Generate Weekly Revenue and Engagement Summary with Pandas

Q: How do I practice SQL interview questions?

PracHub provides an interactive SQL console where you can write and test queries against real database schemas. Get instant feedback and compare your solution with the expected output.

Q: What difficulty level is this coding question?

This is a Medium difficulty Data Manipulation (SQL/Python) question, commonly asked during Technical Screen rounds at DoorDash.

Q: What role is this question designed for?

This question is commonly asked for Data Scientist candidates at DoorDash during technical interviews.

Question

events

| user_id | event_time          | event_type | platform | revenue |
|---------|---------------------|------------|----------|---------|
| 101     | 2024-04-01 12:01:00 | click      | iOS      | 0.00    |
| 101     | 2024-04-01 12:02:10 | purchase   | iOS      | 4.99    |
| 202     | 2024-04-01 13:05:33 | view       | Android  | 0.00    |
| 303     | 2024-04-01 14:20:05 | click      | Web      | 0.00    |
| 202     | 2024-04-01 14:45:00 | purchase   | Android  | 9.99    |

##### Scenario

You own the clickstream pipeline for a consumer app and must create a weekly revenue and engagement summary.

##### Question

Using Pandas, create a DataFrame that aggregates total revenue and distinct purchase counts per user from the events table. Use a lambda function inside apply to classify users into tiers based on total revenue (e.g., 0, 0–5, 5+). Write a SQL query that returns, for each platform, the daily conversion rate (purchases / clicks) for the last 30 days.

##### Hints

Show familiarity with groupby, apply-lambda, dictionary mapping, and SQL aggregations with conditional filtering.

PracHub · Accepted Answer

This question evaluates proficiency in data manipulation and analytics with Pandas and SQL, covering aggregations, distinct purchase counts, lambda-based user-tier classification, and platform-level conversion-rate computation.

Quick Overview

Quick Overview