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  • python - Vectorizing or Speeding up Fuzzywuzzy String Matching on . . .
    FuzzyWuzzy uses Python-Levenshtein to calculate the similarity between two strings, which uses a weightened Levenshtein distance with a weight of 2 for substitutions This is implemented using Wagner-Fischer RapidFuzz on the other hand uses a bitparallel implementation for this based on BitPal, which is faster
  • Python FuzzyWuzzy ratio: how does it work? - Stack Overflow
    The FuzzyWuzzy ratio sim score is a float in the range [0, 1] and is obtained by dividing the raw score by 100 Then how come this score appears to be different when I change the order of the words? from fuzzywuzzy import fuzz fuzz ratio('EMRE MERT', 'OMER CAN') 100 = 0 35 fuzz ratio('EMRE MERT', 'CAN OMER') 100 = 0 47
  • Checking fuzzy approximate substring existing in a longer string, in . . .
    Python regex module works pretty well, though it is little bit slower than inbuilt re module for fuzzy substring cases, which is an obvious outcome due to extra operations The desired output is good and the control over magnitude of fuzziness can be easily defined
  • Python Fuzzy Matching (FuzzyWuzzy) - Keep only Best Match
    fuzzywuzzy's process extract() returns the list in reverse sorted order , with the best match coming first so to find just the best match, you can set the limit argument as 1, so that it only returns the best match, and if that is greater than 60 , you can write it to the csv, like you are doing now
  • python - String Matching using fuzzywuzzy- is it using Levenshtein . . .
    FuzzyWuzzy ratio using python-Levenshtein doesn't return the Levenshtein score, but rather the Levenshtein ratio, which is (a+b - LevenshteinScore) (a+b), where a and b are the lengths of the two strings being compared If you don't have python-Levenshtein installed then fuzzywuzzy doesn't use Levenshtein at all
  • python - use fuzzywuzzy to compare 2 dataframe columns and change . . .
    I want to check if the name column in df1 exists in df2 using the fuzzywuzzy library and change it according to df2['name'] value Here is my code: from fuzzywuzzy import fuzz df1 loc[fuzz ratio(df1['name'],df2['name']) >= 90, 'name'] = df2['name'] so basically if the matching ratio is equal or above 90 percent, I want to change Sam to Sami
  • python - FuzzyWuzzy String Matching - Stack Overflow
    I'm using the FuzzyWuzzy String Matching module from SeatGeek I find that when using the token_set_ratio search algorithm, small differences in case gives wildly differing results For example, if I am looking for the phrase "I am eating" in a file, I get a 100% match
  • Need more understanding on python fuzz partial ratio
    I am using python fuzzywuzzy on an enterprise level to match 2 strings It works fine in most of the cases but giving unexpected results in the below mentioned scenario: fuzz partial_ratio('ja rule:mesmerize','ja rule feat ashanti:mesmerize') gives output 65 and
  • Finding the similar names in single large df in python using fuzzywuzzy
    Fuzzywuzzy to compare two lists of strings of unequal length and save multiple similarity metrics Hot Network Questions Can God be real in the form of a sublime experience that humans across several different religions have experienced?





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