Skip to main content

A Python package for deduplicating data.

Project description

deduplicate_lib

deduplication algorithms in python


GitHub codecov PyPI version Python 3.9+ License: MIT CI Code style: ruff

Key Features

  • Easy to use deduplication algorithms for any vector array
  • Suite of tolerance tuning algorithms to help you find the right tolerance value for your system
  • Suite of benchmarking tools to ensure rigor, accuracy, and speed (not yet implemented)
  • Factory Plugin architecture, for easy extensibility and modification

Implemented Algorithms

  • Distance Matrix (Simple, accurate, expensive): Computes the distance matrix for all vectors and determines duplicates by finding those that fall below a given distance
  • Multi Hashing (Fast): Smears and rounds the vectors using a normal distribution and computes the hashes for each which are then used to determine duplicates by proportion of hash clashes.

Quick Start

install using pip

pip install deduplicate_lib

load your data into python

from deduplicate_lib.plugins.deduplication_algorithms.multi_hash import MultiHash

# define your paramerters in the MultiHash object
dda=MultiHash(dataset_array=your_data_numpy_array)

# return a list of all unique values
print(dda.deduplicate())

A more detailed example can be seen in the examples directory

Dependencies

  • Python 3.9+
  • numpy
  • numba
  • scipy

🤝 Contributing

We welcome contributions! Please see our Contributing Guide for details.

Development Setup

# Clone the repository
git clone https://github.com/julianholland/deduplicate.git
cd deduplicate

# Install in development mode
pip install -e ".[dev]"

# Run tests
pytest

# Run linting
ruff check .
ruff format .

Running Tests

# Run all tests
pytest

# Run specific test categories
pytest tests/core/
pytest tests/plugins/
pytest tests/plugins/duplicate_detection_algorithms/distance_matrix

# Run with coverage
pytest --cov

📝 Citation

If you use deduplicate_lib in your research, please cite:

@software{deduplicate2026,
  title={deduplicate_lib: Auto Tolerance Finding Deduplication Algorithms in Python},
  author={Julian Holland},
  year={2026},
  url={https://github.com/julianholland/deduplicate},
  version={0.0.5}
}

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

  • The Fritz Haber Institute
  • Juan Manuel Lombardi <3
  • Maximillion Ach
  • Chiara Panosetti

Project Links

Project To-Do

  • Add example.ipynb
  • Create general Pre-allocation protocal
  • Add benchmarks for time and robustness
  • Add Locality-Sensitive Hashing as an option
  • Speedup slow tasks with Numba
  • Set up Read the Docs
  • Create general deduplicate function
  • Speed up NTPP

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

deduplicate_lib-0.0.5.tar.gz (15.0 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

deduplicate_lib-0.0.5-py3-none-any.whl (17.3 kB view details)

Uploaded Python 3

File details

Details for the file deduplicate_lib-0.0.5.tar.gz.

File metadata

  • Download URL: deduplicate_lib-0.0.5.tar.gz
  • Upload date:
  • Size: 15.0 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.10.12

File hashes

Hashes for deduplicate_lib-0.0.5.tar.gz
Algorithm Hash digest
SHA256 de3b2ddbfa85d2ee30c7babd76ab7848f047aa1ab124e828ac5eec9264cb3072
MD5 6f8b8f4e870f44c88dd4be238ef07ab6
BLAKE2b-256 7117c5c48e4a54ab1e54b94a9a5674045ed091dce8562e52bbf1cef977f75e69

See more details on using hashes here.

File details

Details for the file deduplicate_lib-0.0.5-py3-none-any.whl.

File metadata

File hashes

Hashes for deduplicate_lib-0.0.5-py3-none-any.whl
Algorithm Hash digest
SHA256 75a091ab726d2528f17fa70cae7b3874b25ce665c28fa2867e3fa3c4dcb51a9e
MD5 0e6b256b4bd04a6dc56d37ab4e6e301c
BLAKE2b-256 1c836311d45b9fbbd05f107a0d6d5ccbe2a7a9370ed1df2b1ed81accd9112339

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page