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Scientific Computing for Chemists with Python is a free book for teaching basic coding skills to chemists using Python, Jupyter notebooks, and the other Python software. This textbook teaches a variety of Python packages including NumPy, SciPy, matplotlib, pandas, seaborn, nmrglue, SymPy, scikit-image, scikit-learn, and others.
The following is the textbook used for the Scientific Computing for Chemists course intended to teach chemists and chemistry students basic computer programming in Python and Jupyter Notebooks and advanced tools for the processing, visualization, and analysis of digital data. This book serves as an introduction to coding for chemists.
A chapter outline is provided below. This book starts with a streamlined introduction to Python for chemists followed by the introduction of powerful computing tools and numerous applications to chemistry. This book assumes that the student or reader has no prior programming experience and has at least one year of undergraduate chemistry background and ideally some very basic spectroscopy/spectrometry (i.e., NMR, IR, UV-vis, and GC/MS) background. All software used (e.g., Python, NumPy, SciPy, etc.) is free and open source software and runs on macOS, Windows, and Linux.
This book is periodically updated to fix typos, account for new software versions, and add new content. The most recent version can be viewed using the link above, downloaded, or forked using Git or GitHub. Reports of errors and information on how people are using this book are always welcome.
The book is copyright © 2017-2026 Charles J. Weiss and is released under the CC BY-NC-SA 4.0 license. All files with the book are also copyright and released under the CC BY-NC-SA 4.0 license unless otherwise noted (see README.txt files for more information).
Answer keys to exercises are available to instructors upon request by emailing me using your school email address. The answer keys are © Charles J. Weiss and are not released under a Creative Commons license.
First, I'd love to hear how people are using this book. Second, this book may receive updates with additional content, clarifications, and corrections, so if you need a static copy of the book to use during the academic term, below are multiple options. The web version of the book is still the recommended form because of ease-of-use and interactivity (e.g., chapter 11).
(Simplest) Download PDF copies of the chapters to share with your student - at the top right of each chapter page, click the download button and select the PDF option.
(Simple) Download a PDF of the entire book to share with your students using this link. The download button (small down arrow) is on the right. The interactivity features in chapter 11 and appendix 0 do not work in the PDF version.
(Advanced) Fork this repository to your GitHub page and have your students use your copy - create a GitHub account, fork this repository, build the book using Jupyter Book, and have your students use your copy on your GitHub page. You can update your fork of this repository at any time to receive any updates, and unlike a PDF copy, this approach makes it easier for students to copy-and-paste code from the chapters. The approach does require the user to be comfortable using git and GitHub.
A Creative Commons Textbook for Teaching Scientific Computing to Chemistry Students with Python and Jupyter Notebooks. J. Chem. Educ. 2021, 98, 489-494. DOI: 10.1021/acs.jchemed.0c01071
Scientific Computing for Chemists: An Undergraduate Course in Simulations, Data Processing, and Visualization. J. Chem. Educ. 2017, 94, 592-597. DOI: 10.1021/acs.jchemed.7b00078
Introduction to Stochastic Simulations for Chemical and Physical Processes: Principles and Applications. J. Chem. Educ. 2017, 94, 1904-1910. DOI: 10.1021/acs.jchemed.7b00395