Python Coding challenge - Day 739| What is the output of the following Python Code?

 

1. Importing the heapq Module

import heapq

Imports Python’s heapq library.

Provides functions to work with heaps (priority queues).

By default, it creates a min-heap (smallest element always at root).

2. Creating a List

nums = [9, 4, 7, 2]

A normal Python list with values [9, 4, 7, 2].

Not yet a heap — just an unordered list.

3. Converting List into a Heap

heapq.heapify(nums)

Transforms the list into a min-heap in place.

Now, nums is rearranged so that the smallest element (2) is at index 0.

Heap after heapify: [2, 4, 7, 9].

4. Adding a New Element to the Heap

heapq.heappush(nums, 1)

Pushes 1 into the heap while maintaining heap order.

Heap now becomes: [1, 2, 7, 9, 4] (internally ordered as a heap, not a sorted list).

5. Removing the Smallest Element

smallest = heapq.heappop(nums)

Pops and returns the smallest element from the heap.

Removes 1 (since min-heap always gives the smallest).

Now, smallest = 1.

Heap after pop: [2, 4, 7, 9].

6. Finding the Two Largest Elements

largest_two = heapq.nlargest(2, nums)

Returns the 2 largest elements from the heap (or any iterable).

Heap currently is [2, 4, 7, 9].

The two largest are [9, 7].

7. Printing Results

print(smallest, largest_two)

Prints the values of smallest and largest_two.

Final Output

1 [9, 7]

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