Python Threads vs Multiprocessing: Understanding the GIL Impact

Tuples are one of those Python concepts everyone learns early — but many don’t fully use them. They are: • ordered • immutable • fast and memory-efficient You’ll often see tuples used for: - function returns - coordinates (x, y) - configuration values - data that should not change When you understand what tuples are and when to use them, your code becomes safer and more intentional. This infographic covers the essentials you’ll revisit again and again. Save it for a quick refresh later. #Python #LearnPython #PythonBasics #Programming #Coding #SoftwareEngineering #PythonDevelopers

✨ Day 24 of #30DaysOfCode ✨ Today I explored how Python handles calculations. Turns out, it follows the same rules we learned back in school, BODMAS (Brackets, Orders, Division , Multiplication, Addition, Subtraction). Simple, but powerful. One step at a time, I’m building confidence in how code translates logic into results. 🚀 #Python #LearningJourney #CodingChallenge #30DaysOfCode #nxtwave #Nxtwave #CCBP #ccbp NxtWave #intensive3point0

🚀 Day 19/50 – LeetCode Challenge (Python) Continuing the challenge with an array problem focused on in-place marking and efficient space usage. 📌 Problem: Find All Numbers Disappeared in an Array (Easy) 🧠 Approach: Mark visited indices in the array to track missing numbers without extra space ✨ Key Learning: In-place techniques can replace additional data structures while keeping complexity optimal Staying consistent and learning new patterns. #LeetCode #Python #DSA #Consistency #LearningInPublic

# 𝑫𝒂𝒚 - 2 𝑷𝒚𝒕𝒉𝒐𝒏 𝑲𝒂 𝑫𝒂𝒊𝒍𝒚 𝑫𝒐𝒔𝒆 1. 𝐋𝐢𝐬𝐭 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐨𝐧 Instead of creating an empty list and using .append(), you define the logic inside square brackets [ ]. The Syntax: [expression for item in iterable if condition] 2. 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲 𝐂𝐨𝐦𝐩𝐫𝐞𝐡𝐞𝐧𝐬𝐢𝐨𝐧 This works just like list comprehension, but it uses curly brackets {} and requires a key: value pair. The Syntax: {key_expression: value_expression for item in iterable if condition} 𝐖𝐡𝐲 𝐮𝐬𝐞 𝐭𝐡𝐞𝐦? 𝐒𝐩𝐞𝐞𝐝: Comprehensions are usually faster than manual loops because they are optimized at the C-level within Python. 𝐑𝐞𝐚𝐝𝐚𝐛𝐢𝐥𝐢𝐭𝐲: They turn 3–4 lines of boilerplate code into a single, declarative statement. #Python #SoftwareDevelopment #CodingTips #PythonDeveloper #DataStructures #Programming

Mastering Python's conditionals is fundamental for building smart, dynamic applications! 🐍 I've put together a simple visual guide (check out the image below 👇) that covers the basics of control flow: if / else / elif: For standard decision-making logic. Nested if: For handling complex, multi-layered conditions. match-case: The modern pattern matching tool (Python 3.10+). Remember: indentation is mandatory, and always use == for comparison! Save this guide for a quick refresher! How have conditional statements helped you solve a complex problem recently? Share your stories below! 👇 #python #codingtips #programming #datascience #pythonforbeginners

Efficiency is not a choice; it is a necessity Many waste hours refactoring Python for loops. While they toil, the wise utilize Vectorized Universal Functions (ufuncs) to achieve results at a scale with near 0 draw backs. In data automation, as in statecraft, speed and precision are the only metrics that matter. If your processes are slow, you are vulnerable. If they are optimized, you are indispensable. #Python #NumPy #DataStrategy #StrategicAutomation

💡 A small Python lesson from LeetCode While solving LeetCode #8 (String to Integer – atoi), I wrote two solutions that were both O(n) and logically identical. But one was faster. The reason? Not the algorithm but the way the code was written. Using elif instead of multiple if checks and avoiding extra branching made a noticeable difference. In Python, fewer condition checks = fewer instructions = better runtime. Nice reminder that Big-O isn’t everything, especially in interpreted languages. Learning moments like these are why I enjoy solving problems 😄 #Python #LeetCode #LearningInPublic #SoftwareEngineering #ProblemSolving

monty-near-cli: Python smart contracts on NEAR Compiles Python to a Monty bytecode VM (~800KB WASM). Top-level def becomes a method. Still very experimental. Feedback welcome. Built by community member Ricky. GitHub link: https://lnkd.in/eTN2REXm

Python Tip of the Day 🐍 id() returns the unique identity of an object in memory. Objects with the same immutable value may sometimes share the same identity. Useful when understanding how Python stores and references objects. Day 6 of building Python basics. #PythonDaily #PythonBasics #DataAnalytics

📅 Day 13/30 – Threading Module in Python Today I explored the Threading module, which allows running multiple tasks concurrently to make programs faster and more efficient. What I learned: • Creating threads using Thread class • Starting and joining threads (start(), join()) • Using threading to run tasks in parallel • Thread safety basics • Practical use cases for concurrency Understanding threading is helping me write more efficient Python programs 💪 📚 Learning resource: HackerBytez – https://lnkd.in/gzKTANVt Excited to apply concurrency in real-world projects 🚀 #Day13 #PythonChallenge #30DaysOfPython #ThreadingModule #Python #Concurrency #LearningInPublic #CodingJourney