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- Representing & checking Dataset schemas · 3 ✖
id | html_url | issue_url | node_id | user | created_at | updated_at ▲ | author_association | body | reactions | performed_via_github_app | issue |
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938409463 | https://github.com/pydata/xarray/issues/1900#issuecomment-938409463 | https://api.github.com/repos/pydata/xarray/issues/1900 | IC_kwDOAMm_X8437v33 | shoyer 1217238 | 2021-10-08T07:25:44Z | 2021-10-08T07:25:44Z | MEMBER |
Awesome -- would love to hear how this goes! |
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Representing & checking Dataset schemas 295959111 | |
364602759 | https://github.com/pydata/xarray/issues/1900#issuecomment-364602759 | https://api.github.com/repos/pydata/xarray/issues/1900 | MDEyOklzc3VlQ29tbWVudDM2NDYwMjc1OQ== | shoyer 1217238 | 2018-02-09T23:53:49Z | 2018-02-09T23:53:49Z | MEMBER |
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Representing & checking Dataset schemas 295959111 | |
364532198 | https://github.com/pydata/xarray/issues/1900#issuecomment-364532198 | https://api.github.com/repos/pydata/xarray/issues/1900 | MDEyOklzc3VlQ29tbWVudDM2NDUzMjE5OA== | shoyer 1217238 | 2018-02-09T19:16:26Z | 2018-02-09T19:16:26Z | MEMBER | I think the right word for this may be "schema". For applications and models (rather than data analysis), these sort of conventions can be super-valuable. I like the idea of declarative spec that can be validated. Just googling around, I came up with pandas-validator: https://github.com/c-data/pandas-validator |
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Representing & checking Dataset schemas 295959111 |
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