[jira] [Commented] (ARROW-12970) [Python] Efficient "row accessor" for a pyarrow RecordBatch / Table

["Wes McKinney (Jira)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/ARROW-12970?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17375719#comment-17375719 ] 

Wes McKinney commented on ARROW-12970:
--------------------------------------

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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