Python Coding challenge - Day 467| What is the output of the following Python Code?

Code Explanation:

1. Importing heapq Module

import heapq

The heapq module provides an implementation of the heap queue algorithm, also known as the priority queue algorithm.

A heap is a binary tree where the parent node is smaller (for a min-heap) or larger (for a max-heap) than its child nodes.

The heapq module in Python supports min-heaps by default.

2. Initializing a List

heap = [3, 1, 4, 5, 2]

Here, we define a list called heap that contains unsorted elements: [3, 1, 4, 5, 2].

This list is not yet in heap order (i.e., not arranged according to the heap property).

3. Applying heapify() to the List

heapq.heapify(heap)

The heapq.heapify() function transforms the list into a valid min-heap.

After calling this function, the smallest element will be at the root (the first element of the list).

The list heap will now be rearranged into the heap order. The smallest element (1) will be at the root, and the children nodes (2, 5, etc.) will satisfy the heap property.

The list after heapq.heapify() becomes:

[1, 2, 4, 5, 3]

Explanation:

1 is the smallest element, so it stays at the root.

The heap property is maintained (parent is smaller than its children).

4. Pushing a New Element into the Heap

heapq.heappush(heap, 6)

The heapq.heappush() function is used to push a new element (in this case, 6) into the heap while maintaining the heap property.

After inserting 6, the heap will rearrange itself to keep the smallest element at the root.

The list after heappush() becomes:

[1, 2, 4, 5, 3, 6]

The element 6 is added, and the heap property is still preserved.

5. Printing the Resulting Heap

print(heap)

Finally, the print() function displays the heap after performing the heap operations.

The printed output will be the heapified list with the new element pushed in, maintaining the heap property.

Output:

[1, 2, 4, 5, 3, 6]