Quick Sort Implementations

Quick sort is a highly efficient sorting algorithm that uses a divide-and-conquer approach. It works by selecting a 'pivot' element from the array and partitioning the other elements into two sub-arrays, according to whether they are less than or greater than the pivot. The sub-arrays are then sorted recursively.

Implementation Details

Here are some common ways to implement quick sort:

In-Place Quick Sort: This version of quick sort sorts the array in place, meaning that it doesn't require any additional storage space beyond the input array.

def quick_sort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) // 2]
    left = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right = [x for x in arr if x > pivot]
    return quick_sort(left) + middle + quick_sort(right)

Iterative Quick Sort: This version of quick sort uses an explicit stack to simulate the recursion stack.

def iterative_quick_sort(arr):
    stack = [(0, len(arr) - 1)]
    while stack:
        start, end = stack.pop()
        if start >= end:
            continue
        pivot = arr[(start + end) // 2]
        left, right = start, end
        while left <= right:
            while arr[left] < pivot:
                left += 1
            while arr[right] > pivot:
                right -= 1
            if left <= right:
                arr[left], arr[right] = arr[right], arr[left]
                left, right = left + 1, right - 1
        stack.append((start, right))
        stack.append((left, end))
    return arr

Why Use Quick Sort?

Quick sort is preferred over other sorting algorithms due to its efficiency. It has an average time complexity of O(n log n) and a worst-case time complexity of O(n^2), although the worst case is rare in practice.

Learn More

For more information on sorting algorithms and their implementations, check out our Sorting Algorithms Guide.