Why Is Python So Popular in 2025?

My opinion: Python may not be the best at everything it does, but it's in the top 3-5 languages in the following areas:

Very easy to install, write and understand
Great libraries for a lot of applications
Large community, lots of people with experience in it

It will always be a practical choice for those reasons. There are probably a lot more as well that I can't think of at the moment.

I would add its a easy Scripting language. No compilation problem, richer that shell/bash makes it a powerfull choice.
And a really dont like it.
- It is far more then that. It is a full up programming language.
  I never understand why people think compilation is a barrier. But sure most python is not compiled.
- Yeah, you can use it both for full applications (web or desktop) as well as simple scripts. The flow of getting from something simple to a full blown application is pretty smooth and natural - better than just starting out in Java or C++ and taking at least an hour before you get anything that runs.
Python is not the best at anything, but is the second best at almost everything. That's the reason I use it. Everything I need to do, I know I can do it in python, maybe isn't going to be the better or faster it could be, but it will work.
Very easy to install
This has to be a joke.
- Yeah, thought the same. Python isn't intuitive to install new packages.
The libraries part is enormous. Researchers just pull up math engines, database access is a breeze, and so on.
You got a specific need, there is a library for it. It's like C/C++ without the hassle.
Python is popular because it's whitespace based syntax make noobs think it's easy to read. This and it's pre-installation on Linux made it popular amongst academics who embraced it for data analysis. This lead to a lot of data scientists embracing it and writing libraries for it which created a virtuous cycle in that arena.
And it's a damn shame because Python, and it's whitespace nonsense and lack of type system, is a horribly impractical language for building anything at scale.
- White space nonsense. Yes and no. I do not enjoy tracking down white space issues until I remember the alternative. Matching brackets, writing begin/end statements, and worst of all looking at code that appears well indented until later you find it is not. and it runs totally differently then it looks. Similar issues with semicolons.
  Pretty much everyone uses LSEs these days so frankly matters less either way most of the time.
- It's not made for scale. Use C/C++ or something if you want scale.
- It does make it easier to read. We primarily use indentation to match brackets, so clearly, indentation is the most important readability feature between the two, not brackets.
  Try reading the same piece of code: without brackets, and then without whitespaces and tell me which is better.
  Whitespaces as syntax force you to indent your code properly and not be misleading.
- Typing thing is way over blown. What did suck before Python 3 was indeterminate type result from the / operator. You never know if it would be integer or floating division looking at the code. Now it is floating.
- It has optional typing

It’s kind of crazy how problematic pip is, though. There are enormous ecosystems like conda, poetry, arguably Docker all built around “pip not working right.”

I see so many people want to install vllm or something with like a 95% crash and burn rate if they aren’t already proficient with Docker, complete with the spare disk space to basically ship a whole other machine.

Meanwhile, massively complex Rust or Go or whatever packages… just work. With the native tooling, for me.

To be clear, I like Python, and I believe many issues can be smoothed with time (like improving its JIT and maybe encouraging more typing in codebases). But pip and its ecosystem are forever cursed.

People used to shit on npm all the time, and I believed them, because I didn't have experience with anything else.
Then I tried pip. Holy...I had to beg npm forgiveness for ever doubting it
- npm was bad, for a very long time. Then they fixed it, because they actually listened to other languages and did what they did. Python still refuses to do that. It’s quite insane because Ruby has had a solution for 15+ years at this point and Python could have directly copied it but refuses to. Python is absolutely the worst language I know of in regards to tooling.
- Yeah.
  I mean, npm deserves some healthy fear/skepticism, but everything is relative.
pip is easily the worst thing about Python. But now that we have uv I would say the worst thing is the package/import system. I'm pretty sure only 1% of developers understand it, and it only really works properly if your Python code is a Python package.
If you treat Python as a scripting language and just scatter loose files around your project and run them directly, it doesn't work at all. Pain everywhere. Which is dumb as fuck because that's like 80% of how people use Python.
- Ugh yes I hate the import system too. I have to look it up every time and still don’t understand it, and it’s a hair away from messing up existing projects to the point where sometimes it does.
  I want to love uv, but:
  It breaks some random compiled C packages. I ran into this the other day, and the associated issue on the package was basically “shrug we see it’s breaking, this dev is doing some weird shit”
  I’d prefer to use the optimized/patched build of Python CachyOS provides (and the optimized Python compiled system packages), though this is largely OCD.
  It’s not optimal for some PyTorch stuff, which is its own little ecosystem nightmare
I'm glad it's not just me lol every so often I'll think "I haven't done a python project in a while I'll try FastAPI" or something and I have so many issues just getting the env set up.
- It's so ridiculous that many projects don't even support pip+venv (much less system python packages. shivers). They literally check if that's what you're trying and pre-emptively fail.
  Some projects become impossible to run in any combination because some dependency (looking at you, sentencepiece) no longer works outside a narrow set of circumstances, unless you hand build some obscure github PR, disable all the dependency checks and cross your fingers and hope the wheel builds.
  And I'm sorry, but I don't have 200GB of space, tons of spare RAM, and an intricate docker passthrough setup for every random python project. I just have like four different 3rd party managers (conda/mamba, uv, poetry... what's the other one? Ugh)
uv. Is like cargo for Python.
- It doesn't work with some projects, unfortunately.

I think it's just because it is always recommended as an "easy" language that's good for beginners.

The only other thing it has going for it is that it has a REPL (and even that was shit until very recently), which I think is why it became popular for research.

It doesn't have anything else going for it really.

It's extraordinarily slow
The static type hints are pretty decent if you use Pyright but good luck convincing the average Python dev to do that.
The tooling is awful. uv is a lifesaver there but even with uv it's a bit of a mess.
The package system is a mess. Most people just want to import files using a relative path, but that's pretty much impossible without horrible hacks.
The official documentation is surprisingly awful.
Implicit variable declaration is a stupid footguns.

The actual syntax is not too bad really, but everything around it is.

syntax is bad for list operations, also there are situations where you need to count the number of parentheses you closed, which wouldn't happen of you were able to use fluent interfaces
I think it’s just because it is always recommended as an “easy” language that’s good for beginners.
The only other thing it has going for it is that it has a REPL (and even that was shit until very recently), which I think is why it became popular for research.
If that's the case, then why didn't Javascript take its place instead? It's arguably even better at Python in both of those areas....
- I think Python is superficially easier since you don't have to declare variables, printing is a little easier, etc. And in some ways it is actually easier, e.g. arbitrary precision integers, no undefined, less implicit type coercion.
  But I agree JavaScript is generally a better choice. And it is actually more popular than Python so...
- But worse in everything else. And the runtime-platform is a mess.

The blog gives a few vague answers, none of which shed any light on how Python became so popular. If I had to guess, much of it's popularity came from being embraced as a data analytics tool -- which is honestly a great use case for an interpreted scripting language -- and its subsequent adoption by academia, introducing it to an entire generation of CS grads.

Python has advantages and drawbacks like any programming language. It's not my favorite language and it's not my least favorite. It's not going anywhere anytime soon.

I would argue it's because it is Good Enough. The most popular solutions to things are rarely the optimum ones but the ones that are generally applicable.

For example, I could fight with bash's unpleasant syntax or I could do it more easily (but less efficiently) with Python. Would it be as performant? Absolutely not - but the performance gains wouldn't be worth the time and maintainability.

From an administrator POV, python is great for scripting, automation, and AWS boto work.

Presumably because it minimizes developer time in writing code and is easy to use and it come with great libraries. That is why I have been using it since 1998.

Edit: People use to think I was crazy using Python. It was interesting seeing people slowly get a clue over the years.

It minimizes greenfield development time and increases it later for anything at scale. It's the mongodb of programming languages.
- That doesn't sound like much of a "gotcha". The requirements for small stuff and big stuff are drastically different.

In the Python documentary they mention the early creation of numeric as an alternative for closed options like Matlab; which led to Python being used in the sciences and data processing. Then later it became the natural choice for machine learning in the early 2010s and the AI explosion of 2020+.

Because most people are lazy hacks.

What is so lazy about Python?
- Lazy is an overly harsh and judgemental way to put it (virtually all programmers start with some high level simplified language), but the sentiment arises because it's syntax is designed to be easy for people writing code, but at the cost of people maintaining code.
  The whitespace delimiters, the lack of type system, the lack of semi-colons ... They're all things that people who haven't programmed before think make programming easier. In reality they all make in the wild production programs waaay harder to maintain.
  There's a reason that JavaScript has been surpassed by TypeScript for professional developers, and it did so remarkably quickly. All that 'extra' information that seems pointless for a new dev to express, in reality constrains your program, makes it more readable and understandable, reduces the amount of tests you have to write, and makes it easier for someone else to come in and make a change to it and be confident they haven't broken anything.
- You can write Python code about 5x faster then C. Add libraries even faster.
- Dynamic type systems are meant for beginner/toy languages. Hacks that don't care about understanding their own code tend to use Python. Their code is often riddled with bugs that they are none-the-wiser about.
  If you write your code in Python, you might as well admit that you don't care to understand what your code is actually doing.
Nah, I recently had to create a program that turned a bunch of extracted CSV files into an XML file for government reporting. I also had to parse some provided government XML files to add things into my output.
This was going to be run by non-technical people on any OS so I went for python because "install python, download this file and click on it" was easy. Python has a big standard library so I could do everything I needed in it. I was considering using Go but asking people to open the terminal and build something was probably a bridge too far.
- Distribution usually isn't considered a strong point for Python, though.
  For other languages that build a static executable, the more expected method of distribution would be some automated workflow that builds artifacts for various os/architecture-triplets, that you can then just download off the project page.
I use it as a beginner cuz it’s easy to use but guess I’m lazy since I didn’t start on a language that’s more in depth
- As a beginner, you'll eventually run into enough issues with your code that you will start to ask "is there a better way?" My answer will be: yes. Strongly typed languages are FAR superior because they force you to make your code robust and eliminate most runtime errors by default. You'll eventually come around (unless you're in a company with a hack culture or are surrounded by hacks that don't know any better).

I'd say libraries is why it is popular. Also, I'm not a Python developer, and I don't bother with libraries.

Python is one of 4 key programming languages in world. C as a systems/performance/building block language (which I get has competitors). Javascript for node performance and web language. J as being the most powerful syntax/paradigm and best designed language.

Python has to be the most important due to all of the support it has, including C++ and other building block language/library imports that allows it to be the LLM integrator choice. A field that moves so fast, you couldn't import libraries fast enough in another environment. Another shoutout to J is that its terseness is a good LLM fit due to less token consumption. But integrating with python is essential.

I started using it as an alternative to Octave/Matlab and Perl. Python is better at general programming than Octave/Matlab, and better syntax than Perl (IMO) while being almost as easy to do the same stuff I was using Perl for. It's very good for quickly writing small scripts. Issues can arise on large projects/teams because of stuff like type safety, and it also has issues with performance.