Python Error Handling with Try Except Else Finally Blocks

So I learned Python the unconventional way. It just clicked. I started by taking everyday things and breaking them down into simple, manageable parts - then I'd write a Python script to replicate the process. This approach felt incredibly natural, like I was solving puzzles, not just memorizing lines of code. And that's what made it stick, you know? I'm passing on three beginner-friendly Python projects that I think are perfect for anyone looking to dive in. Each one introduces a new concept, but in a super straightforward way - we're talking making decisions, adding some randomness to your programs, and even getting user input. It's all about learning by doing, not just reading about it. For instance, imagine you're trying to decide what to eat for dinner - you could write a Python script that randomly selects a restaurant for you. Or, you could create a program that asks you a series of questions and then recommends a movie based on your answers. These are the kinds of projects that'll get you comfortable with Python in no time. Here's the gist: - You can use Python to make decisions, like a flowchart. - You can add randomness to your programs, making them more interesting. - You can even interact with the user, getting input and responding accordingly. It's all about starting small, experimenting with simple programs that do something useful - and before you know it, you'll have a solid grasp on how Python works, including how it handles randomness. Check out this article for more: https://lnkd.in/gCPW8xnw #PythonForBeginners #LearningByDoing #RealLifeCoding

🧠 Python Memory Management: The del Myth Explained Many Python beginners believe that using del immediately frees memory —but that’s NOT how Python actually works internally. This image breaks down how Python manages memory behind the scenes and why understanding this is important for real-world development and interviews. 🔹 Reference Counting Python keeps track of how many references point to an object. Every new reference increases the count Removing a reference decreases the count Memory is freed only when the reference count becomes 0 The del keyword removes a reference, not the object itself 🔹 Circular References Sometimes, objects reference each other in a loop. Reference count never reaches zero Reference counting alone fails to clean up memory 👉 This is where Python needs extra help. 🔹 Garbage Collection (GC) To handle such cases, Python uses a Garbage Collector that runs in the background. Detects unreachable objects Cleans circular references Uses a generational approach for better performance: Generation 0: New objects (frequent cleanup) Generation 1: Survived objects (less frequent leanup) Generation 2: Long-lived objects (rare cleanup 📌 Most Important Takeaway ❌ del = free memory → WRONG ✅ del = remove reference ✅ Garbage Collector = frees memory 💡 Why does this matter? Helps prevent memory leaks Crucial for long-running applications (APIs, servers) Frequently asked in Python interviews Understanding Python internals helps you move from just writing code to understanding how code works 🚀 💬 What was the first Python concept that confused you when you started? Let’s discuss 👇 #Python #PythonInternals #MemoryManagement #GarbageCollection #BackendDevelopment #SoftwareEngineering #CodingLife #LearningJourney

String Formatting in Python: F-Strings vs. Format Method String formatting is essential when you need to generate dynamic messages or output, especially in applications that handle variable user input. Python provides multiple ways to format strings, but the two most common methods are f-strings and the `format()` method. F-strings, introduced in Python 3.6, allow you to embed expressions directly within string literals. This means you can utilize variables and even expressions inside curly braces. For example, the expression `f"My name is {name} and I am {age} years old."` illustrates how user-friendly it is to create personalized messages. The key advantage of f-strings is not only their clarity but also their readability, as they visually connect the message content with the variables that define them. Conversely, the `format()` method offers more flexibility, particularly for earlier Python versions. This method uses placeholders in the string, such as `"My name is {} and I am {} years old.".format(name, age)`. With this approach, you can rearrange the order of the placeholders or even assign names for clarity. However, this can sometimes feel less intuitive than f-strings, especially when you're dealing with multiple variables. Mastering these string formatting techniques is vital, as they enhance your code's clarity and maintainability. Selecting the right method can save frustration when you are updating messages or debugging your code. Quick challenge: How would you modify the f-string to include an additional variable for a hobby, such as "hiking"? #WhatImReadingToday #Python #PythonProgramming #StringFormatting #LearnPython #Programming

PYTHON JOURNEY - Day 36 / 50..!! TOPIC – Python Tuples Today I explored Tuples — the "reliable cousin" of the list. While they look similar, they have one major difference that makes them special! 1. Creating a Tuple Tuples use parentheses () instead of square brackets. Python coordinates = (10.5, 20.0) colors = ("Red", "Green", "Blue") print(colors) 2. The Golden Rule: Immutability Once a tuple is created, you cannot change, add, or remove its items. This makes them "read-only." Python # This will cause an ERROR: # colors[0] = "Yellow" 3. Packing and Unpacking A cool Python feature where you can assign tuple values to multiple variables at once! Python point = (5, 10) x, y = point # Unpacking print(f"X: {x}, Y: {y}") Why Use Tuples? Safety: Use them for data that should never change (like GPS coordinates or constants). Speed: Tuples are slightly faster than lists in terms of performance. Integrity: Prevents accidental data modification in larger programs. Mini Task Write a program that: Creates a tuple containing the name of a country and its capital city. Try to change the capital city and observe the TypeError. Unpack the tuple into two variables: country and capital, and print them. #Python #PythonLearning #50DaysOfPython #DailyCoding #LearnPython #CodingJourney #PythonForBeginners #LinkedInLearning #DeveloperCommunity

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

Python Learning Journey (Day-17) : Constructor (__init__) & Instance Variables in Python Yesterday, we learned about Class and Object. Today, we learn how objects get their initial data and how that data is stored. This is where OOP starts to feel real. ⭐ What is a Constructor? A constructor is a special method in Python that runs automatically when an object is created. In Python, the constructor is named __init__. Its main purpose is to: • Initialize object data • Assign values to variables • Prepare the object for use You don’t call the constructor manually — Python does it for you. ⭐ What are Instance Variables? Instance variables are variables that: • Belong to an object • Store data specific to that object • Are different for each object They are defined inside the constructor using self. Example (conceptually): • One student object has its own name • Another student object has its own name Same class, different data. ⭐ Role of self self represents the current object. It is used to: • Access instance variables • Differentiate object variables from local variables • Allow each object to store its own data Without self, Python cannot know which object the data belongs to. ⭐ How Object Creation Works (Conceptual Flow) Class is defined Object is created Constructor (__init__) runs automatically Instance variables get initialized Object becomes ready to use This happens every time a new object is created. ⭐ Why Constructors Are Important? Constructors ensure that: • Objects start with valid data • No uninitialized variables exist • Code is organized and predictable • Each object has its own identity Almost every real-world class uses a constructor. ⭐ Real-Life Analogy Think of a constructor like: • Filling a form when creating a new account • Entering details when buying a ticket • Setting initial values when registering a user Each user/object has unique data. ⭐ Instance Variables vs Local Variables • Instance Variables Stored inside the object Used throughout the object’s lifetime • Local Variables Exist only inside a method Destroyed after method execution Understanding this avoids confusion later. ⭐Conclusion The constructor (__init__) gives life to objects. Instance variables store the object’s personal data. Together, they make OOP practical and powerful. #Python #Day17 #OOPInPython #Constructor #InstanceVariables #LearnPython #CodingJourney #ProgrammingDaily #TechLearning

🐍 Python Challenge: The Classic Mutable Default Argument Trap! This is perhaps one of the most infamous "gotchas" in Python Interview questions. It catches beginners and even experienced developers off guard if they aren't paying close attention to how Python handles memory. Take a look at the function definition in the image. We are calling f(1) followed immediately by f(2). What exactly will be printed to the console? 💡 The Answer & Explanation: The actual output is: ---------------- [1] [1, 2] ---------------- Why does this happen? Why isn't the second output just [2]? The secret lies in when Python evaluates default arguments. Default parameter values (like l=[ ] in the image) are evaluated only once when the function definition (def f...) is executed, not every time the function is called. Because a list ([ ]) is a mutable object, Python creates one list object in memory when the function is defined. If you don't provide a value for l, Python reuses that exact same list object for every subsequent call. 1. Call f(1): It uses the default empty list created at definition time. It appends 1. The default list object in memory is now [1]. 2. Call f(2): It uses the same default list object (which now contains [1]). It appends 2. The list becomes [1, 2]. How to fix it in real code? Use None as the default and initialize inside the function: def f(x, l=None): if l is None: l = [ ] ... Did you spot the trap immediately, or did you fall for it? Be honest in the comments! 👇 #Python #Programming #SoftwareEngineering #CodingChallenge #PythonDeveloper #TechTips #DigitalFinancialServices #BackendDevelopment

🚀 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.

DAY1 So The Video Starts With Introduction About Python The Language And There Are 13 Chapters And 2Mega Projects Also He Has Coverd A Practise Set After Every Chapter...;) The CHAPTER 0 is The First Chapter of this video it is about the basics about python. How Does The Python Works? Why Python is the most loved language all accross the globe? The community of python and many other knowledgeble things comes in this chapter Also He Told About How To Create The Setup And How To Download and Install Python 3.12.3, VS Code And How To Setup Python Inside VS Code.. THE CHAPTER 1 "Modules Comments And Pip" The Python Language Is Created In 1991 By Guido Van Rossum After Created The Language He Is Actually Finding The Name For His Language And The One Day When He Is Watching A British Comedy Show "Monty Python's Flying Circus" But This Name Is Too Long And He Want A Short Name For His Programming Language So He Decided To Name "Python". Guido Van Rossum Is A Genius He Has Worked In Google Then He Joined DropBox And Now He Is Working In Microsoft.... Lets Get Started print ("Hello world") This Is Our First Python Program What are Modules? Module is A code already written by anybody else we can use it to work faster and efficiently.. Modules are of 2 Types 1) Built in Module 2) External Module What Is Pip? Pip is the package manager for python. We can use Pip to install a module on our system. When we write python in Terminal it becomes A Calculator because this opens REPL (Read Evaluate Print Loop) (COMMENT) Comment are used to write something a programmer does not want to execute.This can be used to write Authour name Date etc There are 2 Types Of Commments 1) Single-Line Comment (written with a # in start of the line) 2) Multi-Line Comment (written with """ in start and end of the Lines You Want To Comment) We can also use Triple Single Or Double quote"' To Print The Whole Sentance At Once... PRACTISE SET 1 Questions.>....... 1) Write Twinkle Twinkle Little Star Poem In Python.. 2)Use REPL and print to write the table of 5. 3)Install an external module and use it To perform an operationof your intrest. 4)Write a Python program to print the contents of a Directory using the os module.. 5)Lable the program written in problems 4 with comments. Thats All Everyone This Is What We Have Learned On DAY1 of our Python Learning

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