SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

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 waves1 and in the first imaging of a black hole2. 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.

The affiliation for Evgeni Burovski was given as Higher School of Economics; the correct affiliation is National Research University, Higher School of Economics. In Box 1, "SciPy is an open-source package that builds on the strengths of Python and Numeric, providing a wide range of fast scientific and numeric functionality" was used as the box title; this has been moved to the beginning of the box text and a new title has been provided: "Excerpt from the SciPy 0.1 release announcement (typos corrected), posted 20 August 2001 on the Python-list mailing list. " From the original first sentence of this box, "(text following the % symbol indicates that a typo in the original text has been corrected in the version reproduced here)" has been deleted, and "% hanker to Hankel" and "% Netwon to Newton" have been deleted from the ends of the special functions row and the optimization row, respectively. In the first sentence of the ndimage section of Box 2, "nonlinear filter" has been changed to plural. At the end of the first paragraph of the section "SciPy matures, " "The library was expanded carefully, with the patience affordable in open-source projects and via best practices common in industry" has been changed to "The library was expanded carefully, with the patience affordable in open-source projects and via best practices, which are increasingly common in the scientific Python ecosystem and industry. " In Table 2, "Inequality constraint" has been changed to plural. In the "Nonlinear optimization: global minimization" section, "scipy.optimize.differentialevolution" had been changed to "scipy.optimize.differential_evolution. " In the first sentence of the section "Maintainers and contributors, " "SciPy developer guide" has been changed to "SciPy contributor guide" and the URL has been changed from

MDAnalysis (http://mdanalysis.org) is a library for structural and temporal analysis of molecular dynamics (MD) simulation trajectories and individual protein structures. MD simulations of biological molecules have become an important tool to elucidate the relationship between molecular structure and physiological function. Simulations are performed with highly optimized software packages on HPC resources but most codes generate output trajectories in their own formats so that the development of new trajectory analysis algorithms is confined to specific user communities and widespread adoption and further development is delayed. MDAnalysis addresses this problem by abstracting access to the raw simulation data and presenting a uniform object-oriented Python interface to the user. It thus enables users to rapidly write code that is portable and immediately usable in virtually all biomolecular simulation communities. The user interface and modular design work equally well in complex scripted work flows, as foundations for other packages, and for interactive and rapid prototyping work in IPython / Jupyter notebooks, especially together with molecular visualization provided by nglview and time series analysis with pandas. MDAnalysis is written in Python and Cython and uses NumPy arrays for easy interoperability with the wider scientific Python ecosystem. It is widely used and forms the foundation for more specialized biomolecular simulation tools. MDAnalysis is available under the GNU General Public License v2.