Package: dedupe
Dedupe Python Librarydedupe is a python library that uses machine learning to perform fuzzy matching, deduplication and entity resolution quickly on structured data.dedupewill help you:remove duplicate entriesfrom a spreadsheet of names and addresseslink a listwith customer information to another with order history, even without unique customer IDstake a database of campaign contributions andfigure out which ones were made by the same person, even if the names were entered slightly differently for each recorddedupe 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 linksDocumentation:https://docs.dedupe.io/Repository:https://github.com/dedupeio/dedupeIssues:https://github.com/dedupeio/dedupe/issuesMailing list:https://groups.google.com/forum/#!forum/open-source-deduplicationExamples:https://github.com/dedupeio/dedupe-examplesdedupe library consultingIf 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 dedupeDedupe.ioA 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.Dedupe.ioalso supports record linkage across data sources and continuous matching and training through anAPI.For more, see theDedupe.io product site,tutorials on how to use it, anddifferences between it and the dedupe library.Dedupe is well adopted by the Python community. Check out thisblogpost,
a YouTube video on how to useDedupe with Pythonand a Youtube video on how to applyDedupe at scale using Spark.csvdedupeCommand line tool for de-duplicating andlinkingCSV files. Read about it onSource Knight-Mozilla OpenNews.InstallationUsing dedupeIf you only want to use dedupe, install it this way:pipinstalldedupeFamiliarize yourself withdedupe's API, and get started on your project. Need inspiration? Have a look atsome examples.Developing dedupeWe recommend usingvirtualenvandvirtualenvwrapperfor working in a virtualized development environment.Read how to set up virtualenv.Once you have virtualenvwrapper set up,mkvirtualenvdedupe
gitclonehttps://github.com/dedupeio/dedupe.gitcddedupe
pipinstall-e.--config-settingseditable_mode=compat
pipinstall-rrequirements.txtIf these tests pass, then everything should have been installed correctly!pytestAfterwards, whenever you want to work on dedupe,workondedupeTestingUnit tests of core dedupe functionspytestTest using canonical dataset from Bilenko's researchUsing Deduplicationpython-mpipinstall-e./benchmarks
pythonbenchmarks/benchmarks/canonical.pyUsing Record Linkagepython-mpipinstall-e./benchmarks
pythonbenchmarks/benchmarks/canonical_matching.pyTeamForest Gregg, DataMadeDerek Eder, DataMadeCreditsDedupe is based on Mikhail Yuryevich Bilenko's Ph.D. dissertation:Learnable Similarity Functions and their Application to Record Linkage and Clustering.Errors / BugsIf something is not behaving intuitively, it is a bug, and should be reported.Report it hereNote on Patches/Pull RequestsFork the project.Make your feature addition or bug fix.Send us a pull request. Bonus points for topic branches.CopyrightCopyright (c) 2022 Forest Gregg and Derek Eder. Released under theMIT License.Third-party copyright in this distribution is noted where applicable.Citing DedupeIf you use Dedupe in an academic work, please give this citation:Forest Gregg and Derek Eder. 2022. Dedupe.https://github.com/dedupeio/dedupe.