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"description": "Python Record Linkage Toolkit\n=============================\n\n|pypi| |travis| |codecov| |docs|\n\n.. |travis| image:: https://travis-ci.org/J535D165/recordlinkage.svg?branch=master\n :target: https://travis-ci.org/J535D165/recordlinkage\n :alt: TravisCI Status\n\n.. |pypi| image:: https://badge.fury.io/py/recordlinkage.svg\n :target: https://pypi.python.org/pypi/recordlinkage/\n :alt: Pypi Version\n\n.. |codecov| image:: https://codecov.io/gh/J535D165/recordlinkage/branch/master/graph/badge.svg\n :target: https://codecov.io/gh/J535D165/recordlinkage\n :alt: Code Coverage\n\n.. |docs| image:: https://readthedocs.org/projects/recordlinkage/badge/?version=latest\n :target: https://recordlinkage.readthedocs.io/en/latest/?badge=latest\n :alt: Documentation Status\n\nThe **Python Record Linkage Toolkit** is a library to link records in or\nbetween data sources. The toolkit provides most of the tools needed for\nrecord linkage and deduplication. The package contains indexing methods,\nfunctions to compare records and classifiers. The package is developed for\nresearch and the linking of small or medium sized files.\n\nThis project is inspired by the `Freely Extensible Biomedical Record Linkage\n(FEBRL) `__ project, which is a great\nproject. In contrast with FEBRL, the recordlinkage project uses `pandas\n`__ and `numpy `__ for data\nhandling and computations. The use of *pandas*, a flexible and powerful data\nanalysis and manipulation library for Python, makes the record linkage process\nmuch easier and faster. The extensive *pandas* library can be used to\nintegrate your record linkage directly into existing data manipulation\nprojects.\n\nOne of the aims of this project is to make an easily extensible record \nlinkage framework. It is easy to include your own indexing algorithms,\ncomparison/similarity measures and classifiers.\n\n\nBasic linking example\n---------------------\n\nImport the ``recordlinkage`` module with all important tools for record\nlinkage and import the data manipulation framework **pandas**.\n\n.. code:: python\n\n import recordlinkage\n import pandas\n\nLoad your data into pandas DataFrames. \n\n.. code:: python\n\n df_a = pandas.DataFrame(YOUR_FIRST_DATASET)\n df_b = pandas.DataFrame(YOUR_SECOND_DATASET)\n\nComparing all record can be computationally intensive. Therefore, we make \nset of candidate links with one of the built-in indexing techniques like\n**blocking**. In this example, only pairs of records that agree on the surname\nare returned.\n\n.. code:: python\n\n indexer = recordlinkage.Index()\n indexer.block('surname')\n candidate_links = indexer.index(df_a, df_b)\n\nFor each candidate link, compare the records with one of the\ncomparison or similarity algorithms in the Compare class.\n\n.. code:: python\n\n c = recordlinkage.Compare()\n\n c.string('name_a', 'name_b', method='jarowinkler', threshold=0.85)\n c.exact('sex', 'gender')\n c.date('dob', 'date_of_birth')\n c.string('str_name', 'streetname', method='damerau_levenshtein', threshold=0.7)\n c.exact('place', 'placename')\n c.numeric('income', 'income', method='gauss', offset=3, scale=3, missing_value=0.5)\n\n # The comparison vectors\n feature_vectors = c.compute(candidate_links, df_a, df_b)\n\nThis Python Record Linkage Toolkit contains multiple classification algorithms.\nPlenty of the algorithms do need training data (supervised learning) while\nothers are unsupervised. An example of supervised learning:\n\n.. code:: python\n\n logrg = recordlinkage.LogisticRegressionClassifier()\n logrg.fit(TRAINING_COMPARISON_VECTORS, TRAINING_CLASSES)\n\n logrg.predict(feature_vectors)\n\nand an example of unsupervised learning (the well known ECM-algorithm):\n\n.. code:: python\n\n ecm = recordlinkage.ECMClassifier()\n ecm.fit_predict(feature_vectors)\n\nMain Features\n-------------\n\nThe main features of the **Python Record Linkage Toolkit** are:\n\n\n- Clean and standardise data with easy to use tools\n- Make pairs of records with smart indexing methods such as\n **blocking** and **sorted neighbourhood indexing**\n- Compare records with a large number of comparison and similarity\n measures for different types of variables such as strings, numbers and dates.\n- Several classifications algorithms, both supervised and unsupervised\n algorithms.\n- Common record linkage evaluation tools\n- Several built-in datasets. \n\nDocumentation \n-------------\n\nThe most recent documentation and API reference can be found at\n`recordlinkage.readthedocs.org\n`__. The documentation\nprovides some basic usage examples like deduplication_ and linking_ census\ndata. More examples are coming soon. If you do have interesting examples to\nshare, let us know.\n\n.. _deduplication: http://recordlinkage.readthedocs.io/en/latest/notebooks/data_deduplication.html\n.. _linking: http://recordlinkage.readthedocs.io/en/latest/notebooks/link_two_dataframes.html\n\nDependencies, installation and license\n--------------------------------------\n\nInstall the Python Record Linkage Toolkit easily with pip\n\n.. code:: sh\n\n pip install recordlinkage\n\nThe toolkit depends on Pandas_ (>=18.0), Numpy_, `Scikit-learn`_, Scipy_ and\nJellyfish_. You probably have most of them already installed. The package\n``jellyfish`` is used for approximate string comparing and string encoding.\nThe package Numexpr_ is an optional dependency to speed up numerical\ncomparisons.\n\n.. _Numpy: http://www.numpy.org\n.. _Pandas: https://github.com/pydata/pandas\n.. _Scipy: https://www.scipy.org/\n.. _Scikit-learn: http://scikit-learn.org/\n.. _Jellyfish: https://github.com/jamesturk/jellyfish\n.. _Numexpr: https://github.com/pydata/numexpr\n\nThe license for this record linkage tool is BSD-3-Clause.\n\nNeed help?\n----------\n\nStuck on your record linkage code or problem? Any other questions? Don't\nhestitate to send me an email (jonathandebruinos@gmail.com).\n\n\n",
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