Data Visualization
Data Visualization teaches you to build clear, compelling charts and graphs using matplotlib and seaborn. Learn distribution plots, scatter plots, heatmaps, and more to uncover patterns and communicate findings to stakeholders.
Data Visualization helps you create effective charts and graphs with matplotlib and seaborn for analysis and storytelling.
AI-generated summary based on this skill's SKILL.md
Install
aj-geddes/useful-ai-prompts/data-visualization · repository language: Shell
git clone https://github.com/aj-geddes/useful-ai-prompts
cp -r useful-ai-prompts/skills/data-visualization ~/.claude/skills/data-visualizationFrequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How do I create matplotlib charts for data analysis?
Data Visualization covers matplotlib fundamentals for building charts from scratch. You'll learn to create line plots, scatter plots, bar charts, and histograms using matplotlib's object-oriented API. The skill teaches you how to customize axes, labels, legends, and styling to make your charts clear and professional. With matplotlib as your foundation, you can build any chart type and have full control over every visual element.
What seaborn visualization types does Data Visualization cover?
Data Visualization teaches seaborn's high-level plotting functions for statistical graphics. You'll learn to create distribution plots, scatter plots, heatmaps, box plots, violin plots, and pair plots. Seaborn integrates with pandas DataFrames and handles data aggregation automatically, making it faster than matplotlib for exploratory analysis. The skill shows how to layer seaborn plots on matplotlib axes for maximum flexibility.
How can I use Data Visualization to communicate insights to stakeholders?
Data Visualization teaches you to design charts that tell clear stories to business audiences. You'll learn which chart types work best for different data patterns—bar charts for categories, line plots for trends, scatter plots for relationships. The skill covers labeling, color choices, and layout techniques that make insights immediately obvious. You'll also learn to build dashboards and publication-ready visualizations that stakeholders can understand without explanation.
How do I visualize distributions and detect outliers visually?
Data Visualization shows multiple approaches to understanding data distributions. Histograms reveal the overall shape of data, box plots highlight quartiles and outliers, and violin plots show the full distribution density. You'll learn when each plot type is most effective and how to layer them for deeper insight. Visual inspection using these techniques helps you spot anomalies and unusual patterns before statistical testing.
Can Data Visualization help me build dashboards and publication-ready charts?
Yes. Data Visualization covers gridspec and subplot layouts for organizing multiple charts into dashboards. You'll learn figure sizing, DPI settings, and export formats for publication. The skill teaches color palette selection for accessibility and visual impact, ensuring your dashboards work in print and on screens. You'll combine matplotlib and seaborn techniques to create polished, professional visualizations ready for reports and presentations.
What data visualization best practices does this skill teach?
Data Visualization emphasizes design principles like choosing appropriate chart types, using color effectively, and minimizing visual clutter. You'll learn when to use different plot types for exploratory analysis versus stakeholder communication. The skill covers accessibility considerations like colorblind-friendly palettes and clear labeling. These best practices help you create visualizations that reveal truth in data rather than obscure it.
SKILL.md
rendered from the published skill — quoted content, verbatim
Data Visualization
Overview
Data visualization transforms complex data into clear, compelling visual representations that reveal patterns, trends, and insights for storytelling and decision-making.
When to Use
- Exploratory data analysis and pattern discovery
- Communicating insights to stakeholders
- Comparing distributions and relationships
- Presenting findings in reports and dashboards
- Identifying outliers and anomalies visually
- Creating publication-ready charts and graphs
Visualization Types
- Distributions: Histograms, KDE, violin plots
- Relationships: Scatter plots, line plots, heatmaps
- Comparisons: Bar charts, box plots, ridge plots
- Compositions: Pie charts, stacked bars, treemaps
- Temporal: Line plots, area charts, time series
- Multivariate: Pair plots, correlation heatmaps
Design Principles
- Choose appropriate chart type for data
- Minimize ink-to-data ratio
- Use color purposefully
- Label clearly and
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skills/data-visualization/SKILL.md
skills/data-visualization/scripts/scaffold-analysis.sh
skills/data-visualization/templates/notebook-template.py