Heap Basics 10 XP Easy

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A Heap (Priority Queue) is a special tree where the smallest (or largest) element is ALWAYS at the top. Perfect when you repeatedly need the min/max! 🏔️

💡 Think of it like this: Imagine an emergency room. Patients aren't served in arrival order — the most critical patient is always treated first. That's a priority queue!

import heapq

# Min-heap: smallest always on top
heap = []
heapq.heappush(heap, 5)   # [5]
heapq.heappush(heap, 1)   # [1, 5]
heapq.heappush(heap, 3)   # [1, 5, 3]

print(heap[0])             # Peek: 1 (smallest) — O(1)
print(heapq.heappop(heap)) # Pop: 1 (removed) — O(log n)
print(heapq.heappop(heap)) # Pop: 3 — O(log n)

Heap operations:

📥 heappush(heap, val) — Insert: O(log n)
📤 heappop(heap) — Remove smallest: O(log n)
👀 heap[0] — Peek at smallest: O(1)
🔄 heapify(list) — Convert list to heap: O(n)
🔄 For max-heap: negate values! Push -val, negate after pop

Classic heap interview problems:

🏆 Top K elements: Use a min-heap of size k
📊 Find median: Two heaps (max-heap for left, min-heap for right)
🔀 Merge K sorted lists: Push first element of each, pop smallest, push next
🗺️ Dijkstra's algorithm: Uses a min-heap for shortest paths

📋 Instructions

Practice heap operations. Push values [4, 1, 7, 3, 8, 5] one at a time. Pop all elements and print them (they'll come out sorted!).

Use heapq.heappush() to add each element, then while heap: print(heapq.heappop(heap)). Elements come out in ascending order.

🧪 Test Cases

Input

result

Expected

[1, 3, 4, 5, 7, 8]

Test case 1

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main.py

Hi! I'm Rex 👋

Output

Ready. Press ▶ Run or Ctrl+Enter.

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