demographic data about the users (agezip codegenderand occupationsuch data is often of interest in the development of recommendation systems based on machine learning algorithms while will not be exploring machine learning techniques in great detail in this booki will show you how to slice and dice data sets like these into the exact form you need the movielens data set contains million ratings collected from users on movies it' spread across tablesratingsuser informationand movie information after extracting the data from the zip fileeach table can be loaded into pandas dataframe object using pandas read_tableimport pandas as pd unames ['user_id''gender''age''occupation''zip'users pd read_table('ml- /users dat'sep='::'header=nonenames=unamesrnames ['user_id''movie_id''rating''timestamp'ratings pd read_table('ml- /ratings dat'sep='::'header=nonenames=rnamesmnames ['movie_id''title''genres'movies pd read_table('ml- /movies dat'sep='::'header=nonenames=mnamesyou can verify that everything succeeded by looking at the first few rows of each dataframe with python' slice syntaxin [ ]users[: out[ ]user_id gender age occupation in [ ]ratings[: out[ ]user_id movie_id rating in [ ]movies[: out[ ]movie_id zip timestamp title toy story ( jumanji ( grumpier old men ( waiting to exhale ( genres animation|children' |comedy adventure|children' |fantasy comedy|romance comedy|drama movielens data set www it-ebooks info