Arjan Egges’ Post

If you’ve ever used `@property` in Python, you’ve already used descriptors. Most developers rely on them every day without realizing what’s actually happening under the hood. And once you understand how descriptors work, a lot of “Python magic” suddenly becomes much easier to reason about. In today’s video, I build descriptors step by step. I recreate a simple version of `@property`, explore why assigning to `__dict__` sometimes overrides attributes and sometimes doesn’t, and use descriptors to implement reusable validation and lazy cached properties. If you want to deepen your understanding of Python and write cleaner, more expressive code, descriptors are a feature worth learning. 👉 Watch here: https://lnkd.in/eXDTNvPg. #python #softwaredesign #cleancode #pythoninternals #developers

🚀 Python Cheat Sheet Every Developer Should Bookmark A well-structured Python cheat sheet that brings together essential concepts—from string operations and data structures to file handling, math functions, and system utilities—all in one place. Instead of digging through documentation every time, having a quick reference like this can significantly speed up development, debugging, and learning. 🔗 Explore it here: https://overapi.com/python Whether you're a beginner building your fundamentals or an experienced developer needing quick recalls, resources like this make a real difference in productivity. 👉 Clean code starts with clarity in basics 👉 Faster problem-solving comes from knowing your tools well 👉 Efficiency improves when references are within reach If you're working with Python, this is definitely worth keeping handy. #Python #Programming #Developers #SoftwareDevelopment #Coding #Tech #Learning

Rules for declaring python veriables:- 1) Must start with letters (a-z, A-Z) or underscore _ 2)Must not start with numbers (1 to .... ) 3) Variables are case sensitive ( python and Python both are different) 4) We cannot use keywords as variables ( if, def, while ...) Variable declaration is main part of any program. First impression will be starting with it, so while declaring variables need to take care. #python #learn #fast #beginner #automation

I finally understood what actually happens when we run Python code… 🤯 Before this, I thought: You write code → It runs → Done. But today I learned something deeper. Here’s what actually happens behind the scenes: 👉 Your Python code gets converted into BYTE CODE 👉 This byte code is NOT machine code 👉 It runs inside something called the Python Virtual Machine (PVM) Basically… Python doesn’t directly talk to your system. It uses a middle layer. And that’s why it’s: ✔ Platform independent ✔ Easy to run anywhere Also learned: 📁 .pyc files = compiled bytecode ⚙ PVM = runtime engine (interpreter) Honestly… Things feel less “magic” now and more “logical” 🧠 Still a beginner. But slowly understanding what’s happening inside. #Python #MachineLearning #Developers #BuildInPublic

🐍📰 Python 3's pathlib Module: Taming the File System How to effectively work with file system paths in Python 3 using the new "pathlib" module in the standard library #python

📅 Day 4 – Python Sets 🐍 Today I learned one of the most useful concepts in Python – Sets and practiced different operations on them 👇 🧠 What is a Set? A set is a collection of unique elements stored in a single variable. It does not allow duplicates and does not follow any specific order. 📚 What I learned: • Sets are unordered and mutable • Duplicate values are automatically removed • Useful for storing unique data • Fast operations compared to lists 🔄 Operations I practiced: • Union → combine sets • Intersection → common elements • Difference → unique elements from one set • Symmetric Difference → uncommon elements 📸 I practiced these operations with small programs (screenshots attached 👇) Sets are very helpful when working with unique values and performing mathematical operations efficiently. Consistent daily practice is helping me improve step by step 💪 #Python #100DaysOfCode #CodingJourney #LearningPython #Developers

Python list: a simple tool with real power In Python, list is one of the most commonly used data structures. It’s simple, flexible, and essential for everyday development. A list is an ordered, mutable collection: items = [1, "text", True] You can easily modify it: items.append(4) items[0] = 10 One important detail: because lists are mutable, they should not be used as default arguments in functions. def add_item(item, my_list=[]): # ⚠️ bad practice my_list.append(item) return my_list This can lead to unexpected behavior because the same list is reused between function calls. Better approach: def add_item(item, my_list=None): if my_list is None: my_list = [] my_list.append(item) return my_list One of the most powerful features is list comprehension, which makes code concise and readable: squares = [x**2 for x in range(10)] Why it matters Lists are everywhere - from API responses to data processing and backend logic. Understanding their behavior helps you avoid subtle bugs and write more reliable code. #Python #Programming #SoftwareEngineering

When I started learning Python, I kept forgetting small but important things… Things like: • Why mutable default arguments break code • When to use is vs == • Differences between shallow & deep copy These small mistakes cost time and caused bugs. So I built a Python Core Cheat Sheet — something I wish I had earlier.  It includes everything from basics to advanced topics like concurrency and performance tips. If you’re learning or working with Python, this might help #Python #Learning #Developers #Coding #SoftwareEngineering #Mounesh

🧵 **Understanding Multithreading in Python — Simplified** While working with Python, I recently explored **Multithreading** — and it completely changed how I think about performance 🚀 💡 **What is Multithreading?** Multithreading allows a program to run multiple tasks (threads) *concurrently* within the same process. 👉 Instead of waiting for one task to finish, Python can handle multiple operations at the same time (especially useful for I/O tasks). 🔹 **Where is it useful?** * API calls 🌐 * File handling 📂 * Web scraping 🕸️ * Background tasks ⚠️ **Important Note:** Due to the **GIL (Global Interpreter Lock)** in Python, multithreading doesn’t always speed up CPU-bound tasks—but it works great for I/O-bound operations. 📌 **Key Learning:** Choosing the right approach (Multithreading vs Multiprocessing) is what makes your code efficient. 🚀 Small optimization → Big performance impact Have you used multithreading in your projects? Share your experience 👇 #Python #Multithreading #Programming #DataEngineering #Coding #TechLearning #CareerGrowth

😳 Looks easy… but 90% developers get this wrong! 🧠 Quick Python Check: print(bool("False")) 💬 What will be the output? A) True B) False C) Error D) None At first glance, many assume the answer immediately… but this question actually tests your understanding of truthy vs falsy values in Python. 💡 Small concepts like this often make a big difference in debugging and real-world coding. 👇 Drop your answer in the comments Bonus: explain your reasoning! #Python #SoftwareEngineering #CodingChallenge #DeveloperSkills #Learning