Visualization libraries · Plotly, Inc.

Plotly

Open-source charting libraries for Python, R, and JavaScript producing interactive, zoomable charts with built-in hover and export tools.

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Plotly is a family of open-source charting libraries — Plotly.py, Plotly.js, and a Plotly R package — that render interactive charts with zooming, panning, hover tooltips, and legend toggling out of the box, without extra event-handling code. It covers statistical, scientific, financial, and 3D chart types, and Plotly.py integrates closely with Pandas DataFrames for quick exploratory plotting in Jupyter notebooks. Because Plotly.js is the rendering engine underneath, charts built in any language look and behave the same and can be exported to standalone HTML or embedded in web apps. Plotly, Inc. also builds Dash, a separate commercial app framework built on top of these libraries for teams that want to ship full analytical web apps rather than individual charts.

At a glance

Vendor Plotly, Inc.
Pricing model Open source + paid options
Free tier Yes
Deployment Self-hosted
Open source Yes (MIT)
Best for Analysts and developers who want interactive charts with minimal setup across Python, R, or JS.

Pricing

The charting libraries themselves are free and open source; Plotly's paid products (Dash Enterprise) are separate.

Pricing has not been verified yet — see the vendor's site.

Features

  • Interactive zoom, pan, and hover tooltips out of the box
  • Statistical, scientific, financial, and 3D chart types
  • Pandas DataFrame integration
  • Consistent rendering across Python, R, and JavaScript
  • Export to standalone HTML
  • WebGL rendering for large datasets

Integrations

Profile last reviewed September 21, 2026

Alternatives

D3.js JavaScript library for binding data to the DOM and drawing fully custom, interactive visualizations with SVG or Canvas. Apache ECharts JavaScript charting library with a large catalog of built-in chart types and strong performance on large datasets. Matplotlib Python's foundational plotting library for static, publication-quality charts, from quick line plots to fully custom figures.

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Terms to know

Data visualization Representing data graphically so that patterns, trends and outliers are easier to see than in a table of numbers. Exploratory data analysis An open-ended first pass through a dataset to understand its structure and spot patterns before formal analysis.

Related guides

How to choose a visualization library Pick a visualization library by language, how much custom control you need, and whether commercial licensing is worth it — not by demo gallery polish.