SciPy (pronounced /ˈsp/ "sigh pie"[2]) is a free and open-source Python library used for scientific computing and technical computing.[3]

SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

SciPy is also a family of conferences for users and developers of these tools: SciPy (in the United States), EuroSciPy (in Europe) and (in India).[4] Enthought originated the SciPy conference in the United States and continues to sponsor many of the international conferences as well as host the SciPy website.

The SciPy library is currently distributed under the BSD license, and its development is sponsored and supported by an open community of developers. It is also supported by NumFOCUS, a community foundation for supporting reproducible and accessible science.


The SciPy package is at the core of Python's scientific computing capabilities. Available sub-packages include:

Snapshot showing SciPy ndimage source code

Data structures

The basic data structure used by SciPy is a multidimensional array provided by the NumPy module. NumPy provides some functions for linear algebra, Fourier transforms, and random number generation, but not with the generality of the equivalent functions in SciPy. NumPy can also be used as an efficient multidimensional container of data with arbitrary datatypes. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. Older versions of SciPy used Numeric as an array type, which is now deprecated in favor of the newer NumPy array code.[6]


In the 1990s, Python was extended to include an array type for numerical computing called Numeric (This package was eventually replaced by Travis Oliphant who wrote NumPy in 2006 as a blending of Numeric and Numarray which had been started in 2001). As of 2000, there was a growing number of extension modules and increasing interest in creating a complete environment for scientific and technical computing. In 2001, Travis Oliphant, Eric Jones, and Pearu Peterson merged code they had written and called the resulting package SciPy. The newly created package provided a standard collection of common numerical operations on top of the Numeric array data structure. Shortly thereafter, Fernando Pérez released IPython, an enhanced interactive shell widely used in the technical computing community, and John Hunter released the first version of Matplotlib, the 2D plotting library for technical computing. Since then the SciPy environment has continued to grow with more packages and tools for technical computing.[7][8][9]

See also


  1. ^ SciPy Team. "How can SciPy be fast if it is written in an interpreted language like Python?". Retrieved 2022-04-11.
  2. ^ "SciPy (pronounced "Sigh Pie")"
  3. ^ Pauli Virtanen; Ralf Gommers; Travis E. Oliphant; et al. (3 February 2020). "SciPy 1.0: fundamental algorithms for scientific computing in Python" (PDF). Nature Methods. 17 (3): 261–272. doi:10.1038/S41592-019-0686-2. ISSN 1548-7091. PMC 7056644. PMID 32015543. Wikidata Q84573952. {{cite journal}}: |author35= has generic name (help) (erratum)
  4. ^ "Upcoming SciPy Conferences 2023". SciPy Conferences. Retrieved May 11, 2023.{{cite web}}: CS1 maint: url-status (link)
  5. ^ "SciPy 0.15.0 Release Notes — SciPy v1.6.2 Reference Guide". Retrieved 2021-04-13.
  6. ^ "NumPy Homepage".
  7. ^ "History of SciPy".
  8. ^ "Guide to NumPy" (PDF).
  9. ^ "Python for Scientists and Engineers".

Further reading

  • Nunez-Iglesias, Juan; van der Walt, Stéfan; Dashnow, Harriet (2017). Elegant SciPy: The Art of Scientific Python. O'Reilly. ISBN 978-1-4919-2287-3.

External links