dedupe is a python library that uses machine learning to perform de-duplication 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 id's
• 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.

• Documentation: https://dedupe.rtfd.org/
• Repository: https://github.com/datamade/dedupe
• Issues: https://github.com/datamade/dedupe/issues
• Examples: https://github.com/datamade/dedupe-examples
• IRC channel, #dedupe on irc.freenode.net

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

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

pip install "numpy>=1.9"
pip install dedupe

git clone git:https://github.com/datamade/dedupe.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!
nosetests

Unit tests of core dedupe functions

nosetests

Using Deduplication

python tests/canonical.py

Using Record Linkage

python tests/canonical_matching.py

• Forest Gregg
• Derek Eder

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

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

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

Copyright (c) 2016 Forest Gregg and Derek Eder. Released under the MIT License.

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

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

Gregg, Forest, and Derek Eder. 2016. Dedupe. https://github.com/datamade/dedupe.