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BUG: BDay() offsetting not behaving as expected #38324

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Merged
merged 1 commit into from
Dec 18, 2020
Merged

BUG: BDay() offsetting not behaving as expected #38324

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34 changes: 31 additions & 3 deletions pandas/tests/tseries/offsets/test_offsets.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,11 +19,9 @@
from pandas.compat.numpy import np_datetime64_compat
from pandas.errors import PerformanceWarning

from pandas import DatetimeIndex, Series, Timedelta, date_range, read_pickle
import pandas._testing as tm
from pandas.core.indexes.datetimes import DatetimeIndex, date_range
from pandas.core.series import Series

from pandas.io.pickle import read_pickle
from pandas.tseries.holiday import USFederalHolidayCalendar
import pandas.tseries.offsets as offsets
from pandas.tseries.offsets import (
Expand Down Expand Up @@ -2326,6 +2324,36 @@ def test_datetimeindex(self):
for idx in [idx1, idx2, idx3]:
tm.assert_index_equal(idx, expected)

def test_bday_ignores_timedeltas(self):
idx = date_range("2010/02/01", "2010/02/10", freq="12H")
t1 = idx + BDay(offset=Timedelta(3, unit="H"))

expected = DatetimeIndex(
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i don't feel strongly about this but is there not a more concise way to generate this

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Reducing the date range seems to be the way to go here. Maybe extend the freq too?

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Done

[
"2010-02-02 03:00:00",
"2010-02-02 15:00:00",
"2010-02-03 03:00:00",
"2010-02-03 15:00:00",
"2010-02-04 03:00:00",
"2010-02-04 15:00:00",
"2010-02-05 03:00:00",
"2010-02-05 15:00:00",
"2010-02-08 03:00:00",
"2010-02-08 15:00:00",
"2010-02-08 03:00:00",
"2010-02-08 15:00:00",
"2010-02-08 03:00:00",
"2010-02-08 15:00:00",
"2010-02-09 03:00:00",
"2010-02-09 15:00:00",
"2010-02-10 03:00:00",
"2010-02-10 15:00:00",
"2010-02-11 03:00:00",
],
freq=None,
)
tm.assert_index_equal(t1, expected)


class TestCustomBusinessHour(Base):
_offset = CustomBusinessHour
Expand Down