Python reduce() Function Tutorial

🐍📰 Namespaces and Scope in Python In this tutorial, you'll learn when namespaces are created, how they are implemented, and how they define variable scope #python

💡 “Simple code is powerful code — that’s the Python way.” 🚀 “Python doesn’t make you smarter, it makes you faster.” 📌 “If your Python code is hard to read, it’s time to refactor.” 🔥 “Great Python code looks obvious in hindsight.” 🧠 “Think first, then let Python do the heavy lifting.” 📈 “One small Python trick today becomes big efficiency tomorrow.”

🐍📰 Python's deque: Implement Efficient Queues and Stacks Use a Python deque to efficiently append and pop elements from both ends of a sequence, build queues and stacks, and set maxlen for history buffers https://lnkd.in/dwJM6in9

Discover a beginner-friendly way to create drawings with code. This guide by Mahnoor Javed introduces the fundamentals of Python's Turtle module for generating visual patterns.

🐍📰 Python Scope and the LEGB Rule: Resolving Names in Your Code Understanding Python's variable scope and the LEGB rule helps you avoid name collisions and unexpected behavior. Learn to manage scope and write better code #python

🐍 Python Type Annotations 101: 📘 Understanding Type Hints, Optional Types, and Tools Learn how Python’s optional type annotations give you many of the benefits of static typing —better tooling, safer refactors, clearer code— without sacrificing the flexibility of a dynamic language. https://lnkd.in/ePnpmfBa

🔐 Understanding Python’s GIL (in simple terms) If you’ve ever heard “Python doesn’t scale with threads”, the reason is usually the GIL. GIL (Global Interpreter Lock) is a rule in CPython that says: 👉 Only one thread can execute Python code at a time. Think of it like this 👇 You have one whiteboard (Python interpreter) and multiple people (threads). Even if everyone is ready to write, only one person can use the marker at any moment. 🧠 Why does Python do this? Python uses a simple memory management system. The GIL keeps things safe and fast for single-threaded code, but limits parallel execution. 📌 What does this mean in practice? ❌ CPU-heavy tasks (calculations, loops) don’t get faster with threads ✅ I/O-heavy tasks (API calls, DB queries, file reads) do benefit from threads Why? Because during I/O, Python waits, and the GIL is released for other threads. 🚀 How developers work around GIL Use multiprocessing (multiple processes, multiple GILs) Use NumPy / Pandas (they release GIL internally) Use async for I/O-heavy workloads 🎯 Key takeaway GIL doesn’t make Python slow — it makes it predictable. The trick is choosing the right concurrency model.

Why range(1,000,000) is cheap, but list(range(1,000,000)) is costly in Python? TL;DR: Iteration Protocol in Python needs to know only next item and not full list. The "Next Page" Rule Iteration in Python isn't about having a collection of items; it’s about knowing how to get the next item. Two special methods make this possible: 1. __iter__() → tells Python “I can be looped over” 2. __next__() → returns the next value, one at a time When there’s nothing left, StopIteration tells Python to stop the loop. Why this matters? When we use a list, we pay for all the memory upfront. When we use the Iteration Protocol, we only pay for one item at a time. This is called Lazy Evaluation. Takeaway - If the object represents a collection or a stream of data, implement __iter__ and __next__. It makes the code more memory-efficient and much more "Pythonic." I’m deep-diving into the Python protocols this week and will share my learnings. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

🐍📰 Unicode & Character Encodings in Python: A Painless Guide Handling character encodings and numbering systems can at times seem painful and complicated, but this guide is here to help with easy-to-follow Python examples #python

🐍📰 In this tutorial, you'll learn about the issues that can occur when your code is run in a multithreaded environment. Then, you'll explore the synchronization primitives in the threading module, such as locks, which help you make your code safe #python