holoviz/panel

★ 5,676PythonAudience · dataComplexity · 2/5LicenseSetup · easy

Mindmap

mindmap
  root((panel))
    What it does
      Python to web app
      Interactive widgets
      Chart layouts
    Deployment
      Jupyter notebook
      Web server
      Static HTML
      WebAssembly
    Compatible charts
      Matplotlib
      Plotly
      Bokeh
      Altair
    Audience
      Data scientists
      Analysts
    HoloViz ecosystem
      HoloViews
      hvPlot
      Datashader

mindmap root((panel)) What it does Python to web app Interactive widgets Chart layouts Deployment Jupyter notebook Web server Static HTML WebAssembly Compatible charts Matplotlib Plotly Bokeh Altair Audience Data scientists Analysts HoloViz ecosystem HoloViews hvPlot Datashader

Click or tap to explore — scroll the page freely

Things people build with this

USE CASE 1

Add sliders and dropdowns to a Matplotlib chart and share it as a live interactive web app

USE CASE 2

Build a data dashboard inside a Jupyter notebook without writing any HTML or JavaScript

USE CASE 3

Export a visualization as a standalone HTML file that runs in the browser with no Python server required

USE CASE 4

Serve a multi-user interactive app on FastAPI or Django for a team

Tech stack

PythonBokehTornadoFlaskDjangoFastAPIJupyterWebAssembly

Getting it running

Difficulty · easy Time to first run · 30min

Install via pip or conda, works immediately inside Jupyter notebooks with no extra configuration.

Open source under a permissive BSD-style license, use freely for any purpose including commercial.

In plain English

Panel is a Python library that lets you turn data analysis code into interactive web applications without knowing anything about web development. A data scientist or analyst who has written Python code to explore a dataset can add sliders, dropdowns, tables, and charts, and then share the result as a live web page or a standalone app, all while staying inside Python the whole time. The core idea is that Panel acts as glue between Python data tools and the web. It works with the charting libraries that data scientists already use: Matplotlib, Plotly, Bokeh, Altair, Seaborn, and many others. Rather than forcing you to pick one charting tool, Panel can display outputs from whichever ones you already use and lay them out together on a page with interactive controls. Users can click, select, filter, and the charts update in response without any page reloads. Deployment is flexible. The same code can run as a web application on a server (using Tornado, Flask, Django, or FastAPI), as an interactive component inside a Jupyter notebook while you're still working on the analysis, as a static HTML file you can email to someone, or even as a fully client-side app that runs in the browser with no server required using WebAssembly technology. That last option is useful for sharing with people who don't have Python installed. Panel is part of a broader collection of Python visualization tools called HoloViz. The other tools in that ecosystem (HoloViews, hvPlot, Datashader, and others) are each designed to solve a specific piece of the data visualization problem, and Panel is the piece that handles layout, widgets, and serving those visuals to users. Installing Panel is done with pip or conda, and development can happen in Jupyter notebooks or standard Python editors like VS Code or PyCharm. The project is open source and has active community support through Discourse and Discord.

Copy-paste prompts

Prompt 1

Build a Panel dashboard that loads a CSV, shows a Pandas DataFrame, and adds a dropdown to filter rows by a column value.

Prompt 2

Convert this Matplotlib chart into an interactive Panel app with a date-range slider that filters the underlying data.

Prompt 3

Deploy a Panel app as a static HTML file using WebAssembly so users can run it in the browser without installing Python.

Prompt 4

Create a Panel layout with two Plotly charts side by side and a shared text search widget that filters both.

Prompt 5

Serve a Panel app on FastAPI so multiple users can interact with it simultaneously from a web browser.

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