dedupe is a 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.
Tools built with dedupe¶
Dedupe.io A full service web service powered by dedupe for de-duplicating and find matches in your messy data. It provides an easy-to-use interface and provides cluster review and automation, as well as advanced record linkage, continuous matching and API integrations. See the product page and the launch blog post.
- machine learning - reads in human labeled data to automatically create optimum weights and blocking rules
- runs on a laptop - makes intelligent comparisons so you don’t need a powerful server to run it
- built as a library - so it can be integrated in to your applications or import scripts
- extensible - supports adding custom data types, string comparators and blocking rules
- open source - anyone can use, modify or add to it
pip install "numpy>=1.9" pip install dedupe
Dedupe is a library and not a stand-alone command line tool. To demonstrate its usage, we have come up with a few example recipes for different sized datasets for you (repo, as well as annotated source code:
Errors / Bugs¶
If something is not behaving intuitively, it is a bug, and should be reported. Report it here
Contributing to dedupe¶
Check out dedupe repo for how to contribute to the library.
Check out dedupe-examples for how to contribute a useful example of using dedupe.
If you use Dedupe in an academic work, please give this citation:
Gregg, Forest and Derek Eder. 2015. Dedupe. https://github.com/dedupeio/dedupe.