Data Visualization Studio
Create professional data visualizations from raw data or existing datasets.
When to Use
- Creating charts and graphs from CSV, JSON, or database data
- Building interactive dashboards for data exploration
- Generating statistical plots and visual analytics
- Exporting visualizations in multiple formats (PNG, SVG, HTML, PDF)
- Creating publication-ready figures and reports
Quick Start
Basic Chart Creation
# Example: Create a simple bar chart
import pandas as pd
import matplotlib.pyplot as plt
data = 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 px
df = pd.read_csv('data.csv')
fig = px.scatter(df, x='x_column', y='y_column', color='category')
fig.write_html('dashboard.html')Supported Libraries
- Matplotlib: Static plots, publication-quality figures
- Plotly: Interactive visualizations, web dashboards
- Seaborn: Statistical graphics, beautiful default styles
- Bokeh: Interactive web plots, streaming data support
- Altair: Declarative visualization, Vega-Lite integration
Output Formats
- PNG/JPEG: High-resolution static images
- SVG: Scalable vector graphics for web/print
- HTML: Interactive web pages with embedded JavaScript
- PDF: Publication-ready documents
- JSON: Data export for further processing
Best Practices
- Data Preparation: Clean and validate data before visualization
- Color Schemes: Use accessible color palettes (avoid red-green)
- Labels: Always include clear axis labels and titles
- Resolution: Use appropriate DPI for intended use (72 for web, 300+ for print)
- File Size: Optimize file sizes for web delivery when needed
Advanced Features
- Animation: Create animated transitions and time-series visualizations
- Geospatial: Map-based visualizations with geographic data
- 3D Plots: Three-dimensional data representation
- Custom Styling: Brand-consistent themes and styling
- Real-time: Live updating visualizations from streaming data
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 guidelinesreferences/performance.md- Optimization for large datasets