Numerical Python

A Practical Techniques Approach for Industry
1. Auflage 2015
ISBN: 978-1-4842-0554-9
Verlag: Apress

Buch, Englisch, 487 Seiten, Book, Format (B × H): 178 mm x 254 mm, Gewicht: 9511 g

Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business management and more. After reading and using this book, you'll get some takeaway case study examples of applications that can be found in areas like business management, big data/cloud computing, financial engineering (i.e., options trading investment alternatives), and even games.Up until very recently, Python was mostly regarded as just a web scripting language. Well, computational scientists and engineers have recently discovered the flexibility and power of Python to do more. Big data analytics and cloud computing programmers are seeing Python's immense use. Financial engineers are also now employing Python in their work. Python seems to be evolving as a language that can even rival C++, Fortran, and Pascal/Delphi for numerical and mathematical computations.

Zielgruppe

Popular/general

Autoren/Hrsg.

Johansson, Robert

Fachgebiete

• Mathematik | Informatik EDV | Informatik Technische Informatik Grid-Computing & Paralleles Rechnen
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Numerische Mathematik
• Technische Wissenschaften Technik Allgemein Computeranwendungen in der Technik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Computeranwendungen in Wissenschaft & Technologie
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen

Inhaltsverzeichnis

1. Introduction to computing with Python.-2. Vectors, matrices and multidimensional arrays.-3. Symbolic computing.-4. Plotting and visualization.-5. Equation solving.-6. Optimization.-7. Interpolation.-8. Integration.-9. Ordinary differential equations.-10. Sparse matrices and graphs.-11. Partial differential equations.-12. Data processing and analysis.-13. Statistics.-14. Statistical modeling.-15. Machine learning.-16. Bayesian statistics.-17. Signal and image processing.-18. Data input and output.-19. Code optimization.-20. Appendix: Installation.-

Über Autor(innen)

Robert Johansson is a numerical Python expert, computational scientist.  He has experience with SciPy, NumPy and works on QuTiP, an open-source python framework for simulating the dynamics of quantum systems.