SymPy — Symbolic Mathematics

Overview

SymPy is a Python library for symbolic mathematics that performs exact computation using mathematical symbols rather than numerical approximations. It covers algebra, calculus, equation solving, linear algebra, physics, and code generation — all within pure Python with no external dependencies.

When to Use

• Solving equations symbolically (algebraic, systems, differential equations)
• Performing calculus operations (derivatives, integrals, limits, series expansions)
• Simplifying and manipulating algebraic expressions
• Working with matrices symbolically (eigenvalues, determinants, decompositions)
• Converting symbolic expressions to fast numerical functions (lambdify → NumPy)
• Generating code from math expressions (C, Fortran, LaTeX)
• Needing exact results (e.g., sqrt(2) not 1.414...)
• For numerical computing (array operations, linear algebra on data), use numpy/scipy
• For statistical modeling (regression, hypothesis testing), use statsmodels

Prerequisites