#### 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

# Exercise

1. If an array `arr = {55, 42, 4, 31}` is given and bubble sort is used to sort the array elements, then how many iterations will be required to sort the array?
1. 3
2. 2
3. 1
4. 0
2. What is the worst-case complexity of bubble sort?
1. `O(n` `log` `n)`
2. `O(log` `n)`
3. `O(n)`
4. `O(n`2`)`
3. Apply quicksort to the sequence (`56, 89, 23, 99, 45, 12, 66, 78, 34`). What is the sequence after the first phase, and what pivot is the first element?
1. 45, 23, 12, 34, 56, 99, 66, 78, 89
2. 34, 12, 23, 45, 56, 99, 66, 78, 89
3. 12, 45, 23, 34, 56, 89, 78, 66, 99
4. 34, 12, 23, 45, 99, 66, 89, 78, 56
4. Quicksort is a ___________
1. Greedy algorithm
2. Divide and conquer algorithm
3. Dynamic programming algorithm
4. Backtracking algorithm
5. Consider a situation where a swap operation is very costly. Which of...