Scientific Computing for Chemists with Python.

Format:

Book

Author/Creator:

Weiss, Charles J., author.

Language:

English

Subjects (All):

Chemistry--Textbooks.

Chemistry.

Computer science--Textbooks.

Computer science.

Science--Textbooks.

Science.

Physical Description:

1 online resource

Place of Publication:

Sioux Falls, SD Charles J. Weiss [2025]

Language Note:

In English.

Summary:

This book serves as an introduction to coding for chemists. The tools employed in this book are the powerful and popular combination of Jupyter notebooks and the Python programming language. No background beyond first-year college chemistry and occasionally some very basic spectroscopy (for advanced chapters) is assumed for most of this book. This book starts with a brief primer on Jupyter notebooks in chapter 0 and computer programming with Python in chapters 1 and 2. If you already have a background in these tools, feel free to skip ahead. The rest of the book dives into applications of Python to solving chemical problems. Python and Jupyter were chosen for a variety of reasons, including that they are: Relatively easy to use and learn Powerful and well-suited for solving chemical problems Free, open-source software Cross-platform (e.g., runs on Windows, macOS, and Linux) Supplemented with numerous, specialized libraries for handling specific types of data or problems (e.g., machine learning) Supported by a helpful and welcoming community Learning to use a number of popular Python scientific libraries to solve chemical problems is one of the themes of this book. A Python library can be thought of as a tool pack with premade functions for performing common tasks in scientific data processing, analysis, and visualization. For example, the matplotlib library provides a variety of functions for creating a wide range of plots, while the scikit-learn library contains functions and resources for machine learning.

Contents:

Chapter 0: Python & Jupyter Notebooks

Chapter 1: Basic Python

Chapter 2: Intermediate Python

Chapter 3: Plotting with Matplotlib

Chapter 4: NumPy

Chapter 5: Pandas

Chapter 6: Signal & Noise

Chapter 7: Image Processing & Analysis

Chapter 8: Mathematics

Chapter 9: Simulations

Chapter 10: Plotting with Seaborn

Chapter 11: Plotting with Altair

Chapter 12: Nuclear Magnetic Resonance with nmrglue & nmrsim

Chapter 13: Machine Learning using Scikit-Learn

Chapter 14: Optimization & Root Finding

Chapter 15: Cheminformatics with RDKit

Chapter 16: Bioinformatics with Biopython & Nglview

Chapter 17: Command Line & Spyder

Appendix 0: Ipython Widgets

Appendix 1: Remote Requests

Appendix 2: Visualizing Atomic Orbitals

Appendix 3: Uncertainty Propagation

Appendix 4: Regular Expressions

Index

Notes:

Description based on online resource