issue_comments

I am closing this. Feel free to re-open/ or open a new issue.

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.

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() `` If you have precision problems - that might be because you havefloat32` values.

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])})

```

Yes but there are less - so as mentioned it removes all columns/ rows with only nans, if there is at least one non-nan value the row is kept.

What is the reason that you want to get rid of the nan values?

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)

What is the reason that you want to get rid of the nan values?

The reason they come back is that are needed to fill the grid again. The dataframe is 1D but the dataarray is 2D.

What you can try is to use where:

python da = ds['z'] da = da.where(da.isnull(), drop=True) but it will only drop the values if the entire row/ column is nan.