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https://issues.apache.org/jira/browse/ARROW-12970?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=17375732#comment-17375732
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Joris Van den Bossche commented on ARROW-12970:
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I think we can discuss those performance related aspects of the arrow->python
object conversion (implementing it in C++ vs in python/cython) in ARROW-12976.
For bulk conversion like here, we probably want to avoid creating a Scalar at
all (so also not going through Scalar.as_py, even when impemented in C++, based
on the experiments described in ARROW-12976).
(of course, ARROW-12976 uses "to_pylist" as example, and for this issue it
might also be interesting to look into directly creating tuples instead of
lists for each columns which gets zipped)
> [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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