Uv Workspaces Enhance Python Monorepo Management with Scalable Approach

una: tool to build and productionise Python monorepos with uv., https://una.rdrn.me/ IA Summary: Una addresses the crucial gap in `uv`'s workspace support, offering a streamlined way to build and productionize Python monorepos with inter-package dependencies. Discover how Una simplifies complex Python project development by enabling robust production builds and dependency management, bridging the gap between simple `uv` workspaces and full-fledged build systems. #python #dev #softwareengineering #uv #monorepo #workspace #una

This article focuses on Google Colab , an increasingly popular, free, and accessible, cloud-based Python environment that is well-suited for prototyping data analysis workflows and experimental code befo... #teachthemachine https://lnkd.in/gbtzKk96

Every Python developer eventually learns this lesson the hard way: Never install everything globally. Virtual environments exist for a reason. They allow you to: • isolate dependencies • avoid version conflicts • keep projects reproducible Example workflow: python -m venv venv source venv/bin/activate pip install -r requirements.txt Without virtual environments, Python projects quickly turn into dependency chaos. With them, your environment becomes predictable. And predictable systems are easier to maintain.

💡 8 Python Functions to Master for Cleaner Code Writing clean and efficient Python code is not about using more code — it’s about using the right tools. Here are some powerful built-in Python functions every developer should master: 🔹 print() customization for better output formatting 🔹 help() for instant documentation 🔹 open() with context managers for safe file handling 🔹 map() for transforming data efficiently 🔹 filter() for selecting elements from datasets 🔹 zip() to combine iterables 🔹 enumerate() for indexed loops 🔹 sorted() with key for precise sorting

Efficiency = PySceneDetect + Python 💻 I’ve been experimenting with PySceneDetect to streamline my video processing pipeline, and the results are impressive. The library makes it incredibly easy to detect shot changes and split videos automatically. Whether you're using the CLI or the Python API, the level of control over threshold sensitivity is a game-changer. Here’s my basic workflow: 1. Initialize the SceneManager. 2. Add a ContentDetector (better for cuts than simple thresholding). 3. Perform the detection and save the list of scenes. Total time saved? Massive. If you're building anything involving video analysis or automated clip generation, this library is a must-have in your toolkit. #PythonProgramming #OpenSource #PySceneDetect #ComputerVision #SoftwareEngineering

Python Virtual Environments: What I Wish I Knew Earlier Just set up my first production-ready development environment, and here's why venv is non-negotiable: Before venv ❌ → One Python installation → Package version conflicts → Projects breaking randomly → Dependency nightmare After venv ✅ → Isolated environments per project → Zero conflicts → Team-ready code → Professional workflow The Command That Changes Everything: python -m venv venv venv\Scripts\activate Current Project: Building an AI Book Builder SaaS Stack: Python | FastAPI | Clean Architecture Are you using virtual environments in your Python projects? Drop a ✅ if yes!

Data visualization using ipychart #machinelearning #datascience #datavisualization #pythonlibrary #ipychart ipychart is a Python library designed for data visualization. It empowers users to craft dynamic, sophisticated, and customizable charts using Python. It's especially tailored for Data Scientists seeking an efficient method to visualize and showcase their data directly within the output cells of their Jupyter notebooks https://lnkd.in/gkkJW2hi

🧩 The Registry Pattern isn’t new… but it’s often overlooked. Beyond just replacing if/elif chains, it can: - Break circular dependencies - Delay initialization of resources - Enable plugin architectures In my latest article, I break down how and why to use a registry in Python. 👉 https://lnkd.in/dX7a438u #python #softwaredevelopment #backend #architecture #designpatterns #engineering

𝗔𝘀𝘆𝗻𝗰𝗜𝗢 𝗣𝘆𝘁𝗵𝗼𝗻 𝗧𝘂𝘁𝗼𝗿𝗶𝗮𝗹 I recently worked through Corey Schafer's Python AsyncIO: Complete Guide to Asynchronous Programming, and it's one of the most thorough technical tutorials for Python developers of any level that I've come across. Here's what stood out: 𝗧𝗵𝗲 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗰𝗵𝗮𝗻𝗴𝗲𝗱 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴. Corey created custom animations that show exactly how the event loop manages coroutines and tasks in real time. If you've ever looked at async/await syntax and felt confused, these animations are exactly what you need. 𝗧𝗵𝗲 𝗿𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗶𝘀 𝗲𝘆𝗲-𝗼𝗽𝗲𝗻𝗶𝗻𝗴. A synchronous image downloader and processor that took ~23 seconds to finish was refactored step by step until it finished in under 5 seconds. This was not done by guessing, but rather by first profiling with Scalene to identify what was I/O-bound versus CPU-bound, and then applying the right tool: AsyncIO with HTTPX was used for downloads and multiprocessing was used for image processing. 𝗧𝗵𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗶𝘀 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹: I/O-bound + async library available → asyncio I/O-bound + no async alternative → threads CPU-bound → multiprocessing 💡 The tutorial also covers semaphores for rate limiting, TaskGroup versus gather, pitfalls of blocking code, and asynchronous context managers. These are all things that you will quickly encounter in production code. 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 🔗 Python Tutorial: AsyncIO - Complete Guide to Asynchronous Programming with Animations by Corey Schafer, 20th August 2025, https://lnkd.in/dgJWtkm3 #Python #AsyncIO #AsynchronousProgramming #Concurrency #Multithreading

I’ve published my first technical article: a walkthrough of the SOLID principles—with Python examples. It started as “I’ve heard these letters everywhere—what do they actually mean in code?” Turning that into something concrete helped me more than skimming another diagram. In the post I break things down into bite-sized pieces, including: • Single Responsibility: One job per module—easier to reason about and change. • Open/Closed: Extend behavior without rewriting existing code. • Liskov Substitution: Subtypes that don’t break expectations. • Interface Segregation: Small, focused contracts instead of fat interfaces. • Dependency Inversion: Depend on abstractions, not concrete details. Beyond the theory, each section includes short Python snippets so the ideas map to something you can run and tweak—not just memorize. The full post is here: https://lnkd.in/gFXSE4d9 #SoftwareEngineering #SOLID #Python #CleanCode #OOP #DesignPatterns