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Python for scientists /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Python for scientists // John M. Stewart, Department of Applied Mathematics & Theoretical Physics, University of Cambridge.
Author:	Stewart, John,
Description:	1 online resource (xii, 220 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 05 Oct 2015).
Subject:	Science - Data processing. -
Online resource:	https://doi.org/10.1017/CBO9781107447875
ISBN:	9781107447875 (ebook)

Python for scientists /
Stewart, John,1943 July 1-

Python for scientists /John M. Stewart, Department of Applied Mathematics & Theoretical Physics, University of Cambridge. - 1 online resource (xii, 220 pages) :digital, PDF file(s).

Title from publisher's bibliographic system (viewed on 05 Oct 2015).

Machine generated contents note: Preface; 1. Introduction; 2. Getting started with IPython; 3. A short Python tutorial; 4. Numpy; 5. Two-dimensional graphics; 6. Three-dimensional graphics; 7. Ordinary differential equations; 8. Partial differential equations: a pseudospectral approach; 9. Case study: multigrid; 10. Appendix A. Installing a Python environment; Appendix B. Fortran77 subroutines for pseudospectral methods; References; Index.

Python is a free, open source, easy-to-use software tool that offers a significant alternative to proprietary packages such as MATLAB and Mathematica. This book covers everything the working scientist needs to know to start using Python effectively. The author explains scientific Python from scratch, showing how easy it is to implement and test non-trivial mathematical algorithms and guiding the reader through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the program's capabilities. In particular, readers are shown how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. Instead of exercises the book contains useful snippets of tested code which the reader can adapt to handle problems in their own field, allowing students and researchers with little computer expertise to get up and running as soon as possible.

ISBN: 9781107447875 (ebook)Subjects--Topical Terms:

528623
Science
--Data processing.

LC Class. No.: Q183.9 / .S865 2014

Dewey Class. No.: 005.13/3

Python for scientists /
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245 1 0 $a Python for scientists / $c John M. Stewart, Department of Applied Mathematics & Theoretical Physics, University of Cambridge.
264 1 $a Cambridge : $b Cambridge University Press, $c 2014.
300 $a 1 online resource (xii, 220 pages) : $b digital, PDF file(s).
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500 $a Title from publisher's bibliographic system (viewed on 05 Oct 2015).
505 8 $a Machine generated contents note: Preface; 1. Introduction; 2. Getting started with IPython; 3. A short Python tutorial; 4. Numpy; 5. Two-dimensional graphics; 6. Three-dimensional graphics; 7. Ordinary differential equations; 8. Partial differential equations: a pseudospectral approach; 9. Case study: multigrid; 10. Appendix A. Installing a Python environment; Appendix B. Fortran77 subroutines for pseudospectral methods; References; Index.
520 $a Python is a free, open source, easy-to-use software tool that offers a significant alternative to proprietary packages such as MATLAB and Mathematica. This book covers everything the working scientist needs to know to start using Python effectively. The author explains scientific Python from scratch, showing how easy it is to implement and test non-trivial mathematical algorithms and guiding the reader through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the program's capabilities. In particular, readers are shown how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. Instead of exercises the book contains useful snippets of tested code which the reader can adapt to handle problems in their own field, allowing students and researchers with little computer expertise to get up and running as soon as possible.
650 0 $a Science $x Data processing. $3 528623
650 0 $a Python (Computer program language) $3 566246
776 0 8 $i Print version: $z 9781107061392
856 4 0 $u https://doi.org/10.1017/CBO9781107447875