Обзорная статья

Array programming with NumPy

Harris, CRMillman, KJBrain Imaging Center, University of California, Berkeley, Berkeley, CA, USAvan der Walt, SJBrain Imaging Center, University of California, Berkeley, Berkeley, CA, USAGommers, RQuansight, Austin, TX, USAVirtanen, PDepartment of Physics, University of Jyväskylä, Jyväskylä, FinlandCournapeau, DWieser, EDepartment of Engineering, University of Cambridge, Cambridge, UKTaylor, JIndependent researcher, Karlsruhe, GermanyBerg, SBerkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USASmith, NJKern, REnthought, Austin, TX, USAPicus, MBerkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USAHoyer, SGoogle Research, Mountain View, CA, USAvan Kerkwijk, MHDepartment of Astronomy and Astrophysics, University of Toronto, Toronto, Ontario, CanadaBrett, MSchool of Psychology, University of Birmingham, Edgbaston, Birmingham, UKHaldane, ADepartment of Physics, Temple University, Philadelphia, PA, USAdel Río, JFGoogle, Zurich, SwitzerlandWiebe, MDepartment of Physics and Astronomy, The University of British Columbia, Vancouver, British Columbia, CanadaPeterson, PQuansight, Austin, TX, USAGérard-Marchant, PDepartment of Biological and Agricultural Engineering, University of Georgia, Athens, GA, USASheppard, KDepartment of Economics, University of Oxford, Oxford, UKReddy, TCCS-7, Los Alamos National Laboratory, Los Alamos, NM, USAWeckesser, WBerkeley Institute for Data Science, University of California, Berkeley, Berkeley, CA, USAAbbasi, HQuansight, Austin, TX, USAGohlke, CLaboratory for Fluorescence Dynamics, Biomedical Engineering Department, University of California, Irvine, Irvine, CA, USAOliphant, TEQuansight, Austin, TX, USA,

2020en

ABI

Аннотация

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.

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