我有两个清单：ref_list和inp_list。如何使用FuzzyWuzzy从引用列表中匹配输入列表？

inp_list = pd.DataFrame(['ADAMS SEBASTIAN',  'HAIMBILI SEUN',  'MUTESI 
                          JOHN', 'SHEETEKELA MATT', 'MUTESI JOHN KUTALIKA', 
                          'ADAMS SEBASTIAN HAUSIKU', 'PETERS WILSON', 
                          'PETERS MARIO', 'SHEETEKELA  MATT NICKY'],
                          columns =['Names'])


ref_list = pd.DataFrame(['ADAMS SEBASTIAN HAUSIKU', 'HAIMBILI MIKE', 'HAIMBILI SEUN', 'MUTESI JOHN 
                         KUTALIKA', 'PETERS WILSON MARIO', 'SHEETEKELA  MATT NICKY MBILI'], columns = 
                        ['Names'])

经过一些研究，我修改了一些代码，我发现在互联网上。这些代码的问题-他们工作得很好，在小样本大小。在我的情况下，inp_list和ref_list分别是29 k和18 k的长度，它需要一天多的时间来运行。
下面是代码，首先定义了一个helper函数。

def match_term(term, inp_list, min_score=0):
    # -1 score in case I don't get any matches
    max_score = -1
    
    # return empty for no match 
    max_name = ''
    
    # iterate over all names in the other
    for term2 in inp_list:
        # find the fuzzy match score
        score = fuzz.token_sort_ratio(term, term2)
    
        # checking if I am above my threshold and have a better score
        if (score > min_score) & (score > max_score):
            max_name = term2
            max_score = score
    return (max_name, max_score)

# list for dicts for easy dataframe creation
dict_list = []

#iterating over the sales file
for name in inp_list:
    #use the defined function above to find the best match, also set the threshold to a chosen #
    match = match_term(name, ref_list, 94)
    
    #new dict for storing data
    dict_ = {}
    dict_.update({'passenger_name': name})
    dict_.update({'match_name': match[0]})
    dict_.update({'score': match[1]})
    
    dict_list.append(dict_)

这些代码在哪里可以改进，以顺利运行，并可能避免评估已经评估的项目？