Python Programming for Mathematics focuses on the practical use of the Python language in a range of different areas of mathematics. Through fifty-five exercises of increasing difficulty, the book provides an expansive overview of the power of using programming to solve complex mathematical problems.
This book is intended for undergraduate and graduate students who already have learned the basics of Python programming and would like to learn how to apply that programming skill in mathematics.
Features
Innovative style that teaches programming skills via mathematical exercises.
Ideal as a main textbook for Python for Mathematics courses, or as a supplementary resource for Numerical Analysis and Scientific Computing courses.
Reihe
Sprache
Verlagsort
Verlagsgruppe
Zielgruppe
Für höhere Schule und Studium
Postgraduate and Undergraduate Core
Illustrationen
69 farbige Zeichnungen, 69 farbige Abbildungen
69 Line drawings, color; 69 Illustrations, color
Maße
Höhe: 234 mm
Breite: 156 mm
Dicke: 14 mm
Gewicht
ISBN-13
978-1-032-91011-6 (9781032910116)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
Julien Guillod is an Associate Professor of Applied Mathematics at Laboratoire Jacques-Louis Lions of Sorbonne University in Paris, a part-time member of the Department of Mathematics and Applications of ENS Paris, and a member of an Inria team. He earned a PhD in Physics from the University of Geneva in 2015.
Guillod's research focuses mainly on the analysis of partial differential equations in fluid mechanics, involving both traditional analysis and numerical simulations. The numerical aspects are mainly used to gain insight into the problems considered, or to discover fundamental properties of the equations studied. His favorite and most commonly used language for these simulations is Python. Most of his research is related in one way or another to the Navier-Stokes equations.
1. Introduction. 2. Data structures. 3. Homogeneous structures. 4. Plotting. 5. Integration. 6. Algebra. 7. Graph theory. 8. Symbolic calculation. 9. Root finding. 10. Probability and statistics. 11. Differential equations. 12. Data Science. 13. Cryptography.
