國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Python for scientists

Stewart, John M., (1943 July 1-)

Python for scientists

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Python for scientistsJohn M. Stewart.
作者:	Stewart, John M.,
出版者:	Cambridge :Cambridge University Press,2014.
面頁冊數:	xii, 220 p. :ill., digital ;24 cm.
標題:	ScienceData processing.
電子資源:	https://doi.org/10.1017/CBO9781107447875
ISBN:	9781107447875$q(electronic bk.)

Python for scientists
Stewart, John M.,1943 July 1-

Python for scientists[electronic resource] /John M. Stewart. - Cambridge :Cambridge University Press,2014. - xii, 220 p. :ill., digital ;24 cm.

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$q(electronic bk.)Subjects--Topical Terms:

180009
Science
--Data processing.

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

Dewey Class. No.: 005.133

Python for scientists
LDR:02182nmm a2200265 a 4500 001 557829
003 UkCbUP
005 20151005020621.0
006 m d
007 cr nn 008maaau
008 191205s2014 enk o 1 0 eng d
020 $a 9781107447875$q(electronic bk.)
020 $a 9781107061392$q(hardback)
020 $a 9781107686427$q(paperback)
035 $a CR9781107447875
040 $a UkCbUP $b eng $c UkCbUP $d GP
041 0 $a eng
050 4 $a Q183.9 $b .S865 2014
082 0 4 $a 005.133 $2 23
090 $a Q183.9 $b .S849 2014
100 1 $a Stewart, John M., $d 1943 July 1- $3 840442
245 1 0 $a Python for scientists $h [electronic resource] / $c John M. Stewart.
260 $a Cambridge : $b Cambridge University Press, $c 2014.
300 $a xii, 220 p. : $b ill., digital ; $c 24 cm.
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 180009
650 0 $a Python (Computer program language) $3 215247
856 4 0 $u https://doi.org/10.1017/CBO9781107447875