Python interviews range from basic syntax to advanced language features, depending on the role. Developers, data engineers, data scientists, DevOps engineers and QA automation engineers all face Python questions. Here are the most common Python interview questions with concise answers you can build on.
Python basics
What are Python’s key data types?
int, float, bool, str, list, tuple, dict, set and None.
What is the difference between a list and a tuple?
Lists are mutable and use square brackets; tuples are immutable and use parentheses. Tuples can be used as dictionary keys and signal fixed data.
What is the difference between == and is?
== compares values; is checks whether two variables refer to the same object.
What are mutable and immutable types?
Mutable objects (lists, dicts, sets) can change after creation. Immutable objects (int, str, tuple) cannot.
How are dictionaries implemented?
As hash tables, giving average O(1) lookup, insertion and deletion.
Intermediate Python questions
What are list comprehensions?
A concise way to create lists: squares = [x*x for x in range(10) if x % 2 == 0]. Similar syntax exists for dicts and sets.
What are args and *kwargs?
args collects extra positional arguments into a tuple; *kwargs collects extra keyword arguments into a dict.
What is a lambda function?
A small anonymous function defined in one expression, often used with sorted(), map() or filter().
How does exception handling work?
Use try, except, else and finally blocks. Catch specific exceptions rather than a bare except, and use finally for cleanup.
What is the difference between deep copy and shallow copy?
A shallow copy creates a new container but references the same nested objects. A deep copy recursively copies everything.
Advanced Python questions
What are decorators?
Functions that wrap other functions to add behavior — such as logging, timing, caching or authentication — without changing the original code.
What are generators?
Functions that use yield to produce values lazily, one at a time. They save memory when processing large datasets or streams.
What is the GIL?
The Global Interpreter Lock lets only one thread execute Python bytecode at a time in CPython. Threads still help with I/O-bound work; multiprocessing or async is better for some workloads.
What are context managers?
Objects used with the with statement to manage resources like files or connections, ensuring cleanup even when errors occur.
Explain Python OOP concepts.
Classes, instances, inheritance, encapsulation, polymorphism, special methods like __init__ and __repr__, and class vs. instance attributes.
Role-specific Python questions
- Data roles: pandas operations (groupby, merge, pivot), handling missing data, vectorization vs. loops — see data analyst interview questions
- DevOps/cloud roles: scripting with subprocess, calling REST APIs, working with AWS SDK (boto3), parsing JSON and YAML — see DevOps interview questions
- Backend roles: Flask/FastAPI/Django, REST APIs, testing with pytest, database ORMs
- ML roles: NumPy, scikit-learn and model evaluation — see machine learning interview questions
Common Python coding exercises
- Reverse a string or check for a palindrome
- Count word frequency in text with a dictionary or Counter
- Find duplicates in a list
- Flatten a nested list
- Merge two sorted lists
- Read a CSV file and compute aggregates
- Implement a simple LRU cache
- Write a function that retries an API call with exponential backoff
Practice the core patterns in our coding interview questions guide.
Tips for Python interviews
- Write Pythonic code: use built-ins like enumerate, zip, sorted and collections
- Explain the time complexity of your solution
- Handle edge cases: empty inputs, None and invalid types
- Write a quick test or example to validate your code
Frequently asked questions
What Python topics are most common in interviews?
Data structures (lists, dicts, sets), comprehensions, functions, OOP, exception handling, generators and decorators are commonly tested.
Is Python good for coding interviews?
Yes. Python’s concise syntax lets you focus on problem-solving, and it is widely accepted in technical interviews.
How do I prepare for a Python interview quickly?
Review core language features, practice common coding patterns daily and prepare role-specific topics such as pandas for data roles or APIs for backend roles.
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