1. What are Python decorators and how do they work?
Answer: Decorators are functions that modify the behavior of other functions or methods. They are used with @decorator_name syntax.
def decorator(func):
def wrapper():
print("Before function call")
func()
print("After function call")
return wrapper
@decorator
def say_hello():
print("Hello!")
say_hello()
---
2. What is the difference between is and == in Python?
Answer:
is checks object identity (whether two variables point to the same object in memory).
== checks value equality (whether the values are the same).
a = [1, 2]
b = [1, 2]
print(a == b) # True
print(a is b) # False
---
3. What is a generator? How is it different from a list?
Answer:
Generators yield items one at a time using yield. They are memory-efficient and lazy-evaluated.
def gen():
yield 1
yield 2
g = gen()
print(next(g)) # 1
Generators don’t store the whole list in memory, unlike a normal list.
---
4. Explain Python's GIL (Global Interpreter Lock).
Answer:
The GIL is a mutex in CPython that allows only one thread to execute at a time, even on multi-core systems. This affects multi-threaded CPU-bound programs but not I/O-bound ones.
---
5. What are metaclasses in Python?
Answer:
Metaclasses are classes of classes — they define how classes behave. You can control class creation using metaclasses.
class Meta(type):
def __new__(cls, name, bases, dct):
print(f"Creating class {name}")
return super().__new__(cls, name, bases, dct)
class MyClass(metaclass=Meta):
pass
---
6. How does *args and **kwargs work?
Answer:
*args captures positional arguments as a tuple.
**kwargs captures keyword arguments as a dict.
def func(*args, **kwargs):
print(args)
print(kwargs)
func(1, 2, a=3, b=4)
---
7. What are closures in Python?
Answer:
Closures are functions that remember the values from their enclosing scope even if the outer function has finished executing.
def outer(x):
def inner():
print(x)
return inner
closure = outer(10)
closure() #
---
8. What is monkey patching in Python?
Answer:
Changing or extending code at runtime, typically used in testing.
import math
math.sqrt = lambda x: "patched"
print(math.sqrt(9)) # patched
9. How does Python’s memory management work?
Answer:
Python uses reference counting and a garbage collector for cyclic references.
Memory is managed in private heaps.
10. Difference between shallow and deep copy?
Answer:
Shallow copy: copies only references for nested objects.
Deep copy: copies all levels recursively.
import copy
a = [[1, 2]]
shallow = copy.copy(a)
deep = copy.deepcopy(a)
Would you like these as a downloadable PDF or want more questions (e.g., multithreading, asyncio, design patterns, etc.)?
