I'm Switching to Python and Actually Liking It

What exactly is the problem with __init__ or __new__? @dataclass is very nice syntactic sugar, but are we arguing here that having access to initializer/allocator/constructor dunder methods is "legacy ugliness"? This is the core of pythonic built-in aware python. Bizarre.

    API_KEY = os.environ.get("YOUTUBE_API_KEY")
    CHANNEL_ID = os.environ.get("YOUTUBE_CHANNEL_ID")
    
    if not API_KEY or not CHANNEL_ID:
        print("Missing YOUTUBE_API_KEY or YOUTUBE_CHANNEL_ID.")
        exit(1)

    if not API_KEY:
        print("Missing YOUTUBE_API_KEY.")
        exit(1)
    if not CHANNEL_ID:
        print("Missing YOUTUBE_CHANNEL_ID.")
        exit(1)

Then, in 2005, Guido van Rossum was hired by Google to work on Google Cloud. That opened the door for wider adoption in academia, since Python had strong math libraries and integrated well with tools researchers were already using, like Hadoop, right around the time big data and ML were starting to take off.

Also, between 2005 and 2006, two important things happened: Ruby on Rails came out and inspired Django, which was starting to gain popularity, and web developers were getting tired of Perl's messy syntax. That's how Python quickly became a solid choice not just for server-side scripts, but for building proper web apps. In the meantime, another language that could be embedded directly into HTML was storming the web: PHP. Its syntax was similar to JavaScript, it was easy to pick up, lowered the barrier to entry for software development, worked straight out of the box, and didn't require thousands of print statements to get things done.

The 3 Ps made history. According to programmers from 20 years ago, they were like religions. Each had its own philosophy and a loyal group of followers crusading online, getting into heated debates, all trying to win over more adopters. The new generation of devs is more pragmatic. These days it's less about language wars and more about picking the right tool for the job.

That's kind of very optimistic evaluation - literally anything beyond "import json" will likely lead you into the abyss of virtual envs. Running something created with say Python 3.13.x on Ubuntu 22.04 or even 24.04 (LTSs) / Rocky 9 and the whole can of worms opened.

things like virtual envs + containers (docker like)/version managers become a must quickly.

I find this depressing. Not only are LLMs covertly reducing our ability to think and make decisions, they’re now also making people voluntarily conform to some lower common denominator.

It’s like humanity decided to stagnate at this one point in time (and what a bad choice of point it was) and stop exploring other directions. Only what the LLM vomits is valid.

It's included in the default install of most desktop/server Linux distros (with plenty of exceptions), but I don't believe any of the BSDs ship it in their base system.

IIRC macOS used to have python 2 in its default install, but I vaguely recall that being deprecated and removed at some point. My only Mac is on the other side of the country at the moment, so I can't check myself.

I got forced to learn it for a project where I was proposing Ruby and the customer insisted on Python. This was years ago when Ruby was much slower. I was annoyed but I got used to it and here I am enjoying it many years later.

I take issue with the description and use of make though! :-D What is the point of it if you're not going to use dependencies? One might as well just write a script with a case statement..... I'm adding smileys because I don't mean to be all that serious but I really do think that the failure of the youth of today to get to grips with Make is a sad reflection on our culture....... and get off my lawn, ok? :-)

I've switched to python primarily (from perl) in early 2010s (I think my first "seriously" used version was 2.6. This is mostly for system management, monitoring, and data transformation /visualisation. Nothing fancy like AI back then in a work setting.

I found the biggest impact was not so much on writing code but on it remaining readable for a very long time, even if it was created hastily "just get this working now" style. Especially in a team.

Python is still one of my favourites and the first tool I reach if bash is not enough for what I'm trying to do.

Why is that?

Why Python for AI?

Two additional suggestions:

* mise to manage system dependencies, including uv version and python itself

* just instead of make; makefile syntax is just too annoying.

Mise actually has a command runner as well which I haven't tried yet, and might work better for running commands in the context of the current environment. It's pretty nice when your GitHub actions workflow is just:

* Install mise

* mise install everything else

That said, I remember writing myself a note a few years ago to avoid Python projects. I had to clean up code from all over the company and make it ready for production. Everyone had their own Python version, dependencies missing from requirements.txt, three way conflicts between 2 dependencies and the python version, wildly different styles, and a habit of pulling in as many libraries as possible [1]. Even recalling those memories makes my stomach turn.

I believe constraints make a project shine and be maintainable. I'd prefer if you throw at me a real python instead of a python project.

[1] Yes, I'm aware of containers, I was the unlucky guy writing them.

That said, call me old-fashioned, but I really take issue with "curl $URL | bash" as an installation method. If you're going to use an install script, inspect it first.

Made me think this is probably normally a Ruby developer indoctrinated against Python. The article doesn’t seem to say what they have come from.