Downloads:
18,062
Downloads of v 1.19.0:
47
Last Update:
21 Jun 2020
Package Maintainer(s):
Software Author(s):
- SciPy developers
Tags:
numpy python scientific computing- Software Specific:
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NumPy
- Software Specific:
- Software Site
- Software Source
- Software License
- Software Docs
- Software Issues
- Package Specific:
- Package Source
- Package outdated?
- Package broken?
- Contact Maintainers
- Contact Site Admins
- Software Vendor?
- Report Abuse
- Download
Downloads:
18,062
Downloads of v 1.19.0:
47
Maintainer(s):
Software Author(s):
- SciPy developers
NumPy
- 1
- 2
- 3
Some Checks Are Exempted or Have Failed
Not All Tests Have Passed
This package was rejected on 29 Jul 2020. The reviewer chtof has listed the following reason(s):
NumPy is the fundamental package for scientific computing with Python. It contains among other things:
- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
- useful linear algebra, Fourier transform, and random number capabilities
Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
NumPy is licensed under the BSD license, enabling reuse with few restrictions.
Getting Started
To install NumPy, we strongly recommend using a scientific Python distribution. See Installing the SciPy Stack for details.
Many high quality online tutorials, courses, and books are available to get started with NumPy. For a quick introduction to NumPy we provide the NumPy Tutorial. We also recommend the SciPy Lecture Notes for a broader introduction to the scientific Python ecosystem.
For more information on the SciPy Stack (for which NumPy provides the fundamental array data structure), see scipy.org.
Documentation
The most up-to-date NumPy documentation can be found at Latest (development) version. It includes a user guide, full reference documentation, a developer guide, meta information, and “NumPy Enhancement Proposals” (which include the NumPy Roadmap and detailed plans for major new features).
A complete archive of documentation for all NumPy releases (minor versions; bug fix releases don’t contain significant documentation changes) since 2009 can be found at https://docs.scipy.org.
python -m pip uninstall numpy -y
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- numpy.1.19.0.nupkg (ecc79a3baff2) - ## / 64
In cases where actual malware is found, the packages are subject to removal. Software sometimes has false positives. Moderators do not necessarily validate the safety of the underlying software, only that a package retrieves software from the official distribution point and/or validate embedded software against official distribution point (where distribution rights allow redistribution).
Chocolatey Pro provides runtime protection from possible malware.
Add to Builder | Version | Downloads | Last Updated | Status |
---|---|---|---|---|
NumPy 1.26.4 | 237 | Tuesday, February 6, 2024 | Approved | |
NumPy 1.26.3 | 154 | Wednesday, January 3, 2024 | Approved | |
NumPy 1.26.1 | 424 | Sunday, October 15, 2023 | Approved | |
NumPy 1.26.0 | 156 | Sunday, September 17, 2023 | Approved | |
NumPy 1.25.2 | 285 | Monday, July 31, 2023 | Approved | |
NumPy 1.25.1 | 155 | Sunday, July 9, 2023 | Approved | |
NumPy 1.25.0 | 141 | Saturday, June 17, 2023 | Approved | |
NumPy 1.24.3 | 298 | Sunday, April 23, 2023 | Approved | |
NumPy 1.24.2 | 559 | Monday, February 6, 2023 | Approved | |
NumPy 1.24.1 | 216 | Monday, December 26, 2022 | Approved | |
NumPy 1.24.0 | 115 | Monday, December 19, 2022 | Approved | |
NumPy 1.23.5 | 189 | Sunday, November 20, 2022 | Approved | |
NumPy 1.23.4 | 236 | Wednesday, October 12, 2022 | Approved | |
NumPy 1.23.3 | 273 | Saturday, September 10, 2022 | Approved | |
NumPy 1.23.2 | 290 | Monday, August 15, 2022 | Approved | |
NumPy 1.23.1 | 259 | Saturday, July 9, 2022 | Approved | |
NumPy 1.23.0 | 144 | Thursday, June 23, 2022 | Approved | |
NumPy 1.22.4 | 181 | Saturday, May 21, 2022 | Approved | |
NumPy 1.22.3 | 372 | Tuesday, March 8, 2022 | Approved | |
NumPy 1.22.2 | 241 | Friday, February 4, 2022 | Approved | |
NumPy 1.22.1 | 213 | Saturday, January 15, 2022 | Approved | |
NumPy 1.22.0 | 164 | Saturday, January 1, 2022 | Approved | |
NumPy 1.21.5 | 188 | Monday, December 20, 2021 | Approved | |
NumPy 1.21.4 | 320 | Friday, November 5, 2021 | Approved | |
NumPy 1.21.3 | 149 | Thursday, October 21, 2021 | Approved | |
NumPy 1.21.2 | 571 | Monday, August 16, 2021 | Approved | |
NumPy 1.21.1 | 168 | Monday, July 19, 2021 | Approved | |
NumPy 1.21.0 | 194 | Tuesday, June 22, 2021 | Approved | |
NumPy 1.20.3 | 227 | Monday, May 10, 2021 | Approved | |
NumPy 1.20.2 | 236 | Sunday, March 28, 2021 | Approved | |
NumPy 1.20.1 | 280 | Monday, February 8, 2021 | Approved | |
NumPy 1.20.0 | 169 | Sunday, January 31, 2021 | Approved | |
NumPy 1.19.5 | 232 | Wednesday, January 6, 2021 | Approved | |
NumPy 1.19.4 | 481 | Monday, November 2, 2020 | Approved | |
NumPy 1.19.3 | 189 | Thursday, October 29, 2020 | Approved | |
NumPy 1.19.2 | 527 | Friday, September 11, 2020 | Approved | |
NumPy 1.19.1 | 344 | Wednesday, July 22, 2020 | Approved | |
NumPy 1.18.5 | 356 | Friday, June 5, 2020 | Approved | |
NumPy 1.18.4 | 209 | Sunday, May 3, 2020 | Approved | |
NumPy 1.18.3 | 294 | Monday, April 20, 2020 | Approved | |
NumPy 1.18.2 | 335 | Tuesday, March 17, 2020 | Approved | |
NumPy 1.18.1 | 578 | Tuesday, January 7, 2020 | Approved | |
NumPy 1.18.0 | 262 | Monday, December 23, 2019 | Approved | |
NumPy 1.17.4 | 627 | Monday, November 11, 2019 | Approved | |
NumPy 1.17.3 | 432 | Thursday, October 17, 2019 | Approved | |
NumPy 1.17.2 | 359 | Saturday, September 7, 2019 | Approved | |
NumPy 1.17.1 | 409 | Tuesday, August 27, 2019 | Approved | |
NumPy 1.17.0 | 338 | Saturday, July 27, 2019 | Approved | |
NumPy 1.16.4 | 404 | Wednesday, May 29, 2019 | Approved | |
NumPy 1.16.3 | 238 | Tuesday, May 14, 2019 | Approved | |
numpy 1.8.1 | 3387 | Tuesday, July 15, 2014 | Approved |
© Copyright 2019 SciPy developers
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- python (≥ 3.7.3)
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chocolatey-ops (reviewer) on 21 Jun 2020 00:59:33 +00:00:
numpy has failed automated validation.
Requirements
Requirements represent the minimum quality of a package that is acceptable. When a package version has failed requirements, the package version requires fixing and/or response by the maintainer. Provided a Requirement has flagged correctly, it must be fixed before the package version can be approved. The exact same version should be uploaded during moderation review.
Guidelines
Guidelines are strong suggestions that improve the quality of a package version. These are considered something to fix for next time to increase the quality of the package. Over time Guidelines can become Requirements. A package version can be approved without addressing Guideline comments but will reduce the quality of the package.
chtof (maintainer) on 29 Jul 2020 16:22:50 +00:00:
Replaced by a newer version
Status Change - Changed status of package from 'submitted' to 'rejected'.