Learning scientific programming with Python / Christian Hill, University College London and Somerville College, University of Oxford.
By: Hill, Christian.
Publisher: Cambridge, United Kingdom : Cambridge University Press, 2015Description: pages cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781107428225 (paperback).Subject(s): Science -- Data processing | Science -- Mathematics | Python (Computer program language) | SCIENCE / Mathematical PhysicsGenre/Form: Print books.DDC classification: 005.13/3Current location | Call number | Status | Date due | Barcode | Item holds |
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On Shelf | Q183.9 .H58 2015 (Browse shelf) | Available | AU0000000006113 |
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Q183.3.A1 R828 2019 How we teach science : what's changed, and why it matters / | Q183.9 .B56 2014 Using R for numerical analysis in science and engineering / | Q183.9 .F37 2008 Mathematical principles for scientific computing and visualization / | Q183.9 .H58 2015 Learning scientific programming with Python / | Q183.9 .P734 2017 Getting started with MATLAB : a quick introduction for scientists and engineers / | Q185 .E69 2016 Hands-on introduction to LabVIEW for scientists and engineers / | Q223 .A38 2013 The craft of scientific presentations : critical steps to succeed and critical errors to avoid / |
Machine generated contents note: 1. Introduction; 2. The core Python language I; 3. Interlude: simple plotting with Pylab; 4. The core Python language II; 5. IPython and IPython notebook; 6. NumPy; 7. Matplotlib; 8. SciPy; 9. General scientific programming; Appendix A; Solutions; Index.
"Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to quickly gain proficiency. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation, this textbook also discusses the use of IPython notebooks to build rich-media, shareable documents for scientific analysis. Including a final chapter introducing challenging topics such as floating-point precision and algorithm stability, and with extensive online resources to support advanced study, this textbook represents a targeted package for students requiring a solid foundation in Python programming"-- Provided by publisher.