Panel is one of the leading choices for building dashboards in Python. In this talk, we discuss the practical aspects of complex data-driven dashboards. There are tutorials and guides available which help teach new users the basics, but this talk focuses on the challenges of building more complex, industry-ready, deployed dashboards. There are a variety of niche issues which arise when you push the limits of complexity, and we will share the solutions we have developed. We will demonstrate these solutions as we walk through the entire lifecycle from data ingestion, though exploratory analysis to deployment as a finished website.
Panel is a mature tool for quickly creating custom, interactive web apps and dashboards by connecting user-defined widgets to plots, images, tables, or text. Panel allows scientists to create their own apps for data exploration quickly and easily without requiring them to know javascript or work with an external developer team. It provides the framework for building much more complex dashboards which look and feel like a mature web app. By allowing prototyping inside Jupyter and deployment outside Jupyter, it bridges the gap between exploration and production.
In this talk, we discuss the practical aspects of complex data-driven dashboards. There are tutorials and guides available which help teach new users the basics, but this talk focuses on the challenges of building and sharing more complex, industry-ready, applications. There are a variety of issues which arise when you build fully featured applications, and we will share the solutions we have developed. We will demonstrate these solutions as we walk through the entire lifecycle from data, though exploratory analysis to deployment as a finished website.
Examples of the topics we will cover include: best practices; creating multi-page apps where you may not know the number of pages ahead of time; Debugging cascading effects of user interactions; Deployment in air-gapped environments; using templates and customizing Panel’s look and feel.
These lessons were distilled from Panel apps we created for clients in finance, manufacturing and science. The datasets and code for these examples in the talk will be made available via a github repository.
