Python Programming Challenge

This repository contains solutions to the Modest Tree 2022 Python programming test.
It demonstrates practical algorithm design, reasoning, and implementation choices,
with a focus on correctness, clarity, and efficiency.

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Project Structure

programming_python/
│── __main__.py        # Entry point to run all questions
│── data
│── docs
│── Dockerfile
│── README.md

You can either:

• Run directly with Python:

  python -m programming_python

• Or build and run with Docker:

  docker build -t python-challenge .
  docker run --rm -it python-challenge

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Problem Breakdown

Question 1 – [Problem Title]

• Algorithm Used: [e.g., Sorting + Binary Search]
• Why: Chosen for simplicity and logarithmic search speed.
• Time Complexity: O(n log n) (due to sort), O(log n) for lookups.
• Space Complexity: O(1) extra (in-place operations).
• Expected Input/Output:

  Input: [sample input]
  Output: [expected result]
  

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Question 2 – [Problem Title]

• Algorithm Used: [e.g., Hash Map lookups]
• Why: Provides O(1) average-time checks compared to O(n) linear scans.
• Time Complexity: O(n).
• Space Complexity: O(n) for the hash table.
• Expected Input/Output:

  Input: [sample input]
  Output: [expected result]
  

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Question 3 – [Problem Title]

• Algorithm Used: [e.g., Two-pointer technique]
• Why: Optimizes space usage, avoids nested loops.
• Time Complexity: O(n).
• Space Complexity: O(1).
• Expected Input/Output:

  Input: [sample input]
  Output: [expected result]
  

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Question 4 – [Problem Title]

• Algorithm Used: [e.g., Dynamic Programming]
• Why: Avoids recomputation by caching subproblems.
• Time Complexity: O(n·m).
• Space Complexity: O(n·m).
• Expected Input/Output:

  Input: [sample input]
  Output: [expected result]
  

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Question 5 – [Problem Title]

• Algorithm Used: [e.g., Greedy approach]
• Why: Locally optimal choices lead to globally optimal results.
• Time Complexity: O(n log n).
• Space Complexity: O(1).
• Expected Input/Output:

  Input: [sample input]
  Output: [expected result]
  

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Full Expected Results

When running the full program:

$ python -m programming_python
Question 1 → Result: [value]
Question 2 → Result: [value]
Question 3 → Result: [value]
Question 4 → Result: [value]
Question 5 → Result: [value]

All questions executed successfully!

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Design Philosophy

• Prefer Python built-ins where they offer efficient, clear solutions.
• Explicit over implicit: every step is clear to the reader/interviewer.
• Balanced trade-offs: correctness and maintainability are prioritized, while also noting potential optimizations.
• Code formatted using Black for consistency.

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Challenges & Solutions

1. Challenge: Efficient lookup and search.
   Solution: Used hash maps and binary search where appropriate.

2. Challenge: Avoiding unnecessary recomputation.
   Solution: Applied memoization / dynamic programming.

3. Challenge: Keeping code clean for interview readability.
   Solution: Modularized by question, added comments, and kept functions concise.

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Conclusion

This project demonstrates:

• Knowledge of multiple algorithmic strategies.
• Awareness of time/space complexity trade-offs.
• Ability to communicate not just what the code does, but why.