SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python
Abstract
SciPy is an open source scientific computing library for the Python programming language. SciPy 1.0 was released in late 2017, about 16 years after the original version 0.1 release. SciPy has become a de facto standard for leveraging scientific algorithms in the Python programming language, with more than 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories, and millions of downloads per year. This includes usage of SciPy in almost half of all machine learning projects on GitHub, and usage by high profile projects including LIGO gravitational wave analysis and creation of the first-ever image of a black hole (M87). The library includes functionality spanning clustering, Fourier transforms, integration, interpolation, file I/O, linear algebra, image processing, orthogonal distance regression, minimization algorithms, signal processing, sparse matrix handling, computational geometry, and statistics. In this work, we provide an overview of the capabilities and development practices of the SciPy library and highlight some recent technical developments.
Keywords
Cite
@article{arxiv.1907.10121,
title = {SciPy 1.0--Fundamental Algorithms for Scientific Computing in Python},
author = {Pauli Virtanen and Ralf Gommers and Travis E. Oliphant and Matt Haberland and Tyler Reddy and David Cournapeau and Evgeni Burovski and Pearu Peterson and Warren Weckesser and Jonathan Bright and Stéfan J. van der Walt and Matthew Brett and Joshua Wilson and K. Jarrod Millman and Nikolay Mayorov and Andrew R. J. Nelson and Eric Jones and Robert Kern and Eric Larson and CJ Carey and İlhan Polat and Yu Feng and Eric W. Moore and Jake VanderPlas and Denis Laxalde and Josef Perktold and Robert Cimrman and Ian Henriksen and E. A. Quintero and Charles R Harris and Anne M. Archibald and Antônio H. Ribeiro and Fabian Pedregosa and Paul van Mulbregt and SciPy 1. 0 Contributors},
journal= {arXiv preprint arXiv:1907.10121},
year = {2020}
}
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Article source data is available here: https://github.com/scipy/scipy-articles