This doesn’t answer your question, but Python has no equivalent for Perl Pie or the other inline terminal features, so for shell one-liners Perl is still heavily relevant because Python doesn’t offer the same functionality.
Ask HN:Why is Perl so dwarfed in data science by Python?
41–50 of 120 posts
Re: Ask HN:Why is Perl so dwarfed in data science by Python?
#42I love perl for regex scripts, where I need to quickly filter or transform a text file. I never liked it for other kinds of programming projects, for some reason to me it doesn’t feel as well suited to, say, numeric simulations. > Naturally far more efficient What does this mean exactly? One big reason for Python’s success in data science is numpy, which is far more efficient (especially on large data) than vanilla P…
> I’m unaware of the state of the Perl ecosystem, does it have something similar [to numpy]?
Perl has PDL (http://pdl.perl.org), which I think predates numpy and does the same kind of array manipulation.
As for the original question, Python's dominance for "data science" projects is just a matter of momentum at this point: it's where Google and others have put their money, and that is now affecting undergraduate and graduate courses. Originally, I think it was just that some people didn't like Perl's syntax.
Personally, I prefer to use a mix of Perl and Julia, each for its respective strengths, and find Python all-around mediocre.
Re: Ask HN:Why is Perl so dwarfed in data science by Python?
#43Does Perl have mature equivalents for pandas and sklearn (setting aside what everyone else is saying about numpy)? The python ecosystem has a bunch of killer apps that make the workaday tasks of data science extremely ergonomic. R is similar with the tidyverse imo, but I don’t know of other languages with a comparable package landscape.
Quick addendum: data science != computer science, most data scientists learn coding on top of another skillset, not as their primary area of expertise, so things like under-the-hood efficiency are often second order concerns to learn-ability, ease of use and maintenance.
Re: Ask HN:Why is Perl so dwarfed in data science by Python?
#44Circa 2000 I did a lot of unix scripting with Perl and also wrote cgi-scripts for the web with Perl.
In the cgi-script mode Perl had to start a new process and compile all your code for each request. There was "mod_perl" which was more efficient but frequently you struggled with memory leaks and other reliability problems.
PHP came out and then Apache Tomcat, web hosting systems for Ruby, etc. all of which had efficiency similar to mod_perl but easy and reliable environments to work in. Generally there were many modules in CPAN that were essential to web development (HTML escaping) for which bugs were not getting fixed and that added to the feeling that Perl was slipping behind.
As for more general scripting I think people found Python was better. Even though it is cross-platform, Perl has a strong UNIX feel to it. Python doesn't feel like it belongs to Windows, UNIX or any other environment, rather it feels comfortable anywhere.
Re: Ask HN:Why is Perl so dwarfed in data science by Python?
#45Python has a very simple and consistent and unsurprising syntax compared to Perl. I think most programmers from other languages can look at Python and feel like they're reading pseudo-code that they actually understand for most operations. So for people coming from a mathematical or scientific background, it unlocks computing abilities without having to learn a bunch more knowledge from another domain. Add to that the success of libraries like NumPy and SciPy that round out the capabilities of the language itself and make many practical tasks very accessible, and it's just a nuke-from-orbit type situation for most other languages.
I mean, even the author of Learning Perl, said "sometimes Perl looks like line noise to the uninitiated, but to the seasoned Perl programmer, it looks like checksummed line noise with a mission in life."
Re: Ask HN:Why is Perl so dwarfed in data science by Python?
#46Not much more than an anecdote, but I found working with julia quite a lot of fun. It was not data science, but since julia is more or less aimed at data science, I'd predict that my positive experience means julia is a very good choice there too.
There is the "time to first plot" issue, and I found that the "efficient as C, easy as Python" motto really means "efficient as C XOR easy as Python", but all in all, it's very easy to write stuff very cleanly, and the path to effiency is quite natural if you know where to look, and the metaprogramming makes it that more powerful.
Re: Ask HN:Why is Perl so dwarfed in data science by Python?
#47C is more efficient than both of them. For that matter, so are tons of other languages.
For that matter, I'm not sure what you mean when you say Perl is "naturally more efficient" than Python. There's nothing about Perl that makes it easy to run faster, and the ability to write incomprehensible one-liners is not a very satisfying measure of "efficiency".
As for why people choose Python over Perl, Perl is a pain in the ass in a lot of ways, and I say this having written thousands of lines of Perl back in the day. Dollar signs on variable names? Obvious bad code turning out to be syntactically correct but do weird stuff, because of strange irregular legacy syntax rules? Library code being unreadable because of the aforementioned incomprehensible one-liners?
Python is bad enough about not finding errors until the code blows up in weird ways, but Perl is worse.
Re: Ask HN:Why is Perl so dwarfed in data science by Python?
#48A lot of this ground was covered when PDL - the Perl Data Language, born 1996 - was submitted to HN in its own right last summer: https://news.ycombinator.com/item?id=27439638
Re: Ask HN:Why is Perl so dwarfed in data science by Python?
#49Actually, Perl was a quite natural choice for a while for some computational biology / bioinformatics workloads. In particular, defining scripts where you expect a sequence (like DNA) as input and a filtered or modified sequence as output allowed for processing pipelines that just flowed nicely: script1 | script2 | final_script.
It's been a while since I was in that field, but I suspect those kind of low level operations are now heavily optimized in faster languages as sequences to operate on became longer and operations more complex.
Re: Ask HN:Why is Perl so dwarfed in data science by Python?
#50Perl is expressive but the code can be hard to read. In general, Python is readable because it enforces the indentation and other language design choices. Python also got heavyweight libraries such as Numpy and Pandas which put it in the front. Perl do not have have such well known libraries as far as I know.
The last time I wrote production Perl code (2012) I stressed about keeping it readable, especially by anyone who would be learning Perl. The company was mostly C/C++ focused with maybe Python as a #3. I think Perl-isms can be foreign if you haven't seen them, so they should be avoided in any non-Perl focused company.
Looking now (it's open source) there's two keywords I have forgotten. It's a Perl binding to a C library using an old version of Swig. There's lots of Perl or C helper code to smooth out the bumps. It also looks like it hasn't been updated (bummer).