4 个仓库
Interactive tables that allow users to dynamically reorganize and aggregate data via row and column swapping.
Distinct from Pivot Table Aggregators: Focuses on the visual interactive transformation and layout of the pivot table rather than just the backend aggregation logic.
Explore 4 awesome GitHub repositories matching data & databases · Pivot Table Visualizations. Refine with filters or upvote what's useful.
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。
Implements pivot tables that transform data through row and column swapping with color-grouped metrics.
本项目是一个基于 JSON 的表单渲染框架和可视化创建器,专为动态数据采集而设计。它提供了一个 JavaScript 表单构建器库,将 JSON 模式转换为功能性 Web 表单和多步向导,并配有专门的可视化调查创建器,无需编写代码即可设计布局和分支逻辑。 该库的特色在于其双向 PDF 集成,允许将 JSON 模式转换为可填写的 PDF 文档,并从现有 PDF 中提取结构化数据。它还具备创建评分评估和计算器的高级功能,可处理数字输入以提供实时加权结果。 系统涵盖了广泛的功能,包括条件分支和多阶段输入验证、多语言本地化,以及适用于 React、Angular 和 Vue.js 的框架无关渲染。此外,还包括响应数据可视化、基于 CSS 变量的主题化以及 WCAG 无障碍合规性工具。
Generates interactive charts, tables, and pivot visualizations to analyze response counts and data trends.
This project is a JavaScript pivot table library and client-side data processor. It provides an interactive interface for transforming raw datasets into summarized tables, heatmaps, and charts, allowing for browser-based data analysis without a backend server. The library distinguishes itself through a drag-and-drop interface for dynamic data exploration and the ability to derive new attributes via date binning or custom logic. It supports flexible data rendering by converting analyzed results into HTML tables or graphical representations using integrated or third-party charting libraries. T
Provides a JavaScript library for creating interactive pivot tables with drag-and-drop grouping and aggregation.
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
Summarizes and transforms datasets using an interactive pivot table interface for dynamic data aggregation.