[
https://issues.apache.org/jira/browse/ARROW-13806?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17407179#comment-17407179
]
Joris Van den Bossche commented on ARROW-13806:
-----------------------------------------------
Note that the existing interval types (Month, and DayTime) are also not yet
supported, not even basic bindings of the types / arrays. So I think a first
step would be to add that (with simple conversion based on the raw values?).
For proper conversion to/from Python, the question is also what kind of value
to use on the Python side. AFAIK there is not really a Python scalar from the
standard library that represents such interval values ({{datetime.timedelta}}
maps to our Duration type, I think). The dateutil package has a
{{relativedelta}} object that can be used for this (but it's an external
package, and not sure how widely used it is).
For numpy-based conversion, the Months unit could be represented by
"timedelta64[M]", as both are a count of number of months (although not a
zero-copy conversion, since in numpy it's always 64bit). But for DayTime and
MonthDayNano, there is no equivalent (or maybe as a numpy record/struct array?).
> [Python] Add conversion to/from Pandas/Python for Month, Day Nano Interval
> Type
> -------------------------------------------------------------------------------
>
> Key: ARROW-13806
> URL: https://issues.apache.org/jira/browse/ARROW-13806
> Project: Apache Arrow
> Issue Type: New Feature
> Components: Python
> Reporter: Micah Kornfield
> Assignee: Micah Kornfield
> Priority: Major
>
> [https://github.com/apache/arrow/pull/10177] has been merged we should
> support conversion to and from this type for standard python surface areas.
--
This message was sent by Atlassian Jira
(v8.3.4#803005)
