SciPy 1.0: fundamental algorithms for scientific computing in Python

Research output: Contribution to journalArticlepeer-review

  • External authors:
  • Pauli Virtanen
  • Ralf Gommers
  • Travis E. Oliphant
  • Matt Haberland
  • Tyler Reddy
  • David Cournapeau
  • Evgeni Burovski
  • Pearu Peterson
  • Warren Weckesser
  • Jonathan Bright
  • Stéfan J. Van Der Walt
  • Matthew Brett
  • Joshua Wilson
  • K. Jarrod Millman
  • Nikolay Mayorov
  • Andrew R. J. Nelson
  • Eric Jones
  • Robert Kern
  • Eric Larson
  • C J Carey
  • İlhan Polat
  • Yu Feng
  • Eric W. Moore
  • Jake Vanderplas
  • Denis Laxalde
  • Josef Perktold
  • Robert Cimrman
  • Ian Henriksen
  • E. A. Quintero
  • Charles R. Harris
  • Anne M. Archibald
  • Antônio H. Ribeiro
  • Fabian Pedregosa
  • Paul Van Mulbregt

Abstract

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

Bibliographical metadata

Original languageEnglish
JournalNature Methods
DOIs
Publication statusPublished - 3 Feb 2020