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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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DOC: add examples to BusinessHour and BusinessDay I
natmokval Dec 13, 2022
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DOC: add examples to BusinessHour and BusinessDay II
natmokval Dec 15, 2022
573eaec
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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19 changes: 17 additions & 2 deletions pandas/_libs/tslibs/offsets.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -1638,7 +1638,7 @@ cdef class BusinessHour(BusinessMixin):

Examples
--------
You can use the parameter ``n`` to represent the shift to n hours.
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)
Expand All @@ -1655,11 +1655,26 @@ cdef class BusinessHour(BusinessMixin):
>>> ts + pd.offsets.BusinessHour(end=dt_time(19, 0))
Timestamp('2022-08-08 10:00:00')

The parameter ``normalize`` equal True forces shift to midnight.
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.CustomBusinessHour(start=["06:00", "10:00", "15:00"],
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@MarcoGorelli MarcoGorelli Dec 15, 2022 •
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I guess this should still be BusinessHour? Sorry, I meant "copy-and-paste but still change the offset", I should've clarified

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Oops! I corrected my mistake. The copy-and-paste method can be dangerous sometimes.

... 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='CBH')
"""

_prefix = "BH"
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