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
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