Infinite Loops in Python for Input Validation

Day 16 / 30 - while Loops in Python What is a while Loop? A while loop keeps running its block of code as long as a condition is True. Unlike a for loop which runs a fixed number of times, a while loop runs an unknown number of times, it stops only when the condition becomes False. Perfect for situations where you don't know in advance how many repetitions you'll need. Syntax Breakdown while condition: # runs as long as condition is True # must update something to eventually make condition False while --> checks the condition before every single run condition --> any expression that evaluates to True or False How It Works, step by step Python checks the condition at the top of the loop If True --> it runs the indented block of code Goes back to the top and checks the condition again Keeps repeating until the condition becomes False When False --> exits the loop and continues the program for Loop vs while Loop for loop 1. Use when you know how many times to repeat 2. Looping over a list, range, sequence while loop 1. Use when you don't know how many times 2. Keep going until a condition changes The Infinite Loop -Most Common Mistake If your condition never becomes False, the loop runs forever, freezing your program. Always make sure something inside your loop changes the condition like incrementing a counter or taking user input so the loop can eventually stop. break and continue break - stops the loop immediately, exits even if the condition is still True continue → skips the current iteration and jumps back to the condition check Both break and continue work in for and while loops. Code Example # Count from 1 to 5 count = 1 while count <= 5: print("Count: " + str(count)) count = count + 1 # break — stop early number = 0 while number < 10: if number == 5: break # stops loop at 5 print(number) number += 1 Key Learnings ☑ A while loop runs as long as its condition is True , checks before every iteration ☑ Always update something inside the loop, counter, input, or flag or you'll get an infinite loop ☑ break exits the loop immediately when a condition is met ☑ continue skips the current iteration and jumps back to the condition check ☑ Use for when you know the count — use while when you don't Why It Matters While loops power real systems — PIN verification, login retry limits, game loops, servers waiting for requests. Anywhere your program needs to wait or keep trying until something changes, a while loop is the answer. My Takeaway A for loop is like a to-do list — you know how many items there are. A while loop is like waiting for a bus — you keep waiting until it arrives. Different tools, different situations. #30DaysOfPython #Python #LearnToCode #CodingJourney #WomenInTech

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

Your Python app can look healthy while background jobs are quietly failing. That's the problem with Celery by default. A task can fail, retry, and eventually succeed, while the real issue stays buried in worker logs. From the outside, everything looks fine. Until a user asks why something never happened. Nehemiah Bosire shows how to use AppSignal to track Celery task failures with full context, so you can see retries, exceptions, and patterns before they turn into bigger problems 👇 https://lnkd.in/eiHrDFCc

Python functions with fixed signatures break the moment you need to forward arguments across abstraction boundaries. *args and **kwargs solve that — and this python tutorial goes well past the syntax. — How CPython actually handles variadic calls at the C level (PyTupleObject, PyDictObject, and why there's a real allocation cost) — Why a defaulted parameter before *args is effectively unreachable via positional calling — and the idiomatic fix — The difference between *args isolating the mapping vs sharing mutable values inside it — ParamSpec (PEP 612) for preserving decorator signatures through the type system — TypedDict + Unpack (PEP 692, Python 3.12) for per-key precision on **kwargs — inspect.Parameter.kind for reading variadic signatures at runtime — the foundation of FastAPI and pytest's dispatch logic — Lambda variadic syntax, functools.wraps, kwargs.setdefault patterns, and common SyntaxErrors caught at parse time Includes interactive quizzes, spot-the-bug challenges, a design decision review, and a 15-question final exam with a downloadable certificate of completion. Full guide: https://lnkd.in/gHkdvCn5 #Python #PythonProgramming #SoftwareDevelopment

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

Excited to share a new article I wrote for AppSignal. I wrote about a problem many Python teams run into with Celery: ✅ Retries can make failed tasks look less serious than they really are. ✅ The real exception path is often harder to follow than it should be. ✅ Debugging usually means going through worker logs to piece together what happened. ✅ That delay makes background job failures harder to catch early. In the article, I break down: ✅ Where Celery’s defaults create visibility gaps. ✅ Why retries can make failure tracking messy. ✅ What this looks like in real Python applications. ✅ How AppSignal improves visibility by surfacing failures, retries, and task context more clearly. Here’s the article: https://lnkd.in/d_XgE3g9 #Python #Celery #BackendEngineering #Observability #DevOps #TechnicalWriting

Python: 06 🐍 Python Tip: Master the input() Function! Ever wondered how to make your Python programs interactive? It all starts with taking input from the user! ⌨️ 1) How to capture input? -To get data from a user, we have to use the input() function. To see it in action, you need to write in the terminal using: '$ python3 app.py' 2) The "Type" Trap 🔍 -By default, Python is a bit picky. If you want to know the type of our functions, You can verify this using the type() function: Python code: x = input("x: ") print(type(x)) Output: <class 'str'> — This means 'x' is a string! 3) Converting Types (Type Casting) 🛠️ If you want to do math, you have to convert that string into an integer. Let's take a look at this example- Python code: x = input("x: ") y = int(x) + 4 # Converting x to an integer so we can add 4! [Why do this? Without int(), here we called int() function to detect the input from the user, otherwise Python tries to do "x" + 4. Since you can't add text to a number, your code would crash! 💥] print(f"x is: {x}, y is {y}") The Result 🚀: If you input 4, the output will be: ✅ x is: 4, y is: 8 Happy coding! 💻✨ #Python #CodingTips #Programming101 #LearnPython #SoftwareDevelopment

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

Errors in Python can arise from invalid syntax, unexpected issues during execution, system issues, or flaws in program logic. Learn about the different types of Python errors and practical ways to identify and avoid them to build more reliable programs. #Python