Python Coding challenge - Day 756| What is the output of the following Python Code?

 

Code Explanation:

1. Importing the heapq Module

import heapq

The heapq module provides functions to work with heaps (a type of priority queue).

By default, Python’s heapq implements a min-heap → the smallest element is always at the root (index 0).

2. Creating the List

nums = [6, 2, 9, 1]

A normal Python list is created with numbers [6, 2, 9, 1].

Currently, it’s just a list, not a heap yet.

3. Converting the List into a Heap

heapq.heapify(nums)

This rearranges the list into min-heap order in-place.

After heapify, the smallest element becomes the first element (index 0).

Resulting heap (internally): [1, 2, 9, 6].

Here, 1 is the root, 2 is next, etc. (heap property is satisfied).

4. Pushing a New Element into the Heap

heapq.heappush(nums, 0)

Adds the value 0 to the heap.

Heap automatically rearranges to maintain the min-heap property.

Heap now becomes: [0, 1, 9, 6, 2].

5. Removing and Returning the Smallest Element

heapq.heappop(nums)

Removes and returns the smallest element (root) from the heap.

Smallest = 0.

Heap after removal adjusts to maintain order: [1, 2, 9, 6].

6. Getting the Two Largest Elements

heapq.nlargest(2, nums)

Finds the 2 largest elements from the heap/list.

Current heap: [1, 2, 9, 6].

Largest two = [9, 6].

7. Printing the Results

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

First, it pops the smallest element (1).

Then finds the 2 largest elements ([9, 6]).

Final Output:

1 [9, 6]

500 Days Python Coding Challenges with Explanation