Python Dynamic Typing Explained

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

Integer Division and Modulo in Python — Two Operators Worth Understanding Deeply Most arithmetic operators in Python do exactly what you expect. Addition, subtraction, multiplication — no surprises. But two operators tend to confuse beginners at first glance, and they’re also two of the most practically useful once you understand what they actually do. The first is // — integer division. Instead of returning a precise decimal result, it divides and discards everything after the decimal point: 17 // 5  →  3 Not 3.4. Just 3. The remainder is ignored entirely. The second is % — the modulo operator. It returns exactly what // discards — the remainder after division: 17 % 5  →  2 Together, they give you complete control over how a number divides. And that turns out to be useful in situations that don’t look like math problems at first. The clearest example is time conversion. If a program receives a duration in seconds — say, 7383 seconds — and needs to display it in hours, minutes, and seconds: total_seconds = 7383 hours = total_seconds // 3600 remaining = total_seconds % 3600 minutes = remaining // 60 seconds = remaining % 60 print(f"{hours}h {minutes}m {seconds}s")  →  2h 3m 3s No libraries. No external tools. Just two operators applied in sequence, each doing a precise job. The same pattern appears in pagination — calculating how many full pages a dataset fills and how many items remain on the last one. Or in determining whether a number is even or odd, where n % 2 == 0 is one of the most common checks in programming. What makes // and % worth studying carefully isn’t their complexity — it’s how often a problem that looks complicated turns out to have a clean solution once you think in terms of division and remainder. #Python #PythonMOOC2026 #BackendDevelopment #SoftwareEngineering #LearningInPublic #UniversityOfHelsinki

💡 Python Learning – Handling User Input Errors Today I learned how to handle user input errors using try-except in Python. try → Runs code that may cause an error except → Handles the error and prevents the program from crashing Code Example: try: n = int(input("Enter Number\t")) if n > 0: print("Positive") elif n < 0: print("Negative") else: print("Zero") except ValueError: print("Please enter a valid number") Logic: n > 0 → Positive n < 0 → Negative else → Zero What I learned: input() takes data as a string int() converts it into a number If the input is invalid (like *), it throws an error We can handle this using try-except 📌 Key takeaway: Error handling makes programs more reliable and user-friendly. What should I learn next in Python? 🤔 #Python #DataAnalytics #LearningJourney #Coding #Seaborn #Matplotlib #Analytics #NareshDailyPost #DataAnalyst

🚨 Python Gotcha: Mutable Default Arguments Trap Most beginners (and even experienced developers) make this subtle mistake in Python — and it can lead to unexpected bugs. 🔍 What’s the issue? When you use a mutable object (like a list or dictionary) as a default argument in a function, Python does NOT create a new object every time the function is called. Instead, it reuses the SAME object across all calls. 💡 Example: def add_item(item, my_list=[]): my_list.append(item) return my_list print(add_item(1)) # [1] print(add_item(2)) # [1, 2] ❌ unexpected 👉 Why this happens: The default list my_list is created only once when the function is defined — not each time it is called. So every call keeps modifying the same list. ✅ Correct Approach: def add_item(item, my_list=None): if my_list is None: my_list = [] my_list.append(item) return my_list print(add_item(1)) # [1] print(add_item(2)) # [2] ✅ correct 🧠 Key Takeaway: Never use mutable objects as default arguments. Use None and initialize inside the function instead. #Python #Programming #CodingTips #PythonTips #Developers #LearnPython

F-strings in Python — Not Just Cleaner. Fundamentally Better. Python has had three ways to format strings over its history. If you’ve only learned the language recently, you might not have encountered the older two — but you will, because they still appear in legacy codebases, documentation, and tutorials written before Python 3.6. The first was % formatting, borrowed from C: name = "Andres" print("Hello, %s. You have %d messages." % (name, 5)) It works. But the syntax is cryptic, the order of arguments is error-prone, and it becomes harder to read as soon as you add more than one variable. The second was .format(), introduced in Python 3.0: print("Hello, {}. You have {} messages.".format(name, 5)) An improvement — more explicit, more flexible. But the variables and the placeholders are still separated. To understand what goes where, your eyes have to travel back and forth across the line. Then Python 3.6 introduced f-strings: print(f"Hello, {name}. You have {5} messages.") The variable lives inside the string, exactly where it appears in the output. No positional arguments. No external references. The code reads the way the sentence reads. Beyond readability, f-strings also evaluate expressions directly inline — which means you can do this: hours = 7.5 print(f"Weekly total: {hours * 5} hours") No intermediate variable needed. And in terms of performance, f-strings are consistently faster than .format() because they are parsed closer to compile-time rather than evaluated fully at runtime. Knowing all three methods matters. Understanding why f-strings became the standard tells you something about how Python evolves — always toward clarity. #Python #PythonMOOC2026 #BackendDevelopment #SoftwareEngineering #LearningInPublic #UniversityOfHelsinki

Day 27 Python Full Stack Development Journey Today’s focus was on understanding Polymorphism in Python and its key concepts. 🔹 Introduction to Polymorphism Polymorphism means “many forms”. In Python, it allows the same function, method, or operator to behave differently depending on the object or data type. Example: Python print(len("Hello")) # String length print(len([1,2,3])) # List length 🔹 Important Terminologies in Polymorphism Method Overriding Same method name in parent and child class, but different implementation. Duck Typing Python doesn’t check object type, it checks behavior. (“If it looks like a duck and acts like a duck, it is a duck.”) Dynamic Typing Variable type is decided at runtime, not fixed. 🔹 Operator Overloading Operator overloading allows operators to have different meanings for different objects. Example: Python print(5 + 3) # Addition print("Hello " + "World") # String concatenation We can also overload operators in user-defined classes. 🔹 Magic Methods (Dunder Methods) Magic methods are special methods in Python that start and end with double underscores (__). They are used to define behavior for operators and built-in functions. 👉 Common Magic Methods: __init__ → Constructor __str__ → String representation __add__ → Addition operator __len__ → Length __repr__ → Official representation 👉 Example: Python class Demo: def __init__(self, x): self.x = x def __str__(self): return f"Value is {self.x}" obj = Demo(10) print(obj) Thanks for our CEO G.R NARENDRA REDDY sir and Global Quest Technologies

Stop using + to join strings in Python! 🐍 When you are first learning Python, it is tempting to use the + operator to build strings. It looks like this: name = "Gemini" status = "coding" print("Hello, " + name + " is currently " + status + ".") The Problem? In Python, strings are immutable. Every time you use +, Python has to create a brand-new string in memory. If you are doing this inside a big loop, your code will slow down significantly. The Pro Way: f-strings (Fast & Clean) Since Python 3.6, f-strings are the gold standard. They are faster, more readable, and handle data types automatically. The 'Pro' way: print(f"Hello, {name} is currently {status}.") Why use f-strings? Speed: They are evaluated at runtime rather than constant concatenation. Readability: No more messy quotes and plus signs. Power: You can even run simple math or functions inside the curly braces: print(f"Next year is {2026 + 1}") Small changes in your syntax lead to big gains in performance. Are you still using + or have you made the switch to f-strings? Let’s talk Python tips in the comments! 👇 #Python #CodingTips #DataEngineering #SoftwareDevelopment #CleanCode #PythonProgramming

If you work with Python, have you ever wondered: • What is a list comprehension really? • Is it just a shorter for loop? • When should I NOT use it? List comprehensions are not just syntactic sugar, they are a fundamental part of writing Pythonic code. And no, they are not just “shorter loops”. They express intent. That’s the key difference. Let’s look at a simple example: 𝗿𝗲𝘀𝘂𝗹𝘁 = [] 𝗳𝗼𝗿 𝘅 𝗶𝗻 𝗿𝗮𝗻𝗴𝗲(𝟭𝟬):   𝗿𝗲𝘀𝘂𝗹𝘁.𝗮𝗽𝗽𝗲𝗻𝗱(𝘅 * 𝟮) Now, the same code using list comprehension: 𝗿𝗲𝘀𝘂𝗹𝘁 = [𝘅 * 𝟮 𝗳𝗼𝗿 𝘅 𝗶𝗻 𝗿𝗮𝗻𝗴𝗲(𝟭𝟬)] Both do the same thing. But they are NOT the same. When you write: 𝗿𝗲𝘀𝘂𝗹𝘁 = [𝘅 * 𝟮 𝗳𝗼𝗿 𝘅 𝗶𝗻 𝗿𝗮𝗻𝗴𝗲(𝟭𝟬)] You are telling Python: “I am building a new list from an existing iterable”. That intention is explicit. Now, here is where things go wrong: [𝗽𝗿𝗶𝗻𝘁(𝘅) 𝗳𝗼𝗿 𝘅 𝗶𝗻 𝗿𝗮𝗻𝗴𝗲(𝟭𝟬)] This works, but it should not be written like this. Why? Because list comprehensions are meant to create data, not perform side effects. When you use them like this, you are creating a list you don’t need, hiding the real intention of the code, and making it less readable. Takeaway: “List comprehensions are not about writing less code. They are about writing code that clearly expresses transformation.” Use them when you are building data. Avoid them when you are executing actions. #python #listcomprehension #pythonic

Sometimes everything in programming feels smooth — and then one small thing makes us pause. 🤔 Why does `sorted(my_list)` return a list, but `my_list.sort()` returns `None`? Same job. Different worlds. I wrote about the intuition behind functions vs methods in Python — no heavy theory, just a clear explanation of what's actually going on. 📖 Read it here 👇 https://lnkd.in/ddsbK7mU #Python #Programming #DataScience

"Having a specific use case when learning Python will make your journey 10x easier." That's the advice I'd give my past self if I could. My first attempt at learning Python was over 7 years ago. I remember being so confused, wondering what any of it was for, and I eventually gave up. Since then, I have tried countless other times and found myself staring into that very same void, "Why am I doing this? What will I even use this for?" However, this time I am making steady progress and staying committed because I have a very clear goal: I want to wrangle, clean, analyse, and visualise data with Python. I even have a specific project timeline in mind. And today's lesson on using string methods to build a simple data cleaning function is a reminder that I am on the right path.