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python-dataviz

Generate static and interactive charts from tabular data using matplotlib, seaborn, and plotly.

8|Updated Feb 25, 2026
Search Tags:#charts#interactive#visualization#plotly#matplotlib#seaborn#data-viz
One-click install
npx skills add https://github.com/SenseTime-FVG/sensenova-claw --skill python-dataviz-sensetime-fvg
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-dataviz
Source: https://github.com/SenseTime-FVG/sensenova-claw/tree/main/.sensenova-claw/skills/python-dataviz
Command: npx skills add https://github.com/SenseTime-FVG/sensenova-claw --skill python-dataviz-sensetime-fvg

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, seaborn, pandas, numpy, plotly, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Automates the creation of clear, publication-ready static and interactive charts so you can turn raw tables and arrays into communicative visuals without manually crafting plotting code every time. It reduces iteration time for figures used in reports, presentations, and exploratory analysis and ensures consistent styling and export settings.

Core Features & Use Cases

  • Static visualizations: High-resolution PNG, SVG, and PDF exports via matplotlib and seaborn for papers and reports.
  • Interactive visualizations: HTML exports, hover tooltips, zoom/pan, 3D plots and animations via plotly for dashboards and exploratory analysis.
  • Examples included: Bar, line, scatter, heatmap, distribution, multi-panel layouts, and ready-to-run scripts for common chart types.
  • Use cases: Generating reproducible figures for academic publications, building visual analytics for business reports, and producing interactive web-ready charts for stakeholder review.

Quick Start

Generate a PNG and an interactive HTML chart from my CSV dataset using the python-dataviz skill and save the outputs locally.

Frequently Asked Questions about python-dataviz

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

FAQPage Schema
How do I create publication-ready charts from tabular data in Python?▼

To create publication-ready charts from tabular data, you can use Python visualization libraries like matplotlib and seaborn to generate high-resolution static PNG, SVG, and PDF outputs for academic papers and reports.

Can I build interactive visualizations with hover tooltips and 3D plots using Python?▼

Yes, you can build interactive visualizations with hover tooltips, zoom, pan, 3D plots, and animations using plotly, exporting the final outputs as HTML files for dashboards and exploratory analysis.

What Python packages do I need to generate static and interactive data visualizations?▼

You need the Python packages matplotlib, seaborn, plotly, pandas, and numpy to generate static and interactive data visualizations, with the optional kaleido package required for static plotly exports.

Does plotly support exporting interactive charts to static PDF or PNG formats?▼

Yes, plotly supports exporting interactive charts to static PDF or PNG formats by installing the optional kaleido dependency, allowing you to capture interactive visualizations as high-resolution image files.

What is the best way to automate generating multi-panel layouts and heatmaps from a CSV dataset?▼

The best way to automate generating multi-panel layouts and heatmaps from a CSV dataset is using Python scripts with seaborn and matplotlib, which provide ready-to-run examples for common distribution and chart types.

When should I use matplotlib and seaborn versus plotly for data visualization?▼

Use matplotlib and seaborn for high-resolution static visualizations needed in papers and reports, and use plotly when you need interactive web-ready charts with hover tooltips, zoom, and pan for stakeholder review.

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