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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
919449676 https://github.com/pydata/xarray/pull/5794#issuecomment-919449676 https://api.github.com/repos/pydata/xarray/issues/5794 IC_kwDOAMm_X842zbBM github-actions[bot] 41898282 2021-09-14T19:25:55Z 2021-10-11T19:15:57Z CONTRIBUTOR

Unit Test Results

6 files           6 suites   1h 0m 9s :stopwatch: 16 230 tests 14 494 :heavy_check_mark: 1 736 :zzz: 0 :x: 90 576 runs  82 396 :heavy_check_mark: 8 180 :zzz: 0 :x:

Results for commit f34595fe.

:recycle: This comment has been updated with latest results.

 
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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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919448891 https://github.com/pydata/xarray/pull/5794#issuecomment-919448891 https://api.github.com/repos/pydata/xarray/issues/5794 IC_kwDOAMm_X842za07 pep8speaks 24736507 2021-09-14T19:24:40Z 2021-10-11T18:45:05Z NONE

Hello @Illviljan! Thanks for updating this PR. We checked the lines you've touched for PEP 8 issues, and found:

There are currently no PEP 8 issues detected in this Pull Request. Cheers! :beers:

Comment last updated at 2021-10-11 18:45:05 UTC
 
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921889702 https://github.com/pydata/xarray/pull/5794#issuecomment-921889702 https://api.github.com/repos/pydata/xarray/issues/5794 IC_kwDOAMm_X8428uum DocOtak 868027 2021-09-17T15:32:04Z 2021-09-17T15:32:04Z CONTRIBUTOR

Python's import machinery has a lot of caching going on. In most cases, additional imports of a module that has been imported previously is about as expensive as a dict lookup.

 
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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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