Hi I have a table products, and another table product pricing. How would I use the fuzzywuzzy match so that I can find the products and return the similarity score and also add productpricing tables items?

tables examples:

output:

I am using the fuzz.token_set_ratio to determine the similarity score. If there's another way to do match and return highest score please advice.

Currently I am stuck here:

listMatch=[]
for brandList in scraped['brands'].unique() :
  productItem = list(product[product['brands'] == brandList].itertuples(index=False))
  scrapedItem = list(scraped[scraped['brands'] == brandList].itertuples(index=False))
  for n,item in enumerate(productItem):
    itemHold=[]
    for no,pro in enumerate(scrapedItem):
      extendItem = (item+pro)
      itemHold.append(extendItem)
    listMatch.extend(itemHold)
df =pd.DataFrame(listMatch, columns =['sku','product','Brands','productScraped','unit','unitPrice','web','date','brands2','unitPrice','salesPrice','normalPrice'])

    

so the main idea is to check both by category, get highest similiar value and append to new dataframe.

Done got it to work.