Professional Python Visualization

A modern Python visualization library combining traditional charting with AI-powered features, multiple backend support, and enterprise-grade capabilities.

Explore Gallery View Features
import vizlychart as vc # Create charts with natural language chart = vc.ai.create("scatter plot of sales vs price") # Switch backends seamlessly vc.set_backend('plotly') # Interactive web charts vc.set_backend('matplotlib') # Publication quality # Enterprise exports chart.export('presentation.pptx', branded=True)

Key Features

VizlyChart extends traditional visualization capabilities with modern features for data science, enterprise use, and AI-assisted chart creation.

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AI-Assisted Chart Creation

Generate charts from natural language descriptions and get intelligent recommendations based on your data characteristics.

# Smart recommendations rec = vc.recommend_chart(data, intent='correlation') print(f"Suggested: {rec.chart_type}") # Natural language generation chart = vc.ai.create("line chart of revenue over time")
Production Ready
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Multiple Backend Support

Switch between matplotlib, Plotly, and pure Python backends without changing your code. Each backend optimized for different use cases.

# Same API, different outputs vc.set_backend('matplotlib') # Static, high-quality vc.set_backend('plotly') # Interactive, web-ready vc.set_backend('pure') # Lightweight, fast
Stable
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Specialized Chart Types

Advanced visualizations for machine learning, causal inference, financial analysis, and engineering applications.

# Causal inference dag = vc.CausalDAGChart() dag.add_node("Treatment", "treatment") # ML explainability chart = vc.FeatureImportanceChart() chart.plot(features, shap_values)
Available
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Enterprise Features

Professional exports to PowerPoint, Excel, and PDF with customizable branding, compliance tracking, and audit trails.

# Enterprise export exporter = vc.EnterpriseExporter(branding) exporter.export_chart( chart, format='pptx', branded=True )
Available
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Performance Optimization

GPU acceleration support and optimized rendering for large datasets with automatic performance scaling.

# GPU-accelerated rendering renderer = vc.AcceleratedRenderer() renderer.scatter_gpu(x, y) # Handle large datasets # Performance monitoring stats = renderer.get_performance_stats()
Beta
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Natural Language Styling

Apply styling and themes using natural language descriptions, making chart customization intuitive and accessible.

# Style with natural language vc.style_chart(chart, "professional blue theme") vc.style_chart(chart, "minimal design with thin lines") # Parse styling descriptions style = vc.parse_style("elegant pastel colors")
Ready

Documentation & Resources

Get started quickly with comprehensive guides, API documentation, and example notebooks.

📚 Getting Started

Installation guide and basic usage examples to get you up and running quickly.

View Guide →

🔧 API Reference

Complete API documentation with method signatures and parameter details.

Browse API →

💡 Examples

Jupyter notebooks with practical examples and use cases across different domains.

View Examples →

🏢 Enterprise Guide

Advanced features for enterprise use including branding, exports, and compliance.

Learn More →