Handling multiple nested loops in Python for generating combinations - performance optimization
I'm collaborating on a project where I'm dealing with I'm trying to generate all possible combinations of items from two lists using nested loops in Python 3.10, but I'm working with important performance optimization as the size of the lists increases. Here's the code I'm currently using: ```python list_a = ['a', 'b', 'c'] list_b = [1, 2, 3, 4] combinations = [] for item_a in list_a: for item_b in list_b: combinations.append((item_a, item_b)) ``` This works fine for small lists, but when I increase the size (for example, list_a has 1000 items and list_b has 1000 items), the execution time becomes impractical, taking several seconds. I tried to optimize it by using list comprehensions instead: ```python combinations = [(item_a, item_b) for item_a in list_a for item_b in list_b] ``` While this is more concise, it didn’t improve performance significantly. I also considered using the `itertools.product` function, expecting it to be faster: ```python import itertools combinations = list(itertools.product(list_a, list_b)) ``` However, even that feels slow when the lists are larger. I am seeing a noticeable delay, and I’m worried scalability will be a question if I need to work with larger datasets in the future. Is there a more efficient way to handle this, or am I missing something in terms of optimization? Any advice on best practices or alternative approaches would be greatly appreciated! This is for a web app running on Linux. For reference, this is a production web app. Cheers for any assistance! I'm using Python LTS in this project.