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data-visualization

Generate data visualizations with Matplotlib, Seaborn, and Plotly.

1|Updated Jan 26, 2026
Search Tags:#data visualization#data analysis#design principles#plotly#matplotlib#seaborn#chart selection
One-click install
npx skills add https://github.com/olavocarvalho/data-agents --skill data-visualization-olavocarvalho
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-visualization
Source: https://github.com/olavocarvalho/data-agents/tree/main/skills/data-vizualization
Command: npx skills add https://github.com/olavocarvalho/data-agents --skill data-visualization-olavocarvalho

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill helps users create clear, informative, and aesthetically pleasing data visualizations, overcoming the challenges of choosing the right chart type, implementing effective design principles, and ensuring accessibility.

Core Features & Use Cases

  • Chart Selection Guidance: Provides a decision tree for choosing the best chart type based on the data relationship being shown.
  • Python Code Patterns: Offers ready-to-use Python code snippets for common chart types (line, bar, histogram, heatmap, small multiples) using Matplotlib and Seaborn.
  • Design Principles: Outlines best practices for color, typography, layout, and accuracy in data visualization.
  • Interactive Charts: Includes examples for creating interactive plots with Plotly.
  • Use Case: You have a dataset with sales figures over time across different regions. This skill can guide you to use a line chart, provide the Python code to generate it, and offer tips on making it publication-ready.

Quick Start

Use the data-visualization skill to create a bar chart showing sales by region from the attached 'sales_data.csv' file.

Frequently Asked Questions about data-visualization

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

FAQPage Schema
How do I choose the right chart type for my data analysis?▼

Choosing the right chart type for data analysis involves using a decision tree based on the data relationship being shown. This ensures your visualizations effectively communicate the underlying patterns, whether comparing categories or tracking changes over time.

How do I create interactive charts with Plotly for my dashboard?▼

To create interactive charts with Plotly, you can use provided Python code examples that demonstrate building interactive plots. These examples support creating interactive dashboards and exploratory data analysis visualizations directly from your datasets.

Does this approach provide Python code patterns for Matplotlib and Seaborn?▼

Yes, this approach provides ready-to-use Python code patterns for common chart types using Matplotlib and Seaborn. These snippets cover line, bar, histogram, heatmap, and small multiples charts for your data visualization needs.

What design principles should I apply for publication-quality figures?▼

For publication-quality figures, apply design principles outlining best practices for color, typography, layout, and accuracy. These data visualization guidelines ensure your charts are clear, accessible, and aesthetically pleasing for final publication.

Can I generate a bar chart showing sales by region from a CSV file?▼

Yes, you can generate a bar chart showing sales by region from a CSV file. The skill processes your dataset to provide the exact Python code needed to visualize regional sales figures effectively.

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