Pandas: Data Manipulation - bdate_range() function

Last update on August 19 2022 21:50:52 (UTC/GMT +8 hours)

bdate_range() function

The bdate_range() function is used to concatenate pandas objects along a particular axis with optional set logic along the other axes.

Syntax:

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

Parameters:

Name	Description	Type / Default Value	Required / Optional
start	Left bound for generating dates.	string or datetime-like Default Value: None	Required
end	Right bound for generating dates.	string or datetime-like Default Value: None	Required
periods	Number of periods to generate.	integer Default Value: None	Required
freq	Frequency strings can have multiples, e.g. ‘5H’.	string or DateOffset Default Value: ‘B’ (business daily)	Required
tz	Time zone name for returning localized DatetimeIndex, for example Asia/Beijing.	string or None	Required
normalize	Normalize start/end dates to midnight before generating date range.	bool Default Value: False	Required
name	Name of the resulting DatetimeIndex.	string Default Value: None	Required
weekmask	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’.	string or None Default Value: None	Required
holidays	Dates to exclude from the set of valid business days, passed to numpy.busdaycalendar, only used when custom frequency strings are passed.	list-like or None Default Value: None	Required
closed	Make the interval closed with respect to the given frequency to the 'left', 'right’, or both sides (None).	string Default Value: None	Required
**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.

Example:

Download the Pandas DataFrame Notebooks from here.

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