Description

<div align="center"> <img src="https://raw.githubusercontent.com/J535D165/recordlinkage/master/docs/images/recordlinkage-banner-transparent.svg"><br> </div> # RecordLinkage: powerful and modular Python record linkage toolkit [](https://pypi.python.org/pypi/recordlinkage/) [](https://github.com/J535D165/recordlinkage/actions) [](https://codecov.io/gh/J535D165/recordlinkage) [](https://recordlinkage.readthedocs.io/en/latest/?badge=latest) [](https://doi.org/10.5281/zenodo.3559042) **RecordLinkage** is a powerful and modular record linkage toolkit to link records in or between data sources. The toolkit provides most of the tools needed for record linkage and deduplication. The package contains indexing methods, functions to compare records and classifiers. The package is developed for research and the linking of small or medium sized files. This project is inspired by the [Freely Extensible Biomedical Record Linkage (FEBRL)](https://sourceforge.net/projects/febrl/) project, which is a great project. In contrast with FEBRL, the recordlinkage project uses [pandas](http://pandas.pydata.org/) and [numpy](http://www.numpy.org/) for data handling and computations. The use of *pandas*, a flexible and powerful data analysis and manipulation library for Python, makes the record linkage process much easier and faster. The extensive *pandas* library can be used to integrate your record linkage directly into existing data manipulation projects. One of the aims of this project is to make an easily extensible record linkage framework. It is easy to include your own indexing algorithms, comparison/similarity measures and classifiers. ## Basic linking example Import the `recordlinkage` module with all important tools for record linkage and import the data manipulation framework **pandas**. ``` python import recordlinkage import pandas ``` Load your data into pandas DataFrames. ``` python df_a = pandas.DataFrame(YOUR_FIRST_DATASET) df_b = pandas.DataFrame(YOUR_SECOND_DATASET) ``` Comparing all record can be computationally intensive. Therefore, we make set of candidate links with one of the built-in indexing techniques like **blocking**. In this example, only pairs of records that agree on the surname are returned. ``` python indexer = recordlinkage.Index() indexer.block('surname') candidate_links = indexer.index(df_a, df_b) ``` For each candidate link, compare the records with one of the comparison or similarity algorithms in the Compare class. ``` python c = recordlinkage.Compare() c.string('name_a', 'name_b', method='jarowinkler', threshold=0.85) c.exact('sex', 'gender') c.date('dob', 'date_of_birth') c.string('str_name', 'streetname', method='damerau_levenshtein', threshold=0.7) c.exact('place', 'placename') c.numeric('income', 'income', method='gauss', offset=3, scale=3, missing_value=0.5) # The comparison vectors feature_vectors = c.compute(candidate_links, df_a, df_b) ``` Classify the candidate links into matching or distinct pairs based on their comparison result with one of the [classification algorithms](https://recordlinkage.readthedocs.io/en/latest/ref-classifiers.html). The following code classifies candidate pairs with a Logistic Regression classifier. This (supervised machine learning) algorithm requires training data. ``` python logrg = recordlinkage.LogisticRegressionClassifier() logrg.fit(TRAINING_COMPARISON_VECTORS, TRAINING_PAIRS) logrg.predict(feature_vectors) ``` The following code shows the classification of candidate pairs with the Expectation-Conditional Maximisation (ECM) algorithm. This variant of the Expectation-Maximisation algorithm doesn't require training data (unsupervised machine learning). ``` python ecm = recordlinkage.ECMClassifier() ecm.fit_predict(feature_vectors) ``` ## Main Features The main features of this Python record linkage toolkit are: - Clean and standardise data with easy to use tools - Make pairs of records with smart indexing methods such as **blocking** and **sorted neighbourhood indexing** - Compare records with a large number of comparison and similarity measures for different types of variables such as strings, numbers and dates. - Several classifications algorithms, both supervised and unsupervised algorithms. - Common record linkage evaluation tools - Several built-in datasets. ## Documentation The most recent documentation and API reference can be found at [recordlinkage.readthedocs.org](http://recordlinkage.readthedocs.org/en/latest/). The documentation provides some basic usage examples like [ded