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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

ColumnData typeDescription
order_numberVARCHARUnique order number.
dateDATEDate of the order, from June to August 2021.
warehouseVARCHARThe warehouse that the order was made from— North, Central, or West.
client_typeVARCHARWhether the order was Retail or Wholesale.
product_lineVARCHARType of product ordered.
quantityINTNumber of products ordered.
unit_priceFLOATPrice per product (dollars).
totalFLOATTotal price of the order (dollars).
paymentVARCHARPayment method—Credit card, Transfer, or Cash.
payment_feeFLOATPercentage of total charged as a result of the payment method.

Your query output should be presented in the following format:

product_linemonthwarehousenet_revenue
product_one---------
product_one---------
product_one---------
product_one---------
product_one---------
product_one---------
product_two---------
............
Spinner
DataFrameas
revenue_by_product_line
variable
SELECT *
FROM sales;
Spinner
DataFrameas
df
variable
SELECT date, warehouse, client_type, product_line, total, payment_fee
FROM sales;
Hidden output
Spinner
DataFrameas
df1
variable
SELECT date, warehouse, client_type, product_line, total, payment_fee
FROM sales
WHERE client_type = 'Wholesale';
Hidden output
Spinner
DataFrameas
df3
variable
SELECT date, warehouse, product_line,
       SUM(total) AS total,
	   SUM(payment_fee) AS payment_fee
FROM sales
WHERE client_type = 'Wholesale'
GROUP BY date, warehouse, product_line
ORDER BY date;
Hidden output
Spinner
DataFrameas
df2
variable
SELECT EXTRACT(MONTH FROM date) AS month
FROM sales;
Hidden output
Spinner
DataFrameas
df4
variable
SELECT
       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
FROM sales
WHERE client_type = 'Wholesale';
Hidden output
Spinner
DataFrameas
revenue_by_product_line
variable
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, month, warehouse
ORDER BY product_line ASC, month ASC, net_revenue DESC;