Perl is superior in many ways and I think using it for data-exploration still has tons of merit.
Folks “shoot themselves in the foot” when they don’t understand the language. In Perls case: list/scalar context is usually the culprit, but it is quite easy to understand. It’s more flexible and concise which in many ways makes Perl better at exploratory programming.
Python vs Perl for science has nothing to do with c/c++/fortran. C/C++/fortran have their own place for science computations. Perl does not. For anything involving any kind of numerics Python will be faster/better tested/will have more libraries/will have better visualisation capabilities, so there is no need for perl.
Sure if you are only working with strings, you can use perl, but that's hardly scientific computing (not at least the field that I work in).
It depends on the field. RNA/DNA, Polypeptids and proteins are just long chains of text, therefore Perl can deal easily with the problems of finding things, manipulating them to build a new chain or translating the chain to a different format. This is a significant chunk of what Bioinformatics do all the time, and Bioperl can manage it.
Also a big part of astronomy is analyzing or finding stars in a tridimensional matrix of space. PDL can be useful with that. Is not dificult to extract a slice of interest in the space matrix and focus our research on it. The main problem could be the lack of experienced people available having exactly this problem to solve.
I don't know if original Perl is very good or bad for that, but there are several Math modules that could have what you want and be easily connected with the former stuff. In fact there are a lot of them to choose:
https://metacpan.org/search?q=Math
Raku at least has a Math::Model module to simulate Physics stuff. I ignore how developed is the module or how its perform would compare with Pyton's similar stuff
That’s just the surface. Working with matrices means nested data structures, and Perls syntax for nested data structures makes the list/scalar context look like child’s play.
Having said all than I have no problems using references in Perl, but see why a Pythonista can find Perl code hard to read.
The question is whether Perl's PDL is better than ScyPy/Numpy/Pandas/Cython, the Spyder IDE, bunch of plotting libraries...etc.
The fact is that most people don't even have an opinion about what is Python or Perl.
Python can seem easy to share after you copy part of a python script and miss one blank space somewhere at the end of a line or start copying in the wrong line. If you use a dumb text editor the script will easily turn into a ugly mess. Perl scripts don't have this problem, so some people could say that they are easier to exchange and share in fact.
Many Perl authors will be really glad to share your code with you. This is what they built an online community of knowledge and libraries called CPAN where you can find it easily. To find authors willing to help and explain obscure parts of their own scripts also if asked politely, is not uncommon or particularly difficult.
I think we can bury this 'Python has significant whitespace' criticism for good now. I have taught python to people from hugely diverse backgrounds (including literature and law) and not once has this been an issue (on the contrary it's a massive help to readability).
Also, no sane person teaches people to code python with a 'dumb' text editor - you give them Jupyter notebooks or VSCode or PyCharm (which has an excellent educational version) or Notepad++ or something.
You realize CPAN did it first right?
I like python a lot and actually i wish less scientists used it. Perl/PDL is in my opinion much better suited to this (understandable) just getting the job done approach i often find in sciene or other areas in which writing software is not the primary goal.