plotly

Generate interactive and static data visualizations from Python with pandas DataFrames.

18|2|Updated Jan 10, 2026

Search Tags:#charts #python #interactive #data analysis #visualization #plotting

Ready to run

npx skills add https://github.com/ZanderRuss/obsidian-claude --skill plotly-zanderruss

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: plotly
Source: https://github.com/ZanderRuss/obsidian-claude/tree/main/.claude/skills/plotly
Command: npx skills add https://github.com/ZanderRuss/obsidian-claude --skill plotly-zanderruss

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill empowers users to create a wide range of interactive, publication-quality data visualizations and dashboards directly from Python, eliminating the need for external tools or complex configurations for most charting needs.

Core Features & Use Cases

Versatile Charting: Supports over 40 chart types, from basic scatter plots to complex 3D surfaces and geographic maps.
Interactive Outputs: Generates HTML files with built-in zoom, pan, and hover capabilities, or static images (PNG, PDF, SVG).
Use Case: Visualize sales data with a dynamic bar chart that updates based on user selection, or create an interactive scatter plot to explore correlations between different scientific measurements.

Quick Start

Use the plotly skill to create a scatter plot from the provided dataframe.

Frequently Asked Questions about plotly

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

FAQPage Schema

How do I create interactive data visualizations from a pandas DataFrame in Python?▼

You can generate interactive data visualizations directly from pandas DataFrames in Python, outputting to HTML files with built-in zoom, pan, and hover capabilities without needing external charting tools.

Can I generate static image formats like PNG or PDF for Python plotting?▼

Yes, Python plotting can output both interactive HTML files and static image formats including PNG, PDF, and SVG to meet various publication and reporting requirements.

Does this Python visualization approach support 3D plots and geographic maps?▼

Yes, this Python visualization approach supports over 40 chart types, specifically including complex 3D surface plots and geographic maps alongside standard scatter and bar charts.

What is the best way to build a dynamic dashboard from Python data analysis?▼

The best way to build dynamic dashboards from Python data analysis is generating interactive HTML outputs that feature built-in zoom, pan, and hover capabilities for exploring data correlations.

Do I need external tools or complex configurations to create publication-quality charts?▼

No, you do not need external tools or complex configurations to create publication-quality charts, as this Python approach handles everything from basic scatter plots to interactive dashboards natively.

What are the limitations of using HTML files for interactive data visualizations?▼

HTML files for interactive data visualizations require a web browser to render built-in zoom and pan capabilities, meaning they are not natively viewable as static images within standard document viewers.