Learning scientific programming with Python

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 pack...

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Bibliographic Details
Main Author: Hill, Christian
Format: eBook
Language:English
Published: Cambridge Cambridge University Press 2015
Subjects:
Online Access:
Collection: Cambridge Books Online - Collection details see MPG.ReNa
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100 1 |a Hill, Christian 
245 0 0 |a Learning scientific programming with Python  |c Christian Hill, University College London and Somerville College, University of Oxford 
260 |a Cambridge  |b Cambridge University Press  |c 2015 
300 |a vii, 452 pages  |b digital 
505 0 |a 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 
653 |a Science / Data processing 
653 |a Science / Mathematics 
653 |a Python (Computer program language) 
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520 |a 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