> I think people way over-index Python as the language for data science. It has limitations that I think are quite noteworthy. There are many data-science tasks I’d much rather do in R than in Python.1 I believe the reason Python is so widely used in data science is a historical accident, plus it being sort-of Ok at most things, rather than an expression of its inherent suitability for data-science work.

Python doesn't need to be the best at any one thing; it just has to be serviceable for a lot of things. You can take someone who has expertise in a completely different domain in software (web dev, devops, sysadmin, etc.) and introduce them to the data science domain without making them learn an entirely new language and toolchain.

Article is well written but fails to address its own thesis by postponing it to a sequel article. At its current state only alludes that Python is not great because requires specialized packages. (And counterexample is R for which also used a package.)

A little late for this

From many practical points, Clojure is great for data. And you can even leverage python libs via clj-python.

R is so good in part because of the efforts of people like Di Cook, Hadley Wickham, and Yihui Xie to create an software environment that they like working in.

It also helps that in R any function can completely change how its arguments are evaluated, allowing the tidyverse packages to do things like evaluate arguments in the context of a data frame or add a pipe operator as a new language feature. This is a very dangerous feature to put in the hands of statisticians, but it allows more syntactic innovation than is possible in Python.

I was a bit disappointed to discover that this was essentially an R vs. Python article, which is a data science trope. I've been in the field for 20+ years now and while I used to be firmly on team R, I now think that we don't really have a good language for data science. I had high hopes for Julia and even Clojure's data landscape looks interesting, but given the momentum of Python I don't see how it could be usurped at this point.

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 'first class citizen', but most people don't seem to use it, and use e.g. tibbles from dplyr instead.

The root cause seems to be that we still haven't figured out the best language to use to manipulate tabular data yet (i.e. the way of expressing this). It feels like there's been some convergence on some common ideas. Polars is kindof similar to dplyr. But no standard, except perhaps SQL.

FWIW, I agree that Python is not great, but I think it's also true R is not great. I don't agree with the specific comparisons in the piece.

Seems like their critique boils down to two areas - pandas limitations and fewer built ins to lean on.

Personally I've found polars has solved most of the "ugly" problems that I had with pandas. It's way faster, has an ergonomic API, seamless pandas interop and amazing support for custom extensions. We have to keep in mind Pandas is almost 20 years old now.

I will agree that Shiny is an amazing package, but I would argue it's less important now that LLMs will write most of your code.

R data science people generally come to data science field from life science or stats field. Python data science people generally originate from other fields that are mostly engineering focused. Again this may not apply to all the cases but that is my general observation.

Recently I am seeing that Python is heavily pushed for all data science related things. Sometimes objectively Python may not be the best option especially for stats. It is hard to change something after it becomes the "norm" regardless of its usability.

The problem is there's so much momentum behind it that's hard to course correct. PyTorch is now a goliath.

Excellent article - except that the author probably should have gated their substantiation of the claim behind a cliffhanger, as other commenters have mentioned.

The author's priorities are sensible, and indeed with that set of priorities, it makes sense to end up near R. However, they're not universal among data scientists. I've been a data scientist for eight years, and have found that this kind of plotting and dataframe wrangling is only part of the work. I find there is usually also some file juggling, parsing, and what the author calls "logistics". And R is terrible at logistics. It's also bad at writing maintainable software.

If you care more about logistics and maintenance, your conclusion is pushed towards Python - which still does okay in the dataframes department. If you're ALSO frequently concerned about speed, you're pushed towards Julia.

None of these are wrong priorities. I wish Julia was better at being R, but it isn't, and it's very hard to be both R and useful for general programming.

Edit: Oh, and I should mention: I also teach and supervise students, and I KEEP seeing students use pandas to solve non-table problems, like trying to represent a graph as a dataframe. Apparently some people are heavily drawn to use dataframes for everything - if you're one of those people, reevaluate your tools, but also, R is probably for you.

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 task, there are clearly better languages than Python. If you optimize on what is best for science then I think it is hard not to argue that Python (and R) are the best choices. In science, just getting things done is not enough. Other people need to be able to read and understand what you are doing.

BTW AI is not helping and in fact is leading to a generation of scientists who know how to write prompts, but do not understand the code those prompts generate or have the ability to peer review it.

For what is worth. The Kotlin folks have been adding some cool features and tools for data analysis. https://kotlinlang.org/docs/data-analysis-overview.html

Python is just a language that:

1. Is easy to read

2. Was easy to extend in languages that people who work with scientific data happen to like.

When I did my masters we hacked around in the numpy source and contributed here and there while doing astrophysics.

Stuff existed in Java and R, but we had learned C in the first semester and python was easier to read and contrary to MATLAB numpy did not need a license.

When data science came into the picture, the field was full of physicists that had done similar things. They brought their tools as did others.

Inherited Python code is a mixed bag. Inherited R code is a nightmare.

Languages inherently have network effects; most people around the world learn English so they can talk with other professionals who also know English, not because they are passionate about Charles Dickens.

My take (and my own experience) is that python won because the rest of the team knows it. I prefer R but our web developers don't know it, and it's way better for me to write code that the rest of our team can review, extend, and maintain.

When you think about a data science pipeline, you really have three separate steps:

[Data Preparation] --> [Data Analysis] --> [Result Preparation]

Neither Python or R does a good job at all of these.

The original article seems to focus on challenges in using Python for data preparation/processing, mostly pointing out challenges with Pandas and "raw" Python code for data processing.

This could be solved by switching to something like duckdb and SQL to process data.

As far as data analysis, both Python and R have their own niches, depending on field. Similarly, there are other specialized languages (e.g., SAS, Matlab) that are still used for domain-specific applications.

I personally find result preparation somewhat difficult in both Python and R. Stargazer is ok for exporting regression tables but it's not really that great. Graphing is probably better in R within the ggplot universe (I'm aware of the python port).

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?

Disclaimer: I have nothing against R or Python and I'm not partial to either.

Python, the language itself, might not be a great language for data science. BUT the author can use Pandas or Polars or another data-science-related library/framework in Python to get the job done that s/he was trying to write in R. I could read both her R and Pandas code snippets and understand them equally.

This article reads just like, "Hey, I'm cooking everything by making all ingredients from scratch and see how difficult it is!".

You could end it with "Python is not a great language".

Now, is Python a SUCCESSFUL language? Very.

They basically advocate using R. I think it depends what they mean by "data science" and if the person will be doing just data science. If that's the case then R may be better. As in their whole career is going to built on that domain. But let's say they are on a general computer science track, now they'll probably benefit from learning Python more than R, simply because they can use it for other purposes.

> Either way, I’ll not discuss it further here. I’ll also not consider proprietary languages such as Matlab or Mathematica, or fairly obscure languages lacking a wide ecosystem of useful packages, such as Octave.

I feel, to most programming folks R is in the same category. R is to them what Octave is to the author. R is nice nice, but do they really want to learn a "niche" language, even if it has better some features than Python? Is holding a whole new paradigm, syntax, library ecosystem in your head worth it?