Campesato Oswald. Python Tools for Data Scientists: Pocket Primer

Mercury Learning and Information, 2023. — 323 p. — ISBN: 978-1-68392-823-2.As part of the best-selling Pocket Primer series, this book is designed to provide a thorough introduction to numerous Python tools for data scientists. The book covers features of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks. It includes separate chapters on data visualization and working with Sklearn and SciPy. Companion files with source code are available.This book contains a fast-paced introduction to as much relevant information about Python tools for data scientists as possible that can be reasonably included in a book of this size. If you are a novice, this book will give you a starting point from which you can decide which Python technologies you want to explore in greater detail.You will be exposed to features of NumPy and Pandas, how to write regular expressions, and how to perform data cleaning tasks. Some topics are presented in a cursory manner, which is for two main reasons. First, you must be exposed to these concepts. In some cases, you will find topics that might pique your interest, and hence motivate you to learn more about them through self-study; in other cases, you will probably be satisfied with a brief introduction. In other words, you decide whether to delve deeply into each of the topics in this book.Second, a full treatment of all the topics that are covered in this book would significantly increase its size, and few people are interested in reading technical tomes with 500 or more pages.The Target Audience:This book is intended primarily for people who have worked with Python and are interested in learning about several important Python libraries. Moreover, this book is also intended to reach an international audience of readers with highly diverse backgrounds in various age groups. Consequently, this book uses standard English rather than colloquial expressions that might be confusing to those readers. As you know, many people learn by different types of imitation, which includes reading, writing, or hearing new material. This book considers these points to provide a comfortable and meaningful learning experience for the intended readers.Features:Introduces Python, NumPy, Sklearn, SciPy, and awk.
Covers data cleaning tasks and data visualization.
Features numerous code samples throughout.
Includes companion files with source code.