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https://issues.apache.org/jira/browse/ARROW-15474?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17632256#comment-17632256
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Jacek Pliszka edited comment on ARROW-15474 at 11/11/22 11:04 AM:
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[~westonpace] maybe approach similar to what I proposed, but in better version
whould work?
We need compute function that for given array of values returns the index of
the first/last appearance.
Then all batches can be processed in parallel and at the end merged exactly as
you described.
Once we have index of the first/last appearance - we can use compute.take to
have the output table.
Maybe even ordering function can be specified so there would be no need to sort
the array a priori.
was (Author: jacek.pliszka):
[~westonpace] maybe approach similar to what I proposed, but in better version
whould work?
We need compute function that for given array of values returns the index of
the first/last appearance.
Then all batches can be processed in parallel and at the end merged exactly as
you described.
Once we have index of the first/last appearance - we can use compute.take to
have the output table.
> [Python] Possibility of a table.drop_duplicates() function?
> -----------------------------------------------------------
>
> Key: ARROW-15474
> URL: https://issues.apache.org/jira/browse/ARROW-15474
> Project: Apache Arrow
> Issue Type: Wish
> Components: Python
> Affects Versions: 6.0.1
> Reporter: Lance Dacey
> Priority: Major
>
> I noticed that there is a group_by() and sort_by() function in the 7.0.0
> branch. Is it possible to include a drop_duplicates() function as well?
> ||id||updated_at||
> |1|2022-01-01 04:23:57|
> |2|2022-01-01 07:19:21|
> |2|2022-01-10 22:14:01|
> Something like this which would return a table without the second row in the
> example above would be great.
> I usually am reading an append-only dataset and then I need to report on
> latest version of each row. To drop duplicates, I am temporarily converting
> the append-only table to a pandas DataFrame, and then I convert it back to a
> table and save a separate "latest-version" dataset.
> {code:python}
> table.sort_by(sorting=[("id", "ascending"), ("updated_at",
> "ascending")]).drop_duplicates(subset=["id"] keep="last")
> {code}
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