A python library for accurate and scaleable data deduplication and entity-resolution

These details have not been verified by PyPI

Project links

Project description

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.

Important links

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

Dedupe.io

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.

Dedupe.io also supports record linkage across data sources and continuous matching and training through an API.

For more, see the Dedupe.io product site, tutorials on how to use it, and differences between it and the dedupe library.

Dedupe is well adopted by the Python community. Check out this blogpost, a YouTube video on how to use Dedupe with Python and a Youtube video on how to apply Dedupe at scale using Spark.

csvdedupe

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

Installation

Using dedupe

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

pip install dedupe

Familiarize yourself with dedupe's API, and get started on your project. Need inspiration? Have a look at some examples.

Developing dedupe

We recommend using virtualenv and virtualenvwrapper for working in a virtualized development environment. Read how to set up virtualenv.

Once you have virtualenvwrapper set up,

mkvirtualenv dedupe
git clone https://github.com/dedupeio/dedupe.git
cd dedupe
pip install -e . --config-settings editable_mode=compat
pip install -r requirements.txt

If these tests pass, then everything should have been installed correctly!

pytest

Afterwards, whenever you want to work on dedupe,

workon dedupe

Testing

Unit tests of core dedupe functions

pytest

Test using canonical dataset from Bilenko's research

Using Deduplication

python -m pip install -e ./benchmarks
python benchmarks/benchmarks/canonical.py

Using Record Linkage

python -m pip install -e ./benchmarks
python benchmarks/benchmarks/canonical_matching.py

Team

Forest Gregg, DataMade
Derek Eder, DataMade

Credits

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

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

Citing Dedupe

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

Forest Gregg and Derek Eder. 2022. Dedupe. https://github.com/dedupeio/dedupe.

Project details

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Project links

Release history Release notifications | RSS feed

3.0.3

Aug 15, 2024

3.0.2

Jul 3, 2024

This version

3.0.1

Jul 1, 2024

3.0.0

Jun 27, 2024

2.0.24

Jun 27, 2024

2.0.23

Feb 17, 2023

2.0.22

Jan 18, 2023

2.0.21

Jan 17, 2023

2.0.20

Dec 11, 2022

2.0.19

Oct 10, 2022

2.0.18

Sep 14, 2022

2.0.17

Jul 6, 2022

2.0.16

Jun 11, 2022

2.0.15

May 21, 2022

2.0.14

Apr 20, 2022

2.0.13

Feb 3, 2022

2.0.12

Feb 3, 2022

2.0.11

Jan 27, 2022

2.0.10

Jan 26, 2022

2.0.9

Jan 21, 2022

2.0.8

Apr 17, 2021

2.0.7

Feb 28, 2021

2.0.6

Sep 6, 2020

2.0.5

Sep 2, 2020

2.0.4

Aug 13, 2020

2.0.3

May 26, 2020

2.0.2

Apr 24, 2020

2.0.0

Mar 9, 2020

1.10.0

Jan 8, 2020

1.9.9

Aug 9, 2019

1.9.8

Jul 27, 2019

1.9.7

May 12, 2019

1.9.6

Mar 27, 2019

1.9.5

Mar 20, 2019

1.9.4

Nov 29, 2018

1.9.3

Sep 13, 2018

1.9.2

Apr 4, 2018

1.9.1

Feb 12, 2018

1.9.0

Feb 11, 2018

1.8.2

Dec 27, 2017

1.8.1

Nov 6, 2017

1.8.0

Oct 24, 2017

1.8.0.dev1 pre-release

Oct 20, 2017

1.7.9

Sep 25, 2017

1.7.8

Sep 14, 2017

1.7.7

Aug 31, 2017

1.7.5

Jul 31, 2017

1.7.4

Jul 22, 2017

1.7.3

Jul 21, 2017

1.7.2

Jul 21, 2017

1.7.1

Jul 19, 2017

1.7.0

Jul 9, 2017

1.6.17

Jul 6, 2017

1.6.16

Jul 6, 2017

1.6.15

Jun 5, 2017

1.6.14

Jun 1, 2017

1.6.13

May 12, 2017

1.6.12

May 3, 2017

1.6.11

May 1, 2017

1.6.10

Apr 25, 2017

1.6.9

Apr 21, 2017

1.6.8

Apr 20, 2017

1.6.7

Mar 31, 2017

1.6.6

Mar 31, 2017

1.6.5

Mar 9, 2017

1.6.4

Mar 9, 2017

1.6.3

Mar 8, 2017

1.6.2

Feb 28, 2017

1.6.1

Jan 17, 2017

1.6.0

Jan 7, 2017

1.5.6

Nov 28, 2016

1.5.5

Nov 12, 2016

1.5.4

Nov 2, 2016

1.5.3

Oct 25, 2016

1.5.2

Oct 25, 2016

1.5.1

Oct 24, 2016

1.5.0

Oct 20, 2016

1.4.15

Jul 13, 2016

1.4.14

Jun 19, 2016

1.4.13

Jun 15, 2016

1.4.12.1

Jan 17, 2017

1.4.12

May 17, 2016

1.4.11

May 11, 2016

1.4.10

May 8, 2016

1.4.9

Apr 28, 2016

1.4.8

Apr 28, 2016

1.4.7

Apr 28, 2016

1.4.6

Apr 26, 2016

1.4.5

Apr 26, 2016

1.4.4

Apr 25, 2016

1.4.3

Apr 13, 2016

1.4.2

Apr 13, 2016

1.4.1

Apr 13, 2016

1.4.0

Mar 31, 2016

1.3.8

Mar 14, 2016

1.3.7

Mar 9, 2016

1.3.6

Mar 2, 2016

1.3.5

Mar 2, 2016

1.3.4

Mar 2, 2016

1.3.3

Mar 2, 2016

1.3.2

Mar 2, 2016

1.3.1

Feb 25, 2016

1.3.0

Feb 25, 2016

1.2.8

Feb 25, 2016

1.2.7

Feb 24, 2016

1.2.6

Feb 23, 2016

1.2.5

Feb 23, 2016

1.2.4

Feb 13, 2016

1.2.3

Jan 4, 2016

1.2.2

Dec 1, 2015

1.2.1

Dec 1, 2015

1.2.0

Nov 30, 2015

1.1.4

Oct 12, 2015

1.1.3

Oct 12, 2015

1.1.2

Oct 1, 2015

1.1.1

Sep 19, 2015

1.1.0

Aug 29, 2015

1.0.2

Aug 24, 2015

1.0.1

Aug 19, 2015

1.0.0

Aug 18, 2015

1.0.0rc2 pre-release

Jul 16, 2015

1.0.0rc1 pre-release

Jun 13, 2015

0.8.0.1.7

May 22, 2015

0.8.0.1.6

May 6, 2015

0.8.0.1.5

Apr 28, 2015

0.8.0.1.4

Apr 27, 2015

0.8.0.1.3

Apr 27, 2015

0.8.0.1.1

Apr 16, 2015

0.8.0.1.0

Mar 30, 2015

0.8.0.0.8

Mar 22, 2015

0.8.0.0.7

Mar 20, 2015

0.8.0.0.6

Mar 19, 2015

0.8.0.0.4

Mar 18, 2015

0.8.0.0.3

Mar 18, 2015

0.8.0.0.2

Mar 18, 2015

0.8.0.0.1

Mar 17, 2015

0.8.0.0.0

Mar 10, 2015

0.7.7.1.9

Mar 5, 2015

0.7.7.1.8

Mar 4, 2015

0.7.7.1.7

Mar 4, 2015

0.7.7.1.6

Mar 3, 2015

0.7.7.1.5

Mar 3, 2015

0.7.7.1.4

Mar 3, 2015

0.7.7.1.3

Mar 1, 2015

0.7.7.1.2

Feb 26, 2015

0.7.7.1.1

Feb 20, 2015

0.7.7.1.0

Feb 18, 2015

0.7.7.0.4

Feb 13, 2015

0.7.7.0.3

Feb 12, 2015

0.7.7.0.2

Feb 11, 2015

0.7.7.0.1

Feb 11, 2015

0.7.7.0.0

Feb 9, 2015

0.7.6.9.5

Feb 7, 2015

0.7.6.9.4

Feb 4, 2015

0.7.6.9.3

Feb 2, 2015

0.7.6.9.2

Jan 28, 2015

0.7.6.9.1

Jan 25, 2015

0.7.6.9

Jan 21, 2015

0.7.6.8

Jan 21, 2015

0.7.6.7

Jan 7, 2015

0.7.6.5

Jan 7, 2015

0.7.6.4

Dec 31, 2014

0.7.6.3

Dec 31, 2014

0.7.6.2

Dec 30, 2014

0.7.6.1

Dec 30, 2014

0.7.6.0

Dec 29, 2014

0.7.5.0

Nov 13, 2014

0.7.4.0

Nov 6, 2014

0.7.3.3

Sep 21, 2014

0.7.3.2

Sep 2, 2014

0.7.3.1

Aug 29, 2014

0.7.3

Aug 29, 2014

0.6.1

Aug 12, 2014

0.6.0

Aug 11, 2014

0.5.3.0

May 11, 2014

0.5.2.1

Apr 27, 2014

0.5.2

Apr 27, 2014

0.5.1

Apr 17, 2014

0.5.0

Apr 3, 2014

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dedupe 3.0.1

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Dedupe Python Library

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