How to optimize list comprehension for large datasets in Python 3.9?
I'm working on a personal project and I'm relatively new to this, so bear with me... I'm currently working on a data transformation script that processes large lists in Python 3.9. I'm using list comprehensions to filter and transform my data, but I've noticed important performance optimization when the dataset exceeds 100,000 records. Here’s an example of my current implementation: ```python large_data = [i for i in range(1, 100001)] filtered_data = [x * 2 for x in large_data if x % 3 == 0] ``` While this works, the execution time increases dramatically with larger datasets, and I suspect that using list comprehensions might be causing memory overhead. I’ve tried using a generator expression instead, like this: ```python filtered_data_gen = (x * 2 for x in large_data if x % 3 == 0) ``` However, when I convert it back to a list using `list(filtered_data_gen)`, it doesn’t seem to improve performance noticeably. I also considered using `filter()`, but I found that it complicates the readability of the code: ```python filtered_data_func = list(filter(lambda x: x % 3 == 0, (x * 2 for x in large_data))) ``` What’s confusing me is how to balance performance with code readability. Are there any best practices or patterns in Python for efficiently processing large datasets that I might be overlooking? Should I stick with list comprehensions, or is there a better approach to handle such cases? Any help would be greatly appreciated! For context: I'm using Python on Ubuntu. Am I missing something obvious? For context: I'm using Python on Windows.