Gain insights into array-based and linked list-based data structures, explore advanced structures like skiplists and hashing, and discover template-based collections for efficient data storage and retrieval.

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Data structures and algorithms are essential in computer science since they play a crucial role in efficient information retrieval and processing, dealing with files, storing contacts on phones, social networks and web searches.

In this course, you’ll learn about the array-based implementation of various linear data structures, stack, and queues. You’ll also learn about linked list-based implementation. Next, you’ll explore advanced data structures like skiplists and hashing. You’ll learn how to implement a variety of trees and graphs, and data structures related to bits of an integer. Toward the end of the course, you’ll learn the implementation of structures based on external storage.

After completing this course, you’ll be able to create reusable programs with template-based collections that can efficiently analyze how to optimize the storage and retrieval of very large amounts of data. Overall, this course will enhance your productivity and performance as a software developer.

Data Structures with Generic Types in Python

Here, we describe the `MeldableHeap`, a priority `Queue` implementation in which the underlying structure is also a heap-ordered binary tree. However, unlike a `BinaryHeap` in which the underlying binary tree is completely defined by the number of elements, there are no restrictions on the shape of the binary tree that underlies a `MeldableHeap`; anything goes.

## Operation in `MeldableHeap`



The `add(x)` and `remove()` operations in a `MeldableHeap` are implemented in terms of the `merge(h1, h2)` operation. This operation takes two heap nodes `h1` and `h2` and merges them, returning a heap node that is the root of a heap that contains all elements in the subtree rooted at `h1` and all elements in the subtree rooted at `h2`.


The nice thing about a `merge(h1, h2)` operation is that it can be defined
recursively. See the below illustration:

Here, we describe the `MeldableHeap`, a priority `Queue` implementation in which the underlying structure is also a heap-ordered binary tree. However, unlike a `BinaryHeap` in which the underlying binary tree is completely defined by the number of elements, there are no restrictions on the shape of the binary tree that underlies a `MeldableHeap`; anything goes.

# Operation in `MeldableHeap`



The `add(x)` and `remove()` operations in a `MeldableHeap` are implemented in terms of the `merge(h1, h2)` operation. This operation takes two heap nodes `h1` and `h2` and merges them, returning a heap node that is the root of a heap that contains all elements in the subtree rooted at `h1` and all elements in the subtree rooted at `h2`.


The nice thing about a `merge(h1, h2)` operation is that it can be defined
recursively. See the below illustration:

Learn the meldable heap and its operations.

Overview

Array-Based Lists

Linked Lists

Skiplists

Hash Tables

Binary Trees

Random Binary Search Trees

Scapegoat Trees

Red-Black Trees

Heaps

Sorting Algorithms

Graphs

Data Structures for Integers

External Memory Searching

Wrap Up

MeldableHeap: A Randomized Meldable Heap

Operation in `MeldableHeap`