#### 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

# Summary

Algorithm design techniques are very important in order to formulate, understand, and develop an optimal solution to a complex problem. In this chapter, we have discussed algorithm design techniques, which are very important in the field of computer science. Important categories of algorithm design, such as dynamic programming, greedy approach, and divide and conquer, we discussed in detail along with implementations of important algorithms.

The dynamic programming and divide-and-conquer techniques are quite similar in the sense that both solve a bigger problem by combining the solutions of the sub-problems. Here, the divide-and-conquer technique partitions the problem into disjointed sub-problems, solving them recursively, and then combines the solutions of the sub-problems to obtain the solution of the original problem, whereas, in dynamic programming, this technique is employed when sub-problems overlap, and recomputation of the same sub-problem is avoided. Furthermore...