AI Publishing - Data Visualization with Python for Beginners: Step-by-Step Guide with Hands-on Projects and Exercises

DATA VISUALIZATION WITH PYTHON FOR BEGINNERS Visualize Your Data Using Pandas,
Matplotlib and Seaborn AI PUBLISHING Copyright 2020 by AI Publishing All rights reserved. First Printing, 2020 Edited by AI Publishing Ebook Converted and Cover by Gazler Studio
Published by AI Publishing LLC ISBN-13: 978-1-7330426-8-0 The contents of this book may not be reproduced, duplicated, or transmitted without the direct written permission of the author. Under no circumstances will any legal responsibility or blame be held against the publisher for any reparation, damages, or monetary loss due to the information herein, either directly or indirectly. Legal Notice: You cannot amend, distribute, sell, use, quote, or paraphrase any part of the content within this book without the consent of the author. Disclaimer Notice: Please note the information contained within this document is for educational and entertainment purposes only. No warranties of any kind are expressed or implied.

Readers acknowledge that the author is not engaging in the rendering of legal, financial, medical, or professional advice. Please consult a licensed professional before attempting any techniques outlined in this book. By reading this document, the reader agrees that under no circumstances is the author responsible for any losses, direct or indirect, which are incurred as a result of the use of the information contained within this document, including, but not limited to, errors, omissions, or inaccuracies. How to contact us If you have any feedback, please let us know by sending an email to . This feedback is highly valued, and we look forward to hearing from you. To get the Python codes and materials used in this book, please click the link below: https://www.aispublishing.net/book-data-visualization About the Publisher At AI Publishing Company, we have established an international learning platform specifically for young students, beginners, small enterprises, startups, and managers who are new to data sciences and artificial intelligence. To get the Python codes and materials used in this book, please click the link below: https://www.aispublishing.net/book-data-visualization About the Publisher At AI Publishing Company, we have established an international learning platform specifically for young students, beginners, small enterprises, startups, and managers who are new to data sciences and artificial intelligence.

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The book follows a very simple approach. Table of ContentsPreface The book follows a very simple approach.

It is divided into 10 chapters. Chapter 1 contains an introduction while the 2nd and 3rd chapters cover the Matplotlib library. Pythons Seaborn library is covered in 4th and 5th chapters while the 6th and 7th chapters explore the Pandas library. The 8th chapter covers 3-D plotting, while the 9th chapter explains how to draw maps via the Basemap library. Finally, the 10th chapter covers interactive data visualization via the Plotly library. In each chapter, different types of plots have been explained theoretically, followed by practical examples.

Each chapter also contains an exercise that students can use to evaluate their understanding of the concepts explained in the chapter. The Python notebook for each chapter is provided in the resources. It is advised that instead of copying the code, you write the code yourself, and in case of error, you match your code with the corresponding Python notebook, find, and then correct the error.

Data science and data visualization are two different but interrelated concepts. Data science refers to the science of extracting and exploring data in order to find patterns that can be used for decision making at different levels. Data visualization can be considered as a subdomain of data science where you visualize data with the help of graphs and tables in order to find out which data is most significant and can help in the identification of important patterns.

Data visualization can also be considered as a standalone discipline where you just want to visually analyze data and base your decision on the visual representation of data. This book is dedicated to data visualization and explains how to perform data visualization on a variety of datasets using various data visualization libraries written in the Python programming language. It is suggested that you use this book for data visualization purposes only and not for decision making. For decision making and pattern identification, read this book in conjunction with a dedicated book on machine learning and data science.

This book explains the process of data visualization using various libraries from scratch. Hence, the book is aimed ideally at absolute beginners to data visualization.

Though a background in the Python programming language and data visualization can help speed up learning, the book contains a crash course on Python programming language in the first chapter. Therefore, the only prerequisite to efficiently using this book is access to a computer with the internet. All the codes and datasets have been provided. However, to download data visualization libraries, you will need the internet. In addition to beginners in data visualization, this book can also be used as a reference manual by intermediate and experienced programmers as it contains data visualization code samples using multiple data visualization libraries.

As I said earlier, the data visualization libraries taught in this book have been divided into multiple chapters.

To get the best out of this book, I would suggest that you first get your feet wet with the Python programming language, especially the object-oriented programming concepts. To do so, you can take a crash course on Python in chapter 1 of this book. Also, try to read the other chapters of this book in order since concepts taught in the subsequent chapters are based on previous chapters. In each chapter, try to first understand the theoretical concepts behind different types of plots and then try to execute the example code. I would again stress that rather than copy and pasting code, try to write the code yourself, and in case of any error, you can match your code with the source code provided in the book as well as in the Python notebooks in the resources. Finally, try to answer the questions asked in the exercises at the end of each chapter.

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