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001 9781032673950
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006 m o d
007 cr |||||||||||
008 240416s2025 flu ob 001 0 eng
040 _aOCoLC-P
_beng
_erda
_cOCoLC-P
020 _a9781032673950
_q(ebook)
020 _a1032673958
020 _a9781040119631
_q(electronic bk. : EPUB)
020 _a1040119638
_q(electronic bk. : EPUB)
020 _a9781040119570
_q(electronic bk. : PDF)
020 _a1040119573
_q(electronic bk. : PDF)
020 _z9781032650333
_q(hardback)
020 _z9781032673905
_q(paperback)
024 7 _a10.1201/9781032673950
_2doi
035 _a(OCoLC)1430983275
035 _a(OCoLC-P)1430983275
050 0 0 _aQA76.73.P98
072 7 _aCOM
_x059000
_2bisacsh
072 7 _aCOM
_x007000
_2bisacsh
072 7 _aCOM
_x062000
_2bisacsh
072 7 _aPH
_2bicssc
100 1 _aPine, David J.,
_eauthor.
_1https://id.oclc.org/worldcat/entity/E39PCjwyQgvQ67kfKPmFgBQFGb
_995998
245 1 0 _aIntroduction to Python for science and engineering /
_cDavid J. Pine.
250 _aSecond edition.
264 1 _aBoca Raton, FL :
_bCRC Press,
_c2025.
300 _a1 online resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aSeries in computational biophysics
520 _a"Introduction to Python for Science and Engineering offers a quick and incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical and "bottom up," which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed. Readers will learn the basics of Python syntax, data structures, input and output, conditionals and loops, user-defined functions, plotting, animation, and visualization. They will also learn how to use Python for numerical analysis, including curve fitting, random numbers, linear algebra, solutions to nonlinear equations, numerical integration, solutions to differential equations, and fast Fourier transforms. Readers learn how to interact and program with Python using JupyterLab and Spyder, two simple and widely used integrated development environments. All the major Python libraries for science and engineering are covered, including NumPy, SciPy, Matplotlib, and Pandas. Other packages are also introduced, including Numba, which can render Python numerical calculations as fast as compiled computer languages such as C but without their complex overhead"--
_cProvided by publisher.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aPython (Computer program language)
_964608
650 0 _aComputer programming.
650 0 _aEngineering
_xData processing.
650 0 _aScience
_xData processing.
650 7 _aCOMPUTERS / Computer Engineering
_2bisacsh
_964152
650 7 _aCOMPUTERS / CAD-CAM
_2bisacsh
_995999
650 7 _aCOMPUTERS / Data Modeling & Design
_2bisacsh
_965185
830 0 _aSeries in computational biophysics.
_996000
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9781032673950
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
942 _cE-Books
999 _c68235
_d68235