understanding the code the first thing we're going to do is import the ebub file as part of the movielens datasetand that is tab-delimited file that contains every rating in the dataset jnqpsuqboebtbtqe @dpmt sbujoht qesfbe@dtw tvoephdpotvmuqbdluebubtdjfodfnm  ebub tfq == obnft @dpmtvtfdpmt sbohf note that you'll need to add the path here to where you stored the downloaded movielens files on your computer sothe way that this works is even though we're calling sfbe@dtw on pandaswe can specify different separator than comma in this caseit' tab we're basically saying take the first three columns in the ebub fileand import it into new dataframewith three columnsvtfs@jenpwjf@jeand sbujoh what we end up with here is dataframe that has row for every vtfs@jewhich identifies some personand thenfor every movie they ratedwe have the npwjf@jewhich is some numerical shorthand for given movieso star wars might be movie or somethingand their ratingyou know to stars sowe have here databasea dataframeof every user and every movie they ratedokaynowwe want to be able to work with movie titlesso we can interpret these results more intuitivelyso we're going to use their human-readable names instead if you're using truly massive datasetyou' save that to the end because you want to be working with numbersthey're more compactfor as long as possible for the purpose of example and teachingthoughwe'll keep the titles around so you can see what' going on @dpmt npwjft qesfbe@dtw tvoephdpotvmuqbdluebubtdjfodfnm  jufn tfq obnft @dpmtvtfdpmt sbohf there' separate data file with the movielens dataset called jufnand it is pipedelimitedand the first two columns that we import will be the npwjf@je and the ujumf of that movie sonow we have two dataframess@dpmt has all the user ratings and @dpmt has all the titles for every npwjf@je we can then use the magical nfshf function in pandas to mush it all together sbujoht qenfshf npwjftsbujoht