Antoine Pitrou created ARROW-2514:
-------------------------------------
Summary: [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
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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