Anas AlKanaani’s Post

A circular reference happens when two or more objects reference each to other. For example: list A contains a reference to list B and B contains ref back to list A. This creates a cycle. The problem is that Python mainly uses reference counting to delete Objects. An object is removed only when it's reference count becomes 0. In a circular reference, each object still has at least one reference form the other, so their reference count never reaches 0, and they are automatically deleted. This can cause memory to stay used longer than needed. How can python delete this circular reference ? the solution is Python's Garbage collector (GC). It can delect groups of objects that are no longer reachable from the program, even if they reference each other, and it removes them to free memory very simple example : import GC a = [] b = [] a.append(b) b.append(a) del a del b GC.collect() #python #garbageCollector #(GC) #dev #deeplearning #programmation #pythonDev

Python’s “fast enough” era might be ending. 🚗💨 Serdar Yegulalp’s roundup digs into Python’s new *native JIT*—a potentially game-changing speed boost that aims to make existing code run faster with *less* developer contortion (no “rewrite it in C” rites of passage)… though the early ride still has a few bumps. What I found especially interesting: this isn’t just about raw CPU wins. It’s a signal that the Python ecosystem is attacking friction from multiple angles at once: - **Performance** (JIT + real benchmarks, not vibes) - **Data work** (Pandas now, Pandas 3 looming) - **Tooling ergonomics** (SQLite finally getting GUIs worth using) - **Editor competition** (Zed, Rust-powered, eyeing VS Code’s crown) Worth a read if you care about how Python stays *pleasant* while getting *serious* about speed. https://lnkd.in/eNT-5CE7 #Python #CPython #JIT #PerformanceEngineering #Pandas #SQLite #DeveloperTools #SoftwareDevelopment #Programming Security is a streak you can’t afford to break.

🧠 Python Feature That Makes Code Faster: functools.lru_cache Sometimes Python isn’t slow… it’s just repeating work 😴 ❌ Without Caching def fib(n): if n <= 1: return n return fib(n-1) + fib(n-2) This recalculates the same values again and again 🐌 ✅ Pythonic Way (Cache the result) from functools import lru_cache @lru_cache def fib(n): if n <= 1: return n return fib(n-1) + fib(n-2) Same code. ⚡ Massive speed boost. 🧒 Simple Explanation Imagine doing homework 📚 If you already solved a question once, why solve it again? Python remembers the answer 🧠✨ 💡 Why This Is Powerful ✔ Faster programs ✔ Less CPU usage ✔ One-line optimization ✔ Used in real systems ⚡ Bonus Tip Limit memory usage: @lru_cache(maxsize=100) Before optimizing your code… check if you’re repeating work. Sometimes performance is just memory 🐍⚡ #Python #PythonTips #AdvancedPython #CleanCode #Programming #SoftwareDevelopment #PerformanceOptimization #Caching #DeveloperLife #LearnPython

Why does a += 10 give an error, but a = 2 then a += 10 works perfectly? 🤔 I made this mistake today and learned something crucial about Python's compound assignment operators. The wrong way: a += 10 # ERROR: name 'a' is not defined ❌ The right way: a = 2 # Initialize first a += 10 # Now it works! ✅ # Result: a = 12 Here's why: Compound operators like +=, -=, *=, /= are shortcuts that perform operations AND assignment together. When you write a += 10, Python actually executes a = a + 10 To add 10 to a, Python first needs to READ the current value of a. If a doesn't exist yet, there's nothing to read — hence the error! Key takeaway: Always initialize your variable before using compound operators. It's not just syntax — it's logic. Have you encountered this error before? What was your "aha!" moment with Python operators? 💡 #Python #CompoundOperators #AssignmentOperators #PythonBasics #CodingMistakes #LearnPython #ProgrammingFundamentals

💡 Python Insight from Production Code One mistake I often see—even in mature codebases—is confusing raise with return. They may look similar, but they serve very different purposes. 🔸 raise Used to stop execution immediately and signal that something went wrong. It enforces correctness and makes failures explicit. 🔹 return Used to exit a function gracefully and pass a result back to the caller. It keeps control flow predictable. 📌 Real-world rule: Use raise when continuing execution would hide a bug Use return when the outcome is expected and handled Clear error handling is not about writing more code — it’s about writing honest code. 👉 Save this if you write Python professionally 👉 Share with someone who’s still mixing these up #Python #PythonProgramming #BackendDevelopment #CleanCode #SoftwareEngineering #ProgrammingTips #CodeQuality #DeveloperCommunity #TechLeadership #LearnPython #CodingBestPractices #EngineeringMindset #100DaysOfCode

>>> import this The Zen of Python, by Tim Peters Beautiful is better than ugly. Explicit is better than implicit. Simple is better than complex. Complex is better than complicated. Flat is better than nested. Sparse is better than dense. Readability counts. Special cases aren't special enough to break the rules. Although practicality beats purity. Errors should never pass silently. Unless explicitly silenced. In the face of ambiguity, refuse the temptation to guess. There should be one-- and preferably only one --obvious way to do it. Although that way may not be obvious at first unless you're Dutch. Now is better than never. Although never is often better than *right* now. If the implementation is hard to explain, it's a bad idea. If the implementation is easy to explain, it may be a good idea. Namespaces are one honking great idea -- let's do more of those! . . The Zen of Python is a: Listing of Python design principles and philosophies that are helpful in understanding and using the language. The listing can be found by typing “import this” at the interactive prompt. (Source) . #DataScience #MachineLearning #ArtificialIntelligence #Tech #Programming #SoftwareEngineer #datos #datasets

Most Python programs fail not because the logic is wrong, but because the code is not prepared for unexpected situations. Users give wrong inputs. Files go missing. APIs fail. Systems behave unpredictably. If your code crashes in these scenarios, that is not bad luck. That is missing exception handling. Exception Handling is what separates code that works in notebooks from code that survives in real-world applications. It helps you write programs that are stable, readable, and production-ready, rather than fragile scripts that break at runtime. 🎯 That’s exactly what the new video covers. I have just launched a new video under my Python Programming playlist, where I explain Exception Handling in Python in depth. We go beyond syntax and focus on:  ✅Why exceptions exist and how Python handles them  ✅How try, except, else, and finally actually work  ✅Common mistakes learners make Practical, real-world examples you will face in projects and interviews If you are learning Python seriously or aiming to write professional-grade code, this is a topic you cannot skip. 📌 Watch the video here: https://lnkd.in/gxBUcce6 Be intentional about the fundamentals. Strong basics always compound faster. #PythonProgramming #ExceptionHandling #LearnPython #CodingFundamentals #SoftwareEngineering #YouTubeLaunch

Python Logic Practice – Building Foundations from Scratch Today I continued my self-practice on Python logic building, focusing purely on conditional statements. Problems I worked on: character type detection vowel vs consonant checks even / odd validation positive, negative, zero classification leap year logic What this practice keeps reminding me is how easy it is to write code that runs but gives wrong results. Python doesn’t warn you when your logic is incorrect. It only executes what you tell it to do. One example was leap year validation. A small mistake in condition ordering silently breaks the logic, especially for century years. No exception. Just a wrong outcome. This kind of practice has reinforced something important for me: strong software isn’t built on frameworks first, it’s built on clear and correct decision-making. I’m documenting these practice problems as part of my preparation. The code and daily practice logs are here: 👉 GitHub: https://lnkd.in/dNq2aiyk Back to practice. #python #learninginpublic #logicbuilding

Ever wondered how to break down your age into decades and years using just two Python operators? My latest article explores the Floor Division (//) and Modulus (%) operators through a simple age calculator. The cool part? These operators aren't just for age calculations. They're used in: • Time conversions (seconds → hours:minutes:seconds) • Currency breakdown (bills and coins) • Pagination systems • Data distribution 📖 Read the full breakdown: https://lnkd.in/gb5HDjGT Following along with my Python revision journey? This is article #2 in my fundamentals series. Perfect for beginners or anyone refreshing their basics like me! Let's learn together! If you're new to Python or revisiting fundamentals, happy to connect. What's your favorite use case for the modulus operator? Share below! #Python #ProgrammingBasics #LearnPython #CodingTutorial #TechEducation #PythonFundamentals #LearningTogether

⚠️ Python Gotcha: Defining the Same Method Twice in a Class Did you know that Python does NOT support method overloading by definition order inside a class? Consider this scenario 👇 You define the same method name twice inside a class, expecting both to exist… Only the LAST definition survives. What actually happens? Python reads the class top to bottom When it sees the second func1, it completely overwrites the first one The first method is lost and ignored No warning. No error. Just replacement. Example outcome Nirmal.func1(2, 4) Runs the second version only Output: Good Morning Result is: 6 🚨 Key Takeaways Python does not support traditional method overloading Method names inside a class must be unique If you need different behaviors: Use different method names Or use default parameters / *args / conditional logic 🧠 Pro Tip If your logic seems to “mysteriously change” — check whether a method name was accidentally redefined. Learning these small details makes a big difference in writing clean, predictable Python code 🐍 #Python #OOP #ProgrammingTips #LearningPython #Developers #CodeSmart