000			02173nam a2200301 i 4500
003			IN-BdCUP
005			20250103125024.0
008			201215s2023||||enk o ||1 0|eng|d
020			_a9781009029728 (ebook) _z9781009014809 (paperback)
040			_aIN-BdCUP _beng _cIN-BdCUP _erda
041			_aeng
050			_aQ183.9 _b.S865 2023
082			_a005.13/3
100			_aStewart, John M. _eAuthor
245		0	_aPython for scientists / _cJohn M. Stewart, University of Cambridge, Michael Mommert, University of St Gallen, Switzerland.
264			_aCambridge, United Kingdom ;;New York, NY : _bCambridge university Press, _c2023
300			_a1 online resource (x, 288 pages) : _bdigital, PDF file(s).
336			_atext _btxt
337			_2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 01 Aug 2023).
520			_aThe third edition of this practical introduction to Python has been thoroughly updated, with all code migrated to Jupyter notebooks. The notebooks are available online with executable versions of all of the book's content (and more). The text starts with a detailed introduction to the basics of the Python language, without assuming any prior knowledge. Building upon each other, the most important Python packages for numerical math (NumPy), symbolic math (SymPy), and plotting (Matplotlib) are introduced, with brand new chapters covering numerical methods (SciPy) and data handling (Pandas). Further new material includes guidelines for writing efficient Python code and publishing code for other users. Simple and concise code examples, revised for compatibility with Python 3, guide the reader and support the learning process throughout the book. Readers from all of the quantitative sciences, whatever their background, will be able to quickly acquire the skills needed for using Python effectively.
650			_aScience _aPython (Computer program language) _xData processing.
700			_aMommert, Michael, _eauthor.
776			_iPrint version: _z9781009029728
856			_3Electronic Book Resource _uhttps://doi.org/10.1017/9781009029728
942			_2ddc _cE
999			_c54696 _d54696