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SciPy

by SciPy developers (NumFOCUS) · Library

Core open-source Python library for scientific computing on NumPy arrays: optimization, integration, interpolation, linear algebra, sparse matrices, statistics, signal processing

BSD-3-Clause licenseFreeOpen sourceLocalSelf-hostable
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Link verified Nov 2, 2026

Overview

SciPy is the foundational scientific-computing library of the Python ecosystem, built on NumPy arrays and released under the BSD 3-Clause license. Its subpackages cover optimization and root finding (scipy.optimize), numerical integration and ODE solvers (integrate), interpolation, dense and sparse linear algebra (linalg, sparse), statistics with distributions and hypothesis tests (stats), signal and image processing (signal, ndimage), fast Fourier transforms (fft), spatial algorithms and distances (spatial) and special functions (special). Much of the heavy lifting is implemented in C, C++, Fortran and Cython.

In econometrics and finance SciPy is rarely the user-facing layer; it sits underneath statsmodels, scikit-learn, arch and linearmodels and is called directly for optimizers, distributions and sparse solvers. The library is maintained by a volunteer community under NumFOCUS fiscal sponsorship with regular releases (1.18.0 at the time of writing) and runs wherever Python does, including Apple silicon. It targets single-machine, in-memory computation; GPU execution and distributed arrays require other projects.

Capabilities

  • Optimization, root finding and curve fitting
  • Numerical integration and ODE/BVP solvers
  • Dense and sparse linear algebra
  • Probability distributions and statistical tests
  • Signal processing, filtering and FFT
  • Interpolation and spatial algorithms
  • Special functions

Typical workflows

  1. Maximum-likelihood estimation with scipy.optimize
  2. Numerical integration in structural models
  3. Sparse linear systems for large fixed-effects problems

Strengths

  • BSD-licensed and ubiquitous
  • Stable, well-documented API
  • Underpins the scientific Python stack

Limitations

  • CPU and single-node only
  • Lower-level than statsmodels for regression work
  • Some legacy APIs coexist with newer ones

Prerequisites

  • Python and NumPy

Who it is for

  • Scientists and engineers using Python
  • Quantitative researchers building custom estimators

Alternatives & comparison

NumPy provides the array type SciPy builds on; statsmodels adds regression and time-series models on top of SciPy. MATLAB and Julia offer comparable numerical toolkits in other languages; SymPy handles symbolic rather than numeric mathematics.

  • Julia

    Dynamically typed, JIT-compiled language for numerical and scientific computing that aims to combine high-level syntax with compiled-code speed

    Open sourceOpen source
  • MATLAB

    MathWorks' commercial numeric computing environment and language for engineers and scientists, with toolboxes, Simulink integration, apps and MATLAB Online

    ProprietaryPaid
  • NumPy

    Foundational Python library for n-dimensional arrays, vectorized math, linear algebra, random numbers and the array API used by the scientific stack

    Open sourceOpen source
  • PyPortfolioOpt

    Python library for portfolio optimization: efficient frontier, Black-Litterman, hierarchical risk parity and discrete allocation

    Open sourceOpen source
  • QuantLib

    Open-source C++ library for quantitative finance covering instruments, term structures, pricing engines and calendars, with Python and other bindings

    Open sourceOpen source
  • statsmodels

    Python library for estimating statistical and econometric models with R-style formulas, rich result summaries and tests: OLS, GLM, discrete choice, ARIMA, state space, VAR and more

    Open sourceFree
  • SymPy

    Pure-Python computer algebra system for symbolic algebra, calculus, equation solving, matrices, physics modules, code generation and LaTeX output

    Open sourceFree
Compare SciPy with its alternatives →

Sources & verification

Primary sources consulted for this record. Claims are tied to the fields each source supports; the automated link checker re-verifies every URL on a schedule.

  • License
    scipy/scipy LICENSE.txt

    https://github.com/scipy/scipy/blob/main/LICENSE.txt

    Supports: license_spdx, source_model

    checked Nov 1, 2026
  • Official documentation
    SciPy documentation

    https://docs.scipy.org/doc/scipy

    Supports: capabilities, description

    Shows version 1.18.0

    checked Nov 1, 2026
  • Official site
    SciPy

    https://scipy.org/

    Supports: name, summary

    checked Nov 1, 2026
Confidence
High
Reviewed
Nov 1, 2026
Links
Verified · Nov 2, 2026

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