Building Django Website: Admin Panel and Pages Live

Day 7/10 🚀 This is where your code grows up. Modules & Packages — the foundation of every scalable Python project. Without them? Spaghetti code, repetition, no structure. With them? Clean, reusable, production-ready code. 📋 What I covered today: 01 → Modules & package structure 02 → Creating & importing .py files 03 → init.py & sub-packages 04 → Import styles — import, from, alias 05 → Relative vs absolute imports 06 → Standard library — os, json, datetime, re 07 → Third-party packages — pip, numpy, pandas 08 → Virtual environments & requirements.txt 09 → Mini Project — Config Loader Package Built a small config package with loader & validator modules — a real-world pattern used in production apps. Day 1 ✅ Day 2 ✅ Day 3 ✅ Day 4 ✅ Day 5 ✅ Day 6 ✅ Day 7 ✅ 3 more to go. Drop a 📦 if you’ve ever put everything in one giant .py file 😄 #Python #Modules #Packages #DataEngineering #LearningInPublic #CleanCode #10DaysOfPython #SoftwareEngineering

🚀 Excited to share my latest project — Smart To-Do & Productivity Tracker! A full-stack web app built with Python Flask + SQLite that helps you organize tasks and stay productive. ✅ User authentication (signup/login) ✅ Task priority levels (High / Medium / Low) ✅ Time scheduling for each task ✅ AI-powered productivity suggestions ✅ Dark mode support ✅ Live productivity summary 🔗 Live Demo: https://lnkd.in/g5iJZBtA Would love your feedback! 🙌 #Python #Flask #WebDevelopment #FullStack #SQLite

Ever wondered how your computer actually manages its memory? 🧠💻 I’m excited to share my latest project: a Memory Management Visualizer that brings complex Operating System concepts to life. Moving from textbook theory to a functional implementation was a huge learning curve, but seeing the logic in action makes it all worth it. Key Features I built: ✅ Paging: Real-time visualization of FIFO and LRU algorithms using a Flask-powered backend. ✅ Segmentation: Implementation of First Fit and Best Fit allocation strategies with a dynamic memory map. ✅ Virtual Memory: A full simulation featuring Page Tables, TLB cache, and page fault handling. Tech Stack: - Backend: Python (Flask) 🐍 Frontend: HTML, CSS, JavaScript, Chart.js 📊 Tools: Conda, REST APIs Check out the repo here: [https://lnkd.in/gdAnxj7E] Team: Shaurya Raj, Sreenath YV Greatful to Arjun Saini for guidance and support throughout the project. #OperatingSystems #ComputerScience #FullStack #Python #Flask #WebDevelopment #CodingProject

FastAPI and Django Production Migration Challenges Revealed 📌 Django’s monolithic power meets FastAPI’s async speed-teams migrating to FastAPI report up to 300% faster dev cycles and razor-sharp I/O performance. But beware: you’ll lose built-in admin tools and session handling. The choice hinges on whether your app thrives on rapid iteration or raw throughput. 🔗 Read more: https://lnkd.in/dsBwaJ-J #Fastapi #Django #Python #Performance #Migration

Just built a **Python-based Text Editor using Tkinter** 🐍📝 as a hands-on project to strengthen my GUI and file-handling skills. Features implemented: • Create new files, open existing ones, and save content • Basic text formatting (Bold and Red color using Tkinter text tags) • Menu-driven interface for better usability • Clean and simple layout for user interaction Key learnings from this project: ✔ How Tkinter GUI components work together (Text, Menu, File Dialogs) ✔ File operations in Python (read/write handling) ✔ Text widget tagging system for formatting selected text ✔ Event-driven programming and user interaction flow This project helped me connect core Python concepts with real application development. I’ll be improving it further by adding keyboard shortcuts, better formatting controls, and UI enhancements. Always building and improving step by step 🚀

🧠 Python Concept: functools.partial Pre-fill function arguments like a pro 😎 ❌ Without partial def power(base, exp): return base ** exp def square(x): return power(x, 2) def cube(x): return power(x, 3) 👉 Repeating logic 👉 Extra functions ✅ With partial from functools import partial def power(base, exp): return base ** exp square = partial(power, exp=2) cube = partial(power, exp=3) print(square(5)) # 25 print(cube(5)) # 125 🧒 Simple Explanation Think of it like preset settings 🎛️ ➡️ Fix some arguments ➡️ Reuse the function easily 💡 Why This Matters ✔ Reduces duplication ✔ Cleaner code ✔ Functional programming style ✔ Useful in callbacks & configs ⚡ Real-World Use ✨ API parameter presets ✨ Event handlers ✨ Reusable utilities 🐍 Don’t repeat functions 🐍 Pre-configure them #Python #AdvancedPython #CleanCode #SoftwareEngineering #BackendDevelopment #Programming #DeveloperLife

I've been using @decorators But Today I finally understood what happens in memory when Python sees that @ symbol. Here's the full picture — with actual RAM addresses 👇 When Python runs your script, it creates function objects in memory immediately: decorator() → stored at address 0x7668...2e0 just_a_func() → stored at address 0x7668...380 The moment you call decorator(just_a_func), something new happens: A brand new object do_something() is born at 0x7668...420 Inside do_something(), the func argument holds a reference to just_a_func's address — 0x7668...380 The original just_a_func() is never modified. It stays at the same address. So @decorator is just clean syntax for: decorated = decorator(just_a_func) That's it. No magic. Just references. Now the FastAPI part — and this is where it gets interesting. @app.get("/ping") is NOT a decorator itself. It's a function that RETURNS a decorator. Under the hood: — app.get("/ping") calls APIRouter.api_route() — api_route() returns a decorator(func) function — that decorator calls add_api_route(path, func) — your route function is now registered in the app's memory state You can actually do this manually without @ syntax: decorator = app.get("/ping") decorated = decorator(new_route) Same result. Just less elegant. The deeper I go into FastAPI internals, the more I appreciate how cleanly it's built. Everything is explicit, traceable, and follows Python's own function object model. This is what "learning in public" looks like — going beyond the docs and into the source code. What concept in your stack did you finally understand by reading the source code? #Python #FastAPI #Decorators #LearningInPublic #BackendDevelopment #100DaysOfCode #SoftwareEngineering #PythonTips #WebDevelopment For More In Dept Visit : https://lnkd.in/d7-rqhzQ

I created a python script called 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗕𝗼𝗺𝗯 to iterate through your entire wordlist e.g. go through all URLs with first line of the wordlist then with the second line to avoid making too many requests to single URL and adding 𝗫-𝗙𝗼𝗿𝘄𝗮𝗿𝗱𝗲𝗱-𝗙𝗼𝗿 , 𝗫-𝗙𝗼𝗿𝘄𝗮𝗿𝗱𝗲𝗱-𝗛𝗼𝘀𝘁 headers with company IPs. https://lnkd.in/dFYrpkiQ

From UI → working product 👇 Here’s a quick demo of the expense tracker I built using Python 💰 In this version (v1), you can: • Add income & expenses • Track balance instantly • View transaction history • View analytics & insights Built this to better understand my own spending habits — and it turned into a full working app. Currently improving it and planning to deploy it soon 🚀 Watch till the end to see insights 👀 Would love your feedback 🙌 #Python #Streamlit #WebDevelopment #BuildInPublic #SoftwareDevelopment #StudentDeveloper #Projects #LearningInPublic

I used to copy-paste the same logging block into every function. Then I learned decorators. And deleted 200 lines of code in one afternoon. Here's what changed: **Before:** ```python def get_user(user_id):   logger.info(f"Calling get_user with {user_id}")   start = time.time()   result = db.query(...)   logger.info(f"get_user took {time.time() - start:.2f}s")   return result ``` Every. Single. Function. 😅 **After:** ```python def log_execution(func):   @wraps(func)   def wrapper(*args, **kwargs):     start = time.time()     result = func(*args, **kwargs)     logger.info(f"{func.__name__} took {time.time() - start:.2f}s")     return result   return wrapper @log_execution def get_user(user_id):   return db.query(...) ``` Clean. Reusable. Readable. The 3 decorator patterns I use every week: - `@log_execution` — timing and logging - `@retry(max_attempts=3)` — auto-retry on transient errors - `@cache_result(ttl=300)` — simple in-memory caching The key insight: decorators are just functions that wrap functions. Once that clicks, you stop copy-pasting and start composing. What's your favorite decorator pattern? Drop it in the comments 👇 #Python #BackendDevelopment #SoftwareEngineering #Programming #Django