Python Mutable vs Immutable Objects: Mastering the Fundamentals

Day 460: 7/1/2026 Why Python Execution Is Slow? Python is expressive, flexible, and easy to use — but when performance matters, it often struggles. This is not because Python is “badly written,” but because of how Python executes code and accesses memory. Let’s break it down. ⚙️ 1. Python Works With Objects, Not Raw Data In Python, data is not stored contiguously like in C or C++. Instead: --> Each value is a Python object --> Objects live at arbitrary locations in memory --> Variables hold pointers to those objects When Python accesses a value: --> The pointer is loaded --> The CPU jumps to that memory location --> The Python object is loaded --> Metadata is inspected --> The actual value is read This pointer-chasing happens for every operation. 🔁 2. Python Is Interpreted, Not Compiled to Machine Code Python source code is not executed directly. Execution flow: --> Python source is compiled into bytecode --> Bytecode consists of many small opcodes --> The Python interpreter: fetches an opcode, decodes it, dispatches it to the corresponding C implementation --> This repeats for every operation --> Each step adds overhead. Even a simple arithmetic operation involves: --> multiple bytecode instructions --> multiple function calls in C --> dynamic type checks at runtime ⚠️ 3. Dynamic Typing Adds Runtime Checks Because Python is dynamically typed: --> Types are not known at compile time --> Every operation checks type compatibility --> Method lookups happen at runtime This flexibility makes Python powerful — but it prevents many low-level optimizations. Stay tuned for more AI insights! 😊 #Python #Performance #SystemsProgramming

🚀 Full Stack Journey Day 37: Advanced Python - Overriding Behavior & Dynamic Duck Typing! 🦆🐍 Day 37 of my #FullStackDevelopment learning series took a deep dive into core OOP principles in Advanced Python: Method Overriding, the nuances of Constructor Overriding, and the elegance of Duck Typing! ✨ These concepts are vital for building adaptable and dynamically typed applications. Today's crucial advanced OOP topics covered: Method Overriding: Re-emphasized method overriding, where a subclass provides a specific implementation for a method already defined in its superclass. This is fundamental for polymorphism, allowing subclasses to specialize or alter inherited behavior while maintaining the same method signature. Constructor Overriding (Python's Approach): Explored how, while Python doesn't have explicit "constructor overriding" in the same way as methods, a subclass's __init__ method can extend or entirely replace its parent's __init__. We specifically looked at how to correctly call the parent's constructor using super().__init__() to ensure proper initialization of inherited attributes. Duck Typing in Python: Mastered Duck Typing, a key aspect of Python's dynamic nature. Understood the principle: "If it walks like a duck and quacks like a duck, then it must be a duck." This means Python focuses on what an object can do (its methods and properties) rather than its explicit type or class hierarchy. This leads to highly flexible and less coupled code. Understanding these concepts empowers you to write highly polymorphic and maintainable object-oriented code, crucial for any complex full-stack development! 📂 Access my detailed notes here: 👉 GitHub: https://lnkd.in/grVzxyDv #Python #AdvancedPython #OOP #ObjectOrientedProgramming #MethodOverriding #ConstructorOverriding #DuckTyping #Polymorphism #FullStackDeveloper #LearningToCode #Programming #TechJourney #SoftwareDevelopment #DailyLearning #CodingChallenge #Day37 LinkedIn Samruddhi P.

Stop repeating yourself—let Python do it for you! 🐍 Are you still manually processing data item by item? If you’re starting your journey in #Python, mastering the for loop is your first real "superpower" for automation. Whether you are preparing for a technical interview or building your first automation script, understanding how to iterate efficiently is non-negotiable. My latest blog post breaks down the Python for loop from the ground up. What you’ll learn: ✅ Basic syntax and the power of the in keyword. ✅ How to use range() for precise control. ✅ Pro-tips: enumerate(), zip(), and the "Pythonic" way. Don't just write code—write efficient code. 🚀 📖 Read the full guide here: https://lnkd.in/d43W3TSM #PythonProgramming #LearnToCode #SoftwareEngineering #DataScience #CodingInterview #TechTips #PythonForBeginners

𝘿𝙤𝙣’𝙩 𝙇𝙖𝙪𝙜𝙝 𝙖𝙩 𝙋𝙮𝙩𝙝𝙤𝙣’𝙨 𝙎𝙡𝙤𝙬𝙣𝙚𝙨𝙨 — 𝙇𝙚𝙖𝙧𝙣 𝙒𝙝𝙮 𝙄𝙩 𝙒𝙤𝙧𝙠𝙨 At first glance: Python feels slow Memory usage feels heavy Big numbers feel expensive But here’s the truth 👇 Python chooses correctness and flexibility over raw speed 𝐖𝐡𝐚𝐭 𝐌𝐨𝐬𝐭 𝐏𝐞𝐨𝐩𝐥𝐞 𝐃𝐨𝐧’𝐭 𝐋𝐞𝐚𝐫𝐧 Python doesn’t use fixed-size integers. It uses binary chunks and arbitrary precision. That means: - No overflow - Safer calculations - Predictable correctness Yes — it costs memory and time. And that’s by design, not a flaw. 💡 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗦𝗸𝗶𝗹𝗹 𝗚𝗮𝗽 Anyone can use Python. Very few understand Python. 𝐖𝐡𝐞𝐧 𝐲𝐨𝐮 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝: Why are large integers slower? Why does time complexity depend on bit length Why NumPy is fast You stop blaming Python… and start using it wisely. 𝐆𝐫𝐨𝐰𝐭𝐡 𝐌𝐢𝐧𝐝𝐬𝐞𝐭 𝐟𝐨𝐫 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐞𝐫 “Python is slow.” “I need the right tool for the job.” C/C++ → raw control Java → balance Python → productivity + correctness Great engineers choose, not complain. ⭐ 𝗙𝗶𝗻𝗮𝗹 𝗧𝗵𝗼𝘂𝗴𝗵 Understanding internals turns limitations into superpowers. - Keep learning. - Keep questioning. - Keep going deeper 🚀 💬 What internals topic changed the way you write code? #Python #LearningJourney #SoftwareEngineering #DeveloperMindset #GrowthMindset #Programming #CareerGrowth #TechMotivation #DataScience #DataAnalysis #MachineLearning #TechJroshan

Stop Guessing Python's Data Structure Rules. (The 60-Second Cheat Sheet Is Below.) Struggling to remember Python’s data structure rules? You’re not alone. Let’s decode them in 60 seconds. Lists are the most popular type of ordered, mutable collections. Here’s your rapid-fire guide: 📌 Lists vs. Others: Lists → are mutable, ordered groups of elements Tuples → are not mutable Sets & Dictionaries → are other types of collections 🔧 List Creation & Syntax: Use: myList = [] Accessed by: myList[Ind] Assignment: myList[Ind] = X ⚙️ Key List Methods: .sort() → sorts in place .append() → adds an element 🔄 How Lists Work with Loops: Are iterated by loops Reviewed sequentially Located by indexes from 0 to length-1 🎯 Why This Matters: Understanding these fundamentals speeds up debugging, improves code clarity, and makes you a more efficient Python developer. 💡 Pro-Tip: Always visualize your data structure. Is it ordered? Mutable? Your choice impacts performance and usability. 👉 Your Turn: Which data structure do you use most often? Lists, dictionaries, or sets? Share your go-to in the comments! #Python #Programming #DataStructures #CodingTips #SoftwareDevelopment #Tech #Developer #LearnPython #PythonProgramming #CodeNewbie

Day 459: 6/1/2026 Why Python Objects Are Heavy? Python is loved for its simplicity and flexibility. But that flexibility comes with a cost — Python objects are memory-heavy by design. ⚙️ What Happens When You Create a Python Object? Let’s take a simple example: a string. When you create a string in Python, you are not just storing characters. Python allocates a full object structure around that value. Every Python object carries additional metadata. 🧱 1. Object Header (Core Overhead) Every Python object has an object header that stores: --> Type Pointer Points to the object’s type (e.g., str, int, list) Required because Python is dynamically typed Enables runtime checks like: which methods are valid whether operations are allowed This is why “a” + 1 raises a TypeError Unlike C/C++, Python must always know the object’s type at runtime. --> Reference Count Tracks how many variables reference the object Used for Python’s memory management When the count drops to zero, the object is immediately deallocated This bookkeeping happens for every object, all the time. 🔐 2. Hash Cache (For Immutable Objects) Immutable objects like strings store their hash value inside the object. Why? Hashing strings is expensive Dictionaries need fast lookups So Python caches the hash: Hash computed once Reused for dictionary and set operations Enables average O(1) lookups This improves speed — but adds more memory per object. 📏 3. Length Metadata Strings also store their length internally. This allows: len(s) to run in O(1) slicing and iteration without recomputing length efficient bounds checking Again: faster execution, but extra memory. Stay tuned for more AI insights! 😊 #Python #MemoryManagement #PerformanceOptimization

🚀 Full Stack Journey Day 43: Python Object Serialization - Mastering Pickling & Unpickling! 📦🐍 Day 43 of my #FullStackDevelopment learning series took a deep dive into an incredibly useful Python feature for data persistence: Pickling (Packing) and Unpickling (Unpacking)! 💡 These processes allow us to convert complex Python objects into a byte stream and back again, making them storable and transferable. Today's crucial advanced Python topics covered: Pickling (Packing) in Python: Explored pickling, the process of converting a Python object (like a list, dictionary, or even custom class instances) into a byte stream. Understood how the pickle module's pickle.dump() function is used to write this byte stream to a file, effectively "packing" the object for storage. This is invaluable for saving the state of an application or complex data structures. Unpickling (Unpacking) in Python: Mastered unpickling, the reverse process of converting a byte stream back into its original Python object. Learned how the pickle.load() function reads the byte stream from a file, "unpacking" it to reconstruct the Python object exactly as it was. This enables seamless retrieval of saved data. Pickling and unpickling are indispensable for tasks like caching results, saving trained machine learning models, persisting user sessions, or transferring objects between different Python programs. They unlock powerful capabilities for data management! 📂 Access my detailed notes here: 👉 GitHub: https://lnkd.in/gvxqqgyj #Python #AdvancedPython #Pickle #Pickling #Unpickling #Serialization #DataPersistence #ObjectSerialization #FullStackDeveloper #LearningToCode #Programming #TechJourney #SoftwareDevelopment #DailyLearning #CodingChallenge #Day43 LinkedIn Samruddhi P.

Getting Started with Python Basics 🚀 In the previous posts, we covered: 1. How to install Python on our systems 2. How to install the Playwright package in Python 3. How to create and download a project inside the IDE Now, we will move forward with the core learning. We will be covering the next topics in two sections: 1. Python Basics 2. Pytest Basics In this post, let’s get started with Python Basics. Creating Our First Python Program Let’s create our first Python file: FirstDemo.py To print anything to the console in Python, we use the print keyword. You can run the program by: Right-clicking on the file and selecting Run FirstDemo Or using the shortcut Ctrl + Shift + F10 Comments in Python When writing programs, it is always a good practice to add comments for better readability. In Python, comments start with the # character. Example Code print('hello') # here are the comments I have defined a = 3 print(a) Str = "Hello World" print(Str) b, c, d = 5, 6.4, "Great" Understanding Variables in Python Here, we declared a variable a and assigned the value 3. One key difference between Python and other programming languages is data type declaration. In Java, we must explicitly define the data type: int a = 3; In Python, we do not specify the data type. Python automatically determines the data type at runtime. Python does have data types, but they do not need to be explicitly mentioned while creating variables. Multiple Variable Assignment Python allows defining multiple variables in a single line, which makes the code clean and concise. Indentation Matters in Python Python is highly sensitive to code indentation. Indentation is not just formatting; it defines code structure. Editors like PyCharm enforce proper indentation by default and help ensure coding standards are followed. There are no semicolons at the end of statements in Python. This forms the foundation of Python basics before moving into pytest and Playwright automation. #Python #PythonBasics #LearningPython #TestAutomation #Playwright #Pytest #SDET #QAEngineering #AutomationTesting #SoftwareTesting #LearningJourney

Ever stared at your Python code and thought, "There *has* to be a simpler way to say this?" 🤯 Well, my friends, let me introduce you to the beautiful difference between telling Python *how* to do something and simply telling it *what* you want! Imagine you want a list of squared numbers. Version A (Imperative): "First, create an empty list. Then, for each number, calculate its square, and finally, add that square to the list. Repeat until done." (Lots of steps, easy to mess up if you forget one!) Version B (Declarative): "This *is* a list of squared numbers for every number." ✨ That's the magic of declarative code, like list comprehensions! It's like going from giving Python a detailed recipe to just handing it a menu and saying, "I'll have the squared numbers, please!" Python gets it, and frankly, it's harder for us to accidentally misspell `.append()` or forget to initialize a list. (Guilty as charged, more times than I care to admit! 🤦♀️) It's not just about looking fancy; it's about writing clearer, more concise, and significantly less error-prone code. Fewer bugs = happier developers! BUT, before you go comprehensions-crazy, remember they're not a one-size-fits-all solution. If your logic gets super complex, you need side effects (like printing), or multiple statements per item, a good old `for` loop is still your best friend. Know your tools! The real "aha!" moment? Think of `[f(x) for x in items]` as an *expression* that *is* a collection, not a step-by-step instruction to *build* one. Once that clicks, your Python game will level up! 🚀 What are your favorite ways to write cleaner, more intentional code? Share your wisdom in the comments below! 👇 If this resonated with you, hit that like button and share the coding love! ❤️ Follow for more insights into making your code shine! #Python #CodingTips #DeclarativeProgramming #ListComprehension #CleanCode #SoftwareDevelopment #TechInsights #Programming Read more: https://lnkd.in/gaZGaEHh

🐍📰 Python's reduce(): From Functional to Pythonic Style In this tutorial, you'll learn how Python's reduce() works and how to use it effectively. You'll also learn some more modern, efficient, and Pythonic ways to gently replace reduce() #python