1. Lighthouse

Level 9 · Route a refund from the web form

The request

From Angela Rivera, Head of Customer Support

“The returns form sends everything as text. For this one: work out the refund, and route it. Damaged or wrong item goes to the warehouse, late goes to the carrier team, changed mind goes to returns, anything else to manual review.”

Your goal

Set result to a dictionary with keys 'input_type' (the type name of qty_text, from type(...).name), 'refund' (quantity * price as a float rounded to 2, after converting the texts to int and float), 'queue' ('warehouse' for codes DMG or WRG, 'carrier' for LATE, 'returns' for CHG, otherwise 'manual'; the code must be stripped and uppercased first) and 'label' (exactly "Order 10482 -> warehouse": 'Order ', the order id, ' -> ', the queue).

  • Use the returns DataFramenot done yet
  • Store a dict in resultnot done yet

Your code

Python

Loading the editor…

    ResultPreview of returns · press Run to see yours
    Preview 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

    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.

    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
    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
    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 type is this value?

    Every value in Python has a type, and type() tells you which one:

    type(10482)        # <class 'int'>
    type("3")          # <class 'str'>
    type(24.99)        # <class 'float'>
    type("3").__name__ # 'str'  just the name, handy in a report

    Data from a web form, a CSV opened by hand or an API usually arrives as text, even when it looks like a number. Text and numbers don’t mix: "3" * 2 is "33" (the text repeated), and "3" * "24.99" raises a TypeError. When a calculation gives a strange answer, type() is the first thing to check.

    Converting between types

    int(), float() and str() build a new value of the type you ask for:

    int("3")             # 3
    float("24.99")       # 24.99
    int("3") * float("24.99")   # 74.97 (after rounding to 2)
    str(10482)           # '10482'
    "Order " + str(10482)       # 'Order 10482'

    + between two strings joins them, but "Order " + 10482 fails because one side is an int. Convert with str() or use an f-string, which converts for you. Conversions can fail too: int("3.0") and float("n/a") raise ValueError, which is often how you discover a bad row.

    match and case

    When one value decides between several fixed options, match reads more cleanly than a long if/elif chain. Python compares the value with each case from top to bottom and runs the first one that fits:

    match "WRG":
        case "DMG" | "WRG":     # | means "either of these"
            queue = "warehouse"
        case "LATE":
            queue = "carrier"
        case _:                 # _ catches everything else
            queue = "manual"

    Each case lists a value to compare with, and the first one that matches wins, so order still matters if two cases could overlap. Compared with if code == "DMG" or code == "WRG": ... elif ..., the match version names the value once and lays the options out like the routing table Angela sent, which makes it easy to check against the policy line by line.

    case _ is the safety net, like a final else. Without it, an unknown code would leave queue unset and the next line would fail with a NameError. match needs Python 3.10 or newer, which is what runs here.

    Common mistakes

    • Multiplying the texts directly. Convert first, then calculate.
    • Matching the raw input. Clean it with .strip().upper() so ” wrg” and “WRG” are treated the same.
    • Forgetting case _, so an unexpected code leaves the queue undefined.
    • Joining a number into text with + without str().

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