Trouble handling large CSV files with Pandas - MemoryError on read
I'm stuck on something that should probably be simple... I tried several approaches but none seem to work. I'm working with a MemoryError when trying to read a large CSV file using Pandas version 1.3.3. The file size is approximately 2GB and contains around 1.5 million rows. My goal is to read this file into a DataFrame while minimizing memory usage and ensuring that the operation does not crash my Python script. I've tried using the `read_csv` method with various parameters such as `chunksize` to read it in smaller parts, but I still encounter issues when processing the chunks individually. Here’s the code snippet I’ve been using: ```python import pandas as pd # Attempting to read the CSV file in chunks chunk_size = 100000 chunks = [] try: for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size): # Processing each chunk chunks.append(chunk) except MemoryError as e: print(f'behavior: {e}') ``` While this approach seems reasonable, after a few iterations, I still run into memory issues, and the script fails. Is there a best practice for handling extremely large CSV files in Pandas, perhaps involving filtering out unnecessary columns or rows during the read operation? Additionally, are there other libraries or methods recommended for this kind of task that might handle memory more efficiently? Thanks in advance for your help! My development environment is Linux. Is there a better approach? Thanks for taking the time to read this! Any examples would be super helpful. This is my first time working with Python stable. Am I missing something obvious?