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modin.pandas.bdate_range

snowflake.snowpark.modin.pandas.general.bdate_range(start: VALID_DATE_TYPE | None = None, end: VALID_DATE_TYPE | None = None, periods: int | None = None, freq: Frequency | str | pd.DateOffset | dt.timedelta | None = 'B', tz: str | tzinfo | None = None, normalize: bool = True, name: Hashable | None = None, weekmask: str | None = None, holidays: ListLike | None = None, inclusive: IntervalClosedType = 'both', **kwargs) → pd.DatetimeIndex[source]

Return a fixed frequency DatetimeIndex with business day as the default.

Parameters:

  • start (str or datetime-like, default None) – Left bound for generating dates.

  • end (str or datetime-like, default None) – Right bound for generating dates.

  • periods (int, default None) – Number of periods to generate.

  • freq (str, Timedelta, datetime.timedelta, or DateOffset, default 'B') – Frequency strings can have multiples, e.g. ‘5h’. The default is business daily (‘B’).

  • tz (str or None) – Time zone name for returning localized DatetimeIndex, for example Asia/Beijing.

  • normalize (bool, default False) – Normalize start/end dates to midnight before generating date range.

  • name (str, default None) – Name of the resulting DatetimeIndex.

  • weekmask (str or None, default None) – Weekmask of valid business days, passed to numpy.busdaycalendar, only used when custom frequency strings are passed. The default value None is equivalent to ‘Mon Tue Wed Thu Fri’.

  • holidays (list-like or None, default None) – Dates to exclude from the set of valid business days, passed to numpy.busdaycalendar, only used when custom frequency strings are passed.

  • inclusive ({"both", "neither", "left", "right"}, default "both") –

    Include boundaries; Whether to set each bound as closed or open.

    New in version 1.4.0.

  • **kwargs – For compatibility. Has no effect on the result.

Return type:

DatetimeIndex

Notes

Of the four parameters: start, end, periods, and freq, exactly three must be specified. Specifying freq is a requirement for bdate_range. Use date_range if specifying freq is not desired.

To learn more about the frequency strings, please see this link.

Examples

Note how the two weekend days are skipped in the result.

>>> pd.bdate_range(start='1/1/2018', end='1/08/2018') 
DatetimeIndex(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-04',
        '2018-01-05', '2018-01-08'],
        dtype='datetime64[ns]', freq=None)
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