[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-798":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":25,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":33,"readmeContent":34,"aiSummary":35,"trendingCount":16,"starSnapshotCount":16,"syncStatus":36,"lastSyncTime":37,"discoverSource":38},798,"PythonDataScienceHandbook","jakevdp\u002FPythonDataScienceHandbook","jakevdp","Python Data Science Handbook: full text in Jupyter Notebooks","http:\u002F\u002Fjakevdp.github.io\u002FPythonDataScienceHandbook",null,"Jupyter Notebook",48494,18978,1815,128,0,17,94,502,80,100,"MIT License",false,"master",true,[27,28,29,30,31,32],"jupyter-notebook","matplotlib","numpy","pandas","python","scikit-learn","2026-06-11 04:00:33","# Python Data Science Handbook\n\n[![Binder](https:\u002F\u002Fmybinder.org\u002Fbadge.svg)](https:\u002F\u002Fmybinder.org\u002Fv2\u002Fgh\u002Fjakevdp\u002FPythonDataScienceHandbook\u002Fmaster?filepath=notebooks%2FIndex.ipynb)\n[![Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fjakevdp\u002FPythonDataScienceHandbook\u002Fblob\u002Fmaster\u002Fnotebooks\u002FIndex.ipynb)\n\nThis repository contains the entire [Python Data Science Handbook](http:\u002F\u002Fshop.oreilly.com\u002Fproduct\u002F0636920034919.do), in the form of (free!) Jupyter notebooks.\n\n![cover image](notebooks\u002Ffigures\u002FPDSH-cover.png)\n\n## How to Use this Book\n\n- Read the book in its entirety online at https:\u002F\u002Fjakevdp.github.io\u002FPythonDataScienceHandbook\u002F\n\n- Run the code using the Jupyter notebooks available in this repository's [notebooks](notebooks) directory.\n\n- Launch executable versions of these notebooks using [Google Colab](http:\u002F\u002Fcolab.research.google.com): [![Colab](https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg)](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fjakevdp\u002FPythonDataScienceHandbook\u002Fblob\u002Fmaster\u002Fnotebooks\u002FIndex.ipynb)\n\n- Launch a live notebook server with these notebooks using [binder](https:\u002F\u002Fbeta.mybinder.org\u002F): [![Binder](https:\u002F\u002Fmybinder.org\u002Fbadge.svg)](https:\u002F\u002Fmybinder.org\u002Fv2\u002Fgh\u002Fjakevdp\u002FPythonDataScienceHandbook\u002Fmaster?filepath=notebooks%2FIndex.ipynb)\n\n- Buy the printed book through [O'Reilly Media](http:\u002F\u002Fshop.oreilly.com\u002Fproduct\u002F0636920034919.do)\n\n## About\n\nThe book was written and tested with Python 3.5, though other Python versions (including Python 2.7) should work in nearly all cases.\n\nThe book introduces the core libraries essential for working with data in Python: particularly [IPython](http:\u002F\u002Fipython.org), [NumPy](http:\u002F\u002Fnumpy.org), [Pandas](http:\u002F\u002Fpandas.pydata.org), [Matplotlib](http:\u002F\u002Fmatplotlib.org), [Scikit-Learn](http:\u002F\u002Fscikit-learn.org), and related packages.\nFamiliarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project,\n[A Whirlwind Tour of Python](https:\u002F\u002Fgithub.com\u002Fjakevdp\u002FWhirlwindTourOfPython): it's a fast-paced introduction to the Python language aimed at researchers and scientists.\n\nSee [Index.ipynb](http:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fjakevdp\u002FPythonDataScienceHandbook\u002Fblob\u002Fmaster\u002Fnotebooks\u002FIndex.ipynb) for an index of the notebooks available to accompany the text.\n\n## Software\n\nThe code in the book was tested with Python 3.5, though most (but not all) will also work correctly with Python 2.7 and other older Python versions.\n\nThe packages I used to run the code in the book are listed in [requirements.txt](requirements.txt) (Note that some of these exact version numbers may not be available on your platform: you may have to tweak them for your own use).\nTo install the requirements using [conda](http:\u002F\u002Fconda.pydata.org), run the following at the command-line:\n\n```\n$ conda install --file requirements.txt\n```\n\nTo create a stand-alone environment named ``PDSH`` with Python 3.5 and all the required package versions, run the following:\n\n```\n$ conda create -n PDSH python=3.5 --file requirements.txt\n```\n\nYou can read more about using conda environments in the [Managing Environments](http:\u002F\u002Fconda.pydata.org\u002Fdocs\u002Fusing\u002Fenvs.html) section of the conda documentation.\n\n\n## License\n\n### Code\nThe code in this repository, including all code samples in the notebooks listed above, is released under the [MIT license](LICENSE-CODE). Read more at the [Open Source Initiative](https:\u002F\u002Fopensource.org\u002Flicenses\u002FMIT).\n\n### Text\nThe text content of the book is released under the [CC-BY-NC-ND license](LICENSE-TEXT). Read more at [Creative Commons](https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby-nc-nd\u002F3.0\u002Fus\u002Flegalcode).\n","该项目是《Python数据科学手册》的完整Jupyter笔记本版本，提供了免费的数据科学学习资源。核心功能包括通过丰富的示例和练习介绍Python中处理数据的关键库，如IPython、NumPy、Pandas、Matplotlib以及Scikit-Learn等。技术特点在于其以交互式Jupyter Notebook的形式呈现内容，支持在线阅读与代码运行，便于读者实践所学知识。适合于希望掌握Python在数据分析、可视化及机器学习领域应用的学生、研究人员或任何对数据科学感兴趣的人士使用。",2,"2026-06-11 02:39:24","top_all"]