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Analyzing Motorcycle Sales
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    You're working for a company that sells motorcycle parts, and they've asked with 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 card, cash, and bank transfer as payment methods. However, each payment type incurs a different fee.

    The board of directors want 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, 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:

    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---------
    ............
    Unknown integration
    DataFrameavailable as
    df
    variable
    SELECT product_line,
    		INITCAP(TO_CHAR(date,'month')) as month,
    		warehouse,
    		ROUND(sum(total*(1-payment_fee))::numeric,2)
    FROM sales
    WHERE client_type = 'Wholesale'
    GROUP BY product_line,month,warehouse
    ORDER BY product_line,month,sum(total*(1-payment_fee)) desc;
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.
    Unknown integration
    DataFrameavailable as
    df1
    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,
        ROUND(SUM(total * (1 - payment_fee))::numeric, 2) AS net_revenue
    FROM sales
    WHERE client_type = 'Wholesale'
    GROUP BY product_line, warehouse, month
    ORDER BY product_line, month, net_revenue DESC
    This query is taking long to finish...Consider adding a LIMIT clause or switching to Query mode to preview the result.