How can I read large text files line by line, without loading them into memory?

Behind the scenes: How it works

Let's deconstruct the mechanics of this approach, dissecting it like a digital frog:

The readlines() method:

• This method loads the entire file into a list in memory. Whoops, we might just run out of memory with large files!

Tuning the buffering parameters:

• In the open() function, you can play around with the buffering parameter to manage memory usage like a pro.

Choosing the right file input mode:

• Make sure the file is opened in 'r' mode or 'rt' mode when reading text. You wouldn't want to load the whole content into memory, would you?

Alternative ways if you want more control

If you are an overachiever and need more flexibility, check out these advanced techniques:

Generators: Python's gift to you

Python's generators can make the whole memory management thing a breeze:

def read_large_file(file_object):
    """Reads a file larger than my list of ignored chores."""
    while True:
        line = file_object.readline()
        if not line:
            break
        yield line

with open('largefile.txt', 'r') as file:
    for line in read_large_file(file):
        process(line) # processes each line, like at a factory conveyor belt.
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The fileinput library: Not a library for reading, sorry

The fileinput module is perfect for in-place editing and streaming lines straight from multiple files:

import fileinput

for line in fileinput.input(files=('largefile1.txt', 'largefile2.txt')):
    process(line) # takes one line at a time, like at the lunch queue.
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While loops: Old but gold

Never underestimate the power of a good old while loop when you have to process each line:

with open('largefile.txt', 'r') as file:
    while (line := file.readline()):
        process(line) # Remember: While there's life, there's hope. And lines.
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Smart practices for smart results

Context management:

• Always make use of context managers (with statement) to make sure the files are closed properly and to keep memory leaks at bay.

File object iteration:

• Avoid creating large lists in memory by directly iterating over the file object. See, going direct is not so bad!

Tackling the unconventional

Every once in a while, you need to deal with files so large that even line-by-line iteration could become problematic, probably because of complex processing of each line. Here we go:

Keep an eye on memory:

Keep tabs on your program's memory usage using Python's tracemalloc module.

Bite sized chunks:

If each line is still too large, read the file in larger chunks and parse each chunk manually into lines.

Trust the Unix guys:

Python combined with Unix tools like grep, awk, or sed can help pre-process files before you analyze them in Python. They can be optimized too, and highly!