A2 Review article, Literature review, Systematic review
SciPy 1.0 : fundamental algorithms for scientific computing in Python (2020)


Virtanen, Pauli, Gommers, Ralf, Oliphant, Travis E., Haberland, Matt, Reddy, Tyler, Cournapeau, David, Burovski, Evgeni, Peterson, Pearu, Weckesser, Warren, Bright, Jonathan, van der Walt, Stéfan J., Brett, Matthew, Wilson, Joshua, Millman, K. Jarrod, Mayorov, Nikolay, Nelson, Andrew R. J., Jones, Eric, Kern, Robert, Larson, Eric, Carey, C. J., Polat, İlhan, Feng, Yu, Moore, Eric W., VanderPlas, Jake, Laxalde, Denis, Perktold, Josef, Cimrman, Robert, Henriksen, Ian, Quintero, E. A., Harris, Charles R., Archibald, Anne M., Ribeiro, Antônio H., Pedregosa, Fabian, van Mulbregt, Paul, SciPy 1.0 Contributors. (2020). SciPy 1.0 : fundamental algorithms for scientific computing in Python. Nature Methods, 17(3), 261-272. https://doi.org/10.1038/s41592-019-0686-2


JYU authors or editors


Publication details

All authors or editors: Virtanen, Pauli; Gommers, Ralf; Oliphant, Travis E.; Haberland, Matt, Reddy, Tyler; Cournapeau, David; Burovski, Evgeni; Peterson, Pearu; Weckesser, Warren; Bright, Jonathan; van der Walt, Stéfan J.; et al.

Journal or series: Nature Methods

ISSN: 1548-7091

eISSN: 1548-7105

Publication year: 2020

Volume: 17

Issue number: 3

Pages range: 261-272

Publisher: Nature Publishing Group

Publication country: United Kingdom

Publication language: English

DOI: https://doi.org/10.1038/s41592-019-0686-2

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/67875

Web address of parallel published publication (pre-print): https://arxiv.org/abs/1907.10121

Additional information: Perspective. Author correction: https://www.nature.com/articles/s41592-020-0772-5


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.


Keywords: computational science; algorithms; open source code; Python (programming languages)

Free keywords: SciPy


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2020

JUFO rating: 3


Last updated on 2021-25-08 at 12:41