issue_comments: 1562698250

xarray handles nan values and ignores them per default - so you don't need to remove them. For example:

```python import numpy as np import xarray as xr

da = xr.DataArray([1, 2, 3, np.nan]) da.mean() ```

This is really helpful as I didn't know this before.

If you have precision problems - that might be because you have float32 values.

Which format would not cause the issue in that case float 64? If yes, can we manually convert?

I don't know what goes wrong with your lon values - that is an issue in the reprojection. You could convert them to 0...360 by using

python lon_dim = "x" new_lon = np.mod(da[lon_dim], 360) da = da.assign_coords(**{lon_dim: new_lon}) da.reindex(**{lon_dim : np.sort(da[lon_dim])})

Yeah. I have done the 180 to 360 deg conversions before. But the issue is more of with rioxarray reprojection I feel The internet data is in meters, as I wanted in degrees/lat-lon format, I converted the data from polar stereographic to wgs84. This converted the datas coordinates to degrees, latitudes are perfect. But longitude are arranged to -180 to +180 instead of 160E to 199W. I as well tried wrapping longitude to 0-360, but it should technically fall in 160-200 range while the long show all 0-360 and stretch throughout, which isn't right.

So, converting the existing gridded data (in meters) to lat-lon projection without affecting the resolution and without nan is my ultimate aim/objective. I successfully converted data to lat-lon and clipped to region but, it drastically changed the resolution like around 20 times maybe. Preserving the resolution is very imp for my work. So, that's the issue with longitudes

Thanks for your time if you went through this.