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Kennedy R. Behrman - Foundational Python for Data Science

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Foundational
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Kennedy R. Behrman

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Library of Congress Control Number: 2021940284

Copyright 2022 Pearson Education, Inc.

All rights reserved. This publication is protected by copyright, and permission must be obtained from the publisher prior to any prohibited reproduction, storage in a retrieval system, or transmission in any form or by any means, electronic, mechanical, photocopying, recording, or likewise. For information regarding permissions, request forms and the appropriate contacts within the Pearson Education Global Rights & Permissions Department, please visit www.pearson.com/permissions/.

ISBN-13: 978-0-13-662435-6
ISBN-10: 0-13-662435-9

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This book is dedicated to Tatiana, Itta, and Maple,
who is probably still under the bed.

Preface

The Python language has been around for a long time and worn many hats. Its original implementation was started by Guido van Rossum in 1989 as a tool for system administration as an alternative to Bash scripts and C programs.

I was first introduced to Python working in the film industry, where we used it to automate data management across departments and locations. In the last decade, Python has become a dominant tool in Data Science.

This dominance evolved due to two developments: the Jupyter notebook, and powerful third-party libraries. In 2001 Fernando Perez began the IPython project, an interactive Python environment inspired by Maple and Mathematica notebooks. By 2014, the notebook-specific part of the project was split off as the Jupyter project. These notebooks have excelled for scientific and statistical work environments. In parallel with this development, third-party libraries for scientific and statistical computing were developed for Python. With so many applications, the functionality available to a Python programmer has grown immensely. With specialized packages for everything from opening web sockets to processing natural language text, there is more available than a beginning developer needs.

This project was the brainchild of Noah Gift. In his work as an educator, he found that students of Data Science did not have a resource to learn just the parts of Python they needed. There were many general Python books and books about Data Science, but not resources for learning just the Python needed to get started in Data Science. That is what we have attempted to provide here. This book will not teach the Python needed to set up a web page or perform system administration. It is also not intended to teach you Data Science, but rather the Python needed to learn Data Science.

I hope you will find this guide a good companion in your quest to grow your Data Science knowledge.

Example Code

Most of the code shown in examples in this book can be found on GitHub at:

https://github.com/kbehrman/foundational-python-for-data-science.


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