Calculate Total Sales and Average Order Value by Customer

PYSPARK coding challenge · Difficulty: medium · Topic: GroupBy Aggregations · +100 XP

Problem

For each customer_id, compute:

• total_sales — the sum of order_value
• avg_order_value — the average of order_value

Return a DataFrame with columns customer_id, total_sales, avg_order_value.

Example Input - `orders`

| order_id | customer_id | order_value |
| --- | --- | --- |
| 1 | 101 | 250.00 |
| 2 | 102 | 300.00 |
| 3 | 101 | 150.00 |
| 4 | 103 | 400.00 |
| 5 | 102 | 200.00 |
| 6 | 101 | 350.00 |

Expected Output

| customer_id | total_sales | avg_order_value |
| --- | --- | --- |
| 101 | 750.0 | 250.0 |
| 102 | 500.0 | 250.0 |
| 103 | 500.0 | 250.0 |

Notes

• The DataFrame is created for you - do not recreate it
• Build a DataFrame called df_result and finish with df_result.show()

What this PYSPARK challenge teaches you

“Calculate Total Sales and Average Order Value by Customer” is a medium-level PYSPARK challenge focused on GroupBy Aggregations. Working through it gives you hands-on practice with groupBy, agg, avg, sum — the kind of transformation you are asked to write in real data engineering work and in technical interviews. You can solve it directly in the browser: the dataset is pre-loaded, so you write the query or DataFrame code, run it, and compare your output against the expected result immediately.

Concepts covered

• groupBy
• agg
• avg
• sum

How to approach it

If you get stuck, work through these steps in order before looking at a full solution — each one narrows the problem down:

1. 1 ≤ number of rows ≤ 1,000,000
2. Each customer_id appears between 1 and 50 times
3. order_value is a float greater than 0
4. Output must have exactly one row per customer_id with columns: customer_id, total_sales, avg_order_value
5. Do not use SQL syntax, use DataFrame API only

How to practise it on PySpark.in

Open the challenge, write your PySpark code in the editor and press Run to execute it against the sample dataset. Submitting checks your output against every test case, including hidden ones, so you find out straight away whether your logic holds up. You can retry as often as you like, and each solved challenge adds to your XP.

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Frequently asked questions

Do I need to install Spark or a database to solve this?

No. The PYSPARK environment runs in your browser with the sample data already loaded, so there is nothing to install or configure.

Is this challenge free?

Yes - the problem, the sample dataset, the hints and unlimited test runs are free.

What level is it?

It is rated medium and covers GroupBy Aggregations.

Solve this challenge free on PySpark.in