GitHub Stats vs Reality: Fixing Jupyter Notebook Misclassification

To all Python devs in my network: As a longtime Python engineer, I have been a power-user of the interactive IPython shell for many years. Yet, I never encountered anyone using IPython the way I do. So let's unbox some secret killer-features. On Thursday april 2 2026, I'll be giving an presentation about IPython on how to supercharge your python shell experience. This is not yet another introductory tutorial. I'll show you in detail why and how I use IPython, and the relevancy in this age of agentic coding. If you’re working with Python in the Flanders/Belgium area, come join us at the next Python User Group Belgium meetup at the Lighthouse office in Ghent! 🔗 https://lnkd.in/eAuv2i4x #Python #IPython #PythonBelgium

🚀 Day 34 of #100DaysOfCode | Scenario-Based Questions in Python OOP Today I practiced solving scenario-based problems using Python Object-Oriented Programming (OOP). These problems help in understanding how OOP concepts are applied in real-world situations. Key Concepts I Practiced: • Designing classes based on real-world scenarios • Implementing Encapsulation to protect class data • Using Inheritance to reuse code between classes • Applying Polymorphism for flexible and reusable methods Practising scenario-based questions improves logical thinking and problem-solving skills, and helps in building scalable and maintainable applications using OOP concepts. GitHub Repository: https://lnkd.in/gTmfXrWP #Python #OOPS #100DaysOfCode #CodingJourney #PythonProjects

GitHub Launches AI-Powered Decision Framework for Structured Thinking 📌 GitHub just dropped think-better - an AI-enhanced CLI tool that turns messy decisions into structured plans using cognitive bias detection and 10+ decision frameworks. Powered by Go and Python, it auto-classifies problems, flags biases like status quo bias, and generates action matrices for platforms like Claude or Copilot. It’s open-source, cross-platform, and ready to upgrade your next big call. 🔗 Read more: https://lnkd.in/ddYDH8kn #Thinkbetter #Decisionframework #Cognitivebias #Clitool #Github

In 2011, I committed my first code to GitHub. Since then, I've curated the best Python code for quant finance on the internet. Here are the 9 best (bookmark this to keep it handy): python-cheatsheet A comprehensive Python cheatsheet. https://t.co/Ok5rjueR8V alphalens-reloaded Performance analysis of predictive (alpha) stock factors. https://t.co/QezeiZguhe optopsy Backtesting and statistics library for option strategies. https://t.co/clLixLM66f quantstats Portfolio profiling, in-depth analytics and risk metrics. https://t.co/BNcstrxuuH ArcticDB High performance, serverless DataFrame database. https://t.co/k0G1nnnxJd OpenBBTerminal Access equity, options, crypto, forex, macro economy, fixed income, alternative datasets, and more. https://t.co/62mrP3QahV zipline-reloaded Event-driven system for backtesting. https://t.co/ruL0dEOMRp PythonDataScienceHandbook Introduces the core libraries essential for working with data in Python. https://t.co/58tKxnyvFl awesome-quant A curated list of insanely awesome libraries, packages and resources for quants. https://t.co/8B5GdCzuMY My 9 favorite GitHub repos (after 13 years of starring them): • optopsy • ArcticDB • quantstats • zipline-reloaded • awesome-quant • OpenBBTerminal • python-cheatsheet • alphalens-reloaded • PythonDataScienceHandbook

Most people consume content about data engineering. Very few build. 674 contributions in the last year. Consistent across October through March, no dead months, no gaps. Not because every day was productive. Because the habit of showing up compounds faster than any course ever will. If you're trying to break into data engineering, close the tutorial. Open a terminal. Build something broken, fix it, commit it, repeat. Your GitHub is either evidence or silence. Make it evidence. What's stopping you from committing something today? #DataEngineering #Python #GitHub

Credentials recovered. 😅 PyStormTracker is officially live on PyPI! 🐍📦 pip install PyStormTracker Up Next: 🧠 In-memory data handling with Xarray 🔄 Automated CI/CD pipelines with GitHub Actions 🐳 Docker containerization for reproducibility 🎯 Better tracking algorithm 📉 Native calculation for Accumulated Track Activity from the original paper Link in the first comment. 👇 #Python #PyPI #Docker #OpenSource #BuildInPublic #ScientificResearch #ClimateChange

𝙂𝙞𝙩 𝙘𝙤𝙢𝙢𝙖𝙣𝙙𝙨 𝙖𝙧𝙚 𝙚𝙖𝙨𝙮. 𝙂𝙞𝙩 𝙥𝙧𝙤𝙗𝙡𝙚𝙢𝙨 𝙖𝙧𝙚 𝙣𝙤𝙩. Everything works fine… until it breaks. And that’s where most developers get stuck. You can clone, commit, and push. But real challenges look like this: ➥ How do you pull without losing your work? ➥ How do you commit only what matters? ➥ How do you undo mistakes safely? ➥ How do you resolve conflicts cleanly? ➥ What do you do when your push gets rejected? This guide focuses on real Git problems you face daily and shows exactly what to do in each situation. Git isn’t about memorizing commands. It’s about knowing what to do when things go wrong. Doc Credit - Respective Owner ♻️ Repost if you found this useful 🤝 Follow Sattari Sateesh Kumar for more  👨💻 For 1:1 guidance → https://topmate.io/sateesh #python #pyspark #pysparklearning #dataengineering #sqllearning #dataengineeringinterview #azuredataengineer #bigdata #spark #datalearning #datacareer #azuredataengineering #dataengineeringjobs #linkedinlearning #dataengineeringlearning

Data visualization using cartopy #machinelearning #datascience #datavisualization #pythonlibrary #cartopy CartoPy is a Python library that specializes in creating geospatial visualizations. It has a slightly different way of representing Coordinate Reference Systems (CRS) as well as constructing plots. https://lnkd.in/gjJ_yjyT

Sometimes, the best way to move forward… is to go back to the basics. 🐍 Over the last few days, I spent time revisiting Python — not by just watching videos or reading notes, but by actually writing code, making mistakes, fixing them, and understanding why things work. I practiced and built small programs around: ✅ Functions ✅ Lists, dictionaries, and sets ✅ File handling ✅ Error handling ✅ OOP concepts ✅ Inheritance ✅ @property and magic methods ✅ Working with files and folders using the os module And honestly, this kind of practice hits differently. Every small bug I fixed taught me something. Every concept I struggled with made more sense after writing code around it. One thing this reminded me of: 👉 Good engineers are built on strong basics. No shortcuts. Just consistency, curiosity, and hands-on practice. I’ve uploaded all my Python practice programs here: 🔗 GitHub Repo: https://lnkd.in/gGUPgkWU Still learning. Still building. And enjoying the process. 🚀 If you’re also going back to the basics right now — you’re not starting over. You’re building stronger this time. #Python #LearningInPublic #PythonProgramming #GitHub #CodingJourney #SoftwareEngineering #DeveloperLife #100DaysOfCode

- Excited to share my latest project: Library Books Management API - GitHub Repository: https://lnkd.in/gWY3DJFB - Project Highlights:- 1.Built using FastAPI 2.CRUD operations for managing books 📚 3.Tested APIs using Swagger UI 4.Clean and structured backend implementation - This project helped me strengthen my understanding of API development and backend architecture. Special thanks to @Innomatics Research Labs for guidance and support #FastAPI #Python #APIs #LearningJourney #Innomatics #GitHubProjects #Innomatics-advanced-GenAI-Internship Innomatics Research Labs