![]() ![]() ![]() Name this new column Cluster and select OK. The Cluster values dialog box appears, where you can specify the name of the new column. To do that task, load the previous table of fruits into Power Query, select the column, and then select the Cluster values option in the Add column tab in the ribbon. United States" Ratio = fuzz.ratio(Str1.lower(),Str2.lower()) Partial_Ratio = fuzz.partial_ratio(Str1.lower(),Str2.lower()) Token_Sort_Ratio = fuzz.token_sort_ratio(Str1,Str2) Token_Set_Ratio = fuzz. Now you're tasked with clustering the values. Str1 = "The supreme court case of Nixon vs The United States" Str2 = "Nixon v. The logic behind these comparisons is that since Sorted_tokens_in_intersection is always the same, the score will tend to go up as these words make up a larger chunk of the original strings or the remaining tokens are closer to each other. S3 = Sorted_tokens_in_intersection + sorted_rest_of_str2_tokens ![]() S2 = Sorted_tokens_in_intersection + sorted_rest_of_str1_tokens Instead of just tokenizing the strings, sorting and then pasting the tokens back together, token_set_ratio performs a set operation that takes out the common tokens (the intersection) and then makes fuzz.ratio() pairwise comparisons between the following new strings: Still, what happens if these two strings are of widely differing lengths? That's where fuzz.token_set_ratio() comes in. ![]()
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