Python is not a great language for data science

> Python is pretty good for deep learning. There’s a reason PyTorch is the industry standard. When I’m talking about data science here, I’m specifically excluding deep learning.

I've written very little deep learning code over my career, but made very frequent use of the GPU and differentiable programming for non-deep learning specific tasks. In general Python is much easier to write quantitative programs that make use of the hardware, and you have a lot more options when your problem doesn't fit into RAM.

> I have been running a research lab in computational biology for over two decades.

I've been working nearly exclusively in industry for these two decades and a major reason I find Python just better is it's much, much easier to interface with other parts of engineering when you're a using truly general purpose PL. I've actually never worked for a pure Python shop, but it's generally much easier to get production ML/DS solutions into prod when working with Python.

> Data science as I define it here involves a lot of interactive exploration of data and quick one-off analyses or experiments

This re-iterates the previous difference. In my experience I would call this "step one" in all my DS related work. The first step is to understand the problem and de-risk. But the vast majority of code and work is related to delivering a scalable product.

You can say that's not part of "data science", but if you did you'd have a hard time finding a job on most of the teams I've worked on.

All that said, my R vs Python experience has boiled down to: If your end result is a PDF report, R is superior. If your end result is shipping a product, then Python is superior. And my experience has been that, outside of university labs, there aren't a lot of jobs out there for DS folks who only want to deliver PDFs.

The difference between the examples is so trivial I'm not really sure why the parent comment felt compelled to complain.

It's pretty clear the post is focused on the context of work being done in an academic research lab. In that context I think most of the points are pretty valid, but most of the real world benefit I've experience from using Python is being able to work more closely with engineering (even on non-Python teams).

I shipped R code to a production environment once over my career and it felt incredibly fragile.

R is great for EDA, but really doesn't work well for iteratively building larger software projects. R is has a great package system, but it's not so great when you need abstraction in between.

Clojure, unlike lists in traditional Lisps, based on composable, unified abstraction for its collections, they are lazy by default and literal readable data structures, they are far easier to introspect and not so "opaque" compared to anything - not just CL (even Python), they are superb for dealing with heterogeneous data. Clojure's cohesive data manipulation story is where Common Lisp's lists-and-symbols just can't match.