In 2025, Python will continue to be one of the most popular programming languages out there. If you’re aiming for a tech job, mastering Python is a must! From data science and artificial intelligence to web development, Python is everywhere. Companies love it because it’s easy to work with, powerful, and incredibly flexible. But here’s the thing: to land a good Python job, you need to be ready for the questions interviewers ask. Python interviews test both your coding skills and your understanding of Python’s core concepts. And let’s be real, tech interviews can be intimidating! That’s why we’ve created this guide to help you feel prepared and confident.
In this blog, we’ve compiled the Top 100 Python Job Interview Questions for 2025. Whether you’re a beginner or an experienced coder, this list will give you a solid foundation to tackle any Python interview. Let’s dive in and get you one step closer to that dream job!
How to use this Guide to ace Python Interview?
This guide is designed to help you feel confident and prepared for any Python interview. We’ve organized the questions into different levels and topics, so you can quickly find what you need:
- Basic Questions – Great if you’re just starting out with Python and want to cover the fundamentals.
- Intermediate Questions – For those with some Python experience, covering loops, functions, and more.
- Advanced Questions – Perfect for experts, focusing on deeper topics like OOP, decorators, and generators.
- Data Structures and Algorithms – Essential for handling questions on lists, dictionaries, and coding efficiency.
- Libraries and Frameworks – Important if you’re applying for roles in data science or web development.
- Coding Challenges – For practicing real-world coding problems and improving problem-solving skills.
- Behavioral Questions – A few soft-skill questions to help you show your personality and teamwork abilities.
By practicing these questions, you’ll build confidence and become familiar with the types of questions you may face. We recommend using this guide as a final review before your interview to refresh your knowledge and boost your readiness.
Basic Python Interview Questions for Beginners
Here’s a list of beginner-friendly Python questions that interviewers commonly ask. These cover Python basics like variables, data types, syntax, and simple functions.
What is Python, and why is it popular?
Answer: Python is a popular programming language known for its easy-to-read syntax and flexibility. It’s widely used in web development, data science, automation, and more.
What are variables in Python?
Answer: Variables store information or data that we can use and change in our code. Think of them as containers that hold values.
What are data types in Python?
Answer: Data types define the kind of data a variable holds. Common ones in Python include integers (numbers), strings (text), floats (decimal numbers), and booleans (True/False).
How do you create a variable in Python?
Answer: You simply type the variable name, an equals sign, and the value. For example, age = 25.
What is a string in Python?
Answer: A string is a type of data used for text. You create a string by putting text in quotes, like “Hello” or ‘Python’.
How do you get the length of a string?
Answer: You can use the len() function. For example, len(“Hello”) gives 5.
What is a list in Python?
Answer: A list is a collection of items, like numbers or strings, that are stored in a specific order. Lists are created with square brackets, like [1, 2, 3].
What is a tuple, and how is it different from a list?
Answer: A tuple is similar to a list but cannot be changed after it’s created. Tuples use parentheses, like (1, 2, 3).
What is a dictionary in Python?
Answer: A dictionary stores data in key-value pairs, like a mini-database. You create one with curly braces, like {“name”: “Alice”, “age”: 25}.
How do you write comments in Python?
Answer: Comments are notes in the code that Python ignores. Use the # symbol for single-line comments.
What is the print() function used for?
Answer: The print() function displays text or other data on the screen. For example, print(“Hello”) shows “Hello”.
How do you take input from the user?
Answer: You can use the input() function, which allows users to type in a response. For example, name = input(“What is your name?”).
What is an if statement?
Answer: An if statement checks a condition. If the condition is true, it runs a specific block of code. For example:
if age > 18:
print(“You are an adult.”)
What are loops in Python?
Answer: Loops repeat a block of code multiple times. Python has two main types: for loops (repeat for each item in a collection) and while loops (repeat as long as a condition is true).
What is a function in Python?
Answer: A function is a block of code that does a specific job. You define a function with def and can call it whenever needed. Example:
def greet():
print(“Hello!”)
How do you call a function in Python?
Answer: You just type the function name followed by parentheses. For example, greet() calls the greet function.
What is a return statement in a function?
Answer: The return statement returns a result when the function is done. For example:
def add(a, b):
return a + b
What are operators in Python?
Answer: Operators perform actions on variables or values. Examples include + (addition), – (subtraction), * (multiplication), and / (division).
How do you check if two values are equal in Python?
Answer: Use the == operator. For example, 5 == 5 is True.
What is type casting in Python?
Answer: Type casting changes the type of a variable. For example, int(“5”) turns the string “5” into the number 5.
These questions cover the basics and help you build a strong foundation in Python. Practice them, and you’ll feel more prepared for any beginner-level Python interview!
Intermediate Python Interview Questions
Here’s a list of intermediate-level Python questions to deepen your understanding. These cover loops, conditionals, functions, file handling, and modules. Let’s dive in!
What is a lambda function, and where is it used?
Answer: A lambda function is a small, anonymous function defined with the lambda keyword. It’s often used for short tasks where defining a full function isn’t necessary. Example: lambda x: x + 2.
How does the for loop work in Python?
Answer: The for loop iterates over items in a collection, like a list or string. For example, for item in [1, 2, 3]: print(item) prints each item in the list.
What is the difference between break and continue in loops?
Answer: break exits the loop entirely, while continue skips the rest of the loop’s code and goes to the next iteration.
How does a while loop work?
Answer: A while loop keeps running as long as a given condition is true. For example:
count = 0
while count < 5:
print(count)
count += 1
What is list comprehension, and why is it useful?
Answer: List comprehension is a concise way to create lists. It’s faster and cleaner. Example: [x**2 for x in range(5)] creates [0, 1, 4, 9, 16].
How do you handle exceptions in Python?
Answer: Use try and except blocks to handle errors. Example:
Try:
10 / 0
except ZeroDivisionError:
print(“Cannot divide by zero”)
What is the purpose of the finally block in exception handling?
Answer: The finally block runs regardless of whether there was an exception, often used for cleanup tasks like closing files.
How can you read a file in Python?
Answer: Use the open() function, then call .read() or .readlines(). Example:
with open(“file.txt”, “r”) as file:
content = file.read()
How do you write to a file in Python?
Answer: Open the file in write (“w”) or append (“a”) mode and use .write(). Example:
with open(“file.txt”, “w”) as file:
file.write(“Hello, World!”)
What is a Python module?
Answer: A module is a file with Python code that you can import into other files to reuse functions, variables, or classes.
How do you import a module in Python?
Answer: Use the import keyword. For example, import math imports the math module.
What does the __name__ == “__main__” do in Python?
Answer: It checks if a script is being run directly or imported as a module, allowing certain code to run only when the file is executed directly.
What is a function argument, and what types exist?
Answer: Function arguments are values passed to functions. Types include positional, keyword, default, and variable-length arguments.
How do you use default arguments in a function?
Answer: You set a default value in the function definition. Example:
def greet(name=”Guest”):
print(“Hello, ” + name)
What are *args and **kwargs?
Answer: *args allows multiple positional arguments, while **kwargs allows multiple keyword arguments.
How do you sort a list in Python?
Answer: Use the sort() method for in-place sorting or sorted() to create a new sorted list.
What is a set in Python?
Answer: A set is an unordered collection of unique items. Created with {} brackets or the set() function.
How do you remove duplicates from a list?
Answer: Convert the list to a set and back to a list. Example: list(set([1, 2, 2, 3])) results in [1, 2, 3].
How do you define a class in Python?
Answer: Use the class keyword. For example:
class Person:
pass
What is the __init__ method?
Answer: The __init__ method initializes an object’s attributes and is automatically called when an object is created.
How do you create an object from a class?
Answer: Use the class name with parentheses. For example, p = Person() creates an object p of class Person.
What is inheritance in Python?
Answer: Inheritance allows a class to inherit attributes and methods from another class. This is helpful for code reuse.
What is the difference between == and is?
Answer: == checks if values are equal, while is checks if they are the same object in memory.
How do you create a generator function?
Answer: Use yield instead of return in a function. Generators allow iteration one item at a time, saving memory.
What are decorators in Python?
Answer: Decorators are functions that modify other functions. You use them with the @ symbol above a function definition.
What is the difference between append() and extend() in lists?
Answer: append() adds a single item, while extend() adds multiple items from another list or iterable.
How do you find the maximum value in a list?
Answer: Use the max() function. Example: max([1, 2, 3]) gives 3.
What is the map() function used for?
Answer: map() applies a function to each item in an iterable. Example: map(str, [1, 2, 3]) turns numbers into strings.
How do you filter items in a list based on a condition?
Answer: Use the filter() function with a condition. Example: filter(lambda x: x > 5, [3, 5, 7]) keeps items greater than 5.
What is the difference between mutable and immutable data types?
Answer: Mutable types (like lists) can change their content, while immutable types (like strings) cannot be modified after creation.
These questions help you investigate Python’s functions, loops, and more complex structures. Practicing them can boost your confidence for intermediate-level interviews!
Advanced Python Interview Questions for Experts
Here’s a list of advanced Python questions focusing on object-oriented programming (OOP), decorators, generators, and exception handling. These will challenge you to think about Python’s deeper features and how they work under the hood.
Explain the difference between shallow and deep copy in Python.
Answer: A shallow copy copies only the outer object, not nested objects, while a deep copy duplicates everything, including nested objects. Use copy.copy() for shallow and copy.deepcopy() for deep copying.
What is a metaclass in Python?
Answer: A metaclass is a “class of a class” that defines how classes behave. Metaclasses allow customization of class creation and are defined using type or inheriting from type.
How do you create a property in Python?
Answer: Use the @property decorator to define a method as a property, making it accessible like an attribute but still capable of logic.
What are static methods in Python?
Answer: Static methods don’t access or modify class or instance variables. They’re defined with @staticmethod and are called on the class, not an instance.
What is a class method, and how is it different from an instance method?
Answer: A class method is defined with @classmethod and uses cls as its first parameter, accessing the class itself, while instance methods use self and work with instances.
How does multiple inheritance work in Python?
Answer: Multiple inheritance allows a class to inherit from multiple classes, but it can create ambiguity. Python uses the Method Resolution Order (MRO) to handle this, following the C3 linearization algorithm.
What is a generator expression, and how is it different from a list comprehension?
Answer: A generator expression is like a list comprehension but uses () instead of []. It produces items one at a time, saving memory.
How does Python’s garbage collection work?
Answer: Python’s garbage collector removes unused objects. It uses reference counting and cycles detection for objects that reference each other.
What is the Global Interpreter Lock (GIL)?
Answer: The GIL is a mutex that allows only one thread to execute Python bytecode at a time, impacting multi-threaded programs in CPython.
What is method overriding in Python?
Answer: Method overriding allows a subclass to change the behavior of a method defined in its superclass.
What are __call__ methods, and where are they used?
Answer: The __call__ method makes an instance callable like a function. It’s used in callable objects.
How do you implement a singleton pattern in Python?
Answer: Use a metaclass, or keep a class-level instance reference to ensure only one instance is created.
What is duck typing in Python?
Answer: Duck typing is a concept where the type of an object is determined by its behavior (methods/attributes) rather than its class.
How does the super() function work?
Answer: super() lets you access methods from a superclass in a subclass, helpful in multiple inheritance for following the MRO.
What is polymorphism in OOP, and how does Python support it?
Answer: Polymorphism lets objects of different classes be treated as instances of a parent class. Python supports this through inheritance and method overriding.
How do you create a custom exception in Python?
Answer: Define a new class inheriting from Exception. Example:
class CustomError(Exception):
pass
Explain the use of __slots__ in a Python class.
Answer: __slots__ saves memory by restricting object attributes to only those listed, preventing dynamic attribute creation.
What is memoization, and how can it be implemented in Python?
Answer: Memoization caches results of function calls for faster execution. You can use functools.lru_cache() for automatic memoization.
What is the yield from statement?
Answer: yield from allows a generator to delegate part of its operations to another generator, simplifying nested generators.
How does the with statement work in Python?
Answer: The with statement manages resources, automatically handling setup and cleanup, often used for file handling and managing database connections.
What are context managers?
Answer: Context managers handle resource management, like files or connections, using __enter__ and __exit__ methods. The with statement invokes them.
Explain the purpose of the assert statement.
Answer: assert is used for debugging. It checks if a condition is true; if not, it raises an AssertionError.
What is a coroutine in Python?
Answer: Coroutines are functions that can pause and resume execution. They’re defined with async and await, useful for asynchronous programming.
What is the difference between threading and multiprocessing in Python?
Answer: Threading is for concurrent tasks in the same memory space, affected by the GIL. Multiprocessing runs tasks in separate memory spaces, avoiding the GIL.
What is method chaining?
Answer: Method chaining lets you call multiple methods on the same object in a single statement. Example: obj.method1().method2().
What are weak references, and when are they useful?
Answer: Weak references don’t prevent an object from being garbage-collected. Useful for caching and avoiding memory leaks.
How do decorators work in Python?
Answer: Decorators modify a function or class behavior. Defined with @decorator_name, they wrap functions to add functionality.
What are dunder (double underscore) methods? Give examples.
Answer: Dunder methods, like __init__, __str__, and __len__, are special methods with predefined behaviors, commonly used for operator overloading.
How does eval() work, and what are the risks?
Answer: eval() evaluates and runs expressions passed as strings, but it’s risky because it can execute harmful code if input is not sanitized.
How do you ensure a Python script is only run as a script, not imported as a module?
Answer: Use if __name__ == “__main__”: to check if the script is run directly, and if true, run specific code.
These questions cover advanced topics that help you understand Python’s inner workings. Practicing them will prepare you for expert-level Python interviews!
Python Data Structures and Algorithms Questions
Here’s a list of Python questions focusing on data structures like lists, dictionaries, tuples, and fundamental algorithms. These will test your understanding of how to use and optimise these structures effectively.
How would you implement a queue in Python?
Answer: Use the collections.deque class, which is optimized for fast appends and pops from both ends. Example:
from collections import deque
queue = deque()
queue.append(1)
queue.popleft()
Efficiency: Both append and popleft are O(1) operations, making deque ideal for queues.
What is the time complexity for accessing an element in a list?
Answer: Accessing an element by index in a list is O(1) (constant time), as Python lists are implemented as dynamic arrays.
How would you remove duplicates from a list efficiently?
Answer: Convert the list to a set and then back to a list:
unique_list = list(set(my_list))
Efficiency: Converting to a set and back has a time complexity of O(n) on average, where n is the list’s length.
What’s the difference between a list and a tuple?
Answer: Lists are mutable, meaning you can change them after creation, while tuples are immutable. Lists are used for dynamic collections, and tuples for fixed data.
Efficiency: Tuples use less memory and are faster for iteration, as they are immutable.
How do you perform a binary search on a sorted list?
Answer: Use the bisect module, which provides functions for binary searching in sorted lists.
from bisect import bisect_left
index = bisect_left(sorted_list, target_value)
Efficiency: Binary search has a time complexity of O(log n), making it efficient for large, sorted lists.
What is the time complexity of inserting and deleting elements in a dictionary?
Answer: Both insertions and deletions are O(1) on average, thanks to hash tables used in Python dictionaries. However, in the worst case, they could degrade to O(n) if there are many hash collisions.
How would you implement a stack in Python?
Answer: You can use a list with append() for pushing and pop() for popping:
stack = []
stack.append(1)
stack.pop()
Efficiency: append() and pop() are O(1) operations when applied to the end of the list, making it efficient for stack operations.
What is a hash table, and how does Python implement it in dictionaries?
Answer: A hash table is a data structure that maps keys to values using a hash function. Python dictionaries use hash tables to store key-value pairs, providing average O(1) access, insertion, and deletion times.
How do you find the most frequent element in a list?
Answer: Use the collections.Counter class:
from collections import Counter
most_common = Counter(my_list).most_common(1)[0][0]
Efficiency: Counting elements with Counter is O(n), where n is the list’s length.
How would you implement a linked list in Python?
Answer: Define a Node class to store data and a reference to the next node. Create a LinkedList class to manage nodes.
class Node:
def __init__(self, data):
self.data = data
self.next = None
class LinkedList:
def __init__(self):
self.head = None
- Efficiency: Linked lists provide O(1) insertions and deletions but O(n) access time, as you must traverse the list to access an element by index.
These questions provide a solid foundation for understanding Python’s data structures and their efficiency. They’re essential for answering common algorithmic questions in interviews.
Python Libraries and Frameworks Questions
Here are five questions about popular Python libraries and frameworks that are often used in data science, web development, and scientific computing.
What are some common uses for the NumPy library in Python?
Answer: NumPy is widely used for numerical computing, especially for handling large arrays and matrices. It’s known for fast operations on arrays, mathematical functions, and is a foundation for other libraries like pandas and SciPy.
How is the pandas library used in data analysis?
Answer: Pandas provides data structures like DataFrames and Series, which make data manipulation easy and efficient. It’s commonly used for cleaning, transforming, analyzing, and visualizing data, particularly in structured, tabular formats.
What is Django, and why is it popular for web development?
Answer: Django is a high-level web framework for building robust and scalable web applications. It follows the MVC (Model-View-Controller) pattern, has built-in features like an admin interface, ORM, and authentication, and encourages rapid development with its “batteries-included” approach.
How does Flask differ from Django?
Answer: Flask is a lightweight, micro-framework for web development. Unlike Django, Flask gives developers more control and flexibility by providing fewer built-in features, making it ideal for smaller projects or those requiring a customized setup.
What is Matplotlib, and how is it used in data visualization?
Answer: Matplotlib is a plotting library used to create a variety of static, animated, and interactive visualizations in Python. It’s popular for creating charts, graphs, and other visuals, especially in data science and analytics for data representation.
These questions cover essential libraries and frameworks, helping you demonstrate your familiarity with popular Python tools in a technical interview.
Coding Challenges for Python Interviews
Here are five common coding challenges you might encounter in a Python interview. Each challenge includes tips on optimizing solutions and writing clean, efficient code.
Write a Python program to check if a number is prime.
Solution:
def is_prime(n):
if n <= 1:
return False
for i in range(2, int(n**0.5) + 1):
if n % i == 0:
return False
return True
Tips: Use the square root of n as the limit for checking divisors to improve efficiency. This reduces time complexity to O(sqrt(n)).
Write a function to reverse a string.
Solution:
def reverse_string(s):
return s[::-1]
Tips: Using slicing ([::-1]) is concise and efficient in Python. Alternatively, you can use a loop, but slicing is often cleaner and faster.
Find duplicates in a list.
Solution:
def find_duplicates(lst):
seen = set()
duplicates = set()
for item in lst:
if item in seen:
duplicates.add(item)
else:
seen.add(item)
return list(duplicates)
Tips: Using sets makes the solution efficient, as set lookups are O(1). This approach has an O(n) time complexity, making it optimal for large lists.
Generate the Fibonacci sequence up to n numbers.
Solution:
def fibonacci(n):
sequence = [0, 1]
for i in range(2, n):
sequence.append(sequence[-1] + sequence[-2])
return sequence[:n]
Tips: This solution uses a list to store Fibonacci numbers, with each new term generated by adding the last two terms in the list. For space efficiency, consider using variables to track only the last two values.
Check if a string is a palindrome.
Solution:
def is_palindrome(s):
return s == s[::-1]
Tips: This one-liner compares the string to its reverse (using slicing). For optimization, you can ignore case by converting the string to lowercase first (s.lower()).
These challenges cover basic problem-solving skills and help demonstrate an understanding of efficient, Pythonic coding techniques. Practicing these will prepare you for a range of coding tasks in interviews!
Behavioural Interview Questions for Python Developers
In addition to technical skills, interviewers look for soft skills in Python developers, including problem-solving, teamwork, communication, adaptability, and time management. Here are five common behavioral questions along with sample answers to help you prepare.
Tell me about a time when you solved a difficult problem in a Python project.
Sample Answer: “In a recent project, I faced an issue with slow data processing because the dataset was huge. The original code used nested loops, which was very inefficient. I researched and implemented a solution using NumPy arrays and vectorized operations, reducing processing time by over 50%. This experience taught me how important it is to continuously look for ways to optimize code.”
This question helps assess problem-solving abilities and how you handle challenges. Be specific about the problem, your approach, and the outcome.
Describe a situation where you had to work as part of a team to complete a Python project.
Sample Answer: “In my previous role, I collaborated on a data analytics project with data scientists and front-end developers. My responsibility was to build a backend API in Flask that could quickly process and serve data. To ensure seamless integration, I held regular check-ins with the team, documented the API thoroughly, and created sample data to help others test their components. This experience emphasized the importance of communication and flexibility in team projects.”
This question focuses on teamwork and communication skills. Emphasize how you handled differing opinions professionally, listened to others, and collaborated to find a solution.
How do you handle situations when your code doesn’t work as expected?
Sample Answer: “When my code doesn’t work as expected, I first review it carefully to pinpoint the issue, then isolate and test each component to narrow down the problem. I also make use of debugging tools and print statements. For example, in one project, I couldn’t get a function to return the correct results. After systematically testing each part, I realized there was a data type issue, which I quickly fixed. This process helps me stay calm and approach problems logically.”
This question shows your accountability and problem-solving mindset. Be honest about the mistake, explain how you corrected it, and describe what you learned to avoid similar mistakes in the future.
Can you give an example of how you handled tight deadlines on a Python project?
Sample Answer: “In one project, we had a strict deadline to deliver a machine learning model. I organized my tasks by priority, focusing on core functions first and saving additional features for later. I also set mini-deadlines to track my progress. By breaking down the work and communicating regularly with my manager, I managed to meet the deadline without sacrificing code quality. This taught me how to manage time effectively under pressure.”
This question gives you a chance to showcase your accomplishments. Focus on the project goals, your role, and the impact or outcome that made it successful.
How do you stay updated on the latest Python trends and technologies?
Sample Answer: “I stay updated through several methods, including following reputable tech blogs, participating in Python developer communities, and taking online courses. I also enjoy working on side projects to test out new libraries or frameworks. For instance, I recently started learning FastAPI, a newer framework, to expand my web development skills. Staying updated helps me continually improve and bring fresh ideas to my work.”
These answers showcase problem-solving, teamwork, adaptability, time management, and a commitment to continuous learning—all key soft skills for Python developers. Practising these examples can help you respond effectively to behavioural questions in interviews.
Tips for cracking Python Interviews in 2025
It’s essential to go beyond basic preparation to stand out in Python interviews. Here are some final tips to help you excel:
Study the Python Documentation
The official Python documentation is a great resource to understand Python’s built-in functions, libraries, and best practices. Familiarize yourself with it to solidify your fundamentals.
Practice on Coding Platforms
Regular practice on coding platforms like LeetCode, HackerRank, or CodeSignal helps you get comfortable with algorithm-based questions and improves your problem-solving speed.
Build and Showcase Projects
Real-world projects demonstrate your coding skills in action. Work on projects that solve practical problems or showcase your ability to use Python libraries and frameworks, like data analysis with pandas or a Flask web app.
Prepare a Portfolio or GitHub Repository
A GitHub profile or online portfolio is a fantastic way to show employers what you’ve built. Include code samples, project descriptions, and any contributions to open-source projects to illustrate your skills and commitment to learning.
Review Common Algorithms and Data Structures
Refresh your knowledge of algorithms and data structures, as they’re often tested in technical interviews. Focus on lists, dictionaries, recursion, sorting, and searching algorithms, as these are frequently covered.
Prepare for Behavioral Questions
Along with technical skills, companies look for team players and problem-solvers. Prepare examples that highlight your adaptability, teamwork, and any unique contributions you’ve made to past projects.
Expert Corner
Preparing for Python interviews can feel overwhelming, but consistent practice is key. Use this guide to tackle common questions, refine your projects, and polish your portfolio. With the right preparation and a clear understanding of Python basics and advanced concepts, you’ll be ready to approach your interviews confidently. Good luck, and happy coding!
Python Developer Practice Test
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