Within the for loop, use an if statement to check whether the similarity score in each match is greater than or equal to

Here's the code to check whether the similarity score in each match is greater than or equal to 80, and if it is, use `.loc` to select rows where `cuisine_type` in `restaurants` is equal to the current match (which is the first element of `match`), and reassign them to be 'italian': ```python # Create a list of matches, comparing 'italian' with the cuisine_type column matches = process.extract('italian', restaurants['cuisine_type'], limit=len(restaurants.cuisine_type)) # Iterate through the list of matches to italian for match in matches: # Check whether the similarity score is greater than or equal to 80 if match[1] >= 80: # Select all rows where the cuisine_type is spelled this way, and set them to the correct cuisine restaurants.loc[restaurants['cuisine_type'] == match[0], 'cuisine_type'] = 'italian' ``` In this code, we use the `process.extract()` function from the `fuzzywuzzy` library to create a list of matches between 'italian' and the values in the `cuisine_type` column of the `restaurants` DataFrame. We pass 'italian' as the first argument, `restaurants['cuisine_type']` as the second argument, and `limit=len(restaurants.cuisine_type)` to ensure that we get a match score for every value in the `cuisine_type` column. We then iterate through the list of matches using a `for` loop. For each match, we check whether the similarity score (which is the second element of the `match` tuple) is greater than or equal to 80. If it is, we use `.loc` to select all rows where the `cuisine_type` is spelled this way (which is the first element of the `match` tuple), and set them to be 'italian'. We do this by passing two arguments to `.loc`: a boolean condition that selects the relevant rows (`restaurants['cuisine_type'] == match[0]`), and the name of the column we want to modify (`'cuisine_type'`). We then use the assignment operator (`=`) to set the selected rows to 'italian'.