Claus Führer - Scientific Computing with Python

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First published: December 2016
Second edition: July 2021

Production reference: 1180621

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Claus Fhrer is a professor of scientific computations at Lund University, Sweden. He has an extensive teaching record that includes intensive programming courses in numerical analysis and engineering mathematics across various levels in many different countries and teaching environments. Claus also develops numerical software in research collaboration with industry and received Lund University's Faculty of Engineering Best Teacher Award in 2016.

Olivier Verdier began using Python for scientific computing back in 2007 and received a Ph.D. in mathematics from Lund University in 2009. He has held post-doctoral positions in Cologne, Trondheim, Bergen, and Ume and is now an associate professor of mathematics at Bergen University College, Norway.

Jan Erik Solem is a Python enthusiast, former associate professor, and computer vision entrepreneur. He co-founded several computer vision startups, most recently Mapillary, a street imagery computer vision company, and has worked in the tech industry for two decades. Jan Erik is a World Economic Forum technology pioneer and won the Best Nordic Thesis Award 2005-2006 for his dissertation on image analysis and pattern recognition. He is also the author of Programming Computer Vision with Python.

Helmut Podhaisky works in the Institute of Mathematics at the Martin Luther University Halle-Wittenberg, where he teaches mathematics and scientific computing. He has co-authored a book on numerical methods for time integration and several papers on numerical methods. For work and fun, he uses Python, Julia, Mathematica, and Rust.

We want to acknowledge the competent and helpful comments and suggestions by Helmut Podhaisky, Halle University, Germany. To have such a partner in the process of writing a book is big luck and chance for the authors.
A book has to be tested in teaching. And here, we had fantastic partners: the teaching assistants from the course "Berkningsprogramering med Python" during the years, especially, Najmeh Abiri, Christian Andersson, Peter Meisrimel, Azahar Monge, Fatemeh Mohammadi, Tony Stillfjord, Peter Meisriemel, Lea Versbach, Sadia Asim and Anna-Mariya Otsetova, Lund University.
A lot of input to the book came from a didactic project in higher education leading to a Ph.D. thesis by Dara Maghdid. Together with him, the material of the book was tested and commented on by students from Soran University in Kurdistan Region, Iraq.
Most of the examples in the new chapter on GUI's in this second edition were inspired by our colleague Malin Christersson. Co-teaching this course with her, Alexandros Sopasakis, Tony Stillfjord, and Robert Klfkorn were fun. Hopefully not only for the teaching team but also for our studentsundergraduates and Ph.D. students. Special thanks also to Anne-Maria Persson, friend, director of studies, and supporter of Python in mathematics and physics higher education.
A book has not only to be written, but it also has to be published, and in this process, Sean Lobo and Gebin George, Packt Publishing, were always constructive, friendly, and helpful partners bridging different time zones and often quite challenging text processing tools. They gave this book project momentum in its final stage to be completedeven under hard Covid19 work conditions.
Claus Fhrer, Jan-Erik Solem, Olivier Verdier , 2021

Python has tremendous potential in the scientific computing domain. This updated edition of Scientific Computing with Python features new chapters on graphical user interfaces, efficient data processing, and parallel computing to help you perform mathematical and scientific computing efficiently using Python.

This book will help you to explore new Python syntax features and create different models using scientific computing principles. The book presents Python alongside mathematical applications and demonstrates how to apply Python concepts in computing with the help of examples involving Python 3.8. You'll use pandas for basic data analysis to understand the modern needs of scientific computing and cover data module improvements and built-in features. You'll also explore numerical computation modules such as NumPy and SciPy, which enable fast access to highly efficient numerical algorithms. By learning to use the plotting module Matplotlib, you will be able to represent your computational results in talks and publications. A special chapter is devoted to SymPy, a tool for bridging symbolic and numerical computations. The book introduces also to the Python wrapper, mpi4py, for message passing parallel programming.

By the end of this Python book, you'll have gained a solid understanding of task automation and how to implement and test mathematical algorithms along within scientific computing.

This book is for students with a mathematical background, university teachers designing modern courses in programming, data scientists, researchers, developers, and anyone who wants to perform scientific computation in Python. The book evolved from 13 years of Python teaching in undergraduate science and engineering programs, as special industry in-house courses and specialization courses for high school teachers. The typical reader has the need to use Python in areas like mathematics, big data processings, machine learning and simulation. Therefore a basic knowledge of vectors and matrices as well of notions like convergence and iterative processes is beneficial.