issue_comments: 412319738
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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/2059#issuecomment-412319738 | https://api.github.com/repos/pydata/xarray/issues/2059 | 412319738 | MDEyOklzc3VlQ29tbWVudDQxMjMxOTczOA== | 1217238 | 2018-08-12T05:27:10Z | 2018-08-12T05:27:10Z | MEMBER |
Unfortunately, there is a frustrating disconnect between string data types in NumPy and netCDF. This could be done in principle, but it would require adding our xarray specific convention on top of netCDF. I'm not sure this would be worth it -- we already end up converting np.unicode_ to object dtypes in many operations because we need a string dtype that can support missing values. For reading data from disk, we use object dtype because we don't know the length of the longest string until we actually read the data, so this would be incompatible with lazy loading. |
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