Python Program for Heap Sort

Heapsort is a comparison based sorting technique based on a Binary Heap data structure. It is similar to selection sort where we first find the maximum element and place the maximum element at the end. We repeat the same process for the remaining element.

Python

# Python program for implementation of heap Sort

# To heapify subtree rooted at index i.
# n is size of heap
def heapify(arr, n, i):
 largest = i # Initialize largest as root
 l = 2 * i + 1  # left = 2*i + 1
 r = 2 * i + 2  # right = 2*i + 2

 # See if left child of root exists and is
 # greater than root
 if l < n and arr[i] < arr[l]:
  largest = l

 # See if right child of root exists and is
 # greater than root
 if r < n and arr[largest] < arr[r]:
  largest = r

 # Change root, if needed
 if largest != i:
  arr[i],arr[largest] = arr[largest],arr[i] # swap

  # Heapify the root.
  heapify(arr, n, largest)

# The main function to sort an array of given size
def heapSort(arr):
 n = len(arr)

 # Build a maxheap.
 # Since last parent will be at ((n//2)-1) we can start at that location.
 for i in range(n // 2 - 1, -1, -1):
  heapify(arr, n, i)

 # One by one extract elements
 for i in range(n-1, 0, -1):
  arr[i], arr[0] = arr[0], arr[i] # swap
  heapify(arr, i, 0)

# Driver code to test above
arr = [ 12, 11, 13, 5, 6, 7]
heapSort(arr)
n = len(arr)
print ("Sorted array is")
for i in range(n):
 print ("%d" %arr[i]),
# This code is contributed by Mohit Kumra

Output

Sorted array is
5 6 7 11 12 13

Time Complexity: O(n*log(n))

• Time complexity of heapify is O(log(n)).
• Time complexity of createAndBuildHeap() is O(n).
• And, hence the overall time complexity of Heap Sort is O(n*log(n)).

Auxiliary Space: O(logn)

Please refer complete article on Heap Sort for more details!