## Problem A "run" is a maximal contiguous subarray of equal elements. Given an array, return the number of runs. ```python def count_runs(arr: list) -> int: pass ``` **Example:** ``` Input: [1, 1, 2, 3, 3, 3, 1] Runs: [1,1], [2], [3,3,3], [1] Output: 4 Input: [5] Output: 1 Input: [] Output: 0 ``` ## Round 2 - Variant Return not just the count but also the encoded form: `[(value, length)]` pairs (run-length encoding). ```python def run_length_encode(arr: list) -> list[tuple]: pass ``` **Example:** ``` Input: [1, 1, 2, 3, 3, 3, 1] Output: [(1,2), (2,1), (3,3), (1,1)] ``` Then decode it back: ```python def run_length_decode(encoded: list[tuple]) -> list: pass ``` ## Follow-ups 1. What is the time and space complexity of encoding and decoding? 2. For what types of data does run-length encoding provide the most compression? 3. How would you handle this for a stream of data (you cannot load the full array into memory)? 4. Extend to 2D: encode each row independently, then encode the rows of encodings. What are the tradeoffs?

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Number of Runs: Count Consecutive Equal-Element Segments in an Array

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Problem A "run" is a maximal contiguous subarray of equal elements. Given an array, return the number of runs. Example: Round 2 - Variant Return not just the count but also the encoded form: [(value, length)] pairs (run-length encoding). Example: Then decode it back: Follow-ups What is the time and space complexity of encoding and decoding? For what types of data does run-length encoding provide the most compression? How would you handle this for a stream of data (you cannot load the full array…

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

It covers the following topics: Arrays, Coding, Phone, Onsite .

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Number of Runs: Count Consecutive Equal-Element Segments in an Array

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