dataprof 0.4.53

Fast, lightweight data profiling and quality assessment library

DataProfiler 📊

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!

High-performance data quality and ML readiness assessment library

DataProfiler v0.4.4 delivers 20x better memory efficiency than pandas, unlimited file streaming, 30+ automated quality checks, and NEW: comprehensive ML readiness assessment. Built in Rust with full Python bindings and production-ready database connectivity.

✨ Key Features (v0.4.4)

• 🤖 ML Readiness Assessment: Automated feature analysis, blocking issues detection, preprocessing recommendations
• ⚡ High Performance: 20x more memory efficient than pandas with Apache Arrow integration
• 🌊 Scalable: Stream processing for files larger than RAM (tested up to 100GB)
• 🔍 Smart Quality Detection: 30+ automated checks for nulls, duplicates, outliers, format issues
• 🗃️ Production Database Support: PostgreSQL, MySQL, SQLite, DuckDB with SSL/TLS and retry logic
• 🐍 Complete Python Integration: Native bindings with pandas, scikit-learn, Jupyter support

🚀 Quick Start

Python

pip install dataprof

import dataprof

# NEW v0.4.4: ML readiness assessment
ml_score = dataprof.ml_readiness_score("data.csv")
print(f"ML Readiness: {ml_score.readiness_level} ({ml_score.overall_score:.1f}%)")

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

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

Rust

cargo add dataprof --features arrow

use dataprof::*;

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

CLI

# Basic profiling
dataprof data.csv --quality --html report.html

# Database profiling
dataprof users --database "postgresql://user:pass@host:5432/db" --quality

# Large files with progress
dataprof huge_file.csv --streaming --progress

📊 Performance

Tool	100MB CSV	Memory	Quality Checks	>RAM Support
DataProfiler (Arrow)	0.5s	30MB	✅ 30+ checks	✅
DataProfiler (Standard)	2.1s	45MB	✅ 30+ checks	✅
pandas.describe()	8.4s	380MB	❌ Basic stats	❌
Great Expectations	12.1s	290MB	✅ Rule-based	❌

💡 Real-World Examples

NEW v0.4.4: ML Pipeline Integration

from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.pipeline import Pipeline
import dataprof

# Step 1: ML readiness assessment guides preprocessing
ml_score = dataprof.ml_readiness_score("dataset.csv")
features_df = dataprof.feature_analysis_dataframe("dataset.csv")

# Step 2: Auto-categorize features for scikit-learn pipeline
numeric_features = features_df[features_df['feature_type'] == 'numeric']['column_name'].tolist()
categorical_features = features_df[features_df['feature_type'] == 'categorical']['column_name'].tolist()

# Step 3: Build preprocessing pipeline based on DataProf recommendations
preprocessor = Pipeline([
    ('scaler', StandardScaler())  # Applied to numeric features
])
print(f"✅ Pipeline ready with {len(numeric_features)} numeric, {len(categorical_features)} categorical features")

Production Quality Gate

from dataprof import quick_quality_check, ml_readiness_score

def validate_ml_pipeline_data(file_path):
    quality_score = quick_quality_check(file_path)
    ml_score = ml_readiness_score(file_path)

    if quality_score < 85.0:
        raise Exception(f"Data quality too low: {quality_score:.1f}%")
    if ml_score.overall_score < 70.0:
        raise Exception(f"ML readiness too low: {ml_score.overall_score:.1f}%")

    return quality_score, ml_score.overall_score

Database Monitoring with ML Assessment

# Monitor daily data loads with ML readiness
dataprof daily_sales --database "postgresql://user:pass@prod-db/warehouse" \
  --query "SELECT * FROM sales WHERE date = CURRENT_DATE" \
  --quality --ml-readiness --json | jq '.ml_readiness.overall_score'

📖 Documentation

Guide	Description
Python API Reference	Complete function and class reference
ML Features Guide	NEW: ML readiness assessment and preprocessing recommendations
Python Integrations	Pandas, scikit-learn, Jupyter, Airflow workflows
Database Connectors	Production PostgreSQL, MySQL, SQLite, DuckDB with SSL/TLS
CLI Usage Guide	Comprehensive CLI with progress indicators and validation

Resources: CHANGELOG • CONTRIBUTING • LICENSE

🛠️ Development

git clone https://github.com/AndreaBozzo/dataprof.git
cd dataprof

# Quick setup
bash scripts/setup-dev.sh    # Linux/macOS
pwsh scripts/setup-dev.ps1   # Windows

# Build and test
cargo build --release
cargo test --all

🤝 Contributing

We welcome contributions! Please see CONTRIBUTING.md for guidelines.

• 🐛 Report bugs
• ✨ Request features
• 📖 Improve docs

📄 License

Licensed under GPL-3.0 • Commercial use allowed with source disclosure