Calculate average revenue per user

Last updated: October 25, 2025

Quick Overview

Write a query to compute average revenue grouped by region, handling edge cases like nulls and duplicates.

Lyft
Data Manipulation (SQL/Python)
Data Scientist
Lyft
October 25, 2025
Data Scientist
Take-home Project
Data Manipulation (SQL/Python)
Medium

4

7

1,783 solved


Write a query to compute average revenue grouped by region, handling edge cases like nulls and duplicates.

This question from Lyft's Take-home Project tests practical data skills. The interviewer wants to see clean, efficient queries that handle edge cases like NULLs, duplicates, and large datasets.

What the Interviewer Expects
  • Use advanced SQL features: window functions, CTEs, subqueries
  • Write efficient queries that avoid common performance pitfalls
  • Handle complex data transformations with multiple joins and aggregations
  • Discuss indexing strategy and query optimization
  • Address data quality issues: duplicates, missing values, outliers
Key Topics to Cover
Aggregate functions and GROUP BY
Index optimization and query performance
JOIN types and when to use each
Window functions (ROW_NUMBER, RANK, LAG, LEAD)
Date/time manipulation
How to Approach This
  1. Clarify the schema and expected output format before writing queries.
  2. Use CTEs (WITH clauses) to break complex queries into readable steps.
  3. Consider window functions (ROW_NUMBER, RANK, LAG, LEAD) for ranking and sequential analysis.
  4. Watch for NULLs, duplicates, and edge cases in JOINs and GROUP BY.
  5. For pandas, prefer vectorized operations over row-by-row iteration.
Possible Follow-up Questions
  • How would you handle this if the data was spread across multiple databases?
  • Can you rewrite this without using subqueries?
  • How would you validate the correctness of your query results?
  • How would you optimize this query for a table with 100 million rows?
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