1. Lighthouse

Level 5 · From net sales to net revenue

The request

From Anthony Ramos, Financial Analyst

“For the annual report I need the last step of the waterfall: net sales, refunds, net revenue and the refund rate, for each year. Refunds go in the year of the original order.”

Your goal

From lines (valid lines of booked orders with net and year, built in the starter) and returns, set result to a DataFrame with one row per year, sorted by year, and columns year, net_sales (sum of net), refunds (sum of refund_amount on those lines), net_revenue (net_sales − refunds) and refund_rate_pct (refunds × 100 / net_sales). Round everything to 2 decimals.

  • Use the orders DataFramenot done yet
  • Use the order_items DataFramenot done yet
  • Show exactly year net_sales refunds net_revenue refund_rate_pctnot done yet

Your code

Python

Loading the editor…

    ResultPreview of orders · press Run to see yours
    Preview of orders
    order_idcustomer_idorder_datestatuschannel
    10011252023-01-01 20:51:19completedweb
    10021562023-01-02 07:58:46completedweb
    1003222023-01-02 10:25:27completedmarketplace
    1004482023-01-02 11:21:51completedmarketplace

    Mote checks your result

    Press Run. Each row that matches the goal becomes a firefly in your lantern.

    DataFrames

    Loaded for you as pandas DataFrames, plus pd and np.

    orders12,000 rows · 7 columns
    • order_idinteger
    • customer_idinteger
    • order_datetimestamp
    • statusvarchar
    • channelvarchar
    • shipping_feedecimal
    • coupon_codevarchar
    First 5 rows of orders
    order_idcustomer_idorder_datestatuschannelshipping_feecoupon_code
    10011252023-01-01 20:51:19completedweb0.00NaN
    10021562023-01-02 07:58:46completedweb0.00NaN
    1003222023-01-02 10:25:27completedmarketplace4.99WELCOME10
    1004482023-01-02 11:21:51completedmarketplace4.99WELCOME10
    1005822023-01-02 11:43:36completedweb0.00WELCOME10
    order_items30,338 rows · 6 columns
    • order_item_idinteger
    • order_idinteger
    • product_idinteger
    • quantityinteger
    • unit_pricedecimal
    • discount_pctdecimal
    First 5 rows of order_items
    order_item_idorder_idproduct_idquantityunit_pricediscount_pct
    11001121931.990.00
    21001154311.990.00
    31002154411.990.00
    410021941149.000.00
    51002193219.990.00
    returns1,673 rows · 5 columns
    • return_idinteger
    • order_item_idinteger
    • returned_ondate
    • reasonvarchar
    • refund_amountdecimal
    First 5 rows of returns
    return_idorder_item_idreturned_onreasonrefund_amount
    1142023-01-10damaged83.69
    2422023-01-10wrong_item19.99
    31052023-01-14wrong_item10.99
    4242023-01-16changed_mind26.99
    51262023-01-16late20.99
    customers2,018 rows · 9 columns
    • customer_idinteger
    • first_namevarchar
    • last_namevarchar
    • emailvarchar
    • cityvarchar
    • statevarchar
    • signup_datedate
    • segmentvarchar
    • referral_sourcevarchar
    First 5 rows of customers
    customer_idfirst_namelast_nameemailcitystatesignup_datesegmentreferral_source
    1OwenHudsonowen.hudson@icloud.comSan FranciscoCA2022-06-02consumersearch
    2DouglasMendozadmendoza69@gmail.comAtlantaGA2022-06-02consumerNaN
    3HenryBrookshbrooks@lakesidecafe.comLos AngelesCA2022-06-04businessNaN
    4BrianGrahambgraham@bluecedarhomes.comSeattleWA2022-06-06businesspinterest
    5KyleJimenezkjimenez27@yahoo.comIndianapolisIN2022-06-07consumerNaN
    products120 rows · 8 columns
    • product_idinteger
    • namevarchar
    • categoryvarchar
    • subcategoryvarchar
    • unit_pricedecimal
    • unit_costdecimal
    • launched_ondate
    • is_activeboolean
    First 5 rows of products
    product_idnamecategorysubcategoryunit_priceunit_costlaunched_onis_active
    101Vale Velvet LoveseatFurnitureSofas1659.00NaN2023-09-11true
    102Alder Oak Coffee TableFurnitureTables919.00503.502024-05-16true
    103Orchard Rattan Dining ChairFurnitureChairs789.00429.872023-05-27true
    104Upland Oak BookcaseFurnitureStorage429.00214.702022-12-20true
    105Avery Leather Bed FrameFurnitureBeds449.00232.702021-08-31true
    employees60 rows · 8 columns
    • employee_idinteger
    • first_namevarchar
    • last_namevarchar
    • departmentvarchar
    • titlevarchar
    • manager_idinteger
    • hire_datedate
    • salaryinteger
    First 5 rows of employees
    employee_idfirst_namelast_namedepartmenttitlemanager_idhire_datesalary
    1CatherineHaleExecutiveChief Executive OfficerNaN2016-03-01285000
    2RobertKimExecutiveChief Operating Officer12017-01-09235000
    3MeganCollinsSalesHead of Sales12018-04-16172000
    4ChloeGutierrezSalesAccount Executive32020-01-2172000
    5FrankBellSalesAccount Executive32023-05-2173000
    web_sessions40,000 rows · 7 columns
    • session_idinteger
    • customer_idinteger
    • started_attimestamp
    • devicevarchar
    • sourcevarchar
    • pages_viewedinteger
    • convertedboolean
    First 5 rows of web_sessions
    session_idcustomer_idstarted_atdevicesourcepages_viewedconverted
    1NaN2023-01-01 23:02:24mobilepaid1false
    2182023-01-01 15:07:49desktoppaid1false
    3NaN2023-01-01 16:55:46desktopdirect7false
    4NaN2023-01-01 12:43:28mobileorganic3false
    5NaN2023-01-01 13:06:21mobilesocial2false
    marketing_spend3,276 rows · 3 columns
    • spend_datedate
    • channelvarchar
    • amountdecimal
    First 5 rows of marketing_spend
    spend_datechannelamount
    2023-01-01paid492.99
    2023-01-01email37.38
    2023-01-01social200.53
    2023-01-02paid709.13
    2023-01-02email64.13

    The lesson

    The last step of the waterfall

    Net sales are what customers paid for. Some of it comes back. Refunds are the money returned, recorded per returned line in returns.refund_amount, and net revenue is what is left:

    gross sales − discounts = net sales
    net sales   − refunds   = net revenue

    A refunded order contributes its full value to net sales and the same amount (or nearly, after a restocking fee) to refunds, so it nets close to zero. A completed order with one returned lamp nets the rest of the basket.

    Joining returns without losing sales

    returns has one row per returned line, and most lines are never returned. A left merge keeps every line and puts NaN in refund_amount where nothing came back:

    lines = lines.merge(returns[["order_item_id", "refund_amount"]], on="order_item_id", how="left")

    groupby(...).sum() skips NaN, so the refunds column adds only real refunds. An inner merge would keep only the 1,673 returned lines and destroy the net sales total.

    Which year does a refund belong to?

    A December order returned in January can be booked two ways: in the year of the order (the cohort view) or the year the money left (the cash view). Finance statements usually use the cash date. This track uses the order’s year, so each year’s refund rate describes that year’s orders. Both are valid; mixing them is not. Say which one you used.

    The newest period is never finished

    Returns arrive 4 to 40 days after the order. December 2025 orders have had only days to come back, so December shows $8,573.75 of refunds against $21,306.68 in November. The 2025 refund rate of 5.96% will creep up as January’s returns arrive. Reports that compare the newest month with older ones without that footnote make returns look like they are falling.

    Common mistakes

    • Inner merge. Only returned lines survive; net sales shrink to almost nothing.
    • Counting returned lines as the rate. Units returned and dollars refunded give different rates; this level uses dollars, because the waterfall is in dollars.
    • Subtracting refunds from gross sales. Refunds are paid on discounted prices, so they come off net sales.

    Interview note

    “Why would last month’s return rate look low?” Because returns lag orders. Saying “right-censoring” or simply “the window is not closed yet” shows you know the data is still arriving.

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