To achieve this, we’ve built up a library of “fuzzy” string matching routines to help us along. And good news! We’re open sourcing it. The library is called “Fuzzywuzzy”, the code is pure python, and it depends only on the (excellent)difflibpython library. It is available onGit...
fuzzywuzzy .editorconfig .gitignore .travis.yml CHANGES.rst LICENSE.txt MANIFEST.in README.md benchmarks.py release setup.cfg setup.py test_fuzzywuzzy.py test_fuzzywuzzy_hypothesis.py test_fuzzywuzzy_pytest.py tox.ini Releases23 Add Python 3.7 CompatibilityLatest ...
For a more detailed description, take a look over the documentation. Let’s start by importing the necessary libraries and go over a simple example. Although it isn’t required, python-Levenshtein is highly recommended with FuzzyWuzzy. It makes the string matching process 4–10x faster but the...
I recently released an (other one) R package on CRAN –fuzzywuzzyR– which ports thefuzzywuzzypython library in R. “fuzzywuzzy does fuzzy string matching by using the Levenshtein Distance to calculate the differences between sequences (of character strings).” There is nobig newshere as in R ...
As per the documentation of the library, it is mentioned that it uses Levenshtein distance for computing the differences between sequences. But upon close inspection, I find that it actually uses the SequenceMatcher function from the difflib library. And this function, as per documentation uses th...