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/435#issuecomment-112621215,https://api.github.com/repos/pydata/xarray/issues/435,112621215,MDEyOklzc3VlQ29tbWVudDExMjYyMTIxNQ==,1217238,2015-06-17T01:45:18Z,2015-06-17T01:45:18Z,MEMBER,"To elaborate: even though both pandas and xray use numpy under the hood, I suspect you may see a performance benefit if you switch from pandas to xray, for three reasons:
1. as you noted, you will no longer need repeats for all those independent variables
2. flattening to put things in a 1D column can require a copy (if the data is not already C-contiguous)
3. pandas also often makes copies when you add new dataframe columns, because it tries to consolidate adjacent columns into the same type

To answer your other question about retrieving results for specific conditions: once you put things in xray dataset, that should be as simple as `ds.sel(P=100000, T=300)`.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,88868867
https://github.com/pydata/xarray/issues/435#issuecomment-112617486,https://api.github.com/repos/pydata/xarray/issues/435,112617486,MDEyOklzc3VlQ29tbWVudDExMjYxNzQ4Ng==,1217238,2015-06-17T01:10:45Z,2015-06-17T01:10:45Z,MEMBER,"I suspect that an `xray.Dataset` would indeed be a suitable data structure for your data.

If each of the columns in the `data` dataframe from your notebook were an numpy array, what would their shapes be?

As for iterative updates, arrays in xray objects can be efficiently modified in place just like numpy arrays.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,88868867