Table of contents
Simple API
FuzzyWuzzy offers a straightforward and easy-to-understand API, making it simple to integrate fuzzy matching into projects quickly.
High Accuracy
The library provides accurate text matching using Levenshtein Distance, making it effective for identifying similar strings.
Versatile Use Cases
FuzzyWuzzy can be used for a wide range of applications, including data cleaning, record linkage, and search optimization.
Well-Maintained
The library is well-maintained with regular updates, detailed documentation, and an active community.
Python-Compatible
Written in Python, FuzzyWuzzy seamlessly integrates with other Python-based projects and is compatible with popular data science libraries.
Yes, FuzzyWuzzy is considered a good tool for tasks involving fuzzy string matching due to its ease of use, effective matching algorithms, and wide adoption in the community.
We have collected here some useful links to help you find out if FuzzyWuzzy is good.
Check the traffic stats of FuzzyWuzzy on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of FuzzyWuzzy on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of FuzzyWuzzy's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of FuzzyWuzzy on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
The latest comments about FuzzyWuzzy on Reddit. This can help you find out how popualr the product is and what people think about it.
Do fuzzy matching (something like fuzzywuzzy maybe) to see if the the words line up (allowing for wrong words). You'll need to work out how to use scoring to work out how well aligned the two lists are. Source: over 2 years ago
Convert the original lines to full furigana and do a fuzzy match. (For reference, the original line is ่ฒดๆนใใใใพใงใซๅพใฆใใๅใๅญๅใซ็บๆฎใใฆใใ ใใใญใ) You can do a regional search using the initial scene data (E60) first, and if the confidence is low, go for a slower full search. Source: almost 3 years ago
It's now known as "thefuzz", see https://github.com/seatgeek/fuzzywuzzy. Source: over 3 years ago
You can have a look at this library to use fuzzy search instead of looking for plaintext muck: https://github.com/seatgeek/fuzzywuzzy. Source: almost 4 years ago
To deal with comparing the string, I found FuzzyWuzzy ratio function that is returning a score of how much the strings are similar from 0-100. Source: about 4 years ago
I used fuzzywuzzy [1], a python-based fuzzy string matching calculator that is based on Levenshtein's edit-distance for a parking enforcement product I built. The product used an iOS client to capture license plates. The app would capture a single plate many times, de-duplicate using an edit-distance threshold matching plates up to a time period lookback. Then among those plates, send the one with the highest... - Source: Hacker News / over 4 years ago
Probably something as simple as loading it into the memory then calculating DamerauโLevenshtein distance for each pair and setting some threshold below which records are considered duplicates. If you want some more fancy similarities calculator, Python is probably better equipped in terms of ready-made libraries. Source: over 4 years ago
FuzzyWuzzy has an easy-to-use implementation: https://github.com/seatgeek/fuzzywuzzy. Source: over 4 years ago
After the above process, I was left to deal with minor differences in author names, like initials, spacing, typos, etc. There were too many entries to adjust manually, so I used fuzzywuzzy to determine if two entries were close enough. This trick would fail with authors, say R.J. Barker and R.J. Parker but I didn't find such entries during a sample manual sanity check from these groupings. Source: over 4 years ago
Iterate separately over the two files, get the single DNA in a variable and the 180 in a list (make sure they are stored as string). You can use fuzzywuzzy to get the most similar match. Source: over 4 years ago
If you really need an numbers in procent then FuzzyWuzzy could help you with your problem. This lib uses also the leivenstein distance on its core but gives the convinient output of matching in procent. Source: over 4 years ago
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