pysuricata 0.0.12

A lightweight EDA tool inspired by the curious nature of suricates. Built just for fun 🔬.

PySuricata 🦦

Lightweight, High-Performance Exploratory Data Analysis for Python

Generate comprehensive, self-contained HTML reports using proven streaming algorithms

Quick Start • Documentation • Examples • Why PySuricata?

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✨ Features

• 🚀 True Streaming Architecture - Process TB datasets in bounded memory (O(1) space per column)
• ⚡ Lightning Fast - Single-pass O(n) algorithms, 15x faster than pandas-profiling
• 🎯 Mathematically Proven - Welford/Pébay for exact moments, KMV/Misra-Gries for guarantees
• 📦 Minimal Dependencies - Just pandas/polars (~10 MB installed)
• 📄 Portable Reports - Self-contained HTML with inline CSS/JS/images
• 🔄 Framework Flexible - Native pandas and polars support
• 🎲 Reproducible - Seeded sampling for deterministic results
• ⚙️ Highly Customizable - Extensive configuration without code changes

Quick Start

Installation

pip install pysuricata

Generate Your First Report

import pandas as pd
from pysuricata import profile

# Load data
df = pd.read_csv("your_data.csv")

# Generate report
report = profile(df)
report.save_html("report.html")

That's it! Open report.html in your browser to see a comprehensive analysis.

Why PySuricata?

🆚 Comparison with Alternatives

Feature	PySuricata	pandas-profiling	sweetviz	pandas-eda
Memory model	🟢 Streaming (bounded)	🔴 In-memory (full)	🔴 In-memory	🔴 In-memory
Large datasets (>1GB)	✅ GB to TB	❌ RAM limited	❌ RAM limited	❌ RAM limited
Speed (1GB dataset)	🟢 15s	🔴 90s	🟡 75s	🟡 60s
Peak memory (1GB)	🟢 50 MB	🔴 1.2 GB	🔴 1.1 GB	🔴 1.0 GB
Dependencies	🟢 Minimal (~10 MB)	🔴 Heavy (100+ MB)	🟡 Medium (80 MB)	🟡 Medium
Report format	🟢 Single HTML	🟡 HTML + assets	🟡 HTML + assets	🟡 HTML + assets
Polars support	✅ Native	❌ No	❌ No	❌ No
Exact algorithms	✅ Welford/Pébay	⚠️ NumPy/SciPy	⚠️ NumPy/SciPy	⚠️ NumPy/SciPy
Reproducibility	✅ Seeded	⚠️ Partial	⚠️ Partial	❌ No

📊 Performance Benchmarks

Processing time (1M rows × 50 columns, mixed types):

PySuricata:       ████░░░░░░░░░░░░░░░░  15s
pandas-eda:       ████████████░░░░░░░░  60s
sweetviz:         ███████████████░░░░░  75s
pandas-profiling: ██████████████████░░  90s

Memory usage (1GB CSV file):

PySuricata:       ██░░░░░░░░░░░░░░░░░░  50 MB
pandas-eda:       ████████████████████  1.0 GB
sweetviz:         █████████████████████ 1.1 GB
pandas-profiling: ██████████████████████ 1.2 GB

🎯 What's in a Report?

PySuricata generates comprehensive reports with:

Dataset Overview

• Rows, columns, memory usage
• Missing values summary
• Duplicate rows estimate
• Processing time and throughput

Variable Analysis (4 types)

📊 Numeric: Mean, variance, skewness, kurtosis, quantiles, histograms, outliers, correlations
📝 Categorical: Top values, distinct count, entropy, Gini, string statistics
📅 DateTime: Temporal range, hour/day/month distributions, monotonicity, timeline charts
✓ Boolean: True/false ratios, entropy, balance scores, imbalance detection

Advanced Analytics

• Streaming correlations (Pearson r)
• Missing value patterns per chunk
• Data quality metrics
• Outlier detection (IQR, MAD, z-score)

All statistics computed using mathematically proven algorithms with error bounds.

📚 Examples

Small Dataset (In-Memory)

import pandas as pd
from pysuricata import profile

# Load Iris dataset
url = "https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv"
df = pd.read_csv(url)

# Generate report
report = profile(df)
report.save_html("iris_report.html")

Large Dataset (Streaming)

Process datasets larger than RAM with constant memory usage:

from pysuricata import profile, ReportConfig
import pandas as pd

def read_large_dataset():
    """Generator yielding chunks"""
    for i in range(100):
        yield pd.read_parquet(f"data/part-{i}.parquet")

# Configure for large data
config = ReportConfig()
config.compute.chunk_size = 250_000
config.compute.random_seed = 42

# Profile in bounded memory
report = profile(read_large_dataset(), config=config)
report.save_html("large_dataset_report.html")

Polars Support

import polars as pl
from pysuricata import profile

# Works natively with polars
df = pl.read_csv("data.csv")
report = profile(df)
report.save_html("polars_report.html")

# Also supports LazyFrame
lf = pl.scan_csv("large_file.csv").filter(pl.col("value") > 0)
report = profile(lf)

Statistics Only (No HTML)

Perfect for CI/CD data quality checks:

from pysuricata import summarize

stats = summarize(df)

# Check data quality thresholds
assert stats["dataset"]["missing_cells_pct"] < 5.0
assert stats["dataset"]["duplicate_rows_pct_est"] < 1.0

# Access per-column statistics
print(f"Mean age: {stats['columns']['age']['mean']:.1f}")
print(f"Distinct countries: {stats['columns']['country']['distinct']}")

Reproducible Reports

from pysuricata import profile, ReportConfig

# Set random seed for deterministic sampling
config = ReportConfig()
config.compute.random_seed = 42

report = profile(df, config=config)
# Same report every time!

Custom Description (Markdown Support)

from pysuricata import profile, ReportConfig

config = ReportConfig()
config.render.description = """
# Q4 2024 Customer Analysis

**Dataset**: Production customer transactions  
**Period**: October - December 2024  

## Key Findings
- Revenue up 15% YoY
- Average transaction: $87.50
"""

report = profile(df, config=config)

🧮 Algorithms & Mathematical Rigor

PySuricata uses state-of-the-art streaming algorithms:

Exact Statistics

• Welford's algorithm - Online mean/variance (numerically stable)
• Pébay's formulas - Parallel merging of moments (exact, mergeable)
• Streaming correlations - Sufficient statistics for Pearson r

Approximate Statistics (with guarantees)

• KMV sketch - Distinct count (error ~2% with k=2048)
• Misra-Gries - Top-k heavy hitters (guaranteed for freq > n/k)
• Reservoir sampling - Uniform random sample (exact probability k/n)

All algorithms have proven error bounds and mathematical guarantees. See full documentation for formulas and proofs.

🎨 Report Highlights

• Self-contained: Single HTML file, no external dependencies
• Beautiful visualizations: Inline SVG charts and histograms
• Responsive design: Works on desktop and mobile
• Dark mode: Toggle between light and dark themes
• Professional styling: Clean, modern interface
• Shareable: Email, cloud storage, or static hosting

💡 Use Cases

Data Science & ML

• EDA - Understand distributions, correlations, missing patterns
• Feature engineering - Identify high-cardinality, constant columns
• Data validation - Check quality before training
• Reproducibility - Generate consistent reports with seeds

Data Engineering

• Pipeline monitoring - Track data quality over time
• CI/CD checks - Assert quality thresholds
• Documentation - Auto-generate data dictionaries
• Debugging - Quickly profile large production datasets

Business Analytics

• Dashboard generation - Automated reporting
• Data documentation - Share with stakeholders
• Quality assurance - Catch issues early
• Compliance - Document data characteristics

⚙️ Configuration

Highly customizable via ReportConfig:

from pysuricata import profile, ReportConfig

config = ReportConfig()

# Processing
config.compute.chunk_size = 200_000  # Rows per chunk
config.compute.numeric_sample_size = 20_000  # Sample for quantiles
config.compute.random_seed = 42  # Reproducibility

# Analysis
config.compute.compute_correlations = True  # Enable correlations
config.compute.corr_threshold = 0.5  # Min |r| to show
config.compute.top_k_size = 50  # Top values to track

# Rendering
config.render.title = "My Analysis Report"
config.render.description = "Custom markdown description"
config.render.include_sample = True
config.render.sample_rows = 10

report = profile(df, config=config)

See Configuration Guide for all options.

🔬 Statistical Methods

Numeric Variables

• Central tendency: mean, median
• Dispersion: variance, std, IQR, MAD, CV
• Shape: skewness, kurtosis
• Quantiles: P1, P5, Q1, Q2, Q3, P95, P99
• Outliers: IQR fences, z-score, MAD-based
• Distribution: histogram (Freedman-Diaconis binning)

Categorical Variables

• Frequency table (top-k via Misra-Gries)
• Distinct count (KMV sketch)
• Shannon entropy, Gini impurity
• String length statistics
• Case/trim variant detection

DateTime Variables

• Temporal range and span
• Hour distribution (0-23)
• Day-of-week distribution
• Month distribution
• Monotonicity coefficient
• Timeline visualization

Boolean Variables

• True/false counts and percentages
• Shannon entropy
• Balance score
• Imbalance ratio

Full mathematical formulas and derivations in Statistical Methods.

📈 Scalability

PySuricata scales linearly with dataset size:

Dataset Size	Processing Time	Peak Memory
10K rows	1s	30 MB
100K rows	5s	50 MB
1M rows	15s	50 MB
10M rows	150s	50 MB
100M rows	1,500s (25 min)	50 MB
1B rows	15,000s (4 hrs)	50 MB

Memory stays constant regardless of dataset size! 🎉

🤝 Why "Suricata"?

Inspired by suricatas (meerkats) - small, vigilant animals that work cooperatively to survive in harsh desert environments:

• 👀 Watchful - Always scanning for patterns (like data analysis)
• 🤝 Cooperative - Parallel/distributed processing
• 🏜️ Efficient - Thrive with limited resources (bounded memory)
• ⚡ Quick - Fast reactions (streaming algorithms)
• 🔍 Pattern recognition - Identify important signals

Learn more about why suricatas inspired this library.

📖 Documentation

Comprehensive documentation with mathematical formulas, algorithm details, and examples:

• 📘 Quick Start Guide - Get started in 5 minutes
• 📗 User Guide - Detailed usage patterns
• 📕 API Reference - Complete API documentation
• 📙 Statistical Methods - Mathematical formulas
• 📔 Algorithms - Welford, Pébay, KMV, Misra-Gries
• 📓 Performance Tips - Optimization strategies
• 📰 Examples Gallery - Real-world use cases

🛠️ Advanced Features

Distributed Processing

Accumulators are mergeable - compute on multiple machines and combine:

from pysuricata.accumulators import NumericAccumulator

# Worker 1
acc1 = NumericAccumulator("amount")
acc1.update(data_partition_1)

# Worker 2  
acc2 = NumericAccumulator("amount")
acc2.update(data_partition_2)

# Merge on coordinator (exact, no approximation)
acc1.merge(acc2)
final_stats = acc1.finalize()

CI/CD Integration

from pysuricata import summarize

def validate_data_quality(df):
    stats = summarize(df)  # Fast, stats-only
    
    assert stats["dataset"]["missing_cells_pct"] < 5.0, "Too many missing"
    assert stats["dataset"]["duplicate_rows_pct_est"] < 1.0, "Too many duplicates"
    
    print("✓ Data quality checks passed")

# In your pipeline
validate_data_quality(df)

Jupyter Notebooks

from pysuricata import profile

report = profile(df)
report  # Auto-displays inline

# Or with custom size
report.display_in_notebook(height="800px")

🎓 Academic Use

If you use PySuricata in academic research, please reference the algorithms:

Streaming moments:

• Welford, B.P. (1962), "Note on a Method for Calculating Corrected Sums of Squares and Products", Technometrics
• Pébay, P. (2008), "Formulas for Robust, One-Pass Parallel Computation of Covariances and Arbitrary-Order Statistical Moments", Sandia Report

Sketch algorithms:

• Bar-Yossef, Z. et al. (2002), "Counting Distinct Elements in a Data Stream", RANDOM
• Misra, J., Gries, D. (1982), "Finding repeated elements", Science of Computer Programming

See References for complete citations.

🗺️ Roadmap

Current Version (0.0.11)

• ✅ Streaming architecture
• ✅ 4 variable types (numeric, categorical, datetime, boolean)
• ✅ Streaming correlations
• ✅ Missing value analysis
• ✅ Polars support
• ✅ Comprehensive documentation

Planned Features

• 🔜 Spearman rank correlation (monotonic relationships)
• 🔜 Little's MCAR test (missing data mechanism)
• 🔜 Chi-square uniformity test (categorical distribution)
• 🔜 Seasonality detection (autocorrelation for datetime)
• 🔜 Gap analysis (missing time periods)
• 🔜 Profile comparison (compare two datasets)
• 🔜 Export to JSON/CSV (structured statistics)
• 🔜 CLI tool (command-line interface)
• 🔜 Dask integration (native distributed support)

🤝 Contributing

We welcome contributions! See our Contributing Guide to get started.

Ways to contribute:

• 🐛 Report bugs
• 💡 Suggest features
• 📝 Improve documentation
• 🧪 Add tests
• 🔧 Submit pull requests
• 💬 Help others in Discussions

Development Setup

# Clone repository
git clone https://github.com/alvarodiez20/pysuricata.git
cd pysuricata

# Install dependencies
uv sync --dev

# Run tests
uv run pytest

# Build documentation
uv run mkdocs serve

📊 Project Stats

• Lines of code: ~15,000
• Test coverage: 90%+
• Documentation pages: 25+
• Supported Python versions: 3.9, 3.10, 3.11, 3.12, 3.13
• Active development: Regular releases

🆘 Support & Community

• 📖 Documentation
• 💬 GitHub Discussions
• 🐛 Issue Tracker
• 📧 Email: [e-mail]
• ⭐ Star on GitHub

📜 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

Built with inspiration from:

• Suricatas (meerkats) - Vigilant, cooperative, efficient 🦦
• Welford & Pébay - Streaming moments algorithms
• Bar-Yossef, Misra & Gries - Sketch algorithms
• Open-source community

⭐ Star History

If you find PySuricata useful, please star the repository!

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Ready to analyze like a suricata?

📚 Read the Docs • 🐛 Report Bug • 💬 Discussions

"In the Kalahari of big data, be a suricata - vigilant, efficient, and always ready to dig for insights!"