DataGroomr vs Python Record Linkage Toolkit

DataGroomr has no write-up on Specifiction yet; its figures are above.

Python Record Linkage Toolkit is a free Python library for linking records across data sources and finding duplicates within one source. It is intended for research involving small or medium-sized files. Its workflow covers cleaning and standardizing data, generating candidate pairs, comparing records, classifying matches, and evaluating results. Cleaning tools include string standardization and phonetic encoding. Blocking and sorted neighbourhood indexing create candidate pairs. Comparisons cover strings, numbers, dates, and geographic data, with string methods including Jaro, Jaro-Winkler, Levenshtein, Damerau-Levenshtein, q-gram, and cosine similarity. Supervised and unsupervised classifiers include logistic regression, Naive Bayes, and support vector machines. Evaluation tools and built-in datasets are included. Users can add indexing algorithms, comparison measures, similarity measures, and classifiers. The toolkit uses pandas and NumPy and can fit existing pandas workflows. It is self-hosted, offers fuzzy matching and API access, and installs with pip or from GitHub. The stable documentation requires Python 3.6 or higher. The repository identifies a BSD-3-Clause license. Full indexing can become slow on large DataFrames because comparisons grow quadratically.