Python Coding challenge - Day 1194| What is the output of the following Python Code?

Code 

import heapq nums = [5, 8, 2, 9] heapq.heapify(nums) heapq.heappush(nums, 1) print(heapq.heappop(nums)) 

print(heapq.heappop(nums))

Explanation:

🔹 1. Import the heapq Module

import heapq

✅ Explanation

Python imports the heapq module.

It provides functions to create and manage a Min Heap.

A Min Heap always keeps the smallest value at the top, making it easy to retrieve.

Think of it like a priority queue in a hospital.

Priority Queue

Emergency (1)   ← Served First

Normal (2)

General (5)

Regular (9)

The smallest priority number is always served first.

🔹 2. Create a Normal List

nums = [5, 8, 2, 9]

✅ Explanation

Initially, this is just a normal list.

Index

0   1   2   3

│   │   │   │

5   8   2   9

Python has no idea that this list should behave like a heap.

🔹 3. Convert List into a Min Heap

heapq.heapify(nums)

✅ Explanation

heapify() doesn't sort the list completely.

Instead, it rearranges elements so that every parent is smaller than its children.

Before:

[5, 8, 2, 9]

After:

[2, 8, 5, 9]

Tree representation:

        2

      /   \

     8     5

    /

   9

Notice something interesting:

8 > 5

Yet this is still a valid heap.

Why?

Because the heap only checks the relationship between a parent and its children, not between sibling nodes.

🔹 4. Insert a New Value

heapq.heappush(nums, 1)

✅ Explanation

Python first inserts 1 at the end.

Temporary heap:

[2, 8, 5, 9, 1]

Now the heap rule is broken because:

1 < 8

So Python starts moving 1 upward.

Step 1:

        2

      /   \

     8     5

    / \

   9   1

Swap with parent (8):

        2

      /   \

     1     5

    / \

   9   8

Still,

1 < 2

Swap again:

        1

      /   \

     2     5

    / \

   9   8

Final heap:

[1, 2, 5, 9, 8]

This upward movement is called Heapify Up (or Bubble Up).

🔹 5. First heappop()

print(heapq.heappop(nums))

✅ Explanation

Python removes the root because it is the smallest.

Current heap:

        1

      /   \

     2     5

    / \

   9   8

Output:

1

But removing the root creates an empty space.

Python moves the last element (8) to the root.

Temporary heap:

        8

      /   \

     2     5

    /

   9

Heap rule is broken.

Since:

8 > 2

Swap:

        2

      /   \

     8     5

    /

   9

Heap property restored.

Current heap:

[2, 8, 5, 9]

🔹 6. Second heappop()

print(heapq.heappop(nums))

✅ Explanation

Again, Python removes the root.

Current heap:

        2

      /   \

     8     5

    /

   9

Output:

2

Move last element (9) to the top.

Temporary:

        9

      /   \

     8     5

Compare with children.

Smallest child is:

5

Swap:

        5

      /   \

     8     9

Heap restored.

Remaining heap:

[5, 8, 9]

🎯 Final Output

1

2

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