So I’m no expert, but I have been a hobbyist C and Rust dev for a while now, and I’ve installed tons of programs from GitHub and whatnot that required manual compilation or other hoops to jump through, but I am constantly befuddled installing python apps. They seem to always need a very specific (often outdated) version of python, require a bunch of venv nonsense, googling gives tons of outdated info that no longer works, and generally seem incredibly not portable. As someone who doesn’t work in python, it seems more obtuse than any other language’s ecosystem. Why is it like this?
Python’s packaging is not great. Pip and venvs help but, it’s lightyears behind anything you’re used to. My go-to is using a venv for everything.
It’s something of a “14 competing standards” situation, but uv seems to be the nerd favourite these days.
I still do the python3 -m venv venv && source venv/bin/activate
How can uv help me be a better person?
- let
pyproject.tomltrack the dependencies and dev-dependencies you actually care about
- dependencies are what you need to run your application
- dev-dependencies are not necessary to run your app, but to develop it (formatting, linting, utilities, etc)
- it can track exactly what’s needed ot run the application via the
uv.lockfile that contains each and every lib that’s needed. - uv will install the needed Python version for you, completely separate from what your system is running.
uv syncanduv run <application>is pretty much all you need to get going- it’s blazingly fast in everything
If you’re happy with your solution, that’s great!
uv combines a bunch of tools into one simple, incredibly fast interface, and keeps a lock file up to date with what’s installed in the project right now. Makes docker and collaboration easier. Its main benefit for me is that it minimizes context switching/cognitive load
Ultimately, I encourage you to use what makes sense to you tho :)
This! Haven’t used that one personally, but seeing how good ruff is I bet it’s darn amazing, next best thing that I used has been PDM and Poetry, because Python’s first party tooling has always been lackluster, no cohesive way to define a project and actually work it until relatively recently
I bet it’s darn amazing,
It is. In this older article (by Anna-Lena Popkes) uv is still not in the middle, but I would claim it’s the new King of Project Management, when it comes to Python.
uv init --name <some name> --package --app and you’re off to the races.
Are you cloning a repo that’s uv-enabled? Just uv sync and you’re done!
Heck, you can now add dependencies to a script and just uv run --script script.py (IIRC) and you don’t need to install anything - uv will take care of it all, including a needed Python version.
Only downside is that it’s not 1.0 yet, so the API can change at any update. That is the last hurdle for me.
I moved all our projects (and devs) from poetry to uv. Reasons were poetry’s non standard pyproject.toml syntax and speed, plus some weird quirks, e. g. if poetry asks for input and is not run with the verbose flag, devs often don’t notice and believe it is stuck (even though it’s in the default project README).
Personally, I update uv on my local machine as soon as a new release is available so I can track any breaking changes. Couple of months in, I can say there were some hiccups in the beginning, but currently, it’s smooth sailing, and the speed gain really affects productivity as well, mostly due to being able to not break away from a mental “flow” state while staring at updates, becoming suspicious something might be wrong. Don’t get me wrong, apart from the custom syntax (poetry partially predates the pyproject PEP), poetry worked great for us for years, but uv feels nicer.
Recently, “uv build” was introduced, which simplified things. I wish there was an command to update the lock file while also updating the dependency specs in the project file. I ran some command today and by accident discovered that custom dependency groups (apart from e. g. “dev”) have made it to uv, too.
“uv pip” does some things differently, in particular when resolving packages (it’s possible to switch to pip’s behavior now), but I do agree with the decisions, in particular the changes to prevent “dependency confusion” attacks.
As for the original question: Python really has a bit of a history of project management and build tools, I do feel however that the community and maintainers are finally getting somewhere.
cargo is a bit of an “unfair” comparison since its development happened much more aligned with Rust and its whole ecosystem and not as an afterthought by third party developers, but I agree: cargo is definitely a great benchmark how project and dependency management plus building should look like, along with rustup, it really makes the developer experience quite pleasant.
The need for virtual environments exists so that different projects can use different versions of dependencies and those dependencies can be installed in a project specific location vs a global, system location. Since Python is interpreted, these dependencies need to stick around for the lifetime of the program so they can be imported at runtime. poetry managed those in a separate folder in e. g. the user’s cache directory, whereas uv for example stores the virtual environment in the project folder, which I strongly prefer.
cargo will download the matching dependencies (along with doing some caching) and link the correct version to the project, so a conceptual virtual environment doesn’t need to exist for Rust. By default, rust links everything apart from the C runtime statically, so the dependencies are no longer neesed after the build - except you probably want to rebuild the project later, so there is some caching.
Finally, I’d also recommend to go and try setting up a project using astral’s uv. It handles sane pyproject.toml files, will create/initialize new projects from a template, manages virtual environments and has CLI to build e. g. wheels or source distribution (you will need to specify which build backend to use. I use hatchling), but thats just a decision you make and express as one line in the project file. Note: hatchling is the build backend, hatch is pypa’s project management, pretty much an alternative to poetry or uv.
uv will also install complete Python distributions (e. g. Python 3.12) if you need a different interpreter version for compatibility reasons
If you use workspaces in cargo, uv also does those.
uv init, uv add, uv lock --upgrade, uv sync, uv build and how uv handles tools you might want to install and run should really go a long way and probably provide an experience somewhat similar to cargo.
I think you responded to the wrong comment, I didn’t question the need for uv or other tools like that
Python is the only programming language that has forced me to question what the difference is between an egg and a wheel.
You re not stupid, python’s packaging & versionning is PITA. as long as you write it for yourself, you re good. As soon as you want to share it, you have a problem
No, it’s not just you, Python’s tooling is a mess. It’s not necessarily anyone’s fault, but there are a ton of options and a lot of very similarly named things that accomplish different (but sometimes similar) tasks. (pyenv, venv, and virtualenv come to mind.) As someone who considers themselves between beginner and intermediate proficiency in Python, this is my biggest hurdle right now.
Python’s tooling is a mess.
Not only that. It’s a historic mess. Over the years, growing a better and better toolset left a lot of projects in a very messy state. So many answers on Stack Overflow that mention easy_install - I still don’t know what it is, but I guess it was some kind of proto uv.
Every time I’m doing anything with Python I ask myself if Java’s tooling is this complicated or I’m just used to it by now. I think a big part of the weirdness is that a lot of Python tooling is tied to the Python installation whereas in Java things like Maven and Gradle are separate. In addition, I think dependencies you install get tied to that Python installation, while in Java they just are in a cache for Maven/Gradle. And in the horrible scenario where you need to use different versions of Maven/Gradle (one place I was at specifically needed Maven 3.0.3 for one project and a different for a different, don’t ask, it’s dumb and their own fault for setting it up that way) at least they still have one common cache for everything.
I guess it also helps that with Java you (often) don’t need platform specific jar files. But Python is often used as an easy and dynamic scripting interface over more performant, native code. So you don’t really run into things like “this artifact doesn’t have a 64 bit arm version for python 2” often with Java. But that’s not a fault of Python’s tooling, it’s just the reality of how it’s used.
