Fuzzy matching two dataframes columns without a Key Column and different rows

Question

I have vendor list that i need to match with my internal client names and extract some other internal metrics.

Below is the sample vendor data hat has around 400 client names

Peter Silver & Partners
Patric+Kennedy
WBTA/Worldwide
Equivalence
Mart Gang
Cornard Scofi + Treno

my internal data has around 70k client names. I need to match the vendor list to internal large data.

I have tried below and I get error as ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all(). Also I wanted to output 2 columns from vendor file and 3 columns from internal table together for the matched results, can u advise how i output those??

     import os
        os.chdir("C:\\instantclient_19_10")
        import cx_Oracle
        import pandas as pd
        from fuzzywuzzy import fuzz
        from fuzzywuzzy import process
        
        con = cx_Oracle.connect("xxxnm/pass123@PRD:1522/prd.bnk.com")
        dft = pd.read_sql("select c1, c2, c3 from Table1",con)
        #print(dft)
        
        dfs = pd.read_excel(r"C:\\Users\\nm\\vendor_List.xlsx", sheet_name=None)
        dfv = pd.concat([sheet.assign(src_sheet=sheet_name) for sheet_name,sheet in dfs.items()])
        
        matched_vendors = []
        
        for row in dfv.index:
            vendor_name = dfv.at[row,"Company Name"]
            for columns in dft.index:
                internal_vendor_name=dft.at[columns,"Internal_NM"]
                matched_token=fuzz.partial_ratio(vendor_name,internal_vendor_name)
                if matched_token>80:
                    matched_vendors.append([vendor_name,internal_vendor_name,matched_token])
                   
                    
    df = DataFrame (matched_vendors).transpose()
    df.columns = ['vendor_name','internal_vendor_name','matched_token']
 #print(matched_vendors)
   matched_vendors.to_csv(r"C:\\Users\\nm\\vendor_matched.csv", index=False)

Ok Shifted to https://github.com/maxbachmann/rapidfuzz based on the Vectorizing or Speeding up Fuzzywuzzy String Matching on PANDAS Column and kind of no performance gain.. I guess I'm doing something wrong

    import os
            os.chdir("C:\\instantclient_19_10")
            import cx_Oracle
            import pandas as pd, numpy as np
            from rapidfuzz import process, utils

            
            con = cx_Oracle.connect("xxxnm/pass123@PRD:1522/prd.com")
            dft = pd.read_sql("select c1, c2, c3 from Table1",con)
            dft.apply(lambda x: x.astype(str).str.upper())
            #print(dft)
            
            dfs = pd.read_excel(r"C:\\Users\\nm\\vendor_List.xlsx", sheet_name=None)
            dfv = pd.concat([sheet.assign(src_sheet=sheet_name) for sheet_name,sheet in dfs.items()])
dfv.apply(lambda x: x.astype(str).str.upper())
            
            vendor = list(dfv['Company Name'])
entity_name = list(dft.INTERNAL_NM)

for i in range(0,len(vendor)):
    vendor1 = vendor[i]
    for j in range(0,len(INTERNAL_NM)):
        entity1 = INTERNAL_NM[j]
        match = process.extractOne(vendor1, entity1, processor=None, score_cutoff=93)
#                 Vendor_list.append(vendor1)
#         entity_vendor_list.append(entity1)
#         match_score.append(match)
        print(match)             
                        
        df = DataFrame (match).transpose()
        #df.columns = ['vendor_name','internal_vendor_name','matched_token']
     #print(matched_vendors)
      # matched_vendors.to_csv(r"C:\\Users\\nm\\vendor_matched.csv", index=False)