Lighthouse

Level 3 · Half a month is not a bad month

From Robert Kim, Chief Operating Officer

“It's October 15th and the dashboard says October net sales are $89k against $146k for September. That's almost 40% down. Do I need to call a meeting?”

Your goal

The data runs to 2025-12-31, but answer as if today were 2025-10-15. daily (in the starter) holds net sales per day of booked orders. Set result to a dict with "october_to_date" (2025-10-01 to 2025-10-15), "september" (the whole of September 2025), "trailing_28" (2025-09-18 to 2025-10-15), "prior_28" (2025-08-21 to 2025-09-17), all inclusive and rounded to 2, and "change_pct": (trailing_28 / prior_28 − 1) × 100, rounded to 1.

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…

Reference solution

ResultPreview of orders · press Run to see yours

Preview of orders
order_id	customer_id	order_date	status	channel
1001	125	2023-01-01 20:51:19	completed	web
1002	156	2023-01-02 07:58:46	completed	web
1003	22	2023-01-02 10:25:27	completed	marketplace
1004	48	2023-01-02 11:21:51	completed	marketplace

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_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

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

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

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

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

The partial-period trap

A calendar month is a fine window once it is over. While it is running, “this month” holds fewer days than “last month”, so it is always lower. On October 15th the dashboard compared 15 days of October with 30 days of September and showed a 39% drop:

daily.loc["2025-10-01":"2025-10-15"].sum()   # 89,128.58
daily.loc["2025-09-01":"2025-09-30"].sum()   # 145,543.20

Nothing happened to the business. Half the month had not happened yet.

Trailing windows

A trailing window always covers the same number of days, ending today. Compare the last 28 days with the 28 days before them and both sides are complete:

2025-09-18 → 2025-10-15   154,204.39
2025-08-21 → 2025-09-17   141,267.38    change +9.2%

The trend Robert should see is up, not down. A trailing window also updates every day, so it answers “how are we doing right now?” without waiting for month end.

Why 28 and not 30

Sales differ by weekday. In 2025 Harbor & Pine averaged about $7,500 of net sales on Saturdays and about $5,900 on Wednesdays. A 30-day window holds four or five of each weekday depending on where it starts, so two 30-day windows can differ only because one has an extra Saturday. Windows of 7 or 28 days always hold the same number of each weekday. The same reasoning is why weekly KPIs compare whole weeks, Monday to Sunday.

Run rates

Another fix is a run rate: month to date divided by days elapsed, times days in the month. For October, $89,128.58 / 15 × 31 = $184,199, and October did finish at $184,242. Run rates work in steady months and fail badly at season changes: a run rate taken on November 15th misses Black Friday and Cyber Monday entirely.

Common mistakes

Month to date vs a full month. Compare it with the same days of last month, or use a trailing window.
Windows that are not a multiple of 7. Weekday mix leaks into the comparison.
Calling a partial month “October” on a chart. Mark it as incomplete, or leave it off until it closes.

Level 3 · Half a month is not a bad month

Your goal

Your code

Reference solution

Mote checks your result

What Robert gets, and why

DataFrames

The lesson

The partial-period trap

Trailing windows

Why 28 and not 30

Run rates

Common mistakes

Lantern lit!

Level 3 · Half a month is not a bad month

The request

Your goal

Your code

Reference solution

Mote checks your result

What Robert gets, and why

DataFrames

The lesson

The partial-period trap

Trailing windows

Why 28 and not 30

Run rates

Common mistakes

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