The NumPy array: a structure for efficient numerical computation

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

In the Python world, NumPy arrays are the standard representation for numerical data. Here, we show how these arrays enable efficient implementation of numerical computations in a high-level language. Overall, three techniques are applied to improve performance: vectorizing calculations, avoiding copying data in memory, and minimizing operation counts. We first present the NumPy array structure, then show how to use it for efficient computation, and finally how to share array data with other libraries.

Related collections

Author and article information

Journal

Publication date Created: 08 February 2011

Article

DOI: 10.1109/MCSE.2011.37

ArXiV ID: 1102.1523

SO-VID: 0c78f8fc-cd0f-4bc9-a5e2-e88468f1d3a5

License:

http://arxiv.org/licenses/nonexclusive-distrib/1.0/

History

Custom metadata

Journal reference Computing in Science and Engineering 13, 2 (2011) 22-30

Categories cs.MS

Proxy ccsd

Data availability:

Comments

Comment on this article

scite_

Smart Citations

Citing PublicationsSupportingMentioningContrasting

View Citations

See how this article has been cited at scite.ai

scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.