The Python ecosystem has ample supply of both web development frameworks, and data visualization components. But despite the maturity of the ecosystem, few datavisualization tools are capable of dealing with large amounts of streaming data. Even fewer are able to perform live aggregations, sorting, and filtering on top of this data.

In this talk, we will put together a simple but full-featured website using Perspective. Perspective is an open source interactive analytics and data visualization component, which is especially well-suited for large and/or streaming datasets. It is written in C++ and Rust with bindings to both Python and WebAssembly, making it ideal for data-intensive applications. It comes with a variety of visualization plugins, including a datagrid and various charts. Additionally, it comes with a Jupyter widget, which allows developers to iterate quickly with a clear pathway to their production website.

We will start with a simple FastAPI-based website and some static data. In a few lines of code, we will have the website up and running. Next, we will demonstrate some of the core features of Perspective - pivoting, sorting, filtering, the various visualization plugins, cross-filtering (using one table as a filter on other tables), and computed columns. After this, we will pull in some streaming data and show how the functionality of Perspective demonstrated updates in realtime alongside the data. Finally, we'll crank the speed of updates to the limit.

By the end of this talk, the audience will know how to use Perspective and how to incorporate it into their own applications for both static and streaming data, either as a simple but high performance datagrid or as a full featured set of interconnected visualization components.