Python Coding challenge - Day 819| What is the output of the following Python Code?

 

Code Explanation:

Importing the Required Libraries

import heapq, operator

heapq: A Python module that provides heap queue (priority queue) algorithms.

It maintains a list such that the smallest element is always at index 0.

operator: Provides function equivalents of operators like +, -, *, /.

For example, operator.add(a, b) is equivalent to a + b.

Creating a List of Numbers

nums = [7, 2, 9, 1]

A simple Python list is created with four integers: [7, 2, 9, 1].

At this stage, it is just a normal unsorted list.

Converting the List into a Heap

heapq.heapify(nums)

Converts the list into a min-heap in place.

A min-heap ensures the smallest element is always at index 0.

After heapifying, the internal arrangement becomes something like:

nums = [1, 2, 9, 7]

(The exact internal order can vary, but 1 is always at the root.)

Adding a New Element to the Heap

heapq.heappush(nums, 3)

Adds a new element (3) into the heap while maintaining the heap property.

The heap reorganizes itself so the smallest element remains at the top.

The new heap might look like:

nums = [1, 2, 9, 7, 3]

Removing the Smallest Element

a = heapq.heappop(nums)

Removes and returns the smallest element from the heap.

Here, a = 1 because 1 is the smallest value.

The heap is then rearranged automatically to maintain its structure.

Removing the Next Smallest Element

b = heapq.heappop(nums)

Again removes and returns the next smallest element.

After 1 is removed, the next smallest is 2.

So, b = 2.

The heap now looks like [3, 7, 9].

Adding the Two Smallest Values

print(operator.add(a, b))

operator.add(a, b) performs addition just like a + b.

Adds the two popped values: 1 + 2 = 3.

The result (3) is printed.

Final Output

3

700 Days Python Coding Challenges with Explanation