1. Lighthouse

Level 2 · Save for it, or borrow and repay?

The request

From Laura Bennett, Head of Finance

“Two questions for the planning meeting. If we set aside 5% of an average 2025 month's revenue every month, in an account paying 4% a year, what do we have after three years? And for the racking loan: if we pay $1,500 a month instead of the minimum, how many payments until it's gone?”

Your goal

Set result to a dict: "monthly_saving", 5% of the average monthly completed revenue in 2025 (rounded to 2: this is the amount transferred), "fv", what that saving grows to after 36 monthly deposits at 4% a year compounded monthly (deposits at the end of each month, rounded to 2), and "months", the number of $1,500 monthly payments needed to repay $48,000 at 7.5% a year, rounded up to a whole number.

  • Use the orders DataFramenot done yet
  • Use the order_items DataFramenot done yet
  • Store a dict in resultnot 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
    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
    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
    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

    Future value: what regular savings grow to

    If you put the same amount aside at the end of every month and the account pays a monthly rate r, after n months you have:

    fv = saving * ((1 + r) ** n - 1) / r

    Each deposit earns interest for the months it sits in the account: the first one for 35 months, the last one for none. The formula adds all of that up in one step. In Excel it is =FV(4%/12, 36, -9584.05); the deposit is negative because it leaves your hands, and the result comes back positive.

    Sizing it from the store’s own numbers

    A savings plan pegged to revenue should use real revenue. Harbor & Pine’s completed revenue in 2025 averaged $191,680.90 a month, ranging from $99,903 in February to about $290,000 in November and December:

    monthly_revenue.mean()             # 191680.90
    round(0.05 * monthly_revenue.mean(), 2)   # 9584.05 a month

    The answer assumes 2025 is a typical year and that the 5% is transferred every month even in a slow February. Write both assumptions down; a finance lead will ask.

    Months to repay: NPER

    The reverse question: given a payment, how many months does a loan take? Solve the loan formula for n:

    r = 0.075 / 12
    n = -math.log(1 - 48_000 * r / 1_500) / math.log(1 + r)    # 35.81
    math.ceil(n)                                                # 36 payments

    The answer is rarely a whole number: 35.81 means 35 full payments and a smaller 36th. Report it rounded up. Excel’s =NPER(7.5%/12, -1500, 48000) gives the same 35.81. If the payment is too small to cover even the first month’s interest ($48,000 × r = $300 here), the logarithm has no answer: the loan would never be repaid.

    Common mistakes

    • Annual rate, monthly periods. (1.04) ** 36 compounds 4% thirty-six times and returns $743,706 instead of $365,934.
    • Rounding months down. 35 payments of $1,500 leave money owed; the loan needs a 36th.
    • Forgetting interest exists. 36 × $9,584.05 = $345,025.80 is what you paid in, not what the account holds.
    • Projecting from one good month. Use an average over a full year, so the holiday peak doesn’t set the plan.

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