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https://issues.apache.org/jira/browse/ARROW-428?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15761553#comment-15761553
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Uwe L. Korn commented on ARROW-428:
-----------------------------------
In my local performance benchmarks, the most expensive thing for a single Table
-> DataFrame conversion was the construction of the DataFrame from NumPy arrays
where the BlockManager filled the underlying blocks using {{memmove}} from the
NumPy arrays. There it would be helpful to pre-allocate an empty DataFrame. But
if/how this may work is outside of my knowledge of the Pandas' internals.
Multiple threads for the {{arrow::Column}} -> {{pandas.Series/numpy.ndarray}}
will still be of benefit.
Also, it is quite common to adhere to the environment variable
{{OMP_NUM_THREADS}} for the number of used CPUs. If not this variable
explicitly, we want to at least provide a way to limit the currency.
> [Python] Deserialize from Arrow record batches to pandas in parallel using a
> thread pool
> ----------------------------------------------------------------------------------------
>
> Key: ARROW-428
> URL: https://issues.apache.org/jira/browse/ARROW-428
> Project: Apache Arrow
> Issue Type: New Feature
> Components: C++
> Reporter: Wes McKinney
>
> By default {{to_pandas}} can use {{multiprocessing.cpu_count()}} to select a
> number of threads.
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