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Learning scientific programming with...

Hill, Christian, (1974-)

Learning scientific programming with Python /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Learning scientific programming with Python /Christian Hill, University College London and Somerville College, University of Oxford.
Author:	Hill, Christian,
Description:	1 online resource (vii, 452 pages) :digital, PDF file(s).
Notes:	Title from publisher's bibliographic system (viewed on 05 Feb 2016).
Subject:	ScienceData processing.
Online resource:	https://doi.org/10.1017/CBO9781139871754
ISBN:	9781139871754 (ebook)

Learning scientific programming with Python /
Hill, Christian,1974-

Learning scientific programming with Python /Christian Hill, University College London and Somerville College, University of Oxford. - 1 online resource (vii, 452 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 05 Feb 2016).

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.

ISBN: 9781139871754 (ebook)Subjects--Topical Terms:

180009
Science
--Data processing.

LC Class. No.: Q183.9 / .H58 2015

Dewey Class. No.: 005.13/3

Learning scientific programming with Python /
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050 0 0 $a Q183.9 $b .H58 2015
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100 1 $a Hill, Christian, $d 1974- $e author. $3 759431
245 1 0 $a Learning scientific programming with Python / $c Christian Hill, University College London and Somerville College, University of Oxford.
264 1 $a Cambridge : $b Cambridge University Press, $c 2015.
300 $a 1 online resource (vii, 452 pages) : $b digital, PDF file(s).
336 $a text $b txt $2 rdacontent
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500 $a Title from publisher's bibliographic system (viewed on 05 Feb 2016).
505 8 $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.
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.
650 0 $a Science $x Data processing. $3 180009
650 0 $a Science $x Mathematics. $3 203126
650 0 $a Python (Computer program language) $3 215247
776 0 8 $i Print version: $z 9781107075412
856 4 0 $u https://doi.org/10.1017/CBO9781139871754