Reading Python Tracebacks to Improve Debugging Skills

print() and input() in Python — Two Functions. The Entire Conversation. Before loops, before functions, before data structures — any interactive program needs to solve two basic problems: how to send information to the user, and how to receive information from them. In Python, that’s where print() and input() come in. They’re the first two built-in functions the Helsinki MOOC introduces, and the reason is straightforward: without them, your program runs silently and alone. Nothing goes out, nothing comes in. print() handles output. It takes whatever you pass it and displays it in the terminal. That can be a string, a number, a variable, or a combination of all three: print("Current temperature:", temperature, "°C") Simple. But the moment you add it to your code, your program starts communicating. input() handles the other direction. It pauses the program, displays a message to the user, and waits. Whatever the user types is returned as a string that your program can then work with: city = input("Enter your city: ") One detail worth noting early: input() always returns a string, regardless of what the user types. If you ask for a number and plan to do arithmetic with it, you need to convert it explicitly. That’s not a flaw — it’s Python being precise about types, which is a habit worth developing from the start. Together, these two functions establish a pattern that scales across the entire language: programs receive data, process it, and return a result. print() and input() are just that pattern in its most direct form. Everything more complex is built on top of this. #Python #PythonMOOC2026 #BackendDevelopment #SoftwareEngineering #LearningInPublic #UniversityOfHelsinki

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

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

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

FUNDAMENTALS OF PYTHON It’s safe to say that Python fundamentals go far beyond just assigning variables or understanding iterations. ➠ Iteration, as the name implies, is the process of repeatedly executing a block of code using loops under specific conditions. From what I’ve learned so far: ➠ While loops handle indefinite iteration — they keep running as long as a condition remains true. ➠For loops handle definite iteratio — they run based on a sequence or a fixed range. Now here’s a real question: ➤ How many months do people spend learning functions, commands, operators, and loops… only to later call them “non-fundamentals”? Well… I’m that “someone.” And I moved past all of these about a week ago. Not because they don’t matter—but because this is my way of pushing myself forward and staying motivated for the journey ahead. To anyone on the same path as me: You’re doing well—but we can do even better. April 1st, 2026 marks the first draft on this journey. Hopefully, it won’t be the last. My Current Focus: Algorithm Building & Checking Right now, I’m diving into algorithm design and validation, which is honestly one of the most interesting parts of learning Python. It blends: ➼Basic algebra ➼ Binary thinking ➼Logical problem-solving The math itself is simple enough but improvisation using codes is where the fun begins ➤ First Concept: Guess-and-Check Algorithm (Also known as Exhaustive Enumeration) ➠ This algorithm works when: ➮ You can guess possible solutions, and ➮ You can **check if those guesses are correct** It keeps trying values until: ➮ A solution is found, or all possibilities are exhausted ➤ Simple Applications: ➠Finding square roots and cube roots of integers ➠Solving basic word problems ➠Building number guessing games and simple logic-based games This is just the beginning. More concepts, more challenges, more growth on the road to becoming a Python guru 🐍 Stay tuned. 👨💻👨💻

Bala Priya C walks beginners through recursion in Python, from the basics to real-world use cases like nested data and tree traversal. If you are learning Python and want to understand recursion clearly, this is a great starting point. Read it here:

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

I made a very classic beginner mistake — I underestimated Python. I thought I could skip the "essentials" and jump straight into Machine Learning like I had a mod. But ML doesn’t really do shortcuts. Turns out, it expects you to actually understand what you are doing. After spending time working with fundamentals like lists and dictionaries, I had a simple but uncomfortable realization: If your Python foundation is weak, Machine Learning doesn’t feel advanced— it just feels like you’re constantly struggling to keep up in a language you barely speak. So I’m doing the sensible thing now: going back and strengthening the basics. Yes, the “boring” stuff I tried to rush past. And honestly, Python was never the problem. My overconfidence was. #Python #MachineLearning #ProgrammingBasics #LearnToCode #NeverStopLearning #100DaysOfCode #TechLearning

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

List comprehensions are one of those Python features that look intimidating at first and then become second nature fast. New tutorial on PythonCodeCrack walks through everything from the ground up: — The three-part syntax and what each part does — How a comprehension maps to an equivalent for loop — Adding filter conditions — Using enumerate() and zip() as source iterables — Ternary expressions vs. filter conditions (a common point of confusion) — When not to use a comprehension — How CPython executes them differently from for loops, including what changed in Python 3.12 — Dict and set comprehensions Includes an interactive syntax visualizer, step tracer, spot-the-bug challenges, quizzes, and a final exam with a certificate of completion. https://lnkd.in/g6VisquH #python #FreeCertificationCourse #tutorials