
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def _does_string_look_like_datetime(py_string: str) -> bool: ...
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def quarter_to_myear(year: int, quarter: int, freq: str) -> tuple[int, int]: ...
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def try_parse_dates(
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values: npt.NDArray[np.object_], # object[:]
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parser=...,
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dayfirst: bool = ...,
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default: datetime | None = ...,
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) -> npt.NDArray[np.object_]: ...
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def try_parse_date_and_time(
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dates: npt.NDArray[np.object_], # object[:]
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times: npt.NDArray[np.object_], # object[:]
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date_parser=...,
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time_parser=...,
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dayfirst: bool = ...,
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default: datetime | None = ...,
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) -> npt.NDArray[np.object_]: ...
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def try_parse_year_month_day(
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years: npt.NDArray[np.object_], # object[:]
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months: npt.NDArray[np.object_], # object[:]
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days: npt.NDArray[np.object_], # object[:]
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) -> npt.NDArray[np.object_]: ...
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def try_parse_datetime_components(
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years: npt.NDArray[np.object_], # object[:]
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months: npt.NDArray[np.object_], # object[:]
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days: npt.NDArray[np.object_], # object[:]
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hours: npt.NDArray[np.object_], # object[:]
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minutes: npt.NDArray[np.object_], # object[:]
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seconds: npt.NDArray[np.object_], # object[:]
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) -> npt.NDArray[np.object_]: ...
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def format_is_iso(f: str) -> bool: ...
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def guess_datetime_format(
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dt_str,
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dayfirst: bool = ...,
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dt_str_parse=...,
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dt_str_split=...,
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) -> str | None: ...
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def concat_date_cols(
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date_cols: tuple,
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keep_trivial_numbers: bool = ...,
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) -> npt.NDArray[np.object_]: ...
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def get_rule_month(source: str) -> str: ...
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