Python Coding challenge - Day 743| What is the output of the following Python Code?

Code Explanation:

1. Importing the heapq module

import heapq

heapq is a Python module that implements the heap queue algorithm (also called a priority queue).

In Python, heapq always creates a min-heap (smallest element at the root).

2. Creating a list

nums = [8, 3, 5, 1]

A normal Python list nums is created with values [8, 3, 5, 1].

At this point, it’s just a list, not yet a heap.

3. Converting list to a heap

heapq.heapify(nums)

heapq.heapify(nums) rearranges the list in-place so it follows the min-heap property.

Now, the smallest number is always at index 0.

After heapify, nums becomes [1, 3, 5, 8].

4. Adding a new element to the heap

heapq.heappush(nums, 0)

heappush adds a new element to the heap while keeping the min-heap structure intact.

Here, 0 is inserted.

Now nums becomes [0, 1, 5, 8, 3] internally structured as a heap (not strictly sorted but heap-ordered).

5. Removing and returning the smallest element

heapq.heappop(nums)

heappop removes and returns the smallest element from the heap.

The smallest element here is 0.

After popping, heap rearranges automatically → nums becomes [1, 3, 5, 8].

6. Getting the largest 3 elements

heapq.nlargest(3, nums)

nlargest(3, nums) returns the 3 largest elements from the heap (or list).

Since nums = [1, 3, 5, 8], the 3 largest elements are [8, 5, 3].

7. Printing the result

print(heapq.heappop(nums), heapq.nlargest(3, nums))

First part: heapq.heappop(nums) → prints 0.

Second part: heapq.nlargest(3, nums) → prints [8, 5, 3].

Final Output:

0 [8, 5, 3]

500 Days Python Coding Challenges with Explanation