Data Visualization Studio
by @zhuyu28
Create interactive and static data visualizations from datasets. Supports charts, graphs, dashboards, and statistical plots with multiple output formats (PNG...
clawhub install data-visualization-studioπ About This Skill
name: data-visualization-studio description: Create interactive and static data visualizations from datasets. Supports charts, graphs, dashboards, and statistical plots with multiple output formats (PNG, SVG, HTML, PDF).
Data Visualization Studio
Create professional data visualizations from raw data or existing datasets.
When to Use
Quick Start
Basic Chart Creation
# Example: Create a simple bar chart
import pandas as pd
import matplotlib.pyplot as pltdata = pd.read_csv('data.csv')
plt.bar(data['category'], data['values'])
plt.savefig('chart.png', dpi=300, bbox_inches='tight')
Interactive Dashboard
# Example: Create interactive plot with Plotly
import plotly.express as pxdf = pd.read_csv('data.csv')
fig = px.scatter(df, x='x_column', y='y_column', color='category')
fig.write_html('dashboard.html')
Supported Libraries
Output Formats
Best Practices
1. Data Preparation: Clean and validate data before visualization 2. Color Schemes: Use accessible color palettes (avoid red-green) 3. Labels: Always include clear axis labels and titles 4. Resolution: Use appropriate DPI for intended use (72 for web, 300+ for print) 5. File Size: Optimize file sizes for web delivery when needed
Advanced Features
References
For detailed examples and advanced usage patterns, see the bundled reference files:
references/chart-types.md - Complete catalog of supported chart typesreferences/styling-guide.md - Customization and branding guidelines references/performance.md - Optimization for large datasetsβ‘ When to Use
π‘ Examples
Basic Chart Creation
# Example: Create a simple bar chart
import pandas as pd
import matplotlib.pyplot as pltdata = pd.read_csv('data.csv')
plt.bar(data['category'], data['values'])
plt.savefig('chart.png', dpi=300, bbox_inches='tight')
Interactive Dashboard
# Example: Create interactive plot with Plotly
import plotly.express as pxdf = pd.read_csv('data.csv')
fig = px.scatter(df, x='x_column', y='y_column', color='category')
fig.write_html('dashboard.html')
π Tips & Best Practices
1. Data Preparation: Clean and validate data before visualization 2. Color Schemes: Use accessible color palettes (avoid red-green) 3. Labels: Always include clear axis labels and titles 4. Resolution: Use appropriate DPI for intended use (72 for web, 300+ for print) 5. File Size: Optimize file sizes for web delivery when needed