Jay Sahu’s Post

Is Python still worth learning in 2026? 🤔 Most students think it’s just a “beginner language”… but the truth is far more powerful. Swipe to know the real picture of Python in today’s job market 👉 #Python2026 #IsPythonWorthIt #PythonDeveloper #TechCareer #FutureProofSkills #SCOPE 

Day 6 of learning ML Still working on Python. Not rushing into models yet — trying to get comfortable with the basics first. Practicing things like: • Lists, sets and dictionaries. • Loops and conditions • Writing cleaner code Feels slow, but skipping this would probably make things harder later. Consistency > Perfection

Day 32 of my python learning journey Today’s Python topic: Polymorphism🐍 Polymorphism = One name, many forms. Same function/method behaves differently based on object. Types I learned: 1. Duck Typing → If it walks like a duck and quacks like a duck, it’s a duck. Python cares about methods, not type. `def add(a, b): return a + b` works for int, str, list. 2. Method Overriding→ Child class changes parent class method. `class Dog(Animal): def sound(self): return "Bark"` 3. Method Overloading (sort of)→ Python doesn’t support true overloading, but we use default args or `*args` to handle it. 4. Operator Overloading → `+` works for numbers and strings. We can define `*add*` in our class too. Polymorphism makes code flexible and easy to extend. One interface, multiple behaviors. Special thanks to the CEO G.R NARENDRA REDDY Sir for constant guidance and motivation. #Python #OOP #Polymorphism

Diving Deep into Python Magic Methods . Recently, I started exploring the internals of Python’s magic methods — those special methods that begin and end with double underscores (__). These aren’t typically called directly; instead, Python invokes them behind the scenes to enable powerful behaviors. These methods can be broadly categorized into: * Callable Objects → __call__() * Iterator Pattern → __init__(), __iter__(), __next__() * Finite Iterators * Context Managers ¥ Understanding the for Loop Internals A for loop in Python is actually built on the Iterator Protocol. Under the hood, it relies on three key magic methods: __init__() → initializes the object __iter__() → returns the iterator object (usually self) __next__() → returns the next value in the sequence ⚙️ How the for Loop Works Internally 1)iter(obj) is called automatically This invokes __iter__() and returns an iterator 2) next(obj) is called repeatedly This invokes __next__() and fetches values 3)Loop continues until StopIteration is raised Without it, the loop runs infinitely 🧠 Example: Custom Iterator class Repeat: def __init__(self, msg): self.msg = msg def __iter__(self): return self def __next__(self): return self.msg This will print the same message infinitely because StopIteration is never raised. Key Insight A for loop is essentially syntactic sugar over a while loop using the iterator protocol. Understanding this gives you deeper control over debugging and helps you identify the root cause of issues in iteration-heavy code. ✨ Once you understand what's happening behind the scenes, Python feels a lot less “magical” and a lot more predictable. For better understanding and clarity I suggest referring the image attached. Till then Happy Sunday, Happy learning and Happy growing. #Python #Programming #SoftwareDevelopment #Coding #PythonInternals #OOP #Developers #Learning #Tech #CSStudents #Debugging

Most people learn Python by staring at output. I tried something different. 👇 This is what actually happens when Python executes a basic function — step by step, visually. No theory. No slides. Just execution in real time. Still early in my journey — but this is how I'm learning. If you're learning Python too, drop a 👋 below. 🔧 Tool: pythontutor / staying.fun 🐍 Concept: Functions — how they're called, executed & returned #Python #LearningInPublic #DataAnalytics #BBA #100DaysOfCode #PythonBeginners

Learning Python has shown me that coding is not only about syntax — it is about solving problems step by step. One thing I appreciate about Python is how readable and beginner-friendly it is, while still being powerful enough for automation, data analysis, testing, and AI. Today’s reminder: even writing a small script that saves 10 minutes of manual work is progress. Small improvements create big results over time. Currently focusing on growing my skills in Python and automation, one day at a time. 🚀 #Python #CodingJourney #Automation #LearningEveryday #TechSkills #SoftwareTesting #CareerGrowth

Blog post: Understanding Python Objects: Mutable vs Immutable As part of my journey learning Python at Holberton School, I wrote a blog post about one of the most important concepts in Python: mutable and immutable objects. In this article, I cover: -What id() and type() are -The difference between mutable and immutable objects -Integer caching and string interning How arguments are passed to functions https://lnkd.in/ebnEVa9Y

This is what Python looks like on paper. Structured. Clear. Step by step. But in real life… it never feels this clean. You don’t learn Python by finishing topics. You learn it when: things don’t work, errors don’t make sense, and you still sit there trying to figure it out. That’s where the real growth happens. Because this roadmap is not a checklist. It’s a cycle. You go back to basics. You revisit concepts. You understand things differently each time. And slowly… what once felt confusing starts feeling obvious. Not because Python became easy. But because you changed. So if you ever feel like there’s still so much left to learn you’re probably on the right path. Because Python is not about reaching the end. It’s about how you evolve while learning it. #Python #DSA #OOPs #DataScience #DataAnalytics #Interviews #Preprations #Jobs

When I first started using Pandas, I wrote code the same way I wrote normal Python. Lots of loops. Lots of step-by-step logic. And it worked… at first. But then datasets got bigger. And things slowed down quickly. That’s when I learned something important: 👉 Pandas works best when you think in vectorized operations. Instead of: looping through rows You start thinking in columns. Example mindset shift: Instead of processing each row individually, you transform entire columns at once. This small change made my code: ✔ faster ✔ simpler ✔ easier to read Still learning, but it's one of those small mental shifts that really changes how you work with data. #DataEngineering #Python #Pandas

So I have been leveling up with boot.dev learning python. To this point it has been really simple. Just learning more about variables, something that I really know. However something I just did made me stop to think. Assignment: We need to reduce our hero's health as they take damage. Before each print() function in the provided code, change the value of player_health to 100 less than it was before. The final output should look like this: 900 800 700 600 My line of code player_health = 1000 # reduce by 100 here print(player_health-100) # and here print(player_health-200) # and here print(player_health-300) # and here print(player_health-400) Their line of code player_health = 1000 # reduce by 100 here player_health = 900 print(player_health) # and here player_health = 800 print(player_health) # and here player_health = 700 print(player_health) # and here player_health = 600 print(player_health) I got the correct output however this was always my delima with math. I can overthink and tend to find other ways to "skin the cat," not sure if this is going to continue to give me the output I will need but we will see.