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
Boost Python Install Speed with UV
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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
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turboAPI - FastAPI compatible python framework, but written in Zig Apparently a drop-in replacement with a much better performance footprint by removing GIL and more handlers written in Zig. https://lnkd.in/eCAUHvV7
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🐍 Python PIP – Package Manager Explained PIP (Preferred Installer Program) is a package manager used to install and manage Python packages (modules). It allows developers to easily add external libraries to their projects. If you are using Python 3.4 or later, PIP is included by default. 🔹 What is a Package? A package contains all the files required for a module. These modules are reusable Python code libraries that can be imported into your project to extend functionality. 🔹 Check if PIP is Installed You can verify PIP installation using: pip --version As shown on page 2, this command displays the installed PIP version and confirms that it is available on your system. 🔹 Install a Package Downloading and installing packages is simple with PIP: pip install camelcase This command installs the camelcase package, as demonstrated in the example on page 2. 🔹 Using Installed Packages Once installed, packages can be imported and used in Python programs: import camelcase c = camelcase.CamelCase() txt = "hello world" print(c.hump(txt)) As shown on page 3, this converts text into camel case format. 🔹 Remove a Package To uninstall a package: pip uninstall camelcase PIP will ask for confirmation before removing the package. 🔹 List Installed Packages You can view all installed packages using: pip list The table shown on page 4 displays installed packages along with their versions (e.g., pip, pymongo, setuptools). 💡 PIP is an essential tool for Python developers, making it easy to manage libraries, dependencies, and project environments efficiently. #Python #PIP #PythonPackages #Programming #BackendDevelopment #DataScience #SoftwareDevelopment #AshokIT
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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
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Python Tutorial for Beginners Want to learn Python from the basics? This tutorial explains Python simply. It covers topics like installation, syntax, variables, loops, and functions. You will also learn how Python is used in real areas like web development, data science, and automation This guide is helpful for students, beginners, and anyone starting coding. Read here: https://lnkd.in/gj8Qezm2 #python #pythontutorial #techskills #softwaredevelopment #igmguru
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Start learning Python step by step https://lnkd.in/dtFbRP96 Explore Python certifications https://lnkd.in/dAJCHqaj Python cheatsheet Basic commands print() Display data on the console input() Receive input from the user len() Get the length of a data structure type() Get the data type of a variable range() Generate a sequence of numbers help() Show documentation for functions Variables and data types int Convert to integer float Convert to float bool Convert to boolean list Create a list dict Create a dictionary tuple Create a tuple set Create a set str Convert to string Control structures if elif else Conditional branching for loop Iterate through a sequence while loop Repeat while condition is true break Exit a loop early continue Skip current iteration pass Placeholder statement Functions def Define a function return Return a value from a function lambda Create an anonymous function Classes and OOP class Define a class self Reference the instance init() Constructor method Modules and packages import Load a module from module import Import specific parts of a module Exception handling try except Handle errors finally Execute code after exception block raise Trigger an exception File handling open() Open a file read() Read file content write() Write to file close() Close the file Decorators and generators @decorator Modify function behavior yield Return values from a generator List comprehensions [expression for item in list if condition] Create lists using iteration and filtering More programming resources https://lnkd.in/dqNVJKCS https://lnkd.in/dqQDSEEA Explore more programming guides https://lnkd.in/dBMXaiCv #Python #LearnPython #Programming #Coding #ProgrammingValley
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PythoC is an interesting library. It allows you to create, compile and run C executable code all within your familiar Python eco-system. If you want to learn more, check out my latest article on the Towards Data Science blogging platform for more details on how to install and use it, together with some some coding examples. https://lnkd.in/eWAku8Wg
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