Performance implementing nested loops when processing large CSV files in Python 3.9
I keep running into I'm working on a personal project and I'm sure I'm missing something obvious here, but I'm working with important performance optimization while processing large CSV files using nested loops in Python 3.9... My goal is to filter rows based on certain criteria and then perform additional calculations on the filtered results. However, as the size of the CSV increases, the execution time grows exponentially, leading to high latency. Here's a snippet of my code: ```python import pandas as pd # Load the CSV file large_df = pd.read_csv('large_file.csv') # Assuming the CSV has columns 'category' and 'value' filtered_results = [] for index, row in large_df.iterrows(): if row['category'] == 'A': for i in range(10000): # Some computation that scales with size value = row['value'] * i filtered_results.append(value) ``` The question arises when I process files larger than 1GB. The inner loop scales with the number of iterations, and I noticed that the performance drastically deteriorates—often hitting a timeout. I've tried using `apply()` in Pandas and vectorized operations, but I’m unsure how to properly refactor this nested structure. I also receive a `MemoryError` occasionally, which indicates that the approach I'm taking may not be optimal. Can someone suggest a more efficient way to handle this processing, perhaps without using nested loops, or point out how I can refactor this logic? I'd appreciate any best practices or design patterns that would apply here. Any ideas what could be causing this? What are your experiences with this?