Flask vs FastAPI: Choosing the Best Python Framework for 2024

Built a Personal Library Manager with Python + Streamlit! First 32s: Full code walkthrough (main.py + pyproject.toml) Last 33s: Live UI demo (Add books, Search, Stats, Export) Features: - Add books with Title, Author, Genre, Year, Pages - Inline editing with Read/Unread checkbox - Search by Title or Author instantly - Stats dashboard with genre bar chart - Export your entire library to CSV - Zero database needed - saves locally as CSV - No login, no cloud - 100% private Built with Python + Streamlit + Pandas + uv 106 lines of code. Zero backend. Works offline. This is the kind of tool I use personally - simple, fast, no unnecessary complexity. #Python #Streamlit #Pandas #BuildInPublic #100DaysOfCode #TechPakistan #Programming #OpenSource

FastAPI with SQLAlchemy: A Comprehensive Guide for Beginners Building robust and scalable APIs often involves interacting with databases. SQLAlchemy is a powerful and flexible Python SQL toolkit and Object-Relational Mapper (ORM) that allows you to interact with various databases using Python code. FastAPI, known for its speed and ease of use, pairs perfectly with SQLAlchemy, providing a streamlined experience for building data-driven applications. This tutorial will guide you through the process of integrating SQLAlchemy with FastAPI, equipping you with the knowledge to create and manage database-backed APIs....

🚀 Level Up Your Python APIs with FastAPI! Are you still using older frameworks for your Python web services? It might be time to switch gears. For my latest project, I've been diving deep into FastAPI, and I am genuinely impressed. It’s not just about speed (though it is incredibly fast). It's about a superior developer experience. Here’s why FastAPI is a game-changer: ⚡️ High Performance: Built on Starlette and Uvicorn, it’s one of the fastest Python frameworks available. 🛡️ Robust and Safe: Leverages Pydantic for data validation, catching errors early. 📝 Automatic Docs: Generates interactive Swagger UI and ReDoc documentation instantly. No extra effort required! 💡 Modern Python: Fully supports asynchronous programming (async/await) and type hints. If you’re building production-ready APIs and want a combination of speed, security, and developer joy, you owe it to yourself to try FastAPI. Have you made the switch to FastAPI? What’s your favorite feature? Share your thoughts below! 👇 #Python #WebDevelopment #FastAPI #Backend #API #Programming #Coding #DeveloperExperience #AsyncIO

🚀 Day 34 of #100DaysOfCode | Scenario-Based Questions in Python OOP Today I practiced solving scenario-based problems using Python Object-Oriented Programming (OOP). These problems help in understanding how OOP concepts are applied in real-world situations. Key Concepts I Practiced: • Designing classes based on real-world scenarios • Implementing Encapsulation to protect class data • Using Inheritance to reuse code between classes • Applying Polymorphism for flexible and reusable methods Practising scenario-based questions improves logical thinking and problem-solving skills, and helps in building scalable and maintainable applications using OOP concepts. GitHub Repository: https://lnkd.in/gTmfXrWP #Python #OOPS #100DaysOfCode #CodingJourney #PythonProjects

FastAPI has become the standard for building high-performance Python backends. It successfully combines the developer experience of a lightweight framework with the speed required for modern, asynchronous applications. Here is why it is a go-to for production-ready systems: • Performance: Built on Starlette and Pydantic, it is one of the fastest Python frameworks available. • Efficiency: Features like automatic OpenAPI (Swagger) docs and built-in data validation reduce boilerplate significantly. • Async Support: Native support for asynchronous programming makes it ideal for real-time apps and ML model deployment. • Reliability: Leveraging Python type hints ensures fewer bugs and better editor support during development. Whether you are architecting microservices or a simple REST API, FastAPI provides the scalability and speed that modern software demands. Are you still using Flask for your initial prototypes, or have you made the full switch to FastAPI? . . . #Python #FastAPI #Backend #WebDev #Microservices #Coding

git push -u origin main I just finished building a new python scraper. I wanted a fast way to gather news data for my projects. I built a tool that pulls Google News results directly into a CSV file using Python and SerpAPI. It collects the titles, links, and dates for any topic user search for. I used a .env file to keep my API keys safe and private. This makes it much easier to build datasets for my data science work. The full code and setup instructions are now live on my GitHub. Check it out.

Currently working through a Flask tutorial. At this stage, I’ve been learning and implementing: • Flask routing and request handling (GET vs POST) • User authentication and session management • Extending data models and handling migrations • Server-side forms with validation • Tracking user activity with UTC timestamps • Using Jinja templates and template inheritance It’s been helpful in understanding how backend logic, database state, and templates come together in a real Flask application. #Python #Flask #WebDevelopment #BackendDevelopment #LearningInPublic

I’m really happy to share something small but meaningful to me 😊 I just published my first Python package on PyPI — Notebook-Playlink 🎉 It allows you to render YouTube videos and any web link directly inside Jupyter Notebook or Google Colab. No switching tabs. No breaking workflow. Everything stays inside the notebook. What started as a tiny utility for my own use during notebooks and demos slowly turned into a proper package. Along the way, I learned a lot more than I expected — about packaging, structuring projects, writing cleaner code, handling dependencies, and thinking like a real software engineer instead of just writing scripts. Why I built this? Because during teaching, research demos, and data science workflows, I often needed to show videos or web content — and constantly switching between tabs was annoying. So I thought, why not make it seamless? Now you can simply: pip install notebook-playlink and then: from notebook_playlink import inline_playlink inline_playlink("your_link_here") That’s it. This project may look simple from the outside, but shipping something to PyPI, documenting it properly, and making it usable for others gave me a completely different level of confidence. If you work with Jupyter, Colab, teaching notebooks, or data science reports — you might find it helpful. GitHub: https://lnkd.in/gj5dU98V PyPI: https://lnkd.in/gnUkWFEZ If you check it out and like it, a ⭐ would honestly mean a lot. Small step. Big learning. More to come #Python #PyPI #OpenSource #JupyterNotebook #GoogleColab #SoftwareDevelopment #DataScience #Boktiar Ahmed Bappy #Inception BD

 Stop Blocking — Start Scaling! If you’re writing Python apps that wait on I/O — like web requests, file ops, or socket connections — your code can feel slow even if the hardware isn’t. That’s where modern Python concurrency shines! I just broke down the real magic behind Python’s asyncio — not just theory, but practical, runnable patterns: 🔹 What coroutines actually are and how they pause & resume work 🔹 How to convert a function into a coroutine with async def 🔹 Why coroutines by themselves don’t run — and how asyncio.create_task() changes that! 🔹 How Tasks let you run many coroutines concurrently 🔹 Using Locks & Semaphores to coordinate shared resources safely 🔹 Visualizing the event loop in action so you finally get async behavior 🔹 Handy patterns → real code you can drop into your project Learn how Python can handle thousands of concurrent operations without threads, and how to avoid common mistakes that lead to deadlocks or wasted CPU time. 👉 Read it now: https://lnkd.in/gn-JzHcR 💬 Got an async use case that’s driving you crazy? Drop a comment — I’ll help you optimize it! #Python #Asyncio #AsyncProgramming #SoftwareEngineering #CodingTips #DeveloperCommunity #OpenSource

🚀 Day 13/100: Mastering Python String Methods! Today marks Day 13 of my #100DaysOfCode journey, and I’m diving deep into the world of Python String Methods. 🐍 Strings are everywhere in programming—from data cleaning to building web apps. Understanding these built-in methods is a total game-changer for writing clean, efficient code. 🛠️ Key Takeaways from Today: Case Transformations: Using .upper(), .lower(), and .capitalize() to standardize text data. Searching & Counting: Using .count() to find frequencies and .find() to locate substrings. Data Cleaning: The power of .replace() for formatting (like changing dates from / to -) and .split() for turning strings into lists. Validation: Checking inputs with .isalnum() and .isnumeric(). 💡 Quick Correction & Tip: While learning from cheatsheets, I noticed a tiny detail! In Python, the method is actually .isnumeric() (not just .numeric()). Always double-check your documentation while coding! 💻 I'm feeling more confident with Python every day. Looking forward to what Day 14 brings! #Python #CodingChallenge #100DaysOfCode #SoftwareDevelopment #ProgrammingTips #DataScience #WebDev #LearnToCode #PythonStrings