What problem does it solve?
It helps you perform exact symbolic mathematics—like algebraic simplification, calculus (derivatives/integrals/limits), equation solving, and symbolic matrix work—without losing precision to floating-point rounding.
Core Features & Use Cases
- Exact algebra and expression manipulation: simplify, expand, factor, collect, and keep results exact (e.g., using Rational/S instead of floats).
- Symbolic calculus and limits: compute derivatives, integrals, limits, and series expansions for analytic answers.
- Symbolic solving and linear algebra: solve equations (including systems and ODEs) and work with matrices (determinants, inverses, eigenvalues/eigenvectors, diagonalization).
- Symbolic-to-code output: convert expressions into fast numerical functions with lambdify and generate C/Fortran/LaTeX when needed.
When to use it
Use this Skill for tasks that require exact, human-readable math (e.g., deriving closed-form results, verifying algebraic solutions, analyzing symbolic matrix properties), while using numpy/scipy for purely numerical computing and statsmodels for statistical modeling.
Quick Start
Use sympy-symbolic-math to solve a quadratic equation symbolically from an expression you provide, and return the exact roots.
