Radar check
What we found when we opened it. Every bar is one fact, with its date.
Last code pushed 29 Jul 2025, 15 months before this check.
No releases published on GitHub; the code date above is the one to trust.
Public since April 2012, a little over 14 years.
4,523 stars and 578 forks.
0 commits and 1 new issues in the last 90 days.
72 contributors. No pull requests merged in 90 days; changes go in directly.
No security policy file says how to report a problem. 1 published security advisory.
How the bars are scored
- Alive. Days since the last code push: a week or less 100, a month 90, three months 70, six months 50, a year 30, eighteen months 15, older 0.
- Releases. Age of the latest GitHub release, on a similar scale; no bar when the project publishes no releases.
- Maturity. Age of the project: eight years 100, five 90, three 70, two 55, one 40, six months 25, younger 10.
- Adoption. Stars on a log scale: 100,000 stars is 100, 10,000 is 80, 1,000 is 60, 100 is 40.
- Activity. Commits in the last 90 days: 300 or more 100, 100 85, 30 65, 10 45, any 25.
- Community. People who have contributed: 100 or more 100, 30 80, 10 60, 5 40, 2 25, one 10.
- Security. A published security policy 80, none 40; published advisories are listed, not scored.
Checked by Repo Radar on 26 Oct 2026 from the repository on GitHub.
In simple English
Real-world data is often messy and full of duplicates. Names and addresses get spelled differently in different spreadsheets. This Python library (a collection of ready-to-use programming code) uses machine learning (a way for computers to learn patterns from examples) to match those records up. You give it a few examples of matches by hand. Then it finds the rest of the duplicates across your large datasets automatically.
What it is
Dedupe is an open-source Python library for record deduplication and entity resolution. It reads human training choices to build custom matching rules for your specific data.
What it is good for
Solo builders who need to clean up dirty customer lists, merge two databases without shared keys, or spot identical entries hiding under slight spelling mistakes.
- Learns from your own training data to get more accurate results over time.
- Handles very large datasets quickly using active learning algorithms.
- Free to use under the permissive MIT licence.
- Requires writing Python code and training the model by hand.
- Needs enough computer RAM (temporary memory) to hold your dataset while processing.
- Maintenance falls entirely on you since this is a community code library.
manual spreadsheet cleanup and custom regex scripts
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python3 -m venv venv source venv/bin/activate pip install dedupe