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Learning Scientific Programming with Python

Learning Scientific Programming with Python

Learning Scientific Programming with Python
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Learning Scientific Programming with Python Paperback - 2016

by Hill, Christian

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Used - Very good

Description

Cambridge University Press, 2016-03-29. paperback. Very Good. 6x1x9. ***VERY GOOD*** Contains one or two pages of minor writing or highlights, and Underlines. Legible and in good shape. Minor to slightly heavy wears on cover from warehouse shelves. (Used so may not contain codes/CDs/Inserts that is included with the book.
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Details

  • Title Learning Scientific Programming with Python
  • Author Hill, Christian
  • Binding Paperback
  • Edition Paperback
  • Condition Used - Very good
  • Pages 458
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2016-03-29
  • Bookseller's Inventory # VB480291896
  • ISBN 9781107428225 / 110742822X
  • Weight 1.98 lbs (0.90 kg)
  • Dimensions 9.72 x 6.85 x 0.87 in (24.69 x 17.40 x 2.21 cm)
  • Size 6x1x9
  • Category Science
  • Library of Congress subjects Science - Mathematics, Python (Computer program language)
  • Library of Congress Catalogue Number 2015017085
  • Dewey Decimal Code 005.133
  • Quantity available 1

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Reader reviews for Learning Scientific Programming with Python

From the publisher

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.
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