Python Tools for Scientists: An Introduction to Coding, Anaconda, JupyterLab, and Python's Scientific Libraries

by Lee Vaughan

An introduction to the Python programming language and its most popular tools for scientists, engineers, students, and anyone who wants to use Python for research, simulations, and collaboration.

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

Create isolated projects in virtual environments, build interactive notebooks, test code in the Qt console, and use Spyder’s interactive development featuresUse Python’s built-in data types, write custom functions and classes, and document your codeRepresent data with the essential NumPy, Matplotlib, and pandas librariesUse Python plotting libraries like Plotly, HoloViews, and Datashader to handle large datasets and create 3D visualizations
Regardless of your scientific field, Python Tools for Scientists will show you how to choose the best tools to meet your research and computational analysis needs.

Genres: Computer Science, Programming, Reference, Computers, Nonfiction, Software, Science

740 pages, Kindle Edition

First published November 1, 2022

Reviews

[Shelton D · Rating 5 out of 5]

This is a well written and well organized book presenting essential tools for scientific applications, mainly data analysis. It could be good for an interactive tutorial or as a reference book.

It starts at the very beginning, assuming some knowledge of programming, but very little knowledge of Python. The text, though starting at very basic level, typically moves quickly to more complex concepts for each topic.

There were not as many real-world examples as I had expected (or hoped); but each topic included some examples to demonstrate the concepts. Chapter 20 (next to last) had probably the best presentation moving through the whole process from the first steps for understanding fundamental properties about a dataset (basic statistics) through a comprehensive example of machine learning for pattern classification.

Perhaps the best thing about the book is the number of excellent tables summarizing modules, functions, parameters, etc. Simply compiling all the tables might make an excellent “quick reference guide” which alone could be well worth the price of the book.

[Ben · Rating 5 out of 5]

Up Your Data Science Game

This book is a must-read for anyone in the scientific community looking to enhance their Python skills.

The author, Vaughan, does an excellent job of breaking down complex scientific concepts and making them accessible to readers at all levels.

The examples provided in the book are not only informative, but they are also practical and applicable to real-world problems. I found myself using them in my own data science projects.

One of the things I appreciated most about this book was its emphasis on using Anaconda and JupyterLab, two tools that are essential for any serious scientific work (not to mention Pandas and NumPy).
The author provides detailed instructions on how to set them up and how to use them effectively.

The writing is clear and concise, making it easy to follow along with the examples and concepts presented. The author's expertise in both Python and scientific research is evident throughout the book.

Overall, I highly recommend this book to anyone looking to improve their Python skills for scientific applications.

Whether you are a beginner or an experienced programmer, you will find valuable insights and practical tips in this book. It's definitely one of the best Python books I've read in a while!

4.8/5

[Stuart Payne · Rating 4 out of 5]

This is a good reference book to set Anaconda, JupyterLab for Python.

I just glanced at it and will pick it up when I'm ready to do more in data science.

[Matthew · Rating 4 out of 5]

After coming out of environmental consulting, where my tools involved janky Excel templates, clunky ancient bespoke programs, and absurdly expensive, finicky GUIs for open-source modelling tools, I knew I there had to be a better way. And this book was exactly what I needed. It gives precisely enough Python knowledge to not feel lost, and then introduces all of the essentials to scientific computing and data science. It's not a mastery of any of the tools, but rather a springboard to figure out which tools you need and where to find out more if/when needed. The entire time I was working through the book, I could reach back to examples from my work where having Python would have made my tasks so much more efficient, not to mention the stuff that would've been nice to have but was impossible without actual programmability.

Any complaints I have are minimal, but I think the last few chapters (on the extensive Python tools) would have worked better as a set of guided projects to work through rather than using an assortment of one-liners and code blocks to show off each of the tool's features. I think capabilities with respect to deliverables would have been nice to see, as well as an introduction/reinforcement of programming project management best practices.

[Frank · Rating 5 out of 5]

Excellent book for learning how to use the Anaconda Suite of software and for learning the Python language. I would, however, skip the section on the special libraries NumPy, Pandas, SciKit Learn etc. It only gives a description of what those libraries do.

Great book for learning how to use the Anaconda CLI (i.e. the Command Line Interface, which is the black window that looks like the old DOS window from years ago).

[Görkem Saylam · Rating 4 out of 5]

Python Tools for Scientists | Lee Vaughan
Scoring Rubric
1: baseline
2: creative contextualization bcs of covering almost all tools for scientist to code
1: routine conceptualization bcs of no new holistic and groundbreaking comprehension to code by python for scientist
4: total points by 5

[Anna]

I’m typically a big fan of the No Starch Press Python books. Not this one. Just painful.

[Michiel · Rating 4 out of 5]

A good reference of all the basics a scientist needs: installing environments and the basic numerical, plotting and data libraries.

[Lime Street Labrador · Rating 2 out of 5]

A verbose collection of descriptions on the Anaconda ecosystem and related libraries.