![]() Although Numarray was highly compatible with Numeric, the two packages had enough differences that it divided the community however, in 2005 NumPy emerged as a ‘best of both worlds’ unification 7-combining the features of Numarray with the small-array performance of Numeric and its rich C API. To handle large astronomical images coming from the Hubble Space Telescope, a reimplementation of Numeric, called Numarray, added support for structured arrays, flexible indexing, memory mapping, byte-order variants, more efficient memory use, flexible IEEE 754-standard error-handling capabilities, and better type-casting rules 6. One of its earliest uses was to steer C++ applications for inertial confinement fusion research at Lawrence Livermore National Laboratory 5. It was written in C and linked to standard fast implementations of linear algebra 3, 4. The Numeric package was developed in the mid-1990s and provided array objects and array-aware functions in Python. Two Python array packages existed before NumPy. 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. It is so pervasive that several projects, targeting audiences with specialized needs, have developed their own NumPy-like interfaces and array objects. NumPy is the foundation upon which the scientific Python ecosystem is constructed. Here we review how a few fundamental array concepts lead to a simple and powerful programming paradigm for organizing, exploring and analysing scientific data. ![]() 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. 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. ![]() NumPy is the primary array programming library for the Python language. Nature volume 585, pages 357–362 ( 2020) Cite this articleĪrray programming provides a powerful, compact and expressive syntax for accessing, manipulating and operating on data in vectors, matrices and higher-dimensional arrays. ![]()
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