issue_comments: 271077863
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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-271077863 | https://api.github.com/repos/pydata/xarray/issues/1194 | 271077863 | MDEyOklzc3VlQ29tbWVudDI3MTA3Nzg2Mw== | 500246 | 2017-01-07T11:24:49Z | 2017-01-07T11:32:06Z | CONTRIBUTOR | I don't see how an integer dtype could ever support missing values; float missing values are specifically defined by IEEE 754 but for ints, every sequence of bits corresponds to a valid value. OTOH, NetCDF does have a _FillValue attribute that works for any type including int. If we view xarray as "NetCDF in memory" that could be an approach to follow, but for numpy in general it would fairly heavily break existing code (see also http://www.numpy.org/NA-overview.html) in particular for 8-bit types. If i understand correctly, R uses INT_MAX which would be 127 for 'int8… Apparently, R ints are always 32 bits. I'm new to xarray so I don't have a good idea on how much work adding support for masked arrays would be, but I'll take your word that it's not straightforward. |
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