Статья

Array programming with NumPy

Charles R. HarrisK. Jarrod MillmanBerkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USA. [email protected]Stéfan J. van der WaltApplied Mathematics, Stellenbosch University, Stellenbosch, South Africa. [email protected]Ralf GommersQuansight, Austin, TX, USA. [email protected]Pauli VirtanenDepartment of Physics, University of Jyväskylä, Jyväskylä, FinlandDavid CournapeauEric WieserDepartment of Engineering, University of Cambridge, Cambridge, UKJulian TaylorIndependent researcher, Karlsruhe, GermanySebastian BergBerkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USANathaniel J. SmithRobert KernEnthought, Austin, TX, USAMatti PicusBerkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USAStephan HoyerGoogle Research, Mountain View, CA, USAMarten H. van KerkwijkDepartment of Astronomy and Astrophysics, University of Toronto, Toronto, Ontario, CanadaMatthew BrettBrain Imaging Center, University of California, Berkeley, Berkeley, CA, USAAllan HaldaneDepartment of Physics, Temple University, Philadelphia, PA, USAJaime Fernández del RíoGoogle, Zurich, SwitzerlandMark WiebeAmazon, Seattle, WA, USAPearu PetersonDepartment of Mechanics and Applied Mathematics, Institute of Cybernetics at Tallinn Technical University, Tallinn, EstoniaPierre Gérard-MarchantDepartment of Biological and Agricultural Engineering, University of Georgia, Athens, GA, USAKevin SheppardDepartment of Economics, University of Oxford, Oxford, UKTyler ReddyCCS-7, Los Alamos National Laboratory, Los Alamos, NM, USAWarren WeckesserBerkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USAHameer AbbasiQuansight, Austin, TX, USAChristoph GohlkeLaboratory for Fluorescence Dynamics, Biomedical Engineering Department, University of California, Irvine, Irvine, CA, USATravis E. OliphantQuansight, Austin, TX, USA

2020en

ABI

Аннотация

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 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.

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Идентификаторы

DOI: 10.1038/s41586-020-2649-2

Цитирования и источники

Цитирований: 21Использованных источников: 0

Показатели — AkademScholar