← Apple Interview Insights

Apple·Data Scientist·Technical Phone Screen·Intermediate

Intermediate
Apr 2026

Summary

Apple data scientist interview with a pandas coding task on sales data. Pretty straightforward if you know your groupby and merge patterns, but the follow-up about ranking regions tripped me up a bit.

Questions Asked (1)

Q1

Using pandas, compute total revenue per store per day from a sales table, then merge it with a stores table to identify the top three regions by total revenue.

Product Analytics & MetricsAlgorithms & Data Structures
Author's notes

The groupby part was fine, I've done that a hundred times.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

First, aggregate the sales table to compute total revenue per store per day using groupby and sum. Then merge the result with the stores table on store ID to attach region information, and finally group by region to sum revenue and identify the top three regions.

Pro tip: Always validate your data before and after merging—check for missing store IDs or duplicate entries that could skew results, and consider using merge with validate='many_to_one' to catch unexpected duplicates.

1. Aggregate sales data

Group the sales table by store and date, then sum the revenue column to get total revenue per store per day. Use groupby with as_index=False to keep the grouping columns as regular columns for easier merging.

2. Merge with stores table

Perform an inner merge between the aggregated sales data and the stores table on the store identifier to attach region information. Validate the merge to ensure each store maps to exactly one region.

3. Aggregate by region

Group the merged data by region and sum the total revenue to get overall revenue per region. Sort the results in descending order and select the top three regions.

4. Handle edge cases

Check for missing or null values in the revenue or region columns, and decide on an appropriate strategy (e.g., drop or fill). Also consider if there are stores with no sales—should they be included with zero revenue?

Key Points to Mention

  • Use of groupby with sum aggregation for revenue calculation
  • Importance of merging on the correct key (e.g., store_id) and validating the merge
  • Handling of date granularity—ensure the date column is in the correct format for grouping
  • Sorting and selecting top regions using nlargest or sort_values + head
  • Data quality checks: missing values, duplicates, and referential integrity between tables
  • Efficiency considerations: performing aggregation before merge to reduce data size

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.