andwe end up with new dataframe that looks like the following tableit' kind of amazing how that just put it all together for us nowyou'll see some /bvalueswhich stands for not numberand its just how pandas indicates missing value sothe way to interpret this isvtfs@je number for exampledid not watch the movie but vtfs@je number did watch %bmnbujbot and rated it stars the vtfs@je number also watched "ohsz fo and rated it starsbut did not watch the movie %bztjouif bmmfz for exampleokaysowhat we end up with here is sparse matrix basicallythat contains every userand every movieand at every intersection where user rated movie there' rating value soyou can see nowwe can very easily extract vectors of every movie that our user watchedand we can also extract vectors of every user that rated given moviewhich is what we want sothat' useful for both user-based and item-based collaborative filteringrightif wanted to find relationships between usersi could look at correlations between these user rowsbut if want to find correlations between moviesfor item-based collaborative filteringi can look at correlations between columns based on the user behavior sothis is where the real flipping things on its head for user versus item-based similarities comes into play