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rapidfuzz avatar

rapidfuzz/RapidFuzz

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RapidFuzz

RapidFuzz is a C++ accelerated Python library providing high-performance string comparison and similarity calculations. It functions as a fuzzy string matching toolkit used to quantify the difference between text sequences through Levenshtein distance and other edit distance metrics.

The library focuses on scalable approximate text matching, enabling the identification and ranking of similar strings within large datasets. It provides specialized utilities for finding the best matches in a collection and generating pairwise similarity matrices.

The project covers a broad surface of text processing capabilities, including string similarity scoring, edit distance calculation, and text preprocessing. These tools are used for tasks such as data deduplication, typo correction, and search query suggestion.

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rapidfuzz.github.io/RapidFuzz
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Features

  • Fuzzy Matching - Provides a comprehensive suite of algorithms for fuzzy string matching to identify close matches despite typos or variations.
  • Text Similarity Scoring - Calculates numerical similarity scores between pieces of text using structural and token-based algorithmic comparison.
  • C++ Implementations - Ships a core engine written in C++ to provide high-performance string matching and similarity calculations.
  • High-Performance C++ Libraries - Provides a high-performance C++ library core exposed to Python for scalable string comparison.
  • Edit Distance Calculators - Implements high-performance edit distance calculators to measure the minimum operations required to transform one string into another.
  • String Similarity Metrics - Implements a wide range of string similarity metrics, including Levenshtein, Hamming, and Jaro-Winkler distances.
  • Approximate Matching Tools - Ships a toolkit for approximate matching to identify the most similar strings from candidate sets using distance metrics.
  • Intra-Dataset Deduplication - Enables the identification and merging of nearly identical records to deduplicate datasets.
  • Matching and Ranking Logic - Implements matching and ranking logic to order a list of candidates by similarity to a target string.
  • Fuzzy Query Suggestions - Supports the ranking of candidates to generate fuzzy search query suggestions based on user input.
  • Vector Distance Kernels - Employs SIMD vector distance kernels to process multiple characters simultaneously for accelerated string distance calculations.
  • C-Bindings - Provides a high-performance binding layer that translates Python string objects into C-style arrays for low-latency execution.
  • Raw Pointer Access - Uses raw pointer access to manage string buffers and score arrays, eliminating Python object overhead in tight loops.
  • Bit-Parallel Implementations - Implements high-performance edit distance calculations using bit-parallelism to process state vectors in single CPU cycles.
  • Similarity Matrices - Provides utilities to calculate distance or similarity between two collections of strings as a full matrix or direct mapping.
  • Approximate String Searching - Performs approximate string searching to find the closest textual matches for a query within a collection.
  • Best Match Extraction - Provides utilities to extract the single most similar string from a list of candidate choices.
3,731 stars·151 forks·Python·mit·62 views

Star history

Star history chart for rapidfuzz/rapidfuzzStar history chart for rapidfuzz/rapidfuzz

How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.

Frequently asked questions

What does rapidfuzz/rapidfuzz do?

RapidFuzz is a C++ accelerated Python library providing high-performance string comparison and similarity calculations. It functions as a fuzzy string matching toolkit used to quantify the difference between text sequences through Levenshtein distance and other edit distance metrics.

What are the main features of rapidfuzz/rapidfuzz?

The main features of rapidfuzz/rapidfuzz are: Fuzzy Matching, Text Similarity Scoring, C++ Implementations, High-Performance C++ Libraries, Edit Distance Calculators, String Similarity Metrics, Approximate Matching Tools, Intra-Dataset Deduplication.

Which projects share features with rapidfuzz/rapidfuzz?

Projects with overlapping indexed features include: seatgeek/fuzzywuzzy — Fuzzywuzzy is a Python library and text processing utility designed to calculate similarity scores between strings. It… maxbachmann/rapidfuzz — This is a Python fuzzy string matching library used for calculating string similarity and edit distances. It serves as… google/diff-match-patch — This is a text diffing and patching library used for computing differences between text blocks, calculating edit… skim-rs/skim — Skim is a cross-platform interactive fuzzy finder that runs as a terminal application, a Rust library, a Vim and… mailcheck/mailcheck — Mailcheck is an email domain suggestion library and validation utility designed to identify misspelled email… javve/list.js — list.js is a JavaScript search and sort library used to add real-time filtering, sorting, and pagination to HTML lists…

Projects sharing features with RapidFuzz

These projects share indexed features with RapidFuzz. Shared tags can include platform or build tooling; verify the primary use case before treating a result as a replacement.
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    seatgeek/fuzzywuzzy

    9,258View on GitHub↗

    Fuzzywuzzy is a Python library and text processing utility designed to calculate similarity scores between strings. It functions as a text similarity scoring engine and an approximate string matching tool used to identify the closest textual matches within a list of candidate strings. The library provides a suite of tools for measuring the degree of similarity between pieces of text, accounting for typos and formatting differences. These capabilities include extracting the best match from a candidate list and performing fuzzy string matching through various scoring methods. The toolset cover

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  • maxbachmann/rapidfuzzmaxbachmann avatar

    maxbachmann/rapidfuzz

    3,958View on GitHub↗

    This is a Python fuzzy string matching library used for calculating string similarity and edit distances. It serves as a collection of string distance algorithms, a sequence alignment tool, and an approximate string search engine to measure text similarity. The library provides a wide array of metrics to quantify string closeness, including Levenshtein, Jaro-Winkler, Hamming, and Damerau-Levenshtein distances. It supports similarity analysis through longest common subsequence calculations, token-based comparisons, and weighted scoring to account for differences in content and word order. Bey

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  • google/diff-match-patchgoogle avatar

    google/diff-match-patch

    8,065View on GitHub↗

    This is a text diffing and patching library used for computing differences between text blocks, calculating edit distances, and applying patches to synchronize document versions. It includes a fuzzy text matching engine to locate strings by balancing accuracy with location, and a Levenshtein distance calculator to measure the number of character insertions, deletions, and substitutions between two strings. The library features a semantic diff optimizer that refines raw text differences to align with human-readable word and phrase boundaries. It provides utilities for generating and parsing se

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  • skim-rs/skimskim-rs avatar

    skim-rs/skim

    6,592View on GitHub↗

    Skim is a cross-platform interactive fuzzy finder that runs as a terminal application, a Rust library, a Vim and Neovim plugin, and a shell integration tool. It provides real-time filtering and selection from lists of items, supporting keyboard and mouse navigation, live preview panes, and multi-select functionality across Linux, macOS, and Windows. The tool distinguishes itself through a composable query expression tree that supports fuzzy, exact, inverse, prefix, suffix, and logical AND/OR operators, combined with a Smith-Waterman scoring engine that penalizes typos and gaps for natural rel

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