熊猫的应用功能极其缓慢，有哪些替代方法可以更快地实现

Question

我有2个数据框（员工和招聘），但是，这些df之间没有唯一的密钥。我正在使用Fuzzywuzzy库函数来获得唯一标识符，为此，我正在使用df.apply函数，但是它非常慢。有人可以建议更快的实现方式吗？

#function to compare name beween 2 dfs.
def get_ratio(row, var1):
    #Get Ratio with all names #take highest and subset
    name1 = row.Offer_CandidateName
    name2 = row.Offer_FirstName
    name3 = row.Applicant_FirstName
    name4 = row.Candidate_FirstName
    #Compare dataframe based on name
    if(not pd.isnull(name1)):
        MatchScore1 = fuzz.partial_ratio(name1 , var1)
    if(not pd.isnull(name2)):
        MatchScore2 = fuzz.partial_ratio(name2 , var1)
    if(not pd.isnull(name3)):
        MatchScore3 = fuzz.partial_ratio(name3 , var1)
    if(not pd.isnull(name4)):
        MatchScore4 = fuzz.partial_ratio(name4 , var1)
    #if (MatchScore1>50 |MatchScore1>50 |MatchScore1>50 |MatchScore1>50):
    return max(MatchScore1,MatchScore2,MatchScore3,MatchScore4)

#Values are passed from employee dataframe to below function
def populateMatch(username,Emp_HireDate, Emp_RMID, Emp_FirstName): #Using Fuzzy Logic 
        #temp dataframe has names of candidate which need to be compared to all emp names
        temp = temp[temp.apply(get_ratio, var1 = Emp_FirstName, axis=1) > 50] 

        EmpMasterDF.loc[EmpMasterDF.Username == username , 'c6'] =  \
        str(temp['combined'].unique().tolist())