issue_comments

Yes float64 should cause less imprecision. You can convert using astype:

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

da = xr.DataArray(np.array([1, 2], dtype=np.float32))

da = da.astype(float) ```

As for the other problems I think you are better of asking the people over at rioxarray. However, you should first gather all the steps you did to convert the data as code. This way it is easier to see what you are actually doing.

Thanks for getting back. I did post in rioxarray and yet, the last step I mentioned isn't successful there too. I'll post the code maybe 8hrs from here(can reach out to my sys then). Thanks for all the helpful suggestions so far. Really helpful.

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.

Well, that does makes sense. I want to calculate anomalies along x-y grids and I'm guessing the nan values are interfering with the results. Also, I have another question which isn't regarding Nan's. if it is right here, I may proceed. (else tag/link to other places/forums relevant). Assuming you must be knowing: I reprojected my nc file from meters to degrees Now, although the projection is right, the values of longitude aren't. python x (x) float64 -179.2 -177.7 ... 177.7 179.2 array([-179.217367, -177.65215 , -176.086933, -174.521715, -172.956498, -171.391281, -169.826063, -168.260846, -166.695629, -165.130412, -163.565194, -161.999977, -160.43476 , -158.869542, 163.565218, 165.130436, 166.695653, 168.26087 , 169.826088, 171.391305, 172.956522, 174.521739, 176.086957, 177.652174, 179.217391]) This is not how it is supposed to be: It should fall with 160-200 longitudes (post wrapping 360)

Is there a way xarray can sort this automatically or do I need to manually reset the cordinates?

Can you try notnull instead of isnull - I often get the boolean array wrong in where:

python da = ds['z'] da = da.where(da.notnull(), drop=True)

Yes, I did.

As we can see, the nan values are not completely gone

Thanks alot for responding, but,

python da = ds['z'] da = da.where(da.isnull(), drop=True) is for pciking nan values? Because the data array has all 'nan' values

when I plot: I get,

I need to use data that has no empty cells for further analysis.