Unlock Python Secrets for Efficient Code

🚀 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.

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

🚀 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.

I love Python. But it's slow. So I wrote C code to value an option and call it from Python. C helps Python run up to 45X faster. Here's how: Before you ask, yes I know there are better ways to do this. Here's the problem: A lot of quant code is already written in C. So instead of re-writing it from scratch, most quants wrap existing code in Python. Enjoy: https://lnkd.in/gDnfmtGk

I love Python. But it's slow. So I wrote C code to value an option and call it from Python. C helps Python run up to 45X faster. Here's how: Before you ask, yes I know there are better ways to do this. Here's the problem: A lot of quant code is already written in C. So instead of re-writing it from scratch, most quants wrap existing code in Python. Enjoy: https://lnkd.in/ejFiHXdy

I didn't understand why functions mattered when I first learned Python. My instructor kept saying: "Don't repeat yourself. Use functions" 🤔 I thought: Why? Copy paste works fine. Then I wrote some repeated logic in my code, the same check appeared more than once. When I found a mistake, I had to fix it in multiple places. That's when it clicked.💡 Functions aren’t just a Python feature. They’re a way to write smarter, maintainable code. The rule I follow now: If I’m writing the same logic twice → make it a function. Why functions matter: • One source of truth 📌 • Fewer bugs 🐞 • Easier updates 🔧 • Code that’s easier to read 👀 I wish I had understood this earlier instead of just memorizing syntax. 📚 Now, when I write code, I ask: “Will I need this logic again?” If yes → function. Functions didn’t just make my code shorter. They made it maintainable. If you’re learning Python, don’t skip functions thinking they’re “advanced.” They’ll save you hours of debugging later.⏱️ What Python concept took you time to really understand? #Python #Programming #CleanCode #LearnInPublic #SoftwareDevelopment

🚀 Full Stack Journey Day 38: Advanced Python - Encapsulation, Getters & Setters for Robust Classes! 🔐🐍 Day 38 of my #FullStackDevelopment learning series took a deep dive into a core OOP pillar: Encapsulation in Python, and how we achieve it using Getter and Setter methods! 🔒 This principle is vital for bundling data with the methods that operate on that data, controlling access, and protecting an object's internal state. Today's crucial advanced OOP topics covered: Encapsulation in Python: Explored the concept of encapsulation as the bundling of data (attributes) and methods that operate on that data within a single unit (a class). Understood how it hides the internal state of an object from the outside world, allowing only controlled access, which helps prevent accidental modification and improves maintainability. Learned about the convention of using single (_var) and double (__var) leading underscores for "private" (by convention) and "name-mangled" attributes, respectively. Setter Methods: Mastered setter methods. These are special methods used to modify the value of an object's private (or protected) attributes. Setters often include validation logic to ensure that new values are valid before they are assigned, providing a controlled way to update an object's state. Getter Methods: Dived into getter methods. These are used to retrieve the value of an object's private (or protected) attributes. Getters provide a controlled interface for reading an object's state without directly exposing its internal representation. Encapsulation, implemented effectively with getters and setters (or Python's @property decorator for more advanced use), is fundamental for creating secure, maintainable, and robust object-oriented designs. It's a cornerstone skill for any full-stack developer! 📂 Access my detailed notes here: 👉 GitHub: https://lnkd.in/gRCENQ8Z #Python #AdvancedPython #OOP #ObjectOrientedProgramming #Encapsulation #Getters #Setters #DataHiding #FullStackDeveloper #LearningToCode #Programming #TechJourney #SoftwareDevelopment #DailyLearning #CodingChallenge #Day38 LinkedIn Samruddhi P.

I was installing some Python packages with pip yesterday when my friend told me he's using uv now. He said it's much faster and that it's becoming the new thing. That made me wonder—wait, is pip getting replaced? Am I behind on something? I went on Google to check it out. Turns out, a lot of people are actually using uv. There are posts about how fast it is and how it handles dependencies better. So yeah, it's definitely getting popular. Here's what I learned: pip is still the official package installer for Python. It's not going away. When you install Python, pip comes with it. Most projects still use it, and it works just fine. uv is a newer tool that some people prefer because it's faster. Like, noticeably faster when you're installing a bunch of packages. It's built differently and handles some things better than pip. But it's not replacing pip. It's just another option. Why people think pip is done: When something new shows up and people start talking about it, it feels like everyone's switching. But that's not really what's happening. Some teams are trying uv because they need the speed. Others are sticking with pip because it works for them. I also found out why some people write "python -m pip install" instead of just "pip install." It makes sure you're using the right version of pip for your Python setup. Helps avoid weird issues when you have multiple Python versions on your computer. What I think: If you're happy with pip, keep using it. If you want to try uv because you're curious or your builds are slow, go for it. There's no rush to switch just because other people are doing it. You can stay with pip for now. It does what you need. But it's good to know there are other options when you need them. What are you using? Still on pip or have you tried uv? Let me know in the comments. #Python #DataEngineering #SoftwareDevelopment #DevTools

Many Python beginners struggle not because of syntax, but because of missing concepts. I just published a new article on 12 Python Concepts That Matter More Than Syntax It explains: ✔️ How Python actually works ✔️ Why beginners get stuck ✔️ What data analysts really need If you’re learning Python for data, this will change how you learn. Read it here : https://lnkd.in/dy6y3rjQ

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