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Project: Analyzing Motorcycle Part Sales

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
DataFrameavailable as
revenue_by_product_line
variable
-- Start coding here
WITH my_table AS (
	SELECT
		product_line,
		TO_CHAR(date, 'Month') AS month,
		warehouse,
		total - payment_fee AS revenue
	FROM sales
	WHERE client_type = 'Wholesale')
SELECT
	product_line,
	month,
	warehouse,
	SUM(revenue) AS net_revenue
FROM my_table
GROUP BY product_line, month, warehouse
ORDER BY product_line, month, net_revenue DESC;
Spinner
DataFrameavailable as
df
variable
SELECT *
FROM Sales;
Spinner
DataFrameavailable as
df1
variable
WITH my_table AS (
	SELECT
		product_line,
		TO_CHAR(date, 'Month') AS month,
		warehouse,
		total - payment_fee AS revenue
	FROM sales
	WHERE client_type = 'Wholesale')
SELECT
	product_line,
	month,
	warehouse,
	SUM(revenue) AS net_revenue
FROM my_table
GROUP BY product_line, month, warehouse;