Overview of this book

Choosing the right data structure is pivotal to optimizing the performance and scalability of applications. This new edition of Hands-On Data Structures and Algorithms with Python will expand your understanding of key structures, including stacks, queues, and lists, and also show you how to apply priority queues and heaps in applications. You’ll learn how to analyze and compare Python algorithms, and understand which algorithms should be used for a problem based on running time and computational complexity. You will also become confident organizing your code in a manageable, consistent, and scalable way, which will boost your productivity as a Python developer. By the end of this Python book, you’ll be able to manipulate the most important data structures and algorithms to more efficiently store, organize, and access data in your applications.
Preface
Free Chapter
Python Data Types and Structures
Introduction to Algorithm Design
Algorithm Design Techniques and Strategies
Stacks and Queues
Trees
Heaps and Priority Queues
Hash Tables
Graphs and Algorithms
Searching
Sorting
Selection Algorithms
String Matching Algorithms
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Index

Introduction to Algorithm Design

The objective of this chapter is to understand the principles of designing algorithms, and the importance of analyzing algorithms in solving real-world problems. Given input data, an algorithm is a step-by-step set of instructions that should be executed in sequence to solve a given problem.

In this chapter, we will also learn how to compare different algorithms and determine the best algorithm for the given use-case. There can be many possible correct solutions for a given problem, for example, we can have several algorithms for the problem of sorting n numeric values. So, there is no one algorithm to solve any real-world problem.

In this chapter, we will look at the following topics:

• Introducing algorithms
• Performance analysis of an algorithm
• Asymptotic notation
• Amortized analysis
• Choosing complexity classes
• Computing the running time complexity of an algorithm