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issue 1

  • Single matplotlib import · 4 ✖

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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
950294432 https://github.com/pydata/xarray/pull/5794#issuecomment-950294432 https://api.github.com/repos/pydata/xarray/issues/5794 IC_kwDOAMm_X844pFeg dcherian 2448579 2021-10-24T09:53:44Z 2021-10-24T09:53:44Z MEMBER

Does seem cleaner. Thanks @Illviljan 

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   Single matplotlib import 996352280
940654242 https://github.com/pydata/xarray/pull/5794#issuecomment-940654242 https://api.github.com/repos/pydata/xarray/issues/5794 IC_kwDOAMm_X844ET6i max-sixty 5635139 2021-10-12T04:43:37Z 2021-10-12T04:43:37Z MEMBER

I wouldn't have thought this has any noticeable difference on timing, but it does make the code a bit cleaner. Is there any reason not to do this?

 
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   Single matplotlib import 996352280
940356823 https://github.com/pydata/xarray/pull/5794#issuecomment-940356823 https://api.github.com/repos/pydata/xarray/issues/5794 IC_kwDOAMm_X844DLTX Illviljan 14371165 2021-10-11T18:44:27Z 2021-10-11T18:46:50Z MEMBER

A little better comparison and little more noticeable difference although still in the noise range:

This branch: ```python %timeit -n1 -r1 import xarray

3.81 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.83 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.87 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.7 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.77 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.91 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.8 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

np.mean([3.81, 3.83, 3.87, 3.7, 3.77, 3.91, 3.8]) Out[3]: 3.812857142857143 ```

Main: ```python %timeit -n1 -r1 import xarray

3.93 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.69 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.64 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.76 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.79 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.81 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each) 3.68 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)

np.mean([3.93, 3.69, 3.64, 3.76, 3.79, 3.81, 3.68]) Out[4]: 3.7571428571428567 ```

 
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   Single matplotlib import 996352280
921868793 https://github.com/pydata/xarray/pull/5794#issuecomment-921868793 https://api.github.com/repos/pydata/xarray/issues/5794 IC_kwDOAMm_X8428pn5 TomNicholas 35968931 2021-09-17T15:03:05Z 2021-09-17T15:03:05Z MEMBER

The slowest run took 11.40 times longer than the fastest. This could mean that an intermediate result is being cached.

What happens if you manually time just a single import (I think you can tell timeit to run only once)? It seems like averaging might not be giving an accurate reflection of the import time here.

 
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   Single matplotlib import 996352280

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