👀 Views: 30 💬 Answers: 1 📅 Created: 2025-06-05

algorithm sorting performance Python

I'm stuck trying to I've spent hours debugging this and I'm working on a project and hit a roadblock..... I'm working on a project and hit a roadblock. I'm working with an scenario with my implementation of the Quick Sort algorithm in Python where the performance drastically drops on larger datasets compared to smaller ones. While sorting arrays of size 10,000 or less, the algorithm performs as expected, but when I test it with arrays of size 1 million, the execution time skyrockets to several seconds. Here’s the code I've written: ```python import random def quick_sort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quick_sort(left) + middle + quick_sort(right) if __name__ == '__main__': large_array = [random.randint(0, 100000) for _ in range(1000000)] sorted_array = quick_sort(large_array) print(sorted_array[:10]) # Print first 10 sorted elements ``` I tried using different pivot selection strategies, like picking the first element or the random element, but they didn't seem to improve the performance significantly. I'm also aware that using list comprehensions can lead to high memory usage, but I'm not sure if that's the main scenario here. I get no behavior messages, but the performance is just not what I expected from Quick Sort. Any insights on optimizing this implementation or best practices for handling larger datasets would be greatly appreciated! For context: I'm using Python on Ubuntu. Has anyone else encountered this? This is for a mobile app running on macOS. Thanks for your help in advance!