let' go ahead and run the preceding code it' fairly computationally expensive thing to doso it will take moment to actually come back with result butthere we have itsowhat do we have in the preceding outputwe have here new dataframe where every movie is on the rowand in the column sowe can look at the intersection of any two given movies and find their correlation score to each other based on this vtfs bujoht data that we had up here originally how cool is thatfor examplethe movie %bmnbujbot is perfectly correlated with itself of coursebecause it has identical user rating vectors butif you look at %bmnbujbot movie' relationship to the movie "ohsz fo it' much lower correlation score because those movies are rather dissimilarmakes senserighti have this wonderful matrix now that will give me the similarity score of any two movies to each other it' kind of amazingand very useful for what we're going to be doing now just like earlierwe have to deal with spurious results soi don' want to be looking at relationships that are based on small amount of behavior information it turns out that the pandas dpss function actually has few parameters you can give it one is the actual correlation score method that you want to useso ' going to say use qfbstpo correlation dpss busjy vtfs bujohtdpss nfuipe qfbstpo njo@qfsjpet dpss busjyifbe