Lighthouse

Level 5 · What returns really cost

From Angela Rivera, Head of Customer Support

“Everyone says Furniture has the worst returns because sofas are a pain to send back. My team thinks it's Textiles. Can you show the return rate per category, in dollars and in lines, and how much margin the returns cost us? All of 2023 to 2025.”

Your goal

From costed (built in the starter: valid lines of booked orders, 2023 to 2025, with a known unit cost, with net, cogs, refund (0 if not returned) and returned (True/False)), set result to a DataFrame with one row per category and columns category, net_sales, refunds, refund_rate_pct (refunds × 100 / net_sales), line_return_rate_pct (returned lines × 100 / lines) and margin_lost (the sum of net − cogs on returned lines), all rounded to 2, sorted by refund_rate_pct from high to low.

Use the products DataFramenot done yet
Use the orders DataFramenot done yet
Show exactly category net_sales refunds refund_rate_pct line_return_rate_pct margin_lostnot done yet

Your code

Python

Loading the editor…

Reference solution

ResultPreview of products · press Run to see yours

Preview of products
product_id	name	category	subcategory	unit_price
101	Vale Velvet Loveseat	Furniture	Sofas	1659.00
102	Alder Oak Coffee Table	Furniture	Tables	919.00
103	Orchard Rattan Dining Chair	Furniture	Chairs	789.00
104	Upland Oak Bookcase	Furniture	Storage	429.00

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.

products120 rows · 8 columns

product_idinteger
namevarchar
categoryvarchar
subcategoryvarchar
unit_pricedecimal
unit_costdecimal
launched_ondate
is_activeboolean

First 5 rows of products
product_id	name	category	subcategory	unit_price	unit_cost	launched_on	is_active
101	Vale Velvet Loveseat	Furniture	Sofas	1659.00	NaN	2023-09-11	true
102	Alder Oak Coffee Table	Furniture	Tables	919.00	503.50	2024-05-16	true
103	Orchard Rattan Dining Chair	Furniture	Chairs	789.00	429.87	2023-05-27	true
104	Upland Oak Bookcase	Furniture	Storage	429.00	214.70	2022-12-20	true
105	Avery Leather Bed Frame	Furniture	Beds	449.00	232.70	2021-08-31	true

orders12,000 rows · 7 columns

order_idinteger
customer_idinteger
order_datetimestamp
statusvarchar
channelvarchar
shipping_feedecimal
coupon_codevarchar

First 5 rows of orders
order_id	customer_id	order_date	status	channel	shipping_fee	coupon_code
1001	125	2023-01-01 20:51:19	completed	web	0.00	NaN
1002	156	2023-01-02 07:58:46	completed	web	0.00	NaN
1003	22	2023-01-02 10:25:27	completed	marketplace	4.99	WELCOME10
1004	48	2023-01-02 11:21:51	completed	marketplace	4.99	WELCOME10
1005	82	2023-01-02 11:43:36	completed	web	0.00	WELCOME10

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_id	order_id	product_id	quantity	unit_price
1	1001	121	9	31.99
2	1001	154	3	11.99
3	1002	154	4	11.99
4	1002	194	1	149.00
5	1002	193	2	19.99

returns1,673 rows · 5 columns

return_idinteger
order_item_idinteger
returned_ondate
reasonvarchar
refund_amountdecimal

First 5 rows of returns
return_id	order_item_id	returned_on	reason	refund_amount
1	14	2023-01-10	damaged	83.69
2	42	2023-01-10	wrong_item	19.99
3	105	2023-01-14	wrong_item	10.99
4	24	2023-01-16	changed_mind	26.99
5	126	2023-01-16	late	20.99

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_id	first_name	last_name	email	city	state	signup_date	segment	referral_source
1	Owen	Hudson	owen.hudson@icloud.com	San Francisco	CA	2022-06-02	consumer	search
2	Douglas	Mendoza	dmendoza69@gmail.com	Atlanta	GA	2022-06-02	consumer	NaN
3	Henry	Brooks	hbrooks@lakesidecafe.com	Los Angeles	CA	2022-06-04	business	NaN
4	Brian	Graham	bgraham@bluecedarhomes.com	Seattle	WA	2022-06-06	business	pinterest
5	Kyle	Jimenez	kjimenez27@yahoo.com	Indianapolis	IN	2022-06-07	consumer	NaN

employees60 rows · 8 columns

employee_idinteger
first_namevarchar
last_namevarchar
departmentvarchar
titlevarchar
manager_idinteger
hire_datedate
salaryinteger

First 5 rows of employees
employee_id	first_name	last_name	department	title	manager_id	hire_date	salary
1	Catherine	Hale	Executive	Chief Executive Officer	NaN	2016-03-01	285000
2	Robert	Kim	Executive	Chief Operating Officer	1	2017-01-09	235000
3	Megan	Collins	Sales	Head of Sales	1	2018-04-16	172000
4	Chloe	Gutierrez	Sales	Account Executive	3	2020-01-21	72000
5	Frank	Bell	Sales	Account Executive	3	2023-05-21	73000

web_sessions40,000 rows · 7 columns

session_idinteger
customer_idinteger
started_attimestamp
devicevarchar
sourcevarchar
pages_viewedinteger
convertedboolean

First 5 rows of web_sessions
session_id	customer_id	started_at	device	source	pages_viewed	converted
1	NaN	2023-01-01 23:02:24	mobile	paid	1	false
2	18	2023-01-01 15:07:49	desktop	paid	1	false
3	NaN	2023-01-01 16:55:46	desktop	direct	7	false
4	NaN	2023-01-01 12:43:28	mobile	organic	3	false
5	NaN	2023-01-01 13:06:21	mobile	social	2	false

marketing_spend3,276 rows · 3 columns

spend_datedate
channelvarchar
amountdecimal

First 5 rows of marketing_spend
spend_date	channel	amount
2023-01-01	paid	492.99
2023-01-01	email	37.38
2023-01-01	social	200.53
2023-01-02	paid	709.13
2023-01-02	email	64.13

The lesson

Two return rates

“Return rate” hides a choice of unit:

Dollar rate = refunds ÷ net sales. What share of the money came back.
Line rate = returned lines ÷ all lines. What share of the things sold came back.

When items in a category have similar prices, the two agree. When a category mixes $40 lamps with $400 floor lamps, they can differ, and a slide that says “return rate 6.7%” without the unit can be read either way.

A left join keeps every line

Returns live in their own table, one row per returned line. A left merge from the lines keeps every sale and adds refund_amount where there was a return:

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

refund_amount is NaN on lines that were not returned. notna() turns that into a True/False returned flag, and fillna(0) turns it into a refund of zero, so both rates are plain sums and means over the same rows.

Margin lost

A refund takes back revenue, but the cost of the goods was already paid. If the item comes back damaged, the store loses its cost too; if it can be resold, much of it is saved. This level uses a simple, honest proxy: the gross margin of the returned lines, the profit the store booked on those sales and then gave back. Say on the slide that it is a proxy, and that damaged goods cost more.

Rate versus dollars

The category with the highest rate is not always the one losing the most. Lighting’s rate is in the middle, but it sells so much that its returns cost more margin than any other category. Show the rate to the team that can lower it, and the dollars to the team that sets priorities.

Common mistakes

A rate over the wrong denominator. Refunds over all orders, including cancelled ones, or over gross sales before discounts, understate the rate.
An inner join with returns. Only returned lines remain, so every rate becomes 100%.
Mixing up refunds and net sales of returned lines. Restocking fees make refunds a little smaller than the lines’ net value.

Level 5 · What returns really cost

Your goal

Your code

Reference solution

Mote checks your result

What Angela gets, and why

DataFrames

The lesson

Two return rates

A left join keeps every line

Margin lost

Rate versus dollars

Common mistakes

Lantern lit!

Level 5 · What returns really cost

The request

Your goal

Your code

Reference solution

Mote checks your result

What Angela gets, and why

DataFrames

The lesson

Two return rates

A left join keeps every line

Margin lost

Rate versus dollars

Common mistakes

Cookie choices