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---\u003e\n[![Version](https://img.shields.io/pypi/v/deduplipy)](https://pypi.org/project/deduplipy/)\n![](https://img.shields.io/github/license/fritshermans/deduplipy)\n[![Downloads](https://static.pepy.tech/badge/deduplipy)](https://pepy.tech/project/deduplipy)\n[![Conda - Platform](https://img.shields.io/conda/pn/conda-forge/deduplipy?logo=anaconda\u0026style=flat)][#conda-forge-package]\n[![Conda (channel only)](https://img.shields.io/conda/vn/conda-forge/deduplipy?logo=anaconda\u0026style=flat\u0026color=orange)][#conda-forge-package]\n[![Conda Recipe](https://img.shields.io/static/v1?logo=conda-forge\u0026style=flat\u0026color=green\u0026label=recipe\u0026message=deduplipy)][#conda-forge-feedstock]\n[![Docs - GitHub.io](https://img.shields.io/static/v1?logo=readthdocs\u0026style=flat\u0026color=pink\u0026label=docs\u0026message=deduplipy)][#docs-package]\n\n[#pypi-package]: https://pypi.org/project/deduplipy/\n[#conda-forge-package]: https://anaconda.org/conda-forge/deduplipy\n[#conda-forge-feedstock]: https://github.com/conda-forge/deduplipy-feedstock\n[#docs-package]: https://deduplipy.readthedocs.io/en/latest/\n\u003c!--- BADGES: END ---\u003e\n\n# DedupliPy\n\n\u003ca href=\"https://deduplipy.readthedocs.io/en/latest/\"\u003e\u003cimg src=\"https://deduplipy.readthedocs.io/en/latest/_images/logo.png\" width=\"15%\" height=\"15%\" align=\"left\" /\u003e\u003c/a\u003e\n\nDeduplication is the task to combine different representations of the same real world entity. This package implements\ndeduplication using active learning. Active learning allows for rapid training without having to provide a large,\nmanually labelled dataset.\n\nDedupliPy is an end-to-end solution with advantages over existing solutions:\n\n- active learning; no large manually labelled dataset required\n- during active learning, the user gets notified when the model converged and training may be finished\n- works out of the box, advanced users can choose settings as desired (custom blocking rules, custom metrics,\n  interaction features)\n\nDeveloped by [Frits Hermans](https://fritshermans.github.io/)\n\n## Documentation\n\nDocumentation can be found [here](https://deduplipy.readthedocs.io/en/latest/)\n\n## Installation\n\n### Normal installation\n\n**With pip**\n\nInstall directly from PyPI.\n\n```\npip install deduplipy\n```\n\n**With conda**\n\nInstall using conda from conda-forge channel.\n\n```\nconda install -c conda-forge deduplipy\n```\n\n\n### Install to contribute\n\nClone this Github repo and install in editable mode:\n\n```\npython -m pip install -e \".[dev]\"\npython setup.py develop\n```\n\n## Usage\n\nApply deduplication your Pandas dataframe `df` as follows:\n\n```python\nmyDedupliPy = Deduplicator(col_names=['name', 'address'])\nmyDedupliPy.fit(df)\n```\n\nThis will start the interactive learning session in which you provide input on whether a pair is a match (y) or not (n).\nDuring active learning you will get the message that training may be finished once algorithm training has converged.\nPredictions on (new) data are obtained as follows:\n\n```python\nresult = myDedupliPy.predict(df)\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffritshermans%2Fdeduplipy","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ffritshermans%2Fdeduplipy","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ffritshermans%2Fdeduplipy/lists"}