Array programming with NumPy

Charles R. Harris, K. Jarrod Millman (), Stéfan J. Walt (), Ralf Gommers (), Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, Robert Kern, Matti Picus, Stephan Hoyer, Marten H. Kerkwijk, Matthew Brett, Allan Haldane, Jaime Fernández Río, Mark Wiebe, Pearu Peterson, Pierre Gérard-Marchant, Kevin Sheppard, Tyler Reddy, Warren Weckesser, Hameer Abbasi, Christoph Gohlke and Travis E. Oliphant
Additional contact information
Charles R. Harris: Independent researcher
K. Jarrod Millman: University of California, Berkeley
Stéfan J. Walt: University of California, Berkeley
Ralf Gommers: Quansight
Pauli Virtanen: University of Jyväskylä
David Cournapeau: Mercari JP
Eric Wieser: University of Cambridge
Julian Taylor: Independent researcher
Sebastian Berg: University of California, Berkeley
Nathaniel J. Smith: Independent researcher
Robert Kern: Enthought
Matti Picus: University of California, Berkeley
Stephan Hoyer: Google Research
Marten H. Kerkwijk: University of Toronto
Matthew Brett: University of California, Berkeley
Allan Haldane: Temple University
Jaime Fernández Río: Google
Mark Wiebe: The University of British Columbia
Pearu Peterson: Quansight
Pierre Gérard-Marchant: University of Georgia
Kevin Sheppard: University of Oxford
Tyler Reddy: CCS-7, Los Alamos National Laboratory
Warren Weckesser: University of California, Berkeley
Hameer Abbasi: Quansight
Christoph Gohlke: University of California, Irvine
Travis E. Oliphant: Quansight

Nature, 2020, vol. 585, issue 7825, 357-362

Abstract: Abstract Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. NumPy is the primary array programming library for the Python language. It has an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, materials science, engineering, finance and economics. For example, in astronomy, NumPy was an important part of the software stack used in the discovery of gravitational waves1 and in the first imaging of a black hole2. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. NumPy is the foundation upon which the scientific Python ecosystem is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Owing to its central position in the ecosystem, NumPy increasingly acts as an interoperability layer between such array computation libraries and, together with its application programming interface (API), provides a flexible framework to support the next decade of scientific and industrial analysis.

Date: 2020
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DOI: 10.1038/s41586-020-2649-2

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