maxbachmann/rapidfuzz
Rapidfuzz
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.
Beyond basic distance metrics, the project covers search and indexing for extracting best matches from collections and performing pairwise distance computations. It includes tools for sequence difference analysis that generate edit operations to transform one string into another, as well as text preprocessing utilities for string cleansing and standardization.
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++.
