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> Examples include converting boxplots into violins or vice versa, turning a line plot into a heatmap, plotting a density estimate instead of a histogram, performing a computation on ranked data values instead of raw data values, and so on.

Most of this is not about Python, it’s about matplotlib. If you want the admittedly very thoughtful design of ggplot in Python, use plotnine

> I would consider the R code to be slightly easier to read (notice how many quotes and brackets the Python code needs)

This isn’t about Python, it’s about the tidyverse. The reason you can use this simpler syntax in R is because it’s non-standard-evaluation allows packages to extend the syntax in a way Python does not expose: http://adv-r.had.co.nz/Computing-on-the-language.html

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"The reason you can use this simpler syntax in R is because it’s non-standard-evaluation ..."

So it actually is about Python vs R.

That said, while this kind of non-standard evaluation is nice when working interactively on the command line, I don't think it's that relevant when writing code for more elaborated analyses. In that context, I'd actually see this as a disadvantage of R because you suddenly have to jump through loops to make trivial things work with that non-standard evaluation.

The increasing prevalence of non-standard evaluation in R packages was one of the major reasons I switched from R to python for my work. The amount of ceremony and constant API changes just to have something as an argument in a function drove me mad.

> nd constant API changes

Yeah, this was so very very painful. I once ended up maintaining a library that basically used all the different NSE approaches, which was not very much fun at all.

>> I would consider the R code to be slightly easier to read (notice how many quotes and brackets the Python code needs)

Oh god no, do people write R like that, pipes at the end? Elixir style pipe-operators at the beginning is the way.

And if you really wanted to "improve" readability by confusing arguments/functions/vars just to omit quotes, python can do that, you'll just need a wrapper object and getattr hacks to get from `my_magic_strings.foo` -> `'foo'`. As for the brackets.. ok that's a legitimate improvement, but again not language related, it's library API design for function sigs.

Upvoted for pipes at the beginning

IIRC, putting pipe operator `|>` at end of line prevents the expression from terminating early. Otherwise the newline would terminate it.

The right way is putting the pipe operator at the beginning of the expression.

  (-> (gather-some-data)
    (map 'Vector #'some-functor)
    (filter #'some-predicate)
    (reduce #'some-gatherer))

Or for those who have an irrational fear of brackets:

  ->
    gather-some-data
    map 'Vector #'some-functor
    filter #'some-predicate
    reduce #'some-gatherer

Or seaborn. It was built exactly for this purpose: abstracting some of the annoying kinks of matplotlib while still offering a rich set of features.

https://seaborn.pydata.org/tutorial/introduction.html

I wonder what the last example of "logistics without libraries" would look like in R. Based on my experience of having to do "low-level" R, it's gonna be a true horror show.

In R it's often that things for which there's a ready made libraries and recipes are easy, but when those don't exist, things become extremely hard. And the usual approach is that if something is not easy with a library recipe, it just is not done.

The way you describe it, can we say that R was AI-first without even knowing?

R is overtly and heavily inspired by Lisp which was a big deal in AI at one point. They knew what they were doing.

Python: easy things are easy, hard things are hard.

R: easy things are hard, hard things are easy.

> This isn’t about Python, it’s about the tidyverse.

> it’s non-standard-evaluation allows packages to extend the syntax in a way Python does not expose

Well this is a fundamental difference between Python and R.

The point is that the ability to extend the syntax of R leads to chaos and mess (in general) but when used correctly and effectively in the tidyverse, improves the experience of writing and reading code.

Python is nothing without it’s batteries.

Python is its batteries.

But why whenever I try to use it, it tries to hurt me like it's kicking me right in my batteries?

The design and success of e.g. Golang is pretty strong support for the idea that you can't and shouldn't separate a language from its broader ecosystem of tooling and packages.

The success of python is due to not needing a broader ecosystem for A LOT of things.

They are of course now abandoning this idea.

> The success of python is due to not needing a broader ecosystem for A LOT of things.

I honestly think that was a coincidence. Perl and Ruby had other disadvantages, Python won despite having bad package management and a bloated standard library, not because of it.

It's because Ruby captured the web market and Python everything else, and I get everything is more timeless than a single segment.

Ruby was competing on the web market and lost to many others, including Python. In part, because python had a much broader ecosystem, and php had wide adoption through wordpress and others, and javascript was expanding from browsers.

The bloated standard library is the only reason I kept using python in spite of the packaging nightmare. I can do most things with no dependencies, or with one dependency I need over and over like matplotlib

If python had been lean and needed packages to do anything useful, while still having a packaging nightmare, it would have been unusable

Well, sure, but equally I think there would have been a lot more effort to fix the packaging nightmare if it had been more urgent.

There was a massive effort though, the proliferation of several different package managers is evidence of that.

Maybe. A lot of them felt like one-person projects that not many people cared about. I think that on the contrary, part of the reason so many different package managers could coexist with no clear winner emerging was that the problem wasn't very serious for a lot of the community.

The bloated standard library is the reason why you can send around a single .py file to others and they can execute it instantly.

Most of the python users are not able nor aware of venv, uv, pip and all of that.

What language is used to write the batteries

C/C++, in large part

And below that, FORTRAN :)

These days it's a whole lot of Rust.

These days it’s still a whole lot of Fortran, with some Rust sprinkled on top. (:

Which since Fortran 2003, or even Fortran 95, has gotten rather nice to use.

IDK it's become too verbose IMHO, looks almost like COBOL now. (I think it was Fortran 66 that was the last Fortran true to its nature as a "Formula Translator"...)

We are way beyond comparing languages to COBOL, now that plenty folks type whole book sized descriptions into tiny chat windows for their AI overloads.

I hear this so much from Python people -- almost like they are paid by the word to say it. Is it different from Perl, Ruby, Java, or C# (DotNet)? Not in my experience, except people from those communities don't repeat that phrase so much.

The irony here: We are talking about data science. 98% of "data science" Python projects start by creating a virtual env and adding Pandas and NumPy which have numerous (really: squillions of) dependencies outside the foundation library.

Someone correct me if I'm completely wrong, but by default (i.e. precompiled wheels) numpy has 0 dependencies and pandas has 5, one of which is numpy. So not really "squillions" of dependencies.

pandas==2.3.3

├── numpy [required: >=1.22.4, installed: 2.2.6]

├── python-dateutil [required: >=2.8.2, installed: 2.9.0.post0]

│ └── six [required: >=1.5, installed: 1.17.0]

├── pytz [required: >=2020.1, installed: 2025.2]

└── tzdata [required: >=2022.7, installed: 2025.2]

I don't know about _squillions_, but numpy definitely has _requirements_, even if they're not represented as such in the python graph.

e.g.

  https://github.com/numpy/numpy/blob/main/.gitmodules (some source code requirements)
  https://github.com/numpy/numpy/tree/main/requirements (mostly build/ci/... requirements)
  ...

They're not represented, because those are build-time dependencies. Most users when they do pip install numpy or equivalent, just get the precompiled binaries and none of those get installed. And even if you compile it yourself, you still don't need those for running numpy.

Read https://numpy.org/devdocs/building/blas_lapack.html.

NumPy will fall back to internal and very slow BLAS and LAPACK implementations if your system does not have a better one, but assuming you're using NumPy for its performance and not just the convenience of adding array programming features to Python, you're really gonna want better ones, and what that is heavily depends on the computer you're using.

This isn't really a Python thing, though. It's a hard problem to solve with any kind of scientific computing. If you insist on using a dynamic interpreted language, which you probably have to do for exploratory interactive analysis, and you still need speed over large datasets, you're gonna need to have a native FFI and link against native libraries. Thanks to standardization, you'll have many choices and which is fastest depends heavily on your hardware setup.

The wheels will most likely come with openblas, so while you can get the original blas (which is really only slow by comparison, for small tasks it's likely users won't notice), this is generally not an issue.

It's not about Python, it's about how R lets you do something Python can't?

R is more of a statistical software than a programming language. So, if you are a so-called "statistician," then R will feel familiar to you

No, R is a serious general purpose programming language that is great for building almost any type of complex scientific software with. Projects like Bioconductor are a good example.

Perhaps a in a context of comparison with Python?

In my limited experience, Using R feels like to using JavaScript in the browser: it's a platform heavily focused on advanced, feature-rich objects (such as DataFrames and specialized plot objects). but you could also just build almost anything with it.

No, it's not. Even established packages have bugs caused by R weirdness. I like it nevertheless.

Care to give some examples?

I already did in my comment

Hmm? I was referring to blubber's claim that "established packages have bugs caused by R weirdness."

Yes, R is a proper general purpose programming language. Turing complete, functional, procedural, object oriented.../

Just in case someone reads this far and sees blubber's confident "No." Blubber is definitely wrong here. I used to do all of my programming in R. Throw the question into an LLM if you're wondering if R has a package like ___ in python.

I know people who used Visual Basic for all of their programming. I'd say No either way unless people explained to me without bursting out into laughter that they also have extensive experience with, e.g., Kotlin, Rust, C#, Java etc. and still prefer VB or R for non-trivial programs.

Of course R isn't a complied language and probably not the same category as C/Rust as systems language but is not in the same category as VB. R is a serious scientific programming language used in non-trivial programs for industrial applications. See Posit's customers. I suggest John Chambers ( https://en.wikipedia.org/wiki/John_Chambers_(statistician) ) book, he explain how he designed S language, R's grandfather so to speak, Software for Data Analysis ( https://link.springer.com/book/10.1007/978-0-387-75936-4 ).

This isn't about compilation vs interpretation. R is simply badly designed as a programming language. This doesn't change just because its inventor wrote a book.

I do.

And I'm still waiting for your examples of "established R packages with bugs caused by R weirdness".

blubber, I think there might be some misconceptions. Just for the record.

R is not actually competing with those languages. R's design purpose is different. it is a general purpose computational language for scientists. There are FFIs (Foreign Function Interfaces) for all those languages.

R-Kotlin-Jave: https://journal.r-project.org/articles/RJ-2018-066/ R-Rust: https://cran.r-project.org/web/packages/using_rust.html R-C# : https://github.com/Open-Systems-Pharmacology/rsharp/

R is supporting C integration natively anyhow (see Chambers's book.

Regarding VB reference. VB was used in finance a lot to do some advanced maths. just a side remark.

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