Mastering HTTP Methods in Python Frameworks

🚀 Build Powerful APIs with Python (Django REST Framework & FastAPI) In this post, I've broken down how to create APIs using two of the most popular Python frameworks: Django REST Framework and FastAPI—in a simple, algorithmic, and visual way. 🔹 What's inside the post? Step-by-step API development flow for both frameworks Clear algorithmic approach (from setup -> models -> endpoints -> testing) Practical code snippets to get started quickly Side-by-side comparison of DRF vs FastAPI Tips on when to use each framework 🔹 Django REST Framework Best for large, database-driven applications where you need a complete ecosystem with authentication, ORM, and scalability. 🔹 FastAPI Perfect for high-performance APIs, microservices, and modern apps with automatic validation and interactive docs. 💡 Key Takeaway: Both frameworks are powerful—choose DRF for full-scale applications and FastAPI for speed and lightweight performance. 🔥 Whether you're preparing for interviews or building real-world projects, mastering these tools is essential for every backend developer. #Python #API #Django #FastAPI #BackendDevelopment #WebDevelopment #SoftwareEngineering 🚀

🚀 Day 17 – Python API Integration Today I explored the power of Django REST Framework and how it simplifies building RESTful APIs in Python. 🔹 Key takeaways: Understood how Django REST Framework extends Django to build APIs efficiently Created a Django project and app structure (countryapi, countries) Built a Model (Country) to represent data Learned how Serializers convert Django models into JSON Used ModelViewSet to handle CRUD operations automatically Configured DefaultRouter to generate API endpoints 🔹 Implemented API endpoints: GET → Retrieve countries POST → Create new country PUT / PATCH → Update data DELETE → Remove data 💡 What stood out: Django REST Framework reduces a lot of boilerplate by providing built-in tools for serialization, routing, and request handling — making API development faster and more structured. 📌 This is a big step forward in building production-ready backend systems. #Python #DataEngineering #Django #DjangoRESTFramework #APIs #BackendDevelopment #LearningJourney #selfLearning

Optimizing Django Queries: How to Avoid N+1 Problems One of the quickest ways to slow down your Django backend is the classic N+1 query issue. While working on Inboxit, I had to be deliberate about this especially when dealing with relationships between models. The fix I use most often: prefetch_related() It’s perfect for optimizing reverse relationships (when you have a ForeignKey pointing to your model and you need to access related data). Instead of making one query per object (which explodes with more records), prefetch_related fetches all the related data in just two queries one for the main objects and one for the related ones. This small change keeps response times fast and your API scalable as usage grows. Have you run into N+1 issues in your Django projects? What’s your go-to optimization technique? #Django #DRF #Python #BackendDevelopment #QueryOptimization #TechNigeria #webdev

Most Django developers don’t realize this… The ORM is silently killing their performance. I’ve seen APIs go from: ⚠️ 300 queries → ⚡ 3 queries Just by fixing QuerySet usage. In this carousel, I broke down: - N+1 problem - select_related vs prefetch_related - F expressions - Real production mistakes If you're working with Django, this is a must-know. Full guide here 👇 https://lnkd.in/dVuaXBMq #Django #Python #DjangoORM #WebDevelopment #BackendDevelopment #SoftwareEngineering #DatabaseOptimization #ProgrammingTips #Developers #CodingLife #BuildInPublic

Day 02 of 30 | Django MVT Pattern 🐍 Before writing any code in Django, you need to understand how it thinks. MVT = Model + View + Template. Every request your user makes follows this exact flow: → Browser sends a request → urls.py routes it to the right View → View asks the Model for data → Model queries the database → View sends data to the Template → Template renders HTML and returns it to the browser I made a video explaining each part. My English is A2. The diagram helps. 👀 #Django #Python #30DaysOfDjango #LearningInPublic #Developer #SaaS

⚡ Flask vs FastAPI — Which one should you choose? As a Python backend developer, I’ve worked with both Flask and FastAPI, and here’s a simple breakdown 👇 🔹 Flask ✔️ Lightweight and flexible ✔️ Easy to get started ✔️ Great for small to medium applications 🔹 FastAPI ✔️ High performance (async support) ✔️ Built-in request validation (Pydantic) ✔️ Automatic API documentation (Swagger UI) 👉 My takeaway: - Use Flask when you need simplicity and quick development - Use FastAPI when performance and scalability matter Both are powerful — it’s not about which is better, but which fits your use case. 💬 What do you prefer — Flask or FastAPI? #Python #BackendDevelopment #FastAPI #Flask #SoftwareEngineering #APIs

Your unittest.mock is lying to you. Tests pass in CI, production breaks, and nobody knows why. The problem? Hand-written mocks drift from the real API silently. I've been contributing to the Microcks open-source ecosystem, and I want to share my latest work on Microcks Testcontainers https://lnkd.in/edzprW5k family for Python. Microcks Testcontainers takes a different approach: your OpenAPI spec becomes the mock. Load it into a Microcks container inside your test suite, and it: - Mocks your dependencies using spec-defined examples - Contract tests your implementation against the spec - Catches API drift automatically I also built a demo app with Flask showing the pattern end-to-end. Library: https://lnkd.in/eXyXwpnB Demo app (Flask): https://lnkd.in/eRhXfKeZ Full step-by-step guide: https://lnkd.in/eBvxUJy8 #Python #Testing #Microservices #API #OpenAPI

Every time you write obj.attribute in Python, something runs that most developers have never heard of. Day 05 of 30 -- Descriptors and Properties Advanced Python + Real Projects Series When Django marks a field dirty on assignment, when SQLAlchemy tracks changes for save(), when Pydantic validates on every write -- they all use the same mechanism. The descriptor protocol. A descriptor is any object defining get, set, or delete. When it sits as a class attribute, Python routes all attribute access through it -- before the instance dict is even touched. Today's Topic covers: Why descriptors exist and what problem @property alone cannot solve The full descriptor protocol -- get, set, delete, set_name Data vs non-data descriptors and why the difference controls lookup priority The 3-level attribute lookup chain Python follows on every access Annotated syntax -- from @property to a fully reusable Validated field Real e-commerce scenario -- auto type check, range validation, and audit logging on every field write across 30 model classes with 3 lines of code How Django, SQLAlchemy, Pydantic, and dataclasses all use this internally 4 mistakes including the infinite recursion trap that kills production apps Key insight: When you write user.email in Django, that single line triggers a descriptor that queues the change for save(). No magic. Just the descriptor protocol. #Python #PythonProgramming #Django #SQLAlchemy #SoftwareEngineering #BackendDevelopment #100DaysOfCode #LearnPython #PythonDeveloper #TechContent #DataEngineering #BuildInPublic #TechIndia #CleanCode #LinkedInCreator #PythonTutorial

💡 𝗧𝗶𝗽 𝗼𝗳 𝘁𝗵𝗲 𝗗𝗮𝘆 — 𝗗𝗷𝗮𝗻𝗴𝗼 𝗗𝗶𝗱 𝘆𝗼𝘂 𝗸𝗻𝗼𝘄? Django’s "annotate()" lets you add 𝗰𝗮𝗹𝗰𝘂𝗹𝗮𝘁𝗲𝗱 𝗳𝗶𝗲𝗹𝗱𝘀 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗾𝘂𝗲𝗿𝘆𝘀𝗲𝘁𝘀. Instead of processing data in Python after fetching it, you can compute values at the 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗹𝗲𝘃𝗲𝗹. 🔧 𝗖𝗼𝗺𝗺𝗼𝗻 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀: - Counting related objects ("Count") - Calculating averages ("Avg") - Adding computed fields to API responses This reduces data processing in your app and leverages the power of your database. Smarter queries = faster apps. #Django #Python #BackendDevelopment #WebDevelopment #DatabaseOptimization #PerformanceOptimization #SoftwareEngineering #CodingTips #FullstackDeveloper

Flask vs Django: Key Differences Every Python Developer Should Know #programming #webdesign #rswebsols https://ift.tt/bB6T4co Excited to share our latest dive into Python web frameworks: Flask vs Django. This post breaks down the key differences every developer should know, from project size and complexity to learning curves, speed of development, and ecosystem strength. If you’re choosing the right tool for your next project, this comparison will help you make an informed decision between Django’s batteries-included approach and Flask’s lightweight, flexible design. Read the full analysis and decide which framework aligns with your goals and team needs. Link: https://ift.tt/bB6T4co