Python Tools for Scientists will introduce you to Python tools you can use in your scientific research, including Anaconda, Spyder, Jupyter Notebooks, JupyterLab, and numerous Python libraries. You'll learn to use Python for tasks such as creating visualizations, representing geospatial information, simulating natural events, and manipulating numerical data.
Once you've built an optimal programming environment with Anaconda, you'll learn how to organize your projects and use interpreters, text editors, notebooks, and development environments to work with your code. Following the book's fast-paced Python primer, you'll tour a range of scientific tools and libraries like scikit-learn and seaborn that you can use to manipulate and visualize your data, or analyze it with machine learning algorithms.
You'll also learn how to:
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"Python has a wealth of scientific computing tools, so how do you decide which ones are right for you? This book cuts through the noise to help you deliver results."
--Serdar Yegulalp, InfoWorld
"The book [Python Tools for Scientists] by Lee Vaughan is a critical resource for anyone that is new to Python programming and intends to become a Python expert. It covers all of the critical topics in an easily understandable format and it goes deep enough to be helpful in navigating advanced topics. The book is also true to current Software Engineering standards and gives even new developers the tools to jump start their Python career."
--Dr. Alec Yasinsac, Department of Computer Science, University of South Alabama
"I wish there was a book like this when I started learning Python... [Python Tools for Scientists] is a practical, detailed, hands-on introduction to setting up a local Python workspace and getting started with the basics of Python programming. It was written for scientists, by a scientist who knows what the typical problems are when scientists and engineers start using Python tools in their everyday work. It also introduces the wide variety of packages that can be used in scientific programming and provides guidelines on when to use them. Matplotlib, numpy, and pandas are covered in much more detail - as they should be. The writing and the organization of the material are clear and easy to follow. I have been using Python for many years, but I know I will be using this book both in teaching and research."
--Zoltán Sylvester, Senior Research Scientist, University of Texas at Austin