Mastering Class Relationships in Python: Inheritance, Composition, and MRO

This 30-Day Strong Python Basics Study Plan is a structured roadmap covering: • Core Fundamentals (Variables → Loops → Mini Projects) • Data Structures (List, Tuple, Set, Dictionary + Projects) • Functions & Logic Building • Error Handling • OOP (Class, Inheritance, File Handling) • Final Projects (Student Management System / Expense Tracker) • Daily discipline structure (Learning + Coding + Problem Solving) Over the past month, I focused on: • Core Programming Fundamentals • Data Structures & Logical Thinking • Functions & Error Handling • Object-Oriented Programming (OOP) • File Handling & Real-World Mini Projects • Final Project Implementation & Code Refactoring Each day included: • Focused learning • 60–90 minutes of coding practice • Problem solving for consistency From building console-based applications like ATM systems and To-Do apps to implementing OOP-based projects, this journey was about discipline, structure, and consistency. Strong basics build strong engineers. #Python #CodingJourney #SoftwareDevelopment #100DaysOfCode #Programming #TechGrowth

Another thing python-pptx taught me: document automation is never just “generate a file.” At first, it feels like you’re just placing text and images into slides. But real-world automation quickly becomes about structure, consistency, and edge cases. What happens when content is longer than expected? When layouts shift? When formatting needs to stay identical across dozens of slides? You stop thinking in terms of “fill this slide” — and start thinking in systems. Templates become architecture. Layout becomes logic. Consistency becomes engineering. That’s when automation stops being a shortcut… and starts becoming real software design. #Python #Automation #pythonpptx #SoftwareEngineering #AIEngineering

I’ve just wrapped up a deep dive into the fundamental building blocks of programming: Functions and Conditional Logic. It’s one thing to make a computer run a script; it’s another to teach it how to make decisions. This week was all about: 🔹 Modular Thinking: Breaking down complex problems into reusable functions. 🔹 Conditional Flow: Using if/elif/else structures to guide how a program reacts to different inputs. 🔹 Data Integrity: Ensuring user input is correctly handled and converted (shoutout to float() and int()) before any calculation happens. I also built a Torque Calculator and a Smart Team Router to put these concepts into practice. It’s not just about writing lines of code anymore—it's about building a logical flow that actually solves a problem. Next step: Mastering Loops. The journey to thinking like a programmer continues! 🚀 #Python #Programming #LearningToCode #SoftwareDevelopment #Logic #CodeNewbie

Still using pip?! I recently switched to uv (a modern, high-performance package manager by Astral) — and for someone who codes heavily in Python, the upgrade feels practical and overdue. Why switching to uv? 1. Project initialization friction 𝐖𝐢𝐭𝐡 𝐩𝐢𝐩 (𝐭𝐲𝐩𝐢𝐜𝐚𝐥 𝐟𝐥𝐨𝐰): Create project folder → Manually create virtual environment (python -m venv venv) → Activate it (different commands per OS) → Upgrade pip (optional but common) → Install dependencies → Sometimes fix path or interpreter issues → Maintain separate requirements file It works — but it’s fragmented and repetitive. 𝐖𝐢𝐭𝐡 𝐮𝐯: Initialize project in one step -- That's it. The virtual environment handled automatically, Dependencies managed cleanly, Lockfile(UV's requirements.txt) generated for reproducibility Less ceremony. Fewer steps. Cleaner workflow. 2. Faster installs → pip installs packages sequentially. → uv performs parallel installation and dependency resolution. The difference becomes obvious in larger projects or fresh environment setups. 3. Shipping accuracy pip + requirements.txt often lists package names only conflicts when different python version is being used. uv generates a lockfile with exact resolved versions, ensuring consistent installs across machines and deployments. Better reproducibility. Fewer works on my machine issues. For anyone working in AI/ML, backend, automation, or tooling-heavy Python workflows, uv reduces overhead and speeds up iteration. Check out the Complete guide by Corey Schafer (one of the most respected educators in the Python community) to know more about it: https://lnkd.in/gN2BbbSy stay tuned for more updates✌️. #Python #PythonDevelopment #UV #PythonTools #DevTools #SoftwareDevelopment #BackendDevelopment #AIML #DataScienc #DeveloperProductivity #OpenSource #Programming #TechCommunity #BuildInPublic #ModernDevelopment

Built scenario-based Python programs today focused on real-world decision making and structured conditional logic. Instead of simple syntax practice, I worked on problems that simulate practical systems and business rules using nested conditions and logical operators. Solved problems like: • 🚦 Smart Traffic Signal with emergency override handling • 🏦 Loan Eligibility Checker based on multiple financial conditions • 🔺 Triangle validation and type identification • ⚡ Electricity Bill calculation using slab logic • 🎓 Exam Result Analyzer with classification rules These exercises improved my ability to design clear decision flows and handle edge cases logically — a key skill for technical interviews and real-world applications. Full code available here: https://lnkd.in/gpj3TJ8i Consistent practice. Clear logic. Stronger problem-solving mindset. #python #pythonprogramming #placementpreparation #codingpractice #logicalthinking #10000coders #softwaredevelopment #problemsolving #logicalthinking #100daysofcode #codeeveryday #learningjourney #skilldevelopment #buildinpublic

Over the weekend, I explored Python's built-in standard GUI library, Tkinter, and created a simple tool/app to make a standard workflow like the DPWH standard superstructure bridge design just more fun and intuitive. One feature is a built-in moving load simulator that visualizes moment envelopes. There are a lot of other ways to increase engineering productivity using Python - from automating manual design tasks, running parametric studies, and interacting with engineering software like SAP2000, ETABS, or STAAD. Still curious how it could be enhanced, better GUI, interactive 3D visualization, parametric design & detailing for other typical bridge components (bearings, piers, foundations), and machine learning for quick preliminary sizing and reinforcement optimization. #StructuralEngineering #Python #BridgeDesign

🚀 New milestone in my Python journey (42 / Growing Code series) After sharing my C projects, I’m starting a new chapter: a structured path through Python modules—each one built around a different “world” and a different set of skills. Here’s the roadmap I’m working through: 🌱 Module 00 — Growing Code: Python fundamentals (syntax, variables, functions, control flow) 🌿 Module 01 — CodeCultivation: OOP foundations (classes, systems thinking) 🛡️ Module 02 — Garden Guardian: exceptions & robust error handling 🎮 Module 03 — Data Quest: collections + data structures (lists/tuples/sets/dicts, comprehensions, generators) 📚 Module 04 — Data Archivist: files, streams, and resilient I/O patterns 🕸️ Module 05 — Code Nexus: inheritance, overriding, and polymorphism 📦 Module 06 — The Codex: packages, imports, relative/absolute paths, circular dependencies 🃏 Module 07 — DataDeck: abstract classes, interfaces, and modular architecture 💊 Module 08 — The Matrix: virtual envs, dependency management, environment config 🪐 Module 09 — Cosmic Data: Pydantic models, validation, nested structures 🧙 Module 10 — FuncMage: functional programming (lambda, HOFs, decorators, scope) What I like about this path: it’s not just “learning Python”—it’s building cleaner design habits, more reliable code, and stronger engineering thinking module by module. #Python #42Network #42Firenze #SoftwareEngineering #OOP #DataEngineering #FunctionalProgramming #Pydantic #LearningInPublic #CodingJourney

We’ve been exploring SOLID principles — the foundation of scalable system design. Today’s principle: Liskov Substitution Principle (LSP) Most developers understand inheritance. Fewer understand correct inheritance. LSP states: If a class is a child of another class, you should be able to replace the parent with the child without breaking the system. In simple terms — 👉 A subclass must behave like its parent. If replacing it causes unexpected errors, broken logic, or special-case handling… your design violates LSP. This principle protects polymorphism and ensures your abstractions are truly reliable. In the carousel, I’ve explained LSP with simple real-world examples and interview clarity. If you're preparing for system design or backend interviews, this principle is critical. 👉 Have you ever seen inheritance break production code? #SOLID #OOP #SoftwareEngineering #SystemDesign #CleanCode #Programming #CodingInterview #DeveloperJourney #BackendDevelopment #DotNet #Java #Python #FullStackDeveloper #SoftwareArchitecture

Most developers spend days understanding a new codebase before writing a single line. Documentation gets outdated, and architecture diagrams are created once and never updated. So here is HIRO a tool that scans any Python file or entire project folder and automatically generates a live architectural diagram with zero manual input. No drag-and-drop, no describing your system just point it at your code and generate a real map of how everything actually connects. Under the hood, HIRO uses Python’s AST module for 100% accurate extraction of classes, functions, imports, and call relationships not AI guesses. A local LLM (LLaMA 3.2) then reads this verified structure to generate architectural insights like dependency chains, layer classification, data flow, and pattern detection. With multi-file analysis, parallel dependency detection strategies, and Mermaid.js diagram generation, it produces a unified, interactive architecture directly from the source of truth: your code.