dataprof 0.4.85

Profile 50GB datasets in seconds on your laptop.

DataProf is built for Data Scientists and Engineers who need to understand their data fast. No more MemoryError when trying to profile a CSV larger than your RAM.

Pandas-Profiling vs DataProf on a 10GB CSV:

🔒 Privacy First: 100% local processing, no telemetry. See what dataprof analyzes →

🚀 Quick Start

Installation

The easiest way to get started is via pip:

pip install dataprof

Python Usage

Forget complex configurations. Just point to your file:

import dataprof

# Analyze a huge file without crashing memory
# Generates a report.html with quality metrics and distributions
dataprof.profile("huge_dataset.csv").save("report.html")

CLI & Rust Usage (Advanced)

If you prefer the command line or are a Rust developer:

# Install via cargo
cargo install dataprof

# Generate report from CLI
dataprof-cli report huge_data.csv -o report.html

More options: dataprof-cli --help | Full CLI Guide

💡 Key Features

• No Size Limits: Profiles files larger than RAM using streaming and memory mapping.
• Blazing Fast: Written in Rust with SIMD acceleration.
• Privacy Guaranteed: Data never leaves your machine.
• Format Support: CSV, Parquet, JSON/L, and Databases (Postgres, MySQL, etc.).
• Smart Detection: Automatically identifies Emails, IPs, IBANs, Credit Cards, and more.

📊 Beautiful Reports

Documentation

• Python API Reference
• CLI Guide
• Performance & Benchmarks
• Developer Guide

Advanced Examples

Batch Processing (Python)

# Process a whole directory of files in parallel
result = dataprof.batch_analyze_directory("/data_folder", recursive=True)
print(f"Processed {result.processed_files} files at {result.files_per_second:.1f} files/sec")

Database Integration (Python)

# Profile a SQL query directly
await dataprof.profile_database_async(
    "postgresql://user:pass@localhost/db",
    "SELECT * FROM sales_data_2024"
)

Rust Library Usage

use dataprof::*;

let profiler = DataProfiler::auto();
let report = profiler.analyze_file("dataset.csv")?;
println!("Quality Score: {}", report.quality_score());

Development

# Setup
git clone https://github.com/AndreaBozzo/dataprof.git
cd dataprof
cargo build --release

# Test databases (optional)
docker-compose -f .devcontainer/docker-compose.yml up -d

# Common tasks
cargo test          # Run tests
cargo bench         # Benchmarks
cargo clippy        # Linting

Development Guide | Performance Guide

Feature Flags

# Minimal (CSV/JSON only)
cargo build --release

# With Apache Arrow (large files >100MB)
cargo build --release --features arrow

# With Parquet support
cargo build --release --features parquet

# With databases
cargo build --release --features postgres,mysql,sqlite

# Python async support
maturin develop --features python-async,database,postgres

# All features
cargo build --release --all-features

When to use Arrow: Large files (>100MB), many columns (>20), uniform types When to use Parquet: Analytics, data lakes, Spark/Pandas integration

Documentation

User Guides: CLI Reference | Python API | Python Integrations | Database Connectors | Apache Arrow

Developer: Development Guide | Performance Guide | Benchmarks

Privacy: What DataProf Does - Complete transparency with source verification

🤝 Contributing

We welcome contributions from everyone! Whether you want to:

• Fix a bug 🐛
• Add a feature ✨
• Improve documentation 📚
• Report an issue 📝

Quick Start for Contributors

1. Fork & clone:

   git clone https://github.com/YOUR-USERNAME/dataprof.git
   cd dataprof
   

2. Build & test:

   cargo build
   cargo test
   

3. Create a feature branch:

   git checkout -b feature/your-feature-name
   

4. Before submitting PR:

   cargo fmt --all
   cargo clippy --all --all-targets
   cargo test --all
   

5. Submit a Pull Request with clear description

📖 Full Contributing Guide →

All contributions are welcome. Please read CONTRIBUTING.md for guidelines and our Code of Conduct.

License

MIT License - See LICENSE for details.