SciPy Scientific Computing Library

SciPy is a fundamental open-source library for scientific and technical computing in Python, providing algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics, signal processing, image processing, sparse matrices, and many other classes of problems. The latest version 1.18.0 was released on June 19, 2026, continuing over two decades of continuous development since the project was founded in 2001. The library extends NumPy by providing additional tools for array computing along with specialized data structures such as sparse matrices for memory-efficient representation of large sparse datasets, k-dimensional trees for efficient spatial queries, and masked arrays for handling missing data. Performance is achieved by wrapping highly optimized implementations written in low-level languages including Fortran libraries like LAPACK, BLAS, and ARPACK, C libraries like FFTW for Fourier transforms, and C++ implementations, giving users the flexibility of Python with the execution speed of compiled code. Key submodules include scipy.optimize for function minimization and root finding with algorithms like L-BFGS-B, Nelder-Mead, and SLSQP, scipy.integrate for numerical integration using quadrature and ODE solvers, scipy.interpolate for spline and radial basis function interpolation, scipy.stats for 100 plus continuous and discrete probability distributions with statistical tests, scipy.signal for filtering, convolution, and spectral analysis, scipy.sparse for efficient sparse matrix operations and iterative solvers, and scipy.ndimage for multi-dimensional image processing including filtering, morphology, and measurements. The library is distributed under the liberal BSD license, developed publicly on GitHub by a vibrant community of contributors, and supported by the Scientific Python ecosystem organization NumFOCUS with an annual international conference.