issue_comments: 1498403174

This data as json

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/7723#issuecomment-1498403174 https://api.github.com/repos/pydata/xarray/issues/7723 1498403174 IC_kwDOAMm_X85ZT9Fm 2448579 2023-04-06T02:24:34Z 2023-04-06T02:24:34Z MEMBER

See https://github.com/pydata/xarray/pull/5680#issuecomment-895508489

To follow up, from a practical perspective, there are two problems with assuming that there are always "truly missing values" (case 2):

It makes it impossible to represent the full range of values in a data type, e.g., 255 for uint8 now means "missing". Due to unfortunately limited options for representing missing data in NumPy, Xarray represents truly missing values in its data model with "NaN". This is more or less OK for floating point data, but means that integer data gets converted into floats. For example, uint8 would now get automatically converted into float32.

Both of these issues are problematic for faithful "round tripping" of Xarray data into netCDF and back. For this reason, Xarray needs an unambiguous way to know if a netCDF variable could contain semantically missing values. So far, we've used the presence of missing_value and _FillValue attributes for that.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   1655569401