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python-dataviz

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

39|1|Updated Jul 2, 2026
Search Tags:#data visualization#charts#python#plotly#matplotlib#statistical plots#seaborn
One-click install
npx skills add https://github.com/HKU-MMLab/UniClawBench --skill python-dataviz-hku-mmlab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: python-dataviz
Source: https://github.com/HKU-MMLab/UniClawBench/tree/main/injection/101_skill_usage/task_101_12_csv_pipeline_merge/skills/python-dataviz
Command: npx skills add https://github.com/HKU-MMLab/UniClawBench --skill python-dataviz-hku-mmlab

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve? Creating publication-quality charts and statistical visualizations from data requires knowing which library and chart type to use, plus correct styling and export settings. This Skill provides ready-to-run examples and reference guides so agents can produce professional PNG, SVG, and HTML visualizations without trial and error. ## Core Features & Use Cases - Static Charts: Bar, line, scatter, heatmap, and distribution plots via matplotlib and seaborn, exported at 300 DPI in PNG or SVG. - Interactive Charts: Plotly-based scatter, line, bar, heatmap, 3D, and pie charts exported as HTML with hover tooltips and zoom. - Reference Guides: Color palette theory, accessibility guidance, and statistical visualization patterns with anti-patterns to avoid. - Use Case: Given a CSV of quarterly sales, load it with pandas, build a grouped bar chart with seaborn, and export a 300 DPI PNG for a report. ## Quick Start Ask the agent to create a grouped bar chart from your CSV data and save it as a high-resolution PNG using this data visualization skill.

Frequently Asked Questions about python-dataviz

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

FAQPage Schema
How do I create a bar chart in Python with matplotlib?▼

Use plt.bar() with your category labels and values, set a figure size like (10, 6), then call plt.savefig with dpi=300 and bbox_inches='tight'. The included bar_chart.py script demonstrates simple, grouped, horizontal, and stacked variations.

Matplotlib vs seaborn vs plotly: which should I use?▼

Matplotlib gives full control for static publication figures, seaborn adds statistical plots and better default styling on top of matplotlib, and plotly creates interactive HTML charts with hover tooltips and zoom. Choose based on whether you need static export or web interactivity.

How do I export a plotly chart as HTML?▼

Build the figure with plotly.express or plotly.graph_objects, then call fig.write_html('chart.html'). For static PNG export from plotly, the kaleido package is required.

Why are my matplotlib chart labels cut off when saving?▼

Labels get clipped when the figure layout exceeds the canvas. Call plt.tight_layout() before saving, or pass bbox_inches='tight' to plt.savefig() to expand the output bounds automatically.

What DPI should I use for publication-quality charts?▼

Use dpi=300 in plt.savefig() for publication or print quality. For web display, 72-150 DPI is sufficient. SVG export is resolution-independent and better for graphics that need scaling.

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