## Problem Implement a pretty printer that takes a nested Python object (dicts, lists, primitives, custom objects) and renders it as formatted, indented text. It must handle: cycles (print ` `), custom `__repr__` on objects, and a max-depth cutoff (print `...` beyond depth `d`). ```python def pretty_print(obj: Any, indent: int = 2, max_depth: int = 5) -> str: pass ``` **Example:** ``` data = {"users": [{"name": "Alice", "scores": [10, 20]}, {"name": "Bob"}], "count": 2} pretty_print(data, indent=2) ``` ``` { "users": [ { "name": "Alice", "scores": [10, 20] }, {"name": "Bob"} ], "count": 2 } ``` ## Follow-ups 1. How do you detect circular references — using `id()` and a seen set? 2. What is the time and space complexity of your traversal? 3. How would you add colorized terminal output (keys in blue, strings in green, numbers in yellow)? 4. Extend to support line-length limits — if a list fits on one line, keep it inline.

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Type Print: Implement a Type-Safe Pretty Printer That Renders Nested Data Structures

phone

Question Details

Problem Implement a pretty printer that takes a nested Python object (dicts, lists, primitives, custom objects) and renders it as formatted, indented text. It must handle: cycles (print <circular ref>), custom repr on objects, and a max-depth cutoff (print ... beyond depth d). Example: Follow-ups How do you detect circular references — using id() and a seen set? What is the time and space complexity of your traversal? How would you add colorized terminal output (keys in blue, strings in green, n…

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About This Question

This is a reported interview question from a merge interview during the phone round.

It covers the following topics: Coding, Phone, Strings .

Topics

Coding Phone Strings

About Merge Interview Reports

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How To Practice This Type of Question

Solve similar problems on LeetCode under timed conditions (25-35 minutes per medium difficulty). The goal is pattern recognition: recognize the underlying technique (sliding window, two-pointer, BFS, memoized recursion, etc.) within 60-90 seconds of reading. Strong candidates verbalize their hypothesis out loud before coding, then iterate based on feedback. Weak candidates dive into implementation immediately, lose time on the wrong approach, and run out of time for follow-ups.

Companies update their question pools every 2-4 months. The exact wording of any given question may have been retired by the time you interview. Focus your prep on the pattern, not the specific problem. The patterns that appear in Merge reports consistently are the ones worth investing in; one-off niche problems are not.

During Your Merge Round

Apply the standard interview round template: clarify requirements (2-3 minutes), state your approach out loud and confirm direction with the interviewer (3-5 minutes), code with narration (15-25 minutes), test with concrete examples including edge cases (5 minutes), discuss optimization or trade-offs if time permits (5 minutes). This template is universally accepted across FAANG and adjacent companies; deviating from it produces weaker interviewer feedback signal.

The single most predictive failure mode in Merge reports tagged "no hire": not asking clarifying questions. Interviewers are explicitly trained to weight this. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's written notes.