Introduction to Python for Science and Engineering

2. Auflage 2024
ISBN: 978-1-032-65033-3
Verlag: Taylor & Francis Ltd (Sales)

Buch, Englisch, 444 Seiten, Format (B × H): 156 mm x 234 mm, Gewicht: 794 g

Reihe: Series in Computational Biophysics

Introduction to Python for Science and Engineering offers a quick and incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical and “bottom up,” which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed.

Readers will learn the basics of Python syntax, data structures, input and output, conditionals and loops, user-defined functions, plotting, animation, and visualization. They will also learn how to use Python for numerical analysis, including curve fitting, random numbers, linear algebra, solutions to nonlinear equations, numerical integration, solutions to differential equations, and fast Fourier transforms.

Readers learn how to interact and program with Python using JupyterLab and Spyder, two simple and widely used integrated development environments.

All the major Python libraries for science and engineering are covered, including NumPy, SciPy, Matplotlib, and Pandas. Other packages are also introduced, including Numba, which can render Python numerical calculations as fast as compiled computer languages such as C but without their complex overhead.

Zielgruppe

Professional Practice & Development and Undergraduate Advanced

Autoren/Hrsg.

Pine, David J

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Professionelle Anwendung Computer-Aided Design (CAD)
• Naturwissenschaften Physik Physik Allgemein
• Mathematik | Informatik EDV | Informatik Technische Informatik
• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software

Inhaltsverzeichnis

1. Introduction

2. Launching Python

3. Integrated Development Environments

4. Strings, Lists, Arrays, and Dictionaries

5. Input and Output

6. Conditionals and Loops

7. Functions

8. Plotting

9. Numerical Routines: SciPy and NumPy

10. Python Classes: Encapsulation

11. Data Manipulation and Analysis: Pandas

12. Animation

13. Speeding up numerical calculations

Appendix A Maintaining your installation Python

Appendix B Glossary

Appendix C Python Resources

Index Index

Über Autor(innen)

David J. Pine has taught physics and chemical engineering for over 40 years at four different institutions: Cornell University (as a graduate student), Haverford College, UCSB, and NYU, where he is a Professor of Physics, Mathematics, and Chemical & Biomolecular Engineering. He has taught a broad spectrum of courses, including numerical methods. He does research on optical materials and in experimental soft-matter physics, which is concerned with materials such as polymers, emulsions, and colloids.