Python Data Transformation Function | Dataford Interview Questions

Write a Python function to perform data transformation on a dataset.

Implement transform_dataset(records). Each record is a dictionary with id, category, and numeric value. Keep only the last record for each ID, discard records with missing or invalid fields, normalize categories by trimming whitespace and converting them to lowercase, then return category aggregates sorted alphabetically.

Input and Output

Return dictionaries with category, count, and total, where total is the sum of valid values.

Example: [{"id": 1, "category": " A ", "value": 2}, {"id": 2, "category": "a", "value": 3}] returns [{"category": "a", "count": 2, "total": 5}].

Constraints

1 <= len(records) <= 1000
Each record is a dictionary
Each record may contain id, category, and value fields
IDs are hashable
A valid category is a nonempty string after trimming whitespace
A valid value is an int or float, excluding booleans

Function Signature

def transform_dataset(records):

Your question is Python Data Transformation Function. Start with the requirements in the Question tab.

Run and submit as often as you like. When you're ready, talk me through your approach or go straight to the code.

Problem