5 to 7 years of experience - Python developer interview questions and sample answers

1. How does Python manage memory and what role does garbage collection play?

Answer: Python uses a private heap for all object storage and relies primarily on reference counting for memory management. When an object's reference count drops to zero, memory is freed. Python also uses a cyclic garbage collector to clean up reference cycles that reference counting alone cannot handle.

2. Explain shallow copy vs deep copy with use cases.

Answer: A shallow copy creates a new object but copies references to nested objects, so changes to nested objects affect both copies. Deep copy recursively copies all objects, so the copies are independent. Shallow copies are faster and suitable when nested objects won't be changed; deep copies are used when full independence is needed, such as cloning complex data structures.

3. What is the Global Interpreter Lock (GIL) in Python, and how do you work around its limitations?

Answer: The GIL allows only one thread to execute Python bytecode at a time, limiting multi-threaded CPU-bound applications. To work around this, multiprocessing can be used to spawn separate processes, or native extensions written in C can release the GIL during intensive computation.

4. How do you optimize Python code for better performance?

Answer: I profile code using tools like cProfile and line_profiler to identify bottlenecks, use built-in efficient data structures, replace loops with list comprehensions where appropriate, minimize global variable access, employ lazy evaluations (generators), and when necessary, offload performance-critical parts to extensions or C modules.

5. Describe the difference between iterables and iterators.

Answer: An iterable is any Python object (like lists, tuples) capable of returning an iterator. An iterator is an object with a next() method that fetches successive elements, raising StopIteration when exhausted.

6. How do you handle dependencies in a Python project?

Answer: I use virtual environments to isolate dependencies and package managers like pip. For complex projects, I prefer pipenv or Poetry which handle virtual environments and lock files automatically to ensure reproducible builds.

7. What is your experience with asynchronous programming in Python?

Answer: I use the asyncio library for concurrency when handling I/O-bound tasks. I create coroutines with async/await syntax, manage event loops, and use libraries like aiohttp for async HTTP calls or databases for improved responsiveness.

8. How do you ensure code quality and testing?

Answer: I write unit tests using pytest or unittest frameworks, apply test-driven development where feasible, use linters like flake8 and type checkers like mypy, and incorporate CI pipelines with test automation.

9. Explain how you manage large datasets with Python.

Answer: For large datasets, I use efficient libraries like NumPy and Pandas with memory optimization strategies such as chunk processing, categoricals for repeated labels, and vectorized operations to avoid loops.

10. How do you approach debugging and profiling Python applications?

Answer: I use pdb for step-by-step debugging, logging for runtime insights, and profiling with cProfile and memory_profiler to identify CPU and memory bottlenecks, allowing targeted optimization.

These questions reflect advanced Python knowledge, architecture understanding, performance tuning, and best practices expected from 5-7 years experienced developers. Preparing real-world examples for your answers based on projects will further strengthen your interview readiness.