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https://issues.apache.org/jira/browse/ARROW-12970?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17375719#comment-17375719
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Wes McKinney commented on ARROW-12970:
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It would probably be worth the effort to implement the "tuplization" of
RecordBatch in the libarrow_python C++ library to make it reasonably efficient.
This would also be a good opportunity to move the implementation of the
{{*Scalar.as_py}} methods into libarrow_python since you would only want to
have one canonical implementation of boxing Arrow array values as Python
objects. This relates to ARROW-12976 also. I can't find the Jira issue about
moving the as_py implementations into C++, but I recall there was one in the
past that [~kszucs] may have been working on at some point.
> [Python] Efficient "row accessor" for a pyarrow RecordBatch / Table
> -------------------------------------------------------------------
>
> Key: ARROW-12970
> URL: https://issues.apache.org/jira/browse/ARROW-12970
> Project: Apache Arrow
> Issue Type: New Feature
> Components: Python
> Reporter: Luke Higgins
> Priority: Minor
> Fix For: 6.0.0
>
>
> It would be nice to have a nice row accessor for a Table akin to
> pandas.DataFrame.itertuples.
> I have a lot of code where I am converting a parquet file to pandas just to
> have access to the rows through iterating with itertuples. Having this
> ability in pyarrow natively would be a nice feature and would avoid memory
> copy in the pandas conversion.
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