My teacher often tells me that I don't take full advantage of Python capabilities

Try partitioning with list comprehensions.

import random


def quicksort(s):
    len_s = len(s)
    if len_s < 2:
        return s

    pivot = s[random.randrange(0, len_s)]

    L = [x for x in s if x < pivot]
    E = [x for x in s if x == pivot]
    G = [x for x in s if x > pivot]

    return quicksort(L) + E + quicksort(G)

This is Pythonic, compact and faster than your partition function.

Furthermore, notice the pivot is random. Your original code always uses zero for the pivot. This results in worst-case O(n^2) complexity for sorted inputs. A random pivot mitigates this risk.

As for style, the answer from @janos offers solid guidance also.

My teacher often tells me that I don't take full advantage of Python capabilities

Try partitioning with list comprehensions.

import random


def quicksort(s):
    len_s = len(s)
    if len_s < 2:
        return s

    pivot = s[random.randrange(0, len_s)]

    L = [x for x in s if x < pivot]
    E = [x for x in s if x == pivot]
    G = [x for x in s if x > pivot]

    return quicksort(L) + E + quicksort(G)

This is Pythonic, compact and faster than your partition function.

Furthermore, notice the pivot is random. Your original code always uses zero for the pivot. This results in worst-case O(n^2) complexity for sorted inputs. A random pivot mitigates this risk.

As for style, the answer from @janos offers solid guidance also.

My teacher often tells me that I don't take full advantage of Python capabilities

Try partitioning with list comprehensions.

def quicksort(s):
    len_s = len(s)
    if len_s < 2:
        return s

    pivot = s[random.randrange(0, len_s)]

    L = [x for x in s if x < pivot]
    E = [x for x in s if x == pivot]
    G = [x for x in s if x > pivot]

    return quicksort(L) + E + quicksort(G)

This is Pythonic, compact and faster than your partition function.

Furthermore, notice the pivot is random. Your original code always uses zero for the pivot. This results in worst-case O(n^2) complexity for sorted inputs. A random pivot mitigates this risk.

As for style, the answer from @janos offers solid guidance also.

My teacher often tells me that I don't take full advantage of Python capabilities

Try partitioning with list comprehensions.

import random


def quicksort(s):
    len_s = len(s)
    if len_s < 2:
        return s

    pivot = s[random.randrange(0, len_s)]

    L = [x for x in s if x < pivot]
    E = [x for x in s if x == pivot]
    G = [x for x in s if x > pivot]

    return quicksort(L) + E + quicksort(G)

This is Pythonic, compact and faster than your partition function.

Furthermore, notice the pivot is random. Your original code always uses zero for the pivot. This results in worst-case O(n^2) complexity for sorted inputs. A random pivot mitigates this risk.

As for style, the answer from @janos offers solid guidance also.