Remapping categories II

In the last exercise, you determined that the distance cutoff point for remapping typos of 'american', 'asian', and 'italian' cuisine types stored in the cuisine_type column should be 80.

In this exercise, you're going to put it all together by finding matches with similarity scores equal to or higher than 80 by using fuzywuzzy.process's extract() function, for each correct cuisine type, and replacing these matches with it. Remember, when comparing a string with an array of strings using process.extract(), the output is a list of tuples where each of tuple is as such:

(closest match, similarity score, index of match)

The restaurants DataFrame is in your environment, and you have access to a categories list containing the correct cuisine types.

Iterate over each cuisine in the categories list.
For each cuisine, find its similarity to entries in the cuisine_type column of restaurants, while returning all possible matches and store them in matches.
For each possible match in matches equal or higher than 80, find the rows where the cuisine_type in restaurants is equal to that possible match. Assign this to matching_cuisine.
Use the Boolean series matching_cuisine to filter restaurants and replace that match with the correct cuisine.
Print the new unique values of cuisine_type in restaurants.

Exercise

Remapping categories II

Instructions