Python Data Structures and Algorithms Questions

Core Python data structure and algorithm knowledge used for manipulating collections and solving common data processing problems. Candidates should know built in types such as lists, dictionaries, sets, and tuples and their performance characteristics; be able to implement and reason about searching, sorting, counting, deduplication, and frequency analysis tasks; and choose appropriate algorithms and data structures for time and space efficiency. Familiarity with Python standard library utilities such as collections.Counter, defaultdict, deque, and heapq is expected, as is writing Pythonic, clear code that handles edge cases. Questions may include algorithmic trade offs, complexity analysis, and applying these techniques to practical data manipulation problems where custom logic is required beyond what pandas or NumPy provide.

HardTechnical

17 practiced

Write a Python implementation of Quickselect to find the k-th largest element and explain pivot selection trade-offs. Then describe how to modify your implementation to guarantee worst-case O(n) time (median-of-medians) and why that increases constant factors.

HardTechnical

16 practiced

Write a Python function smallest_k_pairs(A: List[int], B: List[int], k: int) that returns the k pairs (a,b) with smallest sums where A and B are sorted ascending. Implement with O(k log k) time using a heap and avoid generating all pairs. Provide code and explain correctness and complexity.

HardTechnical

22 practiced

Implement a stable in-place partition of a Python list according to predicate p(x) returning True/False such that all True elements come before False elements but relative order within each partition is preserved. Aim to minimize additional memory; explain trade-offs and time complexity.

HardTechnical

19 practiced

Implement an LRU cache class in Python 3 with methods get(key) and put(key, value) that run in O(1) time. Do not use functools.lru_cache or collections.OrderedDict; implement using a dict and a doubly linked list. Provide code and explain how operations maintain O(1) time complexity.

MediumTechnical

20 practiced

Design a Python class StreamingMedian that supports add(num: float) and median() operations for a stream of numbers. Implement it using two heaps so that add runs in O(log n) and median in O(1). Provide code and explain why two heaps produce the median efficiently.

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