SciPy 1.0: fundamental algorithms for scientific computing in Python
Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew Nelson, Eric D. Jones, Robert Kern, Eric R. Larson, CJ Carey, İlhan Polat, Yu Feng, Eric Moore, Jake Vanderplas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, C. R. Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa, Paul van Mulbregt, A. Vijaykumar, Alessandro Pietro Bardelli low, Alex Rothberg low, Andreas Hilboll, Andreas Kloeckner low, Anthony Scopatz, Antony Lee, Ariel Rokem low, Charles Woods, Chad Fulton, Charles Masson low, Christian Häggström low, C Fitzgerald low, David Nicholson, David Hagen, Dmitrii V. Ṗasechnik, Emanuele Olivetti, Éric Martin, Eric Wieser, Fabrice Silva, Felix Lenders, Florian Wilhelm, George S. Young, Gavin A Price low, Gert‐Ludwig Ingold, Gregory E. Allen low, Gregory R. Lee, Hervé Audren, Irvin Probst low, J. P. Dietrich, Jacob Silterra, James T. Webber, Janko Slavič, Joel Nothman, Johannes Büchner, Johannes Kulick low, Johannes L. Schönberger low, José Vinícius de Miranda Cardoso, Joscha Reimer, Joseph Harrington, Juan Luis Cano, Juan Nunez-Iglesias, Justin Kuczynski low, K. Tritz low, Martin Thoma, M. Newville, Matthias Kümmerer, Maximilian Bolingbroke low, Michael Tartre low, M. Pak, Nathaniel J. Smith, Nikolai Nowaczyk low, Nikolay Shebanov low, Oleksandr Pavlyk low, Per A. Brodtkorb low, Perry Lee low, Robert T. McGibbon, Roman Feldbauer, Sam Lewis low, Sam Tygier, Scott Sievert, Sebastiano Vigna, Stefan Peterson, Surhud More, Tadeusz Pudlik low, 拓也 大嶋
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.
What this paper cites, inside the corpus
What cites it, inside the corpus
| Paper | Year | Cited |
|---|---|---|
| Array programming with NumPy | 2020 | 18,812 |
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Topics
| Computational Physics and Python Applications | Computer Science |
| Scientific Computing and Data Management | Decision Sciences |
| Particle physics theoretical and experimental studies | Physics and Astronomy |
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