Quickstart

This page shows bizdays 2.0. A single bizdays.Calendar instance works with scalar inputs, vectorized inputs, NumPy arrays, and pandas date-like values.

from bizdays import Calendar

cal = Calendar.load(name="ANBIMA")
cal

Calendar: ANBIMA
Start: 2000-01-01
End: 2099-12-25
Weekdays: Saturday, Sunday
Holidays: 1275
Financial: True

Business-day counts are available through bizdays.

cal.bizdays("2014-01-13", "2015-01-13")

np.float64(253.0)

Date adjustment methods return NumPy dates.

cal.following("2015-12-25")

np.datetime64('2015-12-28')

Vectorized methods return NumPy arrays.

cal.offset(["2014-01-02", "2014-01-03"], [1, 5])

array(['2014-01-03', '2014-01-10'], dtype='datetime64[D]')

cal.seq("2014-01-02", "2014-01-07")

array(['2014-01-02', '2014-01-03', '2014-01-06', '2014-01-07'],
      dtype='datetime64[D]')

getdate resolves date expressions against month, year, or date references.

cal.getdate("15th day", "2002-05")

np.datetime64('2002-05-15')

getbizdays counts the business days in a whole year, or in one month.

cal.getbizdays(2024)

np.float64(253.0)

Missing values give missing results. A vector of flags is a masked array: a NumPy array with a mask that marks the missing elements (shown as --).

cal.isbizday(["2024-01-01", None, "2024-01-02"])

masked_array(data=[False, --, True],
             mask=[False,  True, False],
       fill_value=False)

Next: the API reference lists every method with examples, and the migration guide covers code written for bizdays 1.x.