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bigdata-visualization

Creates static and interactive data charts using Matplotlib, Seaborn, and Plotly.

1|Updated Mar 18, 2026
Search Tags:#data visualization#charts#dashboard#big data#plotly#matplotlib#seaborn
One-click install
npx skills add https://github.com/MARUCIE/openclaw-foundry --skill bigdata-visualization-marucie
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: bigdata-visualization
Source: https://github.com/MARUCIE/openclaw-foundry/tree/main/web/public/packs/data-analyst/skills/bigdata-viz
Command: npx skills add https://github.com/MARUCIE/openclaw-foundry --skill bigdata-visualization-marucie

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires matplotlib, seaborn, plotly.

What problem does it solve? Big data teams often struggle to choose the right charting library and produce consistent, publication-quality visuals for reports, dashboards, and exploratory analysis. This Skill standardizes visualization workflows across Matplotlib, Seaborn, and Plotly so outputs follow team conventions. ## Core Features & Use Cases - Library Selection Guide: Decision tree mapping output targets (reports, internal sharing, dashboards, real-time monitoring) to the right library. - Ready-to-Use Patterns: Code templates for Seaborn statistical charts (histograms, correlation heatmaps) and Plotly interactive dashboards (scatter, box plots, HTML export). - Big Data Techniques: Sampling, aggregation, and quantile-based visualization strategies for large datasets that cannot be plotted raw. - Team Standards: Enforced conventions for color schemes, font sizes, resolution (300dpi for reports, 72dpi for web), and output formats (PDF vs HTML). - Use Case: A data analyst needs a quarterly report chart and an interactive dashboard from the same dataset; the Skill provides the Seaborn template for the 300dpi report figure and the Plotly template for the shareable HTML dashboard. ## Quick Start Ask the AI to load the bigdata-viz skill and generate a correlation heatmap of your dataframe using Seaborn, saved as a 300dpi PNG for a report.

Frequently Asked Questions about bigdata-visualization

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

FAQPage Schema
How do I visualize large datasets in Python without performance issues?▼

Sample the dataframe before plotting, for example df.sample(n=10000), or aggregate with groupby before rendering. Quantile-based plots like Plotly box plots also summarize large distributions without drawing every point.

Matplotlib vs Seaborn vs Plotly, which should I use?▼

Use Matplotlib for fine-grained control in papers and reports, Seaborn for quick statistical charts like heatmaps and distributions, and Plotly for interactive dashboards and web display. The choice depends on whether the output is static or interactive.

How do I create an interactive dashboard with Plotly?▼

Use plotly.express functions such as px.scatter with color, size, and hover_data parameters, then export with fig.write_html to produce a shareable HTML file. For full dashboards, combine Plotly with Dash.

How do I make a correlation heatmap in Seaborn?▼

Call sns.heatmap with df.corr(), setting annot=True and a colormap such as coolwarm, then save with plt.savefig at 300dpi for reports. This produces an annotated correlation matrix suitable for publication.

What resolution should charts be for reports versus web?▼

Reports and papers should use 300dpi output, typically saved as PNG or PDF via Matplotlib's savefig dpi parameter. Web and internal sharing outputs use 72dpi or interactive HTML exported from Plotly.

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