Built a Python script to fetch top Stack Overflow questions

Hi connections, I’ve been learning how modern web applications are built using Flask, a lightweight web framework in Python. This diagram shows how the pieces fit together: 🔹 Python — Handles business logic & processing 🔹 Flask — Connects browser requests with Python code 🔹 Jinja2 Templates — Dynamically generate HTML pages 🔹 Browser — Displays the final output to the user When a user visits a URL, Flask processes the request with Python and sends data to a Jinja2-powered HTML template. #Python #Flask #WebDevelopment #Jinja2 templates

🚀 10 Python Libraries You’ve Never Heard Of — But Will Change Your Life! 🐍✨ Most developers use Pandas, NumPy & Requests… but Python hides some insanely powerful gems that can 10x your productivity. From Rich (beautiful terminals) to Polars (super-fast DataFrames) and MoviePy (video editing in Python!) — these libraries will upgrade your entire workflow. If you want to level up your Python skills in 2025, this is your sign. 💡🔥 Medium - https://lnkd.in/dz5xy_KW Google Blogs - https://lnkd.in/dDVujqN3 Personal Site - https://lnkd.in/dF6xifBW Medium - https://lnkd.in/dz5xy_KW #Python #Developers #Coding #Programming #PythonLibraries #Productivity #TechCommunity #DataScience #Automation #MachineLearning #SoftwareEngineering

Day 45 – Packages vs Modules (The Missing Piece) Heard of modules and packages but not sure what’s what? Let’s simplify 👇 📦 Module → A single .py file 📦 Package → A folder that contains multiple modules + an __init__.py file Example: my_package/ __init__.py math_ops.py string_ops.py You can use them like: from my_package import math_ops math_ops.add(5, 10) Packages = modularity + reusability. This is how Python frameworks like Django or Flask are structured internally.💡 👉 Small, modular codebases are easier to grow and maintain. #Python #SoftwareEngineering #CleanCode #100DaysOfCode

Built a Weather App using Python 🐍, Streamlit ⚡, and the Weatherstack API 🌦️ Get real-time weather 🌍 for any city ✈️ — shows temperature 🌡️ and weather description ☁️🌤️ Had fun exploring API integration 🔗 and Streamlit deployment 🚀 🔗 Live Demo: 👉 https://lnkd.in/dVVKeJeK 💾 GitHub: 📂 https://lnkd.in/dtNiH4m5 #Python #Streamlit #WeatherApp #APIIntegration #OpenSource #WebApp #Coding #MadeWithPython #Developer #TechFun

Ever feel like your Python code just refuses to cooperate when things get heavy? Imagine you’ve crafted the perfect string matching algorithm. It works fine with small data, but when you scale up, everything slows to a crawl. Frustrating, right? That’s when I remembered an old trick: offloading critical parts to C. Turns out, a little C integration can make your Python programs run up to 150 times faster. I’ve tried subprocess calls, ctypes, and even full C extensions, and the speed gains are staggering. It’s like giving your code a turbo boost without abandoning Python’s simplicity. The key takeaway? 🧩 Use subprocess for quick wins—compile C code to executable and call from Python. 🚀 Load your C functions directly into Python with ctypes for seamless speed. 🎯 For the fastest performance, write Python C extensions to get native-like speed. When your code hits a wall, consider whether a C shortcut can get you across faster. Are we just coding in Python, or starting to think like data scientists with a performance edge? What’s your favorite way to speed up Python? #DataScience #PythonTips #Performance #CodingHacks

We analyzed more than 10,000 MCP repositories for the State of Dependency Management 2025. Here’s the snapshot: • 44% built in JavaScript/TypeScript • 36% in Python • 82% use sensitive APIs • 66% have under 10 GitHub stars Lots of innovation. Little maturity. Dive into the data: https://lnkd.in/gstQaGDK #MCP #AIAgents #DMR2025

Over the weekend, I delved into Python's functions, uncovering some valuable insights: - Functions streamline your code by allowing you to write a block once and reuse it, ultimately saving time and simplifying maintenance efforts. - Apart from lambda functions which deviate from the standard syntax, All functions typically start with "def ***(a, b): " where *** denotes the function's name, with relevant variables within the parentheses like (a, b) in the example. - The function's body dictates the iteration process over the variables, resulting in a specific output through the "return" statement. Functions serve as a cornerstone for automation. - Demonstrated below is a 'fizzBuzz' function, designed to list numbers from 1 to a specified number "n". Notably, multiples of 3 are replaced by "Fizz", multiples of 5 are replaced by "Buzz", and multiples of 15 are replaced by "FizzBuzz". Fizzbuzz(5) Upon calling the function with "n" assigned as 5 (per the line of code above), the result is returned as: ['1', '2', 'Fizz', '4', 'Buzz'] #Python #Functions

Come hear about various automations I use to alleviate python package maintenance. It'll cover pre-commit, GitHub Actions, and publishing to PyPI in the context of a package I've been developing called `frame-search`. Check it out if you are interested in sharing your own python project on PyPI for the first time or want to pick up some techniques I've found to focus more time on development! Link is below 👇

Python feels easy… until it doesn’t. You fly through the basics. And then suddenly: dunder methods, decorators, functools, typy annotations, protocols... boom 💥. Sound familiar? It took me a while before I actually started feeling comfortable writing Python code. Though I still have no love for "Callable[[arg1, arg2], return_value]" annotations 😬. In this week's video, I break down the exact steps I’d take if I had to learn Python fast from scratch, covering everything I wish I knew earlier. 👉 Watch here: https://lnkd.in/evzGVC_A #Python #SoftwareEngineering #LearningPython #CleanCode #ArjanCodes