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However, it might be a bit too much if you are from other fields and just want to get your sums in order.
dataframe["A"] + dataframe["B"]
Both Python and Perl made bold transitions, Python from 2 to 3 and Perl from 5 to 6.
The Python transition was difficult but ultimately successful. On the other hand, Perl rolled the dice and lost. In some alternate universe it might have been the other way around.
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 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.
This. For the kind of tasks data science involves, you either write C++ or you need the equivalent of Numpy. No Numpy means no Pandas, which means no sane way to wrangle datasets.
For all the hate it gets, Numpy is a world-class library.
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.
Amen to this point. I am currently the single engineer in a small software biz and after the last engineer left I thought to myself "well, at least I won't have to decipher his code anymore"...needless to say, I've been shocked by how much time I can spend on a system only to revisit it within a year and have little to no memory of how or why I did something a certain way!
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.
I think R is worse but it is bundled with a very good stats library. R users aren't so interested in "programming in the large" or even the small so it is OK.
Other than the meaningful indentation Python strikes me as a generic and normal programming language. I'm quite amused at how Python and Java got pattern matching at about the same time with a broadly similar approach to "fit this new feature in this language so that it feels like it belongs and plays well with decades of existing practice".
> 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 Python. I’m unaware of the state of the Perl ecosystem, does it have something similar?
> how come Perl lags far behind or gets dwarfed in data science by Python?
Perl seems to be lagging behind in general, no? Is there anything specific about data science where Perl should be shining?