Arjan Egges’ Post

This is how a lot of Python code starts: A simple function. A few flags. A couple of if-statements. And then one more rule gets added… and another… and suddenly everything lives in one place. The code still works, but it becomes harder to extend, test, and reason about. In today’s video, I show how the Policy Pattern helps you move away from big conditional functions toward small, composable rules. Instead of branching logic, you build a pipeline of policies that you can easily reorder, enable, or disable. It’s a simple shift, but it scales much better as your codebase grows. 👉 Watch the full video here: https://lnkd.in/dWuQ_wu3. #python #softwaredesign #designpatterns #cleancode #developers

Looking to make your Python code more memory-efficient and readable? Check out my short guide to Python Generators—a powerful tool for handling large datasets without crashing your system. Instead of loading everything into memory at once, generators let you yield items on the fly, keeping your applications fast and responsive. #pythonprogramming #generators #yield #memorymanagement

Tiny Python upgrade for cleaner analytics code: Use Counter instead of manual dict increments. You get frequency maps in one pass and top-N insights with most_common(). Less boilerplate, fewer mistakes, faster review. Mini pattern: - counts = Counter(values) - top = counts.most_common(3) - act on thresholds #Python #CodingTips #Backend #DevTips #SoftwareEngineering

Stop writing print("x:", x) 👇 There’s a much better way in Python. Using f-strings with =: → Less typing → Cleaner output → Faster debugging Example: print(f"{name=}, {age=}") Output: name='Purnendu', age=28 Small trick. Big productivity boost. Once you start using this, you won’t go back. Did you know this before? 👇 #Python #PythonTips #Coding #Programming #Developers #SoftwareEngineering #LearnToCode #Tech #Debugging #Productivity

🚀 List vs Tuple in Python — A Fundamental Yet Overlooked Concept Many developers underestimate the importance of choosing the right data structure. In Python: 🔹 Lists are mutable, allowing dynamic changes such as adding or removing elements 🔹 Tuples are immutable, ensuring data integrity and better performance 💡 Why it matters: Tuples are generally faster and more memory-efficient, while lists offer flexibility for dynamic operations Choosing the right structure can improve performance, readability, and scalability of your code. 👉 Read more info: https://lnkd.in/dBs3ikTU #Python #Programming #SoftwareDevelopment #Coding #Developers #DataStructures #CleanCode #TechCareers

How async/await Works in Python (Simple Explanation) Async programming in Python allows multiple tasks to run without blocking each other. Instead of waiting for one task to finish, Python can switch to another task. Key Concepts: - async → defines a function that runs asynchronously - await → pauses execution until the task is complete How it works: 1. Task starts (e.g., API call) 2. Instead of waiting, Python moves to another task 3. When result is ready → execution continues Example Use Cases: - API requests - Database queries - File handling - Web scraping Why it’s important: - Faster performance for I/O tasks - Better resource utilization - Handles multiple operations efficiently Final Insight: Async is not about doing things faster… It’s about not wasting time while waiting. Follow Saif Modan #Python #Async #Backend #Programming #Tech #LearningInPublic

Ever confused between List, Tuple, and Set in Python? 🤔 Here’s the simplest way to understand it: => List [] → Ordered, Mutable, Allows Duplicates => Tuple () → Ordered, Immutable, Allows Duplicates => Set {} → Unordered, Unique Elements Only 💡 Quick Tip: Use List when you need flexibility Use Tuple when data should not change Use Set when you need unique values Mastering these basics makes your Python code cleaner and more efficient. What do you use the most in your projects? 👇 #Python #Programming #BackendDevelopment #Coding #Developers #FastAPI #AI

🚀 Today I learned one of the most powerful concepts in Python — map(), filter(), and reduce()! These functions help you write cleaner, faster, and more efficient code by working with data in a functional way. 🔹 map() → Applies a function to every item in an iterable 🔹 filter() → Filters items based on a condition 🔹 reduce() → Reduces a list into a single value (from functools) 💡 Example: - map → Square all numbers - filter → Get only even numbers - reduce → Find sum of all elements Understanding these can level up your problem-solving skills and make your code more elegant ✨ If you're starting your Python journey, this is definitely something you should explore! 👉 Want to learn with me? Drop a comment and let’s grow together. #Python #Coding #Programming #100DaysOfCode #LearnToCode #Developers #PythonBasics

Most developers fail this simple Python test. Can you guess the correct output before the video ends? It looks like a straightforward list comprehension. But Python handles closures a bit differently than you might expect: It uses "late-binding" for lambdas The functions don't evaluate the variable until they are called By then, the loop has already finished! This tiny detail causes massive debugging headaches in production. Did you originally guess [0, 1, 2] or [2, 2, 2]? Drop your very first guess in the comments below! 👇 #Python #Coding #SoftwareEngineering #Developer #Programming

If you work with Python, here’s a small concept that can make your code more efficient: generator expressions. Most developers learn list comprehensions early: 𝘀𝗾𝘂𝗮𝗿𝗲𝘀 = [𝘅 * 𝘅 𝗳𝗼𝗿 𝘅 𝗶𝗻 𝗿𝗮𝗻𝗴𝗲(𝟭𝟬)] But if you only need to iterate once, a generator expression may be a better choice: 𝗳𝗼𝗿 𝘀𝗾𝘂𝗮𝗿𝗲 𝗶𝗻 (𝘅 * 𝘅 𝗳𝗼𝗿 𝘅 𝗶𝗻 𝗿𝗮𝗻𝗴𝗲(𝟭𝟬)):   𝗽𝗿𝗶𝗻𝘁(𝘀𝗾𝘂𝗮𝗿𝗲) Key differences betwen list-comp and generators is:   • Generator expressions use parentheses () and produce values one at a time, only when needed.   • List comprehensions use brackets [] and create the full list in memory immediately. Why does this matter? • Lower memory usage • Faster startup for large datasets • Better for streaming data • Ideal for one-pass processing Imagine processing 1 million records. A list comprehension builds 1 million items first, a generator expression yields one item at a time. That difference can be huge in real systems. Rule of thumb: • Need all values now or multiple times? Use a list comprehension • Need to consume items once? Use a generator expression Efficient Python is often about choosing the right tool, not writing more code. #python #programming #softwareengineering #cleancode #performance #generators