father of the bride part ii ( comedy in [ ]ratings out[ ]int index entries to data columnsuser_id non-null values movie_id non-null values rating non-null values timestamp non-null values dtypesint ( note that ages and occupations are coded as integers indicating groups described in the data set' readme file analyzing the data spread across three tables is not simple taskfor examplesuppose you wanted to compute mean ratings for particular movie by sex and age as you will seethis is much easier to do with all of the data merged together into single table using pandas' merge functionwe first merge ratings with users then merging that result with the movies data pandas infers which columns to use as the merge (or joinkeys based on overlapping namesin [ ]data pd merge(pd merge(ratingsusers)moviesin [ ]data out[ ]int index entries to data columnsuser_id non-null values movie_id non-null values rating non-null values timestamp non-null values gender non-null values age non-null values occupation non-null values zip non-null values title non-null values genres non-null values dtypesint ( )object( in [ ]data ix[ out[ ]user_id movie_id rating timestamp gender age occupation zip title toy story ( genres animation|children' |comedy name introductory examples www it-ebooks info