The similarity of strings is applied in many contexts, such as spelling errors, text weighting, context similarities, etc。
The most common way to evaluate the similarity of a string is to convert a string into another string by inserting, deleting or replacing such an editorial operation, with the minimum number of editings required, which is the method of measuring the editing distance, also known as the leventein distance. Seam distance is a special case of editorial distance, which is only calculated for the number of editings that replace the operation in an equal length and can only be applied to the distance between two equal long strings。
Other commonly used measurement methods include jaccard distance, j-w distance (jaro-winkler distance), cosine similarity, european distance, etc。
Use python-levenshtein
Install leveshtein using pip initial python-levenshtein command
1. Difflib
2. Hamming distance, str1 and st2 lengths must be consistent, describing the number of characters in the corresponding position between two equal long strings
3. Edit distance, describing the minimum number of operations in which one string is converted to another, including insertion, deletion, replacement
4. Calculating levinsteinby
5. Calculation of jaro distance
Jaro-winkler distance
Output:
