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https://issues.apache.org/jira/browse/ARROW-12787?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17653446#comment-17653446
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Apache Arrow JIRA Bot commented on ARROW-12787:
-----------------------------------------------
This issue was last updated over 90 days ago, which may be an indication it is
no longer being actively worked. To better reflect the current state, the issue
is being unassigned per [project
policy|https://arrow.apache.org/docs/dev/developers/bug_reports.html#issue-assignment].
Please feel free to re-take assignment of the issue if it is being actively
worked, or if you plan to start that work soon.
> [Python] pyarrow.compute not consistent on memory_pool usage
> ------------------------------------------------------------
>
> Key: ARROW-12787
> URL: https://issues.apache.org/jira/browse/ARROW-12787
> Project: Apache Arrow
> Issue Type: Improvement
> Components: Python
> Reporter: Weston Pace
> Assignee: Miles Granger
> Priority: Major
>
> Generally it seems that pyarrow is pretty consistent about offering an
> optional memory_pool parameter if a function might allocate. However, some
> of the compute work is a little inconsistent...
> pa.Array.unique does not accept a memory_pool
> pa.Array.cast does not accept a memory_pool
> pc.cast does not accept a memory_pool
> pc.count does accept a memory_pool but should it?
> pc.fill_null does not accept a memory_pool
> pc.filter does not accept a memory_pool
> pc.match_substring* does not accept a memory_pool
> pc.mean does accept a memory_pool while pc.mode does not
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