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numpy

> 编程语言
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The fundamental package for scientific computing with Python.

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The fundamental package for scientific computing with Python.


NumPy is the fundamental package for scientific computing with Python. - **Website:** https://numpy.org - **Documentation:** https://numpy.org/doc - **Mailing list:** https://mail.python.org/mailman/listinfo/numpy-discussion - **Source code:** https://github.com/numpy/numpy - **Contributing:** https://numpy.org/devdocs/dev/index.html - **Bug reports:** https://github.com/numpy/numpy/issues - **Report a security vulnerability:** https://github.com/numpy/numpy/security/policy (via Tidelift) It provides: - 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 Testing: NumPy requires `pytest`. In addition, there are a number of optional test dependencies, like Meson for testing the NumPy C API, Cython for testing the NumPy Cython API, and Hypothesis for additional property-based tests. Tests can then be run after installation with: python -c "import numpy, sys; sys.exit(numpy.test() is False)" Code of Conduct ---------------------- NumPy is a community-driven open source project developed by a diverse group of [contributors](https://numpy.org/teams/). The NumPy leadership has made a strong commitment to creating an open, inclusive, and positive community. Please read the [NumPy Code of Conduct](https://numpy.org/code-of-conduct/) for guidance on how to interact with others in a way that makes our community thrive. Call for Contributions ---------------------- The NumPy project welcomes your expertise and enthusiasm! Small improvements or fixes are always appreciated. If you are considering larger contributions to the source code, please contact us through the [mailing list](https://mail.python.org/mailman/listinfo/numpy-discussion) first. Writing code isn’t the only way to contribute to NumPy. You can also: - review pull requests - help us stay on top of new and old issues - develop tutorials, presentations, and other educational materials - maintain and improve [our website](https://github.com/numpy/numpy.org) - develop graphic design for our brand assets and promotional materials - translate website content - help with outreach and onboard new contributors - write grant proposals and help with other fundraising efforts For more information about the ways you can contribute to NumPy, visit [our website](https://numpy.org/contribute/). If you’re unsure where to start or how your skills fit in, reach out! You can ask on the mailing list or here, on GitHub, by opening a new issue or leaving a comment on a relevant issue that is already open. Our preferred channels of communication are all public, but if you’d like to speak to us in private first, contact our community coordinators at [email protected] or on Slack (write [email protected] for an invitation). We also have a biweekly community call, details of which are announced on the mailing list. You are very welcome to join. If you are new to contributing to open source, [this guide](https://opensource.guide/how-to-contribute/) helps explain why, what, and how to successfully get involved.

核心特点

  • •Website: https://numpy.org
  • •Documentation: https://numpy.org/doc
  • •Mailing list: https://mail.python.org/mailman/listinfo/numpy-discussion
  • •Source code: https://github.com/numpy/numpy
  • •Contributing: https://numpy.org/devdocs/dev/index.html
  • •Bug reports: https://github.com/numpy/numpy/issues
  • •Report a security vulnerability: https://github.com/numpy/numpy/security/policy (via Tidelift)
  • •a powerful N-dimensional array object
  • •sophisticated (broadcasting) functions
  • •tools for integrating C/C++ and Fortran code

> 标签

Pythonnumpypython

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> 工具信息

发布日期2026年8月1日
最后更新2026年9月9日
分类编程语言
定价开源

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