How to prevent MemoryError when processing large CSV files with pandas in Python 3.10?
I'm learning this framework and I'm stuck on something that should probably be simple. I'm stuck on something that should probably be simple. I'm currently working on a data processing task that involves reading and analyzing a very large CSV file (over 10GB) using pandas in Python 3.10. However, when I attempt to load the entire file into a DataFrame, I encounter a `MemoryError`. Here's the code I'm using: ```python import pandas as pd # Attempting to read the large CSV file try: df = pd.read_csv('large_file.csv') except MemoryError as e: print(f'MemoryError: {e}') ``` To mitigate this scenario, I tried using the `chunksize` parameter to read the file in smaller batches. While this does work, it complicates my workflow since I need to manually concatenate the chunks afterward. Here's my modified approach: ```python chunk_size = 100000 # Adjust chunk size based on testing chunks = [] try: for chunk in pd.read_csv('large_file.csv', chunksize=chunk_size): chunks.append(chunk) df = pd.concat(chunks, ignore_index=True) except Exception as e: print(f'behavior: {e}') ``` This method works but still leads to high memory usage, which is concerning. I would prefer a more efficient way to handle this large dataset without running into memory issues. I've also explored using Dask, but I find the API somewhat different from pandas, making it a bit challenging to transition my existing code. Is there a better strategy or best practice to efficiently handle large CSV files in pandas while keeping memory usage manageable? Any suggestions would be greatly appreciated! My development environment is Linux. This is part of a larger service I'm building. Has anyone else encountered this? This issue appeared after updating to Python stable. What's the best practice here?