How to Get a Python Job in 2025: Top Interview Questions & Expert Tips

Eight years in this industry. Not a lifetime, but enough. Enough to see hopeful candidates walk in, some leave victorious, others crushed. Enough to see hiring managers raise their eyebrows when they hear the right answer — or sigh when they hear the wrong one for the hundredth time.

I’ve been on both sides. I’ve been the anxious interviewee, gripping the chair, trying to decipher the interviewer’s poker face. I’ve also been the one asking the tough questions, deciding who makes the cut. I know exactly what’s coming your way, because I’ve been through every stage of the process.

So, let me do you a favor.

Let me show you the real Python interview questions. The ones that actually matter. The ones that get asked over and over.

If you get these wrong? You’re out. If you get them right? You’re one step closer.

And if you make it all the way? Welcome to the big leagues.

The Five Most Common Python Questions

1. What’s the difference between a list and a tuple?

You might think, Oh, easy, lists are mutable, tuples are not. But everyone says that. You need more.

• Mutability: Lists can be changed. Tuples are locked down.
• Performance: Tuples are faster because they’re immutable.
• Use Case: Lists are for dynamic data. Tuples are for fixed data, like database records.

Follow-up question:
“When would you prefer a tuple over a list?”

A smart answer? When you need data integrity. A function returning fixed values? Tuple. Storing unchangeable settings? Tuple. Passing data between processes? Tuple — less overhead.

2. Explain Python’s Global Interpreter Lock (GIL).

• What it is: A mutex that ensures only one thread executes Python bytecode at a time.
• Why? Because CPython needs it for memory management.
• Does it kill multithreading? No — but it means CPU-heavy tasks should use multiprocessing instead.

Follow-up question:
“So, should we never use threads in Python?”

Wrong. Threads are great for I/O-bound tasks (like network requests, file reading). The GIL is only a problem when your code is CPU-intensive.

3. How do you handle exceptions in Python?

• Basic: try-except.
• Better: try-except-finally (for cleanup).
• Best: Catch specific exceptions — never use except Exception: unless absolutely necessary.

Follow-up question:
“Can you show me a real-world scenario where exception handling is crucial?”

API calls. Network failures. User input validation. Think practical.

4. What are decorators, and why do we use them?

• Simple definition: A function that modifies another function without changing its code.
• Common use cases: Logging, authentication, execution timing.
• Example: @login_required in Django.

Follow-up question:
“Can you write a decorator that logs function execution time?”

If you pause too long, the interview room gets colder. But you? You’re ready.

import time

def timing_decorator(func):
    def wrapper(*args, **kwargs):
        start_time = time.time()  # Start the clock
        result = func(*args, **kwargs)
        end_time = time.time()  # Stop the clock
        print(f"{func.__name__} executed in {end_time - start_time:.5f} seconds")
        return result
    return wrapper

@timing_decorator
def example_function():
    time.sleep(1)  # Simulating a slow operation

example_function()

Break it down for them.

• timing_decorator wraps around any function.
• Captures start time and end time.
• Computes the execution duration and prints it.

Now they’re nodding. You nailed it.

5. Explain list comprehensions and when to use them.

• What it is: A shorter, faster way to create lists.
• Example: [x*x for x in range(5)] produces [0, 1, 4, 9, 16].
• When to avoid? When it reduces readability.

Follow-up question:
“What’s the difference between a list comprehension and a generator expression?”

Generator expressions save memory. They don’t store the full list in RAM. If you’re working with huge datasets, use generators.

Three HARD Python Questions That Separate Good from Great

These are the real tests. If you answer them well? You stand out.

1. What happens when you run python script.py in the terminal?

They want to see how deep your knowledge goes.

• Python loads the script, compiles it to bytecode (.pyc files).
• It executes the __main__ block.
• The interpreter starts managing memory and garbage collection.

Follow-up question:
“How does Python’s garbage collector work?”

Here I let you dear reader to answer, write in the comments and I’ll answer you if it’s good or not. (I won’t give more details not to influence the other comments, but if you want to know why I answered you like that, enter my discord channel)

2. What’s the difference between deep copy and shallow copy?

• Shallow copy: Copies only references to nested objects.
• Deep copy: Recursively copies everything.
• When it matters? When working with mutable nested structures.

Follow-up question:
“What Python module would you use for deep copies?”

copy.deepcopy(). If you don’t mention it, you’re not ready.

3. How would you implement a Least Recently Used (LRU) cache?

• Theoretical answer: A dictionary + a doubly linked list.
• Python answer: Use functools.lru_cache().

Follow-up question:
“How does LRU cache improve performance?”

If you understand cache eviction policies, memory efficiency, and time complexity trade-offs, you’re already ahead of most candidates.

Domain-Specific Questions: Web Dev, Machine Learning & More

Knowing Python isn’t enough. Your role matters.

Web Development

• What’s the difference between Django and Flask?
• How does Django ORM work?
• How do you scale a web application?

Machine Learning

• How does Python handle large datasets?
• What’s the difference between TensorFlow and PyTorch?
• How do you optimize a machine learning model for performance?

Automation & Scripting

• How would you automate a repetitive task using Python?
• What’s the difference between subprocess and threading?
• How do you parse a huge CSV file efficiently?

They expect you to know this. If you don’t, you won’t make it past technical screening.

I’ve seen every possible Python interview question. The common ones. The weird ones. The ones that make you sweat.

So I put them all in one place.

• 130+ of the most frequently asked Python questions.
• Step-by-step solutions with real-world applications.
• Advanced topics that separate juniors from seniors.

Want to crush your next interview? 👉 Check out my eBook.

If I had this guide when I started? I would’ve gotten hired twice as fast.

Now — go land that Python job. 🚀