Python 2.7: How to effectively manage large lists in memory and avoid MemoryError during processing?
I'm maintaining legacy code that I've been banging my head against this for hours. This might be a silly question, but I'm currently working on a Python 2.7 application that processes a substantial amount of data stored in a list. The application reads data from a file, processes it, and then performs calculations to generate results. However, when the list size exceeds a certain threshold (around 1 million entries), I encounter a `MemoryError`. Here's a simplified version of my code: ```python import json # Function to load data def load_data(filename): with open(filename, 'r') as f: return json.load(f) # Function to process data def process_data(data): results = [] for item in data: # Some complex processing results.append(item * 2) # Example operation return results # Main execution if __name__ == '__main__': data = load_data('large_data.json') results = process_data(data) print "Processed {} items".format(len(results)) ``` When I run this code with large datasets, I receive `MemoryError: want to allocate memory`. I've tried optimizing my data structure by using generators instead of lists in some areas. For instance, I attempted to change the `results` list to a generator, but I still ended up with memory issues when trying to store the final results. I also considered chunking my data processing to reduce the memory footprint, but I'm uncertain how to implement that effectively. Here's what I have in mind for chunking: ```python # Potential chunking implementation def process_in_chunks(data, chunk_size=100000): for i in range(0, len(data), chunk_size): chunk = data[i:i + chunk_size] yield process_data(chunk) ``` Could anyone provide insights on best practices for this scenario? Are there any alternative strategies, such as using numpy arrays or other libraries that can guide to handle large datasets more efficiently without running out of memory? This is part of a larger application I'm building. Any ideas what could be causing this? My team is using Python for this CLI tool.