# TheFuzz

Fuzzy String Matching in Python

Repository: https://github.com/seatgeek/thefuzz
Canonical: https://ross.abutalabs.com/products/thefuzz
Language: Python
License: MIT
License Family: permissive
Last push: 2025-03-03T17:02:53+00:00
Link (homepage): http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/

## Health v2 (maintenance only)
Score: 36/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 9, release rhythm 35, longevity 100
- inputs: {"age_days": 2007, "days_push": 548, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3645, forks 167 (observed 2026-08-28T04:08:12.913691+00:00)

## What it is
TheFuzz (formerly FuzzyWuzzy) is a Python library for fuzzy string matching that uses Levenshtein distance to compute similarity ratios between strings. It provides scoring functions like ratio, partial_ratio, token_sort_ratio, and token_set_ratio to match strings that are similar but not identical.

## Use cases
- match misspelled product or event names across data sources
- deduplicate records with slightly different text labels
- fuzzy search for the closest string in a list
- compare user input against known values with typos
- link entity names between two datasets
- score similarity of two strings in Python

## When to choose
- you need simple, well-tested fuzzy string similarity scoring in Python
- you want token-based matching that tolerates word order and partial matches
- you need a lightweight pure-Python library with optional C acceleration via python-Levenshtein

## When to avoid
- you need high-performance matching over millions of strings at scale (consider RapidFuzz)
- you need semantic or embedding-based similarity rather than edit-distance
- GPL-2.0 licensing of the legacy fuzzywuzzy package is a problem (note thefuzz itself is MIT)

## Facets
- artifact type: library
- maturity: active
- function: nlp
- domain: developer-tools, data-science
- platform: python, cross-platform
- tags: fuzzy-matching, string-similarity, levenshtein-distance, record-linkage, text-matching, natural-language-processing

## Member repositories
- seatgeek/thefuzz (main) score 36
- seatgeek/fuzzywuzzy (mirror) score 10

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:12.913691+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:23:13.228087+00:00, confidence not recorded.
  - readme: https://github.com/seatgeek/thefuzz (fetched 2026-08-28T04:08:12.913691+00:00, sha fd271b33d756)
  - homepage: http://chairnerd.seatgeek.com/fuzzywuzzy-fuzzy-string-matching-in-python/ (fetched 2026-08-29T08:22:55.844997+00:00, sha 8f782e9d559e)
  - registry_pypi: https://pypi.org/pypi/fuzzywuzzy/json (fetched 2026-08-29T08:22:55.854186+00:00, sha 283445edea7f)
  - registry_pypi: https://pypi.org/pypi/thefuzz/json (fetched 2026-08-29T08:22:55.856040+00:00, sha 85c7d29084d8)
- Data as of 2026-08-30T08:39:29.467469+00:00.