Python Coding challenge - Day 772| What is the output of the following Python Code?

 

Code Explanation:

1. Importing the heapq Module

import heapq

The heapq module provides an implementation of the min-heap data structure in Python.

A heap always keeps the smallest element at the root (index 0).

This allows efficient retrieval of the minimum value.

2. Creating a List of Numbers

nums = [8, 3, 5, 1]

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

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

3. Converting the List into a Heap

heapq.heapify(nums)

The heapify() function rearranges the elements in-place to satisfy the min-heap property.

After this operation, the smallest element becomes the first element (nums[0]).

Example heap (internally):

nums becomes [1, 3, 5, 8]

Note: The order may vary slightly, but the heap property (smallest at root) is maintained.

4. Inserting a New Element into the Heap

heapq.heappush(nums, 0)

heappush() adds a new element (0) to the heap.

After insertion, the heap automatically reorders to maintain the heap property.

Now the heap looks like:

[0, 1, 5, 8, 3]

(0 is now the smallest element)

5. Removing and Returning the Smallest Element

heapq.heappop(nums)

heappop() removes and returns the smallest element from the heap.

Here, that’s 0.

After popping, the heap reorders itself again.

Remaining heap might look like: [1, 3, 5, 8]

6. Getting the Two Largest Elements

heapq.nlargest(2, nums)

The nlargest(n, iterable) function returns the n largest elements from the given heap or list.

It doesn’t modify the heap.

Here, the two largest elements are [8, 5].

7. Printing the Results

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

The first value printed → smallest element popped (0)

The second value printed → list of two largest numbers ([8, 5])

Output:

0 [8, 5]

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