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!

You could convert your data into pandas and use .itertuples(), e.g., ```python import xarray import itertools

ds = xarray.tutorial.open_dataset('air_temperature') records = ds.to_dataframe().reset_index().itertuples(index=False, name='Record') print(list(itertools.islice(records, 5))) Outputs: [Record(lat=75.0, lon=200.0, time=Timestamp('2013-01-01 00:00:00'), air=241.1999969482422), Record(lat=75.0, lon=200.0, time=Timestamp('2013-01-01 06:00:00'), air=242.09999084472656), Record(lat=75.0, lon=200.0, time=Timestamp('2013-01-01 12:00:00'), air=242.29998779296875), Record(lat=75.0, lon=200.0, time=Timestamp('2013-01-01 18:00:00'), air=241.88999938964844), Record(lat=75.0, lon=200.0, time=Timestamp('2013-01-02 00:00:00'), air=243.1999969482422)] ```