Python OOP: Destructors & Object Relationships

🚀 Full Stack Journey Day 42: Mastering File Handling in Python - Open, Close & File Attributes! 📁🐍 Day 42 of my #FullStackDevelopment learning series took a deep dive into an absolutely essential skill: File Handling in Python! 💾 Understanding how to programmatically interact with files – reading from them, writing to them, and managing their state – is fundamental for almost any data-driven application. Today's crucial practical topics covered: File Handling in Python: Explored the core concepts of working with files, including opening them for various operations (read, write, append), processing their contents, and ensuring they are properly closed. open() function: Mastered the open() function, the primary way to open a file in Python, specifying the file path and the mode of operation (e.g., 'r' for read, 'w' for write, 'a' for append, 'r+' for read/write). close() function: Understood the importance of close() to release system resources held by an open file. Emphasized using with open(...) as f: for automatic resource management, which ensures files are closed even if errors occur. name attribute: Learned how to access the name attribute of a file object, which returns the name of the file. mode attribute: Explored the mode attribute, which returns the mode in which the file was opened. closed attribute: Discovered the closed attribute, a boolean flag indicating whether the file is closed (True) or open (False). readable() method: Understood the readable() method, which checks if the file was opened in a mode that allows reading. writable() method: Explored the writable() method, which checks if the file was opened in a mode that allows writing. Proficiency in file handling is non-negotiable for tasks like data logging, configuration management, processing text files, and interacting with external data sources. This is a foundational skill for any full-stack developer! 📂 Access my detailed notes here: 👉 GitHub: https://lnkd.in/g5MDW_hh #Python #FileHandling #IOOperations #PythonFunctions #PythonAttributes #FullStackDeveloper #LearningToCode #Programming #TechJourney #SoftwareDevelopment #DailyLearning #CodingChallenge #Day42 LinkedIn Samruddhi P.

🐍Py/D6🟩Python Comparison Operators – Smart Decision Making in Code ⚖️🚀 Continuing my AI-Powered Python Learning Series, today I learned about Comparison Operators, which help Python compare values and make logical decisions—an essential part of programming, data analysis, and AI workflows 💻🤖 Under the guidance of Mr. Satish Dhawale sir, Founder & CEO of SkillCourse, I explored how Python uses comparison operators to control program flow and evaluate conditions in real-world scenarios. 🔸 What I Learned Today ✔ What comparison operators are and why they are important ✔ How Python compares values to return True or False ✔ How decisions are made using conditions 🔸 Comparison Operators Explained 🔹 == → Equal to 🔹 != → Not equal to 🔹 > → Greater than 🔹 < → Less than 🔹 >= → Greater than or equal to 🔹 <= → Less than or equal to 🔹 Key Understanding Comparison operators help Python: 🔸 Make decisions using conditions (if, else, loops) 🔸 Validate data and check correctness 🔸 Control program flow logically 🔸 Build intelligent systems and automation logic They are widely used in eligibility checks, performance evaluation, filtering data, AI decision rules, and automation workflows. Just like we compare options before making decisions in real life, comparison operators help Python think logically and act smartly ⚡🧠 Excited to move ahead with D7 and continue strengthening my Python fundamentals 🌟🚀 #Day6 #Python #ComparisonOperators #PythonBasics #LearningJourney #ArtificialIntelligence #SkillCourse #ProgrammingLogic #DataSkills #SatishDhawale #ContinuousLearning

🚀 Full Stack Journey Day 33: Mastering Inheritance Flow - MRO & super() in Advanced Python! 🐍 Day 33 of my #FullStackDevelopment learning series took a deep dive into two essential tools for navigating and managing Python's powerful inheritance structures: the mro() function and the super() keyword! 🗺️ These concepts are crucial for understanding method resolution and ensuring correct behavior in complex class hierarchies. Today's advanced OOP topics covered: mro() function in Python: Explored the mro() (Method Resolution Order) function. Understood how ClassName.mro() returns a list of classes that Python searches to resolve methods and attributes, following the C3 linearization algorithm. This is especially vital in multiple inheritance scenarios to predict which method implementation will be called. Working on super() keyword: Mastered the super() keyword. Learned how super() provides a way to refer to the parent class (or a sibling class in more complex MROs) without explicitly naming it. It's primarily used to call methods (especially __init__) of a parent class from within a child class, ensuring that parent class functionalities are properly initialized and extended without hardcoding parent names. Understanding mro() demystifies how Python resolves method calls in intricate inheritance chains, while super() provides a robust and maintainable way to interact with parent classes. Both are cornerstones of well-designed, extensible OOP code! 📂 Access my detailed notes here: 👉 GitHub: https://lnkd.in/gz3YfMN4 #Python #AdvancedPython #OOP #ObjectOrientedProgramming #Inheritance #MRO #MethodResolutionOrder #SuperKeyword #FullStackDeveloper #LearningToCode #Programming #TechJourney #SoftwareDevelopment #DailyLearning #CodingChallenge #Day33 LinkedIn Samruddhi P.

🚀 Full Stack Journey Day 48: Python Error Mastery - Syntax vs. Runtime Exceptions! 🛠️🐍 Day 48 of my #FullStackDevelopment series is all about understanding the "why" behind broken code! I’ve been diving into Exception Handling in Python, specifically learning how to distinguish between Syntax Errors and Runtime Errors (Exceptions). 🧠 Understanding these two categories is the first step toward building resilient, "unbreakable" applications: Syntax Errors (The Grammar Mistake): These occur when Python doesn't understand your code because it violates the language rules—like a missing colon :, mismatched parentheses (), or incorrect indentation. Python won't even start running your code if it finds a syntax error. It's like trying to drive a car with no engine! ❌ Runtime Errors / Exceptions (The Execution Problem): Your code is grammatically perfect, but something goes wrong while it's running—like dividing by zero, trying to open a file that doesn't exist, or using a variable that hasn't been defined. Unlike syntax errors, these can be caught and handled gracefully using try and except blocks. ✅ Knowing the difference allows me to fix my own typos faster and write code that can handle real-world "surprises" from users or external systems without crashing. 📂 Access my detailed notes here: 👉 GitHub: https://lnkd.in/g_WzXQKf #Python #AdvancedPython #ExceptionHandling #SyntaxError #RuntimeError #CodingFundamentals #CleanCode #FullStackDeveloper #LearningToCode #Programming #TechJourney #SoftwareDevelopment #DailyLearning #CodingChallenge #Day48 LinkedIn Samruddhi P.

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

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

I have been revisiting Python fundamentals as part of my transition from Full Stack development toward AI. This time, instead of rushing, I documented my understanding step by step, exactly the way I would have liked to read it as a beginner. Simple explanations, small examples, and real connections to how Python actually behaves under the hood. This chapter focuses on Object Oriented Programming in Python. Classes, objects, self, constructors, inheritance, MRO, static methods, class methods, and properties, all broken down calmly and practically. 𝐀𝐫𝐭𝐢𝐜𝐥𝐞 𝐥𝐢𝐧𝐤 𝐢𝐧 𝐜𝐨𝐦𝐦𝐞𝐧𝐭𝐬 👇 One question for my network: When did OOP in Python really start making sense to you? Was it when you understood how self works, how attributes are shared between class and object, or how inheritance and MRO behave in real code? I would love to learn from your experiences. More chapters coming soon. 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭𝐢𝐧𝐠 𝐦𝐲 𝐅𝐮𝐥𝐥 𝐒𝐭𝐚𝐜𝐤 𝐭𝐨 𝐀𝐈 𝐣𝐨𝐮𝐫𝐧𝐞𝐲, 𝐬𝐭𝐞𝐩 𝐛𝐲 𝐬𝐭𝐞𝐩. — Payal Kumari 👩💻🌱 #PayalLearnsAI #LearningInPublic #Python #FullStackDeveloper #DeveloperJourney #FullStackToAI #WomenInTech #TechCommunity #AI #payalkumari10

PYTHON JOURNEY - Day 47 / 50..!! TOPIC – List Comprehensions Today I explored one of Python’s most "elegant" features — List Comprehensions. It’s a shorthand way to create new lists based on existing ones, turning 4 lines of code into just 1! 1. The Traditional Way vs. Comprehension Normally, to create a list of squares, you’d need a for loop and .append(). With comprehension, it’s a single line! Python # Traditional Way nums = [1, 2, 3] squares = [] for x in nums: squares.append(x * x) # List Comprehension (The Pythonic Way) squares = [x * x for x in nums] print(squares) # Output: [1, 4, 9] 2. Adding a Condition (The if part) You can filter items while creating the list. Python prices = [10, 55, 80, 25, 100] # Only keep prices over 50 expensive = [p for p in prices if p > 50] print(expensive) # Output: [55, 80, 100] 3. String Manipulation It works perfectly for transforming text data too. Python names = ["srikanth", "python", "dev"] capitalized = [n.capitalize() for n in names] print(capitalized) # Output: ['Srikanth', 'Python', 'Dev'] Why Use List Comprehensions? Readability: Once you learn the syntax, it's much easier to read at a glance. Performance: They are generally faster than standard for-loops for creating lists. Professional: It is a hallmark of "Pythonic" code—showing you really know the language! Mini Task Write a program that: Creates a list of numbers from 1 to 10. Uses List Comprehension to create a new list containing only the even numbers. Prints the resulting list. #Python #PythonLearning #50DaysOfPython #DailyCoding #LearnPython #CodingJourney #PythonForBeginners #LinkedInLearning #DeveloperCommunity

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘆𝗼𝘂𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗰𝗼𝗱𝗲 𝗰𝗼𝘂𝗹𝗱 𝗿𝘂𝗻 𝟮𝟬× 𝗳𝗮𝘀𝘁𝗲𝗿 — 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘁𝗼𝘂𝗰𝗵𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗹𝗼𝗴𝗶𝗰? Sounds impossible, right? But it’s not. I just published an article that shows how you can dramatically boost Python performance using modern execution techniques like JIT compilation, smarter runtimes, and optimized libraries — all without rewriting your existing code. If you’re working with: ✅ Backend APIs ✅ Data processing ✅ Automation scripts ✅ Performance-critical Python apps …this is a must-read 👉 Read here: https://lnkd.in/gykbHu-z 💡 Faster code 💡 Same logic 💡 Zero pain refactoring #Python #PythonPerformance #JIT #SoftwareEngineering #BackendDevelopment #Developer #ProgrammingTips

This was a great read! 🔥 I liked how it highlights that Python performance bottlenecks are often caused by how we use the language rather than the language itself. Techniques like switching interpreters, relying on optimized C-backed libraries, and profiling hot paths instead of rewriting logic shows that scalability and maintainability don’t have to be trade-offs. Very relevant for real-world backend and automation systems. #Python #PythonProgramming #PythonPerformance #PythonTips #PythonOptimization #PyPy #Programming #Developers #CodingLife