Flattening a shallow list in Python

python

list-flattening

iterators

performance

byAnton Shumikhin·Oct 2, 2024

Quickly flatten a shallow list with the extend method within a list comprehension for a speedy, efficient approach:

flattened_list = [item for sublist in shallow_list for item in sublist]

This dynamic line of code restructures the list, flattening it by directly adding each element from each sublist into a newly created list. This prevents the unneeded overhead of list concatenation.

Unraveling list flattening

When discussing flattening a list, what we aim to achieve is converting a list of lists into a single, consolidated list containing all elements.

Various tools for list flattening

Aside from the swift solution above, Python avails a plethora of techniques to achieve this:

itertools.chain:

from itertools import chain
flattened_list = list(chain(*shallow_list))

itertools.chain.from_iterable: Slightly superior in performance when handling large lists as the * operator, used for unpacking the list, is not required:
```
from itertools import chain
flattened_list = list(chain.from_iterable(shallow_list))
```
sum Function: Alternatively, sum with an empty list as the start value proves useful:
```
flattened_list = sum(shallow_list, [])
```
However, it is worth noting that this method's efficiency dwindles with larger lists.

Prioritizing performance

When performance is key, both itertools.chain and itertools.chain.from_iterable outrun nested list comprehensions, particularly evident with larger datasets. timeit module is your go-to for benchmarking:

import timeit

# Benchmarking example
time_taken = timeit.timeit(lambda: list(chain.from_iterable(shallow_list)), number=1000)
print(f"Time used: {time_taken} seconds, but no time wasted!")

However, always uphold the principle that readability is key.

Navigating special cases

Dealing with strings in lists: It is crucial to distinguish strings from iterables in the context of list flattening to prevent unintended splitting of characters:

shallow_list = ['hello', 'world']
# Whoops! Plunged headfirst into letter splitsville!
wrong_flatten = [char for string in shallow_list for char in string]

Deep nesting? Recursion to the rescue!: Dealing with a deeply nested list? Recursion might just save the day:

def flatten(deep_list):
    for el in deep_list:
        if isinstance(el, list):
            yield from flatten(el)
        else:
            yield el
            
deep_list = [[1, 2], [[3, 4], [5, 6]]]
list(flatten(deep_list))

There are a few significant considerations and pro tips to bear in mind while treading the list flattening path:

Iterability confirmation

In the uncertain scenario where all the list elements are iterable, it's prudent to confirm iterability to deflect runtime errors.

Django QuerySets

Django users, be cautious when attempting to flatten QuerySet objects. Always put readability first, as luminously complex list comprehensions can swiftly become a challenging puzzle.

Accuracy over assumption

When in doubt, harness the timeit module to accurately gauge performance rather than guessing. Our intuitions often betray us, particularly across various Python versions and environments.