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ASF GitHub Bot updated ARROW-2514: ---------------------------------- Labels: pull-request-available (was: ) > [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 > Priority: Major > Labels: pull-request-available > > 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} -- This message was sent by Atlassian JIRA (v7.6.3#76005)