issue_comments: 271058005
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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/1194#issuecomment-271058005 | https://api.github.com/repos/pydata/xarray/issues/1194 | 271058005 | MDEyOklzc3VlQ29tbWVudDI3MTA1ODAwNQ== | 1217238 | 2017-01-07T02:54:54Z | 2017-01-07T02:54:54Z | MEMBER | I answered your question on StackOverflow. I agree that this is unfortunate. The cleanest solution would be an integer dtype with missing value support in NumPy itself, but that isn't going to happen anytime soon. I'm not entirely opposed to the idea of adding (limited) support for masked arrays in xarray (see also https://github.com/pydata/xarray/pull/1118), but this could be a lot of work for relatively limited return. I definitely recommend trying dask for processing multi-gigabyte arrays. You might even find the performance boost compelling enough that you could forgive the limitation that it doesn't handle masked arrays, either. |
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