👀 Views: 61 💬 Answers: 1 📅 Created: 2025-07-11

python-2.7 memory-management performance Python

I'm building a feature where I've hit a wall trying to I'm trying to implement I'm working on a Python 2.7 script that processes a large list of dictionaries, and I'm running into performance optimization, particularly regarding memory usage..... The list can contain up to a million entries, and each dictionary has multiple key-value pairs. My current approach involves using a standard `for` loop to iterate over each dictionary and perform some calculations. However, I'm experiencing slowdowns and, occasionally, a `MemoryError` when the list is particularly large. Here’s a simplified version of my code: ```python large_data = [{"id": i, "value": i * 2} for i in range(1000000)] results = [] for item in large_data: result = item['value'] ** 2 # Some computation results.append(result) # Storing results ``` Although the computations are straightforward, the memory usage spikes significantly. I've tried using generators and reducing the size of the list, but the scenario continues. I also attempted to use the `del` statement to free up memory within the loop, but it didn’t seem to make a major difference. Is there a better way to manage this kind of data processing in Python 2.7? Should I consider refactoring my code to use more efficient data structures or processes? What patterns or techniques can I apply to avoid exceeding memory limits while still achieving satisfactory performance? I'm working with Python in a Docker container on Windows 11. I'm open to any suggestions. This is for a microservice running on Windows 11. I'd be grateful for any help. Am I missing something obvious?