Symbolic Computation with Python and SymPy

SymPy is an open-source, lightweight and cross-platform Computer Algebra System written in Python that enables the manipulation of mathematical expressions in an analytical form. It can be used in a variety of disciplines in engineering and science to perform common analytical computations such as differentiation and integration, simplifying and manipulating expressions for greater insight, solving algebraic and differential equations, plotting, mathematical modeling and more.

Symbolic Computation with Python and SymPy takes you through SymPy's capabilities, with sample code and in-depth guided exercises. If you are just starting with SymPy, or want to go deeper, you will learn how to integrate this library into your workflow.

• Use Jupyter Notebook for interactive computing.
• Create and inspect symbolic expressions.
• Gain hands-on experience in expression manipulation with guided exercises and many Cheat Sheets.
• Solve algebraic equations, inequalities and differential equations.
• Use calculus, multi-dimensional and plotting functionalities.
• Generate Equations of Motion of mechanical systems, set up numerical simulations and optimization problems.
• Convert symbolic expressions into numerical functions for fast evaluation with NumPy and SciPy and leverage Cython to get the best speed up.
• Translate symbolic expressions to a target programming language, such as C/C++, Javascript, Matlab/Octave, Julia and more.
• Explore SymPy architecture and use Object-Oriented Programming to extend its capabilities.

Source Code available at Github