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Python for scientists

Scientific Python is a significant public domain alternative to expensive proprietary software packages. This book teaches from scratch everything the working scientist needs to know using copious, downloadable, useful and adaptable code snippets. Readers will discover how easy it is to implement an...

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Detalles Bibliográficos
Autor principal: Stewart, John M
Lenguaje:eng
Publicado: Cambridge University Press 2017
Materias:
Acceso en línea:https://dx.doi.org/10.1017/9781108120241
http://cds.cern.ch/record/2277229
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author Stewart, John M
author_facet Stewart, John M
author_sort Stewart, John M
collection CERN
description Scientific Python is a significant public domain alternative to expensive proprietary software packages. This book teaches from scratch everything the working scientist needs to know using copious, downloadable, useful and adaptable code snippets. Readers will discover how easy it is to implement and test non-trivial mathematical algorithms and will be guided through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the language's capabilities. The author also shows how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. In this new edition, several chapters have been re-written to reflect the IPython notebook style. With an extended index, an entirely new chapter discussing SymPy and a substantial increase in the number of code snippets, researchers and research students will be able to quickly acquire all the skills needed for using Python effectively.
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spelling cern-22772292021-04-21T19:07:40Zdoi:10.1017/9781108120241http://cds.cern.ch/record/2277229engStewart, John MPython for scientistsComputing and ComputersScientific Python is a significant public domain alternative to expensive proprietary software packages. This book teaches from scratch everything the working scientist needs to know using copious, downloadable, useful and adaptable code snippets. Readers will discover how easy it is to implement and test non-trivial mathematical algorithms and will be guided through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the language's capabilities. The author also shows how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. In this new edition, several chapters have been re-written to reflect the IPython notebook style. With an extended index, an entirely new chapter discussing SymPy and a substantial increase in the number of code snippets, researchers and research students will be able to quickly acquire all the skills needed for using Python effectively.Cambridge University Pressoai:cds.cern.ch:22772292017
spellingShingle Computing and Computers
Stewart, John M
Python for scientists
title Python for scientists
title_full Python for scientists
title_fullStr Python for scientists
title_full_unstemmed Python for scientists
title_short Python for scientists
title_sort python for scientists
topic Computing and Computers
url https://dx.doi.org/10.1017/9781108120241
http://cds.cern.ch/record/2277229
work_keys_str_mv AT stewartjohnm pythonforscientists