With a comprehensive philosophy, Panel integrates seamlessly with the PyData ecosystem, offering powerful, interactive data tables, visualizations, and much more, to unlock, visualize, share, and …

Walks you through setting up your Python environment, installing Panel into it and how to configure your editor, IDE or notebook environment appropriately.

These Panel applications demonstrate what you can build with Panel and how to do it. Click on each thumbnail to see the app running live, and click on “See source” to look at how each of the …

Make sure Panel is installed in the same environment as JupyterLab/Jupyter Notebook (pip install panel or conda install panel) to ensure all features work correctly.

The Panel library is a project that provides a wide range of data interfaces and an extensible set of plotting backends, which means the development and testing process involves a broad set of libraries.

Panel aims to seamlessly integrate with all your favorite Python libraries and automatically infer how to render a particular object, whether it’s a DataFrame, a plotting Figure, or any other Python object.

How to build a Panel Pipeline that connects multiple panels into a sequential user interface.

By now, you should have set up your environment and installed Panel, so you’re all set to dive in! In this section, we’ll walk through creating a basic interactive application using NumPy, Pandas, and hvPlot.

The Python-based .link() method present on all viewable Panel objects is a convenient API to link the parameters of two objects together, uni- or bi-directionally.

The Matplotlib pane allows displaying Matplotlib figures inside a Panel app. This includes figures created by Seaborn, Pandas .plot, Plotnine and any other plotting library building on top of Matplotlib.