Dedupe Python Library
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.
- Documentation: https://docs.dedupe.io/
- Repository: https://github.com/dedupeio/dedupe
- Issues: https://github.com/dedupeio/dedupe/issues
- Mailing list: https://groups.google.com/forum/#!forum/open-source-deduplication
- Examples: https://github.com/dedupeio/dedupe-examples
dedupe library consulting
If you or your organization would like professional assistance in working with the dedupe library, Dedupe.io 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.
If you only want to use dedupe, install it this way:
pip install dedupe
Once you have virtualenvwrapper set up,
mkvirtualenv dedupe git clone git://github.com/dedupeio/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!
Afterwards, whenever you want to work on dedupe,
Unit tests of core dedupe functions
Test using canonical dataset from Bilenko’s research
Using Record Linkage
- 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.
If you use Dedupe in an academic work, please give this citation:
Forest Gregg and Derek Eder. 2019. Dedupe. https://github.com/dedupeio/dedupe.