SymPy
SymPy is an open-source Python library for symbolic mathematics. It provides computer algebra capabilities to manipulate mathematical expressions, calculate limits, solve equations, perform symbolic integration and differentiation, and more.
mathematics
symbolic-math
computer-algebra
SymPy: Open-Source Python Library for Symbolic Mathematics
SymPy is an open-source Python library for symbolic mathematics. It provides computer algebra capabilities to manipulate mathematical expressions, calculate limits, solve equations, perform symbolic integration and differentiation, and more.
What is SymPy?
SymPy is an open-source Python library for symbolic mathematics. It enables users to manipulate mathematical expressions, solve equations, expand functions into series, calculate limits, take derivatives and integrals, work with matrices, plot graphs, and more.
Some key features of SymPy include:
- Symbolic calculation engine that can perform arithmetic, algebra, calculus, discrete math, quantum physics, number theory, geometry, logic, and more
- Printable 2D and 3D plotting to visualize mathematical functions
- Solvers for algebraic and differential equations
- Matrices, vectors, tensors with standard operations
- Expand functions into series and calculate limits
- Symbolic integration, differentiation, Laplace transforms, Fourier series, z-transforms
- Code generation to export expressions to other languages like C, Fortran, Java, JavaScript
- A large standard library of mathematical functions and constants
- Import and export to multiple data formats like LaTeX, MathML, NumPy, Matlab, Mathematica
SymPy is used in fields like scientific computing, engineering, data science, artificial intelligence, computer algebra research, quantum computing, finance, and education. It can serve for mathematical modeling, simulation, algorithm implementation, and more thanks to its flexible symbolic representation.
SymPy Features
Features
- Symbolic mathematics
- Computer algebra system
- Mathematical expressions manipulation
- Equation solving
- Symbolic integration
- Symbolic differentiation
Pricing
- Open Source
Pros
Open source
Free to use
Large community support
Extensive documentation
Integrates well with NumPy and SciPy
Cons
Steep learning curve
Not as fast as optimized commercial CAS
Limited plotting capabilities
Not ideal for numerical computations
Official Links
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