Rapid fuzzy string matching in Python and C++ using the Levenshtein Distance
RapidFuzz is a fast string matching library for Python and C++, which is using the string similarity calculations from FuzzyWuzzy. However there are two aspects that set RapidFuzz apart from FuzzyWuzzy:
- It is MIT licensed so it can be used whichever License you might want to choose for your project, while your forced to adopt the GPLv2 license when using FuzzyWuzzy
- It is mostly written in C++ and on top of this comes with a lot of Algorithmic improvements to make string matching even faster, while still providing the same results. These changes result in a 5-100x Speedup in String Matching. More details on benchmark results can be found here
RapidFuzz can be installed using pip
We currently have pre-built binaries (wheels) for
RapidFuzz and its dependencies for MacOS (10.14 and later), Linux x86_64 and Windows.
For any other architecture/os
RapidFuzz can be installed from the source distribution. To do so, a C++17 capable compiler must be installed before running the
pip install rapidfuzz command. While Linux and MacOs usually come with a compiler it is required to install C++-Buildtools on Windows.
When using RapidFuzz with PyInstaller it is required to pass the following option to it:
The reason for this is that
rapidfuzz included all the C++ components from a Module called
_rapidfuzz_cpp. Since this import is hidden from PyInstaller it is required to tell it about it, so it included the Module in the binary.
> from rapidfuzz import fuzz > from rapidfuzz import process
> fuzz.ratio("this is a test", "this is a test!") 96.55171966552734
> fuzz.partial_ratio("this is a test", "this is a test!") 100.0
Token Sort Ratio
> fuzz.ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear") 90.90908813476562 > fuzz.token_sort_ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear") 100.0
Token Set Ratio
> fuzz.token_sort_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear") 83.8709716796875 > fuzz.token_set_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear") 100.0
> choices = ["Atlanta Falcons", "New York Jets", "New York Giants", "Dallas Cowboys"] > process.extract("new york jets", choices, limit=2) [('new york jets', 100), ('new york giants', 78.57142639160156)] > process.extractOne("cowboys", choices) ("dallas cowboys", 90)
RapidFuzz is licensed under the MIT license since we believe that everyone should be able to use it without being forced to adopt our license. Thats why the library is based on an older version of fuzzywuzzy that was MIT licensed aswell.
A Fork of this old version of fuzzywuzzy can be found here.