issue_comments: 1326262197
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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/7045#issuecomment-1326262197 | https://api.github.com/repos/pydata/xarray/issues/7045 | 1326262197 | IC_kwDOAMm_X85PDSe1 | 4160723 | 2022-11-24T10:35:02Z | 2022-11-24T10:35:02Z | MEMBER | I find the analogy with relational databases quite meaningful! Rectangular grids likely have been the primary use case in Xarray for a long time, but I wonder to which extent it is the case nowadays. Probably a good question to ask for the next user survey? Interestingly, the 2021 user survey results (*) show that "interoperability with pandas" is not a critical feature while "label-based indexing, interpolation, groupby, reindexing, etc." is most important, although the description of the latter is rather broad. It would be interesting to compute the correlation between these two variables. The results also show that "more flexible indexing (selection, alignment)" is very useful or critical for 2/3 of the participants. Not sure how to interpret those results within the context of this discussion, though. (*) The 2022 user survey results doesn't show significant differences in general
Not that improbable for unstructured meshes, curvilinear grids, staggered grids, etc. Xarray is often chosen to handle them too (e.g., uxarray, xgcm). |
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