Fundamental algorithms for scientific computing in Python
SciPy 1.14.0 released!
Fundamental algorithms
SciPy provides algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics and many other classes of problems.
Broadly applicable
The algorithms and data structures provided by SciPy are broadly applicable across domains.
Extends NumPy providing additional tools for array computing and provides specialized data structures, such as sparse matrices and k-dimensional trees.
SciPy wraps highly-optimized implementations written in low-level languages like Fortran, C, and C++. Enjoy the flexibility of Python with the speed of compiled code.
Easy to use
SciPy’s high level syntax makes it accessible and productive for programmers from any background or experience level.
Open source
Distributed under a liberal BSD license, SciPy is developed and maintained publicly on GitHub by a vibrant, responsive, and diverse community.