[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-756":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":23,"hasPages":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":31,"readmeContent":32,"aiSummary":33,"trendingCount":16,"starSnapshotCount":16,"syncStatus":34,"lastSyncTime":35,"discoverSource":36},756,"pandas","pandas-dev\u002Fpandas","pandas-dev","Flexible and powerful data analysis \u002F manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more","https:\u002F\u002Fpandas.pydata.org",null,"Python",48956,20009,1116,2953,0,6,54,258,39,45,"BSD 3-Clause \"New\" or \"Revised\" License",false,"main",[26,27,28,29,5,30],"alignment","data-analysis","data-science","flexible","python","2026-06-12 02:00:18","\u003Cpicture align=\"center\">\n  \u003Csource media=\"(prefers-color-scheme: dark)\" srcset=\"https:\u002F\u002Fpandas.pydata.org\u002Fstatic\u002Fimg\u002Fpandas_white.svg\">\n  \u003Cimg alt=\"Pandas Logo\" src=\"https:\u002F\u002Fpandas.pydata.org\u002Fstatic\u002Fimg\u002Fpandas.svg\">\n\u003C\u002Fpicture>\n\n-----------------\n\n# pandas: A Powerful Python Data Analysis Toolkit\n\n| | |\n| --- | --- |\n| Testing | [![CI - Test](https:\u002F\u002Fgithub.com\u002Fpandas-dev\u002Fpandas\u002Factions\u002Fworkflows\u002Funit-tests.yml\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fpandas-dev\u002Fpandas\u002Factions\u002Fworkflows\u002Funit-tests.yml) [![Coverage](https:\u002F\u002Fcodecov.io\u002Fgithub\u002Fpandas-dev\u002Fpandas\u002Fcoverage.svg?branch=main)](https:\u002F\u002Fcodecov.io\u002Fgh\u002Fpandas-dev\u002Fpandas) |\n| Package | [![PyPI Latest Release](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fpandas.svg)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fpandas\u002F) [![PyPI Downloads](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fdm\u002Fpandas.svg?label=PyPI%20downloads)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fpandas\u002F) [![Conda Latest Release](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fpandas\u002Fbadges\u002Fversion.svg)](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fpandas) [![Conda Downloads](https:\u002F\u002Fimg.shields.io\u002Fconda\u002Fdn\u002Fconda-forge\u002Fpandas.svg?label=Conda%20downloads)](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fpandas) |\n| Meta | [![Powered by NumFOCUS](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fpowered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https:\u002F\u002Fnumfocus.org) [![DOI](https:\u002F\u002Fzenodo.org\u002Fbadge\u002FDOI\u002F10.5281\u002Fzenodo.3509134.svg)](https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.3509134) [![License - BSD 3-Clause](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fl\u002Fpandas.svg)](https:\u002F\u002Fgithub.com\u002Fpandas-dev\u002Fpandas\u002Fblob\u002Fmain\u002FLICENSE) [![Slack](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fjoin_Slack-information-brightgreen.svg?logo=slack)](https:\u002F\u002Fpandas.pydata.org\u002Fdocs\u002Fdev\u002Fdevelopment\u002Fcommunity.html?highlight=slack#community-slack) [![LFX Health Score](https:\u002F\u002Finsights.linuxfoundation.org\u002Fapi\u002Fbadge\u002Fhealth-score?project=pandas-dev-pandas)](https:\u002F\u002Finsights.linuxfoundation.org\u002Fproject\u002Fpandas-dev-pandas) |\n\n\n## What is it?\n\n**pandas** is a Python package that provides fast, flexible, and expressive data\nstructures designed to make working with \"relational\" or \"labeled\" data both\neasy and intuitive. It aims to be the fundamental high-level building block for\ndoing practical, **real-world** data analysis in Python. Additionally, it has\nthe broader goal of becoming **the most powerful and flexible open-source data\nanalysis\u002Fmanipulation tool available in any language**. It is already well on\nits way towards this goal.\n\n## Table of Contents\n\n- [Main Features](#main-features)\n- [Where to get it](#where-to-get-it)\n- [Dependencies](#dependencies)\n- [Installation from sources](#installation-from-sources)\n- [License](#license)\n- [Documentation](#documentation)\n- [Background](#background)\n- [Getting Help](#getting-help)\n- [Discussion and Development](#discussion-and-development)\n- [Contributing to pandas](#contributing-to-pandas)\n\n## Main Features\nHere are just a few of the things that pandas does well:\n\n  - Easy handling of [**missing data**][missing-data] (represented as\n    `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data\n  - Size mutability: columns can be [**inserted and\n    deleted**][insertion-deletion] from DataFrame and higher dimensional\n    objects\n  - Automatic and explicit [**data alignment**][alignment]: objects can\n    be explicitly aligned to a set of labels, or the user can simply\n    ignore the labels and let `Series`, `DataFrame`, etc. automatically\n    align the data for you in computations\n  - Powerful, flexible [**group by**][groupby] functionality to perform\n    split-apply-combine operations on data sets, for both aggregating\n    and transforming data\n  - Make it [**easy to convert**][conversion] ragged,\n    differently-indexed data in other Python and NumPy data structures\n    into DataFrame objects\n  - Intelligent label-based [**slicing**][slicing], [**fancy\n    indexing**][fancy-indexing], and [**subsetting**][subsetting] of\n    large data sets\n  - Intuitive [**merging**][merging] and [**joining**][joining] data\n    sets\n  - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of\n    data sets\n  - [**Hierarchical**][mi] labeling of axes (possible to have multiple\n    labels per tick)\n  - Robust I\u002FO tools for loading data from [**flat files**][flat-files]\n    (CSV and delimited), [**Excel files**][excel], [**databases**][db],\n    and saving\u002Floading data from the ultrafast [**HDF5 format**][hdfstore]\n  - [**Time series**][timeseries]-specific functionality: date range\n    generation and frequency conversion, moving window statistics,\n    date shifting and lagging\n\n\n   [missing-data]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Fmissing_data.html\n   [insertion-deletion]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Fdsintro.html#column-selection-addition-deletion\n   [alignment]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Fdsintro.html?highlight=alignment#intro-to-data-structures\n   [groupby]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Fgroupby.html#group-by-split-apply-combine\n   [conversion]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Fdsintro.html#dataframe\n   [slicing]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Findexing.html#slicing-ranges\n   [fancy-indexing]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Fadvanced.html#advanced\n   [subsetting]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Findexing.html#boolean-indexing\n   [merging]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Fmerging.html#database-style-dataframe-or-named-series-joining-merging\n   [joining]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Fmerging.html#joining-on-index\n   [reshape]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Freshaping.html\n   [pivot-table]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Freshaping.html\n   [mi]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Findexing.html#hierarchical-indexing-multiindex\n   [flat-files]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Fio.html#csv-text-files\n   [excel]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Fio.html#excel-files\n   [db]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Fio.html#sql-queries\n   [hdfstore]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Fio.html#hdf5-pytables\n   [timeseries]: https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fuser_guide\u002Ftimeseries.html#time-series-date-functionality\n\n## Where to get it\nThe source code is currently hosted on GitHub at:\nhttps:\u002F\u002Fgithub.com\u002Fpandas-dev\u002Fpandas\n\nBinary installers for the latest released version are available at the [Python\nPackage Index (PyPI)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fpandas) and on [Conda](https:\u002F\u002Fanaconda.org\u002Fconda-forge\u002Fpandas).\n\n```sh\n# conda\nconda install -c conda-forge pandas\n```\n\n```sh\n# or PyPI\npip install pandas\n```\n\nThe list of changes to pandas between each release can be found\n[here](https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Fwhatsnew\u002Findex.html). For full\ndetails, see the commit logs at https:\u002F\u002Fgithub.com\u002Fpandas-dev\u002Fpandas.\n\n## Dependencies\n- [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https:\u002F\u002Fwww.numpy.org)\n- [python-dateutil - Provides powerful extensions to the standard datetime module](https:\u002F\u002Fdateutil.readthedocs.io\u002Fen\u002Fstable\u002Findex.html)\n- [tzdata - Provides an IANA time zone database](https:\u002F\u002Ftzdata.readthedocs.io\u002Fen\u002Flatest\u002F) (Only required on Windows\u002FEmscripten)\n\nSee the [full installation instructions](https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002Finstall.html#dependencies) for minimum supported versions of required, recommended and optional dependencies.\n\n## Installation from sources\nTo install pandas from source you need [Cython](https:\u002F\u002Fcython.org\u002F) in addition to the normal\ndependencies above. Cython can be installed from PyPI:\n\n```sh\npip install cython\n```\n\nIn the `pandas` directory (same one where you found this file after\ncloning the git repo), execute:\n\n```sh\npip install .\n```\n\nor for installing in [development mode](https:\u002F\u002Fpip.pypa.io\u002Fen\u002Flatest\u002Fcli\u002Fpip_install\u002F#install-editable):\n\n\n```sh\npython -m pip install -ve . --no-build-isolation --config-settings editable-verbose=true\n```\n\nSee the full instructions for [installing from source](https:\u002F\u002Fpandas.pydata.org\u002Fdocs\u002Fdev\u002Fdevelopment\u002Fcontributing_environment.html).\n\n## License\n[BSD 3](LICENSE)\n\n## Documentation\nThe official documentation is hosted on [PyData.org](https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fstable\u002F).\n\n## Background\nWork on ``pandas`` started at [AQR](https:\u002F\u002Fwww.aqr.com\u002F) (a quantitative hedge fund) in 2008 and\nhas been under active development since then.\n\n## Getting Help\n\nFor usage questions, the best place to go to is [Stack Overflow](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fpandas).\nFurther, general questions and discussions can also take place on the [pydata mailing list](https:\u002F\u002Fgroups.google.com\u002Fforum\u002F?fromgroups#!forum\u002Fpydata).\n\n## Discussion and Development\nMost development discussions take place on GitHub in this repo, via the [GitHub issue tracker](https:\u002F\u002Fgithub.com\u002Fpandas-dev\u002Fpandas\u002Fissues).\n\nFurther, the [pandas-dev mailing list](https:\u002F\u002Fmail.python.org\u002Fmailman\u002Flistinfo\u002Fpandas-dev) can also be used for specialized discussions or design issues, and a [Slack channel](https:\u002F\u002Fpandas.pydata.org\u002Fdocs\u002Fdev\u002Fdevelopment\u002Fcommunity.html?highlight=slack#community-slack) is available for quick development related questions.\n\nThere are also frequent [community meetings](https:\u002F\u002Fpandas.pydata.org\u002Fdocs\u002Fdev\u002Fdevelopment\u002Fcommunity.html#community-meeting) for project maintainers open to the community as well as monthly [new contributor meetings](https:\u002F\u002Fpandas.pydata.org\u002Fdocs\u002Fdev\u002Fdevelopment\u002Fcommunity.html#new-contributor-meeting) to help support new contributors.\n\nAdditional information on the communication channels can be found on the [contributor community](https:\u002F\u002Fpandas.pydata.org\u002Fdocs\u002Fdevelopment\u002Fcommunity.html) page.\n\n## Contributing to pandas\n\n[![Open Source Helpers](https:\u002F\u002Fwww.codetriage.com\u002Fpandas-dev\u002Fpandas\u002Fbadges\u002Fusers.svg)](https:\u002F\u002Fwww.codetriage.com\u002Fpandas-dev\u002Fpandas)\n\nAll contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.\n\nA detailed overview on how to contribute can be found in the **[contributing guide](https:\u002F\u002Fpandas.pydata.org\u002Fdocs\u002Fdev\u002Fdevelopment\u002Fcontributing.html)**.\n\nYou can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to [subscribe to pandas on CodeTriage](https:\u002F\u002Fwww.codetriage.com\u002Fpandas-dev\u002Fpandas).\n\nOr maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’... you can do something about it!\n\nFeel free to ask questions on the [mailing list](https:\u002F\u002Fgroups.google.com\u002Fforum\u002F?fromgroups#!forum\u002Fpydata) or on [Slack](https:\u002F\u002Fpandas.pydata.org\u002Fdocs\u002Fdev\u002Fdevelopment\u002Fcommunity.html?highlight=slack#community-slack).\n\nAs contributors and maintainers to this project, you are expected to abide by pandas' code of conduct. More information can be found at: [Contributor Code of Conduct](https:\u002F\u002Fgithub.com\u002Fpandas-dev\u002F.github\u002Fblob\u002Fmaster\u002FCODE_OF_CONDUCT.md)\n\n\u003Chr>\n\n[Go to Top](#table-of-contents)\n","pandas 是一个用于 Python 的强大数据处理和分析库，提供了类似 R 语言中 data.frame 的标签化数据结构、统计函数等多种功能。其核心特性包括高效灵活的数据结构（如 DataFrame 和 Series）、便捷的数据清洗与转换工具、强大的数据对齐和合并能力以及丰富的输入输出接口。pandas 支持从多种数据源读取数据，并能轻松进行数据筛选、聚合等操作，非常适合数据分析、科学计算及机器学习预处理等场景使用。",2,"2026-06-11 02:39:08","top_all"]