💡 Stop Overthinking Function Arguments — Bottom-Up Design Works! A lot of new programmers worry: “Do I need to know all the arguments before I write a function?” Here’s the truth: you don’t. Many great devs actually write logic first, then decide what inputs the function needs — it’s called bottom-up design, and it’s totally valid. Here’s how it works in Python: 1️⃣ Write the logic first Copy code Python if calc_op == "+": return operators["+"](num, num2) elif calc_op == "-": return operators["-"](num, num2) You notice: num, num2, calc_op — those are your inputs. 2️⃣ Wrap it in a function Copy code Python def calc(num, num2, calc_op): if calc_op == "+": return operators["+"](num, num2) elif calc_op == "-": return operators["-"](num, num2) The logic hasn’t changed. You just formalized the inputs. ✅ 🔑 Golden Rule: If a variable is used inside a function but not created inside it → it must be an argument. ⚠️ What NOT to do: Copy code Python def calc(): return operators["+"](num, num2) # ❌ num and num2 are undefined Python doesn’t know where these values come from. Your bottom-up approach naturally avoids this. 💡 Why this matters: It aligns with how engineers actually think Scales to bigger programs Makes spotting dependencies intuitive TL;DR: Write logic → identify inputs → promote to arguments → call the function. That’s real-world coding. #Python #Coding #Programming #SoftwareDevelopment #DeveloperLife #ProblemSolving #LearnToCode #CodingMindset #EngineerMindset #TechTips #BottomUpDesign #CareerInTech #ProfessionalGrowth #CodingJourney #LinkedInLearning

How to start automating with Python without the overwhelm: Stop trying to "learn Python." Start solving a specific problem. When I started, I didn’t read a textbook. I looked at the manual document claims that took me hours every month. I was tired of being a human copy-paste machine. Here is your 3-step starter guide: 1. 𝗙𝗶𝗻𝗱 𝗮 "𝗥𝗼𝗯𝗼𝘁𝗶𝗰" 𝗧𝗮𝘀𝗸: If you do it the same way every time in Excel, it’s a candidate. 2. 𝗟𝗲𝗮𝗿𝗻 𝘁𝗵𝗲 "𝗟𝗶𝗯𝗿𝗮𝗿𝘆", 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲: Forget web dev or AI for now. Just learn Pandas. It’s the only tool you need to clean, filter, and move spreadsheet data. 3. 𝗔𝗶𝗺 𝗳𝗼𝗿 𝘁𝗵𝗲 "𝗢𝗻𝗲-𝗠𝗶𝗻𝘂𝘁𝗲 𝗪𝗶𝗻": Don't build a perfect system. Build a script that turns an hour of work into a 60-second execution. Automation isn’t about being a "coder." It’s about buying your time back so you can focus on the work that actually requires your brain. #Python #Automation #Pandas #Efficiency #DataWorkflows

What can’t we do with Python? 🤔 Every time I think I’ve explored enough… Python casually unlocks another door. This week, I stumbled upon a library called FreeSimpleGUI — a lightweight way to build desktop applications without diving into heavy frameworks. Curiosity did what it always does. I couldn’t ignore it. So instead of just reading the docs, I built something simple: 👉 A To-Do List desktop application. Nothing fancy. Just: Add tasks Edit tasks Mark complete Clean, minimal UI And honestly? The result was way better than I expected. No complex boilerplate. No overwhelming setup. Just pure Python doing what it does best — making developers feel powerful. Sometimes we think: Python is for data science. Python is for automation. Python is for ML. Python is for backend. But then it quietly whispers: "Hey, I can build desktop apps too." 😄 The best part? You can prototype a working desktop app in hours, not days. 🎥 I’ve attached a short demo video below. 💻 GitHub link is in the comments if you'd like to explore the code: https://lnkd.in/g4nUBw2m If you’re a Python developer and haven’t explored GUI development yet — this might be your sign. What’s the most unexpected thing you’ve built with Python? #Python #OpenSource #DesktopApp #100DaysOfCode #Learning #Developers #FreeSimpleGUI

🐍 Python Functions — Rules & How to Use Them ⚡ Functions let you reuse code instead of writing the same logic again and again 👇 ✅ Basic Function Syntax def greet(): print("Hello, world!") greet() 👉 Output: Hello, world! 💡 Function Rules (Beginner Friendly) ✔️ Use def keyword to create a function ✔️ Function name should be meaningful ✔️ Parentheses () are required ✔️ Indentation is mandatory ✔️ Must call the function to run it ✅ Function with Parameters (Inputs) def greet(name): print(f"Hello, {name}!") greet("Danial") 👉 Output: Hello, Danial! ✅ Function with Return Value def add(a, b): return a + b result = add(3, 5) print(result) 👉 Output: 8 🔑 Why Functions Are Important • Avoid repeating code • Make programs organized • Easier to debug • Used in every real application 🔥 Simple Idea: Function = A machine Input → Process → Output 🚀 Master functions, and you move from beginner code to real programming skills. #Python #Coding #Programming #LearnToCode #Developer

Still working on my Python Client Management System. After using it for a bit, I noticed something: viewing all clients wasn’t enough. I needed smarter ways to track things. So today I added new features: • Search for a client instantly using name or phone number • View only clients who haven’t paid yet • Automatically show jobs that are overdue based on due dates This is starting to feel less like a script and more like a real admin system. The best part? Every new feature comes from a real problem I face while using it. I’m learning that the best way to improve at coding is to build tools that solve your own problems. More improvements coming soon. #Python #ProblemSolving #BuildInPublic #CodingJourney #Automation

#Python has some drawbacks for vibe coding. In my personal experience the thing manifests in three ways: the model invents nonexistent functions, arbitrarily rewrites portions of working scripts, or substitutes correct logic with unrequested alternatives. The cause is structural: each Python line has high semantic density. A single instruction can invoke complex functions through imported modules (e.g., pandas.DataFrame.merge() internally activates relational join algorithms, type handling, memory allocation). This means that a hallucination on a single line, for example, replacing merge() with concat() in the example above, radically alters the program’s behavior. —- What happens in Python contrasts with what happens in C/C++. In these languages, I observed the opposite behaviour. The generated #code is more stable and adherent to specifications. The probable reason is the peculiar nature of C/C++, in which every operation is explicitly decomposed into elementary instructions (manual allocation, explicit loops, pointer management). Semantic density per line is lower, so a hallucination on a single instruction has a local, limited impact. The model is forced to “reason” step by step rather than relying on opaque high-level functions. On the other hand, however, #security issues are worse on Vibe-Coded C++ #VibeCoding #AI #LLM #Coding #SoftwareEngineering #Python #CPlusPlus #CodeQuality #Hallucinations #DeveloperExperience #SecureCoding #AppSec #Automation #Programming Cefriel Sonia Montegiove Alessandro De Biasio Michele Bonardi Mauro Lomazzi

🐍 lambda vs functools.partial: Which one to choose? If you’ve been coding in Python, you probably know lambdas. But have you tried functools.partial? 🔹 Lambda: Use lambda when you want to create a small, inline function with custom behaviour: Example: square_lambda = lambda x: x ** 2 print(square_lambda(5)) What's good: - Good for short, custom transformations - Anonymous, inline, flexible - Creates new logic from scratch 🔹 Partial: Use partial when you want to reuse an existing function but freeze some arguments: Example: from functools import partial def power(base, exponent): return base ** exponent square = partial(power, exponent=2) print(square(5)) What's good: - Communicates intent clearly - Handles keyword arguments naturally - Supports debugging & introspection (square.func, square.args) - Ideal for callbacks, dependency injection, and pipelines Rule of Thumb: Use lambda: for quick, simple functions you only need once. Use partial: when you want to reuse an existing function with some arguments fixed. #Python #PythonTips #Coding #Programming #CleanCode #DevTips

In plain English, "and" and "or" are just connective words. In Python, they are strict gatekeepers. 🧐 ⠀ Confusing them is the easiest way to break the logic of your application. ⠀ Let's look at a real-world example: Going to the cinema. 🍿 ⠀ 🎬 The "Flexible" Approach (OR): Imagine regular entry to a movie. You write: `if has_paper_ticket OR has_digital_app:` ⠀ The `or` operator is chill. It opens the door if *either* condition is met. Did you forget your paper ticket but have your phone? No problem. You're in. ⠀ 🔞 The "Strict" Approach (AND): Now imagine entry to an age-restricted screening (18+). You write: `if has_ticket AND is_over_18:` ⠀ The `and` operator is the strict manager. It demands *both* requirements be met simultaneously. Have a ticket but forgot your ID? You aren't getting in. ⠀ Logical operators aren't just syntax; they define the rules of your digital world. ⠀ Don't accidentally lock your users out (or let the wrong ones in) because you chose the wrong conjunction. ⠀ We turn boring Python documentation into a friendly, 3-minute daily habit. ☕ ⠀ 👇 Subscribe to the website for free: https://lnkd.in/ducXvs-y ⠀ #Python #Logic #CodingTips #SoftwareDevelopment #LearnToCode #PyDaily

🚀 OOPS in Python – Class & Object Explained (Simple & Clear) 📱🐍 Ever wondered how real-world things like a Mobile can be represented in Python? That’s where OOPS (Object Oriented Programming) comes in! 🔹 Class = Blueprint A class is like a design or template. Example: "Mobile" is a class that defines what every phone should have: Brand, Battery, RAM, Camera, Price. 🔹 Object = Real Product An object is the actual item created using the class. Example: An Apple phone with 8GB RAM and 48MP camera. 💻 Example Code: class Mobile:   def __init__(self, Brand, battery, ram, camera, price):     self.Brand = Brand     self.battery = battery     self.ram = ram     self.camera = camera     self.price = price   def display(self):     print("Brand:", self.Brand)     print("Battery:", self.battery)     print("Ram:", self.ram)     print("Camera:", self.camera)     print("Price:", self.price) obj = Mobile("Apple", "4000mAh", "8GB", "48MP", "90000") obj.display() 🧠 What’s happening here? ✔️ "Mobile" is the class (blueprint) ✔️ "obj" is the object (real mobile) ✔️ __init__ stores details when object is created ✔️ self represents the current object ✔️ display() shows the mobile details 📌 Real-life understanding: Class = Mobile design Object = Your personal phone ✨ This is the basic foundation of OOPS: → Class → Object → Constructor → Methods Start small. Think big. Code smart. 💡 Follow for more 😁 #Python #OOPS #Programming #CodingForBeginners #PythonLearning #Developers #TechEducation

10 Python Mistakes Even Experienced Developers Make (And How to Avoid Them) Whether you're new to Python or a seasoned developer, some common coding pitfalls can lead to unexpected bugs, performance issues, or hard-to-debug errors. Here are 10 Python mistakes I often see—and how to fix them: 🔹 Mutable Default Arguments Using mutable objects (like lists) as default arguments can cause unintended side effects across function calls. ✅ Fix: Use None as the default and assign inside the function. 🔹 Using is Instead of == is checks object identity (memory address), not value equality. ✅ Use == for comparing values. 🔹 Late Binding in Closures Closures capture variables, not their values at creation time, leading to unexpected behavior in loops. ✅ Pass variables as default arguments to capture values early. 🔹 Catching All Exceptions A bare except: hides errors and makes debugging impossible. ✅ Catch specific exceptions and log them appropriately. 🔹 Generator Exhaustion Generators can only be iterated once. Reusing them leads to empty results. ✅ Convert to a list if needed, or re-create the generator. 🔹 Shallow Copy Instead of Deep Copy Using .copy() or list() creates a shallow copy; nested objects still share references. ✅ Use copy.deepcopy() for fully independent copies. 🔹 Shadowing Built-in Names Avoid using names like list, dict, or sum as variables—it overrides built-in functions. ✅ Use descriptive names like user_list or total_sum. 🔹 Floating Point Equality Checks Floats are imprecise; direct equality checks can fail. ✅ Compare with a tolerance: abs(a - b) < 1e-9. 🔹 Using Mutable Objects as Dictionary Keys Dictionary keys must be immutable and hashable (e.g., not lists). ✅ Use tuples or other immutable types instead. 🔹 Ignoring Variable Scope in Comprehensions Variable leaks in list comprehensions can affect outer scope. ✅ Keep comprehensions self-contained and avoid reusing variable names. Understanding these subtle issues can save you hours of debugging and make your Python code more robust and maintainable. Have you encountered any of these? Share your experience in the comments! 👇 #Python #Programming #SoftwareDevelopment #CodingTips #PythonTips #Developer #CodeQuality #Debugging #Tech #LearnPython #ProgrammingMistakes #BestPractices #SoftwareEngineering