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Hands-On Data Structures and Algorithms with Python

By : Dr. Basant Agarwal, Benjamin Baka

3 (13)

Hands-On Data Structures and Algorithms with Python

3 (13)

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.

Preface

Preface

Who this book is for

What this book covers

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Free Chapter

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

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

Principles of Algorithm Design

Principles of Algorithm Design

Technical requirements

An introduction to algorithms

Recursion and backtracking

Big O notation

Summary

Lists and Pointer Structures

Lists and Pointer Structures

Technical requirements

Beginning with an example

Arrays

Pointer structures

Introducing lists

Summary

Stacks and Queues

Stacks and Queues

Technical requirements

Stacks

Queues

Summary

Trees

Trees

Technical requirements

Terminology

Tree nodes

Tree traversal

Binary trees

Heaps

Ternary search tree

Summary

Hashing and Symbol Tables

Hashing and Symbol Tables

Technical requirements

Hashing

Hash tables

Symbol tables

Summary

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

Searching

Searching

Technical requirements

Introduction to searching

Binary search

Interpolation search

Summary

Sorting

Sorting

Technical requirements

Sorting algorithms

Bubble sort algorithms

Insertion sort algorithms

Selection sort algorithms

Quick sort algorithms

Heap sort algorithms

Summary

Selection Algorithms

Selection Algorithms

Technical requirements

Selection by sorting

Randomized selection

Deterministic selection

Summary

String Algorithms and Techniques

String Algorithms and Techniques

Technical requirements

String notations and concepts

Pattern matching algorithms

Summary

Design Techniques and Strategies

Design Techniques and Strategies

Technical requirements

Classification of algorithms

Technical implementation

Complexity classes

Summary

Implementations, Applications, and Tools

Implementations, Applications, and Tools

Technical requirements

Knowledge discovery in data

Data preprocessing

Learning about machine learning

Data visualization

Summary

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Weighted graphs

A weighted graph is a graph that has a numeric weight associated with the edges in the graph. It can be either a directed or an undirected graph. This numerical value can possibly be used to indicate distance or cost, depending upon the purpose of the graph. Let's consider an example. The following graph indicates different ways to get from the A node to the D node. You can either go straight from A to D, or choose to pass through B and C, considering that the associated weight with each edge is the amount of time, in minutes, for the journey to the next node:

In this example, AD and ABCD represent two different paths. A path is simply a sequence of edges that you pass through between two nodes. Following these paths, you see that the AD journey takes 40 minutes, whereas the ABCD journey takes 25 minutes. If the only concern is time, then it would be better...

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