Fuzzy string matching in python
Project description
FuzzyWuzzy
Fuzzy string matching like a boss. It uses Levenshtein Distance to help calculate differences between sequences in a simple to use package.
Requirements
Python 2.4 or higher
difflib
python-Levenshtein (optional, provides a 4-10x speedup in String Matching)
Installation
Using PIP via PyPI
pip install fuzzywuzzy
Using PIP via Github
pip install git+git://github.com/seatgeek/fuzzywuzzy.git@0.8.2#egg=fuzzywuzzy
Adding to your requirements.txt file (run pip install -r requirements.txt afterwards)
git+ssh://git@github.com/seatgeek/fuzzywuzzy.git@0.8.2#egg=fuzzywuzzy
Manually via GIT
git clone git://github.com/seatgeek/fuzzywuzzy.git fuzzywuzzy
cd fuzzywuzzy
python setup.py install
Usage
>>> from fuzzywuzzy import fuzz
>>> from fuzzywuzzy import process
Simple Ratio
>>> fuzz.ratio("this is a test", "this is a test!")
96
Partial Ratio
>>> fuzz.partial_ratio("this is a test", "this is a test!")
100
Token Sort Ratio
>>> fuzz.ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
90
>>> fuzz.token_sort_ratio("fuzzy wuzzy was a bear", "wuzzy fuzzy was a bear")
100
Token Set Ratio
>>> fuzz.token_sort_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
84
>>> fuzz.token_set_ratio("fuzzy was a bear", "fuzzy fuzzy was a bear")
100
Process
>>> 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)]
>>> process.extractOne("cowboys", choices)
("Dallas Cowboys", 90)
Known Ports
Some people are porting FuzzyWuzzy to other languages. Here is one port we know about:
Project details
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