html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/18#issuecomment-41122172,https://api.github.com/repos/pydata/xarray/issues/18,41122172,MDEyOklzc3VlQ29tbWVudDQxMTIyMTcy,1217238,2014-04-23T03:54:59Z,2014-04-23T03:54:59Z,MEMBER,"I'm going to close this, given that pandas doesn't currently have appropriate data structures for representing arbitrary dimensional NetCDF variables. These data structures (N-dimensional labeled arrays like `xray.DataArray`) are a major motivation for why we wrote xray.

You can represent higher dimensional arrays as a `pandas.Series` with a hierarchical index, but this representation has a much less directly connection to NetCDF datasets on disk. I think it makes more sense to make the objects in xray first (since our data models basically matches netCDF), and then convert xray Datasets into pandas DataFrames. We do in fact support this via the to_series and to_dataframe methods, e.g., `xray.open_dataset('foo.nc').to_dataframe()`.

That said, I am not opposed to integrating some or all of xray into pandas -- but that's a much bigger discussion.
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https://github.com/pydata/xarray/issues/18#issuecomment-36039528,https://api.github.com/repos/pydata/xarray/issues/18,36039528,MDEyOklzc3VlQ29tbWVudDM2MDM5NTI4,514053,2014-02-25T18:16:38Z,2014-02-25T18:16:38Z,CONTRIBUTOR,"@jreback  I'll spend some time getting a better feel for how/if we could push some of the backend into pandas' HDFStore.  Certainly, we'd like to leverage other more powerful packages (pandas, numpy) as much as possible. Thanks for the suggestion.
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