Fast, accurate and scalable probabilistic data linkage using your choice of SQL backend.

splink is a Python package for probabilistic record linkage (entity resolution).

Its key features are:

It is extremely fast. It is capable of linking a million records on a laptop in around a minute.
It is highly accurate, with support for term frequency adjustments, and sophisticated fuzzy matching logic.
Linking jobs can be executed in Python (using the DuckDB package), or using big-data backends like AWS Athena and Spark to link 100+ million records.
Training data is not required because models can be trained using an unsupervised approach.
It produces a wide variety of interactive outputs, helping users to understand their model and diagnose linkage problems.

The core linkage algorithm is an implementation of Fellegi-Sunter's model of record linkage, with various customisations to improve accuracy.

What does Splink do?

Splink deduplicates and links records from datasets that lack a unique identifier.

For example, a few of your records may look like this:

row_id	first_name	surname	dob	city
1	lucas	smith	1984-01-02	London
2	lucas	smyth	1984-07-02	Manchester
3	lucas	smyth	1984-07-02
4	david	jones		Leeds
5	david	jones	1990-03-21	Leeds

Splink produces pairwise predictions of the links:

row_id_l	row_id_r	match_probability
1	2	0.9
1	3	0.85
2	3	0.92
4	5	0.7

And clusters the predictions to produce an estimated unique id:

cluster_id	row_id
a	1
a	2
a	3
b	4
b	5

Documentation

The homepage for the Splink documentation can be found here. Interactive demos can be found here, or by clicking the following Binder link:

The specification of the Fellegi Sunter statistical model behind splink is similar as that used in the R fastLink package. Accompanying the fastLink package is an academic paper that describes this model. A series of interactive articles also explores the theory behind Splink.

Installation

Splink supports python 3.7+. To obtain the latest released version of splink:

pip install splink

Quickstart

The following code demonstrates how to estimate the parameters of a deduplication model, use it to identify duplicate records, and then use clustering to generate an estimated unique person ID.

For more detailed tutorials, please see here.

from splink.duckdb.duckdb_linker import DuckDBLinker
from splink.duckdb.duckdb_comparison_library import (
    exact_match,
    levenshtein_at_thresholds,
)

import pandas as pd

df = pd.read_csv("./tests/datasets/fake_1000_from_splink_demos.csv")

settings = {
    "link_type": "dedupe_only",
    "blocking_rules_to_generate_predictions": [
        "l.first_name = r.first_name",
        "l.surname = r.surname",
    ],
    "comparisons": [
        levenshtein_at_thresholds("first_name", 2),
        exact_match("surname"),
        exact_match("dob"),
        exact_match("city", term_frequency_adjustments=True),
        exact_match("email"),
    ],
}

linker = DuckDBLinker(df, settings)
linker.estimate_u_using_random_sampling(target_rows=1e6)

blocking_rule_for_training = "l.first_name = r.first_name and l.surname = r.surname"
linker.estimate_parameters_using_expectation_maximisation(blocking_rule_for_training)

blocking_rule_for_training = "l.dob = r.dob"
linker.estimate_parameters_using_expectation_maximisation(blocking_rule_for_training)

pairwise_predictions = linker.predict()

clusters = linker.cluster_pairwise_predictions_at_threshold(pairwise_predictions, 0.95)
clusters.as_pandas_dataframe(limit=5)

Acknowledgements

We are very grateful to ADR UK (Administrative Data Research UK) for providing the initial funding for this work as part of the Data First project.

We are extremely grateful to professors Katie Harron, James Doidge and Peter Christen for their expert advice and guidance in the development of Splink. We are also very grateful to colleagues at the UK's Office for National Statistics for their expert advice and peer review of this work. Any errors remain our own.