dataframe implementations

[Jay Norwood via Digitalmars-d-learn]

On Saturday, 21 November 2015 at 14:16:26 UTC, Laeeth Isharc 
wrote:
>
> Not sure it is a great idea to use a variant as the basic 
> option when very often you will know that every cell in a 
> particular column will be of the same type.


I'm reading today about an n-dim extension to pandas named xray.  
Maybe should try to understand how that fits.  They support io 
from netCDF, and are making extensions to support blocked input 
using dask, so they can process data larger than in-memory limits.

http://xray.readthedocs.org/en/stable/data-structures.html
https://www.continuum.io/content/xray-dask-out-core-labeled-arrays-python


In general, pandas and xray are supporting with the requirement 
of pulling in data from storage of initially unknown column and 
index names and data types.  Julia throws in support of jit 
compilation and specialized operations for different data types.

It seems to me that D's strength would be in a quick compile, 
which would then allow you to replace the dictionary tag 
implementations and variants with something that used compile 
time symbol names and data types. Seems like that would provide 
more efficient processing, as well as better tab completion 
support when creating expressions.