模糊字符串匹配使用python比较两个大文件

Question

我有两个大文件File1和File2，每个文件都包含公司的名称。我正在尝试从File2中找到公司名称（“ companyname”）的模糊匹配，以匹配到File1。目前，由于超时，我无法完成处理。有没有更有效的方法来提高处理速度？

这是我的代码：

File1=pd.read_csv("directory/File1.csv")
File2=pd.read_csv("directory/File2.csv")

def match_name(name, list_names, min_score=0):
    # -1 score in case we don't get any matches
    max_score = -1
    # Returning empty name for no match as well
    max_name = ""
    # Iternating over all names in the other
    for name2 in list_names:
        #Finding fuzzy match score
        score = fuzz.ratio(name, name2)
        # Checking if we are above our threshold and have a better score
         if (score > min_score) & (score > max_score):
            max_name = name2
            max_score = score
    return (max_name, max_score)


dict_list = []

for name in File2.companyname:
    # Use our method to find best match, we can set a threshold here
    match = match_name(File1.companyname, File2.companyname, 70)

    # New dict for storing data
    dict_ = {}
    dict_.update({"companyname" : name})
    dict_.update({"match_companyname" : match[0]})
    dict_.update({"score" : match[1]})
    dict_list.append(dict_)
merge_table = pd.DataFrame(dict_list)

# Save results
merge_table.to_csv("directory/Saved.csv")