In 2005 Google hired Guido van Rossum, where he spent half of his time developing the Python language.
As you can see, before 2006, Python was little less popular than Perl(9th vs 8th).
By the time Guido left Google(December 2012), Python became 4th most popular programming language.
> I vehemently disagree. Readability is the most important consideration. It's "subjective", but most human "subjects" prefer Python to Perl on this dimension, and it's very important to them. That's why we are where we are today.
I absolutely agree that readability is most important consideration. I just disagree that it depends on the language. As said before, there is a lot of write-only code written by data scientists in Python.
> 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.
You don't have to. It's called a programming language paradigm, not noise. You either learn it or not when studying a language. If you want to understand something without studying it, it's your choice.
I am proficient in both of these languages and can see the strengths and weaknesses in both of them.
> 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.
Perl was extensively used by scientific community, but Python was the lucky one that was backed by Google in 2005. And that's ok. It is better than Perl for data science now, but not because of its syntax :)
Readability is really a weak argument.
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.
The concept behind sigils is very simple and once you've learned it, it's not that threatening. 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.
In my opinion, Perl is not used for data science because it lacks(or people are not aware of) libraries like numpy, pandas, etc.
Here are some stats with the popularity of various languages over time: https://statisticsanddata.org/data/the-most-popular-programm...
In 2005 Google hired Guido van Rossum, where he spent half of his time developing the Python language. As you can see, before 2006, Python was little less popular than Perl(9th vs 8th). By the time Guido left Google(December 2012), Python became 4th most popular programming language.