## In-place QuickSort in Python

I had to implement the QuickSort algorithm for a homework in a language of my choice and I chose Python.

During the lectures, we've been told that QuickSort is memory efficient because it works in-place; i.e., it has no additional copies of parts of the input array for recursions.

With this in mind, I tried to implement the QuickSort algorithm in Python, but shortly afterwards realized that in order to write an elegant piece of code I would have to pass parts of the array to the function itself while recursing. Since Python creates new lists each time I do this, I have tried using Python3 (because it supports the nonlocal keyword). The following is my commented code.

def quicksort2(array): # Create a local copy of array. arr = array def sort(start, end): # Base case condition if not start < end: return # Make it known to the inner function that we will work on arr # from the outer definition nonlocal arr i = start + 1 j = start + 1 # Choosing the pivot as the first element of the working part # part of arr pivot = arr[start] # Start partitioning while j <= end: if arr[j] < pivot: temp = arr[i] arr[i] = arr[j] arr[j] = temp i += 1 j += 1 temp = arr[start] arr[start] = arr[i - 1] arr[i - 1] = temp # End partitioning # Finally recurse on both partitions sort(start + 0, i - 2) sort(i, end) sort(0, len(array) - 1)

Now, I'm not sure whether I did the job well or am I missing something. I have written a more Pythonic version of QuickSort but that surely doesn't work in-place because it keeps returning parts of the input array and concatenates them.

My question is, is this the way of doing it in Python? I have searched both Google and SO but haven't found a true in-place implementation of QuickSort, so I thought it'd be best to ask.

Sure not the best way, plus this famous algorithm will have dozens of perfect implementations.. this is mine, quite easy to understand

def sub_partition(array, start, end, idx_pivot): 'returns the position where the pivot winds up' if not (start <= idx_pivot <= end): raise ValueError('idx pivot must be between start and end') array[start], array[idx_pivot] = array[idx_pivot], array[start] pivot = array[start] i = start + 1 j = start + 1 while j <= end: if array[j] <= pivot: array[j], array[i] = array[i], array[j] i += 1 j += 1 array[start], array[i - 1] = array[i - 1], array[start] return i - 1 def quicksort(array, start=0, end=None): if end is None: end = len(array) - 1 if end - start < 1: return idx_pivot = random.randint(start, end) i = sub_partition(array, start, end, idx_pivot) #print array, i, idx_pivot quicksort(array, start, i - 1) quicksort(array, i + 1, end)

Ok first a seperate function for the partition subroutine. It takes the array, the start and end point of interest, and the index of pivot. This functions should be clear

Quicksort then call the partition subroutine for the first time on the whole array; then call recursevely itself to sort everything up to the pivot and everything after.

ask if you dont understand something

**Python Program for QuickSort,** doesn't create any copies of the array or any of its subarrays. Introduction Divide and conquer: Quicksort splits the array into smaller arrays until it ends up with an empty array, or one that has In place: Quicksort doesn't create any copies of the array or any of its subarrays. It does however require stack memory Unstable: A stable sorting algorithm is

I have started learning python lately and the following is my attempt at implementing quicksort using python. Hope it is helpful. Feedback is welcomed :)

#!/usr/bin/python Array = [ 3,7,2,8,1,6,8,9,6,9] def partition(a, left, right): pivot = left + (right - left)/2 a[left],a[pivot] = a[pivot], a[left] # swap pivot = left left += 1 while right >= left : while left <= right and a[left] <= a[pivot] : left += 1 while left <= right and a[right] > a[pivot] : right -= 1 if left <= right: a[left] , a[right] = a[right], a[left] # swap left += 1 right -= 1 else: break a[pivot], a[right] = a[right] , a[pivot] return right def quicksort(array , left,right): if left >= right: return if right - left == 1: if array[right] < array[left]: array[right], array[left] = array[left] , array[right] return pivot = partition(array, left, right) quicksort(array, left, pivot -1) quicksort(array, pivot+1,right) def main(): quicksort(Array, 0 , len(Array) -1) print Array main()

**In-place QuickSort in Python,** Python program for implementation of Quicksort Sort. # This function takes last element as pivot, places. # the pivot element at its correct position in sorted. Also, in Python you don't have to use a temporal variable to do an exchange. Instead of: t = x x = y y = t You can do: x, y = y, x

Here is another implementation:

def quicksort(alist): if len(alist) <= 1: return alist return part(alist,0,len(alist)-1) def part(alist,start,end): pivot = alist[end] border = start if start < end: for i in range(start,end+1): if alist[i] <= pivot: alist[border], alist[i] = alist[i], alist[border] if i != end: border += 1 part(alist,start,border-1) part(alist,border+1,end) return alist

**Quicksort tutorial: Python implementation with line by line ,** Sure not the best way, plus this famous algorithm will have dozens of perfect implementations.. this is mine, quite easy to understand Question Description. I had to implement the QuickSort algorithm for a homework in a language of my choice and I chose Python. During the lectures, we’ve been told that QuickSort is memory efficient because it works in-place; i.e., it has no additional copies of parts of the input array for recursions.

Here's what I came up with. The algorithm is in-place, looks nice and is recursive.

# `a` is the subarray we're working on # `p` is the start point in the subarray we're working on # `r` is the index of the last element of the subarray we're working on def part(a,p,r): k=a[r] #pivot j,q=p,p if p<r: # if the length of the subarray is greater than 0 for i in range(p,r+1): if a[i]<=k: t=a[q] a[q]=a[j] a[j]=t if i!=r: q+=1 j+=1 else: j+=1 part(a,p,q-1) # sort the subarray to the left of the pivot part(a,q+1,r) # sort the subarray to the right of the pivot return a def quicksort(a): if len(a)>1: return part(a,0,len(a)-1) else: return a

**Quicksort (In-place): Background & Python Code,** Unlike its competitor Mergesort, Quicksort can sort a list in place, saving the need to create a copy of the list, and therefore saving on memory Quick Sort Algorithm in Python. Quicksort is a divide and conquer algorithm. Quicksort when implemented well it is one of the best sorting algorithm.

Explanation: Pivot is always the last element in given array. In my approach I keep track of the 'border' between numbers smaller and bigger than pivot. Border is an index of first number in 'bigger' group. At the end of each iteration we swap number under 'border' with the pivot number.

And the code:

def qs(ar, start, end): if (end-start < 1): return if (end-start == 1): if(ar[start] > ar[end]): tmp = ar[start] ar[start] = ar[end] ar[end] = tmp return pivot = ar[end - 1] border_index = start i = start while(i <= end - 1): if (ar[i] < pivot): if i > border_index: tmp = ar[i] ar[i] = ar[border_index] ar[border_index] = tmp border_index += 1 i+=1 ar[end-1] = ar[border_index] ar[border_index] = pivot qs(ar, start, border_index) qs(ar, border_index + 1, end) qs(ar, 0, n)

**mertkahyaoglu/inplace-quicksort: Inplace QuickSort Python ,** Inplace QuickSort. Index l scans the sequence from left to right, and index r scans the sequence from right to left. A swap is performed when l is at an element As its quicksort most of the code is straightforward enough that comments would probably only add meaning when it comes to the swapping. You should probably try and use four spaces, rather than tabs, for indenting the code (it's just common practice).

**How to implement QuickSort in Python,** QuickSort is an in-place sorting algorithm with worst-case time complexity of n 2 n^{2} n2. Implementation. QuickSort can be implemented both iteratively In this video we'll take another look at the efficient Quicksort algorithm, specifically, we'll reimplement our prior approach to run in-place. After coding up our new solution, we'll compare the

**QuickSort Algorithm in Python - programminginpython.com,** Personally I like the style (especially the nice clear function and variable names). As its quicksort most of the code is straightforward enough Quicksort. Quicksort is a very efficient sorting algorithm invented by C.A.R. Hoare. It has two phases: the partition phase; the sort phase; Most of the work is done in the partition phase - it works out where to divide the work. The sort phase simply sorts the two smaller problems that are generated in the partition phase.

**In-Place Quicksort in Python,** Quicksort in Python is a divide and conquer sorting algorithm that sorts the items in O(nlogn). Python Program for QuickSort Like Merge Sort , QuickSort is a Divide and Conquer algorithm. It picks an element as pivot and partitions the given array around the picked pivot.