Explore how to use Deno's Jupyter kernel for interactive data analysis and visualization with TypeScript. This guide covers setting up the environment, loading and cleaning data from the National Gallery of Art's Open Access dataset, and using tools like Polars for data manipulation and Observable Plot for visualization. Custom interactive components such as widgets are also demonstrated.
Table of contents
The datasetLoading and cleaning the dataDetermining whether art is in public domainJoin to single unified tableExploring art, who created it, when, and more with plotsDeeper exploration with interactivityExploring data with DataFrame viewer anywidgetsCreating a custom <Gallery/> componentDrilling down on when the art was createdConclusionSort: