معرفی شرکت ها

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

تبلیغات ما

مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.

مشاهده بیشتر

توضیحات

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

ویژگی	مقدار
سیستم عامل	-
نام فایل	dedupe-2.0.9
نام	dedupe
نسخه کتابخانه	2.0.9
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	-
ایمیل نویسنده	Forest Gregg <fgregg@datamade.us>
آدرس صفحه اصلی	-
آدرس اینترنتی	https://pypi.org/project/dedupe/
مجوز	The MIT License (MIT) Copyright (c) 2014 Forest Gregg, Derek Eder, DataMade and Contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

# Dedupe Python Library [![Tests Passing](https://github.com/dedupeio/dedupe/workflows/tests/badge.svg)](https://github.com/dedupeio/dedupe/actions?query=workflow%3Atests)[![codecov](https://codecov.io/gh/dedupeio/dedupe/branch/main/graph/badge.svg?token=aauKUrTEgh)](https://codecov.io/gh/dedupeio/dedupe) _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](https://dedupe.io/pricing/#consulting). ## Tools built with dedupe ### [Dedupe.io](https://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](https://dedupe.io/) also supports record linkage across data sources and continuous matching and training through an [API](https://apidocs.dedupe.io/en/latest/). For more, see the [Dedupe.io product site](https://dedupe.io/), [tutorials on how to use it](https://dedupe.io/tutorial/intro-to-dedupe-io.html), and [differences between it and the dedupe library](https://dedupe.io/documentation/should-i-use-dedupeio-or-the-dedupe-python-library.html). Dedupe is well adopted by the Python community. Check out this [blogpost](https://medium.com/district-data-labs/basics-of-entity-resolution-with-python-and-dedupe-bc87440b64d4), a YouTube video on how to use [Dedupe with Python](https://youtu.be/McsTWXeURhA) and a Youtube video on how to apply [Dedupe at scale using Spark](https://youtu.be/q9HPUYmiwjE?t=2704). ### [csvdedupe](https://github.com/dedupeio/csvdedupe) Command line tool for de-duplicating and [linking](https://github.com/dedupeio/csvdedupe#csvlink-usage) CSV files. Read about it on [Source Knight-Mozilla OpenNews](https://source.opennews.org/en-US/articles/introducing-cvsdedupe/). ## Installation ### Using dedupe If you only want to use dedupe, install it this way: ```bash pip install dedupe ``` Familiarize yourself with [dedupe's API](https://docs.dedupe.io/en/latest/API-documentation.html), and get started on your project. Need inspiration? Have a look at [some examples](https://github.com/dedupeio/dedupe-examples). ### Developing dedupe We recommend using [virtualenv](http://virtualenv.readthedocs.org/en/latest/virtualenv.html) and [virtualenvwrapper](http://virtualenvwrapper.readthedocs.org/en/latest/install.html) for working in a virtualized development environment. [Read how to set up virtualenv](http://docs.python-guide.org/en/latest/dev/virtualenvs/). Once you have virtualenvwrapper set up, ```bash 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! ```bash pytest ``` Afterwards, whenever you want to work on dedupe, ```bash workon dedupe ``` ## Testing Unit tests of core dedupe functions ```bash pytest ``` #### Test using canonical dataset from Bilenko's research Using Deduplication ```bash python -m pip install -e ./benchmarks python benchmarks/benchmarks/canonical.py ``` Using Record Linkage ```bash 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*](http://www.cs.utexas.edu/~ml/papers/marlin-dissertation-06.pdf). ## Errors / Bugs If something is not behaving intuitively, it is a bug, and should be reported. [Report it here](https://github.com/dedupeio/dedupe/issues) ## 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 Copyright (c) 2022 Forest Gregg and Derek Eder. Released under the [MIT License](https://github.com/dedupeio/dedupe/blob/main/LICENSE). 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.

نیازمندی

مقدار	نام
-	scikit-learn
>=1.3	affinegap
>=1.9	categorical-distance
-	dedupe-variable-datetime
>=1.20	numpy
-	doublemetaphone
>=0.2.0	highered
>=1.2	simplecosine
>=0.4.1	haversine
>=4.1.4	BTrees
-	zope.index
-	dedupe-Levenshtein-search
-	typing-extensions

زبان مورد نیاز

مقدار	نام
>=3.7	Python

نحوه نصب

نصب پکیج whl dedupe-2.0.9:

pip install dedupe-2.0.9.whl

نصب پکیج tar.gz dedupe-2.0.9:

pip install dedupe-2.0.9.tar.gz