Python uses Fuzzywuzzy to calculate the similarity between strings, including Levenshtein distance, Jaro distance, etc

Environmental construction and preparation work: 1. Install Python: First, ensure that Python has been installed and can be accessed from the official website（ https://www.python.org/downloads/ ）Download and install the required Python version. 2. Install the Fuzzywuzzy class library: Fuzzywuzzy is a Python library used to calculate the similarity between strings. You can use the pip command to install the Fuzzywuzzy library, open a terminal or command prompt window, and run the following command: pip install fuzzywuzzy Dependent class libraries: - fuzzywuzzy - Levenshtein Data sample (sample dataset): For string similarity calculation, the following data examples can be used: python string1 = "Hello World" string2 = "Hello World!" string3 = "Hello Python" The complete sample code is as follows: python from fuzzywuzzy import fuzz, process from fuzzywuzzy import fuzz from Levenshtein import distance #Sample data string1 = "Hello World" string2 = "Hello World!" string3 = "Hello Python" #Calculate the similarity between strings #Calculate similarity using the ratio function of the fuzzy module ratio = fuzz.ratio(string1, string2) print(f"Ratio similarity between '{string1}' and '{string2}' is: {ratio}") #Partial using the fuzzy module_ Ratio function calculates similarity partial_ratio = fuzz.partial_ratio(string1, string2) print( f"Partial ratio similarity between '{string1}' and '{string2}' is: {partial_ratio}" ) #Using the token of the fuzzy module_ Sort_ Ratio function calculates similarity token_sort_ratio = fuzz.token_sort_ratio(string1, string2) print( f"Token sort ratio similarity between '{string1}' and '{string2}' is: {token_sort_ratio}" ) #Using the token of the fuzzy module_ Set_ Ratio function calculates similarity token_set_ratio = fuzz.token_set_ratio(string1, string2) print( f"Token set ratio similarity between '{string1}' and '{string2}' is: {token_set_ratio}" ) #Using the Levenshtein class library to calculate the Levenshtein distance levenshtein_distance = distance(string1, string3) print( f"Levenshtein distance between '{string1}' and '{string3}' is: {levenshtein_distance}" ) Code parsing: 1. Import functions from the fuzzy and process modules to calculate string similarity. 2. Import the distance function to calculate the Levenshtein distance between strings. 3. Define example data: string1, string2, string3. 4. Use the fuzz.ratio function to calculate the similarity between two strings. 5. Using fuzz.partial_ The ratio function calculates the partial similarity between two strings. 6. Using fuzz.token_ Sort_ The ratio function calculates the similarity between sorted words in two strings. 7. Using fuzz.token_ Set_ The ratio function calculates the similarity between the sets of words in two strings. 8. Use the Levenshtein. distance function to calculate the Levenshtein distance between strings. Summary: -The Fuzzywuzzy class library provides multiple methods for calculating the similarity between strings, including ratio, partial_ Ratio, token_ Sort_ Ratio and token_ Set_ Ratio, etc. -The Levenshtein class library provides a function for calculating the Levenshtein distance between strings. -Choose an appropriate method for string similarity calculation based on specific needs, and choose to use the Fuzzywuzzy or Levenshtein class library to calculate similarity according to actual situations.