Building Patient Management API with FastAPI and Python

Exploring FastAPI and tried applying it to a real-world problem. In many clinics, patient records are still stored on paper, making them hard to manage and update. So I began building a Patient Management System API. What I implemented: • Stored patient data in a JSON file (simulating a database) • Built a GET endpoint to retrieve all patient records • Understood how client-server interaction works What I learned: • Difference between static and dynamic software • CRUD operations and how they map to HTTP methods (GET, POST, PUT, DELETE) • How APIs power real-world applications This felt like a shift from just learning concepts to actually thinking in systems. Next step: Adding POST, PUT, DELETE endpoints and expanding functionality. #FastAPI #BackendDevelopment #Python #APIs #MachineLearning

These past few days I've been diving into middleware in FastAPI — and honestly it clicked better than I expected. I implemented 4 types: → CORS — to control which frontends can talk to my API → GZip — to compress large responses and reduce payload size → HTTPS Redirect — to force secure connections automatically → Custom Timer Middleware — my favorite one, built from scratch using BaseHTTPMiddleware The custom one was the most interesting. I wrapped every request with a timer to measure how long each endpoint takes to respond. Something like this: start = time.time() response = await call_next(request) duration = time.time() - start Simple concept, but it made me realize how powerful middleware is — you intercept every request and response without touching a single endpoint. One thing that surprised me: even a basic loop of 10 million iterations is visible in the timing output. That's when I understood why performance monitoring at the middleware level actually matters in production. Still learning, but these small wins keep me going. Code here if you want to check it out 👇 https://lnkd.in/e773_smX #FastAPI #Python #WebDevelopment #BackendDevelopment #Learning

🚀 FastAPI Final Project Completed! Built a - Library Book Management System - using FastAPI with real-world features like CRUD operations, validation, and advanced APIs. 🔧 Highlights: • REST APIs with FastAPI • Pydantic validation • Search, Filter, Sort & Pagination • Swagger API testing 💻 GitHub: 👉 https://lnkd.in/gZAKgB2S Grateful to @Innomatics Research Labs for this opportunity. #FastAPI #Python #Backend #APIs #Projects #Learning

🎯 Precision Engineering: Beyond Basic Queries "A great API doesn't just give you data—it gives you the right data, or a clear reason why it can't. 🛡️ Today I expanded my TodoApp by implementing Path Parameters. Moving beyond fetching 'all' records, I’ve added logic to retrieve specific tasks by their ID. Key technical highlights from this update: ✅ Input Validation: Used FastAPI’s Path to ensure only valid IDs (greater than 0) are processed. ✅ Robust Error Handling: Integrated HTTPException to return a clean 404 Not Found status if a user requests an ID that doesn't exist. ✅ Clean Code: Refactored using Annotated dependencies to keep the route handlers lean and readable. Building a backend isn't just about the 'Happy Path'—it's about handling every edge case with precision. Next: Implementing POST requests to allow users to create their own tasks! 🚀" #FastAPI #Python #BackendDevelopment #WebAPI #CleanCode #SoftwareEngineering

What if every node in your knowledge graph could talk, and what they say dictates how the network evolves? Holonic v0.3.0 is live 🚀 What's new? - 📦 pip install holonic (conda coming soon) - 📄Sphinx docs with full API reference -🧪 Expanded test coverage across backends and traversal paths -📓 New example notebooks (portal traversal, translation, visualization) Built on rdflib + Apache Jena Fuseki, with a pluggable GraphBackend protocol if you want to bring your own quad store (more coming soon!). Portals are RDF triples, discovered via SPARQL. Traversal runs CONSTRUCT queries. Nothing magic, nothing hidden, FULL provenance. Still early (5 ⭐ and 0 issues) so if you're doing anything in the knowledge graph / semantic web space and want to take a test drive, I'd genuinely appreciate it. 🔗 github.com/zwelz3/holonic #RDF #KnowledgeGraphs #SemanticWeb #Python #OpenSource

I was reading the FastAPI docs today and stumbled upon a new chapter: "Vibe Coding" 👀 Turns out you can now do this: @app.vibe(   "/vibe/",   prompt="return hello world as json", ) async def health_check(body: Any): ... No validation. No schemas. No function body. Just vibes ✨ I genuinely got curious for a second. Opened my editor. Started typing. Then I ran it. AttributeError. 💀 Yep. April Fools' by FastAPI team — and honestly one of the best ones I've seen this year. The joke hits because it's a little too real. Anyway. Back to writing actual typed, validated, documented code. Like a boring person. 😂😂😂 check the docs - https://lnkd.in/gh4iSHqP #Python #FastAPI #VibeCoding #AprilFools #SoftwareEngineering

Most operational software I encounter wasn't built to talk to anything else. With FastAPI, you can build a lightweight API layer on top of almost any system, whether it's a database, a legacy application, or a third-party platform. Once that layer is in place, other systems can pull data from it, push data to it, or trigger actions automatically. The result isn't just a technical improvement. It means processes that used to require manual exports, emails back and forth, or someone running a report every morning can simply run on their own. The only thing required is a small Python application. Deployed, maintained, and adapted when business requirements change. No large dev team needed. How many manual actions does your most painful data process require? Drop a number below! D-Data #Python #FastAPI #DataEngineering #SoftwareEngineering #BusinessAutomation #APIIntegration

I spent weeks doing the same feature engineering steps manually on every project. Missing value maps. Outlier detection. Linearity checks. Cramér's V. VIF. RFECV. So I built a Python package that does all of it automatically. Introducing featurewiz-pro — a 7-phase feature engineering pipeline I designed, built, and published to PyPI from scratch. One command. 47 seconds. Clean, model-ready data. What it does: → Profiles your data and drops useless columns automatically → Detects which features are linear vs non-linear → Expands non-linear features with splines → Screens for multicollinearity and interactions → Selects the best features using RFECV + permutation importance → Audits for data leakage before you ever touch a model Tested across Python 3.9–3.12. 55 tests. 0 failures. Live on PyPI now → pip install featurewiz-pro Walkthrough in the video Git: https://lnkd.in/dHPV7xNK #Python #MachineLearning #DataScience #OpenSource #FeatureEngineering #PyPI

Stop writing manual validation logic In traditional frameworks, you spend a lot of time writing code like: if not data.get("email"): raise ValueError... With FastAPI, you stop writing "checks" and start defining Schemas. By using Pydantic models, FastAPI does the heavy lifting for you: ✅ Automatic Parsing: Converts incoming JSON directly into Python objects. ✅ Data Validation: If a user sends a string where an integer should be, FastAPI catches it instantly. ✅ Clear Errors: It sends a detailed 400 error back to the client automatically—your function logic doesn't even have to run. The result? Cleaner code, fewer bugs, and a backend that "just works." Check out the snippet below to see how 5 lines of code can replace dozens of if/else statements. #Python #FastAPI #Pydantic #WebDevelopment #Backend #CleanCode