CODEWITHSUNDEEP
pythonarrayssorting
nick
nick

Reputation: 1207

How can I improve my quick sort algorithm (Python)

After reading about the quick sort algorithm, I decided to write my own implementation before looking at any code. The code below is what I came up with. Upon comparing my code with other implementations I have observed that rather than returning the sorted array from the quick sort function, other implementations tend to take advantage of a list's mutability and simply run the function on the unsorted array, which in turn will sort the array without having to reference the function call. I am curious about the space time comparison with my code and the code from the book I am using, which I have provided below. I am assuming that in terms of time the algorithims perform rather similarly, maybe the concatenation operation I am performing has a negative impact? In terms of space, since I am not modifying the input array directly, I am assuming that I am creating/ returning a new array which is obviously inefficient, and important because the main advantage of quick sort over merge sort is the saved space. Overall I am just looking for some additional insight and any way to improve my algorithm's efficiency.

My code:

from random import randint

def quick(arr):
  if len(arr) == 1:
    return arr
  else:

    pivot = arr[0]
    R = len(arr)-1
    L = 1

    while L <= len(arr)-1 and R >= 1:
        if R == L:
            if arr[0] > arr[R]:
                arr[0], arr[R] = arr[R], arr[0]
            break
        if arr[R] >= pivot:
            R = R - 1
            continue
        if arr[L] <= pivot:
            L = L + 1
            continue
        arr[L], arr[R] = arr[R], arr[L]
    return quick(arr[:R]) + quick(arr[R:])

print quick([randint(0,1000) for i in range(1000)])

The book I am using, Problem Solving With Algorithms and Data Structures Using Python By Brad Miller and David Ranum, provides this quick sort code: 

def quickSort(alist):
  quickSortHelper(alist,0,len(alist)-1)

def quickSortHelper(alist,first,last):
  if first<last:

   splitpoint = partition(alist,first,last)

   quickSortHelper(alist,first,splitpoint-1)
   quickSortHelper(alist,splitpoint+1,last)


def partition(alist,first,last):
  pivotvalue = alist[first]

  leftmark = first+1
  rightmark = last

 done = False
 while not done:

   while leftmark <= rightmark and alist[leftmark] <= pivotvalue:
     leftmark = leftmark + 1

   while alist[rightmark] >= pivotvalue and rightmark >= leftmark:
     rightmark = rightmark -1

     if rightmark < leftmark:
       done = True
     else:
       temp = alist[leftmark]
       alist[leftmark] = alist[rightmark]
       alist[rightmark] = temp

 temp = alist[first]
 alist[first] = alist[rightmark]
 alist[rightmark] = temp

return rightmark

# alist = [54,26,93,17,77,31,44,55,20]
# quickSort(alist)
# print(alist)

Upvotes: 0

Views: 591

Answers (1)

T. Claverie
T. Claverie

Reputation: 12256

This is nice code.

Compared to a quicksort version that is done in place (using only one array), yours may be a bit slower because of the copy/concatenation.

Quicksort performances rely a lot on the choice of the pivot. By choosing the first element, there are some cases where your code runs in quadratic time, for example while sorting an already sorted array. The most known optimizations are:

  • Choosing a better pivot, for example by applying Tukey's ninther (you avoid those worst cases almost certainly).
  • Performing an Insertion sort when the subarray is small enough (< 10 for example).

Else, there are a few variants of quicksort which run faster, like 3-way quicksort using Bentley-McIlroy's sheme or dual-pivot quicksort (used to sort arrays of primitive in java). The Insertion speedup is still applicable for those.

Upvotes: 1

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