Oct 18, 2025 · To build a Max Heap from an array, treat the array as a complete binary tree and heapify nodes from the last non-leaf node up to the root in reverse level order.

2 days ago · To create a heap, use a list initialized as [], or transform an existing list into a min-heap or max-heap using the heapify() or heapify_max() functions, respectively.

Heapify is a broader term used to describe the process of converting an entire array into a heap, whether it's a min-heap or a max-heap. Heapify down is a specific operation used to maintain …

Jan 26, 2025 · The heapify function in Python's heapq module takes a list and rearranges its elements in-place to form a heap. It does this by comparing and swapping elements in such a …

Heap data structure is a complete binary tree that satisfies the heap property, where any given node is. always greater than its child node/s and the key of the root node is the largest among …

Depending on the types of heap property violation at any node, there are two types of heapify operation: 1) Top-down heapify 2) Bottom-up heapify. Understanding these two process are …

Heapify is the process of transforming a binary tree into a heap data structure, ensuring that the tree maintains the heap property. This property can be either a max-heap or a min-heap, …

Jul 23, 2025 · The heapq.heapify () function in Python is used to transform a regular list into a valid min-heap. A min-heap is a binary tree where the smallest element is always at the root.

Transform a list into a heap The heapify() function, which is a part of the standard library heapq module, is used to transform a list object into a heap data structure.

Oct 18, 2025 · Using an iterative heapify () avoids additional stack space, so apart from storing the array itself, no extra memory is required.