issue_comments

@jonathanstrong this is really helpful feedback! You are right to be suspicious of academics when it comes to file formats :) If you have concrete suggestions for doc improvements along these lines, please do put together a PR!

I've thought about the "magic name" approach, too -- my only concern is that it would be weird to get a DataArray back from xarray.open_dataset. But maybe xarray.open is a better name, anyways...

I hadn't, for a number of reasons. First, I've used csv, hdf, sql, json, yaml and other formats but never came across netcdf until using this library as someone who isn't working in the physical sciences. Second, the documentation on netcdf is fairly dense. Third, didn't want to deal with installing the library.

OK, these are all fair points. Though you probably already have SciPy installed, which is enough for basic netCDF support.

I just did use it and seems like it is great for Datasets. As far as I can tell there is no way to save DataArrays directly, though?

This is true. But converting a DataArray to a Dataset is quite simple: arr.to_dataset(name='foo'), so I'm not sure it's worth adding.

Finally, would note that pandas has io methods for csv, excel, hdf, sql, json, msgpack, html, gbq, stata, "clipboard", and pickle. I think it's a strength to offer more choices.

Yes, choice is good -- but also note that none of those are invented file formats for pandas! I am slightly wary of going down this path, because at the point at which you have a file format that can faithfully represent every xarray object, you have basically reinvented netCDF :).

That said, something like JSON is generally useful enough (with a different niche than netCDF) that it could make sense to add IO support.

This is a pretty reasonable way to save data, but my only concern is that it's not clear to me that we need another file format when netCDF already solves this problem, in a completely portable way. Have you tried using xarray's netCDF IO?