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DOC: updated documentation for BusinessHour and BusinessDay #50240

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Merged
merged 6 commits into from
Dec 17, 2022
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DOC: updated documentation for BusinessHour and BusinessDay #50240

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71cae1d
DOC: add examples to BusinessHour and BusinessDay I
natmokval Dec 13, 2022
7aa4747
DOC: add examples to BusinessHour and BusinessDay II
natmokval Dec 15, 2022
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DOC: add examples to BusinessHour and BusinessDay III
natmokval Dec 15, 2022
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DOC: add examples to BusinessHour and BusinessDay IV
natmokval Dec 15, 2022
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DOC: add examples to BusinessHour and BusinessDay V
natmokval Dec 16, 2022
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fixup! DOC: add examples to BusinessHour and BusinessDay V
natmokval Dec 16, 2022
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77 changes: 60 additions & 17 deletions pandas/_libs/tslibs/offsets.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -1494,11 +1494,29 @@ cdef class BusinessDay(BusinessMixin):
"""
DateOffset subclass representing possibly n business days.

Parameters
----------
n : int, default 1
The number of days represented.
normalize : bool, default False
Normalize start/end dates to midnight

Examples
--------
>>> ts = pd.Timestamp(2022, 8, 5)
>>> ts + pd.offsets.BusinessDay()
Timestamp('2022-08-08 00:00:00')
You can use the parameter ``n`` to represent a shift of n business days.

>>> ts = pd.Timestamp(2022, 12, 9, 15)
>>> ts.strftime('%a %d %b %Y %H:%M')
'Fri 09 Dec 2022 15:00'
>>> (ts + pd.offsets.BusinessDay(n=5)).strftime('%a %d %b %Y %H:%M')
'Fri 16 Dec 2022 15:00'

Passing the parameter ``normalize`` equal True, you shift the start
of next business day to midnight.
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equal True -> equal to True

of next business day -> of the next business day

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I corrected my grammar mistakes and updated my PR.


>>> ts = pd.Timestamp(2022, 12, 9, 15)
>>> ts + pd.offsets.BusinessDay(normalize=True)
Timestamp('2022-12-12 00:00:00')
"""
_period_dtype_code = PeriodDtypeCode.B
_prefix = "B"
Expand Down Expand Up @@ -1610,29 +1628,53 @@ cdef class BusinessHour(BusinessMixin):
Parameters
----------
n : int, default 1
The number of months represented.
The number of hours represented.
normalize : bool, default False
Normalize start/end dates to midnight before generating date range.
weekmask : str, Default 'Mon Tue Wed Thu Fri'
Weekmask of valid business days, passed to ``numpy.busdaycalendar``.
start : str, time, or list of str/time, default "09:00"
Start time of your custom business hour in 24h format.
end : str, time, or list of str/time, default: "17:00"
End time of your custom business hour in 24h format.

Examples
--------
>>> from datetime import time
You can use the parameter ``n`` to represent a shift of n hours.

>>> ts = pd.Timestamp(2022, 12, 9, 8)
>>> ts + pd.offsets.BusinessHour(n=5)
Timestamp('2022-12-09 14:00:00')

You can also change the start and the end of business hours.

>>> ts = pd.Timestamp(2022, 8, 5, 16)
>>> ts + pd.offsets.BusinessHour()
Timestamp('2022-08-08 09:00:00')
>>> ts + pd.offsets.BusinessHour(start="11:00")
Timestamp('2022-08-08 11:00:00')
>>> ts + pd.offsets.BusinessHour(end=time(19, 0))
Timestamp('2022-08-05 17:00:00')
>>> ts + pd.offsets.BusinessHour(start=[time(9, 0), "20:00"],
... end=["17:00", time(22, 0)])
Timestamp('2022-08-05 20:00:00')

>>> from datetime import time as dt_time
>>> ts = pd.Timestamp(2022, 8, 5, 22)
>>> ts + pd.offsets.BusinessHour(end=dt_time(19, 0))
Timestamp('2022-08-08 10:00:00')

Passing the parameter ``normalize`` equal True, you shift the start
of next business hour to midnight.
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same comment as above


>>> ts = pd.Timestamp(2022, 12, 9, 8)
>>> ts + pd.offsets.BusinessHour(normalize=True)
Timestamp('2022-12-09 00:00:00')

You can divide your business day hours into several parts.

>>> import datetime as dt
>>> freq = pd.offsets.BusinessHour(start=["06:00", "10:00", "15:00"],
... end=["08:00", "12:00", "17:00"])
>>> pd.date_range(dt.datetime(2022, 12, 9), dt.datetime(2022, 12, 13), freq=freq)
DatetimeIndex(['2022-12-09 06:00:00', '2022-12-09 07:00:00',
'2022-12-09 10:00:00', '2022-12-09 11:00:00',
'2022-12-09 15:00:00', '2022-12-09 16:00:00',
'2022-12-12 06:00:00', '2022-12-12 07:00:00',
'2022-12-12 10:00:00', '2022-12-12 11:00:00',
'2022-12-12 15:00:00', '2022-12-12 16:00:00'],
dtype='datetime64[ns]', freq='BH')
"""

_prefix = "BH"
Expand Down Expand Up @@ -3536,6 +3578,7 @@ cdef class CustomBusinessDay(BusinessDay):
Parameters
----------
n : int, default 1
The number of days represented.
normalize : bool, default False
Normalize start/end dates to midnight before generating date range.
weekmask : str, Default 'Mon Tue Wed Thu Fri'
Expand Down Expand Up @@ -3624,7 +3667,7 @@ cdef class CustomBusinessHour(BusinessHour):
Parameters
----------
n : int, default 1
The number of months represented.
The number of hours represented.
normalize : bool, default False
Normalize start/end dates to midnight before generating date range.
weekmask : str, Default 'Mon Tue Wed Thu Fri'
Expand Down Expand Up @@ -3662,7 +3705,7 @@ cdef class CustomBusinessHour(BusinessHour):
>>> ts + pd.offsets.CustomBusinessHour(end=dt_time(19, 0))
Timestamp('2022-08-08 10:00:00')

In the example below we divide our business day hours into several parts.
You can divide your business day hours into several parts.

>>> import datetime as dt
>>> freq = pd.offsets.CustomBusinessHour(start=["06:00", "10:00", "15:00"],
Expand Down Expand Up @@ -3692,7 +3735,7 @@ cdef class CustomBusinessHour(BusinessHour):
'Fri 16 Dec 2022 12:00'],
dtype='object')

In the example below we define custom holidays by using NumPy business day calendar.
Using NumPy business day calendar you can define custom holidays.

>>> import datetime as dt
>>> bdc = np.busdaycalendar(holidays=['2022-12-12', '2022-12-14'])
Expand Down