Bokeh vs. PyQtGraph vs. Webix Grid vs. python-sql Comparison Chart

Bokeh makes it simple to create common plots, but also can handle custom or specialized use-cases. Plots, dashboards, and apps can be published in web pages or Jupyter notebooks. Python has an incredible ecosystem of powerful analytics tools: NumPy, Scipy, Pandas, Dask, Scikit-Learn, OpenCV, and more. With a wide array of widgets, plot tools, and UI events that can trigger real Python callbacks, the Bokeh server is the bridge that lets you connect these tools to rich, interactive visualizations in the browser. Microscopium is a project maintained by researchers at Monash University. It allows researchers to discover new gene or drug functions by exploring large image datasets with Bokeh’s interactive tools. Panel is a tool for polished data presentation that utilizes the Bokeh server. It is created and supported by Anaconda. Panel makes it simple to create custom interactive web apps and dashboards by connecting user-defined widgets to plots, images, tables, or text.

Webix Grid is a standalone JavaScript DataGrid component (table/grid UI widget) that is optimized for high-performance, large-dataset scenarios, and is designed to be dropped into web applications where tabular data needs to be displayed, edited, filtered, sorted, etc. Key positioning points: Lightweight: you don’t have to bring in the full Webix UI library if you only need the grid. Focused on “just the grid” use-case rather than a full UI framework. Feature-rich, offering a wide set of capabilities for enterprise-style data apps: - Virtual scrolling - Frozen columns - Inline editing - Sorting - Filtering - Grouping - Column resizing - Column reordering - Multi-line headers - Row selection - Cell selection - Copy/paste - Excel export - PDF export - CSV export - Paging - Validation - Undo/redo - Drag and drop - Spanning cells - Context menus - Keyboard navigation - Conditional formatting - Column summaries - Custom rendering - Live updates