1. Import the files data/movies.csv and data/ratings.csv as pandas DataFrames called movies and ratings respectively.

• Create a new DataFrame avg_ratings that contains the average ratings for each movie. This should only have two columns movieId and rating.
• Combine the movies and avg_ratings by joining on movieID. The resulting DataFrame should contain all named movies even if they were not rated. Call this combined DataFrame movie_ratings.

2. Convert movie_ratings into a tidy DataFrame with the following columns - movieID, title, year, genre, rating. Save the movie_ratings DataFrame to an sqlite3 database named movids.db with a single table movie_ratings.

3. Sort the movie_rating DataFrame first by year (most recent first), then by genre in increasing alphabetical order, and finally by title in increasing alphabetical order. Display the first 10 rows from this table.

4. Create a table showing the average rating of shows in each genre over time, such that rows represent different genres and columns represent different years.