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Short answers · Lists and dictionaries

How do I flatten a list of lists in Python?

[x for row in nested for x in row] flattens one level, and itertools.chain does it lazily. Avoid sum(nested, []): it slows down fast as the list grows.

A list comprehension with two fors, read left to right like the nested loops it replaces.

python
weeks = [[3, 5, 2], [8, 1], [4, 4, 6, 0]]

days = [n for week in weeks for n in week]
print(days)
output
[3, 5, 2, 8, 1, 4, 4, 6, 0]

If the order of the fors looks backwards, write out the loop it stands for; the comprehension keeps exactly that order:

python
weeks = [[3, 5, 2], [8, 1]]

days = []
for week in weeks:
    for n in week:
        days.append(n)
print(days)
output
[3, 5, 2, 8, 1]

Without building the list: chain

itertools.chain.from_iterable walks through the inner lists one after another without creating a new list. Good when you're going to loop over the result once, or sum it:

python
from itertools import chain

weeks = [[3, 5, 2], [8, 1], [4, 4, 6, 0]]
print(sum(chain.from_iterable(weeks)))
print(list(chain.from_iterable(weeks)))
output
33
[3, 5, 2, 8, 1, 4, 4, 6, 0]

Skip sum(nested, [])

It looks clever and gives the right answer:

python
print(sum([[1, 2], [3], [4, 5]], []))
output
[1, 2, 3, 4, 5]

But each + copies everything collected so far into a new list. With 10 inner lists that's nothing; with 10,000 it copies millions of items. The comprehension and chain touch each item once.

Deeper nesting

The methods above flatten one level. For lists nested to any depth, recurse:

python
def flatten(items):
    for item in items:
        if isinstance(item, list):
            yield from flatten(item)
        else:
            yield item

print(list(flatten([1, [2, [3, [4]], 5], [[6]]])))
output
[1, 2, 3, 4, 5, 6]

Check for list specifically, not "anything you can loop over": a string is iterable too, and each character is a one-character string, so a general check would never stop.

Working with numbers in a grid? NumPy's array.ravel() flattens a whole matrix in one call.

Go deeper: Comprehensions: Lists, Dicts, Sets, Generators, Itertools: Composable Iterator Building Blocks, Python Comprehensions Cheat Sheet

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