![]() You can create a heap data structure in Python using the heapq module. Priority queues, which are commonly used in task scheduling and network routing, are also implemented using the heap. It is useful for keeping track of the largest and smallest elements in a collection, which is a common task in many algorithms and data structures. Python uses the heap data structure as it is a highly efficient method of storing a collection of ordered elements. Let us understand them below but before that, we will study the heapify property to understand max-heap and min-heap. These algorithms can be used in priority queues, order statistics, Prim's algorithm or Dijkstra's algorithm, etc.Īs learned earlier, there are two categories of heap data structure i.e. ![]() The heap data structure is basically used as a heapsort algorithm to sort the elements in an array or a list. Now, the root node key value is compared with the children’s nodes and then the tree is arranged accordingly into two categories i.e., max-heap and min-heap. In the heap data structure, we assign key-value or weight to every node of the tree. Now, you must be wondering what is the heap property. In the binary tree, it is possible that the last level is empty and not filled. Therefore, it is also known as a binary heap.Īs we all know, the complete binary tree is a tree with every level filled and all the nodes are as far left as possible. It follows a complete binary tree's property and satisfies the heap property. So, let's get started! What is Heap in Python?Ī heap in Python is a data structure based on a unique binary tree designed to efficiently access the smallest or largest element in a collection of items. We will also understand how to implement max heap and min heap concepts and the difference between them. In this article, we will learn what a heap is in Python. ![]() While they are not as commonly used, they can be incredibly useful in certain scenarios. ![]() Python is versatile with a wide range of data structures. ![]()
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