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?
Deze oefening maakt deel uit van de cursus
Building Recommendation Engines in Python
Praktische interactieve oefening
Zet theorie om in actie met een van onze interactieve oefeningen.
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