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Array programming with NumPy

Author

Listed:
  • Charles R. Harris

    (Independent researcher)

  • K. Jarrod Millman

    (University of California, Berkeley
    University of California, Berkeley
    University of California, Berkeley)

  • Stéfan J. Walt

    (University of California, Berkeley
    University of California, Berkeley
    Applied Mathematics, Stellenbosch University)

  • Ralf Gommers

    (Quansight)

  • Pauli Virtanen

    (University of Jyväskylä
    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
    University of Birmingham)

  • Allan Haldane

    (Temple University)

  • Jaime Fernández Río

    (Google)

  • Mark Wiebe

    (The University of British Columbia
    Amazon)

  • Pearu Peterson

    (Quansight
    Independent researcher
    Institute of Cybernetics at Tallinn Technical University)

  • Pierre Gérard-Marchant

    (University of Georgia
    France-IX Services)

  • 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)

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.

Suggested Citation

  • 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 Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    • Handle: RePEc:nat:nature:v:585:y:2020:i:7825:d:10.1038_s41586-020-2649-2
    DOI: 10.1038/s41586-020-2649-2
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