Top 100 Python Interview Questions and Answers

here we discuss questions regarding all aspects that are mostly asked in Python Interviews

Python is one of the most popular programming languages in the world today. It is known for its simple and readable syntax, making it a great choice for beginners as well as experienced developers. Python is used in many areas of technology, including web development, data science, machine learning, automation, artificial intelligence, game development, and more.

Because of its wide usage, Python is a common topic in job interviews for software developers, data analysts, machine learning engineers, and other tech roles. Whether you’re a fresher preparing for your first job, or a professional looking to switch careers, understanding the most common Python interview questions can help you feel more confident and perform better.

In this guide, we have gathered the top 100 Python interview questions and answers, starting from the basics and moving to more advanced topics. Each answer is written in simple words and explained clearly with examples so that anyone can understand — even if you’re just starting your Python journey.

Let’s dive into the questions and get ready to ace your Python interview!

1. What is Python?

Python is a popular, high-level programming language that is easy to read and write. It was created by Guido van Rossum and released in 1991. One of the main reasons why Python is so widely used is because of its simple syntax that looks like English. This makes it a great choice for beginners. Python is also versatile — you can use it for web development, data science, artificial intelligence, machine learning, automation, scripting, and more. It is free to use and open-source, which means anyone can download and use it. Big companies like Google, Instagram, and Netflix use Python in their systems.

2. What are the key features of Python?

Python has many features that make it a popular choice among developers. First, it is easy to learn and use. The syntax is simple, which helps beginners write code quickly. Second, it is an interpreted language, meaning Python executes code line by line, which makes debugging easier. Python is cross-platform, so it works on Windows, Mac, and Linux. It also supports object-oriented and functional programming. Another great feature is its huge standard library — it includes many tools and modules to do different tasks without writing everything from scratch. Python also has a large community, so getting help and learning resources is easy.

3. What are variables in Python?

Variables in Python are used to store information that you want to use later. Think of a variable as a box where you can keep something, like a number or a name. You don’t need to mention the data type when creating a variable — Python figures it out. For example:

name = "Alice"
age = 25

Here, name stores a string and age stores an integer. You can also change the value of a variable anytime. Variables make it easy to write flexible programs where data can change. You just use the variable name wherever you need that value in your code.

4. What are data types in Python?

Data types tell Python what kind of value a variable holds. For example, int is for whole numbers, float is for decimal numbers, and str is for text. Python also has bool for True or False values, list for a collection of items, tuple for an unchangeable group of items, dict for key-value pairs, and set for unique items. These types help Python decide what kind of operations can be done with the data. You don’t have to define the type—Python does it automatically. Knowing data types is important to avoid errors and to use functions correctly in your code.

5. What is a list in Python?

A list in Python is a collection of items that are ordered and changeable. Lists are written with square brackets like this:

fruits = ["apple", "banana", "cherry"]

You can add, remove, or change items in a list using functions like append(), remove(), or by using indexes like fruits[1] = "orange". Lists can contain different data types—numbers, strings, even other lists. They are useful when you want to store multiple items in one variable. You can also loop through a list using a for loop. Lists are one of the most used data structures in Python because they are simple and powerful.

6. What is a tuple in Python?

A tuple is similar to a list, but the key difference is that you cannot change the items in a tuple once it is created. Tuples are immutable, which means unchangeable. They are written with parentheses like this:

colors = ("red", "green", "blue")

You can access items in a tuple using an index like colors[0]. Tuples are useful when you want to make sure that the data does not change. They are also slightly faster than lists. Tuples can be used as keys in dictionaries, but lists cannot because they are mutable. So, if you want to store fixed data, tuples are a good choice.

7. What is a dictionary in Python?

A dictionary in Python stores data in key-value pairs. Each key is linked to a value, like a word and its meaning in a real dictionary. Dictionaries are written using curly braces:

person = {"name": "Alice", "age": 30}

You can access the value by using the key: person["name"] returns "Alice". You can also add new key-value pairs or update existing ones. Dictionaries are very useful when you need to store related information, like user details or configuration settings. Keys must be unique and immutable, like strings or numbers. Dictionaries are fast and great for looking up data by name or key.

8. What is a set in Python?

A set in Python is a collection of unique items. It is unordered, which means the items do not have a fixed position. Sets are written using curly braces:

numbers = {1, 2, 3, 2}

The set automatically removes duplicates, so the output will be {1, 2, 3}. Sets are useful when you want to remove duplicates or check for common values between groups using operations like union, intersection, and difference. Since sets are unordered, you cannot access items using indexes. Sets are also faster than lists when checking if an item exists. They are simple but powerful for handling unique data.

9. What is the difference between list and tuple in Python?

Both lists and tuples can store multiple items, but they have some key differences. Lists are mutable, which means you can change, add, or remove items after the list is created. Tuples are immutable, which means you cannot change them after they are made. Lists are written with square brackets [ ], while tuples use parentheses ( ). Because of immutability, tuples are faster and can be used as keys in dictionaries, while lists cannot. Use a list when your data might change. Use a tuple when your data should stay the same. Both are useful in different situations.

10. How do you write comments in Python?

Comments in Python are used to explain what your code is doing. Python ignores comments — they are just for humans reading the code. To write a single-line comment, start the line with #:

# This is a single-line comment

You can also write multi-line comments using triple quotes:

'''
This is a
multi-line comment
'''

Good comments help others (and yourself) understand your code, especially when it’s long or complex. They make your code more readable and easier to maintain. Writing clear comments is considered a good programming habit in any language.

11. How is Python interpreted?

Python is an interpreted language, which means the code is not compiled before running. Instead, it is executed line-by-line by the Python interpreter. When you write Python code and run it, the interpreter reads each line, translates it into machine language, and then executes it immediately. This is different from compiled languages like C or Java, where the code is turned into a complete executable file before it runs. Being interpreted makes it easier to test and debug Python code. If there’s an error, Python will stop at that line and show an error message. This feature is great for beginners and for rapid development.

12. What is indentation in Python and why is it important?

Indentation in Python refers to the spaces or tabs at the beginning of a line. Unlike many other programming languages, Python uses indentation to define blocks of code. For example, in loops, conditionals, and functions, the indented lines belong to the same code block. If indentation is not done correctly, Python will raise an error and stop running. This makes Python code more readable and clean. Here is a simple example:

if 5 > 2:
    print("Five is greater than two")

In the above code, the print statement is indented, which shows that it belongs to the if block. Proper indentation is very important in Python programming.

13. What are Python functions?

A function in Python is a block of reusable code that performs a specific task. You can define your own functions using the def keyword, or you can use built-in functions like print(), len(), and type(). Functions help you avoid repeating code and make your programs more organized. Here's an example of a simple function:

def greet(name):
    print("Hello, " + name)

You can call this function like greet("Alice"). Functions can take parameters and can also return values using the return keyword. Using functions makes your code shorter, cleaner, and easier to manage.

14. What is the difference between a function and a method in Python?

In Python, a function is a block of code that performs a task and is defined using the def keyword. It can be used on its own, outside of any class. For example:

def add(a, b):
    return a + b

A method, on the other hand, is a function that is associated with an object. Methods are defined inside classes and are called using dot notation. For example:

name = "Alice"
print(name.upper())  # upper() is a method

In simple terms, all methods are functions, but not all functions are methods. Methods always belong to an object or class, while functions do not.

15. What are arguments and parameters in Python functions?

Parameters are the names you define in a function when you write it. Arguments are the actual values you pass to the function when calling it. For example:

def greet(name):  # 'name' is a parameter
    print("Hello, " + name)

greet("Alice")  # "Alice" is the argument

In this case, name is a parameter, and "Alice" is the argument passed to the function. Python also supports default arguments, keyword arguments, and variable-length arguments. Understanding how parameters and arguments work is important for writing flexible and reusable functions.

16. What is the use of the return statement in Python?

The return statement in Python is used in a function to send a value back to the place where the function was called. It ends the function and passes the result to the caller. Here's an example:

def add(a, b):
    return a + b

result = add(5, 3)
print(result)  # Output: 8

In this case, the function add returns the sum of a and b, which is then stored in the variable result. If there is no return statement, the function will return None by default. Using return makes functions more useful because they can provide output to be used later.

17. What are Python loops?

Loops in Python are used to run a block of code multiple times. Python supports two main types of loops: for and while.

• The for loop is used when you want to repeat something for a specific number of times or through items in a sequence like a list:

for i in range(5):
    print(i)

• The while loop runs as long as a condition is true:

count = 0
while count < 5:
    print(count)
    count += 1

Loops help you automate repetitive tasks and make your code more efficient and shorter.

18. What is the difference between for and while loops in Python?

The for loop and while loop are both used to repeat actions, but they are used in different situations. A for loop is best when you know in advance how many times you want to repeat something. It works well with lists, strings, and ranges:

for i in range(5):
    print(i)

A while loop is better when you don't know how many times you’ll repeat and want to continue until a certain condition is false:

while i < 5:
    print(i)
    i += 1

So, use for when looping through known items, and use while when you need to loop based on a condition.

19. What is a conditional statement in Python?

Conditional statements are used to run certain blocks of code only when specific conditions are met. Python uses if, elif, and else for this:

age = 18
if age >= 18:
    print("You are an adult")
else:
    print("You are a minor")

You can also use elif (short for "else if") to check multiple conditions:

if score >= 90:
    print("A grade")
elif score >= 75:
    print("B grade")
else:
    print("C grade")

Conditional statements help your programs make decisions and behave differently based on input or data.

20. What is the use of the break, continue, and pass statements in Python?

These three statements control how loops behave:

• break: Stops the loop entirely and exits:

for i in range(5):
    if i == 3:
        break
    print(i)  # Prints 0, 1, 2

• continue: Skips the current loop cycle and moves to the next one:

for i in range(5):
    if i == 3:
        continue
    print(i)  # Skips 3

• pass: Does nothing. It’s used as a placeholder where code is needed later:

for i in range(5):
    pass  # To be implemented later

These are useful for controlling loops more precisely based on your program’s needs.

21. What are Python lists and how do you use them?

A list in Python is a collection of items that can hold different types of values like numbers, strings, or even other lists. Lists are ordered and changeable, meaning you can update, add, or remove items. You define a list using square brackets:

fruits = ["apple", "banana", "cherry"]

You can access list items by index, like fruits[0] which gives "apple". You can also change values, like fruits[1] = "orange". Python lists have many useful functions like append() to add an item, remove() to delete an item, and sort() to sort the list. Lists are one of the most used data types in Python.

22. What is the difference between a list and a tuple in Python?

Both lists and tuples are used to store multiple items, but the main difference is that lists are mutable (changeable), while tuples are immutable (cannot be changed). You create a list with square brackets [], and a tuple with parentheses ():

my_list = [1, 2, 3]
my_tuple = (1, 2, 3)

You can change my_list[0] = 10, but you cannot change my_tuple[0]. Tuples are faster and take up less memory than lists. Use tuples when your data should not change, such as coordinates or fixed settings. Lists are better when you need to update, sort, or modify the data.

23. What are Python dictionaries and how are they used?

A dictionary in Python is a collection of key-value pairs. Each key is unique and maps to a value. You create a dictionary using curly braces {}:

person = {"name": "Alice", "age": 25}

You can access values using keys, like person["name"] which gives "Alice". You can also add or update values, like person["age"] = 30. Dictionaries are useful when you want to store and retrieve data using names or identifiers instead of positions. Some helpful functions include keys(), values(), and items(). They are powerful for storing structured data, like JSON responses or configuration settings.

24. What are Python sets and what are they used for?

A set is a collection of unique items. It is unordered, so the items do not have a fixed position and cannot be accessed by index. Sets are defined using curly braces {}:

my_set = {1, 2, 3}

If you try to add a duplicate, it will be ignored. Sets are useful for checking membership and removing duplicates. You can use add() to insert elements and remove() to delete them. Python also supports set operations like union (|), intersection (&), and difference (-). Sets are great when you need fast lookups or want to ensure no duplicates exist.

25. How do you create a class in Python?

A class in Python is a blueprint for creating objects. It defines the structure and behavior (methods and variables) of an object. You create a class using the class keyword:

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

    def greet(self):
        print("Hello, my name is " + self.name)

The __init__ method is the constructor and runs when a new object is created. You can create an object like p1 = Person("Alice") and call its method using p1.greet(). Classes help in object-oriented programming and allow you to create reusable code.

26. What is an object in Python?

An object in Python is an instance of a class. When you create a class, you’re just defining the structure. But when you create an object using that class, you get a working version with real values. For example:

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

dog1 = Dog("Buddy")

Here, dog1 is an object of the Dog class. It has its own copy of data and can use class methods. In Python, almost everything is an object—strings, lists, functions, and even classes. Objects make code modular, reusable, and organized.

27. What is inheritance in Python?

Inheritance in Python allows one class (called a child or subclass) to get features from another class (called a parent or base class). It helps in reusing code and building relationships between classes. Here’s a basic example:

class Animal:
    def speak(self):
        print("Animal speaks")

class Dog(Animal):
    def bark(self):
        print("Dog barks")
d = Dog()
d.speak()
d.bark()

In this example, the Dog class inherits from Animal, so it can use the speak() method. Inheritance supports code reuse and helps organize code better when working with related classes.

28. What is polymorphism in Python?

Polymorphism means “many forms”. In Python, polymorphism allows different classes to have methods with the same name, but different behavior. For example, if two classes have a method named speak(), you can call speak() on any object, and it will behave according to its class:

class Dog:
    def speak(self):
        return "Bark"

class Cat:
    def speak(self):
        return "Meow"
animals = [Dog(), Cat()]
for animal in animals:
    print(animal.speak())

Each object knows how to perform its version of the method. Polymorphism makes code flexible and helps when writing functions that can work with multiple types of objects.

29. What is encapsulation in Python?

Encapsulation is a concept in object-oriented programming that hides the internal details of a class and protects data from outside access. In Python, we use private variables (with a single or double underscore _ or __) to indicate that they should not be accessed directly:

class Person:
    def __init__(self, name):
        self.__name = name  # private variable

    def get_name(self):
        return self.__name

In this example, __name is private. We access it using the get_name() method. Encapsulation helps keep your data safe, and only allows access through defined methods, making your code more secure and easier to maintain.

30. What is abstraction in Python?

Abstraction means showing only the essential features and hiding the unnecessary details. It helps reduce complexity and allows you to focus on what an object does, not how it does it. In Python, abstraction is often implemented using abstract classes and methods from the abc module:

from abc import ABC, abstractmethod

class Animal(ABC):
    @abstractmethod
    def make_sound(self):
        pass
class Dog(Animal):
    def make_sound(self):
        print("Bark")

Here, Animal is an abstract class, and make_sound() must be implemented in any child class. Abstraction helps in designing clean interfaces and focusing on high-level functionality.

Here are Python Interview Questions 31 to 40, each with a simple, clear explanation of at least 100 words:

31. What is the difference between global and local variables in Python?

In Python, a local variable is a variable that is declared inside a function and can only be used within that function. It exists temporarily while the function runs. A global variable, on the other hand, is declared outside of all functions and can be used anywhere in the code. Here’s an example:

x = 10  # global

def my_func():
    y = 5  # local
    print(x, y)
my_func()

In this code, x is global and y is local. If you want to modify a global variable inside a function, you must use the global keyword. Without that, Python will treat it as a local variable, which could lead to errors.

32. What is duck typing in Python?

Duck typing is a concept in Python where the type or class of an object is less important than the methods and properties it defines. The name comes from the saying, “If it walks like a duck and quacks like a duck, it’s probably a duck.” In Python, you don’t check the object’s type explicitly; instead, you assume it has the required behavior. For example, a function that calls .read() on an object doesn’t care if it’s a file, a socket, or a custom class—as long as it has a .read() method. Duck typing supports Python’s flexible, dynamic, and readable style.

33. What are Python decorators and how are they used?

Decorators in Python are a powerful feature that allows you to modify or extend the behavior of functions or classes without changing their code. A decorator is essentially a function that takes another function as input and returns a new function with added functionality. This is commonly used in logging, access control, memoization, and more. You use the @decorator_name syntax above a function to apply a decorator. For example, @login_required in Django ensures the user is authenticated before executing the function. Decorators help keep your code clean, reusable, and easier to manage, especially in larger codebases.

34. What is list comprehension in Python?

List comprehension is a concise way to create lists in Python. Instead of using loops, you can use a single line of code. It improves readability and performance. Here’s a basic example:

squares = [x*x for x in range(5)]

This creates a list [0, 1, 4, 9, 16]. You can also add conditions:

even = [x for x in range(10) if x % 2 == 0]

List comprehensions are useful when you want to transform or filter data quickly. They make your code shorter, cleaner, and easier to understand than using a full for loop.

35. What is the difference between append() and extend() in Python lists?

Both append() and extend() are used to add elements to a list, but they work differently. append() adds a single element to the end of the list, while extend() adds all elements from another list (or iterable). Example:

a = [1, 2]
a.append([3, 4])  # a = [1, 2, [3, 4]]
a = [1, 2]
a.extend([3, 4])  # a = [1, 2, 3, 4]

As you can see, append() adds the entire list as a single element, while extend() breaks it and adds each item. Use append() for one item and extend() for multiple items from another list.

36. How does Python handle memory management?

Python manages memory automatically using a system called garbage collection. This system keeps track of all objects and frees up memory that is no longer being used. Python also uses reference counting — each object keeps a count of how many references point to it. When that count reaches zero, the memory is released. Python’s memory management is done by the Python memory manager, and it includes private heap space where all objects and data structures are stored. As a developer, you don’t usually need to manage memory directly, but understanding how it works can help write better, more efficient code.

37. What is a module in Python?

A module in Python is a file that contains Python code. It can include functions, variables, and classes. You can import a module into another script to reuse the code. Python has many built-in modules like math, datetime, and os. You can also create your own custom modules by saving your code in a .py file. Example:

import math
print(math.sqrt(25))  # Output: 5.0

Using modules helps organize code better, keeps it cleaner, and promotes reuse. When your codebase grows, splitting functionality into modules makes it easier to manage and maintain.

38. What is a package in Python?

A package in Python is a collection of modules organized in directories. It allows you to group related modules together. A package is a folder that contains an __init__.py file, which tells Python that the folder should be treated as a package. Example structure:

mypackage/
│
├── __init__.py
├── module1.py
└── module2.py

You can import modules from the package using dot notation:

from mypackage import module1

Packages help in organizing large applications and reusing code across projects. Python also has many third-party packages that can be installed using tools like pip.

39. What is the purpose of __init__.py in Python packages?

The __init__.py file in Python marks a directory as a package so that its modules can be imported. Without this file, Python won’t recognize the folder as a package in older versions (though in modern Python, it’s optional). This file can be empty or contain initialization code for the package. For example, you might import key modules or define variables inside it:

# __init__.py
from .module1 import function1

With this, users can simply do from package import function1 instead of importing the whole module. It helps organize imports and controls how packages behave during import.

40. How do you handle exceptions in Python?

In Python, you handle errors and exceptions using try, except, and optionally finally. This allows your program to continue running even if something goes wrong. Here's an example:

try:
    num = int(input("Enter a number: "))
    result = 10 / num
except ZeroDivisionError:
    print("Cannot divide by zero!")
except ValueError:
    print("Please enter a valid number.")
finally:
    print("This always runs.")

The try block runs the risky code. If there’s an error, Python checks for a matching except block. The finally block always runs, whether an error occurred or not. Exception handling makes your programs more robust and user-friendly.

41. What is the difference between break, continue, and pass in Python?

In Python, break, continue, and pass are control flow statements, but each one serves a different purpose.

• break: It is used to exit a loop completely, even if the loop condition is still true. Once break is encountered, the loop stops running.

for i in range(5):
    if i == 3:
        break
    print(i)

• continue: It skips the current iteration and moves to the next one without stopping the loop.

for i in range(5):
    if i == 3:
        continue
    print(i)

• pass: It does nothing and is used as a placeholder where code is required syntactically but no action is needed.

for i in range(5):
    if i == 3:
        pass
    print(i)

These are useful for controlling how loops and conditionals behave during execution.

42. What are Python decorators?

A decorator in Python is a function that takes another function as input and adds extra functionality to it, without changing its original structure. It is often used to modify the behavior of a function or method dynamically.

def decorator(func):
    def wrapper():
        print("Before function call")
        func()
        print("After function call")
    return wrapper

@decorator
def greet():
    print("Hello!")
greet()

Here, @decorator wraps the greet() function and adds extra code before and after it. Decorators are used often in logging, authentication, timing, and access control. They help you write cleaner, reusable, and more readable code.

43. What are generators in Python?

Generators are a type of iterable in Python that generate values one at a time using the yield keyword. Unlike lists that store all values in memory, generators produce values only when needed, saving memory and improving performance.

def my_generator():
    for i in range(3):
        yield i

gen = my_generator()
for value in gen:
    print(value)

Here, yield pauses the function and remembers its state, allowing you to resume later. Generators are ideal for working with large data sets or infinite sequences, where creating the entire list in memory would be inefficient.

44. What is the difference between yield and return in Python?

The return statement ends a function and sends a value back to the caller. It stops the function completely. On the other hand, yield is used in a generator function to produce a value and pause the function without ending it. The next time the generator is called, execution resumes from where it left off.

def use_return():
    return 1
    return 2  # This will never run

def use_yield():
    yield 1
    yield 2  # This will also run
# Return sends one value. Yield can send multiple one-by-one.

Use return when you want a final result, and yield when you want to produce a sequence of results over time.

45. What are *args and kwargs in Python?

In Python, *args and **kwargs are used in function definitions to allow the function to accept a variable number of arguments.

• *args collects extra positional arguments into a tuple.
• **kwargs collects extra keyword arguments into a dictionary.

def example(*args, **kwargs):
    print(args)
    print(kwargs)

example(1, 2, 3, name="Alice", age=25)

This would print:

(1, 2, 3)
{'name': 'Alice', 'age': 25}

These features make your functions flexible and reusable. You can call them with different numbers of parameters without changing the function definition.

46. What is a shallow copy and deep copy in Python?

A shallow copy creates a new object, but it copies references of the original nested objects. Changes to nested elements affect both copies. A deep copy, on the other hand, copies everything recursively, creating entirely new nested objects.

import copy

original = [[1, 2], [3, 4]]
shallow = copy.copy(original)
deep = copy.deepcopy(original)
original[0][0] = 100
print(shallow)  # [[100, 2], [3, 4]]
print(deep)     # [[1, 2], [3, 4]]

Use copy.copy() for shallow copy and copy.deepcopy() for deep copy. Deep copies are safer when working with complex objects where changes shouldn't affect the original.

47. How can you handle file operations in Python?

Python provides easy ways to work with files using built-in functions like open(), read(), write(), and close(). Here's a simple example of reading a file:

with open("example.txt", "r") as file:
    content = file.read()
    print(content)

The with statement automatically closes the file. You can also write to a file:

with open("example.txt", "w") as file:
    file.write("Hello, Python!")

Use modes like "r" for reading, "w" for writing, and "a" for appending. File operations are useful for data storage, configuration, and logging in applications.

48. What are Python’s built-in data types?

Python has several built-in data types that are categorized into different groups:

• Numeric types: int, float, complex
• Sequence types: list, tuple, range
• Text type: str
• Set types: set, frozenset
• Mapping type: dict
• Boolean type: bool
• Binary types: bytes, bytearray, memoryview

Each type serves different purposes. For example, use int for whole numbers, str for text, list for ordered groups, and dict for key-value pairs. Understanding data types helps you store and manipulate data correctly in your programs.

49. What is the difference between mutable and immutable types in Python?

Mutable types can be changed after creation. Examples include list, dict, and set. You can add, remove, or change elements in these types.

Immutable types cannot be changed once created. Examples include int, float, str, and tuple. If you try to modify them, Python creates a new object instead.

x = "hello"
x = x + " world"  # Creates a new string

Knowing which types are mutable and which are not helps you avoid bugs, especially when passing variables into functions. It also affects performance and how memory is managed.

50. How do you define and use a function in Python?

To define a function in Python, you use the def keyword followed by the function name and parentheses. You can pass arguments into the function and return values using the return statement.

def greet(name):
    return f"Hello, {name}!"

message = greet("Alice")
print(message)

Functions make your code reusable and organized. Instead of repeating the same logic, you put it in a function and call it whenever needed. You can also have default arguments, variable-length arguments, and even functions inside functions.

51. What is list comprehension in Python?

List comprehension is a concise way to create lists in Python. It allows you to generate a new list by applying an expression to each item in an existing iterable (like a list, range, or string). Instead of writing multiple lines with loops, list comprehension lets you do it in just one line.

Here’s a simple example:

squares = [x * x for x in range(5)]
print(squares)  # Output: [0, 1, 4, 9, 16]

You can also add conditions:

evens = [x for x in range(10) if x % 2 == 0]

List comprehensions are more readable and faster in many cases. They are great when you need to create a new list from an existing one by applying some logic or filtering.

52. What are Python modules and packages?

A module is simply a Python file (.py) that contains variables, functions, or classes. You can reuse code from a module by importing it into another Python file using the import statement.

Example:

# math_utils.py
def add(a, b):
    return a + b

# main.py
import math_utils
print(math_utils.add(2, 3))

A package is a collection of related modules in a folder that contains a special __init__.py file. Packages help organize code better, especially for larger projects.

For example:

my_package/
├── __init__.py
├── math_utils.py
├── string_utils.py

Modules and packages allow you to break code into reusable parts and keep it organized.

53. What is a lambda function in Python?

A lambda function is a small anonymous function in Python. It’s used when you need a simple function for a short period and don’t want to formally define it using def. Lambda functions can take any number of arguments but only contain one expression.

Basic syntax:

lambda arguments: expression

Example:

add = lambda x, y: x + y
print(add(3, 5))  # Output: 8

Lambdas are useful in places where a quick function is needed, like with map(), filter(), and sorted().

nums = [5, 2, 9]
sorted_nums = sorted(nums, key=lambda x: -x)

Although powerful, for complex operations, using regular functions is more readable.

54. What is the use of the with statement in Python?

The with statement in Python is used to simplify the management of resources like file handling. When working with files, sockets, or database connections, it's important to close them after use to avoid resource leaks. The with statement does this automatically for you, even if an error occurs.

Example:

with open("file.txt", "r") as f:
    content = f.read()

In the above code, the file is automatically closed after the block inside with is executed. It’s cleaner and safer than manually opening and closing resources using open() and close().

It also works with objects that support context management via __enter__ and __exit__ methods.

55. What is exception handling in Python?

Exception handling is used in Python to catch and respond to errors that occur during program execution. Instead of crashing the program, you can handle the error gracefully using try, except, else, and finally blocks.

Here’s how it works:

try:
    result = 10 / 0
except ZeroDivisionError:
    print("You can't divide by zero!")
else:
    print("No errors occurred.")
finally:
    print("This will always run.")

• try block contains code that might raise an error.
• except block handles the error.
• else runs if no error occurs.
• finally always runs, useful for cleanup.

Using exceptions helps in building robust applications that can recover from unexpected situations.

56. What are Python’s magic methods?

Magic methods in Python are special methods with double underscores at the beginning and end of their names. They’re also known as dunder methods. Python uses these methods to perform operator overloading and other behaviors internally.

Some common magic methods are:

• __init__: Constructor, called when an object is created.
• __str__: Returns a string representation of the object.
• __len__: Returns the length using len().
• __add__: Defines behavior for +.

Example:

class Book:
    def __init__(self, title):
        self.title = title

    def __str__(self):
        return f"Book: {self.title}"
book = Book("Python 101")
print(book)  # Output: Book: Python 101

Magic methods make classes act like built-in types, improving readability and flexibility.

57. What is the difference between is and == in Python?

In Python:

• == checks if values of two variables are equal.
• is checks if two variables point to the same object in memory.

Example:

a = [1, 2, 3]
b = [1, 2, 3]

print(a == b)  # True, because contents are same
print(a is b)  # False, because they are two different objects

is is often used when comparing objects like None:

if my_var is None:
    print("Value is None")

Use == for equality of content and is when you care about identity, such as comparing singletons or cached objects.

58. What is the difference between a tuple and a list in Python?

Both tuples and lists are used to store multiple items, but:

• List is mutable: you can add, remove, or change items.
• Tuple is immutable: once created, it cannot be changed.

my_list = [1, 2, 3]
my_tuple = (1, 2, 3)

Lists are defined with square brackets [], and tuples with parentheses ().

Lists are commonly used when data needs to change during runtime, while tuples are ideal for fixed data or when you want to ensure the data stays unchanged. Also, because tuples are immutable, they can be used as dictionary keys, unlike lists.

59. What is duck typing in Python?

Duck typing is a concept from dynamic typing. In Python, it means that the type or class of an object is less important than the methods or operations it supports. The idea is: “If it walks like a duck and quacks like a duck, it’s a duck.”

Example:

class Duck:
    def quack(self):
        print("Quack!")

class Person:
    def quack(self):
        print("I can quack too!")
def make_quack(thing):
    thing.quack()
make_quack(Duck())
make_quack(Person())

Even though Duck and Person are different classes, both can be passed because they have a quack() method. This flexibility makes Python code more reusable and simple.

60. How is memory managed in Python?

Python uses a combination of reference counting and a garbage collector to manage memory.

• Each object has a reference count: when the count reaches zero, it is deleted.
• Python also uses a garbage collector to handle circular references, where two objects refer to each other but are no longer used.

You don’t usually need to manage memory manually. Python handles it for you. But you can still check or influence it using the gc module:

import gc
gc.collect()

Also, memory is allocated in private heaps managed by the Python interpreter. Efficient memory handling is part of Python’s design to help developers focus on logic instead of low-level resource management.

61. What is the purpose of the pass statement in Python?

The pass statement in Python is a placeholder used when a statement is syntactically required but you don’t want any code to run there. It’s often used when you’re writing code but haven’t implemented a part yet, or when you want to create a minimal class or function structure without actual logic.

Example:

def my_function():
    pass

class MyClass:
    pass

Without pass, Python will throw a syntax error if you define an empty block. It allows the code to run without doing anything. It’s also useful in exception handling when you want to ignore certain errors intentionally. Think of pass as “do nothing” but still keep the structure valid.

62. What are Python iterators?

An iterator in Python is an object that allows you to loop over its elements, one at a time. It must implement the __iter__() and __next__() methods. You can get an iterator from any iterable (like lists, tuples, sets) using the iter() function. Then, you use next() to get the next item.

Example:

my_list = [10, 20, 30]
it = iter(my_list)
print(next(it))  # 10
print(next(it))  # 20

When there are no more items, next() raises a StopIteration error. Iterators are memory-efficient because they don’t store the whole sequence in memory, which is helpful for large datasets or file reading.

63. What is the difference between deepcopy() and copy()?

In Python, copy() and deepcopy() both come from the copy module and are used to duplicate objects, but they behave differently:

• copy.copy() creates a shallow copy of an object. It copies the outer object, but not the nested objects inside it. So changes to inner objects affect both copies.
• copy.deepcopy() creates a completely independent clone, including all nested objects. Changes to one object do not affect the other.

Example:

import copy

original = [[1, 2], [3, 4]]
shallow = copy.copy(original)
deep = copy.deepcopy(original)
original[0][0] = 99
print(shallow[0][0])  # 99 (affected)
print(deep[0][0])     # 1 (not affected)

Use deepcopy() when you need a completely separate object structure.

64. What is slicing in Python?

Slicing in Python lets you extract a portion of a list, string, or tuple using a range of indexes. The basic syntax is object[start:stop:step].

Example:

my_list = [0, 1, 2, 3, 4, 5]
print(my_list[1:4])      # [1, 2, 3]
print(my_list[::2])      # [0, 2, 4]
print(my_list[::-1])     # [5, 4, 3, 2, 1, 0]

• start is the index to begin from (inclusive),
• stop is where to end (exclusive)
• step tells how many items to skip.

Slicing is powerful for reversing sequences, picking even-indexed items, or extracting parts of text. It works with strings too:

text = "Python"
print(text[1:4])  # "yth"

65. What is the difference between append() and extend() in lists?

Both append() and extend() are used to add elements to a list, but they do it differently:

• append() adds a single element to the end of the list, even if it’s another list.
• extend() takes an iterable and adds each element from it to the list.

Example:

a = [1, 2]
a.append([3, 4])
print(a)  # [1, 2, [3, 4]]

b = [1, 2]
b.extend([3, 4])
print(b)  # [1, 2, 3, 4]

So, append() adds the whole object, while extend() breaks it apart and adds each item individually. Use append() when you want to keep an item together; use extend() when you want to expand the list.

66. What is the purpose of enumerate() in Python?

The enumerate() function is used when you need both the index and value while looping over an iterable. It adds a counter to the iterable and returns it as an enumerate object, which you can convert into a list or use in a loop.

Example:

fruits = ['apple', 'banana', 'cherry']
for index, fruit in enumerate(fruits):
    print(index, fruit)

Output:

0 apple  
1 banana  
2 cherry

This is much cleaner than using range(len(fruits)). You can also start the index at a custom number by using enumerate(fruits, start=1). It’s helpful when you’re processing lists, keeping track of positions, or labeling items in a report or UI.

67. What is the difference between filter(), map(), and reduce()?

All three are functional programming tools in Python:

• map() applies a function to every item in an iterable and returns a new iterable.
• filter() applies a function that returns True or False, and only keeps items where the function returns True.
• reduce() repeatedly applies a function to accumulate a result (from functools module).

Example:

from functools import reduce

nums = [1, 2, 3, 4]
print(list(map(lambda x: x * 2, nums)))      # [2, 4, 6, 8]
print(list(filter(lambda x: x % 2 == 0, nums)))  # [2, 4]
print(reduce(lambda x, y: x + y, nums))      # 10

map() transforms data, filter() selects data, and reduce() combines data. They help write clean, short code for data processing.

68. What is the use of zip() in Python?

The zip() function is used to combine two or more iterables into a single iterable of tuples, where the i-th tuple contains the i-th element from each iterable. It stops at the shortest input length.

Example:

names = ['Alice', 'Bob']
scores = [85, 90]
zipped = zip(names, scores)
print(list(zipped))  # [('Alice', 85), ('Bob', 90)]

It’s great for looping over multiple lists at once:

for name, score in zip(names, scores):
    print(f"{name} scored {score}")

If the input lists are of unequal length, the extra items are ignored. You can also unzip using zip(*zipped_data).

69. What is *args and **kwargs in Python?

In Python:

• *args lets you pass a variable number of positional arguments to a function.
• **kwargs lets you pass a variable number of keyword arguments (as a dictionary).

Example:

def demo(*args, **kwargs):
    print("Args:", args)
    print("Kwargs:", kwargs)

demo(1, 2, 3, a=4, b=5)

Output:

Args: (1, 2, 3)
Kwargs: {'a': 4, 'b': 5}

These are useful when writing flexible functions that can accept any number of inputs. It helps when wrapping other functions or when you don’t know in advance how many parameters might be passed.

70. What is recursion in Python?

Recursion is when a function calls itself to solve a smaller part of a problem until it reaches a base case. It’s commonly used in problems like calculating factorials, Fibonacci numbers, and tree traversals.

Example:

def factorial(n):
    if n == 0:
        return 1
    return n * factorial(n - 1)

print(factorial(5))  # 120

Every recursive function must have a base case to prevent infinite recursion. Python limits recursion depth by default (about 1000 calls). You can check it using sys.getrecursionlimit().

Recursion makes some problems easier to solve, though it may use more memory than loops.

71. What is a Python module?

A Python module is a file containing Python code, usually saved with a .py extension. Modules help organize and reuse code across multiple programs. You can define functions, variables, and classes inside a module and then import them into other files using the import statement.

For example, if you create a file math_utils.py with a function:

def add(a, b):
    return a + b

You can use it in another file like this:

import math_utils
print(math_utils.add(2, 3))

Python has many built-in modules like math, random, and datetime. You can also create your own custom modules to keep your code clean, organized, and maintainable, especially in large projects.

72. What is a Python package?

A Python package is a way to organize related modules in directories. It’s simply a folder containing one or more .py files (modules) and an __init__.py file. The __init__.py tells Python to treat the directory as a package.

For example, you can have a package like:

my_package/
    __init__.py
    module1.py
    module2.py

You can import modules from this package using:

from my_package import module1

Packages make large codebases easier to manage by grouping functionality together. Python also supports nested packages (sub-packages). External libraries you install with pip, like numpy or requests, are also packages. Packages are essential for creating scalable and reusable code.

73. What is the with statement in Python used for?

The with statement in Python is used to manage resources like files, database connections, or network sockets. It ensures that the resource is automatically cleaned up when the block of code ends, even if an error occurs. This is often referred to as a context manager.

For example, when working with files:

with open("file.txt", "r") as f:
    content = f.read()

This automatically closes the file when the block finishes, so you don’t need to call f.close(). It helps prevent bugs like memory leaks or file locking. You can use with with any object that implements the context management protocol (__enter__ and __exit__ methods). It makes your code cleaner and safer.

74. What is the purpose of __init__.py?

The __init__.py file is used to mark a directory as a Python package. Without it, Python doesn’t recognize the folder as part of the package system. Even if it's empty, it’s still needed (especially in older Python versions).

For example, in this structure:

my_package/
    __init__.py
    module1.py

You can now do:

from my_package import module1

Besides identifying packages, __init__.py can also run initialization code or expose specific classes/functions when the package is imported. For instance, you can import functions from other modules inside __init__.py, making them accessible directly from the package. It helps organize your project structure for modular and maintainable code.

75. What are Python decorators?

Decorators in Python are a special syntax used to modify the behavior of functions or classes without changing their code. They’re often used for logging, access control, memoization, and more. A decorator is a function that takes another function as an argument and returns a modified version of it.

Example:

def my_decorator(func):
    def wrapper():
        print("Before function call")
        func()
        print("After function call")
    return wrapper

@my_decorator
def greet():
    print("Hello")
greet()

Output:

Before function call
Hello
After function call

Decorators use the @ symbol and are commonly used in frameworks like Flask and Django. They're powerful tools to make code cleaner and reusable.

76. What are Python generators?

A generator is a special type of function that returns values one at a time using the yield keyword instead of return. Generators don’t store all values in memory—they generate them on the fly. This makes them more memory-efficient than using lists, especially for large datasets or infinite sequences.

Example:

def count_up_to(n):
    i = 1
    while i <= n:
        yield i
        i += 1

for num in count_up_to(5):
    print(num)

Each time the generator is called, it pauses and resumes where it left off. Generators are useful for file reading, streaming data, and pipelines. They help write clean and efficient code without needing to load everything at once.

77. What are Python comprehensions?

Python comprehensions are a concise way to create lists, sets, or dictionaries using a single line of code. They are often used as a more readable alternative to loops.

Example of list comprehension:

squares = [x*x for x in range(5)]

This creates [0, 1, 4, 9, 16]. Similarly, you can use:

• Set comprehension: {x*x for x in range(5)}
• Dict comprehension: {x: x*x for x in range(5)}

You can also add conditions:

even_squares = [x*x for x in range(10) if x % 2 == 0]

Comprehensions make the code cleaner and easier to read compared to using traditional loops, especially when the operation is simple and directly maps from input to output.

78. What is the difference between @staticmethod and @classmethod in Python?

Both @staticmethod and @classmethod are decorators in Python, but they serve different purposes. A @staticmethod is a function within a class that does not access instance (self) or class (cls) variables. It is used when the logic relates to the class but doesn’t need its state. A @classmethod, on the other hand, takes the class itself as the first parameter (cls) and can modify class-level data. It’s useful for alternative constructors or utility functions that operate on class variables. Use @staticmethod for general utility functions, and @classmethod when you need access to class properties.

79. What is duck typing in Python?

Duck typing is a concept in Python where the type or class of an object is less important than the methods or behavior it supports. The idea comes from the saying: “If it walks like a duck and quacks like a duck, it’s a duck.”

This means you don’t check the type of an object explicitly; you just use it as long as it behaves as expected.

Example:

def quack(thing):
    thing.quack()

class Duck:
    def quack(self):
        print("Quack!")
quack(Duck())  # Works fine

Duck typing makes Python flexible and supports polymorphism. It allows you to write functions that work with many types of objects, as long as they implement the required behavior.

80. What is the purpose of the id() function in Python?

The id() function returns the unique identifier of an object in Python, which typically corresponds to the object’s memory address. For example, id(x) will return a unique integer that identifies the object x during its lifetime. This is useful for checking whether two variables refer to the same object in memory. It’s especially handy when working with mutable data types like lists or dictionaries and you want to confirm whether a new variable points to the same object or a copy. Keep in mind that the ID is unique only while the object exists and may be reused later for other objects.

81. What is a namespace in Python?

A namespace in Python refers to a space where names are mapped to objects. Think of it as a dictionary where the keys are variable names and the values are the objects those names refer to. Python uses namespaces to keep track of all the names in your program, like variable names, function names, class names, etc.

There are four types of namespaces in Python:

• Built-in: Contains built-in functions like print() and len().
• Global: Contains names defined at the top-level of a script or module.
• Enclosing: Relevant for nested functions (outer function scope).
• Local: Inside a function or block.

For example:

x = 10  # Global namespace

def func():
    y = 5  # Local namespace

Namespaces prevent naming conflicts and help Python know which variable you’re referring to in different parts of the program. You can access namespaces using functions like globals() and locals().

82. What is the difference between global and local variables?

A local variable is defined inside a function and can only be used within that function. A global variable, on the other hand, is defined outside any function and can be accessed from anywhere in the code, including inside functions.

Example:

x = 5  # Global

def my_func():
    x = 10  # Local
    print(x)
my_func()  # Prints 10
print(x)   # Prints 5

If you want to change the global variable inside a function, you must use the global keyword:

def my_func():
    global x
    x = 20

Local variables help keep functions independent, while global variables can be accessed across functions but may lead to unexpected behavior if not managed properly.

83. What is a Python set and how is it different from a list?

A set in Python is an unordered collection of unique elements. It is defined using curly braces {} or the set() function. Unlike lists, sets do not allow duplicate values, and they are not indexed, meaning you cannot access items using indexes like my_set[0].

Example:

my_list = [1, 2, 2, 3]
my_set = set(my_list)  # {1, 2, 3}

Key differences:

• Uniqueness: Sets automatically remove duplicates.
• Order: Lists maintain order; sets do not.
• Mutability: Both are mutable, but sets only contain immutable elements.
• Operations: Sets support mathematical operations like union, intersection, and difference.

Use sets when you need to store unique values or perform set-based operations efficiently.

84. How does exception handling work in Python?

Exception handling in Python is done using try, except, finally, and else blocks. It helps your program handle unexpected errors without crashing. You wrap the code that might throw an error in a try block, then use except to handle specific or general exceptions.

Example:

try:
    x = 1 / 0
except ZeroDivisionError:
    print("Cannot divide by zero.")
finally:
    print("This will always run.")

You can also use else to run code if no exception occurs:

try:
    x = 5
except:
    print("Error")
else:
    print("No error")

This system helps in writing robust and error-tolerant applications. Always catch specific exceptions instead of using a broad except:.

85. What is the purpose of the pass statement in Python?

The pass statement in Python is used as a placeholder. It does nothing when executed but is syntactically required when a block of code is expected. It is often used during development when you're planning out the structure of your code but haven't implemented the logic yet.

Example:

def my_function():
    pass  # To be implemented later

Without pass, Python would throw an error because the function body is empty. pass is also used in loops, classes, or conditional blocks that you haven’t filled yet:

for i in range(10):
    pass

It’s useful to keep your code syntactically correct during development and gradually fill in logic.

86. What is None in Python?

None is a special constant in Python that represents the absence of a value or a null value. It is often used to indicate that a variable doesn’t have any meaningful data yet. It’s an object of its own datatype — NoneType.

Example:

x = None
if x is None:
    print("x has no value")

Functions that don’t explicitly return anything will return None by default:

def greet():
    print("Hello")

print(greet())  # Outputs "Hello" and then "None"

None is not the same as 0, False, or an empty string. It’s used in comparisons, default arguments, and when checking if a variable was assigned any value or not.

87. What is the use of the id() function in Python?

The id() function in Python returns the unique identifier of an object, which is its memory address in CPython (the standard Python implementation). This is useful for checking whether two variables refer to the same object in memory.

Example:

a = [1, 2, 3]
b = a
print(id(a), id(b))  # Same id

Even if two objects have the same value, they may not have the same id:

x = [1, 2]
y = [1, 2]
print(id(x) == id(y))  # False

You can combine id() with the is keyword to check identity. This is especially useful when dealing with mutable and immutable types and understanding object references.

88. What is the difference between shallow and deep copy?

A shallow copy creates a new object but does not copy nested objects. Instead, it copies references to those nested objects. A deep copy, on the other hand, creates a new object and also recursively copies all nested objects.

Example using copy module:

import copy

original = [[1, 2], [3, 4]]
shallow = copy.copy(original)
deep = copy.deepcopy(original)

Modifying original[0][0] = 99 will change shallow but not deep.

Use shallow copy when the nested data doesn’t need to be cloned. Use deep copy when you want complete independence from the original structure to avoid unexpected changes.

89. What is a ternary operator in Python?

The ternary operator in Python is a way to write simple if-else statements in one line. It’s also called a conditional expression.

Syntax:

x = value_if_true if condition else value_if_false

Example:

age = 18
status = "Adult" if age >= 18 else "Minor"

It makes code more concise and readable for small decisions. But avoid using it for complex logic, as it can reduce readability. It’s commonly used when assigning values based on a quick condition.

90. What are Python assertions?

Assertions are a debugging tool used to test if a condition is true. If the condition is false, the program will raise an AssertionError. They help catch bugs early by checking if your assumptions in the code hold true.

Example:

x = 5
assert x > 0, "x must be positive"

If x is less than or equal to 0, the assertion will fail. You can provide a custom error message after the comma.

Assertions are mostly used in development and testing phases. In production, they can be disabled with the -O (optimize) switch. They are not a replacement for proper error handling but are very useful in spotting bugs quickly.

91. What is the use of zip() in Python?

The zip() function in Python is used to combine two or more iterables (like lists or tuples) into a single iterable of tuples. It pairs the elements from each iterable based on their position (index). If the iterables are of different lengths, zip() stops at the shortest one.

Example:

names = ["Alice", "Bob", "Charlie"]
scores = [85, 90, 78]
zipped = zip(names, scores)
print(list(zipped))  # [('Alice', 85), ('Bob', 90), ('Charlie', 78)]

You can use zip() for many tasks like combining data, iterating over multiple sequences at once, or transposing rows and columns in matrices. It's efficient and easy to use, often seen in data processing, file merging, or where multiple iterables need to be processed in parallel.

92. What is list comprehension in Python?

List comprehension is a concise way to create lists in Python using a single line of code. Instead of writing a loop to fill a list, you can use list comprehension to generate it in one step. It is faster and more readable.

Basic syntax:

[expression for item in iterable if condition]

Example:

squares = [x**2 for x in range(5)]  # [0, 1, 4, 9, 16]

You can also use an if clause to filter:

even_squares = [x**2 for x in range(10) if x % 2 == 0]

List comprehension is very helpful for data transformation, filtering, and mapping. It’s a cleaner and more Pythonic alternative to using loops and append().

93. What is the map() function in Python?

The map() function applies a given function to all items in an iterable and returns a map object (which is an iterator). It’s a clean way to apply transformations to a list or tuple without writing a loop.

Syntax:

map(function, iterable)

Example:

numbers = [1, 2, 3, 4]
squared = map(lambda x: x**2, numbers)
print(list(squared))  # [1, 4, 9, 16]

map() is often used when you need to apply the same function to every element. It's efficient and can be combined with other functional programming tools like filter() or reduce(). It keeps code short and readable.

94. What is the filter() function in Python?

The filter() function is used to filter elements from an iterable based on a condition. It returns an iterator containing only the elements for which the function returns True.

Syntax:

filter(function, iterable)

Example:

numbers = [1, 2, 3, 4, 5]
even = filter(lambda x: x % 2 == 0, numbers)
print(list(even))  # [2, 4]

It is useful when you want to keep elements that meet certain criteria and discard the rest. filter() makes your code more expressive and avoids manual loops with conditional checks.

95. What is the reduce() function in Python?

The reduce() function from the functools module applies a function to the items of a sequence and reduces it to a single value. It processes the sequence pairwise.

Syntax:

from functools import reduce
reduce(function, iterable)

Example:

from functools import reduce
numbers = [1, 2, 3, 4]
result = reduce(lambda x, y: x + y, numbers)
print(result)  # 10

You can use reduce() for operations like summing, multiplying, or combining values. It's powerful, but sometimes less readable than a loop or sum(). Use it when you need to process a list into a single result step by step.

96. What are generators in Python?

Generators are a way to create iterators in Python using functions with the yield keyword instead of return. They don’t return all values at once but generate them one by one, which saves memory when working with large datasets.

Example:

def count_up_to(n):
    i = 1
    while i <= n:
        yield i
        i += 1

for number in count_up_to(5):
    print(number)

Generators are useful when you want to iterate through a sequence without storing the entire sequence in memory. They are lazy and efficient, making them ideal for reading large files or infinite streams.

97. What is the difference between yield and return in Python?

Both yield and return are used in functions, but they work very differently. return ends a function and sends back a value. Once return is called, the function ends. On the other hand, yield pauses the function and sends a value but keeps the function state alive for the next call.

With yield, the function becomes a generator. You can use next() to get the next value from it.

Example:

def gen():
    yield 1
    yield 2

g = gen()
print(next(g))  # 1
print(next(g))  # 2

yield is memory-efficient and great for iterating over large or infinite data.

98. What is the Global Interpreter Lock (GIL) in Python?

The Global Interpreter Lock (GIL) is a mechanism in the CPython interpreter that prevents multiple native threads from executing Python bytecodes at the same time. It exists to protect access to Python objects, ensuring thread safety. Because of the GIL, multi-threaded Python programs may not achieve true parallelism on multi-core CPUs for CPU-bound tasks. However, for I/O-bound operations like file handling or network requests, threading can still be beneficial. If you need real parallelism in CPU-heavy operations, consider using multiprocessing or external tools like NumPy or C extensions.

99. How do you handle memory management in Python?

Python handles memory management automatically using a technique called garbage collection. The Python interpreter keeps track of objects and their references using a reference counting system. When an object’s reference count drops to zero, it is deleted automatically.

In addition to reference counting, Python has a cyclic garbage collector to clean up objects involved in reference cycles. You can also manage memory manually using the gc module, but usually, it's not needed.

Example:

import gc
gc.collect()  # Triggers garbage collection

Good practices like avoiding unnecessary global variables and closing files and connections help manage memory better in Python.

100. What is the with statement in Python and why is it used?

The with statement in Python is used for resource management, such as working with files or network connections. It ensures that resources are properly closed after they are used, even if an error occurs during their use.

Example:

with open("file.txt", "r") as file:
    data = file.read()

Here, file is automatically closed when the block ends. This is better than manually calling file.close() because it handles exceptions safely.

The with statement works with context managers, which define __enter__() and __exit__() methods. It makes your code cleaner, safer, and easier to maintain.

I’ve collected all this information from various reliable sources, articles, and websites to bring you a comprehensive list of Python interview questions and answers.

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