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plotly

Create interactive scientific and statistical charts in Python with Plotly.

2|Updated Jan 26, 2026
Search Tags:#data visualization#dashboards#charts#python#plotly#graph objects#interactive plots
One-click install
npx skills add https://github.com/Nzettodess/Awesome-Agent-Skills --skill plotly-nzettodess
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: plotly
Source: https://github.com/Nzettodess/Awesome-Agent-Skills/tree/main/Skills/Charts/plotly
Command: npx skills add https://github.com/Nzettodess/Awesome-Agent-Skills --skill plotly-nzettodess

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires plotly, pandas, kaleido, dash, and includes references (resource) components.

What problem does it solve? Building publication-quality charts in Python often requires juggling multiple libraries and verbose configuration. This Skill provides structured guidance for creating interactive visualizations with Plotly, covering both quick one-liner charts and fully customized figures. ## Core Features & Use Cases - 40+ Chart Types: Scatter, line, bar, histogram, box, violin, heatmap, candlestick, choropleth maps, 3D surfaces, sunburst, treemap, and Sankey diagrams. - Two API Levels: Plotly Express for rapid DataFrame-based charts and graph objects for fine-grained control over traces, layouts, subplots, and annotations. - Flexible Output: Export interactive HTML with hover tooltips, zoom, and animations, or static PNG, PDF, and SVG images via Kaleido. - Use Case: A data scientist needs a multi-panel dashboard comparing distributions across groups. Use this Skill to build a subplot figure combining histograms, box plots, and scatter traces with a shared colorscale, then export it as a standalone HTML report. ## Quick Start Ask the AI to create an interactive scatter plot of your dataset with a trendline and export it as an HTML file.

Frequently Asked Questions about plotly

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create an interactive chart in Python with Plotly?▼

Use Plotly Express with a pandas DataFrame, for example px.scatter(df, x='x', y='y'), then call fig.show() to display it. The resulting figure includes hover tooltips, pan, zoom, and legend toggling by default.

What is the difference between Plotly Express and graph objects?▼

Plotly Express is a high-level API for creating standard charts from DataFrames in one to five lines with automatic styling. Graph objects provide low-level control for custom traces, complex multi-trace figures, and chart types not available in Express.

How do I export a Plotly figure as a PNG or PDF?▼

Install the kaleido package, then call fig.write_image('chart.png') or fig.write_image('chart.pdf'). Kaleido v1 and later also requires Chrome or Chromium installed on the system.

Can Plotly create maps and geographic visualizations?▼

Yes, Plotly supports scatter_geo, choropleth maps at country or US-state level, and Mapbox-based density maps. Some Mapbox styles require an access token, while open styles like open-street-map work without one.

Why does my Plotly static image export fail?▼

Static export fails when kaleido is not installed or when Kaleido v1+ cannot find a Chrome or Chromium binary. Install kaleido with pip and ensure a compatible browser is available on the system.

When should I use Dash instead of a standalone Plotly figure?▼

Use Dash when you need a full interactive web application with callbacks responding to user input, such as click events or dropdown filters. Standalone figures are sufficient for reports, notebooks, and static HTML exports.

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