{"categories":[{"label":"Linguistic","url":"https://skillfed.io/packages/category/text-processing-linguistic/2"}],"enrichment":{"capability":"Computes Levenshtein and Damerau-Levenshtein edit distances between strings with optional early cutoff to return -1 when distance exceeds a specified threshold.","skillfed_tags":["string-algorithms","fuzzy-matching"],"use_cases":["Spell-checking or autocorrect: find candidate corrections by computing edit distances from a misspelled word to a dictionary.","Duplicate detection: identify similar product names, user entries, or records that may refer to the same entity.","Fuzzy search: rank search results by string similarity when exact matches are unavailable.","Data deduplication: merge or flag records with similar identifiers or names across datasets.","Typo tolerance in user input: accept user queries that are within a small edit distance of known commands or entities."],"what_it_does":"editdistpy is a compiled Python library that calculates edit distances\u2014the minimum number of single-character edits needed to transform one string into another. It implements two algorithms: the classic Levenshtein distance (insertions, deletions, substitutions) and the Damerau-Levenshtein optimal string alignment distance (which also allows transpositions). The library is ported from a C# implementation and supports an optional `max_distance` parameter; when the distance would exceed this threshold, the function returns -1 instead of computing the full result, which can significantly speed up comparisons when you only care whether strings are \"close enough\" within a bound.\n\nThe package has no runtime dependencies and is distributed as precompiled wheels for modern Python versions (3.10\u20133.14) on common platforms. It is suitable for tasks like fuzzy string matching, spell-checking, duplicate detection, and record linkage where you need to measure how different two strings are. The library is actively maintained and carries an MIT license.","worth_installing":"Yes, if you need fast edit-distance computation in Python. The library is actively maintained, has no external dependencies, installs cleanly on modern Python versions, carries a permissive MIT license, and shows good performance on short and medium strings. Install it if fuzzy string matching or similarity measurement is core to your application; skip it if you only need exact string matching or have no string-comparison requirements."},"id":"editdistpy","links":{"html":"https://skillfed.io/packages/editdistpy","md":"https://skillfed.io/packages/editdistpy.md","pypi":"https://pypi.org/project/editdistpy/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-12","license_spdx":"MIT","license_treatment":"permissive","name":"editdistpy","python_support":"supports_current","summary":"Fast Levenshtein and Damerau optimal string alignment algorithms."},"popularity":{"monthly_downloads":382152,"position":7091,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.4.0"}