dataprof 0.4.61

Fast, lightweight data profiling and quality assessment library

dataprof

DISCLAIMER FOR HUMAN READERS

dataprof, even if working, is in early-stage development, therefore you might encounter bugs, minor or even major ones during your data-quality exploration journey.

Report them appropriately by opening an issue or by mailing the maintainer for security issues.

Thanks for your time here!

A fast, reliable data quality and ML readiness assessment tool built in Rust. Analyze datasets with 20x better memory efficiency than pandas, unlimited file streaming, and 30+ automated quality checks. NEW in v0.4.61: Generate ready-to-use Python code snippets for each ML recommendation. Full Python bindings and production database connectivity included.

Perfect for data scientists, ML engineers, and anyone working with data who needs quick, reliable quality insights.

Try Online

No installation required! Test dataprof instantly with our web interface:

CSV ML Readiness API →

• Drag & drop your CSV (up to 50MB)
• Get ML readiness score in ~10 seconds
• Powered by dataprof v0.4.61 core engine
• Embeddable badges for your README

CI/CD Integration

Automate data quality checks in your workflows with our GitHub Action:

- name: DataProf ML Readiness Check
  uses: AndreaBozzo/dataprof-actions@v1
  with:
    file: 'data/dataset.csv'
    ml-threshold: 80
    fail-on-issues: true

Get the Action →

• Zero setup - works out of the box
• Smart analysis - ML readiness scoring with actionable insights
• Flexible - customizable thresholds and output formats
• Fast - typically completes in under 2 minutes

Perfect for validating datasets before training, ensuring data quality in pipelines, or generating automated quality reports.

Quick Start

Python

pip install dataprof

import dataprof

# ML readiness assessment with actionable code snippets
ml_score = dataprof.ml_readiness_score("data.csv")
print(f"ML Readiness: {ml_score.readiness_level} ({ml_score.overall_score:.1f}%)")

# NEW: Get ready-to-use preprocessing code
for rec in ml_score.recommendations:
    if rec.code_snippet:
        print(f"📦 {rec.framework} code for {rec.category}")
        print(rec.code_snippet)

# Quality analysis with detailed reporting
report = dataprof.analyze_csv_with_quality("data.csv")
print(f"Quality score: {report.quality_score():.1f}%")

# Production database profiling
profiles = dataprof.analyze_database("postgresql://user:pass@host/db", "users")

Rust

cargo add dataprof

use dataprof::*;

// High-performance Arrow processing
let profiler = DataProfiler::columnar();
let report = profiler.analyze_csv_file("large_dataset.csv")?;

CLI Usage

# Generate ML readiness report with actionable code snippets
dataprof data.csv --quality --ml-score --ml-code

# Generate complete Python preprocessing script
dataprof data.csv --quality --ml-score --output-script preprocess.py

# Quick analysis with streaming for large files
dataprof large_dataset.csv --streaming --sample 10000

Note: On Windows, the binary is named dataprof-cli.exe. Use cargo build --release to build from source.

Development

Want to contribute or build from source? Here's what you need:

Prerequisites

• Rust (latest stable via rustup)
• Docker (for database testing)

Quick Setup

git clone https://github.com/AndreaBozzo/dataprof.git
cd dataprof
cargo build --release  # Build the project
docker-compose -f .devcontainer/docker-compose.yml up -d  # Start test databases

Common Development Tasks

cargo test          # Run all tests
cargo bench         # Performance benchmarks
cargo fmt           # Format code
cargo clippy        # Code quality checks

Documentation

• Development Guide - Complete setup and contribution guide
• Python API Reference - Full Python API documentation
• ML Features - Machine learning readiness assessment
• Python Integrations - Pandas, scikit-learn, Jupyter, Airflow, dbt
• Database Connectors - Production database connectivity
• Performance Guide - Optimization and benchmarking
• Apache Arrow Integration - Columnar processing guide
• CLI Usage Guide - Complete CLI reference
• Performance Benchmarks - Benchmark results and methodology

License

Licensed under the MIT License. See LICENSE for details.