implementing reading large CSV files in Python using pandas
I'm experimenting with I've been struggling with this for a few days now and could really use some help... I tried several approaches but none seem to work. I'm trying to read a large CSV file (over 1GB) in Python using the pandas library (version 1.3.3), but I'm running into performance optimization and memory errors. When I execute the following code: ```python import pandas as pd # Using default parameters try: df = pd.read_csv('large_file.csv') except MemoryError as e: print('MemoryError:', e) ``` I often encounter a `MemoryError`, which suggests that my system is running out of memory. I've tried increasing my system's swap space, but the performance remains slow, and the read operation fails intermittently. I've also attempted to specify the `chunksize` parameter to read the file in smaller portions: ```python chunksize = 100000 for chunk in pd.read_csv('large_file.csv', chunksize=chunksize): process(chunk) # Placeholder for data processing function ``` However, even with `chunksize` set, the performance is still not acceptable. Each chunk takes several seconds to process, and I’m not able to fully utilize my CPU resources. I’ve considered loading only certain columns to reduce memory usage by using the `usecols` parameter: ```python cols_to_use = ['column1', 'column2'] df = pd.read_csv('large_file.csv', usecols=cols_to_use) ``` While this did help a bit in terms of memory, it still seems to be a slow operation. Are there any best practices or optimizations I can implement to handle this better? Is there a more efficient way to read large CSV files with pandas or should I consider using a different library like Dask? Also, if you have experience with similar issues, what configurations or techniques have worked for you? Is there a better approach? For context: I'm using Python on Debian. This is happening in both development and production on Ubuntu 20.04. What's the best practice here? The stack includes Python and several other technologies. What's the correct way to implement this?