SciPy Scientific Computing

SciPy is a fundamental open-source library for scientific computing in Python, built on top of NumPy. Originally created by Travis Oliphant, Eric Jones, and Pearu Peterson in 2001, SciPy is maintained by the SciPy community and NumFOCUS, with over 13,000 stars as of 2026. SciPy extends NumPy's array capabilities with modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal processing, image processing, ODE solvers, and statistical distributions. Key features include: scipy.optimize (minimize, least squares, root finding, linear programming, curve fitting, and global optimization via differential evolution and dual annealing), scipy.linalg (LU, Cholesky, QR, Schur decompositions, eigenvalue problems, and solvers), scipy.integrate (numerical integration via quad, dblquad, tplquad, ODE solvers including solve_ivp with RK45, RK23, Radau, BDF, and LSODA methods), scipy.interpolate (1D, 2D, multivariate, spline, and radial basis function interpolation), scipy.signal (FIR and IIR filters, convolution, correlation, spectral analysis, wavelet transforms), scipy.fft (fast Fourier transforms with multi-threaded support), scipy.sparse (CSR, CSC, COO, DOK, LIL formats with sparse linear algebra), scipy.spatial (KDTree, Delaunay triangulation, convex hull, Voronoi diagrams, and distance computations), scipy.stats (over 100 continuous and discrete probability distributions, t-test, chi-square, Kolmogorov-Smirnov, ANOVA, and descriptive statistics), and scipy.ndimage (multi-dimensional image processing).