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The 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.
Scientific 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.
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