Efficiently Reading and Processing Large CSV Files in Python to Avoid Memory Issues

[Grinda21]

I'm trying to read a large CSV file, but I keep running into memory issues. How can I efficiently read and process a large CSV file in Python, ensuring that I don't run out of memory?

Please provide a solution or guidance on how to handle large CSV files in Python to avoid memory problems.

 

[Begbie00]

Some general guidance, since you've given us almost nothing to go on.

1. General trading data rule: Almost any meaningful statistic (e.g, mean, variance, covariance, principle components, etc.) can be cast as an "on-line" calculation. This means with very few exceptions, you probably don't need to operate on the whole table at once as vector calculations.
   
2. General Python rule: Unless you need to operate on a thing all at once (see #1), you should use iterators. For csv files, you can use the reader or DictReader in Python's csv module. These iterate over the csv file from top to bottom (vs. loading it all into memory first).
   
3. If you need to sort the csv before processing it, you can do so on Linux using the command line SORT. This is a highly-optimized, disk-based, parallel sort that works through massive files in seconds.

I have a Python trading module that backtests on Dukascopy FX quotes across 10 currencies over 20 years and never use more than 30MB at a time. Everything I need is built on streams of data (vs. tables). Pandas is a wonderful library, but I don't miss it.