Python Coding challenge - Day 734| What is the output of the following Python Code?

 

Code Explanation:

1) import heapq

Imports Python’s heapq module — a small heap/priority-queue implementation that stores a min-heap in a plain list.

In a min-heap the smallest element is always at index 0.

2) nums = [7, 2, 9, 4]

Creates a regular Python list with the four integers.

At this point it is not a heap yet; just a list containing [7, 2, 9, 4].

3) heapq.heapify(nums)

Converts the list in-place into a valid min-heap (O(n) operation).

Internally it sifts elements to satisfy the heap property (parent ≤ children).

After heapify the list becomes:

[2, 4, 9, 7]

(2 is the smallest and placed at index 0; internal order beyond the heap property may vary but this is the CPython result for this input).

4) heapq.heappush(nums, 1)

Pushes the value 1 onto the heap while maintaining the heap property.

Steps (conceptually):

Append 1: [2, 4, 9, 7, 1]

Sift up to restore heap: swap with parent 4 → [2, 1, 9, 7, 4]

Swap with parent 2 → [1, 2, 9, 7, 4]

Final heap after push:

[1, 2, 9, 7, 4]

(now 1 is the smallest at index 0).

5) heapq.heappop(nums) inside print(...)

heappop removes and returns the smallest element from the heap.

Operation:

Remove root (1) to return it.

Move last element (4) to root position and sift down to restore heap:

start [4, 2, 9, 7] → swap 4 with smaller child 2 → [2, 4, 9, 7]

The popped value returned is:

1

The heap after pop (the nums list used by nlargest next) is:

[2, 4, 9, 7]

6) heapq.nlargest(2, nums) inside print(...)

nlargest(k, iterable) returns the k largest elements in descending order.

It does not require the input to be a heap, but it works fine with one.

Given the current heap list [2, 4, 9, 7], the two largest elements are:

[9, 7]

Final printed output

1 [9, 7]

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