L2 · Working engineerAdvanced L2~4 min · 3 tests#88

Transform JSON Data in Python

Parse a JSON array of people with json.loads and reshape it into a name→age dict with a comprehension. Everyday data transformation, tested in Python.

The problem

json_text is a JSON array of objects with "name" and "age". Return a dict mapping each name to its age.

Examples

Example 1

Input

name_to_age('[{"name": "John", "age": 30}, {"name": "Alice", "age": 25}]')

Expected output

{'John': 30, 'Alice': 25}

+ 2 hidden tests on Submit — later duplicate wins.

Edge cases to ask about

Empty array
Duplicate names

Hints

0/3

How an interviewer scores this

0/9

1. Understand the problemRestate it, ask about edge cases and input size.
2. Write a brute-force solutionCorrect first, clever later.
3. Get the correct outputautoAll visible examples pass.
4. Analyse time complexityautoCommit to a Big-O in the Complexity Lab.
5. Analyse space complexityautoExtra memory, not the input.
6. Identify the bottleneckautoMeasure your code — where does it bend?
7. OptimiseUsually: a set or dict instead of a scan.
8. Explain why it worksSay it out loud to Solvi.
9. Test the edge casesautoSubmit passes every hidden test.

Python 3.13 · name_to_age

def name_to_age(json_text):
    """Parse the JSON array and return {name: age}."""
    # Write your solution here
    pass

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Complexity Lab

What does this cost as n grows?

Interviewers score the analysis as much as the code. Commit to an answer first — then check it, and read why.

Time complexity of the optimal solution

Space complexity (extra memory)

Pick both to reveal the answer.

From brute force to optimal

The progression an interviewer wants to hear, one step at a time.

Approach	Time	Space	Idea
json.loads + dict comprehension	O(n)	O(n)

Walkthrough of the optimal approach (try it yourself first)

json.loads turns the text into a list of dicts; a comprehension reshapes it. Know the loads/load (string vs file) and dumps/dump pairs.

Mention what you would ask: duplicate names (last wins here), missing keys (p.get("age")), and validation with pydantic for anything coming from outside.

Complexity: O(n) time, O(n) space. Parsing and the comprehension are both linear in the size of the input.

Reveal the reference solution

import json

def name_to_age(json_text):
    people = json.loads(json_text)
    return {p["name"]: p["age"] for p in people}

Follow-ups interviewers ask

Group names by age instead.
Validate the input schema.

Frequently asked interview questions

Core interview concepts, complexities, and follow-ups scored by hiring teams.

What is the time complexity of Transform JSON Data in Python?

The optimal solution runs in O(n) time and O(n) auxiliary space. Parsing and the comprehension are both linear in the size of the input.

What follow-up questions do interviewers ask about Transform JSON Data in Python?

Group names by age instead. Validate the input schema.