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Python for Scientists
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    Giuffrè, Orazio Granà, Anna Tumminello, Maria Luisa Sferlazza, Antonino Tiwari, Shailesh Trivedi, Munesh and Kohle, Mohan L. 2018. Calibrating a microscopic traffic simulation model for roundabouts using genetic algorithms. Journal of Intelligent & Fuzzy Systems, Vol. 35, Issue. 2, p. 1791.

    Brown, J. B. 2018. Computational Chemogenomics. Vol. 1825, Issue. , p. 95.

    Rinne, Oliver 2014. Formation and decay of Einstein-Yang-Mills black holes. Physical Review D, Vol. 90, Issue. 12,


Book description

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.


'… the practitioner who wants to learn Python will love it. This is the type of book I have been looking for to learn Python … concise, yet practical.'

Source: European Mathematical Society (

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