Python Coding Interview Questions and Answers

Python has long become one of the most popular programming languages in the world. It's widely used in product development, analytics, machine learning, DevOps, and automation. That’s why most technical interviews for developer, analyst, and data engineer roles include a Python coding section. These tasks don’t just check syntax knowledge — they assess your ability to solve problems efficiently, write readable code, and explain your reasoning.

Python coding interview questions cover a wide range of topics: from working with lists and dictionaries to string processing, using generators, iterators, basic algorithms, and data structures. During an interview, it’s important not only to provide the correct result but also to explain your thought process. Strong Python skills show that you can think like an engineer — not by pattern, but clearly, flexibly, and with a deep understanding of the language.

How to Prepare for Python Coding Interview?

Preparing for a Python programming interview should not be about memorizing syntax, but about systematically reinforcing your skills. The main focus is practice: the more real problems you solve, the higher your chances of passing the interview without stress. It's also important to explain your solutions — the interviewer often evaluates not only your code but also your logic.

It's recommended to split your preparation into several areas: practicing syntax, algorithms, data structures, and building practical solutions. Additionally, it's helpful to train your ability to read and refactor others’ code.

Here’s what to focus on:

• Solve problems on platforms like LeetCode, HackerRank, Codewars
• Review basic data structures: lists, sets, dictionaries
• Master list comprehensions, lambda, map, filter, zip
• Dive into generators and iterators
• Learn to use Counter, defaultdict, set effectively
• Practice writing clean functions with exception handling
• Train to explain your solution out loud
• Understand common beginner mistakes: references, mutability, object comparisons

What Are the Coding Questions Asked in an Interview?

In technical interviews, candidates are often asked to solve a task live — on a whiteboard, in an online editor, or Google Docs. The main goal is to understand how you think, write code, test, and debug. The problems typically involve basic algorithms and realistic scenarios you’d encounter in development.

Interviewers often observe how well you break down the problem into sub-tasks, whether you can propose a naive and then an optimized solution, and how you comment your code. Remember: the ideal answer is not just correct output, but a clean, logical, and understandable approach to the solution.

1. What happens when executing a = [1, 2, 3]; b = a; b.append(4)?

In this example, a and b point to the same list in memory. When I execute b.append(4), the element is added to the original object, and this change is visible in both a and b. That’s because lists in Python are mutable, and the b = a operation is not a copy but a reference assignment. To create a copy, you need to call a.copy() or use slicing a[:]. These kinds of issues are common sources of bugs if you're not aware of how references work in Python.

2. How do you check if a string is a palindrome?

The simplest way is to compare the string with its reversed version: s == s[::-1]. But during an interview, I clarify whether I should consider only letters, ignore spaces, case, punctuation. If yes, I first clean the string: ''.join(c.lower() for c in s if c.isalnum()), then compare it to its reverse. These clarifications show attention to detail and the ability to properly define the problem, not just “guess the answer”.

3. What will print("".join(sorted("bca"))) output and why?

It will print abc. Because sorted() returns a list of characters in sorted order, and "".join(...) converts that list back into a string. It’s important to show that I understand sorted() works with any iterable, and join() glues the elements into a single string. This is a common pattern when processing strings: sorting, filtering, normalization.

4. How do you check that two lists contain the same elements regardless of order?

If I just need to compare the set of elements, I convert the lists to sets and compare: set(a) == set(b). But if the frequency of each element matters too, I use collections.Counter — it gives an exact solution: Counter(a) == Counter(b). In an interview, I always clarify which logic is needed and suggest both options to demonstrate range of understanding.

5. How does list comprehension work and why do you use it?

List comprehension is a compact way to create a list from another sequence. For example, [x * 2 for x in range(5)] creates [0, 2, 4, 6, 8]. I use it when I need to apply the same transformation to every element. It’s more readable and faster than using for + append(). You can also add conditions: [x for x in data if x > 0]. But if the expression gets too complex — I switch back to regular for for readability.

6. How do you work with dictionaries in Python, and how do you handle missing keys?

Dictionaries are one of my favorite data types. To avoid errors when a key is missing, I use dict.get(key) — it returns None or a default value. For nested structures — I use defaultdict, especially defaultdict(list) or defaultdict(int). I also love collections.Counter when counting frequencies. These features simplify the code and avoid verbose if key in dict checks.

7. How does is work and how is it different from ==?

== compares values, while is checks if objects are the same in memory. For example, a == b may be True even if a is b is False. I use is when checking for None: if x is None, not x == None. It’s more reliable and faster. It's also important to know that Python may cache small strings and numbers, so is may behave unexpectedly. So always use == for value comparison, and is only for identity or None checks.

8. How do you find duplicate elements in a list?

I use collections.Counter — build a counter and select keys with values > 1. If I just need to check if duplicates exist — I create a set() and compare lengths: len(lst) != len(set(lst)). That’s a quick check. If I need to return the duplicate values — I either loop through the counter or make a second pass with seen and duplicates. In interviews, I first suggest a simple solution and then clarify whether unique duplicates, first duplicates, or ordering matters.

9. What happens when passing a mutable object to a function?

In Python, arguments are passed by reference. If the object is mutable (like a list or dictionary), the function can change its state. This often causes unexpected behavior. So if I don’t want the function to modify it — I pass a copy. For example, copy() or deepcopy(). I always check whether the object is mutable and never rely on default behavior. This is important, especially in large projects where functions may be reused in multiple places.

10. How would you reverse a list without using built-in functions?

Without using [::-1] or reversed(), you can iterate from the end and append elements to a new list. Or you can swap elements using two pointers: one starting from the beginning, the other from the end, moving toward each other. This is a classic task, and it's important to demonstrate that I understand the algorithm, not just "remember the method". In practice, of course, I use list[::-1], but in test assignments I can implement it manually — and that shows knowledge of the basics.

11. How do you swap the values of two variables without using a third one?

In Python, it's very simple: a, b = b, a. I use this syntax because it's readable and safe — unlike in some languages where you need arithmetic tricks. This is a built-in Python feature — multiple assignment. In interviews, I explain that this is an atomic operation: the values on the right are evaluated first, then assigned on the left without side effects.

12. How does a list comprehension differ from a generator expression?

A list comprehension returns a fully-built list immediately, while a generator expression returns an object that yields values one at a time during iteration. For example, [x * x for x in range(5)] creates a list, while (x * x for x in range(5)) creates a generator. Generators save memory and are suitable when you don't need to store all elements at once. I use them for streaming data, large files, or lazy operations. Python gives flexibility, and I choose the structure depending on the goal.

13. How do you sort a list of dictionaries by the value of a specific key?

I use sorted() with the key parameter. For example: sorted(data, key=lambda x: x['age']). This is a universal approach, and it works even with nested structures if the lambda is written correctly. I also use reverse=True when sorting in descending order. This is common in interview tasks, and it's important to explain that the original list is not modified — a new one is returned.

14. What’s the difference between append() and extend() in Python lists?

append() adds a single element to the end of a list — as an object, even if it’s a list. extend() adds each element from an iterable separately. For example: a.append([1, 2]) adds one element [1, 2], while a.extend([1, 2]) adds two separate values. This is a common beginner mistake, and in practice I always check whether I need a nested element or flat expansion.

15. How do iterators and generators work in Python?

Iterators are objects that implement the __iter__() and __next__() methods. A generator is a simplified way to create an iterator. It remembers the state and resumes from where it left off. I use yield inside functions to return values one at a time. This is useful when saving memory is important and we don’t want to load the entire collection at once. Generators help write clean, efficient code for large datasets or complex sequences.

16. What is a lambda function and when do you use it?

A lambda is an anonymous function that is useful for short operations, especially as a key argument in sorted(), map(), filter(), and others. Example: lambda x: x['price']. I use it when the logic is simple and doesn’t require naming. But if the logic is complex — I prefer a standard def to keep readability. Lambda is a tool, but not a replacement for proper functions.

17. How do you handle exceptions in Python and when is it important?

I always wrap potentially risky code in try-except, especially when working with files, networking, or user input. If I need to catch a specific error — I specify the exception type, like except FileNotFoundError. I also use finally to release resources, and else when the try block runs successfully. I avoid excessive use of bare except, because it may hide bugs. It’s important to catch only what you expect and log errors for debugging.

18. How do you read a file line by line without loading it entirely into memory?

I simply use with open(...) as f: and then iterate over the lines: for line in f:. This is the standard way, and it’s fully lazy — lines are read one by one, and the file is automatically closed after the block. This is especially important when working with large logs, CSVs, or API data. In Python, this approach is convenient, safe, and readable.

19. How do you create a dictionary from two lists?

I use zip() and dict(): dict(zip(keys, values)). This is a readable, fast, and "Pythonic" approach. But I always check that the lists are the same length, otherwise some data will be lost. If the lengths may differ — I use itertools.zip_longest() to provide default values. This is a common interview question, and it’s important to show that you think about edge cases.

20. What is list slicing and how do you use it?

Slicing allows you to get a sublist without modifying the original list. Syntax: a[start:stop:step]. For example, a[::-1] reverses the list. I use slicing to copy a list (a[:]), extract a portion of data, or remove first/last elements. Python makes slicing intuitive, and it’s one of the tools that simplifies many algorithms — especially when working with subsets or buffers.