{"categories":[{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis"}],"enrichment":{"capability":"RecordLinkage identifies and matches records within or across datasets using indexing, comparison, and classification algorithms, supporting both deduplication and cross-dataset linking tasks.","skillfed_tags":["data-deduplication","entity-resolution","pandas-native"],"use_cases":["Deduplicate customer records in a CRM by blocking on surname and comparing name, address, and phone fields.","Link census records across two time periods to track population changes and identify the same individuals.","Match product catalogs from different suppliers to identify equivalent items for price comparison.","Resolve duplicate entries in a research database by comparing author names, publication dates, and keywords.","Merge employee records from multiple HR systems using blocking on department and comparing names and IDs."],"what_it_does":"RecordLinkage is a Python toolkit for matching records within or across datasets, commonly used for deduplication and entity resolution. It wraps pandas and numpy to provide indexing methods (like blocking and sorted neighbourhood indexing), comparison functions for strings, numbers, and dates, and both supervised and unsupervised classifiers to determine which record pairs are matches. The workflow is: create candidate pairs using an indexer, compute similarity features using a comparator, then classify pairs as matches or non-matches with a classifier like Logistic Regression or ECM.\n\nThe package is designed for research and small-to-medium datasets. It integrates directly with pandas DataFrames, making it natural to use in existing data pipelines. Dependencies include scipy and scikit-learn for statistical and machine-learning operations, and jellyfish for string similarity metrics. No known security vulnerabilities are recorded.","worth_installing":"Yes, if you need record linkage or deduplication on small-to-medium datasets and can tolerate dormant maintenance. The package is stable, has low install friction, and integrates cleanly with pandas. However, avoid it for production systems requiring active support or if you need compatibility guarantees with very recent versions of numpy, pandas, or scikit-learn."},"id":"recordlinkage","links":{"html":"https://skillfed.io/packages/recordlinkage","md":"https://skillfed.io/packages/recordlinkage.md","pypi":"https://pypi.org/project/recordlinkage/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2023-07-20","license_spdx":null,"license_treatment":"permissive","name":"recordlinkage","python_support":"supports_current","summary":"A record linkage toolkit for linking and deduplication"},"popularity":{"monthly_downloads":3071659,"position":2766,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.16"}