For example, using the tutorial data:

``` ds = xr.tutorial.load_dataset('air_temperature')

add an empty 2D dataarray

ds['empty']= xr.full_like(ds.air.mean('time'),fill_value=0)

modify one grid point, using where() or loc()

ds['empty'] = xr.where((ds.coords['lat']==20)&(ds.coords['lon']==260), 100, ds['empty']) ds['empty'].loc[dict(lon=260, lat=30)] = 100

modify an area with where() and a mask

mask = (ds.coords['lat']>20)&(ds.coords['lat']<60)&(ds.coords['lon']>220)&(ds.coords['lon']<260) ds['empty'] = xr.where(mask, 100, ds['empty'])

modify an area with loc()

lc = ds.coords['lon'] la = ds.coords['lat'] ds['empty'].loc[dict(lon=lc[(lc>220)&(lc<260)], lat=la[(la>20)&(la<60)])] = 100 ```

these are examples that I am pretty sure are not on the website, they are I think common in climate scientists workflow, and that it took me quite a while to figure out. I was using a boolean dataarray as well as in the SO example, slowing down my work of quite a bit.

Do they make sense? I can try and add them to the documentation at Assigning Values with indexing , or is there another place that is more relevant?