Welcome! You are now in DataLab.
You successfully completed your project and are looking for some additional related challenges. This DataLab workbook contains the official solution from our curriculum staff, along with Additional Challenges at the bottom. If you would like a quick overview of DataLab, please refer to the help menu. You can easily share your project with your friends and colleagues when you're done.
Good luck with your additional challenges!
You're working for a company that sells motorcycle parts, and they've asked for some help in analyzing their sales data!
They operate three warehouses in the area, selling both retail and wholesale. They offer a variety of parts and accept credit cards, cash, and bank transfer as payment methods. However, each payment type incurs a different fee.
The board of directors wants to gain a better understanding of wholesale revenue by product line, and how this varies month-to-month and across warehouses. You have been tasked with calculating net revenue for each product line and grouping results by month and warehouse. The results should be filtered so that only "Wholesale" orders are included.
They have provided you with access to their database, which contains the following table called sales:
Sales
| Column | Data type | Description |
|---|---|---|
order_number | VARCHAR | Unique order number. |
date | DATE | Date of the order, from June to August 2021. |
warehouse | VARCHAR | The warehouse that the order was made from— North, Central, or West. |
client_type | VARCHAR | Whether the order was Retail or Wholesale. |
product_line | VARCHAR | Type of product ordered. |
quantity | INT | Number of products ordered. |
unit_price | FLOAT | Price per product (dollars). |
total | FLOAT | Total price of the order (dollars). |
payment | VARCHAR | Payment method—Credit card, Transfer, or Cash. |
payment_fee | FLOAT | Percentage of total charged as a result of the payment method. |
Your query output should be presented in the following format:
product_line | month | warehouse | net_revenue |
|---|---|---|---|
| product_one | --- | --- | --- |
| product_one | --- | --- | --- |
| product_one | --- | --- | --- |
| product_one | --- | --- | --- |
| product_one | --- | --- | --- |
| product_one | --- | --- | --- |
| product_two | --- | --- | --- |
| ... | ... | ... | ... |
-- Start coding here
SELECT product_line,
CASE WHEN EXTRACT('month' from date) = 6 THEN 'June'
WHEN EXTRACT('month' from date) = 7 THEN 'July'
WHEN EXTRACT('month' from date) = 8 THEN 'August'
END as month,
warehouse,
SUM(total) - SUM(payment_fee) AS net_revenue
FROM sales
WHERE client_type = 'Wholesale'
GROUP BY product_line, warehouse, month
ORDER BY product_line, month, net_revenue DESC
Extended Project below
The finance team is exploring ways to reduce transaction costs and improve profitability. They’ve asked you to determine the most profitable payment method for each warehouse in each month. Calculate the net revenue for each payment method, grouped by warehouse and month, and identify the top payment method for each combination.
WITH revenues_by_payment AS (
SELECT
warehouse,
DATE_TRUNC('month', date) AS month,
payment,
SUM(total) AS revenue,
RANK() OVER(PARTITION BY warehouse, DATE_TRUNC('month', date) ORDER BY SUM(total) DESC) AS rank
FROM public.sales
GROUP BY warehouse, month, payment
ORDER BY month, revenue DESC
)
SELECT
warehouse,
month,
payment,
revenue
FROM revenues_by_payment
WHERE rank = 1The marketing team is planning a targeted campaign and wants to know the most popular product lines for retail and wholesale customers.
They have given you the task to find the top 3 most ordered product lines for each client type.
WITH product_lines AS ( SELECT
client_type,
product_line,
COUNT(*) AS total,
ROW_NUMBER() OVER(PARTITION BY client_type ORDER BY COUNT(*) DESC) AS row
FROM public.sales
GROUP BY client_type, product_line
ORDER BY total DESC
)
SELECT
client_type,
product_line,
total
FROM product_lines
WHERE row <= 3
ORDER BY client_type, total DESC