Downloads:
20,040
Downloads of v 2.0.1:
523
Last Update:
21 Jul 2024
Package Maintainer(s):
Software Author(s):
- NumPy developers
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NumPy
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2.0.1 | Updated: 21 Jul 2024
- 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- Package outdated?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
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Downloads:
20,040
Downloads of v 2.0.1:
523
Maintainer(s):
Software Author(s):
- NumPy developers
NumPy 2.0.1
Legal Disclaimer: Neither this package nor Chocolatey Software, Inc. are affiliated with or endorsed by NumPy developers. The inclusion of NumPy developers trademark(s), if any, upon this webpage is solely to identify NumPy developers goods or services and not for commercial purposes.
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Not All Tests Have Passed
Deployment Method: Individual Install, Upgrade, & Uninstall
To install NumPy, run the following command from the command line or from PowerShell:
To upgrade NumPy, run the following command from the command line or from PowerShell:
To uninstall NumPy, run the following command from the command line or from PowerShell:
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1. Enter Your Internal Repository Url
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1. Ensure you are set for organizational deployment
Please see the organizational deployment guide
2. Get the package into your environment
Option 1: Cached Package (Unreliable, Requires Internet - Same As Community)-
Open Source or Commercial:
- Proxy Repository - Create a proxy nuget repository on Nexus, Artifactory Pro, or a proxy Chocolatey repository on ProGet. Point your upstream to https://community.chocolatey.org/api/v2/. Packages cache on first access automatically. Make sure your choco clients are using your proxy repository as a source and NOT the default community repository. See source command for more information.
- You can also just download the package and push it to a repository Download
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Open Source
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Download the package:
Download - Follow manual internalization instructions
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Package Internalizer (C4B)
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Run: (additional options)
choco download numpy --internalize --source=https://community.chocolatey.org/api/v2/
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For package and dependencies run:
choco push --source="'INTERNAL REPO URL'"
- Automate package internalization
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Run: (additional options)
3. Copy Your Script
choco upgrade numpy -y --source="'INTERNAL REPO URL'" [other options]
See options you can pass to upgrade.
See best practices for scripting.
Add this to a PowerShell script or use a Batch script with tools and in places where you are calling directly to Chocolatey. If you are integrating, keep in mind enhanced exit codes.
If you do use a PowerShell script, use the following to ensure bad exit codes are shown as failures:
choco upgrade numpy -y --source="'INTERNAL REPO URL'"
$exitCode = $LASTEXITCODE
Write-Verbose "Exit code was $exitCode"
$validExitCodes = @(0, 1605, 1614, 1641, 3010)
if ($validExitCodes -contains $exitCode) {
Exit 0
}
Exit $exitCode
- name: Install numpy
win_chocolatey:
name: numpy
version: '2.0.1'
source: INTERNAL REPO URL
state: present
See docs at https://docs.ansible.com/ansible/latest/modules/win_chocolatey_module.html.
chocolatey_package 'numpy' do
action :install
source 'INTERNAL REPO URL'
version '2.0.1'
end
See docs at https://docs.chef.io/resource_chocolatey_package.html.
cChocoPackageInstaller numpy
{
Name = "numpy"
Version = "2.0.1"
Source = "INTERNAL REPO URL"
}
Requires cChoco DSC Resource. See docs at https://github.com/chocolatey/cChoco.
package { 'numpy':
ensure => '2.0.1',
provider => 'chocolatey',
source => 'INTERNAL REPO URL',
}
Requires Puppet Chocolatey Provider module. See docs at https://forge.puppet.com/puppetlabs/chocolatey.
4. If applicable - Chocolatey configuration/installation
See infrastructure management matrix for Chocolatey configuration elements and examples.
This package was approved as a trusted package on 21 Jul 2024.
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.2.0.1.nupkg (0e3c18221d7c) - ## / 68
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 2.0.1 | 523 | Sunday, July 21, 2024 | Approved | |
NumPy 2.0.0 | 159 | Sunday, June 16, 2024 | Approved | |
NumPy 1.26.4 | 454 | Tuesday, February 6, 2024 | Approved | |
NumPy 1.26.3 | 173 | Wednesday, January 3, 2024 | Approved | |
NumPy 1.26.1 | 447 | Sunday, October 15, 2023 | Approved | |
NumPy 1.26.0 | 177 | Sunday, September 17, 2023 | Approved | |
NumPy 1.25.2 | 316 | Monday, July 31, 2023 | Approved | |
NumPy 1.25.1 | 175 | Sunday, July 9, 2023 | Approved | |
NumPy 1.25.0 | 160 | Saturday, June 17, 2023 | Approved | |
NumPy 1.24.3 | 322 | Sunday, April 23, 2023 | Approved | |
NumPy 1.24.2 | 577 | Monday, February 6, 2023 | Approved | |
NumPy 1.24.1 | 244 | Monday, December 26, 2022 | Approved | |
NumPy 1.24.0 | 134 | Monday, December 19, 2022 | Approved | |
NumPy 1.23.5 | 220 | Sunday, November 20, 2022 | Approved | |
NumPy 1.23.4 | 254 | Wednesday, October 12, 2022 | Approved | |
NumPy 1.23.3 | 286 | Saturday, September 10, 2022 | Approved | |
NumPy 1.23.2 | 308 | Monday, August 15, 2022 | Approved | |
NumPy 1.23.1 | 274 | Saturday, July 9, 2022 | Approved | |
NumPy 1.23.0 | 160 | Thursday, June 23, 2022 | Approved | |
NumPy 1.22.4 | 200 | Saturday, May 21, 2022 | Approved | |
NumPy 1.22.3 | 388 | Tuesday, March 8, 2022 | Approved | |
NumPy 1.22.2 | 266 | Friday, February 4, 2022 | Approved | |
NumPy 1.22.1 | 236 | Saturday, January 15, 2022 | Approved | |
NumPy 1.22.0 | 182 | Saturday, January 1, 2022 | Approved | |
NumPy 1.21.5 | 212 | Monday, December 20, 2021 | Approved | |
NumPy 1.21.4 | 341 | Friday, November 5, 2021 | Approved | |
NumPy 1.21.3 | 164 | Thursday, October 21, 2021 | Approved | |
NumPy 1.21.2 | 593 | Monday, August 16, 2021 | Approved | |
NumPy 1.21.1 | 185 | Monday, July 19, 2021 | Approved | |
NumPy 1.21.0 | 215 | Tuesday, June 22, 2021 | Approved | |
NumPy 1.20.3 | 246 | Monday, May 10, 2021 | Approved | |
NumPy 1.20.2 | 261 | Sunday, March 28, 2021 | Approved | |
NumPy 1.20.1 | 305 | Monday, February 8, 2021 | Approved | |
NumPy 1.20.0 | 187 | Sunday, January 31, 2021 | Approved | |
NumPy 1.19.5 | 257 | Wednesday, January 6, 2021 | Approved | |
NumPy 1.19.4 | 502 | Monday, November 2, 2020 | Approved | |
NumPy 1.19.3 | 212 | Thursday, October 29, 2020 | Approved | |
NumPy 1.19.2 | 550 | Friday, September 11, 2020 | Approved | |
NumPy 1.19.1 | 366 | Wednesday, July 22, 2020 | Approved | |
NumPy 1.18.5 | 378 | Friday, June 5, 2020 | Approved | |
NumPy 1.18.4 | 226 | Sunday, May 3, 2020 | Approved | |
NumPy 1.18.3 | 318 | Monday, April 20, 2020 | Approved | |
NumPy 1.18.2 | 365 | Tuesday, March 17, 2020 | Approved | |
NumPy 1.18.1 | 599 | Tuesday, January 7, 2020 | Approved | |
NumPy 1.18.0 | 287 | Monday, December 23, 2019 | Approved | |
NumPy 1.17.4 | 653 | Monday, November 11, 2019 | Approved | |
NumPy 1.17.3 | 451 | Thursday, October 17, 2019 | Approved | |
NumPy 1.17.2 | 383 | Saturday, September 7, 2019 | Approved | |
NumPy 1.17.1 | 430 | Tuesday, August 27, 2019 | Approved | |
NumPy 1.17.0 | 353 | Saturday, July 27, 2019 | Approved | |
NumPy 1.16.4 | 424 | Wednesday, May 29, 2019 | Approved | |
NumPy 1.16.3 | 255 | Tuesday, May 14, 2019 | Approved | |
numpy 1.8.1 | 3423 | Tuesday, July 15, 2014 | Approved |
© 2019-2020 NumPy. All rights reserved.
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