Parse and Clean External Data | Dataford Interview Questions

Implement a function to parse and clean messy data extracted from an external system.

Accept a list of CSV strings containing name,age,email. Trim whitespace, collapse spaces in names, title-case names, normalize emails to lowercase, convert valid ages to integers, discard malformed or invalid rows, and keep only the first record for each normalized email.

The function must return a list of dictionaries with keys name, age, and email, preserving the input order of retained records. A valid age is an integer from 0 through 120, and a valid email contains exactly one @ with nonempty parts on both sides.

Constraints

0 <= records.length <= 1000
Each record is a string containing at most 10,000 characters
Each valid record has exactly three CSV fields: name, age, and email
Names may contain quoted commas
The first valid record for each normalized email must be retained

Function Signature

def parse_and_clean(records):

Your question is Parse and Clean External Data. 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

Implement a function to parse and clean messy data extracted from an external system.

Constraints

0 <= records.length <= 1000
Each record is a string containing at most 10,000 characters
Each valid record has exactly three CSV fields: name, age, and email
Names may contain quoted commas
The first valid record for each normalized email must be retained

Function Signature

def parse_and_clean(records):

Your question is Parse and Clean External Data. 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.