issue_comments: 1280072309
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| html_url | issue_url | id | node_id | user | created_at | updated_at | author_association | body | reactions | performed_via_github_app | issue |
|---|---|---|---|---|---|---|---|---|---|---|---|
| https://github.com/pydata/xarray/issues/6726#issuecomment-1280072309 | https://api.github.com/repos/pydata/xarray/issues/6726 | 1280072309 | IC_kwDOAMm_X85MTFp1 | 90008 | 2022-10-16T22:33:17Z | 2022-10-16T22:33:17Z | CONTRIBUTOR | In developing https://github.com/pydata/xarray/pull/7172, there are also some places where class types are used to check for features: https://github.com/pydata/xarray/blob/main/xarray/core/pycompat.py#L35 Dask and sparse and big contributors due to their need to resolve the class name in question. Ultimately. I think it is important to maybe constrain the problem. Are we ok with 100 ms over numpy + pandas? 20 ms? On my machines, the 0.5 s that xarray is close to seems long... but everytime I look at it, it seems to "just be a python problem". |
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