Comment by rjbwork

thank you for putting this this way. it helped me crack a big unrelated problem

I'm hoping one day people look at Python the same way they look at Perl and PHP.

There has been so much programming language innovation in the past decade that I'm surprised someone hasn't built a better Python. I'd like to see something like Rhai or Roto take off which has a natural upgrade path to Rust.

Maybe it's because interpreted languages just aren't in vogue anymore and probably won't be for a long time if humans aren't even coding anymore.

Roughly the time when Guido stopped fighting the hordes. The hordes won, wrested the language away from its creator and now they own the pieces. 2 to 3 was the worst possible way a language has ever been upgraded bar none. Hilariously, even today Python 2 persists in many places because of this.

> thus hardlinking a whole python installation in and making you "activate" it first

You don't have to activate a venv to use it; `$VENV_PATH/bin/python3` just works.

(It also softlinks/symlinks the binaries.)

Pretty much every other mainstream language bar JS. I'd say also Ruby because I also do not like a lot of its choices, but there's at least a kind of heady elegance to it in a REPL, though I would never want to work on a production system written in Ruby.

Frankly, I just really prefer statically and strongly typed compiled languages.

I did my last company's first Demo and MVP in Python after running through the Koans at my co-founder's behest and ultimately we decided that the production system would not be done in Python. There are some cool things about it - specifically I do like that annotations are real AOP seams, but the numerous footguns and lack of types and a compiler just make it a very annoying ecosystem to work in for me.

They are a heavy C# user. Coke/Pepsi situation.

20 years working on Python. Two things have driven its success.

First, it's easy to learn and reasonably powerful. You can make a lot of useful things really quickly, which was true even 15 years ago. It's only gotten easier since, and the ecosystem has continued to grow.

Second, ML latched onto it. That gave Python a lot of credibility for people coming through CS programs and into industry, especially as AI use cases were picking up momentum. Think Tensorflow starting 10-ish years ago, then Pytorch, and so on.

[opinion time] Unfortunately, ML has contributed to disaster. The quality of software engineering in ML applications lags badly behind that in other segments, and there's where most Python development is happening now. The feedback loop of training Bad Python into LLMs is only making it worse. The skill floor was dropping, and now it's practically zero.

It also wasn't helped much by language design decisions. Gradual typing was a huge mistake. It would have been much better to go the Typescript route and define a superset of the language that compiles to native Python. The grafted-in typing features that rely on runtime behavior and never got an actual spec for a type system haven't helped.

It's incredibly easy for new engineers to pick up. On the spectrum between "features easy for beginners but nightmarish for experienced engineers" and "features complex for beginners but great for experienced engineers" it always makes the tradeoff for the former.

https://en.wikipedia.org/wiki/Worse_is_better

We've known for a very long time that shitty things can become very successful in this field and why.

Probably the batteries included Standard Library.

Cross platform interpreter.

NumPy and SciPy really set the stage.

I'm not the OP, but it seemed to me that major teaching institutions settled on Python as a pedagogic language after previously having rallied around Java for same, and various other languages before that.

BASIC rebooted. Right at the right time too, BASIC was never going to thrive in the days of the internet.

I'll give Python one thing though, it is by far the most elegant way to glue together a large number of otherwise incompatible C libraries.

They provide binaries for almost every open source library for every popular platform. If you need something, whatever it is, python was and is probably the easiest way to install and use it.

well, for one, my boss can use it and kinda understand it, so it must be dead simple

Definitely ease of use...

It was the first language that actually felt ready-to-use, available for free-as-in-speech. Batteries included, robust online development community, sensible featureset, main libs that were designed bottom-up instead of top-down. I remember learning Java 1.2 or 1.4 after learning Python and it felt like a huge regression. Swing was a joke compared to TkInter (which was weak compared to proper GUI editors like VB but at least made nice scalable native-feeling GUIs and had a sensible API) and the standard collections were miserable without generics.

But Python peaked like 20 years ago. The later features were too ugly, and it was too slow to fill the obvious gaps in the language.

Marketing in academia, decent C-API that led to use in scientific Python.

The language is a disaster since it deviated from its original purpose. The community is ruled by insane, mediocre narcissists who drove away all reasonable people.

- Math + visualization libraries (which ironically have to break out of Python to do their job)

- Reasonable ergonomics for describing data flows, as long as you never run the code, which you often don't care about if you're a mathematician

- Adoption by people who optimize pumping citations, not reproducible results -- non-working code is a feature when you're trying to BS

Not having to build still saves wall clock time. Also Python is a concise language (fewer tokens in context) with a monster ecosystem that avoids a lot of code having to written by agents (fewer tokens for the current task and fewer for future tasks by keeping the code base smaller).

Rust has Rhai and Roto too if you need better than Python level of developer considerations.

The issue is that it just becomes a “bad at everything” tarpit.

- performance is awful.

- gradual typing is sort of ok, but relies too much on runtime behaviour.

- async support is a bit all over the place.

- uv has made the packaging experience sort of liveable, but collectively it’s still very limited: feature support, conditional dependencies, rudimentary tree shaking, cargo-style workspaces, etc.

In an era where your options of tech stack are freer than ever, unless you explicitly need PyTorch, I’m not really sure why you’d choose Python over literally everything else.

List comprehensions? Really? I remember when they were pretty and I was learning them alongside other languages with higher order functions and Python's approach seemed to be the ugliest of its cohort. They were neither as logical as simple Lispy "take a function as a parameter" things like Ruby, nor were they the kind of clean and legible stuff we expect from Python.

The fact that they didn't solve Venv when they did the breaking change in Python 3 is absolutely bonkers.

The syntax and meaningful spaces are annoying. The dynamic typing is abused to hell in everything including the standard library. It has wacky paradigms like "exceptions instead of conditionals" or having "else" clause for loops.

It lacks decent packaging and by design cannot handle multiple versions of dependencies or perform metadata-only/declarative resolution. To resolve the dependencies of a Python package you need to install it. Many Python packages resolve their dependencies imperatively depending on the current system state. When you need binary dependencies like numpy it gets even worse since libpython doesn't have a stable ABI.

It is one of the most underdesigned languages but it holds a dear place due to being easier to translate from pseudocode to actual implementation. This resulted many academics to pick it up and write advanced math libraries in it.