Bokeh vs. PyQtGraph vs. UI-licious 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.

Stop writing brittle tests with hard-coded CSS, XPATH selectors and waits. Write tests that are meaningful to people, effortless to maintain, and reusable. The reason for this is because writing your tests using hard-coded CSS and XPATH selectors is like pouring concrete onto a specific UI implementation. This creates very high-maintenance tests that break at the slightest changes to the UI and are very hard for humans to read. UI-licious uses dynamic code analysis to understand the structure of your website based on the use of semantic HTML and ARIA accessibility attributes, as well as context of the previous commands, to identify which is the intended element to target for every command. This means that even if the HTML code for the UI changes underneath the code, the test is valid as long as the user journey remains the same. That said, your website doesn't to be written perfectly for UI-licious to work.