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How I made my Django project almost as fast as FastAPI

Leave a Comment / django, python / Tarun

FastAPI runs on Uvicorn, an ASGI server made for Python code that runs at the same time. Django is older and has more features, but from version 3.0, it can also operate on ASGI with Uvicorn. Once you set up Django on Uvicorn and make queries and caching work better, you can get the same speed for most things. 1. Start Django with Uvicorn The best way to improve performance is to switch to an ASGI server. Install Uvicorn Make sure your project has a asgi.py file, which is made automatically in Django 3+. Then turn on the server: uvicorn myproject.asgi:application –host 0.0.0.0 –port 8000 –workers 4 Why Uvicorn If you use a process manager like Supervisor or systemd, you can add: 2. Use async views where possible Why use httpx instead of requests: It lets you send HTTP requests (GET, POST, etc.) and handle responses, similar to requests, but it also supports asynchronous programming (async/await). That means you can make many API calls at once without blocking your Django or FastAPI app, ideal for performance and concurrency. import httpx from django.http import JsonResponse async def price_view(request): async with httpx.AsyncClient() as client: r = await client.get(‘https://api.example.com/price’) return JsonResponse(r.json()) For ORM queries, still use sync code or wrap it with sync_to_async: from asgiref.sync import sync_to_async from django.contrib.auth.models import User @sync_to_async def get_user(pk): return User.objects.get(pk=pk) async def user_view(request): user = await get_user(1) return JsonResponse({‘username’: user.username}) 3. Optimize your database Example: posts = Post.objects.select_related(‘author’).all() 4. Enable caching with Redis Install Redis and configure Django: pip install django-redis Add this to settings.py: CACHES = { ‘default’: { ‘BACKEND’: ‘django_redis.cache.RedisCache’, ‘LOCATION’: ‘redis://127.0.0.1:6379/1’, ‘OPTIONS’: { ‘CLIENT_CLASS’: ‘django_redis.client.DefaultClient’, } } } Cache heavy views: from django.views.decorators.cache import cache_page @cache_page(60) def home(request): return render(request, ‘home.html’) 5. Offload background work Use Celery or Dramatiq to handle slow tasks like emails or large file uploads asynchronously. 6. Serve static files efficiently Use WhiteNoise for small deployments or a CDN (Cloudflare, S3 + CloudFront) for large ones. MIDDLEWARE = [ ‘django.middleware.security.SecurityMiddleware’, ‘whitenoise.middleware.WhiteNoiseMiddleware’, # … ] 7. Monitor performance Example Benchmark Running the same Django app under Uvicorn vs Gunicorn (WSGI): Server Avg Latency Req/s Gunicorn (WSGI) 90 ms 700 Uvicorn (ASGI) 40 ms 1400 Final Thoughts FastAPI may always win in pure async benchmarks, but Django + Uvicorn can be nearly as fast for most production workloads — and you keep Django’s ORM, admin, and ecosystem. Checklist:

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How to Flatten a List of Lists in Python

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5 Best Python Books for Beginners

Python Cheat Sheet

Leave a Comment / python / Tarun

Whether you’re a beginner just starting with Python or a seasoned developer needing a quick refresher, this Python Cheat Sheet has you covered! This concise guide includes essential syntax, common functions, data structures, loops, conditionals, file handling, and more. Keep it handy while coding or studying to boost your productivity and confidence. Dive in and supercharge your Python skills with this all-in-one reference! Basic Syntax Python uses clean, readable syntax without semicolons or curly braces. Indentation matters – it defines code blocks! Variables and Data Types Python is dynamically typed – you don’t need to declare variable types. Just assign and go! # Variables (no declaration needed) name = “Alice” age = 25 height = 5.6 is_student = True # Data types str_var = “Hello” # String int_var = 42 # Integer float_var = 3.14 # Float bool_var = True # Boolean list_var = [1, 2, 3] # List tuple_var = (1, 2, 3) # Tuple dict_var = {“key”: “value”} # Dictionary set_var = {1, 2, 3} # Set Control Structures These are the building blocks that control how your program flows and makes decisions. If Statements Make your program smart by adding decision-making logic. if age >= 18: print(“Adult”) elif age >= 13: print(“Teenager”) else: print(“Child”) Loops Automate repetitive tasks – let Python do the boring work for you! # For loop for i in range(5): print(i) for item in [1, 2, 3]: print(item) # While loop count = 0 while count < 5: print(count) count += 1 Data Structures Python’s built-in containers for organizing and storing your data efficiently. Lists The Swiss Army knife of Python data structures – ordered, changeable, and versatile. # Creating and modifying lst = [1, 2, 3, 4, 5] lst.append(6) # Add to end lst.insert(0, 0) # Insert at index lst.remove(3) # Remove first occurrence lst.pop() # Remove last item lst[0] = 10 # Change item len(lst) # Length Dictionaries Key-value pairs that let you store and retrieve data like a real-world dictionary. # Creating and accessing person = {“name”: “Alice”, “age”: 25} person[“name”] # Access value person[“city”] = “NYC” # Add new key-value del person[“age”] # Delete key person.keys() # Get all keys person.values() # Get all values person.get(“name”, “Unknown”) # Safe access Sets Collections of unique items – perfect when you need to eliminate duplicates or check membership. # Creating and operations s1 = {1, 2, 3, 4} s2 = {3, 4, 5, 6} s1.add(5) # Add element s1.remove(1) # Remove element s1 & s2 # Intersection s1 | s2 # Union s1 – s2 # Difference Functions Write reusable code blocks that make your programs modular and easier to maintain. Basic Functions Define once, use everywhere – functions are your best friend for organized code. def greet(name, greeting=”Hello”): return f”{greeting}, {name}!” def add_numbers(*args): return sum(args) def person_info(**kwargs): for key, value in kwargs.items(): print(f”{key}: {value}”) # Lambda functions square = lambda x: x**2 List Comprehensions Python’s elegant way to create lists in a single line – concise and powerful! # Basic list comprehension squares = [x**2 for x in range(10)] # With condition evens = [x for x in range(20) if x % 2 == 0] # Dictionary comprehension square_dict = {x: x**2 for x in range(5)} # Set comprehension unique_chars = {char for char in “hello world”} String Operations Text manipulation made easy – Python treats strings like first-class citizens. File Operations Read from and write to files – your gateway to persistent data storage. # Reading files with open(“file.txt”, “r”) as f: content = f.read() lines = f.readlines() # Writing files with open(“file.txt”, “w”) as f: f.write(“Hello World”) f.writelines([“Line 1\n”, “Line 2\n”]) Exception Handling Handle errors gracefully – because things don’t always go as planned! try: result = 10 / 0 except ZeroDivisionError: print(“Cannot divide by zero”) except Exception as e: print(f”An error occurred: {e}”) else: print(“No errors occurred”) finally: print(“This always executes”) Classes and Objects Object-oriented programming in Python – create your custom data types and behaviors. class Person: def __init__(self, name, age): self.name = name self.age = age def greet(self): return f”Hi, I’m {self.name}” def __str__(self): return f”Person(name='{self.name}’, age={self.age})” # Usage person = Person(“Alice”, 25) print(person.greet()) Common Built-in Functions Python’s toolbox of ready-to-use functions that save you time and effort. # Math functions abs(-5) # Absolute value min(1, 2, 3) # Minimum max(1, 2, 3) # Maximum sum([1, 2, 3]) # Sum of iterable round(3.14159, 2) # Round to 2 decimals # Type functions type(42) # Get type isinstance(42, int) # Check type len([1, 2, 3]) # Length # Iteration functions enumerate([1, 2, 3]) # Returns (index, value) pairs zip([1, 2], [‘a’, ‘b’]) # Combine iterables reversed([1, 2, 3]) # Reverse iterator sorted([3, 1, 2]) # Return sorted list Import and Modules Extend Python’s capabilities by using libraries – standing on the shoulders of giants! import math from math import pi, sqrt import numpy as np from datetime import datetime, timedelta # Using imports math.sqrt(16) pi np.array([1, 2, 3]) datetime.now() Common Patterns Real-world examples of frequently used Python patterns that every developer should know. Working with the Os module Files and Directories Navigate and manipulate your file system like a pro. import os os.listdir(‘.’) # List directory contents os.path.exists(‘file.txt’) # Check if file exists os.path.join(‘folder’, ‘file.txt’) # Join paths Date and Time Work with dates and times – essential for logging, scheduling, and data analysis. from datetime import datetime, timedelta now = datetime.now() tomorrow = now + timedelta(days=1) formatted = now.strftime(‘%Y-%m-%d %H:%M:%S’) Regular Expressions Pattern matching and text processing – powerful tools for working with strings. import re pattern = r’\d+’ # Match digits re.findall(pattern, “I have 5 apples and 3 oranges”) re.search(pattern, “Age: 25”) re.sub(r’\d+’, ‘X’, “I have 5 apples”) # Replace Useful Tips Pro tips and Python idioms that will make you more productive and your code more Pythonic. Please download the Full PDF.

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Top 30 Python Interview Questions and Answers (2025)

1 Comment / Interview, python / Tarun

In this blog, I’ll share 30+ real-world Python interview questions and answers — carefully curated from actual company interviews, including those from startups and top tech firms. Whether you’re just starting out or preparing for your next big opportunity, these questions will help you build confidence, sharpen your problem-solving skills, and stand out in competitive hiring rounds. Moreover, they are tailored to match what companies are asking in 2025, making this a practical and up-to-date resource for your next Python coding interview. I’ve included beginner to advanced Python concepts, covering OOP, data structures, algorithms, and Python libraries commonly asked about by recruiters. If you find this helpful, comment below—I’ll post an advanced Python Q&A series next! 1. What is the Difference Between a List and a Tuple? l = [1, 2, 3] # list t = (1, 2, 3) # tuple 2. Difference Between List Comprehension and Dict Comprehension # List squares = [x*x for x in range(5)] # Dict square_dict = {x: x*x for x in range(5)} 3. What is a Lambda Function in Python? A Lambda function in Python is a small, anonymous function that can have any number of arguments but can only have one expression. It’s a concise way to create simple functions without using the def keyword. add = lambda a, b: a + b 4. Examples of Mutable and Immutable Datatypes in Python Basic Difference: # Value equality with == a = [1, 2, 3] b = [1, 2, 3] print(a == b) # True – same values print(a is b) # False – different objects # Identity with is c = a print(a is c) # True – same object print(a == c) # True – same values 5. What is the Difference Between is and ==? a = [1, 2] b = a c = [1, 2] a is b # True a == c # True a is c # False 6. How Are Variables and Objects Stored in Python? In Python, variables and objects are stored using a combination of namespaces and memory management through references. Objects → Stored in Heap MemoryVariables (Names) → Stored in Stack Memory 7. What is a Decorator in Python? A function that modifies another function without changing its structure. def decorator(func): def wrapper(): print(“Before function”) func() print(“After function”) return wrapper @decorator def greet(): print(“Hello”) greet() 8. Difference Between Generators and Iterators def gen(): yield 1 yield 2 9. Difference Between Pickling and Unpickling? import pickle data = pickle.dumps({‘a’: 1}) obj = pickle.loads(data) 10. Difference Between Shallow Copy and Deep Copy import copy copy.copy(obj) # shallow copy.deepcopy(obj) # deep 11. Multiprocessing vs Multithreading in Python 12. How is Memory Managed in Python? Memory management in Python is handled by the Python memory manager, which includes a private heap, automatic garbage collection, and dynamic memory allocation using reference counting and a cyclic garbage collector. 13. What is the Garbage Collector in Python? The garbage collector in Python is a built-in mechanism that automatically frees up memory by reclaiming objects that are no longer in use, primarily using reference counting and cyclic garbage collection. 14. What is GIL (Global Interpreter Lock)? A mutex that allows only one thread to execute Python bytecode at a time, preventing race conditions in CPython. 15. What is a First-Class Function in Python? First-Class Function: In Python, functions are first-class objects, meaning they can be treated like any other data type. They can be: This allows for powerful programming patterns like higher-order functions, decorators, and functional programming techniques. Functions have the same privileges as other objects in Python. 16. What is a Closure in Python? Closure: A closure is a function that captures and retains access to variables from its outer (enclosing) scope, even after the outer function has finished executing. The inner function “closes over” these variables, keeping them alive in memory. def outer_function(message): def inner_function(): print(f”Message: {message}”) return inner_function # Create a closure my_closure = outer_function(“Hello from closure!”) # Call the inner function my_closure() Key characteristics: This enables data encapsulation and creates functions with persistent local state. 17. Different Ways to Read/Write a File in Python # Read with open(‘file.txt’, ‘r’) as f: data = f.read() # Write with open(‘file.txt’, ‘w’) as f: f.write(“Hello”) 18. What is a Context Manager in Python? Context Manager: An object that defines methods to be used with Python’s with statement. It ensures proper resource management by automatically handling setup and cleanup operations, even if an exception occurs. Key Methods: Purpose: Provides a clean way to manage resources like files, database connections, or locks by ensuring they are properly acquired and released, preventing resource leaks and ensuring cleanup code always runs. 19. Types of Inheritance in Python 20. Difference Between Abstraction and Encapsulation Abstraction: The process of hiding complex implementation details and showing only the essential features of an object. It focuses on what an object does rather than how it does it. Achieved through abstract classes, interfaces, and methods that provide a simplified view of functionality. Encapsulation: The bundling of data (attributes) and methods that operate on that data within a single unit (class), while restricting direct access to internal components. It focuses on hiding the internal state and requiring interaction through well-defined interfaces using access modifiers (private, protected, public). Key Difference: Abstraction is about simplifying complexity by hiding unnecessary details, while encapsulation is about protecting data integrity by controlling access to internal components. 21. What is Polymorphism in Python? Polymorphism: The ability of different objects to respond to the same interface or method call in their specific way. It allows objects of different types to be treated uniformly while exhibiting different behaviors based on their actual type. Key Characteristics: Types in Python: This enables writing generic code that can work with various object types without knowing their specific implementation details. 22. What is Function Overloading? Multiple functions with the same name but different parameters (Not natively supported in Python). 23. What is Function Overriding? Function Overriding: The ability of a child class

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