Python skills are hot right now—and for good reason. It’s simple, flexible, and powers everything from websites to cutting-edge AI. But here’s the thing: acing Python interview questions isn’t just about knowing the language. It’s about showing you can use it.
Are you a new grad prepping for your first tech interview? Or maybe a seasoned dev gunning for a FAANG role? Either way, the right Python questions (and answers) can make or break your shot.
We’ve put together the key questions—from beginner to advanced—plus a few under-the-radar tips most candidates miss.
Ready? Let’s get into it.
Basic Python Interview Questions
Question 1: What is Python and what are its key features?
This question tests whether you really get Python—not just the basics, but what makes it powerful (and where it falls short).
How to Nail Your Answer:
“Python’s a high-level programming language that’s ridiculously easy to read and write. It’s like the friendly neighbor of coding languages—flexible enough for OOP, functional, or procedural programming, without making your brain hurt. Here’s why devs love it:
- Beginner-friendly? Absolutely. The syntax reads almost like plain English. No curly braces chaos!
- Plays nice with any OS. Windows, macOS, Linux—it’ll run.
- Libraries? Tons. Need to crunch data, build a website, or automate stuff? There’s probably a library for that.
- Dynamic typing means less boilerplate code (but watch out for runtime errors).
- Memory management? Handled. Garbage collection saves you from manual cleanup duty.
Downsides? It’s not the fastest, and that dynamic typing can backfire. But for productivity? Hard to beat.”
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Question 2: How is Python interpreted?
They’re basically checking if you get how Python runs code. Here’s how to answer it right:
“Python’s an interpreted language—so instead of compiling everything upfront, it runs line by line. But here’s the twist: it first gets converted into bytecode (those .pyc files you might’ve seen), which the Python Virtual Machine (PVM) then executes. This makes things flexible (like dynamic typing) but a bit slower than languages like C that compile directly to machine code.”
Question 3: Explain the difference between list, tuple, set, and dictionary.
They want to know you understand Python’s core data structures—and when to use which. Break it down like this:
- List: Your go-to for ordered, changeable collections. Need to add, remove, or update items? Lists handle duplicates and let you tweak stuff anytime.
- Tuple: Like a list, but locked after creation. Immutable = safer for fixed data (and lighter on memory).
- Set: All about uniqueness. No duplicates allowed, and it’s unordered—perfect for checking membership fast or mathy ops (think unions, intersections).
- Dictionary: Key-value pairs. Keys are unique (like a real dictionary’s words), and values can be anything. Need to look stuff up in a flash? This is it.
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Question 4: What are mutable and immutable types?
They’re probing whether you grasp how Python manages memory—and why it matters.
- Mutable: Can change after creation (lists, dicts, sets). Modify them? Same object, new contents. Handy, but risky if you’re not careful.
- Immutable: Set in stone (strings, tuples, ints). “Changing” them actually makes a new object. Slower? Sometimes. Safer? Absolutely.
Question 5: What is the difference between is and ==?
These trips people up! They’re testing if you know Python’s identity vs. value checks.
- ==: “Are these two things alike?” Compares values—like checking if two coffee cups hold the same amount.
- is: “Are these literally the same thing?” Checks if both point to the exact same object in memory. Like asking if two people are holding the same cup.
(Pro tip: “is” is usually for None, True, or False checks—not everyday comparisons.)
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Question 6. What are Python’s key advantages over other languages?
They’re checking if you actually know why Python stands out.
Good answer:
“Python’s got some killer perks:
- Readability: It’s like writing plain English—clean syntax, fewer headaches, and way easier for newbies.
- Huge ecosystem: Need a tool? There’s probably a library for it (Django for web stuff, Pandas for data, you name it).
- Works everywhere: Write once, run anywhere—Windows, Mac, Linux, no drama.
- Flexible style: Whether you’re into OOP, functional, or just hacking scripts together, Python’s cool with it.”
Question 7: What is PEP 8, and why is it important?
They want to see if you write code other humans can actually read.
Good answer:
“PEP 8 is Python’s rulebook for neat code. It covers stuff like:
- How many spaces to indent (4, no tabs—ever).
- Where to break long lines.
- Naming things clearly (snake_case, not camelCase). Following it keeps your code from looking like a ransom note. Teams love it because nobody wastes time decoding weird formatting.”
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Question 8: How do you handle errors in Python?
They’re testing if you can stop code from blowing up gracefully.
Good answer:
“try” and “except” blocks are your safety nets:
- Risky code goes in “try”.
- If it crashes, “except” catches it (so your whole program doesn’t die).
- “finally” is for cleanup, like closing files no matter what.
Bonus: Use “else” to run code only if nothing goes wrong. It’s like a ‘win’ clause.”
Question 9: What are some common Python built-in functions you use frequently?
They’re probing if you know the toolbox or just Google everything.
Good answer:
“Oh, constantly use these:
- len() – Because guessing how long a list is gets old fast.
- max()/min() – For finding the ‘winner’ in data.
- sum() – Adding things manually is for spreadsheets.
- range() – The OG way to loop without losing your mind.
- map()/filter() – For when you want to sound fancy (but really, list comprehensions are cleaner).”
Question 10. What is the purpose of the self keyword in Python?
They’re checking if you get how classes actually work.
Good answer:
“self” is how a class refers to itself. It’s like saying ‘me’ in real life:
- Need to access an attribute? self.attribute.
- Calling another method? self.method().
Python sneakily passes it for you, but you have to include it in method definitions. No self, no dice.”
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Question 11: What are args and kwargs in Python?
They want to see if you can handle functions that take ‘whatever’.
Good answer:
“These let you make functions that aren’t picky about arguments:
- args sucks up extra positional args into a tuple.
- kwargs hoovers up keyword args into a dict.
Super handy for wrappers or when you’re not sure how many arguments you’ll get.
Pro tip: Naming them things and options works too.
Question 12: What is the difference between a shallow copy and a deep copy?
They want to see if you get how Python handles objects in memory.
Short answer:
- A shallow copy makes a new object but just references whatever’s inside the original. Mess with a nested object in the copy? Boom—it changes the original too.
- A deep copy goes all out, duplicating everything inside, so changes in the copy stay isolated.
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Question 13: What are lambda functions in Python?
They’re checking if you know quick, disposable functions.
Think of them like this:
Lambda functions are those “I’m-too-lazy-to-write-a-full-function” shortcuts. One line, no name (lambda x: x * 2), and perfect for stuff like map() or filter(). Handy, but don’t overuse ’em—they can get messy fast.
Question 14: How does exception handling work in Python?
They want proof your code won’t explode unexpectedly.
The usual playbook:
- try: Risky code goes here.
- except: “Oops, that broke”—handle the error.
- else: Runs if nothing went wrong (rarely used but neat).
- finally: The “no matter what” cleanup, like closing files.
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Question 15: What are Python modules and packages?
They’re making sure you don’t dump everything in one script.
Simple breakdown:
- A module is just a .py file with reusable code (functions, variables).
- A package? A folder stuffed with modules (and an __init__.py to prove it’s legit). It’s how you keep big projects from turning into spaghetti.
Question 16: What are Python’s built-in data types?
This checks if you know Python’s basic building blocks and when to use which.
Good answer:
Python comes packed with handy data types ready to use out of the box. You’ve got:
- Numbers: plain integers (int), decimals (float), and complex numbers
- Collections: flexible lists, immutable tuples, and handy ranges
- Text: good old strings (str) for all your words and messages
- Dictionaries (dict) – perfect for key-value pairs like phonebooks
- Sets (set, frozenset) when you need unique items only
- Simple bool for your True/False needs
- Binary stuff: bytes, bytearray, and memoryview for low-level data
Pick the right tool for the job – each has its sweet spot.
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Question 17: How would you implement a custom iterator in Python?
They want to see if you understand how Python’s iteration magic works under the hood.
How to nail it:
Easy! Just whip up a class with two special methods:
- __iter__() – gets things started (just return self)
- __next__() – does the heavy lifting, returning each item one by one
When there’s nothing left to give? Raise StopIteration. Now your object works perfectly in for loops, just like Python’s built-ins.
Question 18. What are the advantages of using list comprehensions in Python?
They’re checking if you write clean, Pythonic code.
Smart response:
“Oh, they’re fantastic! List comprehensions let you build new lists in one clean line – way neater than writing full loops. Need to filter stuff? Just add an if. They’re not just pretty either; they often run faster than regular loops. Perfect when you’re transforming one list into another.”
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Question 19: What is a Python generator, and how is it different from a function?
This probes your grasp of memory-smart coding.
Good answer:
“Generators are like magic pauseable functions. Instead of return (which ends everything), they use yield to pump out values one by one. The cool part? They remember where they left off between calls. While regular functions crunch all results at once, generators stay lean – perfect for massive datasets that would choke your RAM.”
Question 20. How would you handle large datasets in Python efficiently?
They want to see if you can work smart with big data.
Pro tips:
- Generators are your best friend – process data piece by piece
- Chunk it! Break big files into smaller bites
- Bring in the heavy hitters: Pandas and NumPy are built for this
- Got CPU-heavy work? multiprocessing can split the load
The goal? Keep that memory usage slim while crunching numbers.
Frequently Asked Questions
Well, it usually starts simple—syntax quirks, lists, dictionaries, the basics. Then they’ll throw in OOP principles, problem-solving, maybe even real-world scenarios using popular libraries. Senior roles? Brace yourself for system design, scaling headaches, and performance tweaks.
Grind algorithms and data structures—but in Python. LeetCode and HackerRank are your best friends. Oh, and don’t forget Python’s neat tricks: list comprehensions, collections, itertools. Write code like you own the language.
Maybe. If the job’s web-heavy, absolutely. Backend or scripting? Might dodge that bullet.
Pro tip: Check the job description. If it’s there, don’t wing it.
Yes. Big time. Sloppy loops when a one-liner would work? Cringe. Use context managers, unpacking, clean variable names—show them you get Python, not just use it.
For data jobs? Go wild. But in most coding rounds? Nah. Stick to core Python—no fancy libraries to save you.
Talk like a human. Start with your approach (“I’ll brute-force first, then optimize”). Narrate as you code (“Here’s where I handle edge cases”). Mention trade-offs. Pretend you’re teaching a coworker.
Yep. Think APIs, microservices, data pipelines. They’ll want to hear how Python fits into the bigger picture—and where it falls short.
“Oh, tell me about that Python project that blew up in production.” Or Git, testing, CI/CD. Maybe how you keep up with Python trends (hint: “I read blogs, not just Stack Overflow”).
Conclusion
Getting ready for a Python interview? Don’t just cram syntax—you’ve gotta get how Python really works.
Sure, memorizing for loops is easy. But what about the why behind decorators? Or how list comprehensions actually save memory? And let’s be real, async programming still trips up plenty of devs.
The best candidates don’t just know Python. They understand it deeply—from everyday tricks to gritty stuff like memory optimization. Because when the interview gets tough, theory matters as much as code.
