# pandas.bdate_range

`pandas.``bdate_range`(start=None, end=None, periods=None, freq='B', tz=None, normalize=True, name=None, weekmask=None, holidays=None, closed=None, **kwargs)

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

  Parameters:

  start : string or datetime-like, default None

  > Left bound for generating dates.

  end : string or datetime-like, default None

  > Right bound for generating dates.

  periods : integer, default None

  > Number of periods to generate.

  freq : string or DateOffset, default ‘B’ (business daily)

  > Frequency strings can have multiples, e.g. ‘5H’.

  tz : string 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 : string, default None

  > Name of the resulting DatetimeIndex.

  weekmask : string 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’.

  > New in version 0.21.0.

  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.

  > New in version 0.21.0.

  closed : string, default None

  > Make the interval closed with respect to the given frequency to the ‘left’, ‘right’, or both sides (None).

  **kwargs

  > For compatibility. Has no effect on the result.

  Returns: | 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='B')

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