issue_comments

I'm going to close this in favor of #1887. Not to reject this approach, but just to keep discussion in one place.

Okay will have a look at #1887 first, before going forward with this request :)

Oh, OK, that makes sense now.

Yes, we are definitely interested in supporting multi-dimensional boolean indexing. See https://github.com/pydata/xarray/issues/1887 for thoughts on what this could look.

Hey Shoyer,

sure I am happy to propose one. Given the input from the xarray example page (http://xarray.pydata.org/en/stable/examples/weather-data.html), I would imagine something like this:

python xarr = xarr.loc[xarr['tmin'] > 5]

If the DataArray is one dimensional this is straight forward to achieve by altering the _LocIndexer in the following way: ``` class _LocIndexer(object): def init(self, dataset): self.dataset = dataset

def __getitem__(self, key):
    if not utils.is_dict_like(key):
        selector = {dim: key[dim][key] for dim in key.dims}
        keep_vars = []
        for var in self.dataset.data_vars:
            if np.all(dim in self.dataset[var].dims for dim in key.dims):
                keep_vars.append(var)
        return self.dataset[keep_vars].sel(selector)
    return self.dataset.sel(**key)

```

This does not work for higher dimensions though as 2-dimensional boolean indexing is not supported. It would as well get rid of all other DataArrarys which do not have shared dimensions with the indexer. Probably, there is a better place to do this, that in the loc function. However, I think it would be great in case people need to filter their data by something else than the array dimensions.

Cheers,

Jendrik

Hi Jendrik, thanks for your interest!

Could you please give an example of what exactly your proposed syntax would look like, with example inputs/outputs?