https://github.com/hylang/hy/releases/tag/1.0a4
That's Lisp over Python -- you can do data science properly in Lisp now using all you favourite Python packages.
I repeat you can do data science properly in Lisp now.
If you're forced to use Python it's usually because of one of 2 reasons: Either you need to work with colleagues on something (in which case almost certainly they wouldn't accept a lispified version of it) or because you need some of the libraries that are already implemented.
In the latter case, you might say Hy is a good option. But why bind yourself to the slowness of Python when you could just as well use some fast Lisp (e.g. Common Lisp) that interacts with a Python interpreter similar to nimpy [0] from Nim? Not sure if such a thing already exists for CL, but it should be quite feasible to write and then you get access to all your Python library needs while actually using a better language for the rest of your code.
But I'm all ears on the real advantages Hy provides aside from being neat.
couple of solutions exist for this
I'm not lisp enough to make a macro, but supposedly, isn't let under the hood making a lambda function and calling it? That's within the confines of possibility in Python.
It has a rather fast rate of development, which makes coding in it a bit of a moving target.
https://www.wolfram.com/data-science-platform/
I suppose Hylang is quite slow (and possibly has leaky abstractions). Ideally the main C/C++ libraries like libtorch should be wrapped in Common Lisp and not Python.
My preference is using py4cl to use pre-trained TensorFlow models in Common Lisp.
I wrote a short book on Hy that has some deep learning examples, but I don't use Hy that often any longer. I am looking forward to the 1.0 changes.