Tracking Customer Purchase History
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Objective
In a retail store, tracking customer transactions chronologically is crucial for maintaining inventory, generating sales insights, and building recommendation engines.
Task
Write a PySpark function that processes a transactions DataFrame to compute the following:
- Add a new column called
previous_productthat contains theproduct_idof the previous transaction made by the same customer, in chronological order. If there is no previous transaction for a customer, this column should be null. - Add a new column called
date_and_productthat concatenates the transactiondateand theprevious_product, separated by a space. If the previous product is null, the string should literally say "None" (e.g.,"2023-01-01 None").
Save your result as result_df. Ensure the output matches the exact schema order requested.
File Path
- Dataset:
/home/interview/transactions.csv - Starter script:
/home/interview/purchase_history.py
Schema
transactions.csv
| Column Name | Data Type |
|---|---|
| customer_id | string |
| product_id | string |
| quantity | integer |
| date | string |
Expected Output Schema
| Column Name | Data Type |
|---|---|
| customer_id | string |
| product_id | string |
| quantity | integer |
| date | string |
| previous_product | string |
| date_and_product | string |
Example
Given this sample input:
transactions
| customer_id | product_id | quantity | date |
|---|---|---|---|
| CUST1 | PROD1 | 2 | 2023-01-01 |
| CUST1 | PROD2 | 1 | 2023-01-05 |
| CUST2 | PROD3 | 5 | 2023-02-03 |
| CUST3 | PROD1 | 4 | 2023-02-07 |
| CUST1 | PROD3 | 3 | 2023-02-10 |
The output would be:
| customer_id | product_id | quantity | date | previous_product | date_and_product |
|---|---|---|---|---|---|
| CUST1 | PROD1 | 2 | 2023-01-01 | null | 2023-01-01 None |
| CUST1 | PROD2 | 1 | 2023-01-05 | PROD1 | 2023-01-05 PROD1 |
| CUST1 | PROD3 | 3 | 2023-02-10 | PROD2 | 2023-02-10 PROD2 |
| CUST2 | PROD3 | 5 | 2023-02-03 | null | 2023-02-03 None |
| CUST3 | PROD1 | 4 | 2023-02-07 | null | 2023-02-07 None |
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