Performance hacks for faster Python code
blog.jetbrains.com
Performance hacks for faster Python code
1–10 of 65 posts
Re: Performance hacks for faster Python code
#2Maybe also knowing when not to use python, or finding a solution in python that uses C/rust/etc underneath.
Re: Performance hacks for faster Python code
#3(2) surprised me a little. Not because of the performance consequences, but because I almost never see explicit calls to `copy()` in Python (and I read a lot of Python).
I think maybe a more realistic example there would be people using splatting without realizing/internalizing that it performs a full copy, e.g.
xs = [1, *ys]
Re: Performance hacks for faster Python code
#4(2) surprised me a little. Not because of the performance consequences, but because I almost never see explicit calls to `copy()` in Python (and I read a lot of Python). I think maybe a more realistic example there would be people using splatting without realizing/internalizing that it performs a full copy, e.g. xs = [1, *ys] Another one that stood out was (3). Slots are great, but >95% of the time I'd expect people…
You can use __slots__ for normal classes; it’s not limited to only dataclasses.
Re: Performance hacks for faster Python code
#5(2) surprised me a little. Not because of the performance consequences, but because I almost never see explicit calls to `copy()` in Python (and I read a lot of Python). I think maybe a more realistic example there would be people using splatting without realizing/internalizing that it performs a full copy, e.g. xs = [1, *ys] Another one that stood out was (3). Slots are great, but >95% of the time I'd expect people…
You can use __slots__ for normal classes; it’s not limited to only dataclasses.
I know that; that's why I said "I'd expect" not "you can't."
Re: Performance hacks for faster Python code
#6There's some genuinely interesting tips in here, but #10 is for sure just padding so they could call the article "10 Hacks" haha. Everything else is at least somewhat Python specific, but "Hack 10: Avoid repeated function calls in loops" is just applicable to anything.
Re: Performance hacks for faster Python code
#7Some helpful guidelines, but it's 2025 and people still use time.time and no stats with their benchmarks :(
In general I feel like these kind of benchmarks might change for each python version, so some caveats might apply.
Re: Performance hacks for faster Python code
#8a smart hack for performance is don't use python
Re: Performance hacks for faster Python code
#9> Copying large objects like lists […] can be costly in both time and memory.
> modify[ing] objects in place […] improves performance by avoiding the overhead of allocating and populating new structures.
AFAIK the poor performance of list copies (demonstrated in the article by a million-element list taking 10ms) doesn’t come from memory allocation nor from copying the contents of the list itself (in this case, a million pointers).
Rather it comes from the need to chase all of those pointers, accessing a million disparate memory locations, in order to increment each element’s reference count.
Re: Performance hacks for faster Python code
#10Maybe also knowing when not to use python, or finding a solution in python that uses C/rust/etc underneath.
maybe you can skip C and just use assembly