18.6. Chapter Exercises

The exercises for this chapter use a different database than the rest of the chapter. The movies database has two tables, movies and actors. We will be using the movie table (so queries will look like SELECT * from movies). It features the following columns of data:

Column Name

Description

id

A unique number for each record

imdb_id

The id of the movie in the Internet Movie DataBase (IMDB)

title

Title fo the movie

director

Name of the director

year

Year (number) the movie was released

rating

Rating (R, PG, etc…)

genre

Comma separated list of genres

runtime

Length in minutes

country

Comma separated list of countries it was released in

language

Comma separated list of languages it was released in

imdb_score

Score of movie (1-10) in IMDB

imdb_votes

Number of ratings for the movie in the IMDB

metacritic_score

Score of movie (1-100) on the Metacritic website

Write a query to find the average IMDB score for all the movies.

Write a query to find the highest IMDB score for any movie that was directed at least partially by Quentin Tarantino. (Hint: A movie can have multiple directors, so you will need to use director LIKE '%Quentin Tarantino%' to identify movies where Quentin Tarantino is in the list of directors.)

Write a query to display the total number of movies with a rating of 'PG-13' in the database.

Use GROUP BY to write a query to display each rating category with the number of movies in that category. Your output should look like this:

... PG | 43 PG-13 | 32 ...

Write a query to list each director with the highest (MAX) imdb_score that director’s movies have earned. Order the output so the directors with the highest imdb_score come first.

Hint: You will need to use both GROUP BY and ORDER BY`.

Your output should look like this:

director | score Frank Darabont | 9.3 Francis Ford Coppola | 9.2 ...

Write a query to list each director with the COUNT of the number of films they have made. But only retrieve the data for directors with at least 2 films.

The results should be sorted by director’s name (but that should be the default, you should not have to use ORDER BY).

Hint: Remember that you have to use HAVING to filter grouped results.

Your output should look like this:

director | num_films Akira Kurosawa | 5 Alfred Hitchcock | 6 ...

Write a query to list each director with the number of movies they have made that have an imdb_score of 8.5 or higher.

Sort the results so the directors with the greatest number of highly rated files are first.

Hint: Here you want to filter the movies by IMDB rating before they are aggregated, so you need a WHERE not a HAVING.

Your output should look like this:

director | high_rated_films Peter Jackson | 3 Christopher Nolan | 3 Steven Spielberg | 2 ...

The following problems will also make use of the actors table. It consists of records that match a movie to a particular actor. If an actor appears in multiple movies, they will appear in one record for each movie they appear in.

Column Name

Description

id

A unique number for each record

movie_id

The id of the movie the actor appears in. This will match the id field in the movies table.

imdb_id

ID of the actor on the IMDB website

name

Name of the actor

We would like to know the title of all the movies Marlon Brando acted in.

Write a query that gets just the movies.title from the results of joining the actors table with the movies table (so that actors.movie_id matches with movies.id) and selecting rows in which the actors.name is "Marlon Brando".

Your output should look like this:

title Apocalypse Now On the Waterfront The Godfather

Write a query that displays each actor.name with the highest metacritic_score of any movie that actor has been in (we won’t know what movie the score is from).

You will have to join actors and movies so that you have access to all the needed data. You will also have to use GROUP BY. The GROUP BY should come after the JOIN.

Your output should look like this:

name | max_metacritic A.B. Lane | 0 A.J. O'Connor | 0 A.K. Hangal | 84 A.R. Haysel | 87 A.S. Duggal | 67 ...

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