Array Programming with NumPy

TL;DR

NumPy is a fundamental array programming library in Python that enables efficient data manipulation and analysis across diverse scientific fields, underpinning the entire scientific Python ecosystem.

Contribution

This paper highlights NumPy's role as the core array programming tool in Python, facilitating scientific research and interoperability among various array libraries.

Findings

Integral to research workflows in multiple scientific disciplines

Enabled discovery of gravitational waves and imaging of a black hole

Serves as the foundation for the scientific Python ecosystem

Abstract

Array programming provides a powerful, compact, 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 plays an essential role in research analysis pipelines in fields as diverse as physics, chemistry, astronomy, geoscience, biology, psychology, material 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 and the first imaging of a black hole. Here we show how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring, and analyzing scientific data. NumPy is the foundation upon which the entire scientific Python universe is constructed. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. Because of its central position in the ecosystem, NumPy increasingly plays the role of an interoperability layer between these new array computation libraries.