In fact, Python is still decent even if you do need speed. We compared Python and Rust for algorithm processing, and we got similar-ish numbers when using numba. Rust was certainly faster, but we would need to retrain a lot of our team, and numba was plenty fast.

Python is fast enough, and if it's not, there are libraries to get it there.

[–] grue@lemmy.world 3 points 8 months ago (1 children)

If I needed speed, I'd be programming in Python but then profiling the performance and re-writing the inner loops and such to call C or BLAS.

[–] muntedcrocodile@lemmy.world 2 points 8 months ago

Surly u can use rust these days?

[–] AnUnusualRelic@lemmy.world 2 points 8 months ago

It's fine, I'm not in a hurry.

[–] eclecticprune@lemmy.world 1 points 8 months ago

While the processor I'm working on right now supports 14MIPS...

[–] eager_eagle@lemmy.world 1 points 8 months ago* (last edited 8 months ago)

so 200 to 800 microseconds on a modern cpu? Fast enough.

[–] andnekon@programming.dev 1 points 8 months ago (1 children)

I doubt it's useful for performance evaluation, however, if you are writing a paper and want to compare your algorithm to an existing one, this can be handy

[–] sugar_in_your_tea@sh.itjust.works 2 points 8 months ago

Eh, maybe? It's probably only useful for large jumps, and timing is also probably good enough for that as well. With small jumps, instruction execution order matters, so a bigger number could very well be faster if it improves pipelining.

It's certainly interesting and maybe useful sometimes, but it's probably limited to people working on Python itself, not regular users.

[–] Big_Boss_77@lemmynsfw.com 0 points 8 months ago (3 children)

Is it just me... or is there a lot of python hate lately?

[–] furrowsofar@beehaw.org 1 points 8 months ago* (last edited 8 months ago) (1 children)

Same for me. I have used Python for most things since the late 1990s. Love Python. Have always hated the poor performance... but in my case mostly it was good enough. When it was not good enough, I wrote C code.

Python is good for problems where time to code is the limiting factor. It sucks for compute bound problems where time to execute is the limiting factor. Most problems in my world are time to code limited but some are not.

Python compute performance has always sucked.

[–] Big_Boss_77@lemmynsfw.com 2 points 8 months ago

I get that... I'm not a developer, I'm a network engineer but I use a lot of python in my day to day operations. I always took python to be the "code for non-coders" which made it infinitely more approachable than some of the other languages.

I'm not running the F1 grand prix over here, I'm driving to get groceries, so what if it's not the fastest thing out there. Close enough is good enough for me. And in my experience that's what people are using python for, daily driving.

[–] neo@lemmy.hacktheplanet.be 1 points 8 months ago (1 children)

Nah, my personal hate of indented blocks has been there since the late 90s /s 8-)

[–] furrowsofar@beehaw.org 3 points 8 months ago

Yes, I hate indentation as structure but I hate tracking brackets even more.

[–] dozymoe@mastodon.social 0 points 8 months ago (1 children)

AI crowd using python probably @Big_Boss_77 @rimu

[–] Big_Boss_77@lemmynsfw.com 0 points 8 months ago (1 children)

Ahh...that makes sense. I bet you're right.

[–] furrowsofar@beehaw.org 1 points 8 months ago* (last edited 8 months ago)

People use Python a lot as a Matlab, Excel/VBA, or R alternative. That was my use for many years. Some of these are compute focused problems and if the dataset is large enough and the computations complex enough then speed can be an issue.

As far as loading packages and printing. Who cares. These are not computationally intensive and are typically IO bound.