Min Heap & Priority Queue Explained | Python Implementation & Use Cases | AlgoYogi
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In this video, we dive deep into the *Min Heap* and *Priority Queue* data structure – an essential tool for solving many algorithmic problems, especially in **Greedy algorithms**, **Dijkstra's algorithm**, **Top-K problems**, and **Scheduling**.
We explain the *core concepts**, how heaps are structured, and how to implement and use them efficiently in **Python* with the `heapq` module.
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🔍 *What You’ll Learn*
What is a Min Heap?
Difference between Min Heap and Priority Queue
How insertion and removal work
Why heaps are used in coding interviews
Python implementation using `heapq`
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🧠 *Example Problems That Use Min Heap*
Merge K Sorted Lists
Top K Frequent Elements
Dijkstra’s Shortest Path
Kth Largest Element in an Array
Path With Minimum Effort
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💻 *Python Demo*
```python
import heapq
min_heap = []
heapq.heappush(min_heap, 3)
heapq.heappush(min_heap, 1)
heapq.heappush(min_heap, 4)
print(heapq.heappop(min_heap)) # Output: 1
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