Metadata-Version: 2.4
Name: data-automation-kit
Version: 2.0.1
Summary: A comprehensive Python package for automated data loading, cleaning, visualization, and quality checks with AI integration
Home-page: https://github.com/yourusername/data-automation-kit
Author: Peter Gatitu
Author-email: petergatitu61@gmail.com
Project-URL: Bug Reports, https://github.com/yourusername/data-automation-kit/issues
Project-URL: Source, https://github.com/yourusername/data-automation-kit
Project-URL: Documentation, https://github.com/yourusername/data-automation-kit#readme
Keywords: data-analysis,automation,data-cleaning,visualization,data-quality,ai,machine-learning
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.3.0
Requires-Dist: numpy>=1.21.0
Requires-Dist: matplotlib>=3.5.0
Requires-Dist: seaborn>=0.11.0
Requires-Dist: sqlalchemy>=1.4.0
Requires-Dist: openpyxl>=3.0.0
Requires-Dist: groq>=0.3.0
Requires-Dist: scikit-learn>=1.0.0
Requires-Dist: scipy>=1.7.0
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license-file
Dynamic: project-url
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary

# Data Automation Kit v2.0 🚀

![Python](https://img.shields.io/badge/Python-3.7%2B-blue)
![Version](https://img.shields.io/badge/Version-2.0.0-orange)
![License](https://img.shields.io/badge/License-MIT-green)

**One line to rule them all:** `quick_analyze()` — Your complete data analysis in one function!

## 🎯 What's New in v2.0

- 🚀 One-Command Analysis: `quick_analyze()` does everything
- 🎨 Professional Visualizations: Beautiful, publication-ready charts
- 🤖 Enhanced AI Integration: Smarter insights with Groq
- 📊 Smart Data Detection: Auto-identifies data types and patterns
- 🛠️ Robust Architecture: Clean, maintainable code structure

## ⚡ Quick Start

### Installation
\`\`\`bash
pip install data-automation-kit
\`\`\`

### Usage (30 seconds)
\`\`\`python
from data_automation_kit import quick_analyze
quick_analyze()
\`\`\`

## 🎨 What You Get

| Feature | Description | Output |
|--------|-------------|--------|
| Smart Data Loading 📥 | CSV, Excel, JSON, SQL, or sample data | Clean DataFrame |
| Auto Quality Checks 🔍 | Data quality scoring | Quality report |
| Intelligent Cleaning 🧹 | Handle missing values, duplicates | Cleaned data |
| Beautiful Visualizations 📊 | Automatic charts | PNG files |
| AI-Powered Insights 🤖 | Analysis & recommendations | Text report |

## 📚 Complete Examples

### One-Line Magic
\`\`\`python
from data_automation_kit import quick_analyze
quick_analyze()
\`\`\`

### Step-by-Step Control
\`\`\`python
from data_automation_kit import DataLoader, AutoVisualizer

loader = DataLoader()
data = loader.load_csv("your_data.csv")

visualizer = AutoVisualizer(data)
visualizer.create_comprehensive_dashboard()
\`\`\`

### Interactive Session
\`\`\`python
from data_automation_kit import InteractiveAnalyzer
InteractiveAnalyzer().start_interactive_session()
\`\`\`

## 🎯 Key Features

### Smart Data Loading
- CSV, Excel, JSON, SQL
- Built-in sample datasets
- Smart file type detection
- Graceful error handling

### Professional Visualizations
- Correlation heatmaps
- Histograms, box plots, density plots
- Categorical charts
- Business dashboards

### AI-Powered Intelligence
- Dataset insights
- Cleaning recommendations
- Suggested charts
- Business recommendations

## 🛠️ Installation

### From PyPI
\`\`\`bash
pip install data-automation-kit
\`\`\`

### From Source
\`\`\`bash
git clone https://github.com/yourusername/data-automation-kit
cd data-automation-kit
pip install -e .
\`\`\`

## 📖 Documentation

### Basic Usage
\`\`\`python
from data_automation_kit import DataLoader, AutoVisualizer

loader = DataLoader()
data = loader.load_csv("data.csv")

data = loader.create_sample_data("sales")

viz = AutoVisualizer(data)
plots = viz.create_comprehensive_dashboard()

print(f"Created {len(plots)} visualizations!")
\`\`\`

### AI Integration
\`\`\`python
from data_automation_kit import GroqAnalyzer
report = GroqAnalyzer().generate_data_report(data)
print(report)
\`\`\`

## 🎨 Visualization Gallery

\`\`\`
data_visualizations/
├── 01_correlation_heatmap.png
├── 02_distribution_age.png
├── 03_categorical_department.png
├── 04_missing_values.png
├── 05_data_types.png
└── ...and more!
\`\`\`

## 🔧 Requirements
- Python 3.7+
- pandas, numpy, matplotlib, seaborn
- sqlalchemy
- groq
- scikit-learn, scipy

## 🤝 Contributing
We welcome contributions! See CONTRIBUTING.md.

## 📄 License
MIT License — see LICENSE.

## 🆘 Support
📧 Email: your-email@example.com  
🐛 GitHub Issues  
💬 Discussions

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Built with ❤️ for the data community.
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