Dedupe Python Library

Tests PassingCoverage Status

dedupe is a python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on structured data.

dedupe will help you:

  • remove duplicate entries from a spreadsheet of names and addresses
  • link a list with customer information to another with order history, even without unique customer IDs
  • take a database of campaign contributions and figure out which ones were made by the same person, even if the names were entered slightly differently for each record

dedupe takes in human training data and comes up with the best rules for your dataset to quickly and automatically find similar records, even with very large databases.

Important links

dedupe library consulting

If you or your organization would like professional assistance in working with the dedupe library, LLC offers consulting services. Read more about pricing and available services here.

Tools built with dedupe

A cloud service powered by the dedupe library for de-duplicating and finding matches in your data. It provides a step-by-step wizard for uploading your data, setting up a model, training, clustering and reviewing the results. also supports record linkage across data sources and continuous matching and training through an API.

For more, see the product site, tutorials on how to use it, and differences between it and the dedupe library.


Command line tool for de-duplicating and linking CSV files. Read about it on Source Knight-Mozilla OpenNews.


Using dedupe

If you only want to use dedupe, install it this way:

pip install dedupe

Familiarize yourself with dedupe’s API, and get started on your project. Need inspiration? Have a look at some examples.

Developing dedupe

We recommend using virtualenv and virtualenvwrapper for working in a virtualized development environment. Read how to set up virtualenv.

Once you have virtualenvwrapper set up,

mkvirtualenv dedupe
git clone git://
cd dedupe
pip install "numpy>=1.9"
pip install -r requirements.txt
cython src/*.pyx
pip install -e .

If these tests pass, then everything should have been installed correctly!


Afterwards, whenever you want to work on dedupe,

workon dedupe


Unit tests of core dedupe functions


Test using canonical dataset from Bilenko’s research

Using Deduplication

python tests/

Using Record Linkage

python tests/


  • Forest Gregg, DataMade
  • Derek Eder, DataMade


Dedupe is based on Mikhail Yuryevich Bilenko’s Ph.D. dissertation: Learnable Similarity Functions and their Application to Record Linkage and Clustering.

Errors / Bugs

If something is not behaving intuitively, it is a bug, and should be reported. Report it here

Note on Patches/Pull Requests

  • Fork the project.
  • Make your feature addition or bug fix.
  • Send us a pull request. Bonus points for topic branches.


Copyright (c) 2019 Forest Gregg and Derek Eder. Released under the MIT License.

Third-party copyright in this distribution is noted where applicable.

Citing Dedupe

If you use Dedupe in an academic work, please give this citation:

Forest Gregg and Derek Eder. 2019. Dedupe.