implementing loop performance when processing large JSON files in Python 3.9
I'm getting frustrated with I've tried everything I can think of but I've been researching this but I'm stuck on something that should probably be simple... I am working with performance optimization when using a for loop to iterate over a large JSON file (about 1 GB) in Python 3.9. The JSON file contains nested lists and dictionaries, and I am trying to extract specific data points from it. My current implementation looks something like this: ```python import json with open('large_file.json', 'r') as f: data = json.load(f) for item in data['items']: if item['type'] == 'desired_type': process(item) ``` However, this loop seems to take an extremely long time to complete. I have tried optimizing the `process()` function, but the bottleneck appears to be the iteration itself. I also considered using `ijson`, a library for iterative JSON parsing, but I'm not sure how to implement it correctly to maintain the same functionality. When profiling the code using `cProfile`, I noticed that the majority of the execution time is spent in the `for item in data['items']:` line. I am wondering if there's a more efficient way to handle this or if my current approach is simply not suitable for such a large dataset. Are there best practices for efficiently processing large JSON files in Python, or do I need to consider a different approach altogether? What's the best practice here? Thanks, I really appreciate it!