Analyze sales with groupby
Company: Uber
Role: Machine Learning Engineer
Category: Coding & Algorithms
Difficulty: easy
Interview Round: Technical Screen
You are given three pandas DataFrames representing a food delivery marketplace:
- `orders(order_id, product_id, quantity, unit_price, status)`
- `products(product_id, category_id)`
- `categories(category_id, category_name)`
Write code to join the tables and compute category-level business metrics using `groupby` and aggregation functions.
For each `category_name`, return:
1. `total_sales`: the sum of `quantity * unit_price`
2. `average_sales_per_order`: the mean sales amount per order line
3. `completion_rate`: the fraction of rows where `status == "completed"`
The result should be a DataFrame with one row per category.
Quick Answer: This question evaluates proficiency in data manipulation and analytics using pandas, specifically the ability to join relational tables and apply groupby aggregations to compute business metrics like total sales, average sales per order, and completion rates.