Yet the philosophical motivations behind a fully open tool stack, combined with an excited, friendly community with a singular focus, have proven auspicious in the long run. To even imagine that a small group of ‘rogue’ student programmers could upend the already well-established ecosystem of research software-backed by millions in funding and many hundreds of highly qualified engineers 10, 11, 12-was preposterous. When started in 2001, the library had little funding and was written mainly by graduate students-many of them without a computer science education and often without the blessing of their advisors. SciPy’s arrival at this point is surprising and somewhat anomalous.
This version numbering convention, however, belies the history of a project that has become the standard others follow and has seen extensive adoption in research and industry. Recently, SciPy released version 1.0, a milestone that traditionally signals a library’s API (application programming interface) being mature enough to be trusted in production pipelines. For example, published scripts 5, 6 used in the analysis of gravitational waves 7, 8 import several subpackages of SciPy, and the M87 black hole imaging project cites SciPy 9. Scientists, engineers and others around the world rely on SciPy. SciPy is built on top of NumPy 1, 2, which provides array data structures and related fast numerical routines, and SciPy is itself the foundation upon which higher level scientific libraries, including scikit-learn 3 and scikit-image 4, are built. SciPy includes algorithms for optimization, integration, interpolation, eigenvalue problems, algebraic equations, differential equations and many other classes of problems it also provides specialized data structures, such as sparse matrices and k-dimensional trees. SciPy is a library of numerical routines for the Python programming language that provides fundamental building blocks for modeling and solving scientific problems. Nature Methods volume 17, pages 261–272 ( 2020) Cite this article SciPy 1.0: fundamental algorithms for scientific computing in Python
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