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  3. Building Recommendation Engines in Python

Exercise

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?

Instructions

50 XP

Possible answers