datastory-ai 0.1.0
Automatic Storytelling from Data - Turn raw data into compelling business narratives
License: MIT
Requires Python: >=3.8
Keywords: data analysis, business intelligence, narrative, storytelling, automated reports, data science

# 📊 DataStory - Automatic Storytelling from Data

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[![Python 3.8+](https://img.shields.io/badge/python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

**Turn raw data into compelling business narratives automatically.** 

DataStory analyzes your datasets and generates full written reports with insights, trends, and recommendations - no LLMs needed, pure Python intelligence.

## 🚀 The Problem

- **Dashboards don't explain insights** - They show graphs, not stories
- **People want narratives** - Business stakeholders need context, not just charts
- **Manual analysis takes time** - Writing reports is tedious and repetitive
- **Insights get lost** - Important patterns buried in spreadsheets

## 💡 The Solution

DataStory automatically:
- ✅ Analyzes your data for trends, patterns, and anomalies
- ✅ Generates natural language business narratives
- ✅ Identifies risks and opportunities
- ✅ Provides actionable recommendations
- ✅ Exports to text, markdown, HTML, or PDF

**All with a single line of code!**

## 📦 Installation

```bash
pip install datastory
```

For full features (charts, Excel, PDF):
```bash
pip install datastory[full]
```

## 🎯 Quick Start

### One-Line Magic

```python
from datastory import narrate

report = narrate("sales.csv")
print(report)
```

**Output:**
```
📊 EXECUTIVE SUMMARY
==================================================
Analyzed 1,247 records across 8 dimensions.

🟡 3 high-priority insights identified.

Key Highlights:
1. Sales increased by 12.3% from $450,000 to $505,000.
2. Customer churn rose in April by 8.5%, requiring attention.
3. West Africa region dominates sales, accounting for 45.2% of revenue.

📈 KEY FINDINGS
==================================================

**Performance Trends:**
• Sales Shows Strong Growth: Sales increased by 12.3% from $450,000 to $505,000.
• Revenue per Customer Rising: Average order value grew by 15.7%.

**Notable Anomalies:**
• Unusual Values Detected in Order Quantity: Found 23 outliers (1.8% of data).

**Relationships Discovered:**
• Strong Positive Link: Marketing Spend and Revenue move together (correlation: 0.85).

🔍 DETAILED ANALYSIS
==================================================

**High-Priority Insights:**

🟡 Customer Churn Rising
   Customer churn increased by 8.5% in April. This represents a significant concern.

🟡 Low Stock Risk: Product X
   Minimum inventory is 12 units, significantly below average of 150. Consider restocking.

💡 RECOMMENDATIONS
==================================================
1. Investigate the decline in customer retention and implement recovery strategies
2. Capitalize on the growth in revenue per customer to maximize returns
3. Replenish product_x inventory to avoid stockouts
4. Review outliers in order quantity to identify root causes
5. Leverage identified relationships between metrics for predictive insights

==================================================
Report generated on December 03, 2025 at 1:20 PM
Powered by DataStory - Automatic Storytelling from Data
```

## 🔥 Key Features

### 1. **Pure Python Intelligence**
- No LLMs or AI APIs required
- Works offline
- Fast and deterministic
- Zero-cost analysis

### 2. **Comprehensive Analysis**
- Statistical summaries
- Trend detection
- Anomaly identification
- Correlation discovery
- Time series patterns
- Risk assessment

### 3. **Natural Language Output**
- Business-friendly narratives
- Context-aware descriptions
- Action-oriented recommendations
- Multiple detail levels

### 4. **Flexible Export**
```python
from datastory import DataStory

story = DataStory()
story.load("data.csv")

# Export to different formats
story.export("report.txt", format="text")
story.export("report.md", format="markdown")
story.export("report.html", format="html", include_charts=True)
story.export("report.pdf", format="pdf")
```

### 5. **Multiple Data Sources**
```python
# CSV, Excel, JSON, Parquet
story.load("sales.csv")
story.load("data.xlsx")
story.load("records.json")
story.load("dataset.parquet")

# URLs
story.load("https://example.com/data.csv")

# Pandas DataFrames
import pandas as pd
df = pd.read_sql("SELECT * FROM sales", conn)
story.load(df)
```

## 📖 Advanced Usage

### Customization

```python
from datastory import DataStory

# Configure narrative style
config = {
    "style": "business",  # business, casual, technical
    "detail_level": "detailed",  # brief, medium, detailed
    "include_recommendations": True
}

story = DataStory(config=config)
story.load("sales.csv")
narrative = story.generate_narrative()
print(narrative)
```

### Programmatic Access

```python
# Access insights directly
story = DataStory()
story.load("data.csv")

insights = story.extract_insights()
for insight in insights:
    print(f"{insight.type}: {insight.title}")
    print(f"Priority: {insight.priority}")
    print(f"Description: {insight.description}\n")
```

### Analysis Results

```python
# Get raw analysis results
story = DataStory()
story.load("data.csv")

results = story.analyze()
print(results["trends"])
print(results["anomalies"])
print(results["correlations"])
```

## 🎓 Use Cases

### 1. **Business Intelligence**
Generate executive summaries from sales, marketing, or financial data.

### 2. **Data Science Reports**
Automatically document exploratory data analysis (EDA) findings.

### 3. **Automated Monitoring**
Create daily/weekly reports on KPIs and metrics.

### 4. **Client Reporting**
Transform raw analytics into client-ready narratives.

### 5. **Academic Research**
Quickly summarize dataset characteristics and patterns.

## 🆚 Why DataStory?

| Feature | DataStory | Traditional BI | LLM-based |
|---------|-----------|----------------|-----------|
| **Setup Time** | Instant | Hours/Days | API setup |
| **Cost** | Free | $$$$ | $$$ per call |
| **Offline Use** | ✅ Yes | ❌ No | ❌ No |
| **Customizable** | ✅ Full control | ⚠️ Limited | ❌ Black box |
| **Speed** | ⚡ Instant | 🐌 Slow | ⏳ API delays |
| **Privacy** | 🔒 Local | ⚠️ Cloud | ❌ Sent to API |
| **Deterministic** | ✅ Yes | ✅ Yes | ❌ No |

## 📊 Example Datasets

The `examples/` directory includes sample datasets:
- `sales.csv` - Sales performance data
- `customer_churn.csv` - Customer retention data
- `inventory.csv` - Stock levels and products

## 🛠️ Technical Details

### Architecture
- **Core Analyzer**: Statistical analysis using pandas/numpy
- **Insight Extractor**: Pattern recognition and business logic
- **Narrative Generator**: Template-based natural language generation
- **Data Loaders**: Multi-format support (CSV, Excel, JSON, Parquet)
- **Report Formatters**: Export to text, markdown, HTML, PDF

### Dependencies
- **Core**: `pandas`, `numpy`
- **Optional**: `matplotlib` (charts), `openpyxl` (Excel), `reportlab` (PDF)

### Performance
- Analyzes 100K rows in <2 seconds
- Generates narrative in <1 second
- Low memory footprint

## 🤝 Contributing

Contributions are welcome! Please:
1. Fork the repository
2. Create a feature branch
3. Submit a pull request

## 📝 License

MIT License - see [LICENSE](LICENSE) file for details.

## 🙏 Acknowledgments

Built with ❤️ by [Idriss Bado](https://github.com/idrissbado)

Inspired by the need for better data communication in business.

## 📧 Contact

- GitHub: [@idrissbado](https://github.com/idrissbado)
- PyPI: [datastory](https://pypi.org/project/datastory/)

---

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🐛 **Found a bug?** [Open an issue](https://github.com/idrissbado/DataStory/issues)

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