I'm trying to process a very large CSV file—we're talking hundreds of gigabytes—that contains raw lead data. My goal is to extract only the qualified leads and discard the rest. The problem is, loading the entire file into memory is impossible; my script crashes every time. My current approach is using a simple for loop to iterate through the file line by line, but it’s still incredibly slow, and I’m worried it’s not the most memory-efficient method. I'm looking for a more scalable method or algorithm in Python to process this data. What's a good way to handle this without loading the entire dataset into memory? A specific code example or a proven approach would be really helpful. Continue reading...
