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  • Working with labeled N-dimensional data with combinatoric independent variables · 2 ✖

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112621215 https://github.com/pydata/xarray/issues/435#issuecomment-112621215 https://api.github.com/repos/pydata/xarray/issues/435 MDEyOklzc3VlQ29tbWVudDExMjYyMTIxNQ== shoyer 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).

 
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   Working with labeled N-dimensional data with combinatoric independent variables 88868867
112617486 https://github.com/pydata/xarray/issues/435#issuecomment-112617486 https://api.github.com/repos/pydata/xarray/issues/435 MDEyOklzc3VlQ29tbWVudDExMjYxNzQ4Ng== shoyer 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.

 
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   Working with labeled N-dimensional data with combinatoric independent variables 88868867

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