Hey everyone,
I’m working on a project where I need to process a lot of numerical data efficiently. One common task I have is summing up large lists of numbers (e.g., millions of floats). Naturally, I’ve been using Python’s built-in sum() function for this, but I’ve noticed it’s not as fast as I expected.
Example: https://pastebin.com/embed_js/bfzCs5Hp
For a list this large, sum() takes over a second on my machine. When I compare it to summing in other languages (like C or even NumPy’s np.sum()), the difference is huge!
I’ve done some research and found that sum() operates at the Python level, which adds overhead because of the interpreted nature of the language. But given how common summing is, I’m wondering
pastebin.com
It’s slower than the built-in sum()! So now I’m stuck. Any help?
I’m working on a project where I need to process a lot of numerical data efficiently. One common task I have is summing up large lists of numbers (e.g., millions of floats). Naturally, I’ve been using Python’s built-in sum() function for this, but I’ve noticed it’s not as fast as I expected.
Example: https://pastebin.com/embed_js/bfzCs5Hp
For a list this large, sum() takes over a second on my machine. When I compare it to summing in other languages (like C or even NumPy’s np.sum()), the difference is huge!
I’ve done some research and found that sum() operates at the Python level, which adds overhead because of the interpreted nature of the language. But given how common summing is, I’m wondering
- Why doesn’t Python’s sum() use optimized C code under the hood for large lists?
- re there ways to speed up sum() without switching entirely to something like NumPy?
from functools import reducedef custom_sum(lst): return reduce(lambda x - Pastebin.com
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It’s slower than the built-in sum()! So now I’m stuck. Any help?
