Array programming with NumPy

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 i...

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Veröffentlicht in:Nature (London) Jg. 585; H. 7825; S. 357 - 362
Hauptverfasser: Harris, Charles R., Millman, K. Jarrod, van der Walt, Stéfan J., Gommers, Ralf, Virtanen, Pauli, Cournapeau, David, Wieser, Eric, Taylor, Julian, Berg, Sebastian, Smith, Nathaniel J., Kern, Robert, Picus, Matti, Hoyer, Stephan, van Kerkwijk, Marten H., Brett, Matthew, Haldane, Allan, del Río, Jaime Fernández, Wiebe, Mark, Peterson, Pearu, Gérard-Marchant, Pierre, Sheppard, Kevin, Reddy, Tyler, Weckesser, Warren, Abbasi, Hameer, Gohlke, Christoph, Oliphant, Travis E.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: London Nature Publishing Group UK 17.09.2020
Nature Publishing Group
Schlagworte:
631/378/116
639/705/1042
639/705/117
639/705/794
704/525/870
Arrays (Data structures)
Computational Biology - methods
Humanities and Social Sciences
Management
Mathematics
multidisciplinary
Programming Languages
Python (Programming language)
Review
Review Article
Science
Science (multidisciplinary)
Software Design
United States
ISSN:0028-0836, 1476-4687, 1476-4687
Online-Zugang:Volltext
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Zusammenfassung: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 waves 1 and in the first imaging of a black hole 2 . 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. NumPy is the primary array programming library for Python; here its fundamental concepts are reviewed and its evolution into a flexible interoperability layer between increasingly specialized computational libraries is discussed.
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ISSN:0028-0836
1476-4687
1476-4687
DOI:10.1038/s41586-020-2649-2