Why does my for loop consume excessive memory when processing large lists in Python 3.11?
Does anyone know how to I just started working with I'm currently working on a Python script that processes large datasets, specifically lists of dictionaries. I've noticed that during the processing, my script's memory usage spikes dramatically, which seems to be related to the for loop I'm using. Here’s a simplified version of my code: ```python large_list = [{"id": i, "value": i * 2} for i in range(1000000)] results = [] for item in large_list: processed = {"id": item["id"], "result": item["value"] ** 2} results.append(processed) ``` After running this, I monitor the memory usage and see it climb significantly. I tried using a generator expression to reduce memory consumption like this: ```python results = ({"id": item["id"], "result": item["value"] ** 2} for item in large_list) ``` However, I still notice high memory usage when converting it back to a list with `list(results)`. Is there a more efficient way to handle this that would reduce memory consumption? Additionally, are there any best practices for processing large datasets in Python that I might be overlooking? I’m using Python 3.11 on a 64-bit machine with 8GB RAM, and I also have `pandas` and `numpy` available if they can help with memory management. Any ideas how to fix this? My team is using Python for this application.