Rating = pd.read_csv('data/BX-Book-Ratings.csv', sep=None, error_bad_lines=False) Rating=Rating[:10000]

Great, it looks like you have loaded in the user ratings data from the BX-Book-Ratings.csv file. The [:10000] syntax suggests that you are only working with the first 10,000 rows of the data.

To calculate similarity between users, we will need to pivot the data so that each row represents a user and each column represents a book. The values in the table will be the ratings that each user has given to each book. Here's some code to pivot the data:

ratings_pivot = Rating.pivot(index='User-ID', columns='ISBN', values='Book-Rating')

This will create a new dataframe where each row represents a user and each column represents a book. The values in the table will be the ratings that each user has given to each book. Note that we are assuming that the column containing book ratings is called 'Book-Rating'. If it has a different name in your data, you will need to change the code accordingly.