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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
-- 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

Spinner
DataFrameas
df
variable
WITH revenue_by_payment AS (
    SELECT payment, payment_fee,
        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
    GROUP BY payment, payment_fee, warehouse, month
),
ranked_payments AS (
    SELECT *,
        RANK() OVER (PARTITION BY warehouse, month ORDER BY net_revenue DESC) as rank
    FROM revenue_by_payment
)
SELECT payment, payment_fee, month, warehouse, net_revenue
FROM ranked_payments
WHERE rank = 1
ORDER BY warehouse, month, net_revenue DESC;

The 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.

Spinner
DataFrameas
df1
variable
WITH ret AS (
    SELECT product_line, client_type, COUNT(quantity) AS count
    FROM sales
    WHERE client_type = 'Retail'
    GROUP BY product_line, client_type
    ORDER BY count DESC, product_line
    LIMIT 3
),
whol AS (
    SELECT product_line, client_type, COUNT(quantity) AS count
    FROM sales
    WHERE client_type = 'Wholesale'
    GROUP BY product_line, client_type
    ORDER BY count DESC, product_line
    LIMIT 3
)
SELECT product_line, client_type, count 
FROM ret 
UNION
SELECT product_line, client_type, count 
FROM whol
ORDER BY client_type, count DESC;