New Python Repository for Beginners on GitHub

20/30 learnings: 🚀 Embarking on the Python Automation Journey Starting with Python for Data Automation? You're not alone in finding the roadmap unclear. Many skip over the foundational aspects, but building a strong Python foundation is crucial for effective automation. 🔑 Why Python Fundamentals Matter Before diving into automation, ensure you're comfortable with: Basic syntax and data structures Functions and control flow Modules and libraries This foundational knowledge empowers you to: Write clean, efficient scripts Debug effectively Build scalable automation solutions 📘 A Resource to Guide You: github link: https://lnkd.in/gGBHc_J6 #30dayslearnings #pythonbasics

💻 New Python script has been added to repository https://lnkd.in/dJ5FrAdi What it does? Checks for all projects and it's work item types and if they are used in any projects to help with the cleanup process. Benefit Get rid of the work item types that are only in default type scheme rather having a separate scheme. #AtlassianChampion #Jira #Python

🚀 Test CI/CD Pipeline with GitHub Actions 🧑💻 I have created a simple test CI/CD pipeline using GitHub Actions for a Python project. It's just a basic test to get familiar with the process. This could be helpful for beginners who would like to learn how to set up a CI/CD pipeline. 🚀 🔧 What it does: Automatically runs tests when code is pushed. Deploys if tests pass. Check out the repo here: https://lnkd.in/eB5cyKbZ #GitHubActions #CICD #TestProject #Python #Beginners #Automation

I built a small tracer from scratch in Python. It automatically creates spans for function calls (like requests.get) using: 𝗠𝗼𝗻𝗸𝗲𝘆 𝗽𝗮𝘁𝗰𝗵𝗶𝗻𝗴 – intercepting existing library functions and wrapping them with tracing logic, without modifying the original code. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝘃𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 (𝗰𝗼𝗻𝘁𝗲𝘅𝘁𝘃𝗮𝗿𝘀) – these are like thread-local storage but designed for async and concurrent programs. They let each coroutine or thread safely keep its own tracing context, so even with parallel requests, the tracer knows which span belongs to which flow. 𝗪𝗵𝗮𝘁’𝘀 𝗮 𝘀𝗽𝗮𝗻?  A span represents one unit of work — for example, a function execution or an API call.  Spans can be nested (parent-child), forming a tree that shows the entire flow of a request across different components. Each span is logged to a file (trace_log.jsonl), capturing parent-child relationships and timing details. Later, this data can be visualized to see how requests flow through a system — similar to what full-fledged tracing tools like OpenTelemetry do. GitHub: https://lnkd.in/gcR7XCwc

Extending Machine Learning Exercises using Python to implement an 'End-To-End' ML Project involved Python Modular Programming concepts. Transformed the Regression Model Jupyter Notebook codes to mimic real-time Production-ready project. Created different Python modules utilizing advanced concepts like 'dataclass', 'pipeline', 'Python Virtual Environment', 'git repository', and '.gitignore' etc. Created a production-like CI/CD deployment folder structure with directories such as 'src', 'components', 'pipeline', 'utils', and 'artifacts' housing various Python (.py) files like 'data_ingestion.py', 'data_transformation.py', 'model_trainer.py', 'logger.py', and 'exception.py'. Setup a custom project environment using 'REQUIREMENTS.TXT', and generated a 'setup.py' for package generation and distribution. Maintained Source Versioning through a remote git repository. Acknowledging Krish Naik for his invaluable YouTube learning resources. Stay tuned for further advancements in extending these developments to harness the Cloud resources. For more project details, visit my git repository: https://lnkd.in/eabdr9ir

Library Management System using Python & Streamlit I am pleased to share my recent project — a Library Management System developed using Python, Streamlit, and Pandas. This web-based application allows users to efficiently manage a library’s collection through the following key functionalities: 📘 View Books: Display the complete list of books available in the library. ➕ Add Books: Add new book entries with title, author, and availability status. 📖 Issue Books: Update the record when a book is issued. 🔁 Return Books: Mark an issued book as returned. 💾 Persistent Storage: Data is stored and managed using a CSV file for easy access and updates. 🧠 Technologies Used: Python – Core programming logic and data handling Streamlit – Web interface and user interaction Pandas – Data manipulation and storage File Handling (CSV) – Persistent local data storage This project enhanced my understanding of data management, Streamlit-based UI development, and Python file operations. 🔗 Live Application: https://lnkd.in/gDMXu4-E 💻 GitHub Repository: https://lnkd.in/gdjNRSaa #Python #Streamlit #DataScience #SoftwareDevelopment #WebApplication #ProjectShowcase #LibraryManagementSystem #GitHub #LearningByBuilding

💻 Python Command-Line Quiz Game Project (Project 17)! I just wrapped up a fun little project: a fully functional, object-oriented quiz game implemented right in the Python command line! This project was a great way to practice Object-Oriented Programming (OOP) principles in Python by breaking the game down into logical, reusable components. 🌟 Key Features OOP Structure: The project is organized into three main classes, making the code clean, readable, and easy to maintain: Question: Simple class to hold the text and the correct answer for each question. QuizBrain: The core logic class that manages the question list, tracks the user's score, checks answers, and moves to the next question. main.py: Drives the game. It takes raw data, converts it into Question objects, and uses the QuizBrain to run the quiz loop. Dynamic Question Flow: The QuizBrain class handles the entire game flow, prompting the user for answers and automatically keeping score. Case-Insensitive Answer Checking: The check_answer method ensures the quiz is user-friendly by accepting "True," "true," "tRuE," etc., as correct. 🛠️ Tech Stack & Files File NamePurposequestion_model.pyDefines the Question class.quiz_brain.pyContains the QuizBrain logic (score tracking, answer checking, next question).game_data.pyStores the raw list of questions and answers.main.pyInitializes the game, builds the question bank, and runs the main quiz loop.🚀 How It Runs The game loop continues as long as there are questions left, giving the user their score after every question, and finally printing a final score summary. Python # Output Example: Q.1. Ada Lovelace is often considered the first computer programmer. (true/false)?: true you got it right the correct answer is True your score is : 1/1 Q.2. JavaScript derives from a later version of Java (true/false)?: true that's wrong the correct answer is False your score is : 1/2 you've completed the game, your final score is 8/10 github link: https://lnkd.in/eKCuaQTv This was a great exercise in applying fundamental Python concepts and good coding structure! #Python #OOP #Programming #CodingProject #CommandlineGame #QuizGame

A few days ago, I was watching videos by Hitesh Choudhary Sir (Chai Aur Code) and ThePrimeTime talking about the possible GitHub ban mainly because of fake PRs being made on popular repositories like Express.js during Hacktoberfest. That really got me thinking... 👉 What if one day my GitHub account gets banned or something goes wrong? Years of projects, code, and experiments, all gone forever. 😶 So, I created a Python package called github-backup-supabase that: 🔹 Automatically backs up all your GitHub repositories locally 🔹 (Optionally) uploads them to Supabase Storage for safekeeping You can install it using: pip install github-backup-supabase and run your first github backup. 📦 Official PyPI page: https://lnkd.in/dTzWxkss I also uploaded a YouTube video explaining its development and how to use it, check it out if you’re curious about how it works behind the scenes! https://lnkd.in/dfEjxxTs If you install the package then drop a comment below 👇, I would love to hear your thoughts or any suggestions for improvements!

🤯 Your Terminal Just Got an Upgrade !!! Seriously, this is a huge time-saver. GitHub Copilot is now available right in your command line (CLI). You don't have to leave the terminal to Google how to write a tricky git command, remember a complicated flag, or figure out what a weird error message means. It basically means: * Less Googling: Ask Copilot in the terminal how to do something, like "how to clone a repo but only the last 3 commits." * Faster Work: Keep your hands on the keyboard and stay focused on the code. * Simple Debugging: Get quick suggestions for errors right where they happen. If you spend a lot of time in your terminal (and who doesn't?), you need to check this out. It’s a real game-changer for daily productivity. Anyone else already installing the Copilot CLI? What's the first complicated command you're going to ask it for? Comment Down!!! #GitHub #Copilot #CLI #DevTools #Productivity #TechnicalArchitecture #SoftwareEngineering #MachineLearning #AILearningJourney #TechTalent #TechCareers #Python #WomenInTech #CareerGrowth #ArtificialIntelligence #Development #Deployment #DataScience #CodeDeepDive #ReactJS #FrontendEngineering #CSSAnimations #PortfolioProgress #MLOpsPrep #TechTalent #SoftwareEngineering #MachineLearning #PortfolioProgress #AlLearningJourney #OpenToWork #ReactJS #JobSearch2025 #TechTalent #ProjectShowcase #TechCareers #Hiring #Python #WomenInTech #CareerGrowth #ArtificialIntelligence #Development #Deployment #Animation #DataScience #connections