000			02182nam a2200349 i 4500
001			CR9781108120241
003			UkCbUP
005			20240730160807.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			160805s2017||||enk o ||1 0|eng|d
020			_a9781108120241 (ebook)
020			_z9781316641231 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQ183.9 _b.S865 2017
082	0	0	_a005.13/3 _223
100	1		_aStewart, John, _d1943 July 1- _eauthor. _974806
245	1	0	_aPython for Scientists / _cJohn M. Stewart.
250			_aSecond edition.
264		1	_aCambridge : _bCambridge University Press, _c2017.
300			_a1 online resource (xiv, 257 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 28 Aug 2017).
520			_aScientific 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.
650		0	_aScience _xData processing. _912246
650		0	_aPython (Computer program language) _96666
776	0	8	_iPrint version: _z9781316641231
856	4	0	_uhttps://doi.org/10.1017/9781108120241
942			_cEBK
999			_c84250 _d84250