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/1534#issuecomment-325777712,https://api.github.com/repos/pydata/xarray/issues/1534,325777712,MDEyOklzc3VlQ29tbWVudDMyNTc3NzcxMg==,4992424,2017-08-29T19:42:24Z,2017-08-29T19:42:24Z,NONE,"@mmartini-usgs, an entire netCDF file (as long as it only has 1 group, which it most likely does if we're talking about standard atmospheric/oceanic data) would be the equivalent of an `xarray.Dataset`. Each variable could be represented as a `pandas.DataFrame`, but with a `MultiIndex` - an index with multiple levels, but which are consist across each level. 

To start with, you should read in your data using the **chunks** keyword to `open_dataset()`; this turns all of the data you read into dask arrays. Then, you use xarray Dataset and DataArray operations to manipulate them. So you can start, instead, by opening your data:

``` python
ds = xr.open_dataset('hugefile.nc', chunks={<fill me in>})
ds_lp = ds.resample('H','time','mean')
```

You'd have to choose chunks based on the dimensions of your data. Like @rabernat previously mentioned, it's very likely you can perform your entire workflow within xarray without every having to drop down to pandas; let us know if you can share more details","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,253407851