---
title: "SciPy 1.0 — paper by Virtanen et al. (2019) — summary and ideas"
description: "The paper describes SciPy, the open-source Python library that collects core numerical algorithms… 156 references, cited by 392 papers here."
authors: ["Pauli Virtanen", "Ralf Gommers", "Travis E. Oliphant", "Matt Haberland", "Tyler Reddy", "David Cournapeau", "Evgeni Burovski", "Pearu Peterson", "Warren Weckesser", "Jonathan Bright", "Stéfan J. van der Walt", "Matthew Brett", "Joshua Wilson", "K. Jarrod Millman", "Nikolay Mayorov", "Andrew R. J. Nelson", "Eric Jones", "Robert Kern", "Eric Larson", "CJ Carey", "İlhan Polat", "Yu Feng", "Eric W. Moore", "Jake VanderPlas", "Denis Laxalde", "Josef Perktold", "Robert Cimrman", "Ian Henriksen", "E. A. Quintero", "Charles R Harris", "Anne M. Archibald", "Antônio H. Ribeiro", "Fabian Pedregosa", "Paul van Mulbregt"]
year: 2019
venue: "arXiv:1907.10121 (Nature Methods 17, 261 (2020))"
source: "https://arxiv.org/abs/1907.10121"
doi: "10.1038/s41592-019-0686-2"
arxiv: "1907.10121"
licence: "CC BY 4.0 (arXiv)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/virtanen-2019-scipy-1-0-fundamental-algorithms/"
---
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# SciPy 1.0 — Fundamental Algorithms for Scientific Computing in Python

**Authors:** [Pauli Virtanen](https://md.research.elamur.ai/free/en/authors/pauli-virtanen/), [Ralf Gommers](https://md.research.elamur.ai/free/en/authors/ralf-gommers/), Travis E. Oliphant, Matt Haberland, Tyler Reddy, [David Cournapeau](https://md.research.elamur.ai/free/en/authors/david-cournapeau/), Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, [Stéfan J. van der Walt](https://md.research.elamur.ai/free/en/authors/stefan-j-van-der-walt/), Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa and Paul van Mulbregt · **Year:** 2019 · **Venue:** arXiv:1907.10121 (Nature Methods 17, 261 (2020)) · **Publisher:** [Nature Portfolio](https://md.research.elamur.ai/free/en/publishers/nature-portfolio/) · **Licence:** [CC BY 4.0 (arXiv)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://doi.org/10.1038/s41592-019-0686-2) · [PDF](https://arxiv.org/pdf/1907.10121v1) · [DOI 10.1038/s41592-019-0686-2](https://doi.org/10.1038/s41592-019-0686-2) · [arXiv 1907.10121](https://arxiv.org/abs/1907.10121) · [OpenAlex](https://openalex.org/W3003257820)

**Citations:** 37,925 citations (OpenAlex, 2026-10-14) · [392](https://md.research.elamur.ai/free/en/papers/virtanen-2019-scipy-1-0-fundamental-algorithms/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper describes SciPy, the open-source Python library that collects core numerical algorithms: optimization, integration, interpolation, linear algebra, signal and image processing, sparse matrices, spatial structures such as k-d trees, statistics and more. It marks the 1.0 release in late 2017, about 16 years after version 0.1. It treats that milestone as a statement that the API is stable enough for production pipelines.

The authors trace SciPy's origins in the late 1990s. A shared array object emerged from the Matrix-SIG mailing list, and graduate students wrapped proven Fortran and C codes for Python. These packages were merged into one library in 2001.

The paper then covers how the project runs: release cadence, maintainers, a community roadmap, and exchange with downstream libraries. Its main evidence is adoption. SciPy is a de facto standard, with hundreds of contributors, millions of yearly downloads, and use in gravitational-wave analysis and the M87 black hole image. This matters because large parts of scientific and machine-learning software depend on SciPy, so its quality and governance spread across the whole ecosystem.

## Key points
- SciPy 1.0 came out in late 2017, roughly 16 years after the 0.1 release. The library builds on NumPy and in turn underlies scikit-learn and scikit-image.
- Early components wrapped established libraries such as ODEPACK, QUADPACK and MINPACK (via Multipack). F2PY was built to bind Fortran to Python.
- In 2018 there were 610 unique code contributors and more than 19,000 accepted commits. GitHub recorded about 20,000 unique visitors and 721 clones between May 14 and May 27, 2018.
- Downloads in 2017 were 13,096,468 from PyPI and 5,776,017 from the default conda channel, which the authors treat as a lower bound. SciPy is used by 47% of machine-learning projects on GitHub, and its website has been cited more than 3,000 times.
- The project follows a 6-monthly release cycle and has 15 active maintainers chosen from proven contributors. A living roadmap (Table 2) lists planned work, for example new global optimizers, new spline routines and PROPACK wrappers for faster SVD.

## Interesting ideas
- Agreeing early on one shared core data object, the array, let many independent packages interoperate instead of fragmenting.
- Wrapping mature, well-tested Fortran and C libraries was preferred over rewriting them, which gave reliable algorithms quickly behind a friendlier interface.
- Bumping to version 1.0 is treated as a public promise of API stability, a promise that downstream pipelines can rely on.
- Code often grows in a domain-specific downstream package and is later promoted into the base library, as happened with binned statistics from an Astropy-affiliated package.
- Usage counts are framed explicitly as lower bounds, because they cover only some install channels, which keeps the claims honest.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (tree): Fix one canonical UD tree structure that the perceptron parser, gparse and the dialect converters all read and write, so every downstream stage consumes the same object.
- **I2** (world): Wrap existing resources such as WordNet, FrameNet and Wikidata behind thin explainable adapters rather than re-deriving that knowledge by hand.
- **I3** (frames): Declare a versioned, frozen interface for the frame and semantic-role output of crate prag, so readers and generators can depend on it without breaking.
- **I4** (learn): Let repair rules or heuristics first prove themselves inside one task-specific reader, then promote the ones that generalize into the shared core.
- **I5** (learn): Report silver-data agreement and test scores together with what they fail to cover, such as missing genres or untested constructions, so the evaluation is read correctly.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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Also on the byline: [Tata Institute of Fundamental Research](https://ror.org/03ht1xw27) · [Université de Bordeaux](https://ror.org/057qpr032) · [Universidade Federal de Campina Grande](https://ror.org/00eftnx64) · [Inter-University Centre for Astronomy and Astrophysics](https://ror.org/04etcj997) · [Niigata University](https://ror.org/04ww21r56) · [Technische Universität Braunschweig](https://ror.org/010nsgg66) · [Lawrence Berkeley National Laboratory](https://ror.org/02jbv0t02) · [Pontificia Universidad Católica de Chile](https://ror.org/04teye511) · [Kavli Institute for the Physics and Mathematics of the Universe](https://ror.org/02chw6z69) · [Bruker (United States)](https://ror.org/04r739x86) · [Cincinnati Children's Hospital Medical Center](https://ror.org/01hcyya48) · [Australian Nuclear Science and Technology Organisation](https://ror.org/05j7fep28) · [D. E. Shaw Research](https://ror.org/02s04h872) · [Federal Reserve Board of Governors](https://ror.org/00v448643) · [International Centre for Theoretical Sciences](https://ror.org/0015qa126) · [Max Planck Institute for Extraterrestrial Physics](https://ror.org/00e4bwe12) · [Orexplore](https://ror.org/00n7wkq71) · [Applied BioMath](https://ror.org/00j3vdq65) · [Anaconda](https://ror.org/0164jks53) · [WayRay](https://ror.org/03nrpad60) · [Laboratoire Photonique, Numérique et Nanosciences](https://ror.org/03vdg7e33) · [Centrale Méditerranée](https://ror.org/040baw385) · [Excellence Cluster Universe](https://ror.org/05874db82) · [École nationale supérieure de techniques avancées Bretagne](https://ror.org/059n54003) · [ABB (Germany)](https://ror.org/05yeg2858) · [Utah State University Space Dynamics Laboratory](https://ror.org/02w403h65).

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## Cite

Cite as: Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. van der Walt, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa and Paul van Mulbregt. 2019. SciPy 1.0 — Fundamental Algorithms for Scientific Computing in Python. arXiv:1907.10121 (Nature Methods 17, 261 (2020)).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{virtanen2019scipy,
  title = {{SciPy 1.0 — Fundamental Algorithms for Scientific Computing in Python}},
  author = {Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, Stéfan J. {van der Walt}, Matthew Brett, Joshua Wilson, K. Jarrod Millman, Nikolay Mayorov, Andrew R. J. Nelson, Eric Jones, Robert Kern, Eric Larson, CJ Carey, İlhan Polat, Yu Feng, Eric W. Moore, Jake VanderPlas, Denis Laxalde, Josef Perktold, Robert Cimrman, Ian Henriksen, E. A. Quintero, Charles R Harris, Anne M. Archibald, Antônio H. Ribeiro, Fabian Pedregosa and Paul {van Mulbregt}},
  year = {2019},
  doi = {10.1038/s41592-019-0686-2},
  eprint = {1907.10121},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.1038/s41592-019-0686-2}
}
```

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## References

156 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

1. Oliphant, T. E. Guide to NumPy (Trelgol Publishing USA, 2006), 1st edn. URL [https://oez.es/Guide%20to%](https://oez.es/Guide%20to%) 20NumPy.pdf. [↗](https://oez.es/Guide%20to%)
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