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…
# Array programming with NumPy
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## Bibliographic
- **DOI:** 10.1038/s41586-020-2649-2
- **Year:** 2020
- **Citations:** 22553
- **Open Access:** Yes (hybrid)
- **License:** cc-by
- **Source:** https://www.nature.com/articles/s41586-020-2649-2.pdf
## Authors
- Charles R. Harris
- K. Jarrod Millman
- Stéfan J. van der Walt
- Ralf Gommers
- Pauli Virtanen
- David Cournapeau
- Eric Wieser
- Julian Taylor
- Sebastian Berg
- Nathaniel J. Smith
- Robert Kern
- Matti Picus
- Stephan Hoyer
- Marten H. van Kerkwijk
- Matthew Brett
- Allan Haldane
- Jaime Fernández del Río
- Mark Wiebe
- Pearu Peterson
- Pierre Gérard-Marchant
- Kevin Sheppard
- Tyler Reddy
- Warren Weckesser
- Hameer Abbasi
- Christoph Gohlke
- Travis E. Oliphant
## Abstract
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.
## Keywords
Python (programming language), Interoperability, Syntax, Programming paradigm, Application programming interface, Software, Generic programming, Computation
## Concepts
- Python (programming language)
- Computer science
- Interoperability
- Syntax
- Programming paradigm
- Application programming interface
- Software
- Programming language
- Computational science
- Generic programming
- Computation
- Interface (matter)
- Object-oriented programming
- Stack (abstract data type)
- Graphical user interface
- Data structure
- Software framework
- Software engineering
- Scientific instrument
- Pipeline (software)
- Visual programming language
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*Metadata only — full text not imported unless Open Access license permits.*
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Tóm lược học thuật (đã diễn giải): 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 proje…
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1. Abstract Array programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays.
2. NumPy is the primary array programming library for the Python language.
3. 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.
4. 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 .
5. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data.
6. NumPy is the foundation upon which the scientific Python ecosystem is constructed.
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