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Isn't the author saying that Python + Pandas is almost as good as R, but Python without Pandas is less powerful than R. I can't help to conclude that Python is as good as R because I still have the choice of using Pandas when I need it. What did I get wrong?
also, we didn't define "good".
From many practical points, Clojure is great for data. And you can even leverage python libs via clj-python.
- [1] https://scicloj.github.io
I think a lot of this comes down to the question: Why aren't tables first class citizens in programming languages? If you step back, it's kind of weird that there's no mainstream programming language that has tables as first class citizens. Instead, we're stuck learning multiple APIs (polars, pandas) which are effectively programming languages for tables. R is perhaps the closest, because it has data.frame as a 'firs…
What is a table other than an array of structs?
Best part is, write a --help, and you can load them into LLMs as tools to help the LLMs figure it out for you.
Fight me.
Earlier quoted context omitted.
What is a table other than an array of structs?
I would argue that's about how the data is stored. What I'm trying to express is the idea of the programming language itself supporting high level tabular abstractions/transformations such as grouping, aggregation, joins and so on.
it was easy to think about the structures (iterators) it was easy to extend. it had a good community.
And for that, people start extending it via libraries.
There are plenty more alternatives now.
What makes Python a great language for data science, is that so many people are familiar with it, and that it is an easy language to read. If you use a more obscure language like Clojure, Common Lisp, Julia, etc., many people will not be familiar with the language and unable to read or review your code. Peer review is fundamental to the scientific endeavor. If you only optimize on what is the best language for the ta…
Earlier quoted context omitted.
That's not why it's used in data science though. Lots of data scientists use Python all day and have no concept of ever working in a different field. It's used in data science because it's used in data science.
It's used in data science because no other language has this level of library support. And it got this unprecedented level of support because right from the start it made its focus clear syntax and (perceived) simplicity. There is also a sort of cumulative effect from being nice for algorithmic work. Guido's long-term strategy won over numerous other strong candidates for this role.
1. data scientists aren't programmers, so why do they need a programming language? the tools they should be using don't exist. they'd need programmers to make them, and all we have to offer is... more programming languages.
2. the giant problem at the heart of modern software: the most important feature of a modern programming language is being easy to read and write. this feature is conspicuously absent from most important languages.
they're trapped. they can't do what they need without a programming language but there are only a handful they can possibly use. the real reason python ended up with such good library support is they never really had a choice.