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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
16 practiced
Implement a Trie (prefix tree) in Python that supports insert(word), search(word), and starts_with(prefix). Provide code and discuss memory usage and possible optimizations for storing many English words (e.g., compression, using arrays vs dicts, ternary search trie).
MediumTechnical
20 practiced
Explain how Python's sort (Timsort) achieves stability and good performance on partially ordered data. Give examples of when stability is required in multi-key sorting in a data-science workflow and demonstrate with Python code how to sort by multiple keys preserving stability.
EasyTechnical
19 practiced
You have a list of strings representing event types. Using collections.Counter in Python 3, write code to produce the top 5 most common events and explain its time complexity. When is Counter.most_common more convenient than manual heap approaches for top-k frequency extraction?
EasyTechnical
19 practiced
Explain average and worst-case time complexity for dictionary lookup, insertion, and deletion in CPython. As a data scientist, why is understanding amortized complexity for operations like list.append important when working with large in-memory datasets? Give a short example where ignoring amortized cost could be costly.
HardTechnical
22 practiced
Implement reservoir sampling (select k items uniformly at random from a stream of unknown length) in Python. Provide code for both k=1 and general k, explain why the algorithm produces uniform samples, and analyze time and space complexity.

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