Note

Python - tee() - Duplicate an iterable into multiple iterators


Overview of the Python tee() function. Duplicate your iterable multiple times in one object.

tee(iterable, n) lets you "copy" or "duplicate" your iterable n times. It returns a tuple with lazy iterators.

It's useful if you want to iterate over your iterable several times, while preserving lazy evaluation.

tee needs to be imported from itertools

Please take a look at examples below to better understand tee():

from itertools import tee

l1 = [1, 2, 3]

# Create three independent iterators from l1
three_l1 = tee(l1, 3)  # (first_l1_iterator, second_l1_iterator, third_l1_iterator)
# (<itertools._tee object at 0x10>, <itertools._tee object at 0x10>, <itertools._tee object at 0x10>)

for a, b, c in three_l1:  # Unpack the tuple of iterators
    print(a, b, c)  # 1 2 3

Output:

1 2 3
1 2 3
1 2 3

Note that first_l1_iterator etc. are no longer lists. They are lazy iterators now.

Unpacking tee() Iterators Using *

from itertools import tee

l2 = ["a", "b", "c"]

# Create three independent iterators from l2
three_l2 = tee(l2, 3)  # (first_l2_iterator, second_l2_iterator, third_l2_iterator)
# (<itertools._tee object at 0x10>, <itertools._tee object at 0x10>, <itertools._tee object at 0x10>)

for iterator in three_l2:
    print(*iterator)  # a b c; Unpack the iterator from tuple of iterators here

Output:

a b c
a b c
a b c

Key Points to Remember about the tee() Function

  • Lazy and Independent Iterators: tee() provides multiple, independent iterators that consume elements lazily.

  • Potential Memory Overhead: While tee() enables multiple passes over an iterable, it can increase memory usage due to caching.

  • Single Use: Avoid using the original iterator after applying tee(), as doing so can cause unexpected behavior in the independent iterators.

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