Computational Physics and Python Applications, Scientific Computing and Data Management, Genetics, Bioinformatics, and Biomedical Research

SciPy 1.0: fundamental algorithms for scientific computing in Python

Nature Methods · 2020

PPauli Virtanen·RRalf Gommers·TTravis E. Oliphant·MMatt Haberland·TTyler Reddy·DDavid Cournapeau·EEvgeni Burovski·PPearu Peterson·WWarren Weckesser·JJonathan Bright·SStéfan J. van der Walt·MMatthew Brett·JJoshua Wilson·KK. Jarrod Millman·NNikolay Mayorov·AAndrew R. J. Nelson·EEric Jones·RRobert Kern·EEric Larson·CC J Carey·İİlhan Polat·YYu Feng·EEric W. Moore·JJake VanderPlas·DDenis Laxalde·JJosef Perktold·RRobert Cimrman·IIan Henriksen·EE. A. Quintero·CCharles R. Harris·AAnne M. Archibald·AAntônio H. Ribeiro·FFabian Pedregosa·PPaul van Mulbregt·SSciPy 1.0 Contributors·AAditya Vijaykumar·AAlessandro Pietro Bardelli·AAlex Rothberg·AAndreas Hilboll·AAndreas Kloeckner·AAnthony Scopatz·AAntony Lee·AAriel Rokem·CC. Nathan Woods·CChad Fulton·CCharles Masson·CChristian Häggström·CClark Fitzgerald·DDavid A. Nicholson·DDavid R. Hagen·DDmitrii V. Pasechnik·EEmanuele Olivetti·EEric Martin·EEric Wieser·FFabrice Silva·FFelix Lenders·FFlorian Wilhelm·GG. Young·GGavin A. Price·GGert-Ludwig Ingold·GGregory E. Allen·GGregory R. Lee·HHervé Audren·IIrvin Probst·JJörg P. Dietrich·JJacob Silterra·JJames T Webber·JJanko Slavič·JJoel Nothman·JJohannes Buchner·JJohannes Kulick·JJohannes L. Schönberger·JJosé Vinícius de Miranda Cardoso·JJoscha Reimer·JJoseph Harrington·JJuan Luis Cano Rodríguez·JJuan Nunez-Iglesias·JJustin Kuczynski·KKevin Tritz·MMartin Thoma·MMatthew Newville·MMatthias Kümmerer·MMaximilian Bolingbroke·MMichael Tartre·MMikhail Pak·NNathaniel J. Smith·NNikolai Nowaczyk·NNikolay Shebanov·OOleksandr Pavlyk·PPer A. Brodtkorb·PPerry Lee·RRobert T. McGibbon·RRoman Feldbauer·SSam Lewis·SSam Tygier·SScott Sievert·SSebastiano Vigna·SStefan Peterson·SSurhud More·TTadeusz Pudlik
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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.

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