Interview Questions

Top 30 Python Interview Questions And Answers For Experienced Developer

Siddharth Verma

• 13 min read
Top 30 Python Interview Questions And Answers For Experienced Developer

Are you an experienced Python developer looking to nail your next job interview? Whether you're on the hunt for a new opportunity or simply want to brush up on your skills, you've come to the right place.

I'm Siddharth, a Python generalist with over a decade of experience in this versatile language. I've had the privilege of conducting hundreds of Python interviews, and I understand the challenges and expectations that come with them. In this blog series, I'm excited to share my insights and expertise with you.

So, grab your favorite cup of coffee, settle into a comfortable spot, and let's dive deep into Python interview questions that will help you stand out from the crowd. Whether you're aiming for a senior developer position, a data science role, or any other Python-related opportunity, this guide will provide you with the knowledge and confidence you need to ace your interview.

Let's get started! ☄️

Common Python Interview Questions for Experienced Developers

What is the purpose of PEP 8?

"Python Enhancement Proposal #8", it serves as a style guide encapsulating conventions pertaining to writing readable and consistent code in python ecosystem. What this means – simply put – adherence to PEP-8 makes your code easier for others (as well as future 'you') to read and comprehend

What is the difference between deep and shallow copy in Python?

A simple copy (shallow copy) of a collection constructs a new collection object then populates it with references to child objects found in the original. Meanwhile, for complex objects like lists or dictionaries within lists, a deep copy will construct not just a fresh parent object but infuses with duplicates of all children as well.

What are Lambda functions in python and when to use them ?

Lambda functions in Python are anonymous, small, inline functions defined using lambda. They're used for concise operations, often when passing a simple function as an argument to functions like map(), filter(), or sorted().

For example, you can double each element in a list with map(lambda x: x * 2, numbers). Lambda functions are handy for short, one-off tasks, but for more complex or reusable functions, it's better to use regular def functions.

numbers = [1, 2, 3, 4, 5]
doubled = list(map(lambda x: x * 2, numbers))
# doubled will be [2, 4, 6, 8, 10]

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
even_numbers = list(filter(lambda x: x % 2 == 0, numbers))
# even_numbers will be [2, 4, 6, 8, 10]

What is a decorator in Python ?

A decorator in Python is a design pattern that allows you to modify or enhance the behavior of functions or methods without changing their source code. It's like adding a layer of functionality to an existing function. Decorators are often used for tasks like logging, authorization, or measuring execution time.

In Python, decorators are implemented using functions. They take a function as an argument, add some functionality to it, and then return the modified function. You can apply decorators to functions or methods by using the "@" symbol above the function definition.

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()

What is the purpose of the yield keyword in Python?

The yield keyword in Python is used in the context of defining a generator function. Its purpose is to create an iterable sequence of values without loading them all into memory at once. Instead of returning a result like a regular function, a generator function yields values one at a time, pausing its execution state between each yield. This is incredibly useful for handling large datasets or generating an infinite sequence.

def countdown(n):
    while n > 0:
        yield n
        n -= 1

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

How do you implement inheritance in Python?

In Python, you implement inheritance by creating a subclass (also known as a derived or child class) that inherits attributes and methods from a superclass (also known as a base or parent class). This allows you to create a hierarchy of classes, where the subclass can reuse and extend the functionality of the superclass.

Inheritance allows you to create a hierarchy of classes, promoting code reuse and organization. It also supports polymorphism, where different classes can provide their own implementations of methods with the same name, allowing for flexible and extensible code.

Here's how you implement inheritance:

class Animal:
    def __init__(self, name, species):
        self.name = name
        self.species = species

    def speak(self):
        pass  # Placeholder method to be overridden by subclasses

class Dog(Animal):
    def __init__(self, name, breed):
        super().__init__(name, species="Dog")
        self.breed = breed

    def speak(self):
        return f"{self.name} says Woof!"

dog = Dog("Buddy", "Golden Retriever")
print(dog.name)        # Output: Buddy
print(dog.species)     # Output: Dog
print(dog.speak())     # Output: Buddy says Woof!

What is the Global Interpreter Lock (GIL) in Python, and how does it affect multi-threading?

The Global Interpreter Lock (GIL) in Python is a mutex (short for mutual exclusion) that allows only one thread to execute in the Python interpreter at a time, even on multi-core processors. This means that Python's multi-threading doesn't provide true parallelism for CPU-bound tasks, as only one thread can execute Python bytecode at a given moment.

However, the GIL doesn't significantly impact multi-threading for I/O-bound tasks because threads can release the GIL while waiting for I/O operations, allowing other threads to run.

In essence, the GIL can limit the performance benefits of multi-threading in CPU-bound scenarios, but it doesn't necessarily hinder multi-threading's usefulness for I/O-bound tasks. To achieve true parallelism for CPU-bound operations, Python developers often use multiprocessing, which creates separate processes, each with its own Python interpreter and memory space.

Explain the concept of metaclasses in Python.

Metaclasses in Python are a powerful yet advanced concept that allow you to define the behavior of classes themselves. They are like "classes for classes."

You can use metaclasses to control how classes are created, modified, or customized. One common use is to enforce coding standards or add specific functionality to all instances of a class.

class MyMeta(type):
    def __init__(cls, name, bases, attrs):
        attrs['custom_attr'] = 42
        super().__init__(name, bases, attrs)

class MyClass(metaclass=MyMeta):
    pass

obj = MyClass()
print(obj.custom_attr)  # Outputs: 42

How do you create a custom exception class in Python?

To create a custom exception class in Python, you can define a new class that inherits from the built-in Exception class or one of its subclasses. 

class CustomException(Exception):
    def __init__(self, message):
        super().__init__(message)

# Raise the custom exception
raise CustomException("This is a custom exception message")

What are generators in Python, and how are they different from regular functions?

Generators in Python are functions that use the yield keyword to produce a sequence of values lazily, one at a time. They are different from regular functions because they don't compute and store all values in memory at once. Instead, they generate values on-the-fly, making them memory-efficient for large datasets.

Explain the use of context managers in Python.

Context managers in Python are used to manage resources, such as files or network connections, by ensuring that they are properly acquired and released. They simplify resource management and help avoid issues like resource leaks.

The with statement is used to create a context manager. It sets up the resource before entering the code block and ensures that it is properly cleaned up afterward. For example, when working with files:

with open('example.txt', 'r') as file:
    data = file.read()
# File is automatically closed outside the 'with' block

How does Python's memory management work, including reference counting and garbage collection?

Python's memory management involves two key mechanisms: reference counting and garbage collection.

Reference Counting: Python uses reference counting to keep track of how many references there are to an object. When an object's reference count drops to zero, it means there are no more references to that object, and Python can reclaim its memory automatically. For example:

a = [1, 2, 3]  # Reference count of the list is 1
b = a          # Reference count becomes 2
del a          # Reference count becomes 1
b = None       # Reference count becomes 0, and memory is reclaimed

Garbage Collection: While reference counting is efficient for most cases, it may not handle cyclic references where objects reference each other, preventing their reference counts from reaching zero. Python's garbage collector detects and collects such cyclic references. It uses the cyclic garbage collector module (gc) to identify and clean up these objects.

import gc

class Node:
    def __init__(self):
        self.next = None

a = Node()
b = Node()
a.next = b
b.next = a  # Creates a cyclic reference

gc.collect()  # Garbage collector reclaims memory for the cyclic references

What is a closure in Python, and how can it be used in practice?

A closure in Python is a function that retains access to variables from its containing or outer scope, even after that scope has finished executing. It "closes over" these variables, allowing the function to remember and use them later.

In practice, closures are often used for data encapsulation, creating private variables, and implementing decorators.

def outer_function(x):
    def inner_function(y):
        return x + y
    return inner_function

closure = outer_function(10)
result = closure(5)  # result is 15, as it remembers the value of 'x'

Describe the purpose and usage of the collections module in Python.

The collections module in Python provides specialized container data types beyond the built-in data structures like lists and dictionaries. It's designed to make certain operations more efficient and convenient.

One of its most commonly used classes is collections.Counter, which is used to count the occurrences of elements in a collection, often used with lists:

from collections import Counter

my_list = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4]

count = Counter(my_list)
print(count[3])  # Outputs: 3, as 3 appears three times in the list

Describe the principles of duck typing in Python, and provide an example of its use.

Duck typing in Python is a dynamic typing concept where an object's suitability for a particular operation is determined by its behavior (methods and properties) rather than its type. If it walks like a duck and quacks like a duck, then it's treated as a duck. In other words, Python focuses on what an object can do rather than what it is.

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

class Dog:
    def quack(self):
        print("Woof!")

def make_sound(animal):
    animal.quack()

duck = Duck()
dog = Dog()

make_sound(duck)  # Outputs: "Quack!"
make_sound(dog)   # Outputs: "Woof!"

What is monkey patching in Python, and when might you use it?

Monkey patching in Python refers to the practice of dynamically modifying or extending existing classes or modules at runtime. It allows developers to add, modify, or replace methods and attributes of classes or modules without changing their source code.

Monkey patching is typically used when:

  1. You need to fix a bug or add a feature to a library or module for which you don't have access to the source code.
  2. You want to temporarily modify the behavior of a class or module for testing or debugging purposes.

Here's a simple example of monkey patching:

# Original class definition
class Dog:
    def bark(self):
        return "Woof!"

# Monkey patching to change the bark behavior
def meow(self):
    return "Meow!"

Dog.bark = meow  # Replace the 'bark' method with 'meow'

dog = Dog()
print(dog.bark())  # Output: "Meow!"

Describe the differences between Python's asyncio and multi-threading for concurrent programming.

Concurrency Model:

  • Asyncio: Based on an asynchronous, single-threaded event loop. It allows non-blocking I/O operations.
  • Multi-threading: Uses multiple threads, each running concurrently with its own stack and program counter.

Parallelism:

  • Asyncio: Achieves concurrency without parallelism, suitable for I/O-bound tasks.
  • Multi-threading: Provides true parallelism on multi-core CPUs, beneficial for CPU-bound tasks.

Resource Overhead:

  • Asyncio: Lower resource overhead due to single-threaded nature.
  • Multi-threading: Higher resource overhead, each thread consumes memory.

Synchronization:

  • Asyncio: Synchronization handled with async/await keywords, minimizing race conditions.
  • Multi-threading: Requires locks and synchronization mechanisms, prone to race conditions.

What are static methods in Python?

Static methods in Python are methods that belong to a class rather than an instance of the class. They don't have access to the instance-specific data and are primarily used for utility functions associated with the class. To define a static method, you use the @staticmethod decorator.

class MyClass:
    def __init__(self, value):
        self.value = value

    @staticmethod
    def static_method():
        print("This is a static method")
        #This doesn't have access to self.value

# Usage
obj = MyClass(42)
obj.static_method()

Describe the differences between the pickle and json modules in Python for serializing and deserializing data.


The pickle and json modules in Python are used for serializing and deserializing data, but they have some key differences:

Data Type Support:

  • pickle: Supports serializing a wider range of Python data types, including custom classes and functions.
  • json: Supports a more limited set of data types, primarily built-in types like dictionaries, lists, strings, and numbers.

Human-Readable:

  • pickle: Produces binary data that is not human-readable.
  • json: Produces a text-based format that is human-readable.

Security:

  • pickle: Can execute arbitrary code from unpickled data, making it potentially unsafe when loading data from untrusted sources.
  • json: Is safer for deserializing data from untrusted sources because it doesn't execute code.

What are some common performance optimization techniques in Python for improving code execution speed?

Common performance optimization techniques in Python include:

  1. Use Built-in Functions: Utilize Python's built-in functions and libraries, as they are typically optimized for performance. For example, use sum() instead of manually summing elements in a list.
  2. List Comprehensions: Use list comprehensions for concise and faster list operations. For instance, [x**2 for x in range(1, 11)] is faster than a for loop.
  3. Generator Expressions: Use generators with yield to avoid loading large data sets into memory at once, improving efficiency.
  4. Avoid Global Variables: Minimize the use of global variables, as accessing them can be slower than local variables.
  5. Profile Your Code: Use profilers like cProfile to identify bottlenecks in your code and focus on optimizing the slowest parts.
  6. Cython or Numba: Consider using Cython or Numba to compile Python code to C, enhancing execution speed for critical sections.
  7. Algorithm Optimization: Choose efficient algorithms and data structures for your specific problem. For example, use sets for fast membership tests.
  8. Concurrency: Utilize multiprocessing or threading for CPU-bound tasks, and asyncio for I/O-bound tasks to achieve parallelism.
  9. Avoid Unnecessary Operations: Minimize unnecessary calculations or operations, especially in loops. Cache results if possible.

How can you profile and optimize Python code for performance, and what tools are available for this purpose?

Profiling
 Use the built-in cProfile module to profile your code.

import cProfile

def my_function():
    # Your code here

cProfile.run("my_function()")

Optimizing:

  • Identify bottlenecks using profiling results.
  • Optimize algorithms, data structures, and code logic.
  • Use built-in functions and libraries efficiently.

Tools:
timeit: Measure execution time for small code snippets.

import timeit
execution_time = timeit.timeit("my_function()", globals=globals(), number=1000)

line_profiler: Line-by-line profiling for detailed insights.

from line_profiler import LineProfiler

profiler = LineProfiler()

@profiler.profile
def my_function():
    # Your code here

my_function()
profiler.print_stats()

memory_profiler: Profile memory usage.

from memory_profiler import profile

@profile
def my_function():
    # Your code here

my_function()

What is metaprogramming in Python, and how can you use it to dynamically generate code?

Metaprogramming in Python is a technique that allows you to write code that manipulates or generates other code during runtime. It enables you to create, modify, or inspect code dynamically. Python provides features like introspection, decorators, and functions like eval() and exec() to perform metaprogramming.

For dynamically generating code, you can use tools like string manipulation and template strings. Here's a simple example of dynamically generating a function:

function_name = "greet"
function_code = f"def {function_name}(name): return f'Hello, {name}!'"
exec(function_code)

result = eval(function_name)("Alice")
print(result)  # Output: Hello, Alice!

How can you use the unittest library for writing and running tests in Python?

  1. Import: Import the unittest library.
  2. Create Test Classes: Create classes that inherit from unittest.TestCase.
  3. Define Test Methods: Write test methods within the test classes, using methods like assertEqual() to check expected outcomes.
  4. Run Tests: Use the unittest test runner to execute tests.
import unittest

class MyTestCase(unittest.TestCase):
    def test_addition(self):
        result = 1 + 2
        self.assertEqual(result, 3)

if __name__ == "__main__":
    unittest.main()

What is the purpose of the if __name__ == "__main__": block in a Python script?

The if __name__ == "__main__": block in a Python script serves to check if the script is being run directly as the main program or if it's being imported as a module into another script. It ensures that certain code only runs when the script is executed directly and not when it's imported elsewhere.

# script.py

def main():
    print("This is the main function")

if __name__ == "__main__":
    main()

Describe the use of docstrings in Python, and what tools can be used to generate documentation from them.

Docstrings in Python are used to provide documentation for functions, classes, modules, or methods. They are enclosed in triple-quotes (''' or """) and are placed immediately after the definition of the function, class, or module. Docstrings help developers understand how to use and interact with code, making it more readable and maintainable.

def add(a, b):
    '''This function adds two numbers.'''
    return a + b

Explain the concept of "pythonic" code and provide examples of code that follows Pythonic conventions.

"Pythonic" code refers to writing Python code in a way that follows the idiomatic style and best practices of the Python programming language. It emphasizes readability, simplicity, and adherence to Python's design philosophy. Pythonic code is clean, concise, and makes use of Python's unique features.

What are some common design patterns in Python, and when should they be applied?


Singleton Pattern:

  • When: Use it when you need only one instance of a class.
  • Example: Implement a database connection pool.

Factory Pattern:

  • When: Use it when you want to create objects without specifying the exact class.
  • Example: Create different types of documents (e.g., PDF, Word) using a document factory.

Decorator Pattern:

  • When: Use it to add behavior to individual objects dynamically.
  • Example: Add logging or authorization to functions/methods.

Observer Pattern:

  • When: Use it for implementing event handling systems.
  • Example: Implement a notification system where multiple subscribers react to updates.

Strategy Pattern:

  • When: Use it to define a family of interchangeable algorithms.
  • Example: Implement various payment methods (e.g., credit card, PayPal) for an e-commerce system.

Adapter Pattern:

  • When: Use it to make incompatible interfaces work together.
  • Example: Adapt a third-party library to your system's interface.

Command Pattern:

  • When: Use it to encapsulate requests or operations as objects.
  • Example: Implement an undo/redo functionality in a text editor.

Builder Pattern:

  • When: Use it to construct complex objects step by step.
  • Example: Create custom orders with various options in an online shopping cart.

What are data classes in Python, and how do they simplify the creation of simple classes for storing data?

Data classes in Python are a feature introduced in Python 3.7 (PEP 557) that simplify the creation of classes primarily used for storing data. They are used to reduce boilerplate code typically associated with simple classes by automatically generating special methods like __init__(), __repr__(), and __eq__().

To create a data class, you use the @dataclass decorator and annotate class attributes with their types. Here's a brief example:

from dataclasses import dataclass

@dataclass
class Point:
    x: int
    y: int

p1 = Point(1, 2)
p2 = Point(1, 2)

print(p1 == p2)  # Output: True

What is the purpose of type hinting in Python, and how can it improve code quality and readability?

Type hinting in Python is used to specify the expected data types of variables, function parameters, and return values. It enhances code quality and readability by:

  1. Improved Code Quality: Type hints help catch type-related errors early during development, reducing bugs and improving code reliability.
  2. Enhanced Readability: Type hints make code more self-documenting by providing clear information about the expected data types. This helps developers understand and maintain the code more easily.
def add_numbers(x: int, y: int) -> int:
    return x + y

Describe the benefits of using virtual environments in Python development.

Using virtual environments in Python development offers several key benefits:

Isolation: Virtual environments create isolated environments for Python projects, preventing conflicts between packages and dependencies.

Dependency Management: You can install project-specific packages without affecting the system-wide Python installation. This ensures that your project uses the correct versions of libraries.bash

Version Compatibility: Virtual environments allow you to work with different Python versions for various projects, ensuring compatibility.bash

Clean Project Structure: Virtual environments promote clean project structures, making it easier to share, replicate, and deploy Python projects with their specific dependencies.