vellum VS magicl

That's cool. I want to integrate parquet files into Vellum library https://github.com/sirherrbatka/vellum

Yeah, I use CL for data science, despite lack of suitable tools. I even ended up writing my own: https://github.com/sirherrbatka/clusters https://github.com/sirherrbatka/vellum https://github.com/sirherrbatka/vellum-plot https://github.com/sirherrbatka/statistical-learning

Vellum - Data Frames for Common Lisp. BSD_2Clause.

https://github.com/sirherrbatka/vellum is maturing and already usable as a data frame library.

(Link didn't work for me)

https://github.com/quil-lang/magicl/blob/master/src/high-lev...

use MAGICL. [1] It is optionally and transparently accelerated by BLAS/LAPACK.

[1] https://github.com/quil-lang/magicl/blob/master/doc/high-lev...

Maybe have a look at how magicl does this?

This is essentially the first option, which is already supported by MAGICL by loading MAGICL/EXT-LAPACK [1].

[1] https://github.com/quil-lang/magicl#extensions

I think what the magicl team has done is brilliant - allowing multiple implementations is awesome.

Second up is magicl, especially useful if performance is a concern. This might not be as extensive as numcl, but it's been battle tested in the industry over the last decade or so. Because this uses generic functions, so long as you are using not-very-small arrays, performance should not be a concern for you. And even if you are, you could write your own functions that use the low-level functions that magicl's backends define. Otherwise performance can be at par with numpy.

This Dev How-To describes (I hope in enough detail) how to add these specialized routines to MAGICL.

I agree... and I do don't want be the person who has not rallied. I just took a look at guicho's issue from 2019. And here, you yourself have admitted that the high level interface is less than ideal and needs more work. However, the very point that magicl is an industry standard could imply that potentially radical backward-incompatible changes can be hard. But, honestly, I want to discuss this, time permitting!

Common Lisp is a great language to build new tools for data science, but currently has pretty awful library support existing data science workflows. Common Lisp is sorely lacking in high-quality statistics, plotting, and sparse arrays. There’s been a long work-in-progress library to bring flexible and high-performance linear algebra to Lisp, but it needs more contributors.