Understanding the content-based data
You are now able to convert common attribute data to a DataFrame containing a row per movie, and each of its attributes as columns. You will now take a closer look at the full DataFrame you just created to see if you understand the information within.
A subset of the DataFrame you have created in the last exercise has been loaded as movie_cross_table
.
As a reminder, the genres are stored as individual columns and the movie names are stored as the index.
Inspect the rows corresponding to 'Toy Story' and 'Yogi Bear' in movie_cross_table
. How many genres do they have in common?
This exercise is part of the course
Building Recommendation Engines in Python
Hands-on interactive exercise
Turn theory into action with one of our interactive exercises
