How to efficiently handle large CSV files with Python's built-in csv module?
I'm trying to process a CSV file that contains millions of rows, but I'm running into performance issues when using Python's built-in `csv` module. My current implementation reads the entire file into memory before processing, which is not feasible given the file size. I want to iterate through the CSV file row by row to minimize memory usage. Here’s a simplified version of my current code: ```python import csv def process_csv(file_path): with open(file_path, mode='r', newline='', encoding='utf-8') as csvfile: reader = csv.reader(csvfile) data = list(reader) # This line is causing memory issues for row in data: # Process each row here print(row) process_csv('large_file.csv') ``` I’ve read that using a generator instead of loading the whole file into a list can help. I tried modifying the code like this: ```python import csv def process_csv(file_path): with open(file_path, mode='r', newline='', encoding='utf-8') as csvfile: reader = csv.reader(csvfile) for row in reader: # This should help with memory # Process each row here print(row) process_csv('large_file.csv') ``` While this works, I noticed that the processing is still slow, especially when I try to perform additional operations on each row. I suspect my processing logic is inefficient. For reference, I’m using Python 3.10.0. Could anyone suggest best practices or design patterns I could implement to improve the performance of this code? Also, are there any specific configurations or techniques I should consider to make the row processing faster? I’m open to using libraries if necessary, but I want to stick to built-in modules if possible.