Python all possible interview questions and answers

For IT interviews (AWS Data Engineer, Data Engineer, Cloud Engineer, AI Engineer, Solutions Architect, MLOps Engineer, and Senior Software Engineer), Python is one of the most heavily tested skills.

Interviewers typically assess:

Python Fundamentals
Object-Oriented Programming (OOP)
Data Structures & Algorithms
File Handling
Exception Handling
Multithreading & Multiprocessing
Generators & Iterators
Decorators
Lambda Functions
Memory Management
Python Internals
Database Programming
API Development
Data Engineering Concepts
Pandas & NumPy
AWS + Python Integration
Machine Learning & AI Python Questions
System Design using Python

Python Interview Questions and Answers (100+)

Section 1: Python Fundamentals

1. What is Python?

Python is a high-level, interpreted, object-oriented programming language known for simplicity and readability.

Features:

Easy syntax
Dynamically typed
Cross-platform
Huge ecosystem
Supports OOP and Functional Programming

Example:

print("Hello World")

2. What are Python’s key features?

Answer:

Interpreted
Dynamically Typed
Object-Oriented
Open Source
Extensive Libraries
Portable
Automatic Memory Management

3. Difference between Compiled and Interpreted Languages?

Compiled:

Entire code converted before execution
Example: C++

Interpreted:

Executes line by line
Example: Python

Python first compiles to bytecode then executes using PVM.

4. What is PEP 8?

PEP = Python Enhancement Proposal

PEP 8 provides coding standards.

Examples:

def calculate_salary():
    pass

Recommendations:

4 spaces indentation
Max line length 79
Meaningful variable names

5. What are Python Data Types?

Built-in Data Types:

int
float
str
bool
list
tuple
set
dict

Example:

age = 30
salary = 50000.5
name = "John"

Section 2: List vs Tuple

6. Difference between List and Tuple?

Feature	List	Tuple
Mutable	Yes	No
Speed	Slower	Faster
Syntax	[]	()

Example:

mylist = [1,2,3]
mytuple = (1,2,3)

7. Why are tuples faster?

Tuples are immutable.

Python optimizes memory allocation.

Suitable for:

Configuration values
Database records

Section 3: Dictionary Questions

8. What is Dictionary?

Key-value data structure.

employee = {
    "name":"John",
    "age":30
}

9. Difference between Dictionary and List?

Dictionary:

Key-value access
O(1) lookup

List:

Index-based access

10. How are dictionaries implemented?

Using Hash Tables.

Average complexity:

Search: O(1)
Insert: O(1)
Delete: O(1)

Section 4: OOP

11. What is OOP?

Object-Oriented Programming organizes code into classes and objects.

Principles:

Encapsulation
Inheritance
Polymorphism
Abstraction

12. What is a Class?

Blueprint of objects.

class Employee:
    pass

13. What is an Object?

Instance of class.

emp = Employee()

14. What is Constructor?

Automatically called during object creation.

class Employee:
    def __init__(self,name):
        self.name = name

15. What is Encapsulation?

Binding data and methods together.

class Account:
    def __init__(self):
        self.__balance = 1000

16. What is Inheritance?

Child class inherits parent properties.

class Animal:
    pass

class Dog(Animal):
    pass

17. Types of Inheritance

Single
Multiple
Multilevel
Hierarchical
Hybrid

18. What is Polymorphism?

Same interface different behavior.

class Dog:
    def sound(self):
        print("Bark")

class Cat:
    def sound(self):
        print("Meow")

19. What is Abstraction?

Hiding implementation details.

from abc import ABC

20. Difference between Abstraction and Encapsulation?

Abstraction:

Hides complexity

Encapsulation:

Hides data

Section 5: Exception Handling

21. What is Exception?

Runtime error.

Examples:

ZeroDivisionError
ValueError
IndexError

22. Exception Handling Syntax

try:
    x = 10/0

except ZeroDivisionError:
    print("Error")

finally:
    print("Always Executes")

23. Difference between Exception and Error?

Exception:

Recoverable

Error:

Non-recoverable

24. What is Finally Block?

Executes whether exception occurs or not.

Section 6: Memory Management

25. What is Garbage Collection?

Automatic memory cleanup.

Python uses:

Reference Counting
Generational Garbage Collection

26. What is Reference Counting?

Tracks references.

x = []
y = x

Reference count becomes 2.

27. What is Deep Copy vs Shallow Copy?

Shallow:

copy.copy()

Deep:

copy.deepcopy()

Deep copy copies nested objects.

Section 7: Iterators and Generators

28. What is Iterator?

Object implementing:

__iter__()
__next__()

29. What is Generator?

Produces values lazily.

def nums():
    yield 1
    yield 2

Advantages:

Memory efficient

30. Yield vs Return

Yield:

Pauses execution

Return:

Ends execution

Section 8: Decorators

31. What is Decorator?

Function modifying another function.

def logger(func):
    def wrapper():
        print("Running")
        func()
    return wrapper

32. Real-world Decorator Uses

Logging
Authorization
Retry Logic
Metrics Collection

Section 9: Lambda Functions

33. What is Lambda?

Anonymous function.

square = lambda x: x*x

34. map()

nums=[1,2,3]

list(map(lambda x:x*2,nums))

Output:

[2,4,6]

35. filter()

list(filter(lambda x:x>5,nums))

36. reduce()

from functools import reduce

Combines values into one result.

Section 10: Multithreading

37. What is Thread?

Lightweight execution unit.

38. What is GIL?

Global Interpreter Lock.

Only one thread executes Python bytecode at a time.

Important AWS interview question.

39. When to use Multithreading?

I/O-bound tasks:

API calls
Database queries
File operations

40. When to use Multiprocessing?

CPU-bound tasks:

ML calculations
Data transformations

Section 11: Python Internals

41. How Python Executes Code?

Flow:

Source Code
↓
Bytecode
↓
Python Virtual Machine
↓
Execution

42. What is name ?

if __name__ == "__main__":

Entry point of execution.

43. What are *args and **kwargs?

def func(*args, **kwargs):
    pass

args:

Positional

kwargs:

Keyword arguments

44. What is Monkey Patching?

Changing behavior at runtime.

45. What are Context Managers?

with open("file.txt") as f:
    pass

Automatic resource cleanup.

Section 12: File Handling

46. How to Read File?

with open("sample.txt") as f:
    data=f.read()

47. Difference read(), readline(), readlines()

read()

Entire file

readline()

Single line

readlines()

List of lines

48. How to Write File?

with open("a.txt","w") as f:
    f.write("Hello")

Section 13: Database Questions

49. Connect Python to MySQL

import mysql.connector

50. Prevent SQL Injection

Use parameterized queries.

cursor.execute(
"SELECT * FROM users WHERE id=%s",
(user_id,)
)

Section 14: Advanced Python

51. What is Metaclass?

Class that creates classes.

class MyMeta(type):
    pass

52. What is MRO?

Method Resolution Order.

Class.mro()

Used in multiple inheritance.

53. What is Duck Typing?

“If it walks like a duck and quacks like a duck…”

Focus on behavior rather than type.

54. What are Magic Methods?

Examples:

__init__
__str__
__repr__
__len__

55. Difference str vs repr

str

User friendly

repr

Developer friendly

Section 15: Pandas Questions

56. What is Pandas?

Data analysis library.

import pandas as pd

57. DataFrame vs Series

Series:

One-dimensional

DataFrame:

Two-dimensional

58. Handling Null Values

df.fillna()
df.dropna()

59. Merge vs Join vs Concat

Merge:

SQL Join

Join:

Index-based

Concat:

Append data

60. GroupBy

df.groupby("department").sum()

Section 16: NumPy

61. Why NumPy?

Fast numerical operations.

62. Advantages over List

Faster
Less memory
Vectorized operations

63. Vectorization

arr*2

No loops needed.

Section 17: AWS + Python

64. What is Boto3?

AWS SDK for Python.

import boto3

65. Upload file to S3

s3.upload_file(
"a.txt",
"bucket-name",
"a.txt"
)

66. Read from DynamoDB

table.get_item()

67. Invoke Lambda using Python

lambda_client.invoke()

Section 18: Data Engineering Questions

68. Read Large Files Efficiently

for line in file:

Avoid loading entire file.

69. Generator for ETL Pipelines

yield row

Reduces memory usage.

70. Process Millions of Records

Approaches:

Chunk Processing
Multiprocessing
Spark
Dask

Section 19: Coding Questions Frequently Asked

71. Reverse String

s[::-1]

72. Check Palindrome

s == s[::-1]

73. Fibonacci

def fib(n):

74. Find Duplicate Elements

Use Set.

75. Count Character Frequency

collections.Counter

Senior-Level Python Questions

76. Why is Python Slow?

Because:

Dynamic typing
Interpreted execution
GIL

77. Asyncio vs Threading

Asyncio:

Single-threaded concurrency

Threading:

Multiple threads

78. What is Async Await?

async def main():

Non-blocking execution.

79. Design High Performance ETL Framework

Answer:

Use:

S3
Lambda
Glue
Spark
Python Generators
Thread Pools

80. Design Log Processing System

Components:

Kinesis
S3
Glue
Spark
Redshift

FAANG / Amazon Senior Questions

81. Mutable Default Arguments Problem

Bad:

def func(items=[]):

Good:

def func(items=None):

82. Difference Between is and ==

is

Identity

==

Value comparison

83. Why Python Uses Indentation?

Improves readability and enforces structure.

84. What Happens During Import?

Module loaded
Bytecode generated
Namespace created

85. Explain Python Memory Model

Memory Areas:

Stack
Heap
Object Pool

Must-Know Python Topics for AWS Interviews

OOP
Decorators
Generators
Context Managers
Lambda
Multithreading
Multiprocessing
Asyncio
Pandas
NumPy
Boto3
API Development
ETL Design
Error Handling
Memory Optimization
Python Internals
Data Structures
SQL Integration
Logging
Unit Testing

For AWS Data Engineer, Cloud Engineer, AI Engineer, and Solutions Architect interviews in the U.S. market, a target of 100–150 Python interview questions, plus hands-on coding practice around ETL pipelines, APIs, Pandas, multithreading, and Boto3 automation, is typically sufficient to clear most mid-level and senior rounds.

fundamentals to advanced system design, including common patterns and coding challenges.

Part 1: Core Python & Internals (The “Must-Know” Fundamentals)

These questions test your deep understanding of the language, not just syntax.

1. How is Python different from Java/C++ in terms of execution?

Answer: Python is an interpreted, dynamically-typed language. Code is first compiled to bytecode (.pyc files), which is then executed by the Python Virtual Machine (PVM). This differs from Java (compiled to bytecode, runs on JVM, statically-typed) and C++ (compiled directly to machine code, statically-typed). No explicit compilation step is needed by the user.

2. What is the Global Interpreter Lock (GIL)? How do you work around it?

Answer: The GIL is a mutex that protects access to Python objects, preventing multiple native threads from executing Python bytecodes at once. This means CPU-bound multithreaded Python programs won’t see performance gains.
Workarounds:
- Multiprocessing: Use the multiprocessing module instead of threading. Each process gets its own Python interpreter and memory space, bypassing the GIL.
- C Extensions: Write performance-critical sections in C, where you can release the GIL.
- Async Programming: Use asyncio for I/O-bound concurrency, which doesn’t rely on multiple threads.
- Alternative Interpreters: Use Jython or IronPython, which have no GIL.

3. Explain mutable vs. immutable types with memory implications.

Answer:
- Immutable: int, float, str, tuple, frozenset. Once created, they cannot be changed. Operations create a new object.
- Mutable: list, dict, set, user-defined objects. Can be modified in-place.
Memory Implication:pythona = “hello” b = a a += ” world” # a now points to a NEW string “hello world” # b is still “hello”For mutable types:pythona = [1, 2, 3] b = a a.append(4) # a and b both point to [1, 2, 3, 4]This is a critical concept for function argument passing (pass-by-object-reference).

4. What are *args and **kwargs?

Answer:
- *args: Allows a function to accept any number of positional arguments. It’s a tuple inside the function.
- **kwargs: Allows a function to accept any number of keyword arguments. It’s a dictionary inside the function.
- They are also used for unpacking: func(*my_list) passes list elements as separate args, func(**my_dict) passes key-value pairs as named args.

5. Deep Copy vs. Shallow Copy

Answer:
- Shallow Copy (copy.copy() or list[:]): Creates a new compound object, but inserts references into it to the objects found in the original. If the original contains nested objects, changes to those nested objects will be reflected in the copy.
- Deep Copy (copy.deepcopy()): Creates a new compound object and recursively inserts copies of the original objects. Fully independent.

6. What is a decorator? Write one that times a function’s execution.

Answer: A decorator is a callable that takes another function and extends its behavior without explicitly modifying it. It’s syntactic sugar for time_calc = timer_decorator(my_func).pythonimport time def timer_decorator(func): def wrapper(*args, **kwargs): start = time.time() result = func(*args, **kwargs) end = time.time() print(f”{func.__name__} took {end-start:.4f} seconds”) return result return wrapper @timer_decorator def slow_function(): time.sleep(1) slow_function()

7. Generators vs. Iterators. What’s the yield keyword?

Answer:
- Iterator: An object with a __next__() method and __iter__() method returning itself.
- Generator: A simple way to create an iterator using a function with yield. It automatically implements the iterator protocol and is memory-efficient as it pauses execution, yielding one value at a time instead of building a whole list.
- yield returns a value and pauses the function’s state, resuming from the next line upon the next __next__() call.

Part 2: Object-Oriented Programming (OOP)

8. What are @staticmethod, @classmethod, and instance methods?

Answer:
- Instance Method: Takes self (the instance). Has access to instance state.
- @classmethod: Takes cls (the class). Can modify class state that applies across all instances. Often used as alternative constructors (e.g., datetime.fromtimestamp()).
- @staticmethod: Takes neither self nor cls. It’s a regular function that lives in the class’s namespace for logical grouping. It cannot modify class or instance state.

9. What is Method Resolution Order (MRO) in inheritance?

Answer: It’s the order in which Python searches for a method in a hierarchy of classes. It uses the C3 Linearization algorithm. You can view it with ClassName.mro() or ClassName.__mro__. The rule is depth-first, left-to-right, but ensuring a child class precedes its parents and the order in which parents are listed is preserved.

10. What are Dunder (Magic) Methods? Explain __init__, __new__, __str__, __repr__, __call__.

Answer: They are special methods with double underscores, allowing your objects to interact with language syntax.
- __new__(cls): Class method that creates the object. Called before __init__. Used for singletons or immutable types.
- __init__(self): Instance method that initializes the object.
- __str__(self): For user-friendly string representation (print(obj)).
- __repr__(self): For unambiguous, developer-friendly string representation, ideally one you can eval().
- __call__(self): Allows an instance to be called as a function.

Part 3: Data Structures & Algorithms (LeetCode-Style)

These are the bread and butter of coding interviews.

11. How does a Python dictionary work? What are its time complexities?

Answer: It’s a hash table under the hood. Keys are hashed (using hash()), and the hash value determines the index in an internal array. Collisions are handled by open addressing. Average-case complexity for insertion, lookup, and deletion is O(1). Worst-case (many collisions) is O(n). Keys must be hashable (immutable and implement __hash__ and __eq__).

12. List vs. Tuple: When to use which?

Answer:
- List: Mutable, dynamic array. Use for homogeneous, mutable sequences.
- Tuple: Immutable, static array. Use for heterogeneous data (like a record), dictionary keys, or when immutability guarantees data integrity. Slightly more memory-efficient.

13. Common Coding Challenge: Two Sum

Question: Given a list of integers nums and an integer target, return the indices of the two numbers that add up to target.
Optimal Answer (O(n) time, O(n) space):pythondef two_sum(nums, target): seen = {} # value -> index for i, num in enumerate(nums): complement = target – num if complement in seen: return [seen[complement], i] seen[num] = i return []

14. Common Coding Challenge: Valid Parentheses

Question: Given a string s containing '(', ')', '{', '}', '[', ']', determine if the input is valid.
Optimal Answer:pythondef is_valid(s): stack = [] mapping = {“)”: “(“, “}”: “{“, “]”: “[“} for char in s: if char in mapping: top = stack.pop() if stack else ‘#’ if mapping[char] != top: return False else: stack.append(char) return not stack

15. Implement a Custom Context Manager (with statement)

Two Ways:python# 1. Class-based class ManagedFile: def __init__(self, filename, mode): self.filename = filename self.mode = mode def __enter__(self): self.file = open(self.filename, self.mode) return self.file def __exit__(self, exc_type, exc_val, exc_tb): self.file.close() # Return True to suppress exceptions # 2. Generator-based (using contextlib) from contextlib import contextmanager @contextmanager def managed_file(filename, mode): f = open(filename, mode) try: yield f finally: f.close()

Part 4: Advanced Python & System Design

16. asyncio in Depth: Event Loop, Coroutines, Tasks

Answer: asyncio is for concurrent I/O-bound tasks. An event loop manages the execution of coroutines (defined with async def). await suspends the coroutine, giving control back to the event loop to run something else until the result is ready. A Task wraps a coroutine and schedules its execution on the event loop. asyncio.gather() runs multiple tasks concurrently. This is single-threaded, cooperative multitasking.

17. Memory Management and Garbage Collection

Answer: Memory management is handled by a private heap. A reference counter tracks how many references point to each object. When it hits 0, the memory is deallocated immediately. The Garbage Collector (GC) detects and cleans up cyclic references (e.g., two objects referencing each other), which reference counting can’t handle. gc module allows manual control.

18. System Design: Design a URL Shortener (like Bitly) in Python

Key Components:
- API Framework: FastAPI or Flask.
- Algorithm: Generate a short unique ID (e.g., using hash of URL + timestamp, or a base-62 encoded counter).
- Database: A key-value store like Redis (for speed) or PostgreSQL. short_code -> original_url.
- Caching: Redis cache in front of the DB for frequent reads.
- Rate Limiting: Use a library or middleware to limit API requests per user.
- Deployment: Use Gunicorn with Uvicorn workers behind an Nginx reverse proxy.

19. Testing: Unit Tests vs. Integration Tests. unittest vs. pytest.

Answer:
- Unit Tests: Test a single component in isolation (e.g., a function). Use unittest.mock to patch dependencies.
- Integration Tests: Test how multiple components work together (e.g., API endpoint writing to a test database).
- pytest is the de facto standard due to its simple syntax (plain assert), powerful fixtures, and rich plugin ecosystem, although unittest is in the standard library.

20. What is Metaclass? When would you use one?

Answer: A metaclass is a class of a class; it defines how a class behaves. A class is an instance of a metaclass (default is type). You use them for powerful, but often complex, framework-level patterns like enforcing coding standards, automatic attribute validation, or ORM base classes (like Django models). 99% of developers don’t need to write them.