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      src/data/courses/sql-mastery/chapters/subqueries-and-ctes/lessons/above-average-books.md
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      src/data/courses/sql-mastery/chapters/subqueries-and-ctes/lessons/books-above-average.md
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      src/data/courses/sql-mastery/chapters/subqueries-and-ctes/lessons/low-stock-by-category.md

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---
title: Above Average Books
description: Practice using scalar subqueries to find books above average price in their category
order: 140
type: challenge
setup: |
```sql
CREATE TABLE book (
id INT PRIMARY KEY,
title VARCHAR(100),
category VARCHAR(50),
price DECIMAL(10,2),
published_date DATE
);
INSERT INTO book (id, title, category, price, published_date) VALUES
(1, 'SQL Basics', 'Programming', 29.99, '2024-01-15'),
(2, 'Advanced SQL', 'Programming', 49.99, '2024-02-20'),
(3, 'Data Science', 'Data Analysis', 39.99, '2024-01-10'),
(4, 'Web Development', 'Programming', 34.99, '2024-03-01'),
(5, 'Statistics 101', 'Data Analysis', 24.99, '2024-02-15'),
(6, 'Machine Learning', 'Data Analysis', 44.99, '2024-03-15'),
(7, 'Database Design', 'Programming', 39.99, '2024-01-20'),
(8, 'Python Basics', 'Programming', 29.99, '2024-02-01');
```
---
The bookstore manager wants to identify books that are priced above the average price in their respective categories, but only for books published in 2024. This information will help in adjusting pricing strategies.
Given the following data in table `book`:
| id | title | category | price | published_date |
| --- | ---------------- | ------------- | ----- | -------------- |
| 1 | SQL Basics | Programming | 29.99 | 2024-01-15 |
| 2 | Advanced SQL | Programming | 49.99 | 2024-02-20 |
| 3 | Data Science | Data Analysis | 39.99 | 2024-01-10 |
| 4 | Web Development | Programming | 34.99 | 2024-03-01 |
| 5 | Statistics 101 | Data Analysis | 24.99 | 2024-02-15 |
| 6 | Machine Learning | Data Analysis | 44.99 | 2024-03-15 |
| 7 | Database Design | Programming | 39.99 | 2024-01-20 |
| 8 | Python Basics | Programming | 29.99 | 2024-02-01 |
Write a query that shows:
- Book title
- Category
- Book price
- Category average price (rounded to 2 decimal places)
- Price difference from category average (rounded to 2 decimal places)
Only include books that are:
1. Published in 2024
2. Priced above their category's average price
## Expected Output
| title | category | price | category_avg | price_diff |
| ---------------- | ------------- | ----- | ------------ | ---------- |
| Machine Learning | Data Analysis | 44.99 | 36.66 | 8.33 |
| Data Science | Data Analysis | 39.99 | 36.66 | 3.33 |
| Advanced SQL | Programming | 49.99 | 36.99 | 13.00 |
| Database Design | Programming | 39.99 | 36.99 | 3.00 |
## Solution
```sql
SELECT
b1.title,
b1.category,
b1.price,
ROUND(avg_price, 2) AS category_avg,
ROUND(b1.price - avg_price, 2) AS price_diff
FROM book b1
INNER JOIN (
-- Subquery to calculate average price per category for 2024
SELECT
category,
AVG(price) AS avg_price
FROM book
WHERE EXTRACT(YEAR FROM published_date) = 2024
GROUP BY category
) AS ca ON b1.category = ca.category
WHERE
EXTRACT(YEAR FROM b1.published_date) = 2024 -- Filter for books published in 2024
AND b1.price > ca.avg_price -- Filter books above category average
ORDER BY
b1.category, price_diff DESC; -- Sort results
```

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---
title: Books Above Average
description: Practice using simple subqueries to find books above average price
order: 141
type: challenge
setup: |
```sql
CREATE TABLE book (
id INT PRIMARY KEY,
title VARCHAR(100),
category VARCHAR(50),
price DECIMAL(10,2),
stock INT
);
INSERT INTO book (id, title, category, price, stock) VALUES
(1, 'SQL Basics', 'Programming', 29.99, 10),
(2, 'Advanced SQL', 'Programming', 49.99, 5),
(3, 'Data Science', 'Data Analysis', 39.99, 8),
(4, 'Web Development', 'Programming', 34.99, 12),
(5, 'Statistics 101', 'Data Analysis', 24.99, 15),
(6, 'Machine Learning', 'Data Analysis', 44.99, 6);
```
---
The bookstore manager wants to identify expensive books that might need a price adjustment. They need to find all books that are priced above the average book price.
Given the following data in table `book`:
| id | title | category | price | stock |
| -- | --------------- | ------------- | ----- | ----- |
| 1 | SQL Basics | Programming | 29.99 | 10 |
| 2 | Advanced SQL | Programming | 49.99 | 5 |
| 3 | Data Science | Data Analysis | 39.99 | 8 |
| 4 | Web Development | Programming | 34.99 | 12 |
| 5 | Statistics 101 | Data Analysis | 24.99 | 15 |
| 6 | Machine Learning| Data Analysis | 44.99 | 6 |
Write a query that shows:
- Book title
- Category
- Price
- How much the price is above the average (rounded to 2 decimal places)
Only include books that are priced above the average book price.
## Expected Output
| title | category | price | above_average |
| --------------- | ------------- | ----- | ------------- |
| Advanced SQL | Programming | 49.99 | 12.66 |
| Machine Learning| Data Analysis | 44.99 | 7.66 |
| Data Science | Data Analysis | 39.99 | 2.66 |
> **Note:** The average book price is 37.33
## Solution
```sql
SELECT
title,
category,
price,
ROUND(price - (
SELECT AVG(price)
FROM book
), 2) as above_average
FROM book
WHERE price > (
SELECT AVG(price)
FROM book
)
ORDER BY price DESC;
```
This challenge requires you to:
1. Calculate the average price using a subquery
2. Compare each book's price to this average
3. Show how much each book is above the average price
The solution demonstrates the use of subqueries:
- In the SELECT clause to calculate the difference from average
- In the WHERE clause to filter for above-average prices
---

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---
title: Low Stock by Category
description: Practice using correlated subqueries to find books with low stock in each category
order: 142
type: challenge
setup: |
```sql
CREATE TABLE book (
id INT PRIMARY KEY,
title VARCHAR(100),
category VARCHAR(50),
price DECIMAL(10,2),
stock INT
);
INSERT INTO book (id, title, category, price, stock) VALUES
(1, 'SQL Basics', 'Programming', 29.99, 10),
(2, 'Advanced SQL', 'Programming', 49.99, 5),
(3, 'Data Science', 'Data Analysis', 39.99, 8),
(4, 'Web Development', 'Programming', 34.99, 3),
(5, 'Statistics 101', 'Data Analysis', 24.99, 15),
(6, 'Machine Learning', 'Data Analysis', 44.99, 6),
(7, 'Python Basics', 'Programming', 29.99, 12),
(8, 'R Programming', 'Data Analysis', 34.99, 4);
```
---
The bookstore manager wants to identify books that have lower stock than the average stock level in their respective categories. This will help in planning reorders.
Given the following data in table `book`:
| id | title | category | price | stock |
| -- | --------------- | ------------- | ----- | ----- |
| 1 | SQL Basics | Programming | 29.99 | 10 |
| 2 | Advanced SQL | Programming | 49.99 | 5 |
| 3 | Data Science | Data Analysis | 39.99 | 8 |
| 4 | Web Development | Programming | 34.99 | 3 |
| 5 | Statistics 101 | Data Analysis | 24.99 | 15 |
| 6 | Machine Learning| Data Analysis | 44.99 | 6 |
| 7 | Python Basics | Programming | 29.99 | 12 |
| 8 | R Programming | Data Analysis | 34.99 | 4 |
Write a query that shows books with stock levels below their category average. Show:
- Book title
- Category
- Current stock
- Category average stock (rounded to 1 decimal place)
## Expected Output
| title | category | stock | category_avg |
| --------------- | ------------- | ----- | ------------ |
| Advanced SQL | Programming | 5 | 7.5 |
| Web Development | Programming | 3 | 7.5 |
| R Programming | Data Analysis | 4 | 8.3 |
| Machine Learning| Data Analysis | 6 | 8.3 |
> **Note:** The query must use a correlated subquery to calculate the category average.
## Solution
```sql
SELECT
b1.title,
b1.category,
b1.stock,
ROUND((
SELECT AVG(stock)
FROM book b2
WHERE b2.category = b1.category
), 1) as category_avg
FROM book b1
WHERE b1.stock < (
SELECT AVG(stock)
FROM book b2
WHERE b2.category = b1.category
)
ORDER BY b1.category, b1.stock;
```
This challenge requires you to:
1. Use a correlated subquery to calculate the average stock for each category
2. Compare each book's stock to its category average
3. Show only books with below-average stock
The solution demonstrates:
- Using correlated subqueries in both SELECT and WHERE clauses
- Comparing individual values against group averages
- Working with a single table
---
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