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https://github.com/ndleah/northwind
💼 SQL, company data analysis
https://github.com/ndleah/northwind
sql
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💼 SQL, company data analysis
- Host: GitHub
- URL: https://github.com/ndleah/northwind
- Owner: ndleah
- Created: 2021-09-10T16:23:29.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-11-12T09:52:49.000Z (about 3 years ago)
- Last Synced: 2024-11-13T08:39:21.538Z (2 months ago)
- Topics: sql
- Language: SQL
- Homepage:
- Size: 3.62 MB
- Stars: 11
- Watchers: 2
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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# Northwind Company Analysis 
> The Northwind database contains the sales data for a fictitious company called **Northwind Traders**, which imports and exports specialty foods from around the world.
>
> 👉 **My task in this case study is to perform an analysis of the performance of this company using SQL.**
## 📕 Table Of Contents
* [📂 Dataset](#-dataset)
* [🔎 Data Dictionary](#-data-dictionary)
* [🧙♂️ Business Question](#️-business-question)
## 📂 Dataset
> 📍 SQL script to generate the dataset is [here](/northwind.postgre_renamed.sql).
The dataset is composed of 13 tables:
Show summary
| Table | Rows |
|---------------------|------|
| Categories | 8 |
| Customers | 91 |
| Employees | 9 |
| EmployeeTerritories | 49 |
| Order Details | 2155 |
| Orders | 830 |
| Region | 4 |
| Products | 77 |
| Shippers | 6 |
| Suppliers | 29 |
| Territories | 53 |
| Usstates | 51 |
Here is the entity relationship diagram (ERD) for this dataset
## 🔎 Data Dictionary
Categories Table
| Primary Key | Foreign Key | Index | Name | Data type | Length | Nullable | Default | Description |
|-------------|-------------|-------|--------------|-----------|--------|----------|---------|----------------------------------------------------|
| PK | | IX | CategoryID | int | | | | Categories of Northwind products. |
| | | IX | CategoryName | varchar | -15 | | | Name of food category. |
| | | | Description | text | | Yes | | Full description of the category. Provide samples. |
| | | | Picture | image | | Yes | | A picture representing the food category. |
CustomerCustomerDemo Table
| Primary Key | Foreign Key | Index | Name | Data type | Length | Nullable | Default | Description |
|-------------|-------------|-------|----------------|-----------|--------|----------|---------|-------------|
| PK | FK | IX | CustomerID | varchar | -5 | | | |
| PK | FK | IX | CustomerTypeID | varchar | -10 | | | |
CustomerDemographics Table
| Primary Key | Foreign Key | Index | Name | Data type | Length | Nullable | Default | Description |
|-------------|-------------|-------|----------------|-----------|--------|----------|---------|-------------|
| PK | | IX | CustomerTypeID | varchar | -10 | | | |
| | | | CustomerDesc | ntext | | Yes | | |
Customers Table
| Primary Key | Foreign Key | Index | Name | Data type | Length | Nullable | Default | Description |
|-------------|-------------|-------|--------------|-----------|--------|----------|---------|----------------------------------------------------|
| PK | | IX | CustomerID | varchar | -5 | | | Unique five-character code based on customer name. |
| | | IX | CompanyName | varchar | -40 | | | |
| | | | ContactName | varchar | -30 | Yes | | |
| | | | ContactTitle | varchar | -30 | Yes | | |
| | | | Address | varchar | -60 | Yes | | Street or post-office box. |
| | | IX | City | varchar | -15 | Yes | | |
| | | IX | Region | varchar | -15 | Yes | | State or province. |
| | | IX | PostalCode | varchar | -10 | Yes | | |
| | | | Country | varchar | -15 | Yes | | |
| | | | Phone | varchar | -24 | Yes | | Phone number includes country code or area code. |
| | | | Fax | varchar | -24 | Yes | | Phone number includes country code or area code. |
Employees Table
| Primary Key | Foreign Key | Index | Name | Data type | Length | Nullable | Default | Description |
|-------------|-------------|-------|-----------------|-----------|--------|----------|---------|--------------------------------------------------|
| PK | | IX | EmployeeID | int | | | | Number automatically assigned to new employee. |
| | | IX | LastName | varchar | -20 | | | |
| | | | FirstName | varchar | -10 | | | |
| | | | Title | varchar | -30 | Yes | | Employee's title. |
| | | | TitleOfCourtesy | varchar | -25 | Yes | | |
| | | | BirthDate | datetime | | Yes | | |
| | | | HireDate | datetime | | Yes | | |
| | | | Address | varchar | -60 | Yes | | Street or post-office box. |
| | | | City | varchar | -15 | Yes | | |
| | | | Region | varchar | -15 | Yes | | Street or post-office box. |
| | | IX | PostalCode | varchar | -10 | Yes | | |
| | | | Country | varchar | -15 | Yes | | |
| | | | HomePhone | varchar | -24 | Yes | | Phone number includes country code or area code. |
| | | | Extension | varchar | -4 | Yes | | Internal telephone extension number. |
| | | | Photo | image | | Yes | | Picture of employee. |
| | | | Notes | text | | Yes | | General information about employee's background. |
| | FK | | ReportsTo | int | | Yes | | Employee's supervisor. |
| | | | PhotoPath | varchar | -255 | Yes | | |
EmployeeTerritories Table
| Primary Key | Foreign Key | Index | Name | Data type | Length | Nullable | Default | Description |
|-------------|-------------|-------|-------------|-----------|--------|----------|---------|-------------|
| PK | FK | IX | EmployeeID | int | | | | |
| PK | FK | IX | TerritoryID | varchar | -20 | | | |
OrderDetails Table
| Primary Key | Foreign Key | Index | Name | Data type | Length | Nullable | Default | Description |
|-------------|-------------|-------|-----------|-----------|--------|----------|---------|---------------------------------------|
| PK | FK | IX | OrderID | int | | | | Same as Order ID in Orders table. |
| PK | FK | IX | ProductID | int | | | | Same as Product ID in Products table. |
| | | | UnitPrice | decimal | | | 0 | |
| | | | Quantity | smallint | | | -1 | |
| | | | Discount | decimal | | | 0 | |
Orders Table
| Primary Key | Foreign Key | Index | Name | Data type | Length | Nullable | Default | Description |
|-------------|-------------|-------|----------------|-----------|--------|----------|---------|----------------------------------------------------|
| PK | | IX | OrderID | int | | | | |
| | FK | IX | CustomerID | varchar | -5 | Yes | | |
| | FK | IX | EmployeeID | int | | Yes | | Same entry as in Employees table. |
| | | IX | OrderDate | datetime | | Yes | | |
| | | | RequiredDate | datetime | | Yes | | |
| | | IX | ShippedDate | datetime | | Yes | | |
| | FK | IX | ShipVia | int | | Yes | | Same as Shipper ID in Shippers table. |
| | | | Freight | decimal | | Yes | 0 | |
| | | | ShipName | varchar | -40 | Yes | | Name of person or company to receive the shipment. |
| | | | ShipAddress | varchar | -60 | Yes | | Street address only -- no post-office box allowed. |
| | | | ShipCity | varchar | -15 | Yes | | |
| | | | ShipRegion | varchar | -15 | Yes | | State or province. |
| | | IX | ShipPostalCode | varchar | -10 | Yes | | |
| | | | ShipCountry | varchar | -15 | Yes | | |
Products Table
| Primary Key | Foreign Key | Index | Name | Data type | Length | Nullable | Default | Description |
|-------------|-------------|-------|-----------------|-----------|--------|----------|---------|-----------------------------------------------|
| PK | | IX | ProductID | int | | | | Number automatically assigned to new product. |
| | | IX | ProductName | nvarchar | -40 | | | |
| | FK | IX | SupplierID | int | | Yes | | Same entry as in Suppliers table. |
| | FK | IX | CategoryID | int | | Yes | | Same entry as in Categories table. |
| | | | QuantityPerUnit | nvarchar | -20 | Yes | | "(e.g., 24-count case, 1-liter bottle)." |
| | | | UnitPrice | money | | Yes | 0 | |
| | | | UnitsInStock | smallint | | Yes | 0 | |
| | | | UnitsOnOrder | smallint | | Yes | 0 | |
| | | | ReorderLevel | smallint | | Yes | 0 | Minimum units to maintain in stock. |
| | | | Discontinued | bit | | | 0 | Yes means item is no longer available. |
Region Table
| Primary Key | Foreign Key | Index | Name | Data type | Length | Nullable | Default | Description |
|-------------|-------------|-------|-------------------|-----------|--------|----------|---------|-------------|
| PK | | IX | RegionID | int | | | | |
| | | | RegionDescription | varchar | -50 | | | |
Shippers Table
| Primary Key | Foreign Key | Index | Name | Data type | Length | Nullable | Default | Description |
|-------------|-------------|-------|-------------|-----------|--------|----------|---------|--------------------------------------------------|
| PK | | IX | ShipperID | int | | | | Number automatically assigned to new shipper. |
| | | | CompanyName | varchar | -40 | | | Name of shipping company. |
| | | | Phone | varchar | -24 | Yes | | Phone number includes country code or area code. |
Suppliers Table
| Primary Key | Foreign Key | Index | Name | Data type | Length | Nullable | Default | Description |
|-------------|-------------|-------|--------------|-----------|--------|----------|---------|--------------------------------------------------|
| PK | | IX | SupplierID | int | | | | Number automatically assigned to new supplier. |
| | | IX | CompanyName | varchar | -40 | | | |
| | | | ContactName | varchar | -30 | Yes | | |
| | | | ContactTitle | varchar | -30 | Yes | | |
| | | | Address | varchar | -60 | Yes | | Street or post-office box. |
| | | | City | varchar | -15 | Yes | | |
| | | | Region | varchar | -15 | Yes | | State or province. |
| | | IX | PostalCode | varchar | -10 | Yes | | |
| | | | Country | varchar | -15 | Yes | | |
| | | | Phone | varchar | -24 | Yes | | Phone number includes country code or area code. |
| | | | Fax | varchar | -24 | Yes | | Phone number includes country code or area code. |
| | | | HomePage | ntext | | Yes | | Supplier's home page on World Wide Web. |
Territories Table
| Primary Key | Foreign Key | Index | Name | Data type | Length | Nullable | Default | Description |
|-------------|-------------|-------|----------------------|-----------|--------|----------|---------|-------------|
| PK | | IX | TerritoryID | varchar | -20 | | | |
| | | | TerritoryDescription | varchar | -50 | | | |
| | FK | | RegionID | int | | | | |
## 🧙♂️ Business Question
### ✅ **Question 1**
For their annual review of the company pricing strategy, the Product Team wants to look at the products that are currently being offered for a specific price range ($20 to $50). In order to help them they asked you to provide them with a list of products with the following information:
1. their name
2. their unit price
Filtered on the following conditions:
1. their unit price is between 20 and 50
2. they are not discontinued
Finally order the results by unit price in a descending order (highest first).
Output example
💡 Solution
```sql
-- Solution Query
SELECT
product_name,
unit_price
FROM products
WHERE unit_price BETWEEN 20 AND 50
AND discontinued = 0
ORDER BY unit_price DESC;
```
| "product_name" | "unit_price" |
|------------------------------------|--------------|
| "Tarte au sucre" | 49.3 |
| "Ipoh Coffee" | 46 |
| "Vegie-spread" | 43.9 |
| "Schoggi Schokolade" | 43.9 |
| "Northwoods Cranberry Sauce" | 40 |
| "Gnocchi di nonna Alice" | 38 |
| "Queso Manchego La Pastora" | 38 |
| "Gudbrandsdalsost" | 36 |
| "Mozzarella di Giovanni" | 34.8 |
| "Camembert Pierrot" | 34 |
| "Wimmers gute Semmelknödel" | 33.25 |
| "Mascarpone Fabioli" | 32 |
| "Gumbär Gummibärchen" | 31.23 |
| "Ikura" | 31 |
| "Uncle Bob's Organic Dried Pears" | 30 |
| "Sirop d'érable" | 28.5 |
| "Gravad lax" | 26 |
| "Nord-Ost Matjeshering" | 25.89 |
| "Grandma's Boysenberry Spread" | 25 |
| "Pâté chinois" | 24 |
| "Tofu" | 23.25 |
| "Chef Anton's Cajun Seasoning" | 22 |
| "Flotemysost" | 21.5 |
| "Louisiana Fiery Hot Pepper Sauce" | 21.05 |
| "Queso Cabrales" | 21 |
| "Gustaf's Knäckebröd" | 21 |
| "Maxilaku" | 20 |
### ✅ **Question 2**
The Logistics Team wants to do a retrospection of their performances for the year 1998, in order to identify for which countries they didn’t perform well. They asked you to provide them a list of countries with the following information:
1. their average days between the order date and the shipping date (formatted to have only 2 decimals)
2. their total number of orders (based on the order date)
Filtered on the following conditions:
1. the year of order date is 1998
2. their average days between the order date and the shipping date is greater or equal 5 days
3. their total number of orders is greater than 10 orders
Finally order the results by country name in an ascending order (lowest first).
Output example
💡 Solution
```sql
-- Solution Query
WITH cte_avg_days AS (
SELECT
ship_country,
ROUND(AVG(
EXTRACT(DAY FROM (shipped_date - order_date) * INTERVAL '1 DAY')
)::NUMERIC,
2) AS average_days_between_order_shipping,
COUNT(*) AS total_number_orders
FROM orders
WHERE EXTRACT(YEAR FROM order_date) = 1998
GROUP BY
ship_country
ORDER BY ship_country
)
SELECT * FROM cte_avg_days
WHERE average_days_between_order_shipping >= 5
AND total_number_orders > 10;
```
| "ship_country" | "average_days_between_order_shipping" | "total_number_orders" |
|----------------|---------------------------------------|-----------------------|
| "Austria" | "5.89" | "11" |
| "Brazil" | "8.12" | "28" |
| "France" | "9.43" | "23" |
| "Germany" | "5.38" | "34" |
| "Spain" | "7.83" | "12" |
| "Sweden" | "13.29" | "14" |
| "UK" | "6.25" | "16" |
| "USA" | "7.89" | "39" |
| "Venezuela" | "8.73" | "18" |
### ✅ **Question 3**
The HR Team wants to know for each employee what was their age on the date they joined the company and who they currently report to. Provide them with a list of every employees with the following information:
1. their full name (first name and last name combined in a single field)
2. their job title
3. their age at the time they were hired
4. their manager full name (first name and last name combined in a single field)
5. their manager job title
Finally order the results by employee age and employee full name in an ascending order (lowest first).
Output example
💡 Solution
```sql
-- Solution Query
SELECT
CONCAT(e.first_name, ' ', e.last_name) AS employee_full_name,
e.title AS employee_title,
EXTRACT(YEAR FROM AGE(e.hire_date, e.birth_date))::INT AS employee_age,
CONCAT(m.first_name, ' ', m.last_name) AS manager_full_name,
m.title AS manager_title
FROM
employees AS e
INNER JOIN employees AS m
ON m.employee_id = e.reports_to
ORDER BY
employee_age,
employee_full_name;
```
| "employee_full_name" | "employee_title" | "employee_age" | "manager_full_name" | "manager_title" |
|----------------------|----------------------------|----------------|---------------------|-------------------------|
| "Anne Dodsworth" | "Sales Representative" | 28 | "Steven Buchanan" | "Sales Manager" |
| "Janet Leverling" | "Sales Representative" | 28 | "Andrew Fuller" | "Vice President, Sales" |
| "Michael Suyama" | "Sales Representative" | 30 | "Steven Buchanan" | "Sales Manager" |
| "Robert King" | "Sales Representative" | 33 | "Steven Buchanan" | "Sales Manager" |
| "Laura Callahan" | "Inside Sales Coordinator" | 36 | "Andrew Fuller" | "Vice President, Sales" |
| "Steven Buchanan" | "Sales Manager" | 38 | "Andrew Fuller" | "Vice President, Sales" |
| "Nancy Davolio" | "Sales Representative" | 43 | "Andrew Fuller" | "Vice President, Sales" |
| "Margaret Peacock" | "Sales Representative" | 55 | "Andrew Fuller" | "Vice President, Sales" |
### ✅ **Question 4**
The Logistics Team wants to do a retrospection of their global performances over 1997-1998, in order to identify for which month they perform well. They asked you to provide them a list with:
1. their year/month as single field in a date format (e.g. “1996-01-01” January 1996)
2. their total number of orders
3. their total freight (formatted to have no decimals)
Filtered on the following conditions:
1. the order date is between 1997 and 1998
2. their total number of orders is greater than 35 orders
Finally order the results by total freight (descending order).
Output example
💡 Solution
```sql
-- Solution Query
WITH cte_freight AS (
SELECT
CONCAT(EXTRACT(YEAR FROM order_date),
'-',
EXTRACT(MONTH FROM order_date),
'-01'
) AS year_month,
COUNT(*) AS total_number_orders,
ROUND(
SUM(freight)
)::INT AS total_freight
FROM orders
WHERE order_date >= '1997-01-01' AND order_date < '1998-01-31'
GROUP BY
CONCAT(EXTRACT(YEAR FROM order_date),
'-',
EXTRACT(MONTH FROM order_date),
'-01'
)
)
SELECT * FROM cte_freight
WHERE total_number_orders > 35
ORDER BY total_freight DESC;
```
| "year_month" | "total_number_orders" | "total_freight" |
|--------------|-----------------------|-----------------|
| "1998-4-01" | 74 | 6394 |
| "1998-1-01" | 55 | 5463 |
| "1998-3-01" | 73 | 5379 |
| "1998-2-01" | 54 | 4273 |
| "1997-10-01" | 38 | 3946 |
| "1997-12-01" | 48 | 3758 |
| "1997-9-01" | 37 | 3237 |
### ✅ **Question 5**
The Pricing Team wants to know which products had an unit price increase and the percentage increase was not between 20% and 30%. In order to help them they asked you to provide them a list of products with:
1. their product name
2. their current unit price (formatted to have only 2 decimals)
3. their previous unit price (formatted to have only 2 decimals)
4. their percentage increase as:
*(New Number - Original Number) ÷ Original Number × 100* (with the result formatted to an integer, e.g 50 for 50%)
Filtered on the following conditions:
1. their percentage increase is not between 20% and 30%
2. their total number of orders is greater than 10 orders
Finally order the results by percentage increase (ascending order).
Output example
💡 Solution
```sql
-- Solution Query
WITH cte_price AS (
SELECT
d.product_id,
p.product_name,
ROUND(LEAD(d.unit_price) OVER (PARTITION BY p.product_name ORDER BY o.order_date)::NUMERIC,2) AS current_price,
ROUND(LAG(d.unit_price) OVER (PARTITION BY p.product_name ORDER BY o.order_date)::NUMERIC,2) AS previous_unit_price
FROM products AS p
INNER JOIN order_details AS d
ON p.product_id = d.product_id
INNER JOIN orders AS o
ON d.order_id = o.order_id
)
SELECT
c.product_name,
c.current_price,
c.previous_unit_price,
ROUND(100*(c.current_price - c.previous_unit_price)/c.previous_unit_price) AS percentage_increase
FROM cte_price AS c
INNER JOIN order_details AS d
ON c.product_id = d.product_id
WHERE c.current_price != c.previous_unit_price
GROUP BY
c.product_name,
c.current_price,
c.previous_unit_price
HAVING COUNT(DISTINCT d.order_id) > 10
AND ROUND(100*(c.current_price - c.previous_unit_price)/c.previous_unit_price) NOT BETWEEN 20 AND 30;
```
| "product_name" | "current_price" | "previous_unit_price" | "percentage_increase" |
|---------------------------------|-----------------|-----------------------|-----------------------|
| "Mozzarella di Giovanni" | 27.8 | 34.8 | -20 |
| "Singaporean Hokkien Fried Mee" | 11.2 | 9.8 | 14 |
### ✅ **Question 6**
The Pricing Team wants to know how each category performs according to their price range. In order to help them they asked you to provide them a list of categories with:
1. their category name
2. their price range as:
* “1.Below $20”
* “2. $20 - $50”
* “3. Over $50”
3. their total amount (formatted to be integer)
4. their number of orders
Finally order the results by category name then price range (both ascending order).
Output example
💡 Solution
```sql
--Solution query
SELECT
c.category_name,
CASE
WHEN p.unit_price < 20 THEN '1. Below $20'
WHEN p.unit_price >= 20 AND p.unit_price <= 50 THEN '2. $20 - $50'
WHEN p.unit_price > 50 THEN '3. Over $50'
END AS price_range,
ROUND(SUM(d.unit_price * d.quantity)) AS total_amount,
COUNT(DISTINCT d.order_id) AS total_number_orders
FROM categories AS c
INNER JOIN products AS p
ON c.category_id = p.category_id
INNER JOIN order_details AS d
ON d.product_id = p.product_id
GROUP BY
c.category_name,
price_range
ORDER BY
c.category_name,
price_range;
```
| "category_name" | "price_range" | "total_amount" | "total_number_orders" |
|------------------|----------------|----------------|-----------------------|
| "Beverages" | "1. Below $20" | 111464 | 317 |
| "Beverages" | "2. $20 - $50" | 25079 | 28 |
| "Beverages" | "3. Over $50" | 149984 | 24 |
| "Condiments" | "1. Below $20" | 28622 | 85 |
| "Condiments" | "2. $20 - $50" | 85073 | 121 |
| "Confections" | "1. Below $20" | 57369 | 197 |
| "Confections" | "2. $20 - $50" | 96094 | 106 |
| "Confections" | "3. Over $50" | 23636 | 16 |
| "Dairy Products" | "1. Below $20" | 17886 | 81 |
| "Dairy Products" | "2. $20 - $50" | 157148 | 204 |
| "Dairy Products" | "3. Over $50" | 76296 | 54 |
| "Grains/Cereals" | "1. Below $20" | 25364 | 99 |
| "Grains/Cereals" | "2. $20 - $50" | 75363 | 91 |
| "Meat/Poultry" | "1. Below $20" | 5121 | 36 |
| "Meat/Poultry" | "2. $20 - $50" | 76504 | 96 |
| "Meat/Poultry" | "3. Over $50" | 96563 | 36 |
| "Produce" | "1. Below $20" | 2566 | 13 |
| "Produce" | "2. $20 - $50" | 57960 | 81 |
| "Produce" | "3. Over $50" | 44743 | 39 |
| "Seafood" | "1. Below $20" | 69673 | 217 |
| "Seafood" | "2. $20 - $50" | 39963 | 70 |
| "Seafood" | "3. Over $50" | 31988 | 27 |
### ✅ **Question 7**
The Logistics Team wants to know what is the current state of our regional suppliers' stocks for each category of product. In order to help them they asked you to provide them a list of categories with:
1. their supplier region” as:
* “America”
* “Europe”
* “Asia-Pacific”
2. their category name
3. their total units in stock
4. their total units on order
5. heir total reorder level
Finally order the results by supplier region then category name then price range (each in ascending order).
Output example
💡 Solution
```sql
--Solution query
SELECT
c.category_name,
CASE
WHEN s.country IN ('Australia', 'Singapore', 'Japan' ) THEN 'Asia-Pacific'
WHEN s.country IN ('US', 'Brazil', 'Canada') THEN 'America'
ELSE 'Europe'
END AS supplier_region,
p.unit_in_stock AS units_in_stock,
p.unit_on_order AS units_on_order,
p.reorder_level
FROM suppliers AS s
INNER JOIN products AS p
ON s.supplier_id = p.supplier_id
INNER JOIN categories AS c
ON p.category_id = c.category_id
WHERE s.region IS NOT NULL
ORDER BY
supplier_region,
c.category_name,
p.unit_price;
```
| "category_name" | "supplier_region" | "units_in_stock" | "units_on_order" | "reorder_level" |
|------------------|-------------------|------------------|------------------|-----------------|
| "Condiments" | "America" | 113 | 0 | 25 |
| "Confections" | "America" | 17 | 0 | 0 |
| "Meat/Poultry" | "America" | 21 | 0 | 10 |
| "Meat/Poultry" | "America" | 115 | 0 | 20 |
| "Beverages" | "Asia-Pacific" | 15 | 10 | 30 |
| "Condiments" | "Asia-Pacific" | 24 | 0 | 5 |
| "Confections" | "Asia-Pacific" | 29 | 0 | 10 |
| "Grains/Cereals" | "Asia-Pacific" | 38 | 0 | 25 |
| "Meat/Poultry" | "Asia-Pacific" | 0 | 0 | 0 |
| "Meat/Poultry" | "Asia-Pacific" | 0 | 0 | 0 |
| "Produce" | "Asia-Pacific" | 20 | 0 | 10 |
| "Seafood" | "Asia-Pacific" | 42 | 0 | 0 |
| "Beverages" | "Europe" | 52 | 0 | 10 |
| "Beverages" | "Europe" | 111 | 0 | 15 |
| "Beverages" | "Europe" | 20 | 0 | 15 |
| "Condiments" | "Europe" | 4 | 100 | 20 |
| "Condiments" | "Europe" | 76 | 0 | 0 |
| "Condiments" | "Europe" | 0 | 0 | 0 |
| "Condiments" | "Europe" | 53 | 0 | 0 |
| "Condiments" | "Europe" | 120 | 0 | 25 |
| "Condiments" | "Europe" | 6 | 0 | 0 |
| "Dairy Products" | "Europe" | 22 | 30 | 30 |
| "Dairy Products" | "Europe" | 86 | 0 | 0 |
| "Produce" | "Europe" | 15 | 0 | 10 |
| "Seafood" | "Europe" | 85 | 0 | 10 |
| "Seafood" | "Europe" | 123 | 0 | 30 |
### ✅ **Question 8**
The Pricing Team wants to know for each currently offered product how their unit price compares against their categories average and median unit price. In order to help them they asked you to provide them a list of products with:
1. their category name
2. their product name
3. their unit price
4. their category average unit price (formatted to have only 2 decimals)
5. their category median unit price (formatted to have only 2 decimals)
6. their position against the category average unit price as:
* “Below Average”
* “Equal Average”
* “Over Average”
7. their position against the category median unit price as:
* “Below Median”
* “Equal Median”
* “Over Median”
Filtered on the following conditions:
* They are not discontinued
Finally order the results by category name then product name (both ascending).
Output example
💡 Solution
```sql
--Solution query
WITH cte_price AS (
SELECT
c.category_name,
p.product_name,
p.unit_price,
ROUND(AVG(d.unit_price)::NUMERIC,2) AS average_unit_price,
ROUND((PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY d.unit_price))::NUMERIC,
2) AS median_unit_price
FROM categories AS c
INNER JOIN products AS p
ON c.category_id = p.category_id
INNER JOIN order_details AS d
ON p.product_id = d.product_id
WHERE p.discontinued = 0
GROUP BY
c.category_name,
p.product_name,
p.unit_price
)
SELECT
category_name,
product_name,
unit_price,
average_unit_price,
median_unit_price,
CASE
WHEN unit_price > average_unit_price THEN 'Over Average'
WHEN unit_price = average_unit_price THEN 'Equal Average'
WHEN unit_price < average_unit_price THEN 'Below Average'
END AS average_unit_price_position,
CASE
WHEN unit_price > median_unit_price THEN 'Over Average'
WHEN unit_price = median_unit_price THEN 'Equal Average'
WHEN unit_price < median_unit_price THEN 'Below Average'
END AS median_unit_price_position
FROM cte_price
ORDER BY
category_name,
product_name;
```
| "category_name" | "product_name" | "unit_price" | "average_unit_price" | "median_unit_price" | "average_unit_price_position" | "median_unit_price_position" |
|------------------|------------------------------------|--------------|----------------------|---------------------|-------------------------------|------------------------------|
| "Beverages" | "Côte de Blaye" | 263.5 | 245.93 | 263.50 | "Over Average" | "Equal Average" |
| "Beverages" | "Chartreuse verte" | 18 | 16.68 | 18.00 | "Over Average" | "Equal Average" |
| "Beverages" | "Ipoh Coffee" | 46 | 43.04 | 46.00 | "Over Average" | "Equal Average" |
| "Beverages" | "Lakkalikööri" | 18 | 16.98 | 18.00 | "Over Average" | "Equal Average" |
| "Beverages" | "Laughing Lumberjack Lager" | 14 | 13.72 | 14.00 | "Over Average" | "Equal Average" |
| "Beverages" | "Outback Lager" | 15 | 14.15 | 15.00 | "Over Average" | "Equal Average" |
| "Beverages" | "Rhönbräu Klosterbier" | 7.75 | 7.38 | 7.75 | "Over Average" | "Equal Average" |
| "Beverages" | "Sasquatch Ale" | 14 | 12.97 | 14.00 | "Over Average" | "Equal Average" |
| "Beverages" | "Steeleye Stout" | 18 | 17.00 | 18.00 | "Over Average" | "Equal Average" |
| "Condiments" | "Aniseed Syrup" | 10 | 9.50 | 10.00 | "Over Average" | "Equal Average" |
| "Condiments" | "Chef Anton's Cajun Seasoning" | 22 | 20.68 | 22.00 | "Over Average" | "Equal Average" |
| "Condiments" | "Genen Shouyu" | 13 | 14.47 | 15.50 | "Below Average" | "Below Average" |
| "Condiments" | "Grandma's Boysenberry Spread" | 25 | 24.17 | 25.00 | "Over Average" | "Equal Average" |
| "Condiments" | "Gula Malacca" | 19.45 | 18.13 | 19.45 | "Over Average" | "Over Average" |
| "Condiments" | "Louisiana Fiery Hot Pepper Sauce" | 21.05 | 19.46 | 21.05 | "Over Average" | "Below Average" |
| "Condiments" | "Louisiana Hot Spiced Okra" | 17 | 15.30 | 15.30 | "Over Average" | "Over Average" |
| "Condiments" | "Northwoods Cranberry Sauce" | 40 | 38.77 | 40.00 | "Over Average" | "Equal Average" |
| "Condiments" | "Original Frankfurter grüne Soße" | 13 | 12.11 | 13.00 | "Over Average" | "Equal Average" |
| "Condiments" | "Sirop d'érable" | 28.5 | 27.79 | 28.50 | "Over Average" | "Equal Average" |
| "Condiments" | "Vegie-spread" | 43.9 | 40.79 | 43.90 | "Over Average" | "Over Average" |
| "Confections" | "Chocolade" | 12.75 | 11.90 | 12.75 | "Over Average" | "Equal Average" |
| "Confections" | "Gumbär Gummibärchen" | 31.23 | 28.86 | 31.23 | "Over Average" | "Below Average" |
| "Confections" | "Maxilaku" | 20 | 18.48 | 20.00 | "Over Average" | "Equal Average" |
| "Confections" | "NuNuCa Nuß-Nougat-Creme" | 14 | 13.07 | 14.00 | "Over Average" | "Equal Average" |
| "Confections" | "Pavlova" | 17.45 | 16.38 | 17.45 | "Over Average" | "Over Average" |
| "Confections" | "Scottish Longbreads" | 12.5 | 11.54 | 12.50 | "Over Average" | "Equal Average" |
| "Confections" | "Schoggi Schokolade" | 43.9 | 40.97 | 43.90 | "Over Average" | "Over Average" |
| "Confections" | "Sir Rodney's Marmalade" | 81 | 75.94 | 81.00 | "Over Average" | "Equal Average" |
| "Confections" | "Sir Rodney's Scones" | 10 | 9.38 | 10.00 | "Over Average" | "Equal Average" |
| "Confections" | "Tarte au sucre" | 49.3 | 46.41 | 49.30 | "Over Average" | "Below Average" |
| "Confections" | "Teatime Chocolate Biscuits" | 9.2 | 8.53 | 9.20 | "Over Average" | "Below Average" |
| "Confections" | "Valkoinen suklaa" | 16.25 | 14.95 | 16.25 | "Over Average" | "Equal Average" |
| "Confections" | "Zaanse koeken" | 9.5 | 9.14 | 9.50 | "Over Average" | "Equal Average" |
| "Dairy Products" | "Camembert Pierrot" | 34 | 32.13 | 34.00 | "Over Average" | "Equal Average" |
| "Dairy Products" | "Flotemysost" | 21.5 | 19.76 | 21.50 | "Over Average" | "Equal Average" |
| "Dairy Products" | "Geitost" | 2.5 | 2.33 | 2.50 | "Over Average" | "Equal Average" |
| "Dairy Products" | "Gorgonzola Telino" | 12.5 | 11.67 | 12.50 | "Over Average" | "Equal Average" |
| "Dairy Products" | "Gudbrandsdalsost" | 36 | 33.45 | 36.00 | "Over Average" | "Equal Average" |
| "Dairy Products" | "Mascarpone Fabioli" | 32 | 30.72 | 32.00 | "Over Average" | "Equal Average" |
| "Dairy Products" | "Mozzarella di Giovanni" | 34.8 | 32.04 | 34.80 | "Over Average" | "Below Average" |
| "Dairy Products" | "Queso Cabrales" | 21 | 19.60 | 21.00 | "Over Average" | "Equal Average" |
| "Dairy Products" | "Queso Manchego La Pastora" | 38 | 36.91 | 38.00 | "Over Average" | "Equal Average" |
| "Dairy Products" | "Raclette Courdavault" | 55 | 51.13 | 55.00 | "Over Average" | "Equal Average" |
| "Grains/Cereals" | "Filo Mix" | 7 | 6.76 | 7.00 | "Over Average" | "Equal Average" |
| "Grains/Cereals" | "Gnocchi di nonna Alice" | 38 | 35.42 | 38.00 | "Over Average" | "Equal Average" |
| "Grains/Cereals" | "Gustaf's Knäckebröd" | 21 | 20.40 | 21.00 | "Over Average" | "Equal Average" |
| "Grains/Cereals" | "Ravioli Angelo" | 19.5 | 18.14 | 19.50 | "Over Average" | "Equal Average" |
| "Grains/Cereals" | "Tunnbröd" | 9 | 8.37 | 9.00 | "Over Average" | "Equal Average" |
| "Grains/Cereals" | "Wimmers gute Semmelknödel" | 33.25 | 31.03 | 33.25 | "Over Average" | "Equal Average" |
| "Meat/Poultry" | "Pâté chinois" | 24 | 22.40 | 24.00 | "Over Average" | "Equal Average" |
| "Meat/Poultry" | "Tourtière" | 7.45 | 6.80 | 7.45 | "Over Average" | "Below Average" |
| "Produce" | "Longlife Tofu" | 10 | 8.77 | 8.00 | "Over Average" | "Over Average" |
| "Produce" | "Manjimup Dried Apples" | 53 | 50.55 | 53.00 | "Over Average" | "Equal Average" |
| "Produce" | "Tofu" | 23.25 | 21.35 | 23.25 | "Over Average" | "Equal Average" |
| "Produce" | "Uncle Bob's Organic Dried Pears" | 30 | 29.17 | 30.00 | "Over Average" | "Equal Average" |
| "Seafood" | "Boston Crab Meat" | 18.4 | 17.23 | 18.40 | "Over Average" | "Below Average" |
| "Seafood" | "Carnarvon Tigers" | 62.5 | 59.72 | 62.50 | "Over Average" | "Equal Average" |
| "Seafood" | "Escargots de Bourgogne" | 13.25 | 12.66 | 13.25 | "Over Average" | "Equal Average" |
| "Seafood" | "Gravad lax" | 26 | 23.40 | 23.40 | "Over Average" | "Over Average" |
| "Seafood" | "Ikura" | 31 | 29.68 | 31.00 | "Over Average" | "Equal Average" |
| "Seafood" | "Inlagd Sill" | 19 | 17.90 | 19.00 | "Over Average" | "Equal Average" |
| "Seafood" | "Jack's New England Clam Chowder" | 9.65 | 9.19 | 9.65 | "Over Average" | "Below Average" |
| "Seafood" | "Konbu" | 6 | 5.76 | 6.00 | "Over Average" | "Equal Average" |
| "Seafood" | "Nord-Ost Matjeshering" | 25.89 | 24.27 | 25.89 | "Over Average" | "Below Average" |
| "Seafood" | "Röd Kaviar" | 15 | 14.36 | 15.00 | "Over Average" | "Equal Average" |
| "Seafood" | "Rogede sild" | 9.5 | 9.23 | 9.50 | "Over Average" | "Equal Average" |
| "Seafood" | "Spegesild" | 12 | 11.11 | 12.00 | "Over Average" | "Equal Average" |
### ✅ **Question 9**
The Sales Team wants to build a list of KPIs to measure employees' performances. In order to help them they asked you to provide them a list of employees with:
1. their full name (first name and last name combined in a single field)
2. their job title
3. their total sales amount excluding discount (formatted to have only 2 decimals)
4. their total number of orders
5. their total number of entries (each row of an order)
6. their average amount per entry (formatted to have only 2 decimals)
7. their average amount per order (formatted to have only 2 decimals)
8. their total discount amount (formatted to have only 2 decimals)
9. their total sales amount including discount (formatted to have only 2 decimals)
10. Their total discount percentage (formatted to have only 2 decimals)
Finally order the results by total sales amount including discount (descending).
Output example
💡 Solution
```sql
--Solution query
WITH cte_kpi AS (
SELECT
CONCAT(e.first_name, ' ', e.last_name) AS employee_full_name,
e.title AS employee_title,
ROUND(
SUM(d.quantity * d.unit_price)::NUMERIC,
2) AS total_sale_amount_excluding_discount,
COUNT(DISTINCT d.order_id) AS total_number_orders,
COUNT(d.*) AS total_number_entries,
ROUND(
SUM(d.discount*(d.quantity * d.unit_price))::NUMERIC,
2) AS total_discount_amount,
ROUND(
SUM((1 - d.discount)*(d.quantity * d.unit_price))::NUMERIC,
2) AS total_sale_amount_including_discount
FROM orders AS o
INNER JOIN employees AS e
ON o.employee_id = e.employee_id
INNER JOIN order_details AS d
ON d.order_id = o.order_id
INNER JOIN products AS p
ON d.product_id = p.product_id
GROUP BY
employee_full_name,
employee_title
)
SELECT
employee_full_name,
employee_title,
total_sale_amount_excluding_discount,
total_number_orders,
total_number_entries,
ROUND(
SUM(total_sale_amount_excluding_discount/total_number_entries),
2) AS average_amount_per_entry,
ROUND(
SUM(total_sale_amount_excluding_discount/total_number_orders),
2) AS average_amount_per_order,
total_discount_amount,
total_sale_amount_including_discount,
SUM(ROUND(
(total_sale_amount_excluding_discount-total_sale_amount_including_discount)/
total_sale_amount_excluding_discount*100,
2)) AS total_discount_percentage
FROM cte_kpi
GROUP BY
employee_full_name,
employee_title,
total_sale_amount_excluding_discount,
total_number_orders,
total_number_entries,
total_sale_amount_including_discount,
total_discount_amount
ORDER BY total_sale_amount_including_discount DESC;
```
| "employee_full_name" | "employee_title" | "total_sale_amount_excluding_discount" | "total_number_orders" | "total_number_entries" | "average_amount_per_entry" | "average_amount_per_order" | "total_discount_amount" | "total_sale_amount_including_discount" | "total_discount_percentage" |
|----------------------|----------------------------|----------------------------------------|-----------------------|------------------------|----------------------------|----------------------------|-------------------------|----------------------------------------|-----------------------------|
| "Margaret Peacock" | "Sales Representative" | 250187.45 | 156 | 420 | 595.68 | 1603.77 | 17296.60 | 232890.85 | 6.91 |
| "Janet Leverling" | "Sales Representative" | 213051.30 | 127 | 321 | 663.71 | 1677.57 | 10238.46 | 202812.84 | 4.81 |
| "Nancy Davolio" | "Sales Representative" | 202143.71 | 123 | 345 | 585.92 | 1643.44 | 10036.11 | 192107.60 | 4.96 |
| "Andrew Fuller" | "Vice President, Sales" | 177749.26 | 96 | 241 | 737.55 | 1851.55 | 11211.51 | 166537.76 | 6.31 |
| "Laura Callahan" | "Inside Sales Coordinator" | 133301.03 | 104 | 260 | 512.70 | 1281.74 | 6438.75 | 126862.28 | 4.83 |
| "Robert King" | "Sales Representative" | 141295.99 | 72 | 176 | 802.82 | 1962.44 | 16727.76 | 124568.23 | 11.84 |
| "Anne Dodsworth" | "Sales Representative" | 82964.00 | 43 | 107 | 775.36 | 1929.40 | 5655.93 | 77308.07 | 6.82 |
| "Michael Suyama" | "Sales Representative" | 78198.10 | 67 | 168 | 465.46 | 1167.14 | 4284.97 | 73913.13 | 5.48 |
| "Steven Buchanan" | "Sales Manager" | 75567.75 | 42 | 117 | 645.88 | 1799.23 | 6775.47 | 68792.28 | 8.97 |
### ✅ **Question 10**
The Sales Team wants to build another list of KPIs to measure employees' performances across each category. In order to help them they asked you to provide them a list of categories and employees with:
1. their categories name
2. their full name (first name and last name combined in a 3. single field)
3. their total sales amount including discount (formatted to have only 2 decimals)
4. their percentage of total sales amount against the total sales amount across all employees (formatted to have only 2 decimals)
5. their percentage of total sales amount against the total sales amount for each employees (formatted to have only 2 decimals)
Finally order the results by category name (ascending) then total sales amount (descending).
Output example
💡 Solution
```sql
--Solution query
WITH cte_kpi AS (
SELECT
c.category_name,
CONCAT(e.first_name, ' ', e.last_name) AS employee_full_name,
ROUND(
SUM(d.quantity * d.unit_price)::NUMERIC,
2) AS total_sale_amount_including_discount
FROM employees AS e
INNER JOIN orders AS o
ON e.employee_id = o.employee_id
INNER JOIN order_details AS d
ON o.order_id = d.order_id
INNER JOIN products AS p
ON d.product_id = p.product_id
INNER JOIN categories AS c
ON c.category_id = p.category_id
GROUP BY
category_name,
employee_full_name
)
SELECT
*,
ROUND(
SUM(total_sale_amount_including_discount) /
SUM(SUM(total_sale_amount_including_discount))
OVER (PARTITION BY employee_full_name) * 100,
2) AS percentage_of_employee_sales,
ROUND(
SUM(total_sale_amount_including_discount) /
SUM(SUM(total_sale_amount_including_discount))
OVER (PARTITION BY category_name) * 100,
2) AS percentage_of_category_sales
FROM cte_kpi
GROUP BY
category_name,
employee_full_name,
total_sale_amount_including_discount
ORDER BY
category_name,
total_sale_amount_including_discount DESC;
```
## ✨ Contribution
Contributions, issues, and feature requests are welcome!
To contribute to this project, see the GitHub documentation on **[creating a pull request](https://help.github.com/en/github/collaborating-with-issues-and-pull-requests/creating-a-pull-request)**.
## 👏 Support
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___________________________________
© 2021 Leah Nguyen