maxbachmann/rapidfuzz

Features

• Fuzzy Matching - Provides comprehensive algorithms for fuzzy string matching to find similar strings despite typos or variations.
• C++ Implementations - Implements core string metric algorithms in C++ to ensure high performance and low-level memory control.
• Fuzzy String Matching Libraries - Acts as a high-performance Python library for calculating string similarity and edit distances.
• Edit Distance Calculators - Calculates the minimum number of insertions and deletions needed to transform one sequence into another.
• Hamming Distance Calculators - Measures the number of positions where two equal-length strings differ to determine the required substitutions.
• String Similarity Metrics - Provides a wide range of string similarity metrics including Levenshtein, Hamming, and Jaro-Winkler distances.
• Python-C++ Bindings - Exposes native C++ high-performance functions to Python via C-API bindings.
• Edit Distance Calculators - Computes the Damerau-Levenshtein distance, including transpositions, to measure the difference between two sequences.
• String Distance Algorithms - Offers a comprehensive collection of string distance algorithms for measuring text similarity.
• Approximate String Searching - Implements an approximate search engine to find the most similar entries in a collection using fuzzy scoring.
• String Standardizers - Provides utilities to standardize text by removing non-alphanumeric characters and trimming whitespace.
• Text Preprocessing Pipelines - Includes a preprocessing pipeline to clean and standardize strings before calculating similarity.
• Text Sequence Alignment Tools - Provides a tool for identifying optimal substrings and generating edit operations to transform sequences.
• Text Sequence Alignments - Identifies specific insertions, deletions, and substitutions needed to transform one string into another.
• Direct Memory Buffers - Directly manages memory buffers in the native C++ layer to eliminate Python overhead during pairwise comparisons.
• Edit Operation Opcodes - Represents string differences as operation codes to describe the exact path from source to target.
• Token-Based Similarity Scoring - Implements token-based normalization to make similarity scoring independent of word order.
• SIMD Vectorizations - Uses SIMD vectorization to process multiple characters simultaneously, accelerating distance calculations.
• Longest Common - Computes similarity between two sequences based on their longest shared subsequence using absolute and normalized scores.
• Bit-Parallel Implementations - Implements high-performance edit distance calculations using bit-parallelism to optimize computational complexity.
• Sequence Difference Analysis - The library generates a sequence of edit operations or opcodes that describe how to transform one string into another.
• String Distance Matrices - Provides functionality to compute similarity scores between two collections of strings as a full matrix or matching pairs.
• Weighted Similarity Measures - Implements the ability to combine multiple ratio algorithms into a single weighted similarity score for more robust text comparison.
• Customizable Sequence Differs - Identifies the specific sequence of insertions, deletions, and substitutions required to transform one string into another.
• Edit Operation Sequences - Generates a precise list of insertions and deletions required to transform one sequence into another.
• LCS-Based Sequence Diffing - Uses the Longest Common Subsequence algorithm to identify the minimum set of changes needed to transform sequences.
• Best Match Extraction - Retrieves the most similar entries from a string collection, sorted by score or as a top result.
• Similarity-Based Substring Matching - Searches for the best alignment of a short string within a longer one to score the most similar segment.
• Text Retrieval - Fast string matching library for Python and C++.

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