Show simple item record

dc.creatorHarris, CR
dc.creatorMillman, KJ
dc.creatorvan der Walt, SJ
dc.creatorGommers, R
dc.creatorVirtanen, P
dc.creatorCournapeau, D
dc.creatorWieser, E
dc.creatorTaylor, J
dc.creatorBerg, S
dc.creatorSmith, NJ
dc.creatorKern, R
dc.creatorPicus, M
dc.creatorHoyer, S
dc.creatorvan Kerkwijk, MH
dc.creatorBrett, M
dc.creatorHaldane, A
dc.creatordel Río, JF
dc.creatorWiebe, M
dc.creatorPeterson, P
dc.creatorGérard-Marchant, P
dc.creatorSheppard, K
dc.creatorReddy, T
dc.creatorWeckesser, W
dc.creatorAbbasi, H
dc.creatorGohlke, C
dc.creatorOliphant, TE
dc.date.accessioned2021-01-28T21:11:49Z
dc.date.available2021-01-28T21:11:49Z
dc.date.issued2020-09-17
dc.identifier.issn0028-0836
dc.identifier.issn1476-4687
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5084
dc.identifier.other32939066 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5102
dc.description.abstract© 2020, The Author(s). 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.
dc.format.extent357-362
dc.language.isoen
dc.relation.haspartNature
dc.relation.isreferencedbySpringer Science and Business Media LLC
dc.rightsCC BY
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectComputational Biology
dc.subjectMathematics
dc.subjectProgramming Languages
dc.subjectSoftware Design
dc.titleArray programming with NumPy
dc.typeArticle
dc.type.genreReview
dc.type.genreJournal
dc.relation.doi10.1038/s41586-020-2649-2
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.date.updated2021-01-28T21:11:45Z
refterms.dateFOA2021-01-28T21:11:49Z


Files in this item

Thumbnail
Name:
Array programming with NumPy.pdf
Size:
1.162Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record

CC BY
Except where otherwise noted, this item's license is described as CC BY