Top 100+ Python Interview Questions & Answers (University Viva & Job Prep)

Whether you are preparing for your university semester theory examinations, viva voce lab tests, campus placement drives, or professional software engineering interviews, this curated master repository compiles the most frequently asked Python interview questions and high-scoring model answers.

Category 1: Core Fundamentals & Language Architecture

Q1: What is PEP 8, and why is it important?
Answer: PEP 8 is the official Style Guide for Python Code written by Guido van Rossum and Barry Warsaw. It standardizes naming conventions (snake_case for functions/variables, PascalCase for classes), whitespace indentation (4 spaces per level), line length limits (79 characters), and import styling to ensure that Python code is consistent and readable across teams worldwide.

Q2: What is the Python GIL (Global Interpreter Lock)?
Answer: The GIL is a synchronization lock mechanism used in CPython (the standard reference implementation of Python). It ensures that only one native OS thread executes Python bytecode at any given moment, even on multi-core processors. The GIL was implemented to protect CPython's reference-counting garbage collector from multi-threaded race conditions. For CPU-bound tasks needing true multi-core parallel processing, developers use the multiprocessing module instead of threading.

Q3: How does memory management work in Python?
Answer: Python manages memory through: (1) A private heap space containing all Python objects and data structures, (2) An automatic reference counting mechanism that deallocates objects immediately when their reference counter drops to zero, and (3) A cyclic generational garbage collector that detects and cleans up circular reference loops (e.g., Object A pointing to Object B, which points back to A).

Category 2: Data Structures & Collections

Q4: What is the difference between a List and a Tuple in Python?
Answer: Lists ([]) are mutable (modifiable in place) and consume slightly more memory due to dynamic capacity over-allocation. Tuples (()) are immutable (cannot be modified after creation), occupy less memory, and are hashable, meaning tuples can be safely used as dictionary keys and set elements.

Q5: What is the difference between == and is?
Answer: == tests value equality (checks if the contents of two objects are equal). is tests reference identity (checks if both variables point to the exact same physical memory address in RAM, i.e., id(a) == id(b)).

Q6: Explain Dictionary Key requirements. Why can't a list be a dictionary key?
Answer: Python dictionary lookups rely on hash tables. A dictionary key must be hashable, meaning it must have an immutable hash value that never changes over its lifecycle (implementing __hash__() and __eq__()). Because a list is mutable, its contents can change, which would calculate a different hash value and corrupt the internal hash table bucket indexing.

Category 3: Functions, Scopes & OOP

Q7: What is the difference between shallow copy and deep copy?
Answer: A shallow copy (copy.copy()) constructs a new collection object, but populates it with references to the child objects found in the original. A deep copy (copy.deepcopy()) constructs a new collection and recursively copies all nested objects, creating a 100% independent clone in memory.

Q8: How does Python resolve method calls in multiple inheritance (MRO)?
Answer: Python uses the C3 Linearization Algorithm (Method Resolution Order). You can inspect any class's lookup order using ClassName.mro(). C3 guarantees monotonicity and ensures parent classes appear after their derived child classes.

Q9: What are generators, and why are they preferred over lists for large datasets?
Answer: A generator is an iterator function that uses the yield keyword instead of return. Unlike a list which loads all elements into RAM at once (causing MemoryError on large files), a generator computes each value lazily on demand, maintaining minimal memory consumption (O(1) space complexity).

Q10: What is a closure in Python?
Answer: A closure is an inner function that retains access to variables in its outer enclosing scope even after the outer function has finished executing. Closures form the architectural foundation for Python decorators.

Frequently Asked Questions (FAQ)