Practice / selecting and filtering

Tip-Top Baristas

medium

Corporate at The Daily Grind is launching a "Tip-Top Barista" award, and naturally they want data instead of vibes. The metric: which orders tipped at least 20% of the drink price?

Same orders DataFrame as before:

Your task:

Compute each order's tip percentage as tip / price, in a column named tip_pct.
Keep only orders where tip_pct is at least 0.20.
Return order_id, drink, and tip_pct.

Assign the DataFrame to result. Row order doesn't matter, and tip_pct is compared with a float tolerance — no need to round.

`orders`input DataFrame

Schema

column	type
order_id	long
drink	string
size	string
price	double
tip	double

Sample rows (first few)

order_id	drink	size	price	tip
1001	oat latte	large	6.5	1
1002	drip coffee	small	3	0
1003	caramel macchiato	large	7.25	2
1004	flat white	medium	5	1.5
1005	cold brew	large	5.5	0.5
1006	espresso	small	2.5	0.75
1007	mocha	large	6.75	0.25
1008	latte	medium	5.25	2
1009	gold-dust affogato	small	6.5	0.75
1010	barrel-aged cold brew	large	6	0.5

Expected output shape

order_iddrinktip_pct· 5 rows

›Hint

Build the ratio with withColumn("tip_pct", F.col("tip") / F.col("price")), then filter on the new column. You can reference a column you just created in the very next chained step — transformations stack.

›Lesson refresher

This problem builds on Selecting & Filtering (~6 min). Pop it open in a new tab if you want a quick recap.

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ordersinput DataFrame

`orders`input DataFrame