sympy-symbolic-math
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)not1.414...) - For numerical computing (array operations, linear algebra on data), use numpy/scipy
- For statistical modeling (regression, hypothesis testing), use statsmodels
Prerequisites
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