nowwe're going with item-based collaborative filteringso we want to extract columnsto do this let' run the following codetubs bst bujoht npwjf bujoht ubs bst tubs bst bujohtifbe nowwith the help of thatlet' go ahead and extract all the users who rated ubs bst andwe can see most people havein factwatched and rated ubs bst and everyone liked itat least in this little sample that we took from the head of the dataframe sowe end up with resulting set of user ids and their ratings for ubs bst the user id did not rate ubs bst so we have /bvalueindicating missing value therebut that' okay we want to make sure that we preserve those missing values so we can directly compare columns from different movies sohow do we do thatthe corrwith function wellpandas keeps making it easy for usand has dpssxjui function that you can see in the following code that we can usetjnjmbs pwjft npwjf bujohtdpssxjui tubs bst bujoht tjnjmbs pwjft tjnjmbs pwjftespqob eg qe%bub'sbnf tjnjmbs pwjft egifbe 