Cleaning Data in Python | Dataford Interview Questions

Implement a function to transform and clean a dataset in Python.

Write clean_dataset(records) for a list of dictionaries containing id, name, email, and age. Trim string fields, lowercase emails, convert valid numeric age strings to integers, discard records with missing or invalid required values, and keep only the first valid record for each normalized ID. Return a new list containing dictionaries with the keys id, name, email, and age.

Constraints

0 <= len(records) <= 10000
Each record is a dictionary
Required keys are id, name, email, and age
ID, name, and email must be strings before normalization
Age must be an integer or a string containing decimal digits
Valid ages are between 0 and 120 inclusive
The first valid record for each normalized ID is retained

Function Signature

def clean_dataset(records):

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Problem

Implement a function to transform and clean a dataset in Python.

Constraints

0 <= len(records) <= 10000
Each record is a dictionary
Required keys are id, name, email, and age
ID, name, and email must be strings before normalization
Age must be an integer or a string containing decimal digits
Valid ages are between 0 and 120 inclusive
The first valid record for each normalized ID is retained

Function Signature

def clean_dataset(records):

Your question is Cleaning Data in Python. 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.