issue_comments

Hello guys,

While waiting for a integrated solution. Here is a function that should do the job in a safe way. It returns an iterator

```` def xndindex(ds, dims=None): if dims is None: dims = ds.dims elif type(dims) is str: dims=[dims] else: pass

for d in dims:
    if d not in ds.dims:
        raise ValueError("Invalid dimension '{}'. Available dimensions {}".format(d, ds.dims))

iter_dict = {k:v for k,v in ds.sizes.items() if k in dims}
for d,k in zip(repeat(tuple(iter_dict.keys())),zip(np.ndindex(tuple(iter_dict.values())))):
    yield {k:l for k,l in zip(d,k[0])}

Example of use a = xr.DataArray(np.random.rand(4,3), dims=['x','y'], coords={'x':np.arange(4), 'y':np.arange(3)}) for i in xndindex(a): print(a[i]) ````

Hi guys,

For now, when I want to iterate over all my dataset I use the simple (but dangerous I believe) workaround: for i in np.ndindex(ds.shape): res = ds[{d:j for d,j in zip(ds.dims,i)}] but, I am not sure that ndindex will iterate in the good order relatively to the ds.dims return.

Is there any news on this topic ?

Many thanks !

Hi shoyer, many thanks for your quick reply. Converting the xarray to a DataFrame indeed does the job and I will use this solution for the time being. Nevertheless, to me the approach seems rather like an ad-hoc solution since it requires a series of conversions / function calls and I feel like there should be some built-in solution from xarray. In particular, in the above solution, you lose track of what are the coordinates and what is the actual data (all is stored in a single NamedTuple), which requires an additional step to separate the two data structures. Anyway, thanks for your help! If you find another solution, please let me know!