pip vs uv: Faster Alternative for Python Package Installation

🐍 Back to Basics: The Building Blocks of Python I recently reviewed some comprehensive notes on Python fundamentals, and it’s a great reminder of why this language—developed by Guido Van Rossum—remains so user-friendly and powerful. Whether you are just starting or brushing up on your skills, mastering the core vocabulary is key. Here is a breakdown of the essentials found in these notes: 1. The "Grammar" (Keywords) These are the reserved words that give Python its structure. The notes highlight how we use logic operators like and, or, and not to control flow, or def and class to build functions and objects. Did you know? You can use import keyword and print(keyword.kwlist) to see all of them in your current version! 2. The "Tools" (Built-in Functions) Python comes batteries-included with functions that handle heavy lifting immediately. Math & Numbers: abs(), round(), float(), and int(). Interaction: input() and print(). Sequence Helpers: len(), range(), and the powerful map() function for applying logic across iterables. 3. Data Manipulation (Methods) The notes dive deep into how we handle Lists and Strings: Lists: Modifying data is easy with append(), insert(), pop(), and sort(). Strings: Text processing is a breeze using split(), join(), capitalize(), and checks like isalpha(). Python’s "platform independence" and "open source" nature make it accessible, but its these built-in tools that make it efficient. Which built-in Python function do you find yourself using the most? 👇 #Python #Programming #CodingBasics #SoftwareDevelopment #TechSkills #Learning

Modules, Packages, and Imports in Python Efficiency in Python isn't just about the logic you write it’s about how you organize it. If you want to move from "scripts" to "software," mastering the hierarchy of code organization is essential. Here is a quick breakdown of the Python ecosystem: 1. The Module: The Atomic Unit A Module is simply a .py file. It’s the smallest unit of organization where you define functions, classes, and variables. - The Goal: Break down massive scripts into manageable, reusable pieces. - The Rule: The filename (minus the .py) becomes the module name. 2. The Package: Higher-Order Logic A Package is a directory that houses multiple modules. While Python 3.3+ supports namespace packages, adding an __init__.py file is still the standard way to signal a package directory. - The Goal: Organize related modules into a hierarchy (like NumPy or Django) to prevent naming conflicts. - The Structure: Packages can contain "subpackages," creating a clean, nested architecture. 3. The Import: The Bridge The import statement is the engine that brings your code to life by connecting definitions to your current workspace. Pro Tip: Choose your style based on readability: - Standard: import module (Keeps namespaces clean) - Alias: import pandas as pd (Saves time/keystrokes) - Direct: from math import pi (Fast access to specific tools) - Relative: from . import utils (Best for internal package references) 💡 Why it matters? This system is the backbone of Namespace Management. It ensures your "math_utils" don't clash with someone else's "math_utils," keeping your codebase scalable and easy to maintain. #Python #DataEngineering #DataScience

I didn't understand why functions mattered when I first learned Python. My instructor kept saying: "Don't repeat yourself. Use functions" 🤔 I thought: Why? Copy paste works fine. Then I wrote some repeated logic in my code, the same check appeared more than once. When I found a mistake, I had to fix it in multiple places. That's when it clicked.💡 Functions aren’t just a Python feature. They’re a way to write smarter, maintainable code. The rule I follow now: If I’m writing the same logic twice → make it a function. Why functions matter: • One source of truth 📌 • Fewer bugs 🐞 • Easier updates 🔧 • Code that’s easier to read 👀 I wish I had understood this earlier instead of just memorizing syntax. 📚 Now, when I write code, I ask: “Will I need this logic again?” If yes → function. Functions didn’t just make my code shorter. They made it maintainable. If you’re learning Python, don’t skip functions thinking they’re “advanced.” They’ll save you hours of debugging later.⏱️ What Python concept took you time to really understand? #Python #Programming #CleanCode #LearnInPublic #SoftwareDevelopment

This week I gradually upgraded a small Python project step by step from Python 3.10 to 3.14. I’d heard that Python 3.14 brings noticeable performance improvements, so I was curious to see what I’d observe in practice. For a very small experiment, I sent 10 requests to my service on each version (after updating the code each time) and averaged the results: Python 3.10 — 429 ms Python 3.11 — 395 ms Python 3.12 — 523 ms Python 3.13 — 475 ms Python 3.14 — 467 ms The takeaway? In such a small, I/O-oriented test, it’s hard (and probably wrong) to judge the real performance impact of a language version based on numbers like these alone. The differences exist, but they’re not dramatic — and not always in the direction you might expect. It feels like a good reminder that performance claims only really make sense in the context of real workloads, proper benchmarks, and larger samples. I’m curious — have you noticed any tangible performance wins from upgrading Python versions in your own projects? Or did the benefits show up more in maintainability, tooling, and developer experience than in raw speed?

🚀 pip vs uv: Python Dependency Management Is Changing For years, pip has been the default way to manage Python dependencies. It works — but modern Python projects need speed, reproducibility, and structure, not just installs. I just published an article breaking down: pip vs uv uv’s pip-compatible mode vs native workflow How uv init, uv add, and uv run actually work Whether you really need to activate .venv (spoiler: usually no) If you’re building Python projects in 2025 — APIs, services, or production systems — this is worth understanding. 📖 Read here: https://lnkd.in/g33mw9m7 Would love to hear: 👉 Are you still using pip / requirements.txt, or have you started experimenting with uv? #Python #SoftwareEngineering #DeveloperTools #BackendDevelopment #OpenSource

Staring at the python tutorial videos I have to cover, and honestly, it feels like I have a long way to go. But one thing is sure, I’m not stopping. Today, I opened the YouTube tutorial I’m learning from, and the topic was Comments. Below are few things I learnt about the topic and I did compare it with what I know about comment in SQL: 📍I learned that comments in Python are basically notes inside your code. They’re not for Python, they’re for humans. 📍Once Python sees the '#' symbol, it skips that line completely. It doesn’t affect the logic, it doesn’t change the output, it’s simply there to help me understand what my code is doing. 📍I also found it interesting that SQL uses comments too, but with different characters. One thing I noticed is that in Python, if I want to write multi-line comments, I need to use `#` on every line. In SQL, with '/* */', you can write multiple lines of comments without repeating the symbol, and honestly, I think I prefer that 😅 But hey, Python has its own rules, and I’m learning to respect them. 📌What really stood out to me is why Comments matter. They help explain the steps, the logic, and the intention behind the code. They make code more readable, more professional, and easier to maintain. Even someone who doesn’t know much about Python can look at a well-commented code and still understand what the programmer was trying to do, and that’s powerful. Little lessons like this remind me that progress isn’t always loud. Sometimes, it’s just opening the tutorial again and learning something small… but important. #GWO_Linkedin30daysChallenge #PythonLearning #DataAnalyticsJourney #ContinuousLearning #GrowthMindset

Option 1: The "Simple & Effective" Approach Best for: General tech audiences or beginners. Caption: Python makes everything look easy! 🐍✨ Did you know Python has a built-in calendar module? No need for complex loops or external libraries to display a specific month. Just 4 lines of code and you have a perfectly formatted output. Sometimes the best solution is already built-in. Have you used the calendar module for any of your projects? #Python #CodingTips #SoftwareEngineering #PythonProgramming #TechCommunity Option 2: The "Productivity Hack" Approach Best for: Developers who love clean, efficient code. Caption: Stop overcomplicating your code. 🛠️ In Python, displaying a calendar is a one-liner thanks to the calendar module. Whether you need to check a leap year, find the day of the week, or just print a monthly view, this library is a hidden gem. Why it’s great: * Zero dependencies (standard library). * Highly readable. * Saves time on logic formatting. What’s your favorite "hidden" Python library? Let me know in the comments! 👇 #PythonTips #CleanCode #DeveloperLife #Programming #DataScience Option 3: Short & Punchy (Engaging) Best for: High engagement and quick scrolling. Caption: Python: Less code, more results. 🚀 Check out how easy it is to generate a calendar using only the built-in calendar module. No extra installs, just pure logic. Is Python the most user-friendly language out there? Let’s discuss. 💬 #Python #Coding #Logic #TechTrends #WebDevelopment

Announcing yaml12: High-Performance YAML 1.2 Parsing for R and Python We are pleased to announce the release of yaml12 (for R) and py-yaml12 (for Python), two new packages designed to provide predictable, high-performance YAML 1.2 parsing and emitting. These packages are specifically designed for those who use YAML for configuration files, front matter, or data interchange. While the existing yaml package in R remains maintained for those who rely on it, yaml12 offers a modernized alternative built on a shared Rust-based core. • Built with Rust, the R package is approximately 2x faster than the original yaml package, while the Python version (py-yaml12) has demonstrated speeds over 50x faster than default PyYAML in common benchmarks. • By following the YAML 1.2 specification, the packages provide stricter implicit conversions. • Both the R and Python packages share a consistent API and design philosophy, making it easier for teams working in multi-language environments to maintain the same YAML behavior. The library is designed to preserve mapping order and handle unhandled tags gracefully, ensuring that data written back to YAML retains its original structure and intent. Learn more about the release and its technical capabilities on the Tidyverse blog: https://lnkd.in/gXMBxzdU

🐍 Python Course – Day 7 (Functions in Python) 🔹 What is a Function? A function is a block of code that performs a specific task. Instead of writing the same code again and again, we write it once and reuse it. 🔹 Why Functions are Important? Make code clean and organized Avoid repetition Easy to update and debug Used in every real Python project 🔹 Creating a Function In Python, functions are created using the def keyword. def greet(): print("Hello, Python") Explanation: def means define greet is the function name Code inside runs when the function is called 🔹 Calling a Function greet() Explanation: The function executes only when it is called 🔹 Function with Parameters Parameters allow functions to receive data. def greet(name): print("Hello", name) greet("Hashim") Explanation: name is a parameter "Hashim" is an argument 🔹 Function with Return Value A function can return a result. def add(a, b): return a + b result = add(5, 3) print(result) Explanation: return sends value back to the caller Code stops after return 🔹 Function with User Input def square(): num = int(input("Enter number: ")) return num * num print(square()) 🔹 Important Rules ✔ Function name should be meaningful ✔ Indentation is mandatory ✔ A function runs only when called 🔹 Day 7 Practice Task ✅ Create a function to print your name ✅ Create a function to add two numbers ✅ Create a function to check even or odd def check_even(num): if num % 2 == 0: print("Even") else: print("Odd") check_even(10) 🚀 60 Days of Python – Day 7 Completed 🐍 Today I learned Functions in Python – one of the most powerful concepts. What I practiced today: ✔ Creating functions ✔ Passing parameters ✔ Returning values ✔ Writing reusable code def add(a, b): return a + b Writing clean code starts with good functions 💡 Consistency is my biggest strength. #Python #Programming #LearningJourney #Day7

How to Switch to ty from Mypy Python has supported type hinting for quite a few versions now, starting way back in 3.5. However, Python itself does not enforce type checking. Instead, you need to use an external tool or IDE. The first, and arguably the most popular, is mypy. Microsoft also has a Python type checker that you can use in VS Code called Pyright, and then there’s the lesser-known Pyrefly type checker and language server. The newest type checker on the block is Astral’s ty, the maker of Ruff. Ty is another super-fast Python utility written in Rust. In this article, you will learn how to switch your project to use ty locally and in GitHub Actions… https://lnkd.in/d3i-mgnq