Code Explanation:
🔹 1. Importing heapq
import heapq
✅ Explanation
heapq is Python's built-in module for working with Heap (Priority Queue) data structures.
By default, it creates a Min Heap.
In a Min Heap, the smallest element is always stored at the root (first position).
Internally, a heap is stored as a normal Python list.
heapq Module
│
▼
Min Heap Operations
• heapify()
• heappush()
• heappop()
• heapreplace()
Nothing executes yet.
🔹 2. Creating the List
nums = [8, 1, 5, 3]
✅ Explanation
A normal Python list is created.
Current Memory
nums
[8, 1, 5, 3]
Visual Representation
Index
0 → 8
1 → 1
2 → 5
3 → 3
At this point, it is just a list, not a heap.
🔹 3. Converting List into a Heap
heapq.heapify(nums)
✅ Explanation
heapify() rearranges the existing list into a Min Heap.
Important:
No new list is created.
The original list is modified.
Only the heap property is guaranteed:
Parent ≤ Children
The list is not fully sorted.
Current Memory
Before
[8, 1, 5, 3]
↓
After heapify
[1, 3, 5, 8]
Visual Representation
1
/ \
3 5
/
8
Notice:
Root = 1
Every parent is smaller than its children.
🔹 4. Removing the Smallest Element
heapq.heappop(nums)
✅ Explanation
heappop() removes and returns the smallest element from the heap.
Since this is a Min Heap:
Smallest Element
↓
1
After removing 1, Python rearranges the remaining elements to maintain the heap property.
Current Memory
Removed
1
Remaining Heap
[3, 8, 5]
Visual Representation
Before Pop
1
/ \
3 5
/
8
↓
After Pop
3
/ \
8 5
🔹 5. Printing the Result
print(heapq.heappop(nums))
✅ Explanation
heappop() returns the smallest value.
That returned value is printed.
Output
1
🎯 Final Output
1
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