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Panel Review

Panel is an open-source Python library designed for creating custom interactive web applications and dashboards, integrating with the PyData ecosystem for data visualization and analysis.

shipped Jul 6, 2026aifree
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Panel — product screenshot

Why it matters

1Panel is an open-source Python library for building interactive web applications and dashboards.
2It integrates with the PyData ecosystem, supporting libraries such as Pandas, Bokeh, Plotly, and Matplotlib.
3The library offers reactive applications where only necessary components re-execute, enhancing efficiency.
4Panel 1.9.3 was released in March 2026, with Panel 1.9.0 introducing full typing compatibility with Param 2.4.0.

Specs

GitHub

View Repository →

API Available

Yes, public API

overview

What is Panel?

Panel is an open-source Python library developed by HoloViz that enables developers and data scientists to create custom interactive web applications and dashboards. It integrates with the PyData ecosystem, supporting a wide range of plotting libraries and Jupyter notebooks for data visualization and analysis. The library offers extensive flexibility, allowing for reactive applications where only necessary components re-execute. This provides users with customizability and control over application logic and layout, making it suitable for building complex interactive dashboards and visualizations. Panel is a core component of the HoloViz ecosystem, which aims to simplify browser-based data visualization in Python.

features

Key Features of Panel

Panel provides a comprehensive set of features for building interactive data applications and dashboards within the Python environment, supporting both high-level reactive APIs and lower-level callback-based APIs.

  • Creation of custom interactive web applications and dashboards.
  • Integration with the PyData ecosystem, including Pandas, Dask, and XArray.
  • Support for multiple plotting libraries: Bokeh, Plotly, Matplotlib, Altair, HoloViews, hvPlot, Seaborn, Datashader, Deck.gl.
  • Reactive application architecture for selective component re-execution.
  • Extensive customizability and control over application logic and layout.
  • Widgets for accepting user inputs and controlling application state.
  • Deployment as standalone web applications (Tornado, Flask, Django, FastAPI), client-side applications (Pyodide, PyScript), or static HTML.
  • Seamless integration within Jupyter Notebooks, JupyterLab, VS Code, Spyder, and PyCharm.
  • Built-in support for powerful tables via DataFrame and Tabulator panes, including formatting, filtering, and streaming.
  • Development of AI/ML applications, such as image classifiers and chat bots.

use cases

Who Should Use Panel?

Panel is utilized across various domains for interactive data exploration, application development, and specialized analytical tools, catering to a broad audience of Python users.

  • Interactive Data Exploration: Data scientists and analysts visualizing large datasets (millions or billions of rows) with libraries like Datashader within Jupyter environments.
  • Web Application and Dashboard Development: Developers building multi-page applications with bi-directional communication, cross-filtering, and streaming data capabilities.
  • Financial and Trading Applications: Professionals creating interactive tools with advanced tables for data analysis, monitoring, and model interaction.
  • AI/ML Application Prototyping: Researchers and engineers developing interactive interfaces for machine learning models, including image classifiers and chat bots.
  • Providing Tools for Analysts: Teams building interactive components and analysis tools for notebook users to explore data and models.

how to use

How to Use Panel

Panel is used by installing the Python library and then writing Python code to define interactive components, layouts, and data visualizations, which can be displayed in notebooks or served as web applications.

  • 1Install Panel using pip: pip install panel.
  • 2Import Panel and other necessary libraries (e.g., Pandas, Bokeh) into a Python script or Jupyter Notebook.
  • 3Define widgets (e.g., pn.widgets.IntSlider) for user input and interactivity.
  • 4Create plots or data displays using integrated plotting libraries like Bokeh or Plotly.
  • 5Combine widgets and plots into custom layouts using Panel's layout components (e.g., pn.Column, pn.Row).
  • 6Display the application directly in a Jupyter Notebook or serve it as a standalone web application using panel serve your_app.py.

pricing

Panel Pricing & Plans

Panel is an open-source Python library and is available for free. There are no subscription tiers or usage-based costs associated with the core library, making it accessible for all users.

  • Panel: Free (Open-source Python library for custom interactive web applications and PyData ecosystem integration).

Pros

  • +Free and open-source, fostering community contributions and transparency in development.
  • +Deep integration with the PyData ecosystem and numerous plotting libraries (e.g., Bokeh, Plotly, Matplotlib, HoloViews).
  • +High flexibility and customizability for building complex, reactive web applications and dashboards with fine-grained control.
  • +Supports development and deployment across various environments, including Jupyter Notebooks, VS Code, and standalone web servers (Tornado, Flask, Django, FastAPI).
  • +Efficient reactive architecture, allowing only necessary components to re-execute for optimized performance and responsiveness.
  • +Active development with a clear roadmap, including planned Panel 2.0 and 3.0 releases, ensuring future compatibility and feature enhancements.

Cons

  • −May have a steeper learning curve for beginners compared to simpler frameworks like Streamlit for basic, rapid prototyping.
  • −Requires more explicit coding for layout and interactivity compared to frameworks with more opinionated, high-level abstractions.
  • −Achieving highly polished, pixel-perfect UI/UX might require more effort and custom styling than dedicated front-end frameworks.
  • −While flexible, reliance on Python for all aspects might limit advanced front-end customization without additional web development knowledge.
  • −The extensive options and integrations, while powerful, can sometimes lead to choice paralysis for new users trying to select the optimal approach.

Similar Tools

Panel vs Competitors

Panel operates within a competitive landscape of Python-based dashboarding and web application frameworks, each with distinct strengths and target use cases, offering varying levels of flexibility and ease of use.

1
StreamlitOn StorkCompare

Streamlit focuses on extreme ease of use and rapid prototyping, allowing data scientists to build interactive apps with minimal code.

Streamlit is generally considered easier and faster for simple app development and prototyping than Panel, though Panel offers more flexibility and control for complex, reactive applications and better Jupyter integration. Both are free and open-source.

2
Plotly DashOn StorkCompare

Dash is built on top of Flask, React, and Plotly.js, offering a robust framework for highly customizable and complex analytical web applications with fine-grained control over UI.

Dash provides more granular control over UI and callbacks, making it suitable for enterprise-grade applications, but often has a steeper learning curve and requires more web development knowledge than Panel. While the core library is open-source, Plotly offers commercial extensions.

3
GradioOn StorkCompare

Gradio is specifically designed for quickly creating shareable web interfaces for machine learning models and data science demos, often with pre-built components for common ML tasks.

Gradio is more specialized than Panel, focusing almost exclusively on creating interfaces for ML models, often with fewer general-purpose dashboarding features and less layout customization. Both are free and open-source, but Gradio's target audience is more niche for quick ML demos.

4
Voila↗

Voila renders live Jupyter notebooks as standalone web applications, hiding code cells and exposing interactive widgets, making it ideal for converting existing notebook work.

Voila directly leverages existing Jupyter notebooks, making it ideal for converting existing work into interactive apps without significant refactoring, whereas Panel offers a more programmatic approach and better support for multi-user, stateful applications. Both are free and open-source.

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Connect
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GitHubgithub.com/holoviz/panel
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Discorddiscord.gg/UXdtYyGVQX

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