Implementing Error Handling & Logging in Python Full Stack

Finding and Removing Dead Code in code bases & scripting     Find and remove dead code – before it finds you We’ve all been there: the codebase grows, features come and go, and eventually the code ends up in functions that nobody calls anymore. Particularly tricky: scripting files (Python, Shell, JS), which often exist outside the IDE and are quickly forgotten. 🫵 Why this is problematic: • Maintenance costs: Everyone has to read code that is never executed. • Security risk: Outdated logic may contain vulnerabilities that have never been patched because they were never tested. • Confusion: New team members waste time trying to understand why something exists (“Is this still needed?”) • Slows down builds and increases binary size • Bloats tests and reviews   ➡️ How SciTools’ Understand helps Instead of tedious manual searching, Understand does the work for you: 1️⃣ Find unused functions & variables The static analysis detects code that is never called,  across all files and languages. 2️⃣Visualize dependencies Graphs immediately show you which modules are isolated and can be removed. 3️⃣ Track references Where is this function used? Understand shows you every reference, or, indeed, none. 4️⃣Scripting included Not just compiled code, your build scripts, helper scripts and automations are analyzed too. ✔️ Best practice: Integrate dead code checks into your workflow: 1. Generate reports regularly 2. Check before every release 3. Plan refactoring strategically The result: A leaner, more maintainable and more secure codebase. Free trial www.emenda.com/trial #SoftwareEngineering #DeadCode #CodeQuality #Refactoring #CleanCode #Understand #SciTools #Scripting 

🏗️ Scaling Up: Moving from Scripts to Systems As my Python projects grow, I’m learning that writing code that works is only half the battle. Writing code that is maintainable is where the real skill lies. I’ve started refactoring my automation scripts by breaking them down into reusable functions. Here’s why this shift is a game-changer: ♻️ Reusability (DRY - Don't Repeat Yourself) Instead of copying and pasting logic, I can write a function once and call it whenever I need it. It makes the codebase smaller and much easier to update. 📖 Readability By abstracting complex logic into functions with clear names like clean_data() or export_to_excel(), my main execution flow now reads like a story rather than a wall of text. Anyone (including my future self) can understand the logic at a glance. 🧪 Testability Organizing code into functions allows me to test individual "units" of logic in isolation. If something breaks, I know exactly which function is responsible, making debugging significantly faster. The Evolution: Level 1: Write a long script that runs top-to-bottom. Level 2: Organize logic into functions for better flow. Level 3: Move functions into separate modules for a professional project structure. I’m currently at Level 2 and feeling the difference in how I approach problem-solving! 💻 #PythonProgramming #CleanCode #SoftwareDevelopment #LearningToCode #CodeRefactoring #TechCommunity

𝗨𝗡𝗟𝗘𝗔𝗦𝗛𝗜𝗡𝗚 𝗧𝗛𝗘 𝗨𝗟𝗧𝗜𝗠𝗔𝗧𝗘 𝗣𝗬𝗧𝗛𝗢𝗡 𝗣𝗢𝗪𝗘𝗥𝗛𝗢𝗨𝗦𝗘: 𝗧𝗛𝗘 𝗦𝗘𝗖𝗥𝗘𝗧 𝗧𝗢 𝗕𝗨𝗜𝗟𝗗𝗜𝗡𝗚 𝗟𝗜𝗚𝗛𝗧𝗡𝗜𝗡𝗚 𝗙𝗔𝗦𝗧 𝗔𝗣𝗜𝗦 𝗥𝗘𝗩𝗘𝗔𝗟𝗘𝗗 As we head into 2026, building scalable microservices requires a rock-solid understanding of how your code is organized from the first line. This guide demystifies the structural foundations of FastAPI to ensure your backend remains maintainable as your codebase grows. THE FASTAPI INSTANCE The core of every application begins with initializing the FastAPI class. This instance acts as the central router and configuration hub, serving as the bridge between your incoming HTTP requests and your internal application logic. Understanding how to instantiate this object correctly is the first step toward managing middleware, dependencies, and route decorators effectively. ROUTING AND PATH OPERATIONS Path operations are the fundamental building blocks of your API. By using decorators linked to your FastAPI instance, you define how the server responds to specific HTTP methods like GET or POST. This video breaks down how these functions map directly to URL paths, allowing for clean, readable code that handles client communication without unnecessary complexity. PARAMETER HANDLING AND TYPE HINTS FastAPI leverages Python type hints to perform automatic data validation and documentation. By defining expected types for path parameters, query parameters, and request bodies, you enable the framework to enforce data integrity before your logic even executes. This approach significantly reduces the surface area for bugs while providing built-in Swagger UI documentation for your endpoints. As a Senior Engineer, I cannot overstate that the structure of your application in 2026 is just as important as the logic inside it. FastAPI enforces good habits early by requiring explicit type definitions and clear route mapping, which saves hundreds of hours in debugging and refactoring down the line. Treat your project structure as your primary documentation. Tags: #FastAPI #Python #API #BackendDevelopment 📺 Watch the full breakdown here: https://lnkd.in/d7YeSp75

Day 29: The Anatomy of a Bug — Three Types of Errors 🐞 In programming, not all "crashes" are created equal. We categorize errors into three levels of severity, ranging from "The computer doesn't understand you" to "The computer does exactly what you said, but you said the wrong thing." 1. Syntax Errors (The "Grammar" Mistake) These happen before the code even starts running. Python’s "Parser" looks at your script and realizes it violates the rules of the language. The Cause: Missing colons :, unclosed parentheses (, or incorrect indentation. The Result: The program won't start at all. 💡 The Engineering Lens: These are the "cheapest" errors to fix. Your code editor (IDE) will usually highlight these with a red squiggly line as you type. 2. Runtime Errors (The "Panic" Mistake) The syntax is perfect, and the program starts running—but then it hits a situation it can't handle. The Cause: Dividing by zero, trying to open a file that doesn't exist, or calling a variable that hasn't been defined yet (NameError). The Result: The program "crashes" in the middle of execution. 💡 The Engineering Lens: We handle these using Exception Handling (try/except). Professional code assumes things will go wrong (like the internet cutting out) and builds "safety nets" to keep the program alive. 3. Semantic Errors (The "Logic" Mistake) These are the most dangerous and difficult to find. The program runs perfectly from start to finish. There are no crashes and no red text. But the output is wrong. The Cause: You used + when you meant -, or your loop stops one item too early. The Result: The program gives you the wrong answer (e.g., a calculator saying $2 + 2 = 22$). 💡 The Engineering Lens: The computer is doing exactly what you told it to do; the "error" is in your logic. We find these using Unit Testing and Debugging tools. If you don't test your code, you might not even know a semantic error exists until a customer reports it. #Python #SoftwareEngineering #Debugging #ProgrammingTips #LearnToCode #TechCommunity #PythonDev #CleanCode #BugHunting

🚀 Just wrapped up my latest Python project: A smart Command-Line Task Tracker! I wanted to move beyond basic scripts and tackle real-world data management. Here are the top 3 concepts I leveled up while building this: 💾 File Persistence: Built a permanent "filing cabinet" using Python's with open() to safely serialize, save, and reload user data. ⏳ Dynamic Data vs. Static Data: Used the datetime module to recalculate "days remaining" every time the app boots, rather than hard-coding static dates into the save file. 🌿 Clean Git Workflow: Managed isolated feature testing using git checkout -b and used .gitignore to keep system files out of my remote repo. This project really solidified my understanding of how data flows in and out of a program seamlessly. On to the next build! 💻 #Python #SoftwareDevelopment #CodingJourney #Git

🚀 Day 14 – Logging Module in Python Today I learned why professionals don’t rely on print()… they use logging. 🔹 Logging helps track: ✔ Errors ✔ Program flow ✔ Debugging info 💡 Key Difference: print() → Temporary logging → Stored with time, file & error details 🔹 Logging Levels: DEBUG → Developer info INFO → General updates WARNING → Unexpected events ERROR → Serious issues CRITICAL → System failure 📌 Example: import logging logging.basicConfig(level=logging.DEBUG) logging.info("Application started") 🔥 Realization: Logging is not optional in real-world projects — it’s essential. Ajay Miryala 10000 Coders #Python #Logging #Debugging #BackendDevelopment #CodingJourney

update from previous post[https://lnkd.in/dVx_NrDQ] From "Arrow Code" to Clean Architecture. 🏹 ➡️ 🧱 I’ve hit a major turning point in my Python journey. I realized my code was starting to look like an arrow—layers upon layers of if statements and while loops pushing my logic further and further to the right. In professional dev circles, they call this Arrow Code, and it's a nightmare to maintain. Here’s how I "flattened" my latest project: ✅ Decomposition: I broke down massive, nested blocks into small, dedicated functions. Each function now does one thing well, making the main logic readable at a single glance. ✅ The "Gatekeeper" Pattern: Instead of scattered validation, I built a centralized handler to act as a security guard for all user inputs. ✅ State Management: I’m now mastering the "Baton Pass"—using return values and arguments to move data (like budgets) safely through the app instead of relying on global variables. ✅ Professional Workflow: I’m officially using Git branches and Pull Requests on GitHub to review my own work and track my architectural improvements. The goal isn't just to write code that the computer understands; it’s to write code that other humans can read. 🚀 #CleanCode #Python #SoftwareEngineering #Refactoring #CodingJourney #BuildInPublic

“𝗛𝗮𝗿𝗱𝗲𝘀𝘁 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗳𝗼𝗿 𝗽𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗲𝗿𝘀? 𝗡𝗮𝗺𝗶𝗻𝗴 𝘁𝗵𝗶𝗻𝗴𝘀.” We can build scalable systems… but naming a variable? That takes forever. We’ve all written things like: temp, data, x, finalData_v2 Even programming languages aren’t great at naming: • Python → not about snakes • JavaScript → not really Java • Go → sounds like a command • Rust → not about corrosion • Swift → not just about speed Naming has always been hard. But in real projects, bad names = confusion, bugs, and slow development. Good naming is simple: • Clear • Meaningful • Easy to understand 𝗥𝗲𝗰𝗲𝗻𝘁𝗹𝘆 𝗿𝗲𝗮𝗱 𝗮 𝗱𝗼𝗰 𝘁𝗵𝗮𝘁 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝗵𝗼𝘄 𝗜 𝘁𝗵𝗶𝗻𝗸 𝗮𝗯𝗼𝘂𝘁 𝗻𝗮𝗺𝗶𝗻𝗴. Link: https://lnkd.in/gMgBWdqz #learning

My first version of the Task Manager CLI was embarrassing. Everything in one file. No error handling. Variables named x, temp, data2. It worked — until it didn't. And when it broke, I had no idea where to start debugging. That moment of staring at my own code and not understanding it was one of the most important moments in my development as an engineer. I refactored the entire project. Broke it into modules. Added structured exception handling. Rewrote the validation logic. Debugging effort dropped by 40%. More importantly, I could actually read my own code. The lesson wasn't technical. It was about ego. Bad code is often the result of being in a hurry to "finish" — instead of being willing to do it right. I now build slower and ship cleaner. And I'm better for it. Have you ever looked back at old code and learned something important about yourself? #Refactoring #CleanCode #Python #SoftwareEngineering #LessonsLearned

Most beginner backend projects die in refactoring. Here's the structure I use to prevent that. When I built my Task Manager CLI, I learned this the hard way — a monolithic file that worked until it very much didn't. After refactoring, here's the structure I now start with: Before writing a single line: → Define your data model first → Identify all operations (CRUD) you'll need → Map inputs, outputs, and error states While building: → One module per concern (routes, models, utils, exceptions) → Validate inputs at the boundary — not deep inside logic → Handle errors explicitly — no silent failures Before shipping: → Test the unhappy paths, not just the happy ones → Read your own code like a stranger would This approach reduced my debugging effort by 40% on a real project. It works at any scale — from a CLI tool to a FastAPI service. What's the first thing you do when starting a new backend project? #BackendDevelopment #Python #FastAPI #SoftwareEngineering #CodingTips