cl-ana VS numcl

I understand what you mean; but despite CL appearing as a very similar to EL, the trouble would be probably significant due to missing libraries and semantic differences between CL and EL, in case of any significantly sophisticated plotting library, such as cl-ana for example. It is probably easier to port Emacs to CL, than that thing to Emacs :).

There are some other active Lisp efforts in the data science space, such as cl-ana.[1]

[1] https://github.com/ghollisjr/cl-ana

If you like spreadsheets, you may appreciate the Cells library. I haven't used it myself yet, however it is billed as "Spreadsheet-like expressiveness for CLOS, the Common Lisp Object System." I believe cl-ana also implements some spreadsheet-like functionality. This article demonstrates some trivial usage of Cells.

cl-ana - Common Lisp data analysis library with emphasis on modularity and conceptual clarity. It aims to be a general purpose framework for analyzing small and large scale datasets, including binned data analysis and visualization. [GNU GPL3][2].

Is it me or numcl is faster than magicl? Matrix multiplication on magicl with pure lisp backend is

Interesting, I will, thanks! I am aware of numcl for CL, but I don't think it is "there" yet :).

The first that comes to mind is numcl. This works if (i) performance is not seriously a concern, (ii) you are not annoyed by julia-like JIT/JAOT compilation delays, (iii) copy-based slicing won't be a performance issue for you. To be fair, limitation (i) might be overcome by writing a better (simd-based) backend for numcl. numcl is fast, it compiles to fairly good code, but simd can boost the performance by another 4-8 times or so.

am I missing something that already exists (numcl / Alexandria / core language, etc?)

>Either implement numpy equivalent on your own or half of your code is data massaging data between libraries

I haven't tested this but here you go:

https://github.com/numcl/numcl

Personally, I've been relying on the stream-based method using py4cl/2, mostly because I did not - and perhaps do not - have the knowledge and time to dig into the CFFI based method. The limitation is that this would get you less than 10000 python interactions per second. That is sufficient if you will be running a long running python task - and I have successfully run trivial ML programs using it, but any intensive array processing gets in the way. For this later task, there are a few emerging libraries like numcl and array-operations without SIMD (yet), and numericals using SIMD. For reasons mentioned on the readme, I recently cooked up dense-arrays. This has interchangeable backends and can also use cl-cuda. But barring that, the developer overhead of actually setting up native-CFFI ecosystem is still too high, and I'm back to py4cl/2 for tasks beyond array processing.

numcl - Numpy clone in Common Lisp. [LGPL3][9].

[3] https://github.com/numcl/numcl