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There are a bunch of GH issues relating to arithmetic ops with datetimelike objects behaving unexpectedly when boxed in a (Series|DataFrame). For the issues I have in mind (I'll collect a list later), the operation works as expected when done on a (DatetimeIndex|TimedeltaIndex|PeriodIndex).
The relevant (Series|DataFrame) operations are defined in core.ops. The DatetimeIndex (et al) ops are defined in core.indexes.datetimelike.DatetimeIndexOpsMixin. With a little gumption the relevant ops can be taken out of DatetimeIndexOpsMixin and made into (Index|Series|DataFrame)-agnostic classes TimestampVector, TimedeltaVector, PeriodVector.
Would it be feasible/desirable to have appropriately-dtyped (Series|DataFrame) methods dispatch to these implementations?
The text was updated successfully, but these errors were encountered:
There are a bunch of GH issues relating to arithmetic ops with datetimelike objects behaving unexpectedly when boxed in a
(Series|DataFrame)
. For the issues I have in mind (I'll collect a list later), the operation works as expected when done on a(DatetimeIndex|TimedeltaIndex|PeriodIndex)
.The relevant
(Series|DataFrame)
operations are defined in core.ops. TheDatetimeIndex
(et al) ops are defined incore.indexes.datetimelike.DatetimeIndexOpsMixin
. With a little gumption the relevant ops can be taken out ofDatetimeIndexOpsMixin
and made into(Index|Series|DataFrame)
-agnostic classesTimestampVector
,TimedeltaVector
,PeriodVector
.Would it be feasible/desirable to have appropriately-dtyped
(Series|DataFrame)
methods dispatch to these implementations?The text was updated successfully, but these errors were encountered: