SymPy is a Python library for symbolic mathematics. It aims to become a full-featured computer algebra system (CAS) while keeping the code as simple as possible in order to be comprehensible and easily …

Welcome to SymPy’s documentation! ¶ A PDF version of these docs is also available. SymPy is a Python library for symbolic mathematics. If you are new to SymPy, start with the introductory tutorial. …

The real power of a symbolic computation system such as SymPy is the ability to do all sorts of computations symbolically. SymPy can simplify expressions, compute derivatives, integrals, and …

To give a guide, suitable for someone who has never used SymPy (but who has used Python and knows the necessary mathematics). To be written in a narrative format, which is both easy and fun to follow.

One of the most useful features of a symbolic manipulation system is the ability to simplify mathematical expressions. SymPy has dozens of functions to perform various kinds of simplification.

One advantage of using a symbolic computation system like SymPy is that we can build a symbolic representation for expr, and then substitute x with values. The correct way to do this in SymPy is to …

Basic Operations ¶ Here we discuss some of the most basic operations needed for expression manipulation in SymPy. Some more advanced operations will be discussed later in the advanced …

The Python package SymPy can symbolically solve equations, differential equations, linear equations, nonlinear equations, matrix problems, inequalities, Diophantine equations, and evaluate integrals.

If it is a SymPy Function or Lambda instance, it should be able to accept two arguments which represents the matrix coordinates. If it is a pure string containing Python lambda semantics, it is …

(Recall that in Python if a is a tuple, then f(*a) means to call f with arguments from the elements of a, e.g., f(*(1, 2, 3)) is the same as f(1, 2, 3).) This key invariant allows us to write simple algorithms that …