IMDB+question.sql

USE imdb;

/* Now that you have imported the data sets, let’s explore some of the tables. 
 To begin with, it is beneficial to know the shape of the tables and whether any column has null values.
 Further in this segment, you will take a look at 'movies' and 'genre' tables.*/


-- Segment 1:


-- Q1. Find the total number of rows in each table of the schema?
-- Type your code below:

SELECT table_name, table_rows
FROM INFORMATION_SCHEMA.TABLES
WHERE TABLE_SCHEMA = 'imdb';


-- Q2. Which columns in the movie table have null values?
-- Type your code below:


SELECT COLUMN_NAME 
FROM information_schema.COLUMNS 
WHERE TABLE_SCHEMA='imdb' 
  AND TABLE_NAME='movie' 
  AND IS_NULLABLE='YES';


-- Now as you can see four columns of the movie table has null values. Let's look at the at the movies released each year. 
-- Q3. Find the total number of movies released each year? How does the trend look month wise? (Output expected)

/* Output format for the first part:

+---------------+-------------------+
| Year			|	number_of_movies|
+-------------------+----------------
|	2017		|	2134			|
|	2018		|		.			|
|	2019		|		.			|
+---------------+-------------------+


Output format for the second part of the question:
+---------------+-------------------+
|	month_num	|	number_of_movies|
+---------------+----------------
|	1			|	 134			|
|	2			|	 231			|
|	.			|		.			|
+---------------+-------------------+ */
-- Type your code below:

Select count(*),year from movie group by year;

SELECT
    EXTRACT(month FROM date_published) "Month",
    count(*) 
FROM
    movie 
GROUP BY
    EXTRACT(month FROM date_published)
ORDER BY
    1;
    
/*The highest number of movies is produced in the month of March.
So, now that you have understood the month-wise trend of movies, let’s take a look at the other details in the movies table. 
We know USA and India produces huge number of movies each year. Lets find the number of movies produced by USA or India for the last year.*/
  
-- Q4. How many movies were produced in the USA or India in the year 2019??
-- Type your code below:

SELECT count(*) AS country
FROM movie
WHERE country like 'USA' or 'India'
ORDER BY  '2019';


/* USA and India produced more than a thousand movies(you know the exact number!) in the year 2019.
Exploring table Genre would be fun!! 
Let’s find out the different genres in the dataset.*/

-- Q5. Find the unique list of the genres present in the data set?
-- Type your code below:

SELECT genre,
		count(movie_id) as number_of_movies
from genre
group by genre
order by number_of_movies;

/* So, RSVP Movies plans to make a movie of one of these genres.
Now, wouldn’t you want to know which genre had the highest number of movies produced in the last year?
Combining both the movie and genres table can give more interesting insights. */

-- Q6.Which genre had the highest number of movies produced overall?
-- Type your code below:

select year,
		genre,
		count(movie_id) as number_of_movies
from movie m inner join genre g
on m.id= g.movie_id
where year = '2019'
group by genre
order by number_of_movies desc
limit 1;
 


/* So, based on the insight that you just drew, RSVP Movies should focus on the ‘Drama’ genre. 
But wait, it is too early to decide. A movie can belong to two or more genres. 
So, let’s find out the count of movies that belong to only one genre.*/

-- Q7. How many movies belong to only one genre?
-- Type your code below:

WITH genre_count 
AS
(
SELECT
    COUNT(movie_id) AS no_of_movies_with_one_genre
FROM
    genre
)
SELECT *
FROM
    genre
HAVING
    COUNT(genre) = 1;


/* There are more than three thousand movies which has only one genre associated with them.
So, this figure appears significant. 
Now, let's find out the possible duration of RSVP Movies’ next project.*/

-- Q8.What is the average duration of movies in each genre? 
-- (Note: The same movie can belong to multiple genres.)


/* Output format:

+---------------+-------------------+
| genre			|	avg_duration	|
+-------------------+----------------
|	thriller	|		105			|
|	.			|		.			|
|	.			|		.			|
+---------------+-------------------+ */
-- Type your code below:

select genre,
		round(avg(duration),2) as avg_duration
from movie m inner join genre g
on m.id= g.movie_id
group by genre
order by avg_duration ;


/* Now you know, movies of genre 'Drama' (produced highest in number in 2019) has the average duration of 106.77 mins.
Lets find where the movies of genre 'thriller' on the basis of number of movies.*/

-- Q9.What is the rank of the ‘thriller’ genre of movies among all the genres in terms of number of movies produced? 
-- (Hint: Use the Rank function)


/* Output format:
+---------------+-------------------+---------------------+
| genre			|		movie_count	|		genre_rank    |	
+---------------+-------------------+---------------------+
|drama			|	2312			|			2		  |
+---------------+-------------------+---------------------+*/
-- Type your code below:
with rank_thriller as
(
select genre,
		count(movie_id) as movie_count,
        rank() over(order by count(movie_id) desc) as genre_rank
from genre
group by genre
)
select *
from rank_thriller
where genre= 'thriller';



/*Thriller movies is in top 3 among all genres in terms of number of movies
 In the previous segment, you analysed the movies and genres tables. 
 In this segment, you will analyse the ratings table as well.
To start with lets get the min and max values of different columns in the table*/




-- Segment 2:


-- Q10.  Find the minimum and maximum values in  each column of the ratings table except the movie_id column?
/* Output format:
+---------------+-------------------+---------------------+----------------------+-----------------+-----------------+
| min_avg_rating|	max_avg_rating	|	min_total_votes   |	max_total_votes 	 |min_median_rating|min_median_rating|
+---------------+-------------------+---------------------+----------------------+-----------------+-----------------+
|		0		|			5		|	       177		  |	   2000	    		 |		0	       |	8			 |
+---------------+-------------------+---------------------+----------------------+-----------------+-----------------+*/
-- Type your code below:

select min(avg_rating) as min_avg_rating,
		max(avg_rating) as max_avg_rating,
        min(total_votes) as min_total_votes,
        max(total_votes) as max_total_votes,
        min(median_rating) as min_median_rating,
        max(median_rating) as min_median_rating
from ratings;


/* So, the minimum and maximum values in each column of the ratings table are in the expected range. 
This implies there are no outliers in the table. 
Now, let’s find out the top 10 movies based on average rating.*/

-- Q11. Which are the top 10 movies based on average rating?
/* Output format:
+---------------+-------------------+---------------------+
| title			|		avg_rating	|		movie_rank    |
+---------------+-------------------+---------------------+
| Fan			|		9.6			|			5	  	  |
|	.			|		.			|			.		  |
|	.			|		.			|			.		  |
|	.			|		.			|			.		  |
+---------------+-------------------+---------------------+*/
-- Type your code below:
-- It's ok if RANK() or DENSE_RANK() is used too

SELECT title, SUM(avg_rating) / COUNT(movie_id)
FROM movie 
INNER JOIN ratings r ON movie_id = movie_id
GROUP BY movie_id
ORDER BY 2 DESC
LIMIT 0, 10


/* Do you find you favourite movie FAN in the top 10 movies with an average rating of 9.6? If not, please check your code again!!
So, now that you know the top 10 movies, do you think character actors and filler actors can be from these movies?
Summarising the ratings table based on the movie counts by median rating can give an excellent insight.*/

-- Q12. Summarise the ratings table based on the movie counts by median ratings.
/* Output format:

+---------------+-------------------+
| median_rating	|	movie_count		|
+-------------------+----------------
|	1			|		105			|
|	.			|		.			|
|	.			|		.			|
+---------------+-------------------+ */
-- Type your code below:
-- Order by is good to have

SELECT median_rating, count(movie_id)
from ratings
group by median_rating
order by median_rating;
        


/* Movies with a median rating of 7 is highest in number. 
Now, let's find out the production house with which RSVP Movies can partner for its next project.*/

-- Q13. Which production house has produced the most number of hit movies (average rating > 8)??
/* Output format:
+------------------+-------------------+---------------------+
|production_company|movie_count	       |	prod_company_rank|
+------------------+-------------------+---------------------+
| The Archers	   |		1		   |			1	  	 |
+------------------+-------------------+---------------------+*/
-- Type your code below:

select production_company,
		 count(movie_id) as movie_count,
         dense_rank() over(order by count(movie_id) desc) as prod_company_rank
from movie m inner join ratings r on
m.id=r.movie_id
where avg_rating > 8 and production_company is not null
group by production_company
order by count(movie_id);



-- It's ok if RANK() or DENSE_RANK() is used too
-- Answer can be Dream Warrior Pictures or National Theatre Live or both

-- Q14. How many movies released in each genre during March 2017 in the USA had more than 1,000 votes?
/* Output format:

+---------------+-------------------+
| genre			|	movie_count		|
+-------------------+----------------
|	thriller	|		105			|
|	.			|		.			|
|	.			|		.			|
+---------------+-------------------+ */
-- Type your code below:

select genre,
		count(r.movie_id) as movie_count
from movie m inner join genre g on
m.id=g.movie_id inner join ratings r on 
m.id= r.movie_id
 where month(date_published)= 3 and year(date_published)= 2017 and country= 'usa' and total_votes> 1000
group by genre
order by movie_count;



-- Lets try to analyse with a unique problem statement.
-- Q15. Find movies of each genre that start with the word ‘The’ and which have an average rating > 8?
/* Output format:
+---------------+-------------------+---------------------+
| title			|		avg_rating	|		genre	      |
+---------------+-------------------+---------------------+
| Theeran		|		8.3			|		Thriller	  |
|	.			|		.			|			.		  |
|	.			|		.			|			.		  |
|	.			|		.			|			.		  |
+---------------+-------------------+---------------------+*/
-- Type your code below:

select title,
		avg_rating,
		genre
from movie m inner join genre g on
m.id=g.movie_id inner join ratings r on 
m.id= r.movie_id
where title regexp '^The.*' and avg_rating> 8
order by avg_rating ;


-- You should also try your hand at median rating and check whether the ‘median rating’ column gives any significant insights.
-- Q16. Of the movies released between 1 April 2018 and 1 April 2019, how many were given a median rating of 8?
-- Type your code below:

select  median_rating,count(r.movie_id) as number_of_movies
from movie m inner join ratings r on
m.id=r.movie_id
where date_published between '2018-04-01' and '2019-04-01' and median_rating=8
group by median_rating;



-- Once again, try to solve the problem given below.
-- Q17. Do German movies get more votes than Italian movies? 
-- Hint: Here you have to find the total number of votes for both German and Italian movies.
-- Type your code below:

select 
case 
when  ((select  sum(r.total_votes)
from movie m inner join ratings r on
m.id=r.movie_id
where country='germany')  >
(select  sum(r.total_votes)
from movie m inner join ratings r on
m.id=r.movie_id
where country='italy'))
then  'Yes'
else 'No'
End as German_gets_more_vote_than_italy;

-- Answer is Yes

/* Now that you have analysed the movies, genres and ratings tables, let us now analyse another table, the names table. 
Let’s begin by searching for null values in the tables.*/




-- Segment 3:



-- Q18. Which columns in the names table have null values??
/*Hint: You can find null values for individual columns or follow below output format
+---------------+-------------------+---------------------+----------------------+
| name_nulls	|	height_nulls	|date_of_birth_nulls  |known_for_movies_nulls|
+---------------+-------------------+---------------------+----------------------+
|		0		|			123		|	       1234		  |	   12345	    	 |
+---------------+-------------------+---------------------+----------------------+*/
-- Type your code below:



Select sum(case when name is null then 1 else 0 end) as name_nulls, 
		sum(case when height is null then 1 else 0 end) as height_nulls, 
    sum(case when date_of_birth is null then 1 else 0 end) as date_of_birth_nulls,
	sum(case when known_for_movies is null then 1 else 0 end) as known_for_movies_nulls
from names;



/* There are no Null value in the column 'name'.
The director is the most important person in a movie crew. 
Let’s find out the top three directors in the top three genres who can be hired by RSVP Movies.*/

-- Q19. Who are the top three directors in the top three genres whose movies have an average rating > 8?
-- (Hint: The top three genres would have the most number of movies with an average rating > 8.)
/* Output format:

+---------------+-------------------+
| director_name	|	movie_count		|
+---------------+-------------------|
|James Mangold	|		4			|
|	.			|		.			|
|	.			|		.			|
+---------------+-------------------+ */
-- Type your code below:
with top_genre as
(
select genre,
		count(g.movie_id) as movie_count
from genre g inner join ratings r on
g.movie_id=r.movie_id
where avg_rating>8
group by genre
order by movie_count
limit 3
),

Top_3_director as
(
select nm.name as director_name,
		count(g.movie_id) as movie_count,
        dense_rank() over(order by count(g.movie_id) desc) as director_rank,
        row_number() over(order by count(g.movie_id) desc) as director_rank_row
from names nm inner join director_mapping dm on
nm.id=dm.name_id inner join genre g on
dm.movie_id=g.movie_id inner join ratings r on
r.movie_id= g.movie_id,
top_genre
where g.genre in (top_genre.genre) and avg_rating>8
group by director_name
order by movie_count desc
)
select *
from Top_3_director
where director_rank<=3
limit 3;


/* James Mangold can be hired as the director for RSVP's next project. Do you remeber his movies, 'Logan' and 'The Wolverine'. 
Now, let’s find out the top two actors.*/

-- Q20. Who are the top two actors whose movies have a median rating >= 8?
/* Output format:

+---------------+-------------------+
| actor_name	|	movie_count		|
+-------------------+----------------
|Christain Bale	|		10			|
|	.			|		.			|
+---------------+-------------------+ */
-- Type your code below:

select name as actor_name, 
		count(m.id) as movie_count

from movie m inner join ratings r on
m.id=r.movie_id inner join role_mapping rm on
m.id= rm.movie_id inner join names nm on
rm.name_id=nm.id
where category='actor' and median_rating>=8
group by name
order by movie_count desc
limit 2;




/* Have you find your favourite actor 'Mohanlal' in the list. If no, please check your code again. 
RSVP Movies plans to partner with other global production houses. 
Let’s find out the top three production houses in the world.*/

-- Q21. Which are the top three production houses based on the number of votes received by their movies?
/* Output format:
+------------------+--------------------+---------------------+
|production_company|vote_count			|		prod_comp_rank|
+------------------+--------------------+---------------------+
| The Archers		|		830			|		1	  		  |
|	.				|		.			|			.		  |
|	.				|		.			|			.		  |
+-------------------+-------------------+---------------------+*/
-- Type your code below:
select production_company,
		sum(total_votes) as vote_count,
        rank() over(order by sum(total_votes) desc) as prod_comp_rank
 from movie m inner join ratings r on
 m.id=r.movie_id
 group by production_company
 limit 3;



/*Yes Marvel Studios rules the movie world.
So, these are the top three production houses based on the number of votes received by the movies they have produced.

Since RSVP Movies is based out of Mumbai, India also wants to woo its local audience. 
RSVP Movies also wants to hire a few Indian actors for its upcoming project to give a regional feel. 
Let’s find who these actors could be.*/

-- Q22. Rank actors with movies released in India based on their average ratings. Which actor is at the top of the list?
-- Note: The actor should have acted in at least five Indian movies. 
-- (Hint: You should use the weighted average based on votes. If the ratings clash, then the total number of votes should act as the tie breaker.)

/* Output format:
+---------------+-------------------+---------------------+----------------------+-----------------+
| actor_name	|	total_votes		|	movie_count		  |	actor_avg_rating 	 |actor_rank	   |
+---------------+-------------------+---------------------+----------------------+-----------------+
|	Yogi Babu	|			3455	|	       11		  |	   8.42	    		 |		1	       |
|		.		|			.		|	       .		  |	   .	    		 |		.	       |
|		.		|			.		|	       .		  |	   .	    		 |		.	       |
|		.		|			.		|	       .		  |	   .	    		 |		.	       |
+---------------+-------------------+---------------------+----------------------+-----------------+*/
-- Type your code below:

select name as actor_name,
				total_votes,
                count(m.id) as movie_count,
                round(sum(avg_rating*total_votes)/sum(total_votes),2) as actor_avg_rating,
                rank() over(order by avg_rating desc) as actor_rank
		
from movie m inner join ratings r on
m.id=r.movie_id inner join role_mapping rm on
m.id=rm.movie_id inner join names nm on 
rm.name_id=nm.id
where category='actor' and country= 'india'
group by name
having count(m.id)>=5
limit 1;


-- Top actor is Vijay Sethupathi

-- Q23.Find out the top five actresses in Hindi movies released in India based on their average ratings? 
-- Note: The actresses should have acted in at least three Indian movies. 
-- (Hint: You should use the weighted average based on votes. If the ratings clash, then the total number of votes should act as the tie breaker.)
/* Output format:
+---------------+-------------------+---------------------+----------------------+-----------------+
| actress_name	|	total_votes		|	movie_count		  |	actress_avg_rating 	 |actress_rank	   |
+---------------+-------------------+---------------------+----------------------+-----------------+
|	Tabu		|			3455	|	       11		  |	   8.42	    		 |		1	       |
|		.		|			.		|	       .		  |	   .	    		 |		.	       |
|		.		|			.		|	       .		  |	   .	    		 |		.	       |
|		.		|			.		|	       .		  |	   .	    		 |		.	       |
+---------------+-------------------+---------------------+----------------------+-----------------+*/
-- Type your code below:
select name as actress_name,
				total_votes,
                count(m.id) as movie_count,
                round(sum(avg_rating*total_votes)/sum(total_votes),2) as actress_avg_rating,
                rank() over(order by avg_rating desc) as actress_rank
from movie m inner join ratings r on
m.id=r.movie_id inner join role_mapping rm on
m.id=rm.movie_id inner join names nm on 
rm.name_id=nm.id
where category='actress'  and country= 'india' and languages= 'hindi'
group by name
having count(m.id)>=3
limit 5;



/* Taapsee Pannu tops with average rating 7.74. 
Now let us divide all the thriller movies in the following categories and find out their numbers.*/


/* Q24. Select thriller movies as per avg rating and classify them in the following category: 

			Rating > 8: Superhit movies
			Rating between 7 and 8: Hit movies
			Rating between 5 and 7: One-time-watch movies
			Rating < 5: Flop movies
--------------------------------------------------------------------------------------------*/
-- Type your code below:

with thriller_movies as 
(
select title,
		avg_rating
from genre g inner join movie m on
g.movie_id= m.id inner join ratings r on
m.id= r.movie_id
where genre= 'thriller'
)
select *,
		(case
        when avg_rating >=8 then 'Superhit movie'
        when avg_rating >= 7 and avg_rating <8 then 'Hit movie'
        when avg_rating >= 5.0 and avg_rating < 7 then 'One-time-watch movies'
        when avg_rating < 5.0 then 'Flop movies'
    end) as 'Rating'
from thriller_movies;





/* Until now, you have analysed various tables of the data set. 
Now, you will perform some tasks that will give you a broader understanding of the data in this segment.*/

-- Segment 4:

-- Q25. What is the genre-wise running total and moving average of the average movie duration? 
-- (Note: You need to show the output table in the question.) 
/* Output format:
+---------------+-------------------+---------------------+----------------------+
| genre			|	avg_duration	|running_total_duration|moving_avg_duration  |
+---------------+-------------------+---------------------+----------------------+
|	comdy		|			145		|	       106.2	  |	   128.42	    	 |
|		.		|			.		|	       .		  |	   .	    		 |
|		.		|			.		|	       .		  |	   .	    		 |
|		.		|			.		|	       .		  |	   .	    		 |
+---------------+-------------------+---------------------+----------------------+*/
-- Type your code below:

select genre,
		round(avg(duration),2) as avg_duration,
        sum(avg(duration)) over(order by genre rows unbounded preceding) as running_total_duration,
        avg(avg(duration)) over(order by genre rows 13 preceding) as moving_avg_duration
from movie m inner join genre g on
m.id= g.movie_id
group by genre
order by genre;







-- Round is good to have and not a must have; Same thing applies to sorting


-- Let us find top 5 movies of each year with top 3 genres.

-- Q26. Which are the five highest-grossing movies of each year that belong to the top three genres? 
-- (Note: The top 3 genres would have the most number of movies.)

/* Output format:
+---------------+-------------------+---------------------+----------------------+-----------------+
| genre			|	year			|	movie_name		  |worldwide_gross_income|movie_rank	   |
+---------------+-------------------+---------------------+----------------------+-----------------+
|	comedy		|			2017	|	       indian	  |	   $103244842	     |		1	       |
|		.		|			.		|	       .		  |	   .	    		 |		.	       |
|		.		|			.		|	       .		  |	   .	    		 |		.	       |
|		.		|			.		|	       .		  |	   .	    		 |		.	       |
+---------------+-------------------+---------------------+----------------------+-----------------+*/
-- Type your code below:

-- Top 3 Genres based on most number of movies



with Top_three_genre as
(
select genre,
		count(r.movie_id) as number_of_movies
from genre g inner join movie m on
g.movie_id= m.id inner join ratings r on
m.id=r.movie_id 
group by genre
order by number_of_movies desc
limit 3
)


, Top_5 as
(
select genre,
		year,
        title as movie_name,
        worlwide_gross_income,
        dense_rank() over (partition by year order by worlwide_gross_income desc) as movie_rank
        
from movie m inner join genre g on
m.id= g.movie_id
where genre in (select genre from Top_three_genre)
)
select *
from Top_5
where movie_rank<=5;






-- Finally, let’s find out the names of the top two production houses that have produced the highest number of hits among multilingual movies.
-- Q27.  Which are the top two production houses that have produced the highest number of hits (median rating >= 8) among multilingual movies?
/* Output format:
+-------------------+-------------------+---------------------+
|production_company |movie_count		|		prod_comp_rank|
+-------------------+-------------------+---------------------+
| The Archers		|		830			|		1	  		  |
|	.				|		.			|			.		  |
|	.				|		.			|			.		  |
+-------------------+-------------------+---------------------+*/
-- Type your code below:
select production_company,
		count(m.id) as movie_count,
        row_number() over(order by count(id) desc) as prod_comp_rank
from movie m inner join ratings r on
m.id=r.movie_id
where median_rating>=8 and production_company is not null and position(',' in languages)>0
group by production_company
limit 2;



-- Multilingual is the important piece in the above question. It was created using POSITION(',' IN languages)>0 logic
-- If there is a comma, that means the movie is of more than one language


-- Q28. Who are the top 3 actresses based on number of Super Hit movies (average rating >8) in drama genre?
/* Output format:
+---------------+-------------------+---------------------+----------------------+-----------------+
| actress_name	|	total_votes		|	movie_count		  |actress_avg_rating	 |actress_rank	   |
+---------------+-------------------+---------------------+----------------------+-----------------+
|	Laura Dern	|			1016	|	       1		  |	   9.60			     |		1	       |
|		.		|			.		|	       .		  |	   .	    		 |		.	       |
|		.		|			.		|	       .		  |	   .	    		 |		.	       |
+---------------+-------------------+---------------------+----------------------+-----------------+*/
-- Type your code below:

select name,
		sum(total_votes),
        count(m.id) as movie_count,
        title
from genre g inner join movie m on
g.movie_id= m.id inner join ratings r on
m.id= r.movie_id inner join role_mapping rm on
m.id=rm.movie_id inner join names nm on
rm.name_id=nm.id
where genre= 'drama' and category= 'actress' and avg_rating>=8
group by name
limit 3;






/* Q29. Get the following details for top 9 directors (based on number of movies)
Director id
Name
Number of movies
Average inter movie duration in days
Average movie ratings
Total votes
Min rating
Max rating
total movie durations

Format:
+---------------+-------------------+---------------------+----------------------+--------------+--------------+------------+------------+----------------+
| director_id	|	director_name	|	number_of_movies  |	avg_inter_movie_days |	avg_rating	| total_votes  | min_rating	| max_rating | total_duration |
+---------------+-------------------+---------------------+----------------------+--------------+--------------+------------+------------+----------------+
|nm1777967		|	A.L. Vijay		|			5		  |	       177			 |	   5.65	    |	1754	   |	3.7		|	6.9		 |		613		  |
|	.			|		.			|			.		  |	       .			 |	   .	    |	.		   |	.		|	.		 |		.		  |
|	.			|		.			|			.		  |	       .			 |	   .	    |	.		   |	.		|	.		 |		.		  |
|	.			|		.			|			.		  |	       .			 |	   .	    |	.		   |	.		|	.		 |		.		  |
|	.			|		.			|			.		  |	       .			 |	   .	    |	.		   |	.		|	.		 |		.		  |
|	.			|		.			|			.		  |	       .			 |	   .	    |	.		   |	.		|	.		 |		.		  |
|	.			|		.			|			.		  |	       .			 |	   .	    |	.		   |	.		|	.		 |		.		  |
|	.			|		.			|			.		  |	       .			 |	   .	    |	.		   |	.		|	.		 |		.		  |
|	.			|		.			|			.		  |	       .			 |	   .	    |	.		   |	.		|	.		 |		.		  |
+---------------+-------------------+---------------------+----------------------+--------------+--------------+------------+------------+----------------+

--------------------------------------------------------------------------------------------*/
-- Type your code below

with date_in as
(
select d.name_id, name, d.movie_id,
	   m.date_published, 
       lead(date_published, 1) over (partition by d.name_id order by date_published, d.movie_id) as next_movie_date
from director_mapping d
	 join names n on d.name_id=n.id 
	 join movie m on d.movie_id=m.id
),
date_dif as
(
 select *, datediff(next_movie_date, date_published) as diff
 from date_in
 ),
 avg_inter_days as
 (
 select name_id, avg(diff) as avg_inter_movie_days
 from date_dif
 group by name_id
 ),
 final as
 (
 select d.name_id as director_id,
	 name as director_name,
	 count(d.movie_id) as number_of_movies,
	 round(avg_inter_movie_days) as inter_movie_days,
	 round(avg(avg_rating),2) as avg_rating,
	 sum(total_votes) as total_votes,
	 min(avg_rating) as min_rating,
	 max(avg_rating) as max_rating,
	 sum(duration) as total_duration,
	 row_number() over(order by count(d.movie_id) desc) as director_row_rank
 from
	 names n join director_mapping d on n.id=d.name_id
	 join ratings r on d.movie_id=r.movie_id
	 join movie m on m.id=r.movie_id
	 join avg_inter_days a on a.name_id=d.name_id
 group by director_id
 )
 select *	
 from final;