[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-70729":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":15,"stars7d":15,"stars30d":16,"stars90d":15,"forks30d":15,"starsTrendScore":15,"compositeScore":17,"rankGlobal":9,"rankLanguage":9,"license":18,"archived":19,"fork":19,"defaultBranch":20,"hasWiki":21,"hasPages":19,"topics":22,"createdAt":9,"pushedAt":9,"updatedAt":25,"readmeContent":26,"aiSummary":27,"trendingCount":15,"starSnapshotCount":15,"syncStatus":28,"lastSyncTime":29,"discoverSource":30},70729,"pyecharts","pyecharts\u002Fpyecharts","🎨 Python Echarts Plotting Library","https:\u002F\u002Fpyecharts.org",null,"Python",15760,2855,376,3,0,6,70.6,"MIT License",false,"master",true,[23,24],"echarts","python","2026-06-12 04:00:56","\u003Cp align=\"center\">\n    \u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F71825144-2d568180-30d6-11ea-8ee0-63c849cfd934.png\" alt=\"pyecharts logo\" width=200 height=200 \u002F>\n\u003C\u002Fp>\n\u003Ch1 align=\"center\">pyecharts\u003C\u002Fh1>\n\u003Cp align=\"center\">\n    \u003Cem>Python ❤️ ECharts = pyecharts\u003C\u002Fem>\n\u003C\u002Fp>\n\u003Cp align=\"center\">\n    \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fpyecharts\u002Fpyecharts\u002Factions\">\n        \u003Cimg src=\"https:\u002F\u002Fgithub.com\u002Fpyecharts\u002Fpyecharts\u002Factions\u002Fworkflows\u002Fpython-app.yml\u002Fbadge.svg\" alt=\"Github Actions Status\">\n    \u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fcodecov.io\u002Fgh\u002Fpyecharts\u002Fpyecharts\">\n        \u003Cimg src=\"https:\u002F\u002Fcodecov.io\u002Fgh\u002Fpyecharts\u002Fpyecharts\u002Fbranch\u002Fmaster\u002Fgraph\u002Fbadge.svg\" alt=\"Codecov\">\n    \u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fpyecharts\">\n        \u003Cimg src=\"https:\u002F\u002Fbadge.fury.io\u002Fpy\u002Fpyecharts.svg\" alt=\"Package version\">\n    \u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fpyecharts\u002F\">\n        \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fpyversions\u002Fpyecharts.svg?colorB=brightgreen\" alt=\"PyPI - Python Version\">\n    \u003C\u002Fa>\n\u003C\u002Fp>\n\u003Cp align=\"center\">\n    \u003Ca href=\"https:\u002F\u002Fpypi.org\u002Fproject\u002Fpyecharts\">\n        \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fformat\u002Fpyecharts.svg\" alt=\"PyPI - Format\">\n    \u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fpyecharts\u002Fpyecharts\u002Fpulls\">\n        \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fcontributions-welcome-brightgreen.svg?style=flat\" alt=\"Contributions welcome\">\n    \u003C\u002Fa>\n    \u003Ca href=\"https:\u002F\u002Fopensource.org\u002Flicenses\u002FMIT\">\n        \u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLicense-MIT-brightgreen.svg\" alt=\"License\">\n    \u003C\u002Fa>\n\u003C\u002Fp>\n\n[English README](README.en.md)\n\n## 📣 简介\n\n[Apache ECharts](https:\u002F\u002Fgithub.com\u002Fapache\u002Fecharts) 是一个由百度开源的数据可视化，凭借着良好的交互性，精巧的图表设计，得到了众多开发者的认可。而 Python 是一门富有表达力的语言，很适合用于数据处理。当数据分析遇上数据可视化时，[pyecharts](https:\u002F\u002Fgithub.com\u002Fpyecharts\u002Fpyecharts) 诞生了。\n\n* **`pyecharts like` 的可视化项目**\n  * [py-vchart](https:\u002F\u002Fgithub.com\u002FVisActor\u002Fpy-vchart)\n  * [py-antv](https:\u002F\u002Fgithub.com\u002Fsunhailin-Leo\u002Fpyantv)\n\n## ✨ 特性\n\n* 简洁的 API 设计，使用如丝滑般流畅，支持链式调用\n* 囊括了 30+ 种常见图表，应有尽有\n* 支持主流 Notebook 环境，Jupyter Notebook、JupyterLab 和 [marimo](https:\u002F\u002Fgithub.com\u002Fmarimo-team\u002Fmarimo)\n* 可轻松集成至 Flask，Sanic，Django 等主流 Web 框架\n* 高度灵活的配置项，可轻松搭配出精美的图表\n* 详细的文档和示例，帮助开发者更快的上手项目\n* 多达 400+ 地图文件，并且支持原生百度地图，为地理数据可视化提供强有力的支持\n\n## ⏳ 版本\n\nv0.5.x 和 V1 间不兼容，V1 是一个全新的版本，详见 [ISSUE#892](https:\u002F\u002Fgithub.com\u002Fpyecharts\u002Fpyecharts\u002Fissues\u002F892)，[ISSUE#1033](https:\u002F\u002Fgithub.com\u002Fpyecharts\u002Fpyecharts\u002Fissues\u002F1033)。\n\n### V0.5.x\n\n> 支持 Python 2.7，3.4+\n\n经开发团队决定，0.5.x 版本将不再进行维护，0.5.x 版本代码位于 *05x* 分支，文档位于 [05x-docs.pyecharts.org](http:\u002F\u002F05x-docs.pyecharts.org)。\n\n### V1\n\n> 仅支持 Python 3.7+\n\n新版本系列将从 v1.0.0 开始，文档位于 [pyecharts.org](https:\u002F\u002Fpyecharts.org)；示例位于 [gallery.pyecharts.org](https:\u002F\u002Fgallery.pyecharts.org)\n\n### V2\n\n> 仅支持 Python 3.7+\n\n新版本基于 Echarts 5.4.1+ 进行渲染, 文档和示例位置与 V1 相同\n\n## 🔰 安装\n\n**pip 安装**\n```shell\n# 安装 v1 以上版本\n$ pip install pyecharts -U\n\n# 如果需要安装 0.5.11 版本的开发者，可以使用\n# pip install pyecharts==0.5.11\n```\n\n**源码安装**\n```shell\n# 安装 v1 以上版本\n$ git clone https:\u002F\u002Fgithub.com\u002Fpyecharts\u002Fpyecharts.git\n# 如果需要安装 0.5.11 版本，请使用 git clone https:\u002F\u002Fgithub.com\u002Fpyecharts\u002Fpyecharts.git -b v05x\n$ cd pyecharts\n$ pip install -r requirements.txt\n$ python setup.py install\n```\n\n## 📝 使用\n\n### 本地环境\n\n#### 生成 HTML\n```python\nfrom pyecharts.charts import Bar\nfrom pyecharts import options as opts\n\n# V1 版本开始支持链式调用\nbar = (\n    Bar()\n    .add_xaxis([\"衬衫\", \"毛衣\", \"领带\", \"裤子\", \"风衣\", \"高跟鞋\", \"袜子\"])\n    .add_yaxis(\"商家A\", [114, 55, 27, 101, 125, 27, 105])\n    .add_yaxis(\"商家B\", [57, 134, 137, 129, 145, 60, 49])\n    .set_global_opts(title_opts=opts.TitleOpts(title=\"某商场销售情况\"))\n)\nbar.render()\n\n# 不习惯链式调用的开发者依旧可以单独调用方法\nbar = Bar()\nbar.add_xaxis([\"衬衫\", \"毛衣\", \"领带\", \"裤子\", \"风衣\", \"高跟鞋\", \"袜子\"])\nbar.add_yaxis(\"商家A\", [114, 55, 27, 101, 125, 27, 105])\nbar.add_yaxis(\"商家B\", [57, 134, 137, 129, 145, 60, 49])\nbar.set_global_opts(title_opts=opts.TitleOpts(title=\"某商场销售情况\"))\nbar.render()\n```\n\u003Cp align=\"center\">\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F55270272-d6ff1b80-52d7-11e9-820f-30660a068e3e.gif\"  width=\"85%\" \u002F>\n\u003C\u002Fp>\n\n#### 生成图片\n```python\nfrom snapshot_selenium import snapshot as driver\n\nfrom pyecharts import options as opts\nfrom pyecharts.charts import Bar\nfrom pyecharts.render import make_snapshot\n\n\ndef bar_chart() -> Bar:\n    c = (\n        Bar()\n        .add_xaxis([\"衬衫\", \"毛衣\", \"领带\", \"裤子\", \"风衣\", \"高跟鞋\", \"袜子\"])\n        .add_yaxis(\"商家A\", [114, 55, 27, 101, 125, 27, 105])\n        .add_yaxis(\"商家B\", [57, 134, 137, 129, 145, 60, 49])\n        .reversal_axis()\n        .set_series_opts(label_opts=opts.LabelOpts(position=\"right\"))\n        .set_global_opts(title_opts=opts.TitleOpts(title=\"Bar-测试渲染图片\"))\n    )\n    return c\n\n# 需要安装 snapshot-selenium 或者 snapshot-phantomjs\nmake_snapshot(driver, bar_chart().render(), \"bar.png\")\n```\n\u003Cp align=\"center\">\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F56089096-11fc7400-5ec0-11e9-9c21-551624036836.png\"  width=\"85%\" \u002F>\n\u003C\u002Fp>\n\n### Notebook 环境\n\n#### Jupyter Notebook\n\n![](https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F55270255-b3d46c00-52d7-11e9-8aa5-f7b3819a1e88.png)\n\n#### JupyterLab\n\n![](https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F55270259-c0f15b00-52d7-11e9-8811-93bfca1cc027.png)\n\n#### Web 框架\n\n![](https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F35081158-3faa7c34-fc4d-11e7-80c9-2de79371374f.gif)\n\n## 🔖 Demo\n\n> Demo 代码位于 example 文件夹下，欢迎参考 pyecharts 画廊 [pyecharts-gallery](https:\u002F\u002Fgithub.com\u002Fpyecharts\u002Fpyecharts-gallery)。\n\n\u003Cdiv align=\"center\">\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52197440-843a5200-289a-11e9-8601-3ce8d945b04a.gif\" width=\"33%\" alt=\"bar\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52360729-ad640980-2a77-11e9-84e2-feff7e11aea5.gif\" width=\"33%\" alt=\"boxplot\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52535290-4b611800-2d87-11e9-8bf2-b43a54a3bda8.png\" width=\"33%\" alt=\"effectScatter\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52332816-ac5eb800-2a36-11e9-8227-3538976f447d.gif\" width=\"33%\" alt=\"funnel\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52332988-0b243180-2a37-11e9-9db8-eb6b8c86a0de.png\" width=\"33%\" alt=\"gague\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52344575-133f9980-2a56-11e9-93e0-568e484936ce.gif\" width=\"33%\" alt=\"geo\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F35082102-fd8d884a-fc52-11e7-9e40-5f94098d4493.gif\" width=\"33%\" alt=\"geo\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52727805-f7f20280-2ff0-11e9-91ab-cd99848e3127.gif\" width=\"33%\" alt=\"graph\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52345115-6534ef00-2a57-11e9-80cd-9cbfed252139.gif\" width=\"33%\" alt=\"heatmap\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52345490-4a16af00-2a58-11e9-9b43-7bbc86aa05b6.gif\" width=\"33%\" alt=\"kline\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52346064-b7770f80-2a59-11e9-9e03-6dae3a8c637d.gif\" width=\"33%\" alt=\"line\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52347117-248ba480-2a5c-11e9-8402-5a94054dca50.gif\" width=\"33%\" alt=\"liquid\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52347915-0a52c600-2a5e-11e9-8039-41268238576c.gif\" width=\"33%\" alt=\"map\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F57545910-431c7700-738e-11e9-896b-e071b55115c7.png\" width=\"33%\" alt=\"bmap\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52535013-e48e2f80-2d83-11e9-8886-ac0d2122d6af.png\" width=\"33%\" alt=\"parallel\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52348202-bb596080-2a5e-11e9-84a7-60732be0743a.gif\" width=\"33%\" alt=\"pie\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F35090457-afc0658e-fc74-11e7-9c58-24c780436287.gif\" width=\"33%\" alt=\"ploar\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52533994-932b7380-2d76-11e9-93b4-0de3132eb941.gif\" width=\"33%\" alt=\"radar\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52348431-420e3d80-2a5f-11e9-8cab-7b415592dc77.gif\" width=\"33%\" alt=\"scatter\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F44004598-5636d74e-9e97-11e8-8a5c-92de6278880d.gif\" width=\"33%\" alt=\"tree\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F35082251-b9e23982-fc53-11e7-8341-e7da1842389f.gif\" width=\"33%\" alt=\"treemap\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52348737-01fb8a80-2a60-11e9-94ac-dacbd7b58811.png\" width=\"33%\" alt=\"wordCloud\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52433989-4f075b80-2b49-11e9-9979-ef32c2d17c96.gif\" width=\"33%\" alt=\"bar3D\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52464826-4baab900-2bb7-11e9-8299-776f5ee43670.gif\" width=\"33%\" alt=\"line3D\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52802261-8d0cfe00-30ba-11e9-8ae7-ae0773770a59.gif\" width=\"33%\" alt=\"sankey\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52464647-aee81b80-2bb6-11e9-864e-c544392e523a.gif\" width=\"33%\" alt=\"scatter3D\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52465183-a55fb300-2bb8-11e9-8c10-4519c4e3f758.gif\" width=\"33%\" alt=\"surface3D\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52798246-7ebae400-30b2-11e9-8489-6c10339c3429.gif\" width=\"33%\" alt=\"themeRiver\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F17564655\u002F57567164-bdd5a880-7407-11e9-8d19-9be2776c57fa.png\" width=\"33%\" alt=\"sunburst\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F52349544-c2ce3900-2a61-11e9-82af-28aaaaae0d67.gif\" width=\"33%\" alt=\"overlap\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F35089737-ccc1c01c-fc72-11e7-874d-8ba8b89572eb.png\" width=\"33%\" alt=\"grid\"\u002F>\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F56976071-b9f28c80-6ba4-11e9-8efd-603203c77619.png\" width=\"33%\" alt=\"grid\">\n\u003Cimg src=\"https:\u002F\u002Fuser-images.githubusercontent.com\u002F19553554\u002F35082279-e111743c-fc53-11e7-9362-580160593715.gif\" width=\"33%\" alt=\"timeline\"\u002F>\n\u003C\u002Fdiv>\n\n更多详细文档，请访问\n\n* [中文文档](http:\u002F\u002Fpyecharts.org\u002F#\u002Fzh-cn\u002F)\n* [English Documentation](http:\u002F\u002Fpyecharts.org\u002F#\u002Fen-us\u002F)\n* [示例 Example](https:\u002F\u002Fgallery.pyecharts.org)\n\n## ⛏ 代码质量\n\n### 单元测试\n\n```shell\n$ pip install -r test\u002Frequirements.txt\n$ make\n```\n\n### 集成测试\n\n使用 [Travis CI](https:\u002F\u002Ftravis-ci.org\u002F) 和 [AppVeyor](https:\u002F\u002Fci.appveyor.com\u002F) 持续集成环境。\n\n### 代码规范\n\n使用 [flake8](http:\u002F\u002Fflake8.pycqa.org\u002Fen\u002Flatest\u002Findex.html), [Codecov](https:\u002F\u002Fcodecov.io\u002F) 以及 [pylint](https:\u002F\u002Fwww.pylint.org\u002F) 提升代码质量。\n\n## 😉 Author\n\npyecharts 主要由以下几位开发者开发维护\n\n* [@chenjiandongx](https:\u002F\u002Fgithub.com\u002Fchenjiandongx)\n* [@chfw](https:\u002F\u002Fgithub.com\u002Fchfw)\n* [@kinegratii](https:\u002F\u002Fgithub.com\u002Fkinegratii)\n* [@sunhailin-Leo](https:\u002F\u002Fgithub.com\u002Fsunhailin-Leo)\n\n更多贡献者信息可以访问 [pyecharts\u002Fgraphs\u002Fcontributors](https:\u002F\u002Fgithub.com\u002Fpyecharts\u002Fpyecharts\u002Fgraphs\u002Fcontributors)\n\n## 💡 贡献\n\n期待能有更多的开发者参与到 pyecharts 的开发中来，我们会保证尽快 Reivew PR 并且及时回复。但提交 PR 请确保\n\n1. 通过所有单元测试，如若是新功能，请为其新增单元测试\n2. 遵守开发规范，使用 black 以及 isort 格式化代码（$ pip install -r requirements-dev.txt）\n3. 如若需要，请更新相对应的文档\n\n我们也非常欢迎开发者能为 pyecharts 提供更多的示例，共同来完善文档，文档项目位于 [pyecharts\u002Fwebsite](https:\u002F\u002Fgithub.com\u002Fpyecharts\u002Fwebsite)\n\n## 📃 License\n\nMIT [©chenjiandongx](https:\u002F\u002Fgithub.com\u002Fchenjiandongx)\n","pyecharts 是一个基于 Python 的 ECharts 图表绘制库。它通过简洁流畅的 API 设计，支持链式调用，并提供了超过 30 种常见图表类型，适用于多种数据可视化需求。该库兼容主流的 Notebook 环境（如 Jupyter Notebook 和 JupyterLab）以及流行的 Web 框架（例如 Flask、Sanic 和 Django），便于开发者将生成的图表集成到自己的项目中。此外，pyecharts 还支持丰富的地图文件和原生百度地图功能，为地理信息展示提供了强大支持。无论是数据分析报告还是Web应用中的数据展示，pyecharts 都是一个高效且美观的选择。",2,"2026-06-11 03:33:54","high_star"]