performance optimization When Using Large Dictionaries as Default Values in Python 3.10
I'm a bit lost with I'm migrating some code and I'm trying to implement I'm experimenting with I've looked through the documentation and I'm still confused about I'm working with important performance optimization when using a large dictionary as a default value with the `collections.defaultdict` in Python 3.10... The use case involves categorizing user input data where each category has a complex default structure. However, initializing this large dictionary every time I create an instance of `defaultdict` seems inefficient. For instance, I have a structure like this: ```python from collections import defaultdict def create_default_dict(): return defaultdict(lambda: {'count': 0, 'items': []}) data = defaultdict(create_default_dict) ``` The question arises when I try to populate this `data` dictionary with a large number of entries. The initialization of the default dictionary leads to excessive memory usage and slowdowns. When I do something like: ```python for i in range(100000): category = f'category_{i % 10}' data[category]['count'] += 1 data[category]['items'].append(i) ``` I notice that the performance degrades sharply. I've tried moving the default dictionary creation to a separate function, thinking it would help with initialization, but the scenario continues. I'm also wary of modifying the default factory to return a pre-initialized structure due to the shared reference question. Is there a better pattern or approach I can use to improve performance without running into issues with shared references or unnecessary initialization? This is part of a larger API I'm building. Is there a better approach? Is there a simpler solution I'm overlooking? I'm working on a desktop app that needs to handle this. I'd really appreciate any guidance on this. I recently upgraded to Python 3.10. Cheers for any assistance! I recently upgraded to Python latest. Thanks for taking the time to read this!