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

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

#3

Post by kasperset »

Perl 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.

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

#6

Post by teekert »

I got into Python because I outgrew Excel (and Origin) and started with Jupyter notebooks and Pandas. The Pandas functions DataFrame.from_excel and DataFrame.to_excel combined with how visual a notebook is made the transition very easy for me.

I do come across some perl every now and then but it looks like Bash on steriods to me (perhaps because of the $variables), and I never see any DataFrames or similar structure that looks familiar to me.

Idk, Perl never occurred to me and nobody ever recommended it. Is Perl good for data science? Do you have any examples? I never ran into "efficiency problems" with Python btw, I'm not using it at that scale, at what scale would I notice this? I have run out of ram at times, but that's usually when a dataset on disk is already larger than my ram. But then I usually find a tool to deal with the data anyway (for example pyosmium for super large osm/o5m fles).

Edit: I feel that Python also gives me other nice things to get started with, things like Django and Snakemake. This also leads me to recommend Python to other people, it's a broad basis for a lot of stuff. That's why even though some people recommended R when I got started, I choose Python anyway. I have no regrets, unless you are going to blow my mind with some examples...

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

#8

Post by city41 »

kasperset wrote: post #3

Perl 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.

Perl seems to have died down just about everywhere. I've not used it professionally or seen it in use in well over a decade. It's so expressive and has so many ways to do things that reading other people's Perl can be challenging. I think this contributed to its downfall. And "other people" can often mean "yourself in six months" too.

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

#9

Post by ldiracdelta »

hprotagonist wrote: post #4

tooling and network effects. perl doesn’t have the numerical chops to keep up, and if it started to fix that now, it has 20 years of headwind to fight through for probably marginal gains. Good luck.

Perl is also a nasty language to work with. Incredibly ugly. Even if perl was the standard for data science, I would be looking to escape it at every opportunity. In fact, that's what I did when I worked in a sector that had tooling and network effects for perl. I tried to escape using perl every chance I could.
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