Limited data in your rows
This data sparsity can cause an issue when using techniques like K-nearest neighbors as discussed in the last chapter. KNN needs to find the k most similar users that have rated an item, but if only less than or equal to k users have given an item the rating, all ratings will be the "most similar".
In this exercise, you will count how often each movie in the user_ratings_df DataFrame has been given a rating, and then see how many have only one or two ratings.
This exercise is part of the course
Building Recommendation Engines in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Count the occupied cells per column
occupied_count = user_ratings_df.____().____()
print(occupied_count)