instavizlib 1.0.0

Intelligent Data Visualization - See truth, not just charts. Auto-selects best charts, provides insights, and creates beautiful visualizations.

📊 InstaViz - Intelligent Data Visualization

See truth, not just charts.

InstaViz is a next-generation Python visualization library built on top of Matplotlib that automatically chooses the best chart type for your data and provides natural language insights.

✨ Features

• 🎯 Smart Auto-Selection - Automatically picks the best chart for your data
• 📈 Built-in Insights - Get natural language explanations of your data
• 🎨 Beautiful Themes - Professional themes out of the box (light, dark, minimal, vibrant, corporate)
• 🔍 One-Line EDA - Complete exploratory data analysis with viz.show(df)
• 📊 Statistical Annotations - Automatic outlier detection, trend lines, and statistics
• 🚀 Zero Configuration - Works great with sensible defaults

📦 Installation

pip install instaviz

##🚀 Quick Start

```bash

import instaviz as viz
import pandas as pd
import numpy as np

# Create sample data
df = pd.DataFrame({
    'category': ['A', 'B', 'C', 'D'] * 25,
    'value': np.random.randn(100),
    'score': np.random.exponential(50, 100)
})

# 🔍 Automatic EDA - One line to explore your data!
viz.show(df)

# 📊 Smart Plotting - Automatically chooses the best chart
viz.plot(df, x='category', y='value')

# 📈 Distribution Analysis
viz.distribution(df, 'score')

# 🆚 Category Comparison
viz.compare(df, x='category', y='value')

# 🔗 Correlation Heatmap
viz.correlate(df)

## 📖 API Reference

Core Functions
Function	Description
viz.show(df)	Automatic EDA dashboard
viz.plot(df, x, y)	Smart auto-plot
viz.distribution(df, column)	Distribution analysis
viz.compare(df, x, y)	Category comparison
viz.trend(df, date_col, value_col)	Time series trends
viz.correlate(df)	Correlation heatmap
viz.relationship(df, x, y)	Scatter/relationship plots
viz.target(df, target)	Feature vs target analysis
Themes
Python

# Available themes: 'instaviz', 'dark', 'minimal', 'vibrant', 'corporate'
viz.set_theme('dark')
Configuration
Python

viz.set_config(
    figsize=(12, 8),
    show_insights=True,
    auto_annotate=True,
    max_categories=20
)

## 🎨 Example Gallery

Distribution Analysis
Python

viz.distribution(df, 'price', kind='raincloud')
Time Series
Python

viz.trend(df, 'date', 'revenue', smooth=3)
Relationship with Grouping
Python

viz.relationship(df, 'age', 'income', hue='education')

## 📋 Requirements

Python >= 3.8
NumPy >= 1.20.0
Pandas >= 1.3.0
Matplotlib >= 3.5.0
SciPy >= 1.7.0

## 👨‍💻 Author

Md. Ujayer Hasnat

📧 Email: [e-mail]
💼 LinkedIn: linkedin.com/in/ujayerhasnat
🐙 GitHub: github.com/CodexUjayer

## 📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

## 🤝 Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## ⭐ Star History

If you find this project useful, please consider giving it a star on GitHub!