A new tutorial has been released that guides users through creating an advanced multi-page interactive dashboard using the Panel library. This comprehensive resource is designed for anyone interested in building data visualization tools, especially those who want to analyze synthetic data effectively.
The tutorial breaks down the process step by step. It starts by installing essential libraries like Panel, hvPlot, Pandas, and NumPy, which are crucial for running the dashboard smoothly, particularly in Google Colab. Users will learn how to generate a full year’s worth of synthetic time-series data across different segments and regions. This data serves as the foundation for various visualizations throughout the tutorial.
Interactive widgets are a key feature of the dashboard. The tutorial explains how to create filters that allow users to select segments, regions, metrics, and date ranges. This interactivity is enhanced by using reactive programming, enabling users to see changes in real time as they adjust their selections.
In addition to basic time-series plots, the tutorial shows how to build more complex visualizations, including bar charts and heatmaps. These visual elements respond to the same global filters, providing a cohesive view of the data without duplicating code.
A standout feature of the dashboard is the live KPI updates. The tutorial includes a section on simulating rolling updates for key performance indicators like total revenue, average conversions, and conversion rates. This aspect adds a dynamic element to the dashboard, making it feel like a real-time monitoring tool.
The dashboard is organized into three main sections: an overview page, an insights page, and a live KPI page. This structure helps users easily access different types of analyses and visualizations.
Overall, this tutorial offers a clear and practical approach to building an interactive dashboard that can be used for various data analysis tasks. For those interested in exploring the full code and details, the tutorial is available on GitHub, providing an excellent resource for developers and data enthusiasts alike.