Top 25 Python Interview Questions You Must Know in 2025

Python is one of the most popular programming languages due to its simplicity, versatility, and wide range of applications. Whether you're preparing for a technical interview or just looking to deepen your understanding of Python, this article covers the top 25 Python interview questions with detailed explanations and examples. Let's dive in!

1. What is Python, and what are its key features?

Python is a high-level, interpreted programming language known for its simplicity and readability. It supports multiple programming paradigms, including procedural, object-oriented, and functional programming.

Key Features:

• Easy to Learn: Python's syntax is straightforward and easy to understand, making it ideal for beginners.
• Interpreted Language: Python code is executed line by line, which makes debugging easier.
• Dynamically Typed: Variables do not need to be declared with a type, and the type is determined at runtime.
• Extensive Libraries: Python has a rich set of libraries for various applications, such as web development, data analysis, and machine learning.

Key Takeaways:

• Python is versatile and widely used in web development, data science, automation, and more.
• Its simplicity and readability make it a favorite among developers.

2. What are Python's data types?

Python supports several built-in data types, including:

• Numbers: Integers, floats, and complex numbers.
• Strings: Sequences of characters.
• Lists: Ordered, mutable sequences of elements.
• Tuples: Ordered, immutable sequences of elements.
• Dictionaries: Unordered collections of key-value pairs.
• Sets: Unordered collections of unique elements.

  # Example: Python Data Types
  num = 10  # Integer
  pi = 3.14  # Float
  name = "Python"  # String
  fruits = ["apple", "banana", "cherry"]  # List
  coordinates = (10.0, 20.0)  # Tuple
  person = {"name": "Alice", "age": 30}  # Dictionary
  unique_numbers = {1, 2, 3}  # Set
  

Key Takeaways:

• Python's data types are flexible and easy to use.
• Understanding these data types is essential for effective programming.

3. What is the difference between lists and tuples?

Lists: Lists are ordered, mutable sequences of elements. They can be modified after creation.

Tuples: Tuples are ordered, immutable sequences of elements. They cannot be modified after creation.

  # Example: Lists vs Tuples
  fruits_list = ["apple", "banana", "cherry"]
  fruits_tuple = ("apple", "banana", "cherry")

  fruits_list[1] = "blueberry"  # Valid: Lists are mutable
  # fruits_tuple[1] = "blueberry"  # Invalid: Tuples are immutable
  

Key Takeaways:

• Use lists when you need a collection that can change over time.
• Use tuples for collections that should remain constant.

4. What are Python dictionaries, and how are they used?

Dictionaries are unordered collections of key-value pairs. They are used to store data in a way that allows for fast lookups based on keys.

  # Example: Dictionary
  person = {
      "name": "Alice",
      "age": 30,
      "city": "New York"
  }

  # Accessing values
  print(person["name"])  # Output: Alice

  # Adding a new key-value pair
  person["email"] = "alice@example.com"

  # Iterating over a dictionary
  for key, value in person.items():
      print(f"{key}: {value}")
  

Key Takeaways:

• Dictionaries are ideal for storing and retrieving data using unique keys.
• They are highly efficient for lookups and data organization.

5. What is the difference between `==` and `is` in Python?

`==`: Compares the values of two objects.

`is`: Compares the identities (memory addresses) of two objects.

  # Example: == vs is
  list1 = [1, 2, 3]
  list2 = [1, 2, 3]
  list3 = list1

  print(list1 == list2)  # True: Values are the same
  print(list1 is list2)  # False: Different objects
  print(list1 is list3)  # True: Same object
  

Key Takeaways:

• Use `==` to compare values.
• Use `is` to check if two variables reference the same object.

6. What are Python decorators, and how do they work?

Decorators are functions that modify the behavior of other functions or methods. They are often used for logging, access control, and memoization.

  # Example: Decorator
  def my_decorator(func):
      def wrapper():
          print("Something is happening before the function is called.")
          func()
          print("Something is happening after the function is called.")
      return wrapper

  @my_decorator
  def say_hello():
      print("Hello!")

  say_hello()
  # Output:
  # Something is happening before the function is called.
  # Hello!
  # Something is happening after the function is called.
  

Key Takeaways:

• Decorators are a powerful way to extend the functionality of functions.
• They are widely used in frameworks like Flask and Django.

7. What is the difference between `deepcopy` and `copy`?

`copy`: Creates a shallow copy of an object. Nested objects are not duplicated; only references are copied.

`deepcopy`: Creates a deep copy of an object. Nested objects are duplicated recursively.

  # Example: copy vs deepcopy
  import copy

  list1 = [[1, 2, 3], [4, 5, 6]]
  list2 = copy.copy(list1)
  list3 = copy.deepcopy(list1)

  list1[0][0] = 99

  print(list1)  # [[99, 2, 3], [4, 5, 6]]
  print(list2)  # [[99, 2, 3], [4, 5, 6]] (shallow copy)
  print(list3)  # [[1, 2, 3], [4, 5, 6]] (deep copy)
  

Key Takeaways:

• Use `copy` for shallow copies when nested objects do not need to be duplicated.
• Use `deepcopy` for deep copies when nested objects need to be duplicated.

8. What are Python generators, and how do they work?

Generators are functions that return an iterator. They use the `yield` keyword to produce a sequence of values lazily, one at a time.

  # Example: Generator
  def my_generator():
      yield 1
      yield 2
      yield 3

  gen = my_generator()
  print(next(gen))  # 1
  print(next(gen))  # 2
  print(next(gen))  # 3
  

Key Takeaways:

• Generators are memory-efficient because they produce values on-the-fly.
• They are ideal for working with large datasets or infinite sequences.

9. What is the difference between `*args` and `**kwargs`?

`*args`: Used to pass a variable number of positional arguments to a function.

`**kwargs`: Used to pass a variable number of keyword arguments to a function.

  # Example: *args and **kwargs
  def my_function(*args, **kwargs):
      print("Positional arguments:", args)
      print("Keyword arguments:", kwargs)

  my_function(1, 2, 3, name="Alice", age=30)
  # Output:
  # Positional arguments: (1, 2, 3)
  # Keyword arguments: {'name': 'Alice', 'age': 30}
  

Key Takeaways:

• Use `*args` for handling variable positional arguments.
• Use `**kwargs` for handling variable keyword arguments.

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

The Global Interpreter Lock (GIL) is a mutex that prevents multiple native threads from executing Python bytecode simultaneously. This means that even in a multi-threaded Python program, only one thread can execute Python code at a time.

Key Takeaways:

• The GIL can be a bottleneck for CPU-bound multi-threaded programs.
• For I/O-bound tasks, multi-threading can still be beneficial.

11. What are Python list comprehensions, and how are they used?

List comprehensions provide a concise way to create lists. They consist of an expression followed by a `for` clause and optionally one or more `if` clauses.

  # Example: List Comprehension
  squares = [x ** 2 for x in range(10)]
  print(squares)  # [0, 1, 4, 9, 16, 25, 36, 49, 64, 81]

  # Example: List Comprehension with Condition
  even_squares = [x ** 2 for x in range(10) if x % 2 == 0]
  print(even_squares)  # [0, 4, 16, 36, 64]
  

Key Takeaways:

• List comprehensions are concise and readable.
• They are ideal for transforming and filtering lists.

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

`append()`: Adds a single element to the end of the list.

`extend()`: Adds all elements of an iterable to the end of the list.

  # Example: append() vs extend()
  list1 = [1, 2, 3]
  list2 = [4, 5]

  list1.append(list2)
  print(list1)  # [1, 2, 3, [4, 5]]

  list1.extend(list2)
  print(list1)  # [1, 2, 3, 4, 5]
  

Key Takeaways:

• Use `append()` to add a single element.
• Use `extend()` to add multiple elements from an iterable.

13. What is the purpose of the `with` statement in Python?

The `with` statement is used for resource management, such as opening and closing files. It ensures that resources are properly managed, even if an exception occurs.

  # Example: Using with for File Handling
  with open("example.txt", "r") as file:
      content = file.read()
      print(content)
  # The file is automatically closed after the block
  

Key Takeaways:

• The `with` statement simplifies resource management.
• It is commonly used for file handling, database connections, and more.

14. What are Python lambda functions, and how are they used?

Lambda functions are small, anonymous functions defined using the `lambda` keyword. They are often used for short, throwaway functions.

  # Example: Lambda Function
  add = lambda x, y: x + y
  print(add(2, 3))  # 5

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

Key Takeaways:

• Lambda functions are concise and useful for simple operations.
• They are often used with functions like `map()`, `filter()`, and `sorted()`.

15. What is the difference between `__str__` and `__repr__` in Python?

`__str__`: Provides a user-friendly string representation of an object. It is used by the `str()` function and `print()`.

`__repr__`: Provides a detailed, unambiguous string representation of an object. It is used by the `repr()` function and for debugging.

  # Example: __str__ vs __repr__
  class Person:
      def __init__(self, name, age):
          self.name = name
          self.age = age

      def __str__(self):
          return f"Person(name={self.name}, age={self.age})"

      def __repr__(self):
          return f"Person(name={self.name}, age={self.age})"

  person = Person("Alice", 30)
  print(str(person))  # Person(name=Alice, age=30)
  print(repr(person))  # Person(name=Alice, age=30)
  

Key Takeaways:

• Use `__str__` for a readable representation of the object.
• Use `__repr__` for a detailed representation, especially for debugging.

16. What is the purpose of the `__init__` method in Python?

The `__init__` method is a special method in Python classes that is automatically called when an object is created. It is used to initialize the object's attributes.

  # Example: __init__ Method
  class Person:
      def __init__(self, name, age):
          self.name = name
          self.age = age

  person = Person("Alice", 30)
  print(person.name)  # Alice
  print(person.age)  # 30
  

Key Takeaways:

• The `__init__` method is used to set up the initial state of an object.
• It is called automatically when an object is instantiated.

17. What is the difference between `__init__` and `__new__` in Python?

`__new__`: A static method that creates and returns a new instance of a class. It is called before `__init__`.

`__init__`: Initializes the attributes of an instance after it has been created by `__new__`.

  # Example: __new__ vs __init__
  class MyClass:
      def __new__(cls, *args, **kwargs):
          print("Creating instance")
          instance = super().__new__(cls)
          return instance

      def __init__(self, value):
          print("Initializing instance")
          self.value = value

  obj = MyClass(10)
  # Output:
  # Creating instance
  # Initializing instance
  

Key Takeaways:

• Use `__new__` to control the creation of an instance.
• Use `__init__` to initialize the instance's attributes.

18. What is the purpose of the `super()` function in Python?

The `super()` function is used to call a method from a parent class. It is commonly used in inheritance to avoid hardcoding the parent class name.

  # Example: Using super()
  class Parent:
      def __init__(self, name):
          self.name = name

  class Child(Parent):
      def __init__(self, name, age):
          super().__init__(name)
          self.age = age

  child = Child("Alice", 10)
  print(child.name)  # Alice
  print(child.age)  # 10
  

Key Takeaways:

• `super()` simplifies calling parent class methods.
• It is essential for maintaining clean and reusable code in inheritance.

19. What is the difference between `isinstance()` and `type()` in Python?

`isinstance()`: Checks if an object is an instance of a class or a tuple of classes.

`type()`: Returns the type of an object.

  # Example: isinstance() vs type()
  num = 10

  print(isinstance(num, int))  # True
  print(type(num) == int)  # True

  # isinstance() works with inheritance
  class Parent:
      pass

  class Child(Parent):
      pass

  obj = Child()
  print(isinstance(obj, Parent))  # True
  print(type(obj) == Parent))  # False
  

Key Takeaways:

• Use `isinstance()` to check if an object is an instance of a class or its subclasses.
• Use `type()` to get the exact type of an object.

20. What are Python modules and packages?

Modules: Python files containing reusable code (functions, classes, variables).

Packages: Collections of modules organized in directories with an `__init__.py` file.

  # Example: Module (math_operations.py)
  def add(a, b):
      return a + b

  # Example: Package (my_package/__init__.py)
  # Importing a module from a package
  from my_package import math_operations
  print(math_operations.add(2, 3))  # 5
  

Key Takeaways:

• Modules and packages help organize and reuse code.
• Packages are directories containing modules and an `__init__.py` file.

21. What is the purpose of the `__name__` variable in Python?

The `__name__` variable is a special built-in variable that holds the name of the current module. When a Python file is run directly, `__name__` is set to `"__main__"`. When it is imported as a module, `__name__` is set to the module's name.

  # Example: Using __name__
  def main():
      print("This is the main function.")

  if __name__ == "__main__":
      main()
  

Key Takeaways:

• Use `__name__` to determine if a script is being run directly or imported as a module.
• It is commonly used to define executable code in a module.

22. What are Python iterators, and how do they work?

Iterators are objects that allow you to traverse through all the elements of a collection (e.g., lists, tuples). They implement the `__iter__()` and `__next__()` methods.

  # Example: Iterator
  class MyIterator:
      def __init__(self, data):
          self.data = data
          self.index = 0

      def __iter__(self):
          return self

      def __next__(self):
          if self.index >= len(self.data):
              raise StopIteration
          value = self.data[self.index]
          self.index += 1
          return value

  my_iter = MyIterator([1, 2, 3])
  for item in my_iter:
      print(item)  # 1, 2, 3
  

Key Takeaways:

• Iterators are memory-efficient because they produce elements on-the-fly.
• They are the foundation of Python's `for` loop.

23. What is the difference between `range()` and `xrange()` in Python?

`range()`: Returns a list of numbers in Python 2 and a range object in Python 3. It generates all numbers at once.

`xrange()`: Returns an iterator in Python 2 that generates numbers on-the-fly. It is not available in Python 3.

  # Example: range() in Python 3
  for i in range(3):
      print(i)  # 0, 1, 2

  # Example: xrange() in Python 2 (not available in Python 3)
  # for i in xrange(3):
  #     print(i)  # 0, 1, 2
  

Key Takeaways:

• In Python 3, `range()` behaves like `xrange()` in Python 2, generating numbers lazily.
• Use `range()` for creating sequences of numbers in Python 3.

24. What is the purpose of the `zip()` function in Python?

The `zip()` function combines multiple iterables (e.g., lists, tuples) into a single iterable of tuples. It stops when the shortest iterable is exhausted.

  # Example: Using zip()
  names = ["Alice", "Bob", "Charlie"]
  ages = [25, 30, 35]

  combined = zip(names, ages)
  for name, age in combined:
      print(f"{name} is {age} years old.")
  # Output:
  # Alice is 25 years old.
  # Bob is 30 years old.
  # Charlie is 35 years old.
  

Key Takeaways:

• `zip()` is useful for pairing elements from multiple iterables.
• It stops when the shortest iterable is exhausted.

25. What are Python decorators, and how are they used?

Decorators are functions that modify the behavior of other functions or methods. They are often used for logging, access control, and memoization.

  # Example: Decorator
  def my_decorator(func):
      def wrapper():
          print("Something is happening before the function is called.")
          func()
          print("Something is happening after the function is called.")
      return wrapper

  @my_decorator
  def say_hello():
      print("Hello!")

  say_hello()
  # Output:
  # Something is happening before the function is called.
  # Hello!
  # Something is happening after the function is called.
  

Key Takeaways:

• Decorators are a powerful way to extend the functionality of functions.
• They are widely used in frameworks like Flask and Django.

Congratulations! You've now explored the top 25 Python interview questions. These concepts are essential for mastering Python and excelling in technical interviews. Keep practicing, and happy coding!