record-linkage

问题 I'm somewhat new to Pandas and Python Record Linkage Toolkit, so please forgive me if the answer is obvious. I'm trying to cross-reference one large dataset, "CSV_1", against another, "CSV_2", in order to create a third CSV consisting only of matches that concatenates all columns from CSV_1 and CSV_2 regardless of overlap in order to preserve the original record, e.g. CSV_1 CSV_2 Name City Date Name_of_thing City_of_Origin Time Examp. Bton 7/11 THE EXAMPLE, LLC Bton, USA 7/11/2020 00:00

问题 I am going to work on some projects to deal with entity deduplication. Datasets (one or more) which may contain duplicate entity. In the realtime, entity may represent the name, address, country, email, social media id in the different form. My goal is to identify that these are possible duplicates based on different weightage for the different entity Info. I am trying to look for a library that is open-source & preferably written in Java. As I need to process the millions of data, I need to

问题 How can use fuzzy matching in pandas to detect duplicate rows (efficiently) How to find duplicates of one column vs. all the other ones without a gigantic for loop of converting row_i toString() and then comparing it to all the other ones? 回答1: Not pandas specific, but within the python ecosystem the dedupe python library would seem to do what you want. In particular, it allows you to compare each column of a row separately and then combine the information into a single probability score of a

问题 How can use fuzzy matching in pandas to detect duplicate rows (efficiently) How to find duplicates of one column vs. all the other ones without a gigantic for loop of converting row_i toString() and then comparing it to all the other ones? 回答1: Not pandas specific, but within the python ecosystem the dedupe python library would seem to do what you want. In particular, it allows you to compare each column of a row separately and then combine the information into a single probability score of a

问题 How can use fuzzy matching in pandas to detect duplicate rows (efficiently) How to find duplicates of one column vs. all the other ones without a gigantic for loop of converting row_i toString() and then comparing it to all the other ones? 回答1: Not pandas specific, but within the python ecosystem the dedupe python library would seem to do what you want. In particular, it allows you to compare each column of a row separately and then combine the information into a single probability score of a

问题 I have a dataframe containing the title of an article and the url links associated. My problem is that the url link is not necessary in the row of the corresponding title, example: title | urls Who will be the next president? | https://website/5-ways-to-make-a-cocktail.com 5 ways to make a cocktail | https://website/who-will-be-the-next-president.com 2 millions raised by this startup | https://website/how-did-you-find-your-house.com How did you find your house | https://website/2-millions

问题 Closed. This question is off-topic. It is not currently accepting answers. Want to improve this question? Update the question so it's on-topic for Stack Overflow. Closed 2 years ago . Is there a open source implementation for Fellegi-Sunter? 回答1: Is this what you are looking for? Wikipedia: Record Linkage Check under "Software implementations" for possible solutions. 回答2: Here are some open source implementations: http://github.com/dedupeio/dedupe (author of this) https://sourceforge.net

问题 I'm trying to use the Duke Fast Deduplication Engine to search for some duplicate records in the database at the company where I work. I run it from the command line like this: java -cp "C:\utils\duke-0.6\duke-0.6.jar;C:\utils\duke-0.6\lucene-core-3.6.1.jar" no.priv.garshol.duke.Duke --showmatches --verbose .\config.xml But I get an error: Exception in thread "main" java.lang.UnsupportedOperationException: Operation no t yet supported at sun.jdbc.odbc.JdbcOdbcResultSet.isClosed(Unknown Source

问题 I frequently need to match two datasets by multiple matching columns, for two reasons. First, each of these characteristics are ‘dirty’, meaning a single column does not consistently match even when it should (for a truly matching row). Second, the characteristics are not unique (e.g., male and female). Matching like this is useful for matching across time (pre-test with post-test scores), different data modalities (observed characteristics and lab values), or multiple datasets for research

问题 I'm using the Dedupe library to match person records to each other. My data includes name, date of birth, address, phone number and other personally identifying information. Here is my question: I always want to match two records with 100% confidence if they have a matching name and phone number (for example). Here is an example of some of my code: fields = [ {'field' : 'LAST_NM', 'variable name' : 'last_nm', 'type': 'String'}, {'field' : 'FRST_NM', 'variable name' : 'frst_nm', 'type':