The full problem statement is available to Pro members.
preloaded fixtures
import pandas as pd
import numpy as np
deliveries = pd.DataFrame({
"delivery_id": range(1, 11),
"city": ["Chicago", "Chicago", "Chicago", "Austin", "Austin", "Denver", "Chicago", "Austin", "Chicago", "Denver"],
"cuisine": ["mexican", "asian", "mexican", "salads", "pizza", "breakfast", "asian", "pizza", "mexican", "breakfast"],
"placed_at": pd.to_datetime(["2024-01-10 18:20", "2024-01-10 19:05", "2024-01-11 12:30", "2024-01-11 13:10",
"2024-01-12 20:00", "2024-01-12 08:45", "2024-01-13 19:40", "2024-01-14 18:15",
"2024-01-15 18:55", "2024-01-16 09:05"]),
"minutes": [32, 41, 27, 22, 55, 19, 38, 48, 30, 24],
"total": [24.50, 38.00, 18.75, 15.00, 42.20, 12.40, 29.90, 51.00, 22.10, 14.00],
"tip": [4.00, 6.00, 2.50, 3.00, 5.00, 2.00, 4.50, 8.00, 3.50, 1.50],
})
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Pro problem
“Peak dinner hour volume”
This is one of the real interview questions in Pro. Try Pro free for 7 days to unlock it and everything below:
- 293 interview-grade problems across SQL, Python & pandas
- 104+ real-world SQL problems: SaaS retention, fintech ledgers, supply chain, recursive CTEs
- 18 company interview packs (Amazon, Meta, Google, Uber and more)
- AI "explain my mistake" tutor and mock interviews
- Use QueryLab inside Claude, Cursor & other AI assistants
// built for real interview rounds
Practice the patterns 18 top companies test
329 problems · 36 free forever · 293 unlocked with Pro
Card required. Then $12/month. Cancel before your trial ends and you pay nothing.
or keep practicing free problems →Secure checkout by Paddle · instant access · cancel anytime from your account