Modeling Railway Systems with Python OOP

🚀 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

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

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.

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

Stop doing the same manual and repetitive engineering tasks… …build custom layers on top of your existing software instead. We just published a new tutorial on EngineeringSkills where Hakan Keskin walks through building a professional desktop engineering app using Python and PyQt. The idea is simple but powerful...don't replace your existing engineering software, augment it. In this case study build-along, Hakan adds a custom layer on top of ETABS... ✅ Python orchestrates the workflow ✅ PyQt provides a clean interface ✅ ETABS stays as the analysis engine Exporting tables, reformatting data, assembling reports — all streamlined into one custom tool. The tutorial covers everything from structuring a split-panel UI to integrating an ETABS interface and displaying results in a table. It's an in-depth one, but once you've worked through it, you'll be ready to start building your own custom tools. Link in comments 👇 #StructuralEngineering #CivilEngineering #EngineeringSkills #Python #PyQt #ETABS #EngineeringAutomation

Day 9 of 150: Advanced Class Relationships — Inheritance, Composition, and MRO Building on the foundations of OOP, today’s session focused on how objects interact and share behavior. Understanding the trade-offs between Inheritance and Composition is a high-frequency topic in system design interviews. Technical Focus Areas: • Inheritance vs. Composition: Evaluating "Is-A" vs. "Has-A" relationships to build flexible and maintainable class hierarchies. • Base Class Access: Mastering the three ways to interface with parent classes, including the use of super() for clean, maintainable code. • Method Resolution Order (MRO): A deep dive into the C3 Linearization algorithm that Python uses to navigate multiple inheritance and prevent the "Diamond Problem." • Method Overriding: Implementing specialized behavior in child classes while preserving the integrity of the base class. • Real-World Application: Developed a Smart Home Device Tracker to implement complex device hierarchies and shared attributes across varying hardware types. Mastering how classes relate to one another is key to building modular, enterprise-grade software. 141 days to go. #Python #SoftwareEngineering #OOP #SystemDesign #150DaysOfCode #InterviewPrep

🚀 Python Journey — Day 2 | Strengthening Logic Building Today I focused on decision making and operator-based problems in Python. I practiced multiple logical programs: • Swapping two numbers without a third variable • Checking positive, negative, or zero • Rectangle area and verifying square • Simple Interest calculation • Bitwise operations (AND, OR, XOR, left shift, right shift) • Even or Odd number check • Divisible by 5 but not by 10 • Biggest & smallest among two numbers • Checking divisibility by 2, 3, and 6 Today's learnings: ✅ if-elif-else conditions for decision making ✅ Comparison & logical operators ✅ Modulus (%) for validations ✅ Bitwise operators basics ✅ Taking user input and handling real scenarios Today I understood how programs actually make decisions based on conditions — this felt like real problem solving. Heartfelt thanks to Rudra Sravan kumar sir for the guidance and structured learning. Learning step by step and improving every day 💪 📌 Consistency > Motivation Moving to Day 3 💻 #PythonJourney #Day2 #PythonFullStack #10000Coders #LearningInPublic #ProgrammingBasics #CodeEveryDay #FutureDeveloper #KeepLearning

HI All Factory Automation Professional, In 2026, AUTONDMY’s OPENPLC V4 underwent a major update, with both the Editor and Runtime officially releasing V4! This series will conduct various tests using OPENPLC V4 in practice. Episode 3 introduces creating a Python FB. Additionally, we installed the Open PLC Runtime on the Docker container of Advantech’s AMAX-7580 100D5A. And please connect Thiago Alves, PhD to get the latest update of openplc v4!!! #Advantech #PLC #OPENPLC #Python #Docker #OPENPLCV4 Let’s enjoy the FA! https://lnkd.in/gaJ7r3Dq

✔️ #Day43 of #DSA LeetCode Journey ⛓️💥 🚀 LeetCode #1672 – Richest Customer Wealth | Python Solution Solved an interesting beginner-friendly problem today! 🔹 Problem Statement: Given an m x n grid where accounts[i][j] represents money in the jᵗʰ bank of the iᵗʰ customer, return the wealth of the richest customer. 👉 A customer's wealth = sum of money in all their bank accounts. 👉 We need to return the maximum wealth among all customers. 🧠 Approach: Calculate the sum of each row (customer wealth). Return the maximum row sum. ⏱ Time Complexity: O(m × n) — We visit each element once. 📦 Space Complexity: O(1) — No extra space used. ✨ Key Learning: Even simple problems test your understanding of loops, 2D arrays, and built-in functions like sum() and max(). Small problems build strong foundations 💪 #LeetCode #Python #CodingPractice #ProblemSolving #50DaysOfCode #Learning #PlacementPreparation 10000 Coders Manoj Kumar Reddy Parlapalli