Learning Scientific Programming with Python

Christian Hill

doi:10.1017/CBO9781139871754

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.

'This book is well illustrated and is supported by an extensive collection of resources online in the book’s website, scipython.com. This site has code listings and solutions to exercises. I would readily recommend this book to any student (or even a colleague) who wishes to achieve a solid foundation in Python programming.'

Vasudevan Lakshminarayanan Source: Contemporary Physics

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Learning Scientific Programming with Python

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Contents

Frontmatter
pp i-iv

Contents
pp v-vi

Acknowledgments
pp vii-viii

1 - Introduction
pp 1-7

2 - The core Python language I
pp 8-83

3 - Interlude: simple plotting with pylab
pp 84-101

4 - The core Python language II
pp 102-159

5 - IPython and IPython Notebook
pp 160-183

6 - NumPy
pp 184-279

7 - Matplotlib
pp 280-332

8 - SciPy
pp 333-401

9 - General scientific programming
pp 402-423

Appendix A - Solutions
pp 424-444

Index
pp 445-452

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Learning Scientific Programming with Python

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