How can I efficiently filter a large dictionary by keys without performance degradation in Python?
I'm working on a project and hit a roadblock. I tried several approaches but none seem to work. I'm working on a data processing project where I have a very large dictionary (around 1 million entries) that represents user data, and I need to filter this dictionary based on a specific set of keys. My initial approach was to use a list comprehension, but I'm concerned about performance optimization when the input data scales up. Here's a simplified version of what I've tried: ```python user_data = { f'user_{i}': {'age': i, 'location': 'Location_' + str(i)} for i in range(1, 1000001) } keys_to_filter = [f'user_{i}' for i in range(1, 10001)] # Filtering using a dictionary comprehension filtered_data = {k: user_data[k] for k in keys_to_filter if k in user_data} ``` This works, but I've noticed that as `keys_to_filter` grows, the performance starts to degrade significantly, especially when the count goes above 10,000 keys. I receive no errors, but the execution time becomes noticeably longer. I've also tried to utilize the `.get()` method to avoid KeyErrors, but it didn't improve the performance: ```python filtered_data = {k: user_data.get(k) for k in keys_to_filter} ``` Is there a more efficient way to filter this dictionary, perhaps by using a different data structure or method? Additionally, are there any best practices to keep in mind when dealing with large dictionaries in Python? I'm currently using Python 3.9.5. Any insights or alternative approaches would be greatly appreciated! Cheers for any assistance! I'm working on a microservice that needs to handle this.