Splitting a list into N parts of approximately equal length

Getting precise with splits

When the list cannot be evenly divided, our function may make uneven jigsaw puzzles. To balance the pieces, use floating point division and modulo operator:

split_precise = lambda lst, n: [lst[round(i*len(lst)/n):round((i+1)*len(lst)/n)] for i in range(n)]


When dealing with the fussiness of indices and element distribution, the modulo operator comes to rescue like a super hero:

split_modulo = lambda lst, n: [lst[i*len(lst)//n + min(i, len(lst) % n):(i+1)*len(lst)//n + min(i+1, len(lst) % n)] for i in range(n)]


Dealing with numerical data

If numbers are your cup of tea like Sheldon Cooper, NumPy is your mojo. The array_split function manages order and handles uneven splits gracefully like a pro gymnast:

import numpy as np

def split_numpy(lst, n):
    return np.array_split(lst, n)


Getting, set, use:

lst = np.arange(1, 10)
n = 3
print(split_numpy(lst, n))  # Output: [array([1, 2, 3]), array([4, 5, 6]), array([7, 8, 9])]


Standing on the shoulders of giants

Sometimes, It's like standing on the shoulder of virtual giants like Zero Piraeus and looking further. Respected and tested methods in the community tend to be best practices. Atlas Shrugged but they didn't.

Tackling common pitfalls

When things aren't divisible

Some items don't like to be split, like the One Ring in Lord of the Rings. Modulo, your Gandalf here, can help tackle this by ensuring a fair share for all.

Ensuring order like a British queue

Make sure to maintain the 'Tolkien Rule' - the original order of the journey. Any method you choose should respect this order, more than Gollum respected the ring.

Common mistakes: Whoops!

Round-off errors can transform your list split result into a Fibonacci sequence! Use accurate integer division and consider edge cases when testing to ensure your function is as reliable as a Swiss watch.