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https://issues.apache.org/jira/browse/ARROW-2514?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Rok Mihevc updated ARROW-2514:
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External issue URL: https://github.com/apache/arrow/issues/18790
> [Python] Inferring / converting nested Numpy array is very slow
> ---------------------------------------------------------------
>
> Key: ARROW-2514
> URL: https://issues.apache.org/jira/browse/ARROW-2514
> Project: Apache Arrow
> Issue Type: Bug
> Components: Python
> Affects Versions: 0.9.0
> Reporter: Antoine Pitrou
> Assignee: Antoine Pitrou
> Priority: Major
> Labels: pull-request-available
> Fix For: 0.10.0
>
> Time Spent: 1h 50m
> Remaining Estimate: 0h
>
> Converting a nested Numpy array nested walks over the Numpy data as Python
> objects, even if the dtype is not "object". This makes it pointlessly slow
> compared to the non-nested case, and even the nested Python list case:
> {code:python}
> >>> %%timeit data = list(range(10000))
> ...:pa.array(data)
> ...:
> 746 µs ± 8.36 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
> >>> %%timeit data = np.arange(10000)
> ...:pa.array(data)
> ...:
> 81.1 µs ± 57.7 ns per loop (mean ± std. dev. of 7 runs, 10000 loops each)
> >>> %%timeit data = [np.arange(10000)]
> ...:pa.array(data)
> ...:
> 3.39 ms ± 6.27 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
> {code}
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