1. Lighthouse

Level 8 · From numbers to a takeaway

The request

From Megan Collins, Head of Sales

“The leadership summary has room for two sentences about business customers. No charts, no code: what we found, the numbers, and what it means for us. Make sure every number is exact.”

Your goal

Using order lines with quantity > 0 from orders with status 'completed' placed in 2025, compute each order's value and attach the customer's segment. Set result to this text, with the numbers computed in code: "Business accounts placed {A} of 2025's completed orders but brought {B} of revenue. Their median order was ${C}, against ${D} for consumers, so losing one business account costs as much as losing several consumers." A and B are the business shares of orders and of revenue as whole percents (f-string format :.0%), C and D the business and consumer median order values with thousands separators and no decimals (format :,.0f).

  • Use the customers DataFramenot done yet
  • Use the orders DataFramenot done yet
  • Store a text value (str) in resultnot done yet

Your code

Python

Loading the editor…

    ResultPreview of customers · press Run to see yours
    Preview of customers
    customer_idfirst_namelast_nameemailcity
    1OwenHudsonowen.hudson@icloud.comSan Francisco
    2DouglasMendozadmendoza69@gmail.comAtlanta
    3HenryBrookshbrooks@lakesidecafe.comLos Angeles
    4BrianGrahambgraham@bluecedarhomes.comSeattle

    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.

    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
    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
    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

    What, how much, so what

    A takeaway that a manager can act on has three parts:

    Part Answers Example
    What What is the main result? Business accounts bring a large share of revenue
    How much Which number backs it? 26% of orders, 52% of revenue
    So what What does it mean for a decision? Losing one hurts as much as losing several consumers

    Without “how much” it’s a description of a chart. Without “so what” it’s a fact nobody can use. Compare “The chart shows revenue by segment. Business is high.” with “Business accounts placed 26% of 2025’s completed orders but brought 52% of revenue.” The second one can be acted on.

    Compute the numbers, never copy them

    Every number in a takeaway should come from code that runs right before it, formatted by an f-string. Then a re-run with new data updates the sentence too, and nobody has to retype 52% from a chart.

    order_share = is_b.mean()                               # 0.2638 → "26%"
    f"{order_share:.0%}"                                    # '26%'
    f"{706.9:,.0f}"                                         # '707'
    f"{1200321.6:,.0f}"                                     # '1,200,322'

    :.0% multiplies by 100, rounds to whole percents and adds the sign. :,.0f adds thousands separators. Printing the sentence (print(result)) shows it exactly as the reader will see it.

    Writing it in a notebook

    In a notebook, the takeaway goes in a Markdown cell under the chart, not in a code comment. Markdown is plain text with light markup: # for a title, ## for a section, **bold** for the key numbers, - for a bullet list, and a table with a |---|---| line under its header. Give every question its own heading, then code, chart and takeaway in that order. Put all imports in the first cell and check the notebook with Restart & Run All: if it fails from top to bottom, it isn’t done.

    Other kinds of takeaways

    • A decision about the data: what you decided, why, and its impact. “We kept the 370 outlier orders: they’re real business purchases, and dropping them would remove a large share of revenue.”
    • A correlation: strength, direction, the word “associated”, and what’s not related.
    • A recommendation: an action verb, a concrete place, the number behind it. “Give business accounts a named contact: they bring 52% of revenue from 26% of orders.”

    Common mistakes

    • Describing the chart (“the chart shows…”) instead of stating the finding.
    • Rounding by hand or pasting numbers from a chart.
    • Leaving out the denominator: “52% of revenue” of what, in which year?
    • Writing “causes” for a correlation, or giving a recommendation without the number behind it.

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