How can I efficiently process gigantic CSV files in Python 2.7 without running into memory issues?

Reading Gigantic CSV Files: Optimizing Memory and Speed

When attempting to process massive CSV files with millions of rows and hundreds of columns, traditional approaches using iterators can lead to memory-related issues. This article explores optimized techniques for handling large-scale CSV data in Python 2.7.

Memory Optimization:

The crux of the memory problem lies in constructing in-memory lists to store large datasets. To mitigate this issue, Python offers the yield keyword, which converts functions into generator functions. These functions pause execution after each yield statement, allowing incremental processing of data as it's encountered.

By employing generator functions, you can process data row by row, eliminating the need to store entire files in memory. The following code demonstrates this approach:

import csv

def getstuff(filename, criterion):
    with open(filename, "rb") as csvfile:
        datareader = csv.reader(csvfile)
        yield next(datareader)  # yield header row

        count = 0
        for row in datareader:
            if row[3] == criterion:
                yield row
                count += 1
            elif count:  # stop processing when a consecutive series of non-matching rows is encountered
                return

Speed Enhancements:

Additionally, you can leverage Python's dropwhile and takewhile functions to further improve processing speed. These functions can filter data efficiently, enabling you to quickly locate the rows of interest. Here's how:

from itertools import dropwhile, takewhile

def getstuff(filename, criterion):
    with open(filename, "rb") as csvfile:
        datareader = csv.reader(csvfile)
        yield next(datareader)  # yield header row

        yield from takewhile(  # yield matching rows
            lambda r: r[3] == criterion,
            dropwhile(  # skip non-matching rows
                lambda r: r[3] != criterion, datareader))
        return

Simplified Looped Processing:

By combining generator functions, you can greatly simplify the process of looping through your dataset. Here's the optimized code for getstuff and getdata:

def getdata(filename, criteria):
    for criterion in criteria:
        for row in getstuff(filename, criterion):
            yield row

Now, you can directly iterate over the getdata generator, which produces a stream of rows row by row, freeing up valuable memory resources.

Remember, the goal is to minimize in-memory data storage while simultaneously maximizing processing efficiency. By applying these optimization techniques, you can effectively handle gigantic CSV files without encountering memory roadblocks.

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