# Panel

> Data exploration and web app framework for Python that works with many plotting libraries.

- Category: [Python data visualization libraries](https://awesomedataviz.com/categories/python/)
- Website: https://panel.holoviz.org
- Repository: https://github.com/holoviz/panel
- License: BSD-3-Clause
- Language: Python
- Status: Active (commits in the last 90 days)
- GitHub stars: 5,782
- Commits in the last 12 months: 392
- Contributors: 225
- Last commit: Oct 4, 2026
- Latest release: v1.9.4 (Aug 17, 2026)
- Install (PyPI): `pip install panel`, 279.2K downloads per week
- Topics: Dashboards & BI, Jupyter & notebook visualization, Exploratory data analysis tools

## Overview

Panel is an open-source Python visualization library released under the BSD-3-Clause license. Its GitHub repository has 5,782 stars, 635 forks, and 225 contributors. It is actively developed with 392 commits in the last 12 months. The latest release, v1.9.4, was published on Aug 17, 2026. On PyPI it is downloaded about 279.2K times per week.

## Alternatives

- [Streamlit](https://awesomedataviz.com/tools/streamlit/): Framework for turning Python scripts into interactive data apps. (45.9K stars)
- [Dash](https://awesomedataviz.com/tools/dash/): Framework for building data apps and dashboards in Python, built on Plotly.js and React. (24.4K stars)
- [Matplotlib](https://awesomedataviz.com/tools/matplotlib/): 2D plotting library. (23.3K stars)
- [Bokeh](https://awesomedataviz.com/tools/bokeh/): Interactive Web Plotting for Python. (20.5K stars)
- [Taipy](https://awesomedataviz.com/tools/taipy/): Python framework for building data and AI web applications with interactive charts and dashboards. (19.4K stars)
- [Plotly (Python)](https://awesomedataviz.com/tools/plotly-python/): Interactive web based visualization built on top of plotly.js (18.8K stars)

## Comparisons

- [Panel vs Streamlit](https://awesomedataviz.com/compare/panel-vs-streamlit/)

Source: https://awesomedataviz.com/tools/panel/ · Data updated 2026-10-04 · Content: CC BY 4.0, Awesome Dataviz (https://awesomedataviz.com).