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Binary Heaps

Read heaps first.




A binary heap uses a complete binary tree.

Recall that complete binary trees:

  • have each level filled, with possible exception of last level
  • when the last level is incomplete all nodes are filled from the left

Inserting into a binary heap:

  1. place new element in left most spot (n+1)
  2. "Bubble Up": if and while (new) element dominates parent
  • swap them

Swaps happen between levels, and a tree will have lg(n) levels. Since there are n items to be inserted, insertion will take O(nlogn).

Extracting the dominator from a binary heap:

  1. remove dominating element from top
  2. move last added element (bottom-right most leaf) into top
  3. "Bubble Down": if and while that element does not dominate its children:
  • swap it with lesser of two children

Implementing Binary Heaps

Since a binary heap is a complete binary tree we can implement it using an array.

Example: 2 7 8 1 5 9 3 pushed into min-heap as a tree:

  2   3
 7 5 9 8

But as an array looks like: 1,2,3,7,5,9,8,. Notice this is equivalent to a breadth-first traversal.

This image from Wikipedia explains best: Implicit Binary Tree

So for some zero-based index i:

  • left child = 2i + 1
  • right child = 2i + 2
  • parent = floor((i-1) / 2)

WARNING: Beware zero and one-based version of the above equations. Many references use one-based equations because the math/logic is cleaner, but programmers always use zero-based arrays.

Notice that floor(n/2) is the index of one or more the the middle items of the array:

  • floor(n/2) if n is odd
  • floor(n/2) and floor(n/2) - 1 if n is even

To insert:

  1. append number to list
  2. "Bubble Up":
  • parent = heap[floor((i-1)/2)]

  • if parent < number then swap them

  • Test your inserts

  • Test your removals


  • cannot be efficiently searched because it is not a BST
  • we don't know any facts that will improve a linear search

Heap Interface:

  • get the dominating element, e.g. min or max
  • add or insert an element
  • remove or delete an element

We can store any binary tree in an array without pointers but:

  • array still requires empty spots for missing nodes
  • methods to save memory make it less flexible


8.4. heapq — Heap queue algorithm.