Python Coding challenge - Day 750| What is the output of the following Python Code?

 

Code Explanation:

1. Importing heapq

import heapq

Imports the heapq module, which provides heap (priority queue) operations in Python.

Heaps are special binary trees where the smallest element is always at the root (min-heap by default in Python).

2. Creating a list

nums = [7, 2, 5, 1]

A normal Python list is created with elements [7, 2, 5, 1].

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

3. Converting 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 heapify, the list becomes something like [1, 2, 5, 7] (exact arrangement may vary, but smallest = 1 is always first).

4. Pushing a new element into the heap

heapq.heappush(nums, 0)

Inserts 0 into the heap while maintaining the heap property.

Now the heap is [0, 1, 5, 7, 2] (internally arranged, but 0 is guaranteed to be at root).

5. Removing the smallest element

heapq.heappop(nums)

Pops and returns the smallest element from the heap.

Smallest = 0.

After popping, heap reorganizes itself, smallest at root again.

6. Getting the two largest elements

heapq.nlargest(2, nums)

Finds the 2 largest elements in the heap.

From [1, 2, 5, 7], the two largest are [7, 5].

7. Printing results

print(heapq.heappop(nums), heapq.nlargest(2, nums))

First part (heapq.heappop(nums)) → 0.

Second part (heapq.nlargest(2, nums)) → [7, 5].

Final Output

0 [7, 5]

500 Days Python Coding Challenges with Explanation