data-visualization
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SKILL.md
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Data Visualization Skill
This skill provides capabilities for creating data visualizations.
Quick Start
Using matplotlib for basic charts:
import matplotlib.pyplot as plt
# Simple line chart
plt.plot([1, 2, 3, 4], [1, 4, 2, 3])
plt.title("Sample Chart")
plt.savefig("chart.png")
Capabilities
Chart Types
- Line charts
- Bar charts
- Pie charts
- Scatter plots
- Histograms
- Box plots
- Heatmaps
Libraries Supported
- Matplotlib (static charts)
- Seaborn (statistical visualizations)
- Plotly (interactive charts)
- Altair (declarative visualization)
Advanced Features
- Multi-axis plots
- Subplots and grids
- Custom themes and styling
- Annotations and labels
- Export to various formats (PNG, SVG, PDF)
Best Practices
- Choose the right chart type for your data
- Use clear labels and titles
- Consider color accessibility
- Keep visualizations simple and focused
- Export at appropriate resolution for intended use
Source
git clone https://github.com/aiskillstore/marketplace/blob/main/skills/0xkynz/data-visualization/SKILL.mdView on GitHub Overview
The Data Visualization Skill helps you generate charts and reports from data using popular Python libraries. It covers basic to advanced visuals, across multiple chart types, with options for static and interactive plots and export-ready outputs.
How This Skill Works
Select a chart type and configure axes, labels, and styling using libraries like Matplotlib, Seaborn, Plotly, or Altair. The tool renders the visualization and exports it in formats such as PNG, SVG, or PDF. It supports multi-axis plots, subplots, annotations, and themed styling for polished graphics.
When to Use It
- Visualize time-series data to reveal trends with line charts.
- Compare categories or groups using bar charts or column charts.
- Show composition or proportion with pie charts or stacked visuals.
- Explore relationships with scatter plots or correlation heatmaps.
- Prepare printable or shareable reports by exporting charts to PNG, SVG, or PDF.
Quick Start
- Step 1: Install a visualization library (Matplotlib/Seaborn/Plotly/Altair).
- Step 2: Create a basic chart (e.g., line or bar) and set a title.
- Step 3: Export the chart to PNG, SVG, or PDF for sharing.
Best Practices
- Choose the right chart type for your data and question.
- Label axes, add a clear title, and include a legend when needed.
- Use accessible color palettes and sufficient contrast.
- Keep visuals simple, focused, and free of clutter.
- Export at appropriate resolution and size for the intended medium.
Example Use Cases
- Monthly sales trend line chart showing revenue over the last year.
- Product category distribution pie chart illustrating market share.
- User engagement scatter plot of session duration vs. actions taken.
- Test score histogram to assess distribution across cohorts.
- Feature correlation heatmap to identify strong relationships between metrics.
Frequently Asked Questions
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