bumping to good business day when generating range - python-3.x

Question

I am using pandas pd.bdate_range() to generate a range of dates given a start and end, but it seems to not work as expected.
What I am ultimately after is quarterly dates over a start and end date, but I want the dates to be valid business days.
start = '2015-06-01'
end = '2019-06-01'
dates = pd.bdate_range(start,end,freq='MS')[::3]
unfortunately this includes 2018-09-01 which is a Saturday
is there a more foolproof way to get an index of only business days, also taking account USFederalHolidayCalendar()?

Answer 1

You can take your existing Series and increment to the next business day like so
from pandas.tseries.offsets import BDay
start = '2015-06-01'
end = '2019-06-01'
dates = pd.bdate_range(start,end,freq='MS')[::3]
new_dates = dates.map(lambda x : x + 0*BDay())
Or you can pass BMS to the freq keyword attribute like so
start = '2015-06-01'
end = '2019-06-01'
dates = pd.bdate_range(start,end, freq='BMS')[::3]
Both give this output
DatetimeIndex(['2015-06-01', '2015-09-01', '2015-12-01', '2016-03-01',
'2016-06-01', '2016-09-01', '2016-12-01', '2017-03-01',
'2017-06-01', '2017-09-01', '2017-12-01', '2018-03-01',
'2018-06-01', '2018-09-03', '2018-12-03', '2019-03-01',
'2019-06-03'],
dtype='datetime64[ns]', freq=None)

Answer 2

I think you can pass the following to get what you desire.
freq='BMS' # Business month start
or
freq='BQS' # Business quarter start
Update:
You could do something like this take care of holidays that fall on month/quarter start.
from pandas import DatetimeIndex
from pandas.tseries.holiday import USFederalHolidayCalendar
holidays = USFederalHolidayCalendar().holidays(start, end, return_name=False)
month_dates = pandas.bdate_range(start, end, freq='CBMS', holidays=[holiday for holiday in holidays])
print(month_dates)
print(DatetimeIndex([e[1] for e in zip(month_dates.month, month_dates) if e[0] in {1, 4, 7, 10}]))
DatetimeIndex(['2015-01-02', '2015-02-02', '2015-03-02', '2015-04-01',
'2015-05-01', '2015-06-01', '2015-07-01', '2015-08-03',
'2015-09-01', '2015-10-01', '2015-11-02', '2015-12-01',
'2016-01-04', '2016-02-01', '2016-03-01', '2016-04-01',
'2016-05-02', '2016-06-01', '2016-07-01', '2016-08-01',
'2016-09-01', '2016-10-03', '2016-11-01', '2016-12-01',
'2017-01-03', '2017-02-01', '2017-03-01', '2017-04-03',
'2017-05-01', '2017-06-01', '2017-07-03', '2017-08-01',
'2017-09-01', '2017-10-02', '2017-11-01', '2017-12-01',
'2018-01-02', '2018-02-01', '2018-03-01', '2018-04-02',
'2018-05-01', '2018-06-01', '2018-07-02', '2018-08-01',
'2018-09-04', '2018-10-01', '2018-11-01', '2018-12-03',
'2019-01-02', '2019-02-01', '2019-03-01', '2019-04-01',
'2019-05-01'],
dtype='datetime64[ns]', freq='CBMS')
DatetimeIndex(['2015-01-02', '2015-04-01', '2015-07-01', '2015-10-01',
'2016-01-04', '2016-04-01', '2016-07-01', '2016-10-03',
'2017-01-03', '2017-04-03', '2017-07-03', '2017-10-02',
'2018-01-02', '2018-04-02', '2018-07-02', '2018-10-01',
'2019-01-02', '2019-04-01'],
dtype='datetime64[ns]', freq=None)