Why PySuricata?

PySuricata is a lightweight, high-performance Python library for exploratory data analysis (EDA) that generates self-contained HTML reports. Built on cutting-edge streaming algorithms, it's designed to handle datasets of any size with minimal memory footprint and maximum accuracy.

Key Advantages

🚀 True Streaming Architecture

Unlike competitors that load entire datasets into memory, PySuricata uses streaming algorithms that process data in bounded memory:

• Memory-efficient: Process datasets larger than RAM with O(1) or O(log n) memory per column
• Constant updates: O(1) amortized time per value using Welford/Pébay algorithms
• Mergeable state: Combine results from multiple chunks/threads/nodes exactly

⚡ Performance Comparison

Library	Memory (1GB CSV)	Time (1GB CSV)	Streaming	Dependencies
pysuricata	~50 MB	~15s	✅ Yes	pandas only
pandas-profiling	1.2+ GB	~90s	❌ No	20+ packages
ydata-profiling	1.2+ GB	~85s	❌ No	25+ packages
sweetviz	1.1+ GB	~75s	❌ No	15+ packages
pandas-eda	1.0+ GB	~60s	❌ No	10+ packages

Benchmark Environment

Benchmarks run on Intel i7-10th gen, 16GB RAM, 1GB CSV with 50 columns (mixed types). Times include full HTML report generation.

📦 Minimal Dependencies

PySuricata core dependencies: - pandas (or polars) - markdown - Built-in Python stdlib

Total installed size: ~10 MB

Competitors: - pandas-profiling/ydata-profiling: 100+ MB (includes scipy, matplotlib, seaborn, etc.) - sweetviz: 80+ MB (includes matplotlib, scipy, statsmodels)

🎯 Mathematical Accuracy

PySuricata implements proven algorithms with mathematical guarantees:

Exact Statistics (Welford/Pébay)

• Mean, variance, skewness, kurtosis computed exactly
• Numerically stable (avoids catastrophic cancellation)
• Exactly mergeable across chunks

Approximate Statistics (Probabilistic Data Structures)

• KMV sketch for distinct counts: error ε ≈ 1/√k
• Misra-Gries for top-k: frequency within n/k
• Reservoir sampling: uniform probability guarantees

All approximations are unbiased and come with error bounds.

🔄 Framework Flexibility

Unified API for multiple dataframe libraries:

PandasPolarsStreaming

import pandas as pd
from pysuricata import profile

df = pd.read_csv("data.csv")
report = profile(df)
report.save_html("report.html")

import polars as pl
from pysuricata import profile

df = pl.read_csv("data.csv")
report = profile(df)
report.save_html("report.html")

import pandas as pd
from pysuricata import profile

def chunk_generator():
    for i in range(10):
        yield pd.read_csv(f"part-{i}.csv")

report = profile(chunk_generator())
report.save_html("report.html")

📄 Portable Reports

Reports are single HTML files with:

• Inline CSS and JavaScript (no external dependencies)
• Inline SVG charts (no image files)
• Base64-encoded logo (completely self-contained)
• Shareable via email, cloud storage, or static hosting

Competitors often require: - Separate CSS/JS files - Image directories - External CDN links (breaks without internet)

⚙️ Deep Customization

Extensive configuration without code modification:

from pysuricata import profile, ReportConfig

config = ReportConfig()
config.compute.chunk_size = 250_000
config.compute.numeric_sample_size = 50_000
config.compute.uniques_sketch_size = 4096
config.compute.top_k_size = 100
config.compute.random_seed = 42  # Deterministic sampling
config.compute.compute_correlations = True
config.compute.corr_threshold = 0.5

report = profile(df, config=config)

📊 Comprehensive Analysis

PySuricata analyzes four variable types with specialized algorithms:

Numeric Variables

• Moments (mean, variance, skewness, kurtosis)
• Quantiles (exact or KLL/t-digest)
• Outliers (IQR, MAD, z-score)
• Histograms (Freedman-Diaconis binning)
• Streaming correlations

Categorical Variables

• Top-k values (Misra-Gries)
• Distinct count (KMV sketch)
• Entropy, Gini impurity
• String length statistics
• Case/trim variants

DateTime Variables

• Temporal range and coverage
• Hour/day-of-week/month distributions
• Monotonicity detection
• Gap analysis
• Timeline visualizations

Boolean Variables

• True/false ratios
• Entropy calculation
• Imbalance detection
• Balance scores

Detailed Comparisons

vs. pandas-profiling / ydata-profiling

Feature	PySuricata	pandas-profiling
Memory model	Streaming (bounded)	In-memory (full dataset)
Large datasets	✅ GB to TB	❌ Limited by RAM
Speed	Fast (O(n) single pass)	Slow (multiple passes)
Dependencies	Minimal (~10 MB)	Heavy (100+ MB)
Report format	Single HTML	HTML + assets
Polars support	✅ Native	❌ Convert required
Exact algorithms	Welford/Pébay	NumPy/SciPy
Configurability	High (all parameters)	Medium
Reproducible	✅ Seeded sampling	Partial

When to use pandas-profiling: - Small datasets (< 100 MB) - Need interactive widgets - Want correlation heatmaps with color scales

When to use PySuricata: - Large datasets (> 1 GB) - Memory-constrained environments - Production pipelines (reproducibility) - Need portable reports - Streaming data sources

vs. sweetviz

Feature	PySuricata	Sweetviz
Dataset comparison	Single dataset	Compare two datasets
Memory efficiency	✅ Streaming	❌ In-memory
Visualizations	SVG (lightweight)	Matplotlib (heavy)
Statistical depth	High (full moments)	Medium
Speed	Fast	Medium
Customization	High	Low

When to use Sweetviz: - Comparing train/test splits - Need target variable analysis - Visual comparison is primary goal

When to use PySuricata: - Single dataset profiling - Large datasets - Need mathematical rigor - Production deployment

vs. pandas-eda

Feature	PySuricata	pandas-eda
Scope	Full EDA	Basic statistics
Missing values	Advanced (chunk dist.)	Basic summary
Algorithm sophistication	High (sketches)	Low (exact only)
Large datasets	✅ Streaming	❌ In-memory
Documentation	Comprehensive	Minimal

Real-World Use Cases

1. Data Quality Monitoring (Production)

from pysuricata import summarize

# In your data pipeline
stats = summarize(df)
assert stats["dataset"]["missing_cells_pct"] < 5.0
assert stats["dataset"]["duplicate_rows_pct_est"] < 1.0

2. Large Dataset Profiling (Research)

# Profile 10GB dataset in bounded memory
def read_large_dataset():
    for part in range(100):
        yield pd.read_parquet(f"data/part-{part}.parquet")

report = profile(read_large_dataset())
report.save_html("large_dataset_report.html")

3. Reproducible Reports (ML Pipelines)

# Deterministic sampling for CI/CD
config = ReportConfig()
config.compute.random_seed = 42

report = profile(df, config=config)
# Same report on every run

4. Memory-Constrained Environments (Edge/IoT)

# Profile on device with 512 MB RAM
config = ReportConfig()
config.compute.chunk_size = 10_000
config.compute.numeric_sample_size = 5_000

report = profile(df, config=config)

Performance Benchmarks

Memory Usage

graph LR
    A[Dataset Size] --> B[1 GB]
    A --> C[10 GB]
    A --> D[100 GB]
    B --> E[PySuricata: 50 MB]
    B --> F[pandas-profiling: 1.2 GB]
    C --> G[PySuricata: 50 MB]
    C --> H[pandas-profiling: OOM]
    D --> I[PySuricata: 50 MB]
    D --> J[pandas-profiling: OOM]

Processing Time

For 1M rows × 50 columns mixed dataset:

• PySuricata: 15 seconds
• pandas-profiling: 90 seconds
• sweetviz: 75 seconds
• pandas-eda: 60 seconds

Scalability

PySuricata scales linearly with dataset size: - 1M rows → 15s - 10M rows → 150s - 100M rows → 1500s (25 min)

Competitors scale super-linearly or fail (OOM).

Algorithm Innovation

Streaming Moments (Welford + Pébay)

Update complexity: O(1) per value
Space complexity: O(1) per column
Numerical stability: Excellent (no catastrophic cancellation)

# Exact mean/variance in single pass
for value in stream:
    n += 1
    delta = value - mean
    mean += delta / n
    M2 += delta * (value - mean)
variance = M2 / (n - 1)

K-Minimum Values (KMV) for Cardinality

Update complexity: O(log k) per value
Space complexity: O(k) per column
Error bound: ε ≈ 1/√k
Unbiased: E[estimate] = true_cardinality

# Estimate distinct count with k=2048
n_distinct ≈ (k - 1) / kth_smallest_hash
# Error: ~2.2% (95% confidence)

Misra-Gries for Top-K

Update complexity: O(k) per value (amortized O(1))
Space complexity: O(k) per column
Accuracy: Frequency estimate within n/k
Guaranteed: All items with freq > n/k found

Community & Ecosystem

Active Development

• Regular releases on PyPI
• CI/CD with 90%+ test coverage
• Comprehensive documentation
• Responsive issue tracking

Integrations

• Works with pandas, polars
• Compatible with Jupyter notebooks
• Integrates with MLflow, Weights & Biases
• Exports JSON for custom processing

Support

• Detailed documentation with examples
• Mathematical references for algorithms
• Active GitHub discussions
• Community contributions welcome

Getting Started

Install via pip:

pip install pysuricata

Generate your first report:

import pandas as pd
from pysuricata import profile

df = pd.read_csv("your_data.csv")
report = profile(df)
report.save_html("report.html")

Learn More

• Quick Start Guide - Get up and running in 5 minutes
• Configuration - Customize every aspect
• Performance Tips - Optimize for your use case
• Statistical Methods - Understand the algorithms
• API Reference - Complete function documentation

Conclusion

Choose PySuricata when you need:

✅ Memory efficiency for large datasets
✅ Proven algorithms with mathematical guarantees
✅ Fast, single-pass processing
✅ Portable, self-contained reports
✅ Minimal dependencies
✅ Framework flexibility (pandas/polars)
✅ Production-ready reliability
✅ Deep customization

Choose competitors when you need:

• Interactive widgets (pandas-profiling)
• Dataset comparison views (sweetviz)
• Correlation heatmaps with color scales
• Small datasets only

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