Book Image

Hands-On Data Structures and Algorithms with Python - Second Edition

By : Dr. Basant Agarwal, Benjamin Baka
Book Image

Hands-On Data Structures and Algorithms with Python - Second Edition

By: Dr. Basant Agarwal, Benjamin Baka

Overview of this book

Data structures allow you to store and organize data efficiently. They are critical to any problem, provide a complete solution, and act like reusable code. Hands-On Data Structures and Algorithms with Python teaches you the essential Python data structures and the most common algorithms for building easy and maintainable applications. This book helps you to understand the power of linked lists, double linked lists, and circular linked lists. You will learn to create complex data structures, such as graphs, stacks, and queues. As you make your way through the chapters, you will explore the application of binary searches and binary search trees, along with learning common techniques and structures used in tasks such as preprocessing, modeling, and transforming data. In the concluding chapters, you will get to grips with organizing your code in a manageable, consistent, and extendable way. You will also study how to bubble sort, selection sort, insertion sort, and merge sort algorithms in detail. By the end of the book, you will have learned how to build components that are easy to understand, debug, and use in different applications. You will get insights into Python implementation of all the important and relevant algorithms.

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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Python Objects, Types, and Expressions
Python Objects, Types, and Expressions
Technical requirements
Installing Python
Understanding data structures and algorithms
Flow control and iteration
Overview of data types and objects
Generators and co-routines
Summary
Further reading
2
Python Data Types and Structures
Python Data Types and Structures
Technical requirements
Built-in data types
None type
Numeric types
Representation error
Membership, identity, and logical operations
Sequences
Learning about tuples
Beginning with dictionaries
Sorting dictionaries
Dictionaries for text analysis
Sets
Modules for data structures and algorithms
Summary
3
Principles of Algorithm Design
Principles of Algorithm Design
Technical requirements
An introduction to algorithms
Recursion and backtracking
Big O notation
Summary
4
Lists and Pointer Structures
Lists and Pointer Structures
Technical requirements
Beginning with an example
Arrays
Pointer structures
Introducing lists
Summary
5
Stacks and Queues
Stacks and Queues
Technical requirements
Stacks
Queues
Summary
6
Trees
Trees
Technical requirements
Terminology
Tree nodes
Tree traversal
Binary trees
Heaps
Ternary search tree
Summary
7
Hashing and Symbol Tables
Hashing and Symbol Tables
Technical requirements
Hashing
Hash tables
Symbol tables
Summary
8
Graphs and Other Algorithms
Graphs and Other Algorithms
Technical requirements
Graphs
Directed and undirected graphs
Weighted graphs
Graph representations
Graph traversals
Other useful graph methods
Priority queues and heaps
Selection algorithms
Summary
9
Searching
Searching
Technical requirements
Introduction to searching
Binary search
Interpolation search
Summary
10
Sorting
Sorting
Technical requirements
Sorting algorithms
Bubble sort algorithms
Insertion sort algorithms
Selection sort algorithms
Quick sort algorithms
Heap sort algorithms
Summary
11
Selection Algorithms
Selection Algorithms
Technical requirements
Selection by sorting
Randomized selection
Deterministic selection
Summary
12
String Algorithms and Techniques
String Algorithms and Techniques
Technical requirements
String notations and concepts
Pattern matching algorithms
Summary
13
Design Techniques and Strategies
Design Techniques and Strategies
Technical requirements
Classification of algorithms
Technical implementation
Complexity classes
Summary
14
Implementations, Applications, and Tools
Implementations, Applications, and Tools
Technical requirements
Knowledge discovery in data
Data preprocessing
Learning about machine learning
Data visualization
Summary
15
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