Understanding Python's GIL and No-GIL Mode

Day 3 : 10 Days of Python Loops in Python I spent time understanding how ‘for’ and ‘while’ loops work, and it really clicked how important they are in programming and data science. Loops make it possible to automate repetitive tasks, iterate through data, and write cleaner, more efficient code instead of repeating the same instructions manually. This concept helped me see how Python handles data step by step, whether it’s going through a list, processing values, or preparing data for analysis. It’s a small concept on its own, but it plays a huge role in building scalable and practical solutions. As I continue my Data Science journey, I’m prioritizing a solid understanding of the fundamentals and consistently applying them through practice. Progress may be gradual, but it’s intentional and impactful. #Python #DataScience #10daysofpython

5 Python performance tricks nobody talks about (But Everyone Should Use) Most devs learn the basics and stop there. But Python 3.10+ quietly shipped features that can transform your code's performance. Here are 5 tricks I've been using in production: 👉🏽 𝗕𝘆𝘁𝗲𝗰𝗼𝗱𝗲 𝗽𝗿𝗲-𝗰𝗼𝗺𝗽𝗶𝗹𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝘁𝗵 𝘂𝘃 Add --compile-bytecode to your uv commands. Reduces cold start. Your deployment thanks you. 👉🏽 𝗔𝘀𝘆𝗻𝗰𝗜𝗢 𝗦𝗲𝗺𝗮𝗽𝗵𝗼𝗿𝗲𝘀 𝗳𝗼𝗿 𝗿𝗮𝘁𝗲 𝗹𝗶𝗺𝗶𝘁𝗶𝗻𝗴 Stop hammering APIs. Semaphores let you cap concurrent requests elegantly. No more 429s crashing your pipelines. 👉🏽 __𝘀𝗹𝗼𝘁𝘀__ 𝗳𝗼𝗿 𝗺𝗲𝗺𝗼𝗿𝘆 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 When you're creating millions of objects, __slots__ skips the per-instance __dict__. Memory drops 30-40%. Attribute access gets faster too. 👉🏽 𝗔𝘀𝘆𝗻𝗰 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝗺𝗮𝗻𝗮𝗴𝗲𝗿𝘀 @asynccontextmanager guarantees your resources release properly, even when exceptions hit. DB connections, file handles, API sessions. No more leaks. 👉🏽 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗮𝗹 𝗽𝗮𝘁𝘁𝗲𝗿𝗻 𝗺𝗮𝘁𝗰𝗵𝗶𝗻𝗴 match/case isn't just a switch statement. It destructures nested data structures in one shot. Your if-elif chains become readable again. The best part? Zero external dependencies. It's all built into modern Python. 𝘞𝘩𝘪𝘤𝘩 𝘵𝘳𝘪𝘤𝘬 𝘢𝘳𝘦 𝘺𝘰𝘶 𝘶𝘴𝘪𝘯𝘨 𝘢𝘭𝘳𝘦𝘢𝘥𝘺? #python #agi #llm #ai #opensource

As I’ve been diving deeper into Python, one of the most powerful concepts I’ve explored is Object-Oriented Programming (OOP). At first, it felt abstract, but once I started applying it, I realized how much it changes the way we structure and think about code. • Classes & Objects → Classes are blueprints and objects, actual instances we create from them. • Encapsulation → Grouping data and methods together makes code cleaner and easier to maintain. • Inheritance → I learned how to reuse and extend existing code instead of rewriting everything from scratch. • Polymorphism → Writing methods that behave differently depending on the object has shown me how flexible Python can be. • Abstraction → Focusing on what an object does rather than how it does it keeps things simple and organized. Understanding OOPs has helped me move from writing small scripts to thinking about building scalable applications. Learning Python has ignited a desire within to keep learning it. #Python #LearningJourney #Programming

🚀 Python isn't slow. Your loops are. One of the first things we learn in programming is the for loop. But in Data Science, the loop is often the enemy of performance. 🔹 Iterative Approach: Looping through rows one by one. (Slow, inefficient). 🔹 Vectorized Approach: Performing operations on entire arrays at once using NumPy or Pandas. (Lightning fast). A benchmark I ran recently: 👉 Replacing a simple for loop with a vector operation reduced the execution time from 10 seconds to 0.01 seconds. That is a 1000x speedup. My advice to anyone optimizing their pipelines: 📌 Stop thinking in "rows." Start thinking in "columns" and "matrices." 📌 If you see a for loop in your data processing code, ask yourself: "Can this be broadcasted?" Write code that leverages the C-engine underneath Python, not code that fights against it. #Python #DataScience #Optimization #NumPy #CodingBestPractices

If this image made you pause for a second — good. 👀 Same code. Same value. But Python says True here and False there. This is not: ❌ a bug ❌ randomness ❌ Python being inconsistent This is Python memory management at work. Behind the scenes, Python is deciding: Should I reuse memory? Is this object safe to share? Is this value common enough to cache? Can reference counting clean this up? Or should the Garbage Collector step in? Most developers learn Python syntax. Very few learn how Python thinks about memory. That gap is where: interview traps happen `is` vs `==` confusion starts performance bugs hide I explained this clearly, step-by-step (with diagrams) in my Medium article 👇 👉 Python Memory Management Explained (Interning, GC, Reference Counting) https://lnkd.in/grVdVSns Once you read it, this image will make complete sense — and Python will stop feeling “magical”. #Python #PythonInternals #MemoryManagement #BackendDeveloper #SoftwareEngineering #LearnPython

🚀 Diving into Concurrency and Parallelism in Python! 🐍 Over the past few days, I’ve been exploring multi-threading and multi-processing in Python, and it’s been an eye-opening journey. Here’s a quick summary of what I’ve learned: Threads vs Processes Threads are lightweight, share memory, and are great for I/O-bound tasks. Processes are heavier, have separate memory spaces, and allow true parallelism—perfect for CPU-bound tasks. Threads are limited by Python’s GIL (Global Interpreter Lock), so CPU-heavy threads don’t run in parallel. Concurrency vs Parallelism Concurrency: multiple tasks appear to run at the same time (e.g., using threads or async I/O). Parallelism: tasks actually run at the same time on multiple cores (using processes). Key Python Tools threading.Thread → for concurrent I/O tasks multiprocessing.Process → for CPU-bound parallel tasks asyncio → lightweight asynchronous I/O threading.Lock → safely manage shared resources in threads multiprocessing.Queue / Value → communicate safely between processes Lessons from Practice Using threads incorrectly can actually slow down your program (my initial mistake while downloading images 😅). Always use if __name__ == '__main__' when working with multiprocessing on Windows. To get return values from threads/processes, you need shared objects or queues. What strategies do you use for concurrency or parallelism in Python? Let’s share tips! 🔗 #Python #Programming #Coding #SoftwareEngineering #Concurrency #Parallelism #MultiThreading #MultiProcessing #PythonTips If you want, I can also craft an even shorter, punchy version under 150 words that’s optimized for maximum engagement on LinkedIn. That usually gets more likes and comments. Do you want me to do that?

I had a Python script downloading files one by one. 50 requests × 2 seconds = 100+ seconds of waiting. Then I added threading. ⏱️ Runtime dropped to under 10 seconds — without async. But threading isn’t just about speed. It introduces: • concurrency • race conditions • locks & deadlocks These are the same problems real systems face in production. I wrote a beginner-friendly guide: Python Threading Explained — From Your First Thread to Real-World Problems 👇 🔗 https://lnkd.in/gzW84Yb4 #Python #Threading #Concurrency #SoftwareEngineering #BackendDevelopment

Came across a video that helped clarify something I had long used in Python, but never fully understood under the hood — multithreading and the role of the GIL. In short, when using Python’s default CPython interpreter, threads are protected by a Global Interpreter Lock (GIL). This lock ensures thread safety and prevents race conditions when multiple threads try to access shared memory. However, the trade-off is that only one thread executes Python bytecode at a time, meaning true CPU-bound parallelism isn’t achieved even if multiple threads exist. On reading more about this, it became clear why Python multithreading works well for I/O-bound tasks but struggles with CPU-bound workloads. When a thread is waiting on I/O (like network calls, file reads, or database queries), it releases the GIL, allowing another thread to run. This makes multithreading effective for I/O-heavy applications. However, for CPU-intensive operations, threads continuously compete for the GIL, so execution remains effectively single-core, limiting true parallelism despite multiple threads. Interestingly, this sheds light on why performance-critical libraries like NumPy, Pandas, etc. are largely implemented in C/C++ (and increasingly Rust). These languages operate outside the GIL, allowing actual parallel execution and helping bypass interpreter-level bottlenecks. There are also alternative Python interpreters (and Rust-based extensions) that approach concurrency differently and can utilize multi-core CPUs more effectively — depending on the use case. For a clear visual explanation, here’s the video that sparked this learning: https://lnkd.in/gx8tx82J #Python #Multithreading #GIL #CPython #Concurrency #Parallelism #SoftwareEngineering #BackendEngineering #SystemDesign #Programming