About

Introduction

The Python Record Linkage Toolkit is a library 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.

The project is inspired by the Freely Extensible Biomedical Record Linkage (FEBRL) project, which is a great project. In contrast with FEBRL, the recordlinkage project makes extensive use of data manipulation tools like pandas and numpy. 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 extensible record linkage framework. It is easy to include your own indexing algorithms, comparison/similarity measures and classifiers. The main features of the 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.

What is record linkage?

The term record linkage is used to indicate the procedure of bringing together information from two or more records that are believed to belong to the same entity. Record linkage is used to link data from multiple data sources or to find duplicates in a single data source. In computer science, record linkage is also known as data matching or deduplication (in case of search duplicate records within a single file).

In record linkage, the attributes of the entity (stored in a record) are used to link two or more records. Attributes can be unique entity identifiers (SSN, license plate number), but also attributes like (sur)name, date of birth and car model/colour. The record linkage procedure can be represented as a workflow [Christen, 2012]. The steps are: cleaning, indexing, comparing, classifying and evaluation. If needed, the classified record pairs flow back to improve the previous step. The Python Record Linkage Toolkit follows this workflow.

How to link records?

Import the recordlinkage module with all important tools for record linkage and import the data manipulation framework pandas.

import recordlinkage
import pandas

Consider that you try to link two datasets with personal information like name, sex and date of birth. Load these datasets into a pandas DataFrame.

df_a = pandas.DataFrame(YOUR_FIRST_DATASET)
df_b = pandas.DataFrame(YOUR_SECOND_DATASET)

Comparing all record can be computationally intensive. Therefore, we make smart set of candidate links with one of the built-in indexing techniques like blocking. Only records pairs agreeing on the surname are included.

indexer = recordlinkage.Index()
indexer.block('surname')
candidate_links = indexer.index(df_a, df_b)

Each candidate_link needs to be compared on the comparable attributes. This can be done easily with the Compare class and the available comparison and similarity measures.

compare = recordlinkage.Compare()

compare.string('name', 'name', method='jarowinkler', threshold=0.85)
compare.exact('sex', 'gender')
compare.exact('dob', 'date_of_birth')
compare.string('streetname', 'streetname', method='damerau_levenshtein', threshold=0.7)
compare.exact('place', 'placename')
compare.exact('haircolor', 'haircolor', missing_value=9)

# The comparison vectors
compare_vectors = compare.compute(candidate_links, df_a, df_b)

This record linkage package contains several classification algorithms. Plenty of the algorithms need trainings data (supervised learning) while some others are unsupervised. An example of supervised learning:

true_linkage = pandas.Series(YOUR_GOLDEN_DATA, index=pandas.MultiIndex(YOUR_MULTI_INDEX))

logrg = recordlinkage.LogisticRegressionClassifier()
logrg.fit(compare_vectors[true_linkage.index], true_linkage)

logrg.predict(compare_vectors)

and an example of unsupervised learning (the well known ECM-algorithm):

ecm = recordlinkage.BernoulliEMClassifier()
ecm.fit_predict(compare_vectors)