dataspot 0.1.0

Find data concentration patterns and dataspots. Built for fraud detection and risk analysis.

Dataspot 🔥

Find data concentration patterns and dataspots in your datasets Created by @eliosf27 at Frauddi

Dataspot automatically discovers where your data concentrates, helping you identify patterns, anomalies, and insights in datasets. Originally developed for fraud detection at Frauddi, now available as open source.

✨ Why Dataspot?

• 🎯 Purpose-built for finding data concentrations, not just clustering
• 🔍 Fraud detection ready - spot suspicious behavior patterns
• ⚡ Simple API - get insights in 3 lines of code
• 📊 Hierarchical analysis - understand data at multiple levels
• 🔧 Flexible filtering - customize analysis with powerful options
• 📈 Production tested - battle-tested in real fraud detection systems

🚀 Quick Start

pip install dataspot

import dataspot

# Sample transaction data
data = [
    {"country": "US", "device": "mobile", "amount": 150, "user_type": "premium"},
    {"country": "US", "device": "mobile", "amount": 200, "user_type": "premium"},
    {"country": "EU", "device": "desktop", "amount": 50, "user_type": "free"},
    {"country": "US", "device": "mobile", "amount": 300, "user_type": "premium"},
    # ... more data
]

# Find concentration dataspots
dataspot = dataspot.Dataspot()
concentrations = dataspot.find(data, fields=["country", "device", "user_type"])

# Results show where data concentrates
for pattern in concentrations[:5]:
    print(f"{pattern.path} → {pattern.percentage}% ({pattern.count} records)")

# Output:
# country=US > device=mobile > user_type=premium → 45.2% (127 records)
# country=US > device=mobile → 52.1% (146 records)
# device=mobile → 67.8% (190 records)

🎯 Real-World Use Cases

🚨 Fraud Detection

# Find suspicious transaction patterns
suspicious = dataspot.find(
    transactions,
    fields=["country", "payment_method", "time_of_day"],
    min_percentage=15  # Only significant concentrations
)

# Spot unusual concentrations that might indicate fraud
for pattern in suspicious:
    if pattern.percentage > 30:
        print(f"⚠️ High concentration: {pattern.path}")

📊 Business Intelligence

# Discover customer behavior patterns
insights = dataspot.analyze(
    customer_data,
    fields=["region", "device", "product_category", "tier"]
)

print(f"📈 Found {len(insights.patterns)} concentration patterns")
print(f"🎯 Top opportunity: {insights.top_patterns[0].path}")

🔍 Data Quality Analysis

# Find data quality issues
concentrations = dataspot.find(user_logs, ["source", "event", "status"])

# Look for unusual concentrations that might indicate data issues
anomalies = [p for p in concentrations if p.percentage > 80]
for anomaly in anomalies:
    print(f"⚠️ Possible data quality issue: {anomaly.path}")

🛠️ Advanced Usage

Flexible Filtering

# Complex analysis with multiple criteria
results = dataspot.query(
    min_percentage=10,          # Only patterns with >10% concentration
    max_depth=3,               # Limit hierarchy depth
    contains="mobile",         # Must contain "mobile" in pattern
    min_count=50,             # At least 50 records
    sort_by="concentration"   # Sort by concentration strength
)

Builder Pattern for Complex Queries

from dataspot import QueryBuilder

# Fluent interface for complex filtering
high_value_patterns = QueryBuilder(dataspot) \
    .field("country", "US") \
    .min_percentage(20) \
    .exclude(["test", "internal"]) \
    .sort_by("percentage") \
    .limit(10) \
    .execute()

Custom Analysis

# Add custom preprocessing
def extract_hour(timestamp):
    return timestamp.split("T")[1][:2]  # Extract hour from ISO timestamp

dataspot.add_preprocessor("timestamp", extract_hour)

# Now timestamp field will be analyzed by hour
patterns = dataspot.find(events, ["user_type", "timestamp", "action"])

⚡ Performance

Dataspot is built for speed and scale. Our optimized algorithm delivers exceptional performance across datasets of any size.

🚀 Blazing Fast Performance

Dataset Size	Processing Time	Memory Usage
1K records	~3ms	~2MB
10K records	~30ms	~15MB
100K records	~300ms	~150MB
1M records	~3s	~1.5GB

📊 Algorithm Complexity

• Time Complexity: O(n × f) where n = records, f = fields
• Space Complexity: O(n × f) linear memory usage
• Scalability: Linear scaling - predictable performance growth

🔥 Built for Production

import time
import dataspot

# Large dataset example
data = generate_transactions(100_000)  # 100K records
fields = ["country", "device", "payment_method", "user_tier"]

start = time.time()
patterns = dataspot.find(data, fields, min_percentage=5)
duration = time.time() - start

print(f"Analyzed 100K records in {duration:.2f}s")
# Output: Analyzed 100K records in 0.31s

💡 Performance Tips

Optimize for Speed:

# Use filtering to reduce pattern count
patterns = dataspot.find(
    data,
    fields,
    min_percentage=10,    # Skip low-concentration patterns
    max_depth=3,         # Limit hierarchy depth
    limit=100           # Cap results
)

Memory Efficiency:

# Process large datasets in chunks
def analyze_large_dataset(data, chunk_size=50000):
    results = []
    for i in range(0, len(data), chunk_size):
        chunk = data[i:i + chunk_size]
        patterns = dataspot.find(chunk, fields)
        results.extend(patterns)
    return results

🎯 When to Use Dataspot

✅ Perfect for:

• Real-time fraud detection (millisecond response times)
• Large-scale business intelligence
• High-frequency pattern analysis
• Production systems with strict performance requirements

⚠️ Consider alternatives for:

• Simple data grouping (use pandas.groupby())
• One-time data exploration (any tool works)
• Very small datasets (<100 records)

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Benchmarks run on standard hardware (Intel i7, 16GB RAM). Your results may vary.

📈 What Makes Dataspot Different?

Traditional Clustering	Dataspot Analysis
Groups similar data points	Finds concentration patterns
Equal-sized clusters	Identifies where data accumulates
Distance-based	Percentage and count based
Hard to interpret	Business-friendly hierarchy
Generic approach	Built for real-world analysis

🔧 Installation & Requirements

# Install from PyPI
pip install dataspot

# Development installation
git clone https://github.com/frauddi/dataspot.git
cd dataspot
pip install -e ".[dev]"

Requirements:

• Python 3.8+
• No heavy dependencies (just standard library + optional speedups)

🛠️ Development Commands

The project includes a Makefile with useful development commands:

Command	Description
make lint	Check code for style and quality issues
make lint-fix	Automatically fix linting issues where possible
make tests	Run all tests with coverage reporting
make check	Run both linting and tests
make clean	Remove cache files, build artifacts, and temporary files
make venv-clean	Remove the virtual environment
make venv-create	Create a new virtual environment with Python 3.11
make venv-install	Install the uv package manager
make install	Create virtual environment and install the dependencies

📚 Documentation

• 📖 User Guide - Complete usage documentation
• 🔧 API Reference - Detailed API documentation
• 💡 Examples - Real-world usage examples
• 🤝 Contributing - How to contribute

🌟 Why Open Source?

Dataspot was born from real-world fraud detection needs at Frauddi. We believe powerful pattern analysis shouldn't be locked behind closed doors. By open-sourcing Dataspot, we hope to:

• 🎯 Advance fraud detection across the industry
• 🤝 Enable collaboration on pattern analysis techniques
• 🔍 Help companies spot issues in their data
• 📈 Improve data quality everywhere

🤝 Contributing

We welcome contributions! Whether you're:

• 🐛 Reporting bugs
• 💡 Suggesting features
• 📝 Improving documentation
• 🔧 Adding new analysis methods

See our Contributing Guide for details.

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

• Created by @eliosf27 - Original algorithm and implementation
• Sponsored by Frauddi - Production testing and open source support
• Inspired by real fraud detection challenges - Built to solve actual problems

🔗 Links

• 🏠 Homepage
• 📦 PyPI Package (coming soon)
• 🐛 Issue Tracker
• 💬 Discussions
• 🐦 Follow updates on Twitter

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Find your data's dataspots. Discover what others miss. Built with ❤️ by Frauddi