hereeach row is the vtfs@jethe columns are made up of all the unique movie titles in my datasetand each cell contains ratingwhat we end up with is this incredibly useful matrix shown in the preceding outputthat contains users for every row and movies for every column and we have basically every user rating for every movie in this matrix sovtfs@je number for examplegave %bmnbujbot  -star rating andagain all these /bvalues represent missing data sothat just indicatesfor examplevtfs@je number did not rate the movie againit' very useful matrix to have if we were doing user-based collaborative filteringwe could compute correlations between each individual user rating vector to find similar users since we're doing item-based collaborative filteringwe're more interested in relationships between the columns sofor exampledoing correlation score between any two columnswhich will give us correlation score for given movie pair sohow do we do thatit turns out that pandas makes that incredibly easy to do as well it has built-in dpss function that will actually compute the correlation score for every column pair found in the entire matrix-it' almost like they were thinking of us dpss busjy vtfs bujohtdpss dpss busjyifbe