How Can Python\'s `itertools` Module Help Batch Large Iterators for Efficient Processing?

Batching Iterators with Python's itertools

Iterating over large iterators in Python can be inefficient if you need to process data in smaller chunks. This issue arises when dealing with memory-intensive datasets or when you want to avoid overloading your system.

Enter the itertools module, which provides a suite of tools for working with iterators. One of its lesser-known but incredibly useful features is the ability to batch iterators into smaller chunks.

itertools.batched()

The itertools.batched() function takes an iterator and a chunk size as arguments and returns a new iterator that yields tuples of elements from the original iterator, with each tuple representing a batch.

For example:

import itertools

l = [1, 2, 3, 4, 5, 6, 7]
batched_l = itertools.batched(l, 3)
for batch in batched_l:
    print(batch)

OUTPUT:

(1, 2, 3)
(4, 5, 6)
(7,)

Other Options

While itertools.batched() is the simplest solution, it may not meet all your requirements. If you need more control over how batches are handled, consider the following alternatives:

• grouper(): This recipe from the itertools documentation allows you to specify how incomplete batches should be handled: filled, dropped, or considered an error.
• batched(iterable, n): Another recipe that works similarly to itertools.batched(), but only for sequences and preserving the original sequence type.
• Sequence Slicing: If you're dealing with a list or tuple, simple slicing can also be an effective way to batch data.

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