[jira] [Resolved] (ARROW-8888) [Python] Heuristic in dataframe_to_arrays that decides to multithread convert cause slow conversions

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

     [ https://issues.apache.org/jira/browse/ARROW-8888?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Wes McKinney resolved ARROW-8888.
---------------------------------
    Resolution: Fixed

Issue resolved by pull request 7563
[https://github.com/apache/arrow/pull/7563]

> [Python] Heuristic in dataframe_to_arrays that decides to multithread convert cause slow conversions
> ----------------------------------------------------------------------------------------------------
>
>                 Key: ARROW-8888
>                 URL: https://issues.apache.org/jira/browse/ARROW-8888
>             Project: Apache Arrow
>          Issue Type: Bug
>          Components: Python
>    Affects Versions: 0.16.0
>         Environment: MacOS: 10.15.4 (Also happening on windows 10)
> Python: 3.7.3
> Pyarrow: 0.16.0
> Pandas: 0.25.3
>            Reporter: Kevin Glasson
>            Assignee: Wes McKinney
>            Priority: Minor
>              Labels: pull-request-available
>             Fix For: 1.0.0
>
>          Time Spent: 0.5h
>  Remaining Estimate: 0h
>
> When calling pa.Table.from_pandas() the code path that uses the ThreadPoolExecutor in dataframe_to_arrays (called by Table.from_pandas) the conversion is much much slower.
>  
>  I have a simple example - but the time difference is much worse with a real table.
>  
> {code:java}
> Python 3.7.3 | packaged by conda-forge | (default, Dec 6 2019, 08:54:18)
>  Type 'copyright', 'credits' or 'license' for more information
>  IPython 7.13.0 – An enhanced Interactive Python. Type '?' for help.
> In [1]: import pyarrow as pa
> In [2]: import pandas as pd
> In [3]: df = pd.DataFrame({"A": [0] * 10000000})
> In [4]: %timeit table = pa.Table.from_pandas(df)
>  577 µs ± 15.3 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
> In [5]: %timeit table = pa.Table.from_pandas(df, nthreads=1)
>  106 µs ± 1.65 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)
> {code}
>  



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