10 个仓库
Components for rendering charts, metrics, and informational blocks within user interfaces.
Distinguishing note: Focuses on the visual representation layer rather than the underlying data storage.
Explore 10 awesome GitHub repositories matching user interface & experience · Data Visualization Widgets. Refine with filters or upvote what's useful.
Appsmith is a low-code platform designed for building internal business tools, such as operational dashboards and administrative panels. It enables developers to construct dynamic user interfaces by dragging and dropping modular widgets onto a canvas and binding them directly to backend data sources. The platform utilizes a reactive framework that automatically updates interface elements and triggers functions whenever underlying data or widget properties change, eliminating the need for manual event handling. The platform distinguishes itself through a server-side proxy architecture that exe
Ships a variety of visual widgets for rendering data in tables, charts, and maps.
DearPyGui is a GPU-accelerated, immediate-mode graphical user interface framework for Python. It provides a high-performance toolkit for building interactive desktop applications by leveraging native hardware-accelerated rendering backends across multiple operating systems. By utilizing an immediate-mode execution model, the library offers direct control over the rendering loop and element state, enabling the creation of responsive, dynamic interfaces. The framework distinguishes itself through its ability to handle complex, high-frequency visual updates, making it suitable for real-time data
Renders interactive data plots and histograms with support for embedding complex widgets within tooltips.
This library provides a diagnostic toolkit for automated data profiling and exploratory analysis. It generates comprehensive statistical summaries and visual reports for tabular datasets, enabling users to identify distribution patterns, missing values, and quality anomalies through a unified interface. The project distinguishes itself by offering differential analysis, which allows for the comparison of two dataset versions to track structural and statistical changes over time. It supports large-scale data processing through lazy evaluation and provides interactive widgets that embed directl
Displays interactive profiling widgets directly inside data science environments for immediate exploration.
Perspective is a columnar data analytics engine and high-performance visualization component powered by WebAssembly. It provides a system for analyzing and visualizing large or streaming datasets through interactive data grids and charts, utilizing a compiled binary to achieve near-native performance within the browser. The project distinguishes itself through a WebSocket-based data streaming interface and deep Apache Arrow integration, which minimize memory overhead when synchronizing tables between servers and clients. It acts as a remote query proxy capable of translating visualization con
Provides interactive widgets for transforming and visualizing large datasets within JupyterLab environments.
Conky is a system monitoring application used to create custom desktop widgets that visualize real-time hardware performance and system metrics. It renders data such as CPU, memory, disk, and network usage directly onto the desktop environment as transparent overlays. The project extends its core monitoring capabilities through external data integration, allowing it to fetch information from remote APIs and mail servers. It also includes dedicated tracking for media playback status and current track information from music players. Users can personalize these displays using text, graphs, and
Visualizes system and external data using customizable text, progress bars, and graphs.
Serial Studio is a desktop application for connecting to, decoding, visualizing, and recording data from hardware devices over multiple communication protocols. It functions as an embedded device debugging toolkit that ingests live data from Serial, Bluetooth, CAN, Modbus, MQTT, and network sockets into a unified dashboard, while also serving as a programmatic automation platform with over 320 commands exposed over TCP, gRPC, and MCP for external control. The application distinguishes itself through a scriptable frame pipeline that routes incoming bytes through configurable detection, decodin
Displays incoming data through over 15 interactive widgets including line plots, gauges, bar charts, GPS maps, FFT spectrum, and accelerometer views.
Davinci 是一个商业智能和数据可视化平台,用于构建交互式仪表板和报告。它作为一个基于 SQL 的仪表板构建器和多租户分析服务,通过 JDBC 和 CSV 文件连接到数据库,将原始数据转换为可视化组件。 该平台以其细粒度的安全模型而著称,包括与 LDAP 和 OAuth2 身份验证集成的行级和列级权限。它还提供了一个嵌入式可视化工具,允许通过 URL 和框架将安全的、参数化的图表和仪表板插入到外部应用程序中。 该系统涵盖了广泛的能力,包括使用 SQL 模板进行数据建模、用于响应式仪表板的拖放式布局引擎,以及多种可视化类型,如桑基图、雷达图和地理地图。它还包括用于调度基于电子邮件的报告的自动化功能,并利用键值缓存来优化查询性能。
Transforms data models into interactive visualization widgets by applying secondary aggregation and grouping.
这是一个基于 React 的管理后台模板和 UI 工具包,专为构建仪表盘而设计。它提供了一套完整的预设布局、无障碍组件库以及基于 Sass 的 UI 框架,用于创建管理界面。 该模板专门针对 AI 进行了优化,采用了有助于 AI 助手生成一致且可直接用于生产环境的 React 代码的组织模式。它具有灵活的样式系统,利用 Sass 变量和 CSS 自定义属性来支持可定制的亮色和暗色主题。 该工具包涵盖了广泛的界面功能,包括数据可视化小部件、支持排序和分页的交互式表格,以及多步向导等复杂表单元素。它还包含侧边栏和面包屑等高级导航系统,以及响应式网格、模态对话框和无障碍日历界面等结构化组件。
Renders visual widgets that summarize key data and metrics for an immediate performance overview.
Mercury 是一个将 Jupyter Notebook 转换为交互式 Web 应用、Notebook 执行 API 和静态站点生成器的框架。它作为一个自托管应用服务器,允许用户在不编写前端代码的情况下,将受密码保护的 Notebook 部署为功能性用户界面。 该系统通过将 Notebook 小部件映射到响应式 Web 界面并实时同步多个用户的实时应用会话,展现出其独特之处。它支持通过 API 远程执行 Notebook 以检索计算结果作为结构化数据,并支持将 Notebook 转换为交互式幻灯片或聊天界面。 该平台涵盖了广泛的能力,包括交互式仪表板创建、PDF 和 HTML 格式的自动化报告生成,以及将已执行的 Notebook 嵌入到外部网站的能力。应用品牌和布局通过外部配置文件进行管理。 部署选项包括托管云服务和使用 Docker 容器的自托管私有基础设施。
Triggers the automatic re-execution of notebook cells when a user modifies an input widget.
This project is an interactive, web-based notebook environment designed for distributed data science and large-scale computing. It serves as a development tool for executing code and performing data analysis specifically within the Apache Spark framework, providing a browser-based interface that combines code execution with reactive data visualization. The platform distinguishes itself through its deep integration with distributed infrastructure, allowing users to manage cluster resources, configure runtime dependencies, and isolate execution processes for individual notebooks. It supports co
Integrates interactive visualization components directly into notebook cells for displaying data samples and streaming updates.