nums = [4, 1, 7]
Code Explanation:
🔹 1. Importing heapq
import heapq
✅ Explanation:
heapq is Python's built-in module for working with heaps.
Python uses a Min Heap by default.
Rule:
Smallest element is always at index 0
Example:
Heap
1
/ \
4 7
🔹 2. Creating a List
nums = [4, 1, 7]
✅ Explanation:
A normal list is created.
Current state:
[4, 1, 7]
Visual:
Index: 0 1 2
Value: [4, 1, 7]
At this point:
Not a heap yet
🔹 3. Converting List into Heap
heapq.heapify(nums)
✅ Explanation:
heapify() rearranges elements into a valid Min Heap.
Before:
[4, 1, 7]
After:
[1, 4, 7]
Because:
Smallest element must come first
Visual Heap:
1
/ \
4 7
Current state:
nums
↓
[1, 4, 7]
🔹 4. Pushing a New Element
heapq.heappush(nums, 0)
✅ Explanation:
A new element:
0
is inserted into the heap.
Temporary state:
[1, 4, 7, 0]
Now heap property is broken because:
0 < 1
Python reorganizes the heap.
After adjustment:
[0, 1, 7, 4]
Visual Heap:
0
/ \
1 7
/
4
Current state:
nums
↓
[0, 1, 7, 4]
🔹 5. Understanding heappop()
heapq.heappop(nums)
✅ Explanation:
heappop() removes and returns the smallest element.
Current heap:
[0, 1, 7, 4]
Smallest element:
0
gets removed.
🔹 6. Heap Reorganization
After removing:
0
Remaining elements:
[1, 4, 7]
Heap property is restored automatically.
Visual Heap:
1
/ \
4 7
🔹 7. Return Value
heapq.heappop(nums)
returns:
0
🔹 8. Printing Result
print(heapq.heappop(nums))
✅ Explanation:
Prints:
0
🎯 Final Output
heapq.heapify(nums
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