1. Lighthouse

Level 1 · Products by price band

The request

From Jessica Turner, Merchandising Manager

“How balanced is our range? Count our products in three price bands: under $50, $50 to under $200, and $200 and up.”

Your goal

From products, return price_band and products (number of products), one row per band. price_band is 'under_50' when unit_price < 50, '50_to_199' when unit_price is at least 50 and below 200, and '200_plus' when unit_price is 200 or more.

  • Read from the products tablenot done yet
  • Show exactly price_band productsnot done yet
  • 3 rowsnot done yet

Your query

SQL

Loading the editor…

    ResultPreview of products · press Run to see yours
    Preview of products
    product_idnamecategorysubcategoryunit_price
    101Vale Velvet LoveseatFurnitureSofas1659.00
    102Alder Oak Coffee TableFurnitureTables919.00
    103Orchard Rattan Dining ChairFurnitureChairs789.00
    104Upland Oak BookcaseFurnitureStorage429.00

    Mote checks your result

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

    Tables

    Harbor & Pine store data, ready to query.

    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 LoveseatFurnitureSofas1659.00NULL2023-09-11true
    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
    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.comAtlantaGA2022-06-02consumerNULL
    3HenryBrookshbrooks@lakesidecafe.comLos AngelesCA2022-06-04businessNULL
    4BrianGrahambgraham@bluecedarhomes.comSeattleWA2022-06-06businesspinterest
    5KyleJimenezkjimenez27@yahoo.comIndianapolisIN2022-06-07consumerNULL
    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 20:51:19completedweb0.00NULL
    10021562023-01-02 07:58:46completedweb0.00NULL
    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
    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 OfficerNULL2016-03-01285000
    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
    1NULL2023-01-01 23:02:24mobilepaid1false
    2182023-01-01 15:07:49desktoppaid1false
    3NULL2023-01-01 16:55:46desktopdirect7false
    4NULL2023-01-01 12:43:28mobileorganic3false
    5NULL2023-01-01 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

    CASE is SQL’s if/else. It returns a value based on conditions:

    CASE
      WHEN unit_price < 50  THEN 'under_50'
      WHEN unit_price < 200 THEN '50_to_199'
      ELSE '200_plus'
    END

    It can go anywhere an expression goes: in SELECT, WHERE, ORDER BY, GROUP BY, or inside an aggregate.

    Branches are checked in order

    The first true branch wins and the rest are skipped. That is why the second branch can simply say unit_price < 200: a $30 product already matched the first branch and never reaches it. Order your branches from the most specific or lowest boundary to the highest. If you reversed them and checked < 200 first, every cheap product would land in 50_to_199.

    Be precise about boundaries. A product at exactly $50.00 must go into exactly one band. With < boundaries checked in ascending order, each value falls into exactly one bucket with no gaps.

    ELSE and NULL

    If no branch matches and there is no ELSE, CASE returns NULL. Here every product has a price, so ELSE '200_plus' is safe. When a column can be NULL, handle it explicitly first, or it falls into the ELSE bucket and inflates it:

    CASE
      WHEN unit_cost IS NULL THEN 'unknown'
      WHEN unit_cost < 20    THEN 'low'
      ELSE 'high'
    END

    Grouping by a CASE

    You need the same expression in GROUP BY. Repeating the whole CASE is legal but noisy, so analysts usually write GROUP BY 1 (the first select column) or the alias:

    SELECT
      CASE WHEN unit_price < 50 THEN 'under_50'
           WHEN unit_price < 200 THEN '50_to_199'
           ELSE '200_plus' END AS price_band,
      COUNT(*) AS products
    FROM products
    GROUP BY price_band;

    Grouping by an alias works in DuckDB, Postgres, MySQL and BigQuery. SQL Server requires the full expression.

    Sorting buckets in a sensible order

    Text labels sort alphabetically: 200_plus comes before 50_to_199. To show bands from cheapest to priciest, sort by a second CASE or by MIN(unit_price):

    ORDER BY MIN(unit_price)

    Simple CASE

    There is also a short form that compares one expression to values:

    CASE channel WHEN 'web' THEN 'Website' WHEN 'app' THEN 'Mobile app' ELSE 'Partner' END

    It only handles equality, and CASE col WHEN NULL never matches, for the same reason = NULL never does.

    Common mistakes

    • Overlapping conditions in the wrong order, as above.
    • Mixing types between branches, such as THEN 'low' and THEN 0. Every branch must return the same type, or the database errors (or casts in surprising ways).