Python Continuous Integration Tutorial

🐍 Python Term of the Day: version control (source control) (Python Best Practices) Guidelines and best practices for version-controlling your Python code. https://lnkd.in/gXZTwK4C

🐍📰 Getting Started With Async Features in Python This step-by-step tutorial gives you the tools you need to start making asynchronous programming techniques a part of your repertoire #python

The best Python setup is not the one with the most extensions. It’s the one with the fewest surprises. I shared a practical Medium post on the best VS Code extensions for Python development and how to avoid editor overload. Read here: https://lnkd.in/dVgDwEHG

🐍📰 How to Manage Python Projects With pyproject.toml In this tutorial, you'll explore key use cases of the pyproject.toml file, including configuring your build, installing your package locally, managing dependencies, and publishing your package to PyPI

Python dependency installs shouldn’t take so long⚡ Yet many Python workflows still rely on a stack of tools just to manage environments and packages. Between pip, virtual environments, and dependency managers, installs can become slow and inconsistent across machines. A newer tool is starting to change that. UV is a high-performance Python packaging and environment manager designed to simplify the workflow and dramatically speed it up. A few highlights: • Built in Rust for major performance gains • Package installs can run 10–100× faster than traditional workflows • Handles environments and dependency management in one tool • Uses pyproject.toml as the single source of truth for projects For teams running CI pipelines or managing complex Python environments, improvements like this can significantly reduce setup time and friction across development workflows. If you’re working with Python infrastructure, this is worth a closer look. Read the full breakdown on the blog. https://lnkd.in/gS3mQ7AN #PythonDevelopment #DevOps #CloudEngineering #SoftwareEngineering #DeveloperTools

uv vs pip. Why should you use uv rather than pip for managing packages and dependencies in your Python project? - uv generates a uv.lock file that pins the exact versions of every dependency and sub-dependency. This makes environments reproducible across machines and deployments. - When you add a package with uv add, it updates the project definition for you. With pip, you typically have to remember to run pip freeze to update requirements.txt. - uv handles virtual environments as part of the workflow instead of requiring separate setup. - Dependency resolution with uv is dramatically faster than pip. I wrote a short breakdown explaining how this works and why it matters for production Python projects. #python #packagemanagement #uv #pip #backend #pythonprojects

🐍📰 Socket Programming in Python (Guide) In this tutorial, you'll learn how to build a socket server and client with #python. By the end of this, you'll understand how to write your own networked client-server applications using Python's socket module.

🐍🔖 Python Development Tools — On this page you will find articles that will help you get started on the road to mastering the most common tools used in the Python ecosystem. Knowing how to use these tools will serve you well in your career. https://lnkd.in/g-e3xpA

💻 New Code Commit: Implement encapsulation and abstraction in Python This commit strengthens the object-oriented design of the project by applying two fundamental principles of Python programming: encapsulation and abstraction. Encapsulation means keeping data and the methods that work on that data inside a single unit (a class), while controlling how the data is accessed. In simple terms, we protect internal details and expose only what is necessary. For example, instead of allowing direct modification of a variable, we manage it through methods. This improves data safety and reduces unexpected errors. Abstraction means hiding complex implementation details and showing only the essential features of an object. Think of it like driving a car — you use the steering wheel and pedals without needing to understand how the engine works internally. In Python, abstraction is commonly implemented using abstract base classes or well-defined interfaces. By introducing these principles: 1. The code becomes easier to understand and maintain. 2. Internal logic is protected from accidental misuse. 3. Future updates or changes can be made with minimal impact on other parts of the system. 4. The overall design becomes cleaner and more scalable. These improvements are especially important in real-world projects where multiple developers collaborate and systems grow over time. Strong object-oriented foundations help ensure that the codebase remains organized, flexible, and reliable 🚀

A small Claude Code hook worth adding to any Python project. Every time Claude writes or edits a Python file, this automatically runs ruff check and ruff format. You can tell Claude to always format files in your CLAUDE.md. It will follow that instruction most of the time. But on small followup fixes, a targeted edit, or a multi-step task, it sometimes skips it. Then your CI fails on a ruff check that has nothing to do with the actual change. Hooks run at the tool level, not the conversation level. Formatting is guaranteed regardless of what Claude decided to do or not do, preventing ruff CI errors. Do you have any personal hook worth sharing? Link to code: https://lnkd.in/epQ_-aHu