explaingit

plotly/plotly.py

📈 Trending★ 18,539PythonAudience · developerComplexity · 2/5ActiveLicenseSetup · easy

TLDR

Python library for creating interactive charts and graphs that respond to mouse clicks, hovers, and zooms in web browsers.

Mindmap

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    What it does
      Interactive charts
      30+ chart types
      Web browser display
    Features
      Hover tooltips
      Zoom and pan
      Toggle data series
      3D graphs
    Where to use
      Jupyter notebooks
      Standalone HTML
      Dash dashboards
    Tech stack
      Python
      Plotly.js
      Kaleido optional
    Use cases
      Scientific plots
      Financial charts
      Geographic maps

Things people build with this

USE CASE 1

Build interactive dashboards in Jupyter notebooks to explore data with hover tooltips and zoom controls.

USE CASE 2

Create standalone HTML chart files that you can share via email or embed in websites without needing a server.

USE CASE 3

Generate scientific and financial visualizations with 3D plots, statistical charts, and geographic maps.

USE CASE 4

Build data dashboard applications using Dash that let users interact with charts to filter and explore datasets.

Tech stack

PythonPlotly.jsJupyterDashKaleido

Getting it running

Difficulty · easy Time to first run · 5min
Use freely for any purpose, including commercial use, as long as you keep the copyright notice.

In plain English

plotly.py is a Python library for creating interactive charts and graphs that run in a web browser. Rather than producing static image files, the charts respond to mouse interactions, you can hover to see values, zoom in, pan, and toggle data series on and off. The library is built on top of plotly.js and provides a high-level, declarative interface, meaning you describe what you want the chart to look like rather than writing low-level drawing code. It comes with over 30 chart types including scientific plots, 3D graphs, statistical charts, geographic maps, financial charts, and more. Charts can be displayed inside Jupyter notebooks (interactive coding environments), saved as standalone HTML files, or embedded inside Dash applications (a framework for building data dashboards in Python). Installation is straightforward: pip install plotly. For use as a Jupyter widget, the anywidget package is also needed. For saving charts as static image files (PNG, SVG, etc.) rather than interactive HTML, the kaleido package provides that capability. Extended geographic features like county-level map overlays require a separate plotly-geo package. The library is MIT-licensed and maintained by Plotly, Inc.

Copy-paste prompts

Prompt 1
Show me how to create an interactive line chart with plotly.py that displays values on hover and lets me zoom in.
Prompt 2
How do I save a plotly chart as a static PNG image file instead of interactive HTML?
Prompt 3
Create a plotly.py example that makes a 3D scatter plot and embeds it in a Jupyter notebook.
Prompt 4
How do I add a geographic map visualization using plotly.py to show data by country or region?
Prompt 5
Show me how to build a simple Dash dashboard with multiple plotly charts that users can interact with.
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.