issues: 114732169
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| id | node_id | number | title | user | state | locked | assignee | milestone | comments | created_at | updated_at | closed_at | author_association | active_lock_reason | draft | pull_request | body | reactions | performed_via_github_app | state_reason | repo | type |
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| 114732169 | MDU6SXNzdWUxMTQ3MzIxNjk= | 643 | "naive" iteration is very slow | 1322974 | closed | 0 | 2 | 2015-11-03T02:53:04Z | 2019-01-15T21:09:07Z | 2019-01-15T21:09:07Z | CONTRIBUTOR | ``` $ ipython Python 3.5.0 (default, Sep 20 2015, 11:28:25) Type "copyright", "credits" or "license" for more information. IPython 4.0.0 -- An enhanced Interactive Python. ? -> Introduction and overview of IPython's features. %quickref -> Quick reference. help -> Python's own help system. object? -> Details about 'object', use 'object??' for extra details. Using matplotlib backend: Qt4Agg In [1]: from xray import DataArray Iteration over a Python listIn [2]: %%timeit t = list(range(10000)) for _ in t: pass ...: 10000 loops, best of 3: 87.3 µs per loop Iteration over a ndarrayIn [3]: %%timeit t = np.arange(10000) for _ in t: pass ...: 1000 loops, best of 3: 472 µs per loop Iteration over a DataArrayIn [4]: %%timeit t = DataArray(np.arange(10000)) for _ in t: pass ...: 1 loops, best of 3: 818 ms per loop ``` I'm not sure how much can be done about this as iterating over a DataArray needs to create a bunch of temporary objects (and I understand the emphasis is as usual on vectorized operations, etc.) but a >1500 fold difference certainly doesn't look good. |
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completed | 13221727 | issue |