[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-2014":3},{"id":4,"name":5,"fullName":6,"owner":5,"repo":5,"description":7,"homepage":8,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":9,"rankLanguage":9,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":22,"topics":25,"createdAt":9,"pushedAt":9,"updatedAt":27,"readmeContent":28,"aiSummary":29,"trendingCount":15,"starSnapshotCount":15,"syncStatus":16,"lastSyncTime":30,"discoverSource":31},2014,"numpy","numpy\u002Fnumpy","The fundamental package for scientific computing with Python.","https:\u002F\u002Fnumpy.org",null,"Python",32170,12438,603,2103,0,2,21,173,11,45,"Other",false,"main",true,[5,26],"python","2026-06-12 02:00:35","\u003Ch1 align=\"center\">\n\u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fnumpy\u002Fnumpy\u002Fmain\u002Fbranding\u002Flogo\u002Fprimary\u002Fnumpylogo.svg\" width=\"300\">\n\u003C\u002Fh1>\u003Cbr>\n\n\n[![Powered by NumFOCUS](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpowered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](\nhttps:\u002F\u002Fnumfocus.org)\n[![PyPI Downloads](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdm\u002Fnumpy.svg?label=PyPI%20downloads)](\nhttps:\u002F\u002Fpypi.org\u002Fproject\u002Fnumpy\u002F)\n[![Conda Downloads](https:\u002F\u002Fimg.shields.io\u002Fconda\u002Fdn\u002Fconda-forge\u002Fnumpy.svg?label=Conda%20downloads)](\nhttps:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fnumpy)\n[![Stack Overflow](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fstackoverflow-Ask%20questions-blue.svg)](\nhttps:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fnumpy)\n[![Nature Paper](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FDOI-10.1038%2Fs41586--020--2649--2-blue)](\nhttps:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41586-020-2649-2)\n[![LFX Health Score](https:\u002F\u002Finsights.linuxfoundation.org\u002Fapi\u002Fbadge\u002Fhealth-score?project=numpy)](https:\u002F\u002Finsights.linuxfoundation.org\u002Fproject\u002Fnumpy)\n[![OpenSSF Scorecard](https:\u002F\u002Fapi.securityscorecards.dev\u002Fprojects\u002Fgithub.com\u002Fnumpy\u002Fnumpy\u002Fbadge)](https:\u002F\u002Fsecurityscorecards.dev\u002Fviewer\u002F?uri=github.com\u002Fnumpy\u002Fnumpy)\n[![Typing](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Ftypes\u002Fnumpy)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fnumpy\u002F)\n\n\nNumPy is the fundamental package for scientific computing with Python.\n\n- **Website:** https:\u002F\u002Fnumpy.org\n- **Documentation:** https:\u002F\u002Fnumpy.org\u002Fdoc\n- **Mailing list:** https:\u002F\u002Fmail.python.org\u002Fmailman\u002Flistinfo\u002Fnumpy-discussion\n- **Source code:** https:\u002F\u002Fgithub.com\u002Fnumpy\u002Fnumpy\n- **Contributing:** https:\u002F\u002Fnumpy.org\u002Fdevdocs\u002Fdev\u002Findex.html\n- **Bug reports:** https:\u002F\u002Fgithub.com\u002Fnumpy\u002Fnumpy\u002Fissues\n- **Report a security vulnerability:** https:\u002F\u002Fgithub.com\u002Fnumpy\u002Fnumpy\u002Fsecurity\u002Fpolicy (via Tidelift)\n\nIt provides:\n\n- a powerful N-dimensional array object\n- sophisticated (broadcasting) functions\n- tools for integrating C\u002FC++ and Fortran code\n- useful linear algebra, Fourier transform, and random number capabilities\n\nTesting:\n\nNumPy requires `pytest` and `hypothesis`.  Tests can then be run after installation with:\n\n    python -c \"import numpy, sys; sys.exit(numpy.test() is False)\"\n\nCode of Conduct\n----------------------\n\nNumPy is a community-driven open source project developed by a diverse group of\n[contributors](https:\u002F\u002Fnumpy.org\u002Fteams\u002F). The NumPy leadership has made a strong\ncommitment to creating an open, inclusive, and positive community. Please read the\n[NumPy Code of Conduct](https:\u002F\u002Fnumpy.org\u002Fcode-of-conduct\u002F) for guidance on how to interact\nwith others in a way that makes our community thrive.\n\nCall for Contributions\n----------------------\n\nThe NumPy project welcomes your expertise and enthusiasm!\n\nSmall improvements or fixes are always appreciated. If you are considering larger contributions\nto the source code, please contact us through the [mailing\nlist](https:\u002F\u002Fmail.python.org\u002Fmailman\u002Flistinfo\u002Fnumpy-discussion) first.\n\nWriting code isn’t the only way to contribute to NumPy. You can also:\n- review pull requests\n- help us stay on top of new and old issues\n- develop tutorials, presentations, and other educational materials\n- maintain and improve [our website](https:\u002F\u002Fgithub.com\u002Fnumpy\u002Fnumpy.org)\n- develop graphic design for our brand assets and promotional materials\n- translate website content\n- help with outreach and onboard new contributors\n- write grant proposals and help with other fundraising efforts\n\nFor more information about the ways you can contribute to NumPy, visit [our website](https:\u002F\u002Fnumpy.org\u002Fcontribute\u002F). \nIf you’re unsure where to start or how your skills fit in, reach out! You can\nask on the mailing list or here, on GitHub, by opening a new issue or leaving a\ncomment on a relevant issue that is already open.\n\nOur preferred channels of communication are all public, but if you’d like to\nspeak to us in private first, contact our community coordinators at\nnumpy-team@googlegroups.com or on Slack (write numpy-team@googlegroups.com for\nan invitation).\n\nWe also have a biweekly community call, details of which are announced on the\nmailing list. You are very welcome to join.\n\nIf you are new to contributing to open source, [this\nguide](https:\u002F\u002Fopensource.guide\u002Fhow-to-contribute\u002F) helps explain why, what,\nand how to successfully get involved.\n","NumPy是Python科学计算的基础库。它提供了一个强大的N维数组对象，支持复杂的广播功能、C\u002FC++和Fortran代码集成工具以及线性代数、傅里叶变换和随机数生成等功能。NumPy适合用于需要高效数值运算的场景，如数据分析、机器学习、工程计算等。其简洁高效的API设计使得数据处理变得更加简单快捷。","2026-06-11 02:47:36","top_all"]