1. Why is Python still so popular in 2026?<\/strong><\/h2>\n\n\n\nPython has maintained its #1 position in 2026 due to:<\/p>\n\n\n\n
\n- Readable, beginner-friendly syntax<\/strong><\/li>\n\n\n\n
- Huge ecosystem<\/strong> for AI, ML, web, data, cloud, and analytics<\/li>\n\n\n\n
- Cross-platform support<\/strong><\/li>\n\n\n\n
- Powerful frameworks<\/strong> (Django, Flask, FastAPI, Pandas, NumPy, TensorFlow)<\/li>\n\n\n\n
- Rich standard library<\/strong><\/li>\n\n\n\n
- Strong community support<\/strong><\/li>\n\n\n\n
- Integration with cloud providers<\/strong> (AWS Lambda, GCP Functions, Azure Functions)<\/li>\n<\/ul>\n\n\n\n
Companies rely on Python not just for rapid development but also for scalability, automation, and data-heavy applications. This is why Python Latest interview topics are becoming deeper and more practical each year.<\/p>\n\n\n\n
2. What\u2019s new in Python 3.12 and 3.13? (2026 update)<\/strong><\/h2>\n\n\n\nPython 3.12 and 3.13 delivered major improvements:<\/p>\n\n\n\n
Faster CPython Execution<\/h3>\n\n\n\n
Python 3.12 introduced up to 40% speed improvements, while 3.13 enhances startup performance, garbage collection, and optimization.<\/p>\n\n\n\n
Improved Error Messages<\/h3>\n\n\n\n
Python now provides smart error hints critical for debugging test automation and backend systems.<\/p>\n\n\n\n
Removal of Deprecated Modules<\/h3>\n\n\n\n
Outdated libraries were removed to streamline performance.<\/p>\n\n\n\n
Enhanced asyncio<\/h3>\n\n\n\n
Async programming now matches Node.js-level performance, making Python preferred for microservices.<\/p>\n\n\n\n
Experimental \u201cNo-GIL\u201d Python<\/h3>\n\n\n\n
Python 3.13 includes a nogil test build for true Latest multithreading one of the hottest interview topics for 2026.<\/p>\n\n\n\n
3. What is PEP 8 and why is it important?<\/strong><\/h2>\n\n\n\nPEP 8 is the official Python style guide that defines rules for:<\/p>\n\n\n\n
\n- Indentation<\/li>\n\n\n\n
- Variable naming<\/li>\n\n\n\n
- Spacing<\/li>\n\n\n\n
- Code layout<\/li>\n\n\n\n
- Commenting<\/li>\n\n\n\n
- Import structure<\/li>\n\n\n\n
- Best practices for readability<\/li>\n<\/ul>\n\n\n\n
Following PEP 8 helps maintain clean, readable, and professional code something hiring managers expect from every Python developer.<\/p>\n\n\n\n
4. Explain Latest key differences between List, Tuple, Set, and Dictionary.<\/strong><\/h2>\n\n\n\nType<\/th> Ordered<\/th> Mutable<\/th> Allows Duplicates<\/th> Use Case<\/th><\/tr><\/thead> List<\/strong><\/td>Yes<\/td> Yes<\/td> Yes<\/td> Dynamic collections<\/td><\/tr> Tuple<\/strong><\/td>Yes<\/td> No<\/td> Yes<\/td> Immutable data like config<\/td><\/tr> Set<\/strong><\/td>No<\/td> Yes<\/td> No<\/td> Unique values, membership checks<\/td><\/tr> Dictionary<\/strong><\/td>Yes (Py3.7+)<\/td> Yes<\/td> Keys unique<\/td> Key\u2013value mapping<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nUnderstanding these differences is fundamental for optimizing algorithms and memory usage.<\/p>\n\n\n\n
5. What is the difference between \u201c==\u201d and \u201cis\u201d?<\/strong><\/h2>\n\n\n\n\n- ==<\/strong> compares values<\/li>\n\n\n\n
- is<\/strong> compares memory identity<\/li>\n<\/ul>\n\n\n\n
Example:<\/p>\n\n\n\n
a = [1,2]\nb = [1,2]\nprint(a == b) # True\nprint(a is b) # False\n<\/pre>\n\n\n\n6. What are Python decorators? Give an example.<\/strong><\/h2>\n\n\n\nDecorators allow you to modify Latest function behavior without changing code.<\/p>\n\n\n\n
Example:<\/p>\n\n\n\n
def log(func):\n def wrapper(*args, **kwargs):\n print(\"Function executed\")\n return func(*args, **kwargs)\n return wrapper\n\n@log\ndef greet():\n print(\"Hello!\")\n<\/pre>\n\n\n\nUsed in:<\/p>\n\n\n\n
\n- Django middleware<\/li>\n\n\n\n
- FastAPI routes<\/li>\n\n\n\n
- Logging<\/li>\n\n\n\n
- Authorization<\/li>\n\n\n\n
- Caching<\/li>\n<\/ul>\n\n\n\n
7. What is the GIL (Global Interpreter Lock)? Is Python removing it in 2026?<\/strong><\/h2>\n\n\n\n GIL Definition<\/h3>\n\n\n\n
A mutex that allows only one thread to execute Python bytecode at a time.<\/p>\n\n\n\n![\"\"](\"https:\/\/www.h2kinfosys.com\/blog\/wp-content\/uploads\/2025\/11\/professional-business-woman-is-working-using-keyboard-typing-processed-company-online-1024x683.jpg\" "\\"\\"")
<\/figure>\n\n\n\nProblem<\/h3>\n\n\n\n
Limits true multithreading for CPU-heavy tasks.<\/p>\n\n\n\n
2026 Update<\/h3>\n\n\n\n\n- Python 3.13 includes an Latest experimental No-GIL build<\/li>\n\n\n\n
- Significant progress in PEP 703<\/li>\n\n\n\n
- Expected to roll out in stable versions soon<\/li>\n<\/ul>\n\n\n\n
This is one of the hottest Python interview topics for 2026.<\/p>\n\n\n\n
8. Explain shallow copy vs deep copy.<\/strong><\/h2>\n\n\n\nimport copy\n\noriginal = [[1,2],[3,4]]\nshallow = copy.copy(original)\ndeep = copy.deepcopy(original)\n<\/pre>\n\n\n\n
\n- Shallow copy<\/strong> copies references<\/li>\n\n\n\n
- Deep copy<\/strong> copies entire objects<\/li>\n<\/ul>\n\n\n\n
Useful for understanding memory management in large data structures.<\/p>\n\n\n\n
9. What are lambda functions?<\/strong><\/h2>\n\n\n\nSmall, anonymous functions created using lambda<\/strong><\/code>.<\/p>\n\n\n\nsquare = lambda x: x * x\n<\/pre>\n\n\n\nUsed heavily in:<\/p>\n\n\n\n
\n- Sorting<\/li>\n\n\n\n
- Filtering<\/li>\n\n\n\n
- Machine Learning pipelines<\/li>\n\n\n\n
- Map\/Reduce<\/li>\n<\/ul>\n\n\n\n
10. What are list comprehensions? Why are they faster?<\/strong><\/h2>\n\n\n\nLatest List comprehensions provide a short and optimized way to create lists.<\/p>\n\n\n\n
result = [x*x for x in range(10)]\n<\/pre>\n\n\n\nThey are faster because:<\/p>\n\n\n\n
\n- They run in C-level loops<\/li>\n\n\n\n
- Reduce overhead of Latest Python bytecode<\/li>\n\n\n\n
- Improve readability<\/li>\n<\/ul>\n\n\n\n
11. What is *args and kwargs?<\/strong><\/h2>\n\n\n\nUsed to pass variable arguments:<\/p>\n\n\n\n
def func(*args, **kwargs):\n print(args)\n print(kwargs)\n<\/pre>\n\n\n\n
\n*args<\/code> \u2192 positional arguments<\/li>\n\n\n\n**kwargs<\/code> \u2192 keyword arguments<\/li>\n<\/ul>\n\n\n\n12. What are generators and why are they useful?<\/strong><\/h2>\n\n\n\nGenerators return sequence values using yield<\/code> no need to store everything in memory.<\/p>\n\n\n\ndef counter():\n for i in range(5):\n yield i\n<\/pre>\n\n\n\nBenefits:<\/p>\n\n\n\n
\n- Very memory efficient<\/li>\n\n\n\n
- Used in Latest streaming, data pipelines<\/li>\n\n\n\n
- Essential for large-scale ML\/ETL systems<\/li>\n<\/ul>\n\n\n\n
13. Explain Python\u2019s Memory Management Model.<\/strong><\/h2>\n\n\n\nPython manages memory using:<\/p>\n\n\n\n
\n- Private Heap<\/strong> (for objects)<\/li>\n\n\n\n
- Reference Counting<\/strong><\/li>\n\n\n\n
- Garbage Collector<\/strong><\/li>\n\n\n\n
- Generational GC Algorithm<\/strong><\/li>\n<\/ul>\n\n\n\n
Python abstracts memory details, improving developer productivity.<\/p>\n\n\n\n
14. What is a module vs a package?<\/strong><\/h2>\n\n\n\n\n- Module<\/strong> = single Python file<\/li>\n\n\n\n
- Package<\/strong> = folder with multiple modules<\/li>\n<\/ul>\n\n\n\n
Example:<\/p>\n\n\n\n
mypackage\/\n __init__.py\n module1.py\n module2.py\n<\/pre>\n\n\n\n15. What is virtualenv and why is it used?<\/strong><\/h2>\n\n\n\nIt creates isolated Python environments, preventing version conflicts.<\/p>\n\n\n\n
Tools:<\/p>\n\n\n\n
\n- virtualenv<\/li>\n\n\n\n
- pipenv<\/li>\n\n\n\n
- conda<\/li>\n\n\n\n
- poetry<\/li>\n<\/ul>\n\n\n\n
Essential for multi-project development.<\/p>\n\n\n\n
16. Explain exception handling with an example.<\/strong><\/h2>\n\n\n\ntry:\n x = 10 \/ 0\nexcept ZeroDivisionError:\n print(\"Cannot divide by zero\")\n<\/pre>\n\n\n\nCommon exceptions:<\/p>\n\n\n\n
\n- KeyError<\/li>\n\n\n\n
- TypeError<\/li>\n\n\n\n
- FileNotFoundError<\/li>\n\n\n\n
- IndexError<\/li>\n<\/ul>\n\n\n\n
17. What is asynchronous programming in Python? Why is it trending in 2026?<\/strong><\/h2>\n\n\n\nAsync programming allows concurrent execution<\/strong> of tasks using asyncio<\/code>.<\/p>\n\n\n\nimport asyncio\n\nasync def run():\n print(\"Async function\")\n\nasyncio.run(run())\n<\/pre>\n\n\n\nWhy trending?<\/h3>\n\n\n\n\n- API-first development<\/li>\n\n\n\n
- FastAPI adoption<\/li>\n\n\n\n
- Microservices<\/li>\n\n\n\n
- High-performance backend systems<\/li>\n<\/ul>\n\n\n\n
18. What is structural pattern matching? (PEP 634)<\/strong><\/h2>\n\n\n\nPattern matching, introduced in Python 3.10, functions like a powerful switch-case.<\/p>\n\n\n\n
def check(x):\n match x:\n case 0:\n return \"Zero\"\n case [a, b]:\n return f\"List with two items: {a}, {b}\"\n<\/pre>\n\n\n\nUsed in:<\/p>\n\n\n\n
\n- Data parsing<\/li>\n\n\n\n
- DSL interpreters<\/li>\n\n\n\n
- Routing logic<\/li>\n<\/ul>\n\n\n\n
19. Difference between multithreading and multiprocessing?<\/strong><\/h2>\n\n\n\nMultithreading<\/strong><\/h3>\n\n\n\n\n- Good for I\/O tasks<\/li>\n\n\n\n
- Limited by GIL<\/li>\n<\/ul>\n\n\n\n
Multiprocessing<\/strong><\/h3>\n\n\n\n\n- Bypasses GIL<\/li>\n\n\n\n
- Best for CPU-bound tasks<\/li>\n\n\n\n
- Spawns separate processes<\/li>\n<\/ul>\n\n\n\n
Used in:<\/strong><\/h3>\n\n\n\n\n- ML training<\/li>\n\n\n\n
- Image processing<\/li>\n\n\n\n
- Large dataset transformations<\/li>\n<\/ul>\n\n\n\n
20. Explain the use of __init__, __str__, and __repr__.<\/strong><\/h2>\n\n\n\nclass Student:\n def __init__(self, name):\n self.name = name\n \n def __str__(self):\n return f\"Name: {self.name}\"\n \n def __repr__(self):\n return f\"Student(name={self.name})\"\n<\/pre>\n\n\n\n\n__init__<\/code> \u2192 constructor<\/li>\n\n\n\n__str__<\/code> \u2192 human-readable text<\/li>\n\n\n\n__repr__<\/code> \u2192 debugging text<\/li>\n<\/ul>\n\n\n\n21. What are dataclasses? Why are they useful in 2026?<\/strong><\/h2>\n\n\n\nIntroduced in Python 3.7, dataclasses automatically generate:<\/p>\n\n\n\n
\n__init__<\/code><\/li>\n\n\n\n__repr__<\/code><\/li>\n\n\n\n__eq__<\/code><\/li>\n<\/ul>\n\n\n\nExample:<\/p>\n\n\n\n
from dataclasses import dataclass\n\n@dataclass\nclass User:\n name: str\n age: int\n<\/pre>\n\n\n\nUsed heavily in:<\/p>\n\n\n\n
\n- FastAPI models<\/li>\n\n\n\n
- ML configs<\/li>\n\n\n\n
- ETL pipelines<\/li>\n<\/ul>\n\n\n\n
22. Explain the difference between Django and FastAPI.<\/strong><\/h2>\n\n\n\nDjango<\/strong><\/h3>\n\n\n\n\n- Full-featured web framework<\/li>\n\n\n\n
- Great for dashboards, admin, e-commerce<\/li>\n<\/ul>\n\n\n\n
FastAPI<\/strong><\/h3>\n\n\n\n\n- Extremely fast, async-first<\/li>\n\n\n\n
- Ideal for microservices, ML<\/a> model serving<\/li>\n<\/ul>\n\n\n\n
FastAPI is a must-know for 2026 backend interviews.<\/p>\n\n\n\n
23. What is monkey patching?<\/strong><\/h2>\n\n\n\nChanging a class or function at runtime.<\/p>\n\n\n\n
import math\nmath.pi = 3\n<\/pre>\n\n\n\nUsed in:<\/p>\n\n\n\n
Companies rely on Python not just for rapid development but also for scalability, automation, and data-heavy applications. This is why Python Latest interview topics are becoming deeper and more practical each year.<\/p>\n\n\n\n
2. What\u2019s new in Python 3.12 and 3.13? (2026 update)<\/strong><\/h2>\n\n\n\nPython 3.12 and 3.13 delivered major improvements:<\/p>\n\n\n\n
Faster CPython Execution<\/h3>\n\n\n\n
Python 3.12 introduced up to 40% speed improvements, while 3.13 enhances startup performance, garbage collection, and optimization.<\/p>\n\n\n\n
Improved Error Messages<\/h3>\n\n\n\n
Python now provides smart error hints critical for debugging test automation and backend systems.<\/p>\n\n\n\n
Removal of Deprecated Modules<\/h3>\n\n\n\n
Outdated libraries were removed to streamline performance.<\/p>\n\n\n\n
Enhanced asyncio<\/h3>\n\n\n\n
Async programming now matches Node.js-level performance, making Python preferred for microservices.<\/p>\n\n\n\n
Experimental \u201cNo-GIL\u201d Python<\/h3>\n\n\n\n
Python 3.13 includes a nogil test build for true Latest multithreading one of the hottest interview topics for 2026.<\/p>\n\n\n\n
3. What is PEP 8 and why is it important?<\/strong><\/h2>\n\n\n\nPEP 8 is the official Python style guide that defines rules for:<\/p>\n\n\n\n
\n- Indentation<\/li>\n\n\n\n
- Variable naming<\/li>\n\n\n\n
- Spacing<\/li>\n\n\n\n
- Code layout<\/li>\n\n\n\n
- Commenting<\/li>\n\n\n\n
- Import structure<\/li>\n\n\n\n
- Best practices for readability<\/li>\n<\/ul>\n\n\n\n
Following PEP 8 helps maintain clean, readable, and professional code something hiring managers expect from every Python developer.<\/p>\n\n\n\n
4. Explain Latest key differences between List, Tuple, Set, and Dictionary.<\/strong><\/h2>\n\n\n\nType<\/th> Ordered<\/th> Mutable<\/th> Allows Duplicates<\/th> Use Case<\/th><\/tr><\/thead> List<\/strong><\/td>Yes<\/td> Yes<\/td> Yes<\/td> Dynamic collections<\/td><\/tr> Tuple<\/strong><\/td>Yes<\/td> No<\/td> Yes<\/td> Immutable data like config<\/td><\/tr> Set<\/strong><\/td>No<\/td> Yes<\/td> No<\/td> Unique values, membership checks<\/td><\/tr> Dictionary<\/strong><\/td>Yes (Py3.7+)<\/td> Yes<\/td> Keys unique<\/td> Key\u2013value mapping<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\nUnderstanding these differences is fundamental for optimizing algorithms and memory usage.<\/p>\n\n\n\n
5. What is the difference between \u201c==\u201d and \u201cis\u201d?<\/strong><\/h2>\n\n\n\n\n- ==<\/strong> compares values<\/li>\n\n\n\n
- is<\/strong> compares memory identity<\/li>\n<\/ul>\n\n\n\n
Example:<\/p>\n\n\n\n
a = [1,2]\nb = [1,2]\nprint(a == b) # True\nprint(a is b) # False\n<\/pre>\n\n\n\n6. What are Python decorators? Give an example.<\/strong><\/h2>\n\n\n\nDecorators allow you to modify Latest function behavior without changing code.<\/p>\n\n\n\n
Example:<\/p>\n\n\n\n
def log(func):\n def wrapper(*args, **kwargs):\n print(\"Function executed\")\n return func(*args, **kwargs)\n return wrapper\n\n@log\ndef greet():\n print(\"Hello!\")\n<\/pre>\n\n\n\nUsed in:<\/p>\n\n\n\n
\n- Django middleware<\/li>\n\n\n\n
- FastAPI routes<\/li>\n\n\n\n
- Logging<\/li>\n\n\n\n
- Authorization<\/li>\n\n\n\n
- Caching<\/li>\n<\/ul>\n\n\n\n
7. What is the GIL (Global Interpreter Lock)? Is Python removing it in 2026?<\/strong><\/h2>\n\n\n\n GIL Definition<\/h3>\n\n\n\n
A mutex that allows only one thread to execute Python bytecode at a time.<\/p>\n\n\n\n<\/figure>\n\n\n\nProblem<\/h3>\n\n\n\n
Limits true multithreading for CPU-heavy tasks.<\/p>\n\n\n\n
2026 Update<\/h3>\n\n\n\n\n- Python 3.13 includes an Latest experimental No-GIL build<\/li>\n\n\n\n
- Significant progress in PEP 703<\/li>\n\n\n\n
- Expected to roll out in stable versions soon<\/li>\n<\/ul>\n\n\n\n
This is one of the hottest Python interview topics for 2026.<\/p>\n\n\n\n
8. Explain shallow copy vs deep copy.<\/strong><\/h2>\n\n\n\nimport copy\n\noriginal = [[1,2],[3,4]]\nshallow = copy.copy(original)\ndeep = copy.deepcopy(original)\n<\/pre>\n\n\n\n
\n- Shallow copy<\/strong> copies references<\/li>\n\n\n\n
- Deep copy<\/strong> copies entire objects<\/li>\n<\/ul>\n\n\n\n
Useful for understanding memory management in large data structures.<\/p>\n\n\n\n
9. What are lambda functions?<\/strong><\/h2>\n\n\n\nSmall, anonymous functions created using lambda<\/strong><\/code>.<\/p>\n\n\n\nsquare = lambda x: x * x\n<\/pre>\n\n\n\nUsed heavily in:<\/p>\n\n\n\n
\n- Sorting<\/li>\n\n\n\n
- Filtering<\/li>\n\n\n\n
- Machine Learning pipelines<\/li>\n\n\n\n
- Map\/Reduce<\/li>\n<\/ul>\n\n\n\n
10. What are list comprehensions? Why are they faster?<\/strong><\/h2>\n\n\n\nLatest List comprehensions provide a short and optimized way to create lists.<\/p>\n\n\n\n
result = [x*x for x in range(10)]\n<\/pre>\n\n\n\nThey are faster because:<\/p>\n\n\n\n
\n- They run in C-level loops<\/li>\n\n\n\n
- Reduce overhead of Latest Python bytecode<\/li>\n\n\n\n
- Improve readability<\/li>\n<\/ul>\n\n\n\n
11. What is *args and kwargs?<\/strong><\/h2>\n\n\n\nUsed to pass variable arguments:<\/p>\n\n\n\n
def func(*args, **kwargs):\n print(args)\n print(kwargs)\n<\/pre>\n\n\n\n
\n*args<\/code> \u2192 positional arguments<\/li>\n\n\n\n**kwargs<\/code> \u2192 keyword arguments<\/li>\n<\/ul>\n\n\n\n12. What are generators and why are they useful?<\/strong><\/h2>\n\n\n\nGenerators return sequence values using yield<\/code> no need to store everything in memory.<\/p>\n\n\n\ndef counter():\n for i in range(5):\n yield i\n<\/pre>\n\n\n\nBenefits:<\/p>\n\n\n\n
\n- Very memory efficient<\/li>\n\n\n\n
- Used in Latest streaming, data pipelines<\/li>\n\n\n\n
- Essential for large-scale ML\/ETL systems<\/li>\n<\/ul>\n\n\n\n
13. Explain Python\u2019s Memory Management Model.<\/strong><\/h2>\n\n\n\nPython manages memory using:<\/p>\n\n\n\n
\n- Private Heap<\/strong> (for objects)<\/li>\n\n\n\n
- Reference Counting<\/strong><\/li>\n\n\n\n
- Garbage Collector<\/strong><\/li>\n\n\n\n
- Generational GC Algorithm<\/strong><\/li>\n<\/ul>\n\n\n\n
Python abstracts memory details, improving developer productivity.<\/p>\n\n\n\n
14. What is a module vs a package?<\/strong><\/h2>\n\n\n\n\n- Module<\/strong> = single Python file<\/li>\n\n\n\n
- Package<\/strong> = folder with multiple modules<\/li>\n<\/ul>\n\n\n\n
Example:<\/p>\n\n\n\n
mypackage\/\n __init__.py\n module1.py\n module2.py\n<\/pre>\n\n\n\n15. What is virtualenv and why is it used?<\/strong><\/h2>\n\n\n\nIt creates isolated Python environments, preventing version conflicts.<\/p>\n\n\n\n
Tools:<\/p>\n\n\n\n
\n- virtualenv<\/li>\n\n\n\n
- pipenv<\/li>\n\n\n\n
- conda<\/li>\n\n\n\n
- poetry<\/li>\n<\/ul>\n\n\n\n
Essential for multi-project development.<\/p>\n\n\n\n
16. Explain exception handling with an example.<\/strong><\/h2>\n\n\n\ntry:\n x = 10 \/ 0\nexcept ZeroDivisionError:\n print(\"Cannot divide by zero\")\n<\/pre>\n\n\n\nCommon exceptions:<\/p>\n\n\n\n
\n- KeyError<\/li>\n\n\n\n
- TypeError<\/li>\n\n\n\n
- FileNotFoundError<\/li>\n\n\n\n
- IndexError<\/li>\n<\/ul>\n\n\n\n
17. What is asynchronous programming in Python? Why is it trending in 2026?<\/strong><\/h2>\n\n\n\nAsync programming allows concurrent execution<\/strong> of tasks using asyncio<\/code>.<\/p>\n\n\n\nimport asyncio\n\nasync def run():\n print(\"Async function\")\n\nasyncio.run(run())\n<\/pre>\n\n\n\nWhy trending?<\/h3>\n\n\n\n\n- API-first development<\/li>\n\n\n\n
- FastAPI adoption<\/li>\n\n\n\n
- Microservices<\/li>\n\n\n\n
- High-performance backend systems<\/li>\n<\/ul>\n\n\n\n
18. What is structural pattern matching? (PEP 634)<\/strong><\/h2>\n\n\n\nPattern matching, introduced in Python 3.10, functions like a powerful switch-case.<\/p>\n\n\n\n
def check(x):\n match x:\n case 0:\n return \"Zero\"\n case [a, b]:\n return f\"List with two items: {a}, {b}\"\n<\/pre>\n\n\n\nUsed in:<\/p>\n\n\n\n
\n- Data parsing<\/li>\n\n\n\n
- DSL interpreters<\/li>\n\n\n\n
- Routing logic<\/li>\n<\/ul>\n\n\n\n
19. Difference between multithreading and multiprocessing?<\/strong><\/h2>\n\n\n\nMultithreading<\/strong><\/h3>\n\n\n\n\n- Good for I\/O tasks<\/li>\n\n\n\n
- Limited by GIL<\/li>\n<\/ul>\n\n\n\n
Multiprocessing<\/strong><\/h3>\n\n\n\n\n- Bypasses GIL<\/li>\n\n\n\n
- Best for CPU-bound tasks<\/li>\n\n\n\n
- Spawns separate processes<\/li>\n<\/ul>\n\n\n\n
Used in:<\/strong><\/h3>\n\n\n\n\n- ML training<\/li>\n\n\n\n
- Image processing<\/li>\n\n\n\n
- Large dataset transformations<\/li>\n<\/ul>\n\n\n\n
20. Explain the use of __init__, __str__, and __repr__.<\/strong><\/h2>\n\n\n\nclass Student:\n def __init__(self, name):\n self.name = name\n \n def __str__(self):\n return f\"Name: {self.name}\"\n \n def __repr__(self):\n return f\"Student(name={self.name})\"\n<\/pre>\n\n\n\n\n__init__<\/code> \u2192 constructor<\/li>\n\n\n\n__str__<\/code> \u2192 human-readable text<\/li>\n\n\n\n__repr__<\/code> \u2192 debugging text<\/li>\n<\/ul>\n\n\n\n21. What are dataclasses? Why are they useful in 2026?<\/strong><\/h2>\n\n\n\nIntroduced in Python 3.7, dataclasses automatically generate:<\/p>\n\n\n\n
\n__init__<\/code><\/li>\n\n\n\n__repr__<\/code><\/li>\n\n\n\n__eq__<\/code><\/li>\n<\/ul>\n\n\n\nExample:<\/p>\n\n\n\n
from dataclasses import dataclass\n\n@dataclass\nclass User:\n name: str\n age: int\n<\/pre>\n\n\n\nUsed heavily in:<\/p>\n\n\n\n
\n- FastAPI models<\/li>\n\n\n\n
- ML configs<\/li>\n\n\n\n
- ETL pipelines<\/li>\n<\/ul>\n\n\n\n
22. Explain the difference between Django and FastAPI.<\/strong><\/h2>\n\n\n\nDjango<\/strong><\/h3>\n\n\n\n\n- Full-featured web framework<\/li>\n\n\n\n
- Great for dashboards, admin, e-commerce<\/li>\n<\/ul>\n\n\n\n
FastAPI<\/strong><\/h3>\n\n\n\n\n- Extremely fast, async-first<\/li>\n\n\n\n
- Ideal for microservices, ML<\/a> model serving<\/li>\n<\/ul>\n\n\n\n
FastAPI is a must-know for 2026 backend interviews.<\/p>\n\n\n\n
23. What is monkey patching?<\/strong><\/h2>\n\n\n\nChanging a class or function at runtime.<\/p>\n\n\n\n
import math\nmath.pi = 3\n<\/pre>\n\n\n\nUsed in:<\/p>\n\n\n\n
PEP 8 is the official Python style guide that defines rules for:<\/p>\n\n\n\n
- \n
- Indentation<\/li>\n\n\n\n
- Variable naming<\/li>\n\n\n\n
- Spacing<\/li>\n\n\n\n
- Code layout<\/li>\n\n\n\n
- Commenting<\/li>\n\n\n\n
- Import structure<\/li>\n\n\n\n
- Best practices for readability<\/li>\n<\/ul>\n\n\n\n
Following PEP 8 helps maintain clean, readable, and professional code something hiring managers expect from every Python developer.<\/p>\n\n\n\n
4. Explain Latest key differences between List, Tuple, Set, and Dictionary.<\/strong><\/h2>\n\n\n\n
Type<\/th> Ordered<\/th> Mutable<\/th> Allows Duplicates<\/th> Use Case<\/th><\/tr><\/thead> List<\/strong><\/td> Yes<\/td> Yes<\/td> Yes<\/td> Dynamic collections<\/td><\/tr> Tuple<\/strong><\/td> Yes<\/td> No<\/td> Yes<\/td> Immutable data like config<\/td><\/tr> Set<\/strong><\/td> No<\/td> Yes<\/td> No<\/td> Unique values, membership checks<\/td><\/tr> Dictionary<\/strong><\/td> Yes (Py3.7+)<\/td> Yes<\/td> Keys unique<\/td> Key\u2013value mapping<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n Understanding these differences is fundamental for optimizing algorithms and memory usage.<\/p>\n\n\n\n
5. What is the difference between \u201c==\u201d and \u201cis\u201d?<\/strong><\/h2>\n\n\n\n
- \n
- ==<\/strong> compares values<\/li>\n\n\n\n
- is<\/strong> compares memory identity<\/li>\n<\/ul>\n\n\n\n
Example:<\/p>\n\n\n\n
a = [1,2]\nb = [1,2]\nprint(a == b) # True\nprint(a is b) # False\n<\/pre>\n\n\n\n
6. What are Python decorators? Give an example.<\/strong><\/h2>\n\n\n\n
Decorators allow you to modify Latest function behavior without changing code.<\/p>\n\n\n\n
Example:<\/p>\n\n\n\n
def log(func):\n def wrapper(*args, **kwargs):\n print(\"Function executed\")\n return func(*args, **kwargs)\n return wrapper\n\n@log\ndef greet():\n print(\"Hello!\")\n<\/pre>\n\n\n\n
Used in:<\/p>\n\n\n\n
- \n
- Django middleware<\/li>\n\n\n\n
- FastAPI routes<\/li>\n\n\n\n
- Logging<\/li>\n\n\n\n
- Authorization<\/li>\n\n\n\n
- Caching<\/li>\n<\/ul>\n\n\n\n
7. What is the GIL (Global Interpreter Lock)? Is Python removing it in 2026?<\/strong><\/h2>\n\n\n\n
GIL Definition<\/h3>\n\n\n\n
A mutex that allows only one thread to execute Python bytecode at a time.<\/p>\n\n\n\n
<\/figure>\n\n\n\nProblem<\/h3>\n\n\n\n
Limits true multithreading for CPU-heavy tasks.<\/p>\n\n\n\n
2026 Update<\/h3>\n\n\n\n
- \n
- Python 3.13 includes an Latest experimental No-GIL build<\/li>\n\n\n\n
- Significant progress in PEP 703<\/li>\n\n\n\n
- Expected to roll out in stable versions soon<\/li>\n<\/ul>\n\n\n\n
This is one of the hottest Python interview topics for 2026.<\/p>\n\n\n\n
8. Explain shallow copy vs deep copy.<\/strong><\/h2>\n\n\n\n
import copy\n\noriginal = [[1,2],[3,4]]\nshallow = copy.copy(original)\ndeep = copy.deepcopy(original)\n<\/pre>\n\n\n\n
- \n
- Shallow copy<\/strong> copies references<\/li>\n\n\n\n
- Deep copy<\/strong> copies entire objects<\/li>\n<\/ul>\n\n\n\n
Useful for understanding memory management in large data structures.<\/p>\n\n\n\n
9. What are lambda functions?<\/strong><\/h2>\n\n\n\n
Small, anonymous functions created using
lambda<\/strong><\/code>.<\/p>\n\n\n\nsquare = lambda x: x * x\n<\/pre>\n\n\n\n
Used heavily in:<\/p>\n\n\n\n
- \n
- Sorting<\/li>\n\n\n\n
- Filtering<\/li>\n\n\n\n
- Machine Learning pipelines<\/li>\n\n\n\n
- Map\/Reduce<\/li>\n<\/ul>\n\n\n\n
10. What are list comprehensions? Why are they faster?<\/strong><\/h2>\n\n\n\n
Latest List comprehensions provide a short and optimized way to create lists.<\/p>\n\n\n\n
result = [x*x for x in range(10)]\n<\/pre>\n\n\n\n
They are faster because:<\/p>\n\n\n\n
- \n
- They run in C-level loops<\/li>\n\n\n\n
- Reduce overhead of Latest Python bytecode<\/li>\n\n\n\n
- Improve readability<\/li>\n<\/ul>\n\n\n\n
11. What is *args and kwargs?<\/strong><\/h2>\n\n\n\n
Used to pass variable arguments:<\/p>\n\n\n\n
def func(*args, **kwargs):\n print(args)\n print(kwargs)\n<\/pre>\n\n\n\n
- \n
*args<\/code> \u2192 positional arguments<\/li>\n\n\n\n**kwargs<\/code> \u2192 keyword arguments<\/li>\n<\/ul>\n\n\n\n12. What are generators and why are they useful?<\/strong><\/h2>\n\n\n\n
Generators return sequence values using
yield<\/code> no need to store everything in memory.<\/p>\n\n\n\ndef counter():\n for i in range(5):\n yield i\n<\/pre>\n\n\n\n
Benefits:<\/p>\n\n\n\n
- \n
- Very memory efficient<\/li>\n\n\n\n
- Used in Latest streaming, data pipelines<\/li>\n\n\n\n
- Essential for large-scale ML\/ETL systems<\/li>\n<\/ul>\n\n\n\n
13. Explain Python\u2019s Memory Management Model.<\/strong><\/h2>\n\n\n\n
Python manages memory using:<\/p>\n\n\n\n
- \n
- Private Heap<\/strong> (for objects)<\/li>\n\n\n\n
- Reference Counting<\/strong><\/li>\n\n\n\n
- Garbage Collector<\/strong><\/li>\n\n\n\n
- Generational GC Algorithm<\/strong><\/li>\n<\/ul>\n\n\n\n
Python abstracts memory details, improving developer productivity.<\/p>\n\n\n\n
14. What is a module vs a package?<\/strong><\/h2>\n\n\n\n
- \n
- Module<\/strong> = single Python file<\/li>\n\n\n\n
- Package<\/strong> = folder with multiple modules<\/li>\n<\/ul>\n\n\n\n
Example:<\/p>\n\n\n\n
mypackage\/\n __init__.py\n module1.py\n module2.py\n<\/pre>\n\n\n\n
15. What is virtualenv and why is it used?<\/strong><\/h2>\n\n\n\n
It creates isolated Python environments, preventing version conflicts.<\/p>\n\n\n\n
Tools:<\/p>\n\n\n\n
- \n
- virtualenv<\/li>\n\n\n\n
- pipenv<\/li>\n\n\n\n
- conda<\/li>\n\n\n\n
- poetry<\/li>\n<\/ul>\n\n\n\n
Essential for multi-project development.<\/p>\n\n\n\n
16. Explain exception handling with an example.<\/strong><\/h2>\n\n\n\n
try:\n x = 10 \/ 0\nexcept ZeroDivisionError:\n print(\"Cannot divide by zero\")\n<\/pre>\n\n\n\n
Common exceptions:<\/p>\n\n\n\n
- \n
- KeyError<\/li>\n\n\n\n
- TypeError<\/li>\n\n\n\n
- FileNotFoundError<\/li>\n\n\n\n
- IndexError<\/li>\n<\/ul>\n\n\n\n
17. What is asynchronous programming in Python? Why is it trending in 2026?<\/strong><\/h2>\n\n\n\n
Async programming allows concurrent execution<\/strong> of tasks using
asyncio<\/code>.<\/p>\n\n\n\nimport asyncio\n\nasync def run():\n print(\"Async function\")\n\nasyncio.run(run())\n<\/pre>\n\n\n\n
Why trending?<\/h3>\n\n\n\n
- \n
- API-first development<\/li>\n\n\n\n
- FastAPI adoption<\/li>\n\n\n\n
- Microservices<\/li>\n\n\n\n
- High-performance backend systems<\/li>\n<\/ul>\n\n\n\n
18. What is structural pattern matching? (PEP 634)<\/strong><\/h2>\n\n\n\n
Pattern matching, introduced in Python 3.10, functions like a powerful switch-case.<\/p>\n\n\n\n
def check(x):\n match x:\n case 0:\n return \"Zero\"\n case [a, b]:\n return f\"List with two items: {a}, {b}\"\n<\/pre>\n\n\n\nUsed in:<\/p>\n\n\n\n
- \n
- Data parsing<\/li>\n\n\n\n
- DSL interpreters<\/li>\n\n\n\n
- Routing logic<\/li>\n<\/ul>\n\n\n\n
19. Difference between multithreading and multiprocessing?<\/strong><\/h2>\n\n\n\n
Multithreading<\/strong><\/h3>\n\n\n\n
- \n
- Good for I\/O tasks<\/li>\n\n\n\n
- Limited by GIL<\/li>\n<\/ul>\n\n\n\n
Multiprocessing<\/strong><\/h3>\n\n\n\n
- \n
- Bypasses GIL<\/li>\n\n\n\n
- Best for CPU-bound tasks<\/li>\n\n\n\n
- Spawns separate processes<\/li>\n<\/ul>\n\n\n\n
Used in:<\/strong><\/h3>\n\n\n\n
- \n
- ML training<\/li>\n\n\n\n
- Image processing<\/li>\n\n\n\n
- Large dataset transformations<\/li>\n<\/ul>\n\n\n\n
20. Explain the use of __init__, __str__, and __repr__.<\/strong><\/h2>\n\n\n\n
class Student:\n def __init__(self, name):\n self.name = name\n \n def __str__(self):\n return f\"Name: {self.name}\"\n \n def __repr__(self):\n return f\"Student(name={self.name})\"\n<\/pre>\n\n\n\n- \n
__init__<\/code> \u2192 constructor<\/li>\n\n\n\n__str__<\/code> \u2192 human-readable text<\/li>\n\n\n\n__repr__<\/code> \u2192 debugging text<\/li>\n<\/ul>\n\n\n\n21. What are dataclasses? Why are they useful in 2026?<\/strong><\/h2>\n\n\n\n
Introduced in Python 3.7, dataclasses automatically generate:<\/p>\n\n\n\n
- \n
__init__<\/code><\/li>\n\n\n\n__repr__<\/code><\/li>\n\n\n\n__eq__<\/code><\/li>\n<\/ul>\n\n\n\nExample:<\/p>\n\n\n\n
from dataclasses import dataclass\n\n@dataclass\nclass User:\n name: str\n age: int\n<\/pre>\n\n\n\n
Used heavily in:<\/p>\n\n\n\n
- \n
- FastAPI models<\/li>\n\n\n\n
- ML configs<\/li>\n\n\n\n
- ETL pipelines<\/li>\n<\/ul>\n\n\n\n
22. Explain the difference between Django and FastAPI.<\/strong><\/h2>\n\n\n\n
Django<\/strong><\/h3>\n\n\n\n
- \n
- Full-featured web framework<\/li>\n\n\n\n
- Great for dashboards, admin, e-commerce<\/li>\n<\/ul>\n\n\n\n
FastAPI<\/strong><\/h3>\n\n\n\n
- \n
- Extremely fast, async-first<\/li>\n\n\n\n
- Ideal for microservices, ML<\/a> model serving<\/li>\n<\/ul>\n\n\n\n
FastAPI is a must-know for 2026 backend interviews.<\/p>\n\n\n\n
23. What is monkey patching?<\/strong><\/h2>\n\n\n\n
Changing a class or function at runtime.<\/p>\n\n\n\n
import math\nmath.pi = 3\n<\/pre>\n\n\n\n
Used in:<\/p>\n\n\n\n
- Package<\/strong> = folder with multiple modules<\/li>\n<\/ul>\n\n\n\n
- Reference Counting<\/strong><\/li>\n\n\n\n
- Private Heap<\/strong> (for objects)<\/li>\n\n\n\n
- Deep copy<\/strong> copies entire objects<\/li>\n<\/ul>\n\n\n\n
- Shallow copy<\/strong> copies references<\/li>\n\n\n\n
- is<\/strong> compares memory identity<\/li>\n<\/ul>\n\n\n\n
- ==<\/strong> compares values<\/li>\n\n\n\n