{"categories":[{"label":"Text Processing","url":"https://skillfed.io/packages/category/text-processing"}],"enrichment":{"capability":"Computes the edit distance (Levenshtein distance) between two sequences using a fast C++ and Cython implementation, supporting strings and any hashable iterables.","skillfed_tags":["string-similarity","levenshtein","fuzzy-matching"],"use_cases":["Spell-checking and typo correction by finding the closest matching word in a dictionary.","Fuzzy record matching to identify duplicate or near-duplicate entries in databases.","Approximate string search in text processing pipelines where exact matches are too strict.","Sequence alignment in bioinformatics or computational linguistics for comparing word or token sequences.","Similarity scoring in recommendation systems to find similar user queries or product names."],"what_it_does":"editdistance is a Python library that calculates the edit distance (Levenshtein distance) between two sequences\u2014the minimum number of single-character edits (insertions, deletions, substitutions) needed to transform one sequence into another. It wraps a C++ implementation with Cython bindings to achieve performance significantly faster than pure-Python alternatives. The library accepts strings or any hashable iterables (such as lists of words), making it more flexible than some competitors that only handle strings.\n\nThe package has no runtime dependencies and provides a single main function, `eval()`, that takes two sequences and returns their edit distance. It is commonly used in spell-checking, fuzzy matching, record deduplication, and natural-language processing tasks where approximate string matching is needed. The repository is archived and no longer actively maintained as of February 2024, though the last release (0.8.1) supports Python 3.8 and later with prebuilt wheels for major platforms.","worth_installing":"Yes, if you need fast edit-distance computation and can accept an unmaintained package. The library is stable, has no known vulnerabilities, and prebuilt wheels minimize install friction on common platforms. However, be aware the repository is archived; no new features or security updates will be released. For active maintenance and ongoing support, consider alternatives, but for a straightforward, performant edit-distance calculation in an existing codebase, this remains a solid choice."},"id":"editdistance","links":{"html":"https://skillfed.io/packages/editdistance","md":"https://skillfed.io/packages/editdistance.md","pypi":"https://pypi.org/project/editdistance/"},"maintenance":{"status":"abandoned"},"meta":{"latest_release":"2024-02-10","license_spdx":null,"license_treatment":"permissive","name":"editdistance","python_support":"supports_current","summary":"Fast implementation of the edit distance (Levenshtein distance)"},"popularity":{"monthly_downloads":3940760,"position":2439,"tier":"top_5000"},"security":{"n_vulnerabilities":0},"version":"0.8.1"}