Python Polymorphism Explained

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

Today’s Python lesson felt like learning how to write code in a smarter, cleaner way. 🐍 Day 13 of my #30DaysOfPython journey was all about list comprehension and lambda functions, and this one felt like a nice upgrade in how I think about Python. List comprehension is a compact way to create a list from a sequence. It is also faster and cleaner than writing the same logic with a full for loop. Syntax: [expression for i in iterable if condition] Then came lambda functions — tiny anonymous functions with no name. They can take any number of arguments, but only one expression. They are useful when you need a quick function inside another function. Syntax: lambda param1, param2: expression What stood out to me today was how Python gives you more than one way to solve the same problem. You can write it the long way, or you can write it in a tighter, more elegant way when the situation calls for it. One more day, one more topic, one more step toward writing code that feels sharper and more intentional. Which one clicked faster for you: list comprehension or lambda functions? #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

Dynamic Typing in Python — Flexibility With a Responsibility Attached When you write x = 10 in Python, something specific happens under the hood that most introductory courses don’t explain: Python doesn’t create a variable called x that holds the value 10. It creates an integer object with the value 10 in memory, and then makes x a label that points to it. That distinction matters more than it first appears. Because x is just a reference — not a fixed container — you can reassign it to something of an entirely different type: x = 10 x = "barcelona" x = 3.14 Each reassignment doesn’t overwrite the previous value in the same memory location. Python creates a new object and redirects the label. The old object, now unreferenced, gets collected automatically by the garbage collector. You never have to declare a type, and you never have to free memory manually. This is dynamic typing. The type isn’t attached to the variable — it’s attached to the object the variable currently points to. You can verify this yourself with type() and id(): x = 100 print(type(x), id(x)) x = "hello" print(type(x), id(x)) The id changes with each reassignment because x is now pointing to a completely different object in memory. The flexibility this gives you is real. But so is the responsibility. In a statically typed language, the compiler catches type mismatches before the program ever runs. In Python, those mismatches surface at runtime — which means the burden of keeping track of what a variable holds at any given moment falls on you, the developer. Dynamic typing makes Python fast to write. Understanding what it’s actually doing makes you less likely to be surprised by what it does. #Python #PythonMOOC2026 #BackendDevelopment #SoftwareEngineering #LearningInPublic #UniversityOfHelsinki

🚀 **Day X of My Python Learning Journey – Mastering List Methods!** Today I explored one of the most important concepts in Python — **List Methods** 🐍 From adding elements to sorting and reversing, lists make data handling super powerful and flexible. Here are some key methods I practiced: ✔️ append() – Add elements ✔️ clear() – Remove all items ✔️ copy() – Duplicate lists ✔️ count() – Count occurrences ✔️ extend() – Add multiple elements ✔️ index() – Find position ✔️ insert() – Add at specific index ✔️ pop() – Remove by index ✔️ remove() – Remove specific value ✔️ reverse() – Reverse list ✔️ sort() – Sort elements 💡 **Key takeaway:** Understanding these methods makes your code cleaner, faster, and more efficient. Consistency is the real game changer — small progress every day leads to big results. 🔥 This is part of my **30 Days Python Challenge** — more coming soon! #Python #CodingJourney #100DaysOfCode #Programming #LearnPython #DeveloperLife #TechSkills #PythonLists

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 is one of the easiest languages to start with… and one of the most powerful as you grow. In the beginning, you learn: Variables Loops Functions And things start to click quickly. But what makes Python really valuable comes next. From the fundamentals in , your learning naturally evolves: Writing code → structuring it better Using loops → writing cleaner logic with comprehensions Functions → reusable and readable code Handling errors → building safer programs And then you unlock real-world usage: Working with APIs Handling data (JSON, CSV, Pandas) Writing clean classes (OOP) Using generators and decorators That’s where Python becomes truly useful. A simple way to keep improving: Build small things Automate a task Fetch some data Process a file That’s how concepts stay with you. Python is simple to begin with, and powerful to grow with. Save this for your next revision. Follow Shivam Chaturvedi for more content on practical tech learning

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

🚀 Python Learning Journey – Day 5: Lists in Python 🐍 Continuing my Python journey, today I learned about Lists, one of the most useful data structures in Python 🔥 📌 Key Takeaways: ✔️ Lists can store multiple values of different data types ✔️ Lists support indexing & slicing just like strings ✔️ Lists are mutable (we can change them anytime) 💻 Basic Example: l1 = [7, 9, "siddu"] print(l1[0]) # 7 print(l1[1]) # 9 📌 List Methods I Practiced: ✔️ sort() → Sorts the list ✔️ reverse() → Reverses the list ✔️ append() → Adds element at the end ✔️ insert() → Adds element at a specific index ✔️ pop() → Removes element using index ✔️ remove() → Removes a specific value 💻 Example: l1 = [1, 8, 7, 2, 21, 15] l1.sort() l1.append(8) l1.insert(3, 8) l1.pop(2) l1.remove(21) print(l1) ✨ Slowly building my foundation in Python step by step. Consistency is key! #Day5 #PythonLearning #CodingJourney #LearnPython #ProgrammingBasics #FutureBusinessAnalys

Today’s Python lesson felt less like learning syntax and more like learning how to stay calm when code gets messy. 🐍 Day 17 of my #30DaysOfPython journey was all about exception handling, and this one felt very real because errors are not rare — they are part of the process. Python gives us a way to handle errors without crashing the whole program. That makes code feel a lot more dependable. Today I explored: 1. try → run the risky code 2. except → handle the problem if something goes wrong 3. else → run only when no exception happens 4. finally → run no matter what I also learned about: 1. unpacking lists and tuples using *variable_name 2. unpacking dictionaries using **variable_name 3. packing values with *args and **kwargs 4. spreading values into function calls 5. enumerate() → when you need both index and value 6. zip() → when you want to loop through multiple lists together What stood out to me today was this: good code is not code that never fails — it is code that knows how to handle failure properly. One more day, one more topic, one more reminder that writing Python is also about writing with patience. Which one feels most useful in real code to you: try/except, enumerate(), or zip()? Github Link - #Python #LearnPython #CodingJourney #30DaysOfPython #Programming #DeveloperJourney

Today I learned about Polymorphism in Python, and it completely changed how I think about writing flexible code. Polymorphism is all about using a single interface to work with different types of objects. In simple terms, the same method can behave differently depending on the object that calls it. For example, a speak() method can return “Woof” for a Dog and “Meow” for a Cat — same method name, different behavior. What I found really interesting is how it works behind the scenes. Python allows this through concepts like method overriding, duck typing, and operator overloading. Instead of writing separate logic for every type, we can write more general and reusable code that adapts automatically. The real-world usefulness is huge. Whether it's handling different types of files, working with multiple payment methods, or building scalable systems, polymorphism helps keep code clean, maintainable, and easy to extend. This is a powerful reminder that writing smart code isn’t about making it complex — it’s about making it adaptable. #Python #Programming #OOP #Learning #SoftwareDevelopment