FuzzyWuzzy is a popular Python library used for string matching and comparison. It employs a technique called “fuzzy string matching” to find the similarity between two strings based on the Levenshtein distance (also known as edit distance), which measures the number of single-character edits (insertions, deletions, or substitutions) required to transform one string into another. The library uses the concept of “fuzziness” to quantify the degree of similarity between strings, which can be helpful in situations where strings being compared may have typos, variations in spelling, or other inconsistencies.
FuzzyWuzzy provides various functions to perform different types of string matching, such as:
- Simple Ratio: Computes the similarity between two strings using the basic Levenshtein distance.
- Partial Ratio: Compares the similarity between two strings by considering only the best matching substring in the longer string.
- Token Sort Ratio: Tokenizes the input strings, sorts the tokens, joins them into a single string, and then compares the similarity.
- Token Set Ratio: Tokenizes the input strings, creates sets of unique tokens, and then compares the similarity based on set operations (intersection and difference).
FuzzyWuzzy returns a similarity score ranging from 0 to 100, where 0 indicates no similarity and 100 indicates a perfect match.
Note that FuzzyWuzzy is not the only fuzzy string matching library available for Python, but it is one of the most widely used and straightforward to implement.