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.
This project is inspired by the Freely Extensible Biomedical Record Linkage
(FEBRL) project, which is a great
project. In contrast with FEBRL, the recordlinkage project uses pandas and numpy 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
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.
Load your data into pandas DataFrames.
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. In this example, only pairs of records that agree on the surname
index = recordlinkage.Pairs(df_a, df_b)
candidate_links = index.block('surname')
For each candidate link, compare the records with one of the
comparison or similarity algorithms in the Compare class.
c = recordlinkage.Compare(candidate_links, df_a, df_b)
c.string('name_a', 'name_b', method='jarowinkler', threshold=0.85)
c.string('str_name', 'streetname', method='damerau_levenshtein', threshold=0.7)
c.numeric('income', 'income', method='gauss', offset=3, scale=3, missing_value=0.5)
# The comparison vectors
This Python Record Linkage Toolkit contains multiple classification alogirthms.
Plenty of the algorithms need trainings data (supervised learning) while
others are unsupervised. An example of supervised learning:
logrg = recordlinkage.LogisticRegressionClassifier()
and an example of unsupervised learning (the well known ECM-algorithm):
ecm = recordlinkage.ECMClassifier()
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
- Common record linkage evaluation tools
- Several built-in datasets.
The most recent documentation and API reference can be found at
recordlinkage.readthedocs.org. The documentation
provides some basic usage examples like deduplication and linking census
data. More examples are coming soon. If you do have interesting examples to
share, let us know.
Dependencies, installation and license
Install the Python Record Linkage Toolkit easily with pip
pip install recordlinkage
The toolkit depends on Pandas (>=18.0), Numpy, Scikit-learn, Scipy and
Jellyfish. You probably have most of them already installed. The package
jellyfish is used for approximate string comparing and string encoding.
The package Numexpr is an optional dependency to speed up numerical
The license for this record linkage tool is GPLv3.
Stuck on your record linkage code or problem? Any other questions? Don’t
hestitate to send me an email (email@example.com).