Ask HN:Why is Perl so dwarfed in data science by Python?

I vehemently disagree. When choosing among languages today, performance is mostly good enough across all languages and readability becomes the most important consideration. It's "subjective", but human beings are subjects, and subjective things are important to them.

> I've seen a lot of unreadable code written by data scientist in Python. And a lot of readable Perl code. It's not about the language, it's about the best practices.

This is a bad and cliche argument. Yes, there is well-written code in languages that deprioritize readability and badly-written code in languages that highly prioritize readability. That doesn't mean that any differences in the inherent, built-in readability of X and Y are moot. And "best practices", which are not widely agreed upon or followed, absolutely do not suffice to paper over this.

> The concept behind sigils is very simple and once you've learned it, it's not that threatening.

"Once you've learned it" or "get used to it" is the biggest programming cliche in the world. People say this about all their favorite languages and language features. I'm sure I could complain about doing data science in Brainfuck and get someone in the comments who would say it's not threatening "once you get used to it".

Well sorry, but I don't want to get used to your favorite language's line noise. That's why I choose another language. That's why everyone else chooses another language.

> And when talking about data structures, it is a benefit to have it more structured and know that you are dealing with an array or hash or scalar.

You can keep track of types in Python with gradual typing. Most people don't bother, so evidently it's not that important in most cases, but you can do it.

Other than that, no, I don't need to fill my code with more line noise to constantly remind myself what type of container I'm dealing with. That's obvious enough from the methods I'm using to manipulate the container.

> In my opinion, Perl is not used for data science because it lacks(or people are not aware of) libraries like numpy, pandas, etc.

No, this is after the horse has already left the barn. Those libraries were developed in Python because the authors of those libraries preferred to work in Python. The authors preferred to work in Python because of the legibility considerations I've already mentioned.

Python's design and adoption were both heavily driven by a vision of readability from the very earliest stages in the 1980s/90s. People got into Python because they liked how the code looked:

> A friend of mine who knows nearly all the widely used languages uses Python for most of his projects. He says the main reason is that he likes the way source code looks. That may seem a frivolous reason to choose one language over another. But it is not so frivolous as it sounds: when you program, you spend more time reading code than writing it. You push blobs of source code around the way a sculptor does blobs of clay. So a language that makes source code ugly is maddening to an exacting programmer, as clay full of lumps would be to a sculptor.

- Paul Graham (http://www.paulgraham.com/pypar.html, August 2004)

One thing to consider is that scientists are not necessarily programmers, there is an overlap, especially in data science, but from my experience, they tend to write terrible code. It is not to be dismissive, it is just a different skill set, there would be no need for professional programmers if scientists could do better and vice versa.

And Python is very interesting in that it is actually difficult to write terrible code with it, forced indentation and clean syntax certainly helps, it also heavily promotes the one "pythonic" way. Contrast with Perl "there is more than one way to do it" philosophy.

It is not that you can't make a huge mess with Python, but it only tends to happen at the intermediate level, like when you are starting to write libraries but have not yet reached mastery.

The example for "looks_like_number" has probably be chosen, because many experienced Perl programmers are familiar with that.

In Raku, you can use kebab-case, and if there would be such a routine in Raku, it would probably be called "looks-like-number", and return a Bool, as in either True or False.

"say" is also builtin in Perl, at least for the past 12 years or so. And if you are on a Mac, running "say this is your computer talking to you', it will talk to you.

In any case, the thing you appear to refer to, is in no way a tutorial, but rather documentation intended for a specific group of people: Perl programmers.