That's quite a bold assertion, my friend. I have to say that Python's superiority for that particular field is not obvious to me, if such superiority even exists. I'd be very interested to read how you know this to be true.

First of all, I was sloppy with terminology. I shouldn't have said "scientific computing" because that has a more restricted meaning than what I intended to refer to. I intended to refer to any domain where computers are extensively involved in gathering empirical knowledge, which is basically all over the place. The intended meaning was what scientific computing really means as well as statistics, machine learning, etc.

Anyway, no other very high-level language has anything really comparable to all the advantages offered by numpy, scipy, scikit-learn, TensorFlow, Sage etc. in this area. There's still a lot of stuff I would only do in R (as much as I really don't want to) and Matlab is still pretty widely used, though I don't know much about it. A lot of big data stuff appears to be done in Java and fellow JVM language Scala. Raw C, C++ and Fortran are still relevant if you have a real need for speed. But in many respects Python has become the de facto standard for these things when special requirements don't need to be met. For example, I'm enrolled in the Coursera course Data-Driven Astronomy and it's all in Python. This is equally true of a number of other courses on said website. Results from Google are also indicative of Python's preeminence in data science / machine learning, more than I expected really. In academics, Python is replacing other languages for introductory programming courses and has replaced Common Lisp in the leading AI textbook Artificial Intelligence: A Modern Approach. And so it goes.

Yes. Though Perl once did well with PDL, Python is way ahead of Perl in this field today.

If you need further convincing - as noted in Re: The First Ten Perl Monks - when nate, one of the revered founders of the Perl Monks web site wrote an O'Reilly book on Computer Vision in 2012, he chose Python! for the source code behind the book.

You are quite right of course.

The criterion for relevance is not just technology as such.

Probably (I don't really know to be honest) you could do a lot of things that people use python's numpy & pandas for with perl's PDL, but then EVERYBODY uses numpy & pandas and if I want to learn about it I can even choose among several books if I want, while there is not a single book about PDL.

So on a pure technical level perl may be in contention, but in reality it isn't.

Python seems to be eating R's lunch these times and Perl is not even a contender anymore.

It was not inevitable to play out like this, to me this is (in a way) Perl vs PHP all over again.

there is not a single book about PDL

Other than The PDL Book, of course.

Python seems to be eating R's lunch these times and Perl is not even a contender anymore

I sit near a PhD AI researcher at work and often hear her extolling the virtues of Python. I sometimes hear her defending her choice of Python over C++ ("I'm a scientist, not a professional programmer") but never Perl. Perl is not on her radar. At all. I never suggest Perl to her because, frankly, if I was working in her field I would also choose Python. This is not about technical capability, it's about alignment with colleagues in your field.

It's no wonder academia prefers Python.

They respect well formatted code and clinical documentation, which gives the impression of an axiomatic system.

Novel style perldocs with insider jokes? Golf code in form of an ASCII camel?

Not their style.

Cheers Rolf
_{(addicted to the Perl Programming Language and ☆☆☆☆ :)
Wikisyntax for the Monastery}

Perl should have definitely trounced PHP. Oh well.

I have more respect for R but it still does things like polluting the global namespace with historical datasets and requiring third-party add-ons to have actual hash tables. Its greatest strength is having all these libraries that minimize contact with the terrible underlying language.

For example, I'm enrolled in the Coursera course Data-Driven Astronomy and it's all in Python.

Bummer. The University of Washington teaches Perl in ASTR 300 Introduction to Programming for Astronomical Applications: "Introduction to programming needed for astronomical applications: Linux operating systems, PERL, IDL. Recommended for astronomy majors planning to take 400-level astronomy courses, to pursue individual research projects, or"

https://myplan.uw.edu/course/#/courses/ASTR300

The OP asks about Perl 6. Only the first of the four links does the same but that was back in 2001. I don't know much Python but perhaps more than 15 years later someone could give a better answer about Perl 6.

Update:

Thanks to Laurent_R for coming up with an answer: Re: Curious about Perl's strengths in 2018

There are only two or three real users here; probably more detractors; the majority being like me: I'm still interested in it and hope it becomes what it might but in my view it's a curiosity until it's much faster, a little less buggy, and the "6PAN" has grown. My lack of speaking about it is the intersection of general ignorance and politeness. :P