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https://github.com/pydata/xarray/pull/5680#issuecomment-895508489 https://api.github.com/repos/pydata/xarray/issues/5680 895508489 IC_kwDOAMm_X841YGAJ 1217238 2021-08-09T20:11:24Z 2021-08-09T20:11:24Z MEMBER

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

  1. It makes it impossible to represent the full range of values in a data type, e.g., 255 for uint8 now means "missing".
  2. 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.

 
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