Panel
Claim this toolBuild powerful data exploration tools, dashboards, and web applications entirely in Python.
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Tracked since2026
0 reviews trackedThe Bottom Line
Entry price
Free, no paid tier
Biggest pro
Open-source and free for commercial and non-commercial use
Biggest con
Requires Python knowledge
TL;DR - Panel
- Develop interactive data apps and dashboards entirely in Python.
- Integrates seamlessly with the PyData ecosystem and various plotting libraries.
- Supports rapid development of exploratory apps and complex multi-page applications.
Pricing: Free forever
Best for: Individuals & startups
What is Panel?
Panel is an open-source Python library designed for developing robust data tools, interactive dashboards, and complex web applications using only Python. It seamlessly integrates with the PyData ecosystem, providing capabilities for creating powerful, interactive data tables and visualizations. Panel offers both high-level reactive APIs for rapid development of exploratory applications and lower-level callback-based APIs for intricate, multi-page applications with extensive interactivity.
This tool is ideal for data scientists, developers, and anyone in the PyData community who wants to unlock, visualize, share, and collaborate on their data efficiently. It enables users to combine various PyData tools and plotting libraries, iterate quickly on data tools, and create high-performing, streaming, and production-ready web applications, including those that can run entirely in the browser. Panel is a member of the HoloViz ecosystem, providing a cohesive suite of data exploration tools.
Available on: Web
Pros & Cons
Pros
- Open-source and free for commercial and non-commercial use
- Develops entirely within Python, leveraging existing Python skills
- Seamless integration with the PyData ecosystem
- Supports rapid prototyping and complex application development
- Part of the cohesive HoloViz suite of tools
Cons
- Requires Python knowledge
- Dependency on specific Python libraries (e.g., pandas not installed by default from 1.9.0)
Key Features
High-level reactive APIsLower-level callback-based APIsInteractive data tablesData visualizationsBi-directional communication for data appsInteractive big data applications with crossfilteringStreaming data applicationsIn-browser application execution
Pricing
Free
Panel is completely free to use with no hidden costs.
Reviews
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Panel FAQ
How does Panel facilitate rapid development of data applications?
Panel provides high-level reactive APIs that enable quick prototyping of exploratory applications. It allows users to iterate rapidly on data tools by combining various PyData tools and plotting libraries. This approach helps in efficiently building and refining data exploration tools and dashboards.
Which teams would benefit most from using Panel?
Panel is ideal for data scientists, developers, and anyone in the PyData community. It helps these users unlock, visualize, share, and collaborate on their data efficiently. Teams looking to build powerful data exploration tools, dashboards, and web applications entirely in Python will find it particularly useful.
How does Panel compare to a tool like 1000tools for building data applications?
Panel distinguishes itself by allowing users to build powerful data exploration tools and web applications entirely within Python. It leverages existing Python skills and integrates seamlessly with the PyData ecosystem. This contrasts with other tools that might require different programming languages or have less direct integration with Python's data science stack.
Does Panel include a free tier?
Panel is open-source and entirely free to use, without any paid plans required. This means it can be used for both commercial and non-commercial projects without cost. Its open-source nature also allows for community contributions and transparency.
What kind of web applications can be built using Panel?
Panel enables the creation of high-performing, streaming, and production-ready web applications, including those that can run entirely in the browser. It supports complex, multi-page applications with extensive interactivity through its lower-level callback-based APIs. This flexibility allows for a wide range of web-based data tools.
What are the main trade-offs when choosing Panel for data visualization?
A primary trade-off when using Panel is the requirement for Python knowledge, as development occurs entirely within the Python ecosystem. Additionally, it has dependencies on specific Python libraries, such as pandas, which are not installed by default from version 1.9.0 onwards. Users need to manage these dependencies for full functionality.
Source: panel.holoviz.org