000 03015nam a22003375i 4500
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003 ES-VaUE
005 20220531192455.0
007 ta
008 210309s2021 nju 000 0 eng
010 _a 2021934834
020 _a9780691219288
_q(hardback)
020 _a9780691223650
_q(paperback)
020 _z9780691223667
_q(ebook)
040 _aDLC
_beng
_erda
_cUEV
_dES-VaUE
042 _apcc
050 _aQA76.73 .P98
_bK56 2021
100 1 _aKinder, Jesse M.,
_d1978-
_eauthor.
245 1 2 _aA student's guide to python for physical modeling
_cJesse M. Kinder, Philip Nelson
250 _aSecond ed.
264 1 _aPrinceton
_bPrinceton University Press
_ccop. 2021
300 _a223 p.
_bil.
_c25 cm
336 _atext
_btxt
_2rdacontent
337 _aunmediated
_bn
_2rdamedia
338 _avolume
_bnc
_2rdacarrier
520 _a"Python is an open-source computer programming language that is popular in the sciences. This second edition of A Student's Guide to Python for Physical Modeling offers an up-to-date primer aimed to help students (without previous programming experience) form a enough of a foundation in the Python programming language to get started with physical modeling. Students will learn how to install a Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. The text is applicable to all fields of science, but focuses on the physical sciences. It can be used as a textbook to cover a 2-3 week introductory tutorial on Python within any course in the physical sciences, or it can be used for self-study by individuals wanting to quickly learn Python for use in their own research. Numerous code samples and exercises - with solutions-illustrate new ideas as they are introduced. Web-based resources also accompany this guide and include code samples, data sets, and more. ** 2nd Edition includes the following** - Retains the short, focused format that has been successful for the 1st edition. - Retains the focus on novice readers who need basic skills. - Retain the focus on the needs of physical-science students. - Updated to reflect changes in the Python language (current with Python 3.9) and common practice in plotting and numerical methods. - Updated to reflect changes in the Anaconda software distribution. - Concepts and instructions clarified throughout, based on classroom experience. - Reduced emphasis on the decreasingly popular Spyder platform. Increased coverage of the popular Jupyter Notebook platform. - Additional new appendix on command line tools and version control with Git. - Additional new material on symbolic calculations with SymPy. - Additional new material to introduce basic Python libraries for data science and machine learning (pandas, sklearn)"--
650 7 _2embne
_929326
_aPython (Lenguaje de programación)
700 1 _aNelson, Philip,
_d1957-
_eauthor.
942 _2lcc
_cMSA
998 _da