Isolate Python Dependencies with Virtual Environments

Virtual environments isolate Python dependencies at the project level, preventing version conflicts and keeping experiments contained without affecting system-wide installations. By Jessica Wachtel

Virtual environments isolate Python dependencies at the project level, preventing version conflicts and keeping experiments contained without affecting system-wide installations. By Jessica Wachtel

Moderne has announced that its OpenRewrite code refactoring platform now supports Python, enabling organizations to modernize their systems, fix vulnerabilities, and run change initiatives over a larger proportion of their application and data infrastructure. OpenRewrite is powered by the company’s Lossless Semantic Tree (LST), a code model that resolves symbols, tracks relationships, and preserves intent, … continue reading The post Moderne adds Python support to its OpenRewrite code refactoring platform appeared first on SD Times. #Python #CodeRefactoring #OpenRewrite #SoftwareDevelopment #TechNews

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

With the addition of Python, Moderne customers will now be able to coordinate their modernization efforts across a greater proportion of their codebases. https://lnkd.in/exNEDt4m SD Times Moderne

Hi! Mastering Python Concurrency: A Practical In-Depth Guide to Multiprocessing and Threading Performance Python is often criticized for being "slow" or "single-threaded" due to the Global Interpreter Lock (GIL). However, for many modern applications—from data processing pipelines to high-traffic web servers—concurrency is not just an option; it is a necessity. Understanding when to use `threading` versus `multiprocessing` is the hallmark of a senior Python developer. This guide dives deep into the mechanics of Python concurrency, explores the limitations of the GIL, and provides practical patterns for maximizing performance. Before writing a single line of code, you must categorize your task. The choice between threading and multiprocessing depends entirely on where the bottleneck lies. Read the full guide: https://lnkd.in/dnraAxF3

Hi! Mastering Python Concurrency: A Practical In-Depth Guide to Multiprocessing and Threading Performance Python is often criticized for being "slow" or "single-threaded" due to the Global Interpreter Lock (GIL). However, for many modern applications—from data processing pipelines to high-traffic web servers—concurrency is not just an option; it is a necessity. Understanding when to use `threading` versus `multiprocessing` is the hallmark of a senior Python developer. This guide dives deep into the mechanics of Python concurrency, explores the limitations of the GIL, and provides practical patterns for maximizing performance. Before writing a single line of code, you must categorize your task. The choice between threading and multiprocessing depends entirely on where the bottleneck lies. Read the full guide: https://lnkd.in/dnraAxF3

Python virtual environments: isolation without the chaos Virtual environments isolate Python dependencies at the project level, preventing version conflicts and keeping experiments contained without affecting system-wide installations. Installing packages globally isn’t always a good idea. Different tools inside an application can require specific versions of features, functions, or dependencies. These can conflict with or break other parts of the same application or other projects on your system. There’s a simple solution. Install locally not globally. Favoring more local installations isn’t a new idea in software development. One of the core principles of development is to use lightweight, isolated setups, and modular code. This keeps code contained, modular, and predictable. These same ideas helped drive the rise of container-based development (think Docker). Containers isolate applications and their dependencies so they can run reliably in different environments. Virtual environments apply that same principle at the language level. They let you isolate dependencies for a specific project, no matter how big or small, without affecting anything else on your system. https://lnkd.in/eKwjZJzJ Please follow Divye Dwivedi for such content.  #DevSecOps,#SecureDevOps,#CyberSecurity,#SecurityAutomation,#CloudSecurity,#InfrastructureSecurity,#DevOpsSecurity,#ContinuousSecurity, #SecurityByDesign, #SecurityAsCode, #ApplicationSecurity,#ComplianceAutomation,#CloudSecurityPosture, #SecuringTheCloud,#AI4Security #DevOpsSecurity #IntelligentSecurity #AppSecurityTesting #CloudSecuritySolutions #ResilientAI #AdaptiveSecurity #SecurityFirst #AIDrivenSecurity #FullStackSecurity #ModernAppSecurity #SecurityInTheCloud #EmbeddedSecurity #SmartCyberDefense #ProactiveSecurity

Day 7 of 10: Environment Management & Functional Python 🐍⚙️ We are on Day 7 of my 10-day Python sprint! Today’s module from the CodeWithHarry handbook focused on "Advanced Python 2," covering how to manage project dependencies and utilize functional programming patterns. Coming from an ecosystem that relies heavily on NPM and package.json, seeing how Python handles isolated environments is incredibly refreshing. Here are my top takeaways: 📌 Virtual Environments (virtualenv): Creating an environment isolated from the main system interpreter is crucial for avoiding dependency conflicts across different projects. 📌 Dependency Tracking: Running pip freeze > requirements.txt is the perfect way to snapshot installed packages and their exact versions. Distributing this file allows other developers to perfectly recreate the environment using pip install -r requirements.txt. 📌 Lambda Functions: Python’s version of anonymous or "arrow" functions are created using the lambda keyword. They evaluate a single expression and are perfect for passing quick, throwaway logic into other methods. 📌 Map, Filter, & Reduce: Python brings strong functional programming concepts to the table. map applies a function to all items in an input list, filter creates a list of items that return true for a given condition, and reduce applies a rolling computation to sequential pairs. As I push forward with backend and AI development, mastering how to isolate project dependencies is non-negotiable before deploying to production. Python devs: When manipulating data, do you prefer using map and filter, or do you strictly stick to List Comprehensions for readability? Let’s debate below! 👇 #Python #SoftwareEngineering #BackendDevelopment #10DayChallenge #CodeWithHarry

Unlock Python's full potential for automating file and data tasks in your homelab. Many scripts get stuck because they can't efficiently read or write files, costing hours in repetitive work. Learning proper file handling not only speeds up workflows but opens doors to more advanced automation.   https://lnkd.in/g5a758Wh   #Python #Automation #DataProcessing #Homelab #TechSkills