Python List Mutability Bug Fix

Python Micro-Logics 🚀: Small conditions build strong programming thinking. Here are some quick practical checks every learner should know: ➡️ Checks whether a number is greater than 10. ```python n = int(input()) print(n > 10) ``` ➡️ Checks whether the last digit of a number is greater than 5. python n = int(input()) print((n % 10) > 5) ➡️ Checks whether the last digit of a number is divisible by 3. python n = int(input()) print((n % 10) % 3 == 0) ➡️ Checks whether a string is a palindrome using slicing. python s = input() print(s == s[::-1]) ➡️ Checks whether the first two and last two characters of a string are equal. python s = input() print(s[:2] == s[-2:]) ➡️ Checks whether a digit character represents a value greater than 6. python ch = input() print((ord(ch) %10) > 6) Consistent practice with these small logical expressions improves interview readiness, debugging skills, and coding confidence faster than memorizing theory. Which beginner Python logic problem challenged you the most when you started? 👇 #Python #LearnPython #CodingPractice #ProgrammingLogic #BeginnerDevelopers #PythonTips #CodingJourney #DataScience #PythonFullStack

New Project: Python CLI Test Generator I built a simple command-line tool that automatically generates Python unittest test files for a given Python function using an LLM. Idea Provide a Python file containing a function, and the tool will analyze it and generate a ready-to-run test file automatically. Tools Used • Python ast – to parse and validate the structure of the input file • argparse – to build the command-line interface • unittest – for generating structured test cases • Ollama + qwen3-coder-next – LLM used to generate the tests Simple Pipeline CLI → Validate file with AST → Build prompt → Call LLM → Generate test file The tool outputs a complete unittest file covering possible edge cases for the function. 🔗 GitHub: https://lnkd.in/dXbqUh7e #Python #LLM #AI #Testing #Automation #GenAi

Understanding Python's Logical Operators: And, Or, Not Logical operators are essential in Python for evaluating conditions and controlling the flow of a program. The `and`, `or`, and `not` operators combine boolean expressions effectively. The `and` operator requires both conditions to be `True` for the overall result to be `True`. If even one condition is `False`, the entire expression evaluates to `False`. This functionality is crucial in scenarios like validating user input, where all specified conditions must be met for a successful operation. Conversely, the `or` operator offers a broader approach, returning `True` if at least one condition is `True`. This characteristic allows you to implement logic that requires only one of multiple conditions to be satisfied, perfect for handling varied decision-making frameworks. Finally, the `not` operator reverses the boolean value of its operand. It's useful in scenarios where you want to take action only if a condition is not met, such as prompting users when required credentials are missing. Using these logical operators greatly enhances the complexity and expressiveness of your code, allowing you to align it more closely with real-world decision processes. They enable you to develop conditions that reflect the intricacies of user behavior and system requirements. Quick challenge: How would you modify the example code to print `True` when both variables are `False` using a logical operator? #WhatImReadingToday #Python #PythonProgramming #LogicalOperators #Programming

Context managers — Python’s most unappreciated feature. If you’ve written Python, you’ve probably used this: with open("file.txt", "r") as f: data = f.read() But do you actually know what "with" is doing? In simple terms, "with" makes sure something is properly cleaned up after you’re done using it. Without "with" , you would write: f = open("file.txt", "r") data = f.read() f.close() Now imagine you forget f.close()… Or your program crashes before it runs. That’s where with shines. When you use "with", Python automatically: • Sets things up • Runs your code • Cleans everything up — even if errors happen It’s not just for files. You can use with for: - Database connections - Locks in multithreading - Opening network connections - Even capturing print output And here’s something many beginners don’t realize: You can create your own custom context managers. Yes — you can teach Python how to automatically handle setup and cleanup for your own logic. That means: Start something → Use it → Safely finish it All enforced by the language itself. For beginners, this is powerful. It makes your code safer. It reduces hidden bugs. And it builds strong engineering habits early. The with statement isn’t complicated. It’s just disciplined coding — made simple. If you're learning Python and treating with like magic syntax, dig deeper. It’s one of the cleanest tools Python gives you. #Python #Programming #SoftwareEngineering #CleanCode

🚀 Stop Reinventing the Wheel: Master Python’s Built-in Modules One of the reasons Python is so popular is its "batteries included" philosophy. You don't always need complex external libraries to get the job done! For my students and fellow learners, here are three essential modules you’ll use in almost every project: 📅 1. datetime | Handling Time Don’t try to manually calculate dates. The datetime module helps you grab the current date, format it, or even calculate the number of days until a deadline. Key takeaway: datetime.date.today() is your go-to for simple timestamping. 📁 2. os | Talking to your Computer Want to know where your script is running or create a new folder? The os module bridges the gap between your code and your Operating System. Key takeaway: os.getcwd() (Get Current Working Directory) is a lifesaver when debugging file path errors! 🔢 3. json | The Language of the Web Data today moves in JSON format. Whether you're saving user settings or fetching data from an API, the json module is how you translate Python dictionaries into shareable strings. Key takeaway: Use json.dumps() to turn an object into a string, and json.loads() to bring it back! 💡 Pro-Tip for Students: Before you pip install a new library, check the Python Standard Library documentation. There's a good chance Python already has a built-in tool to solve your problem. Which built-in module do you find most useful? Let’s discuss in the comments! 👇 #PythonProgramming #CodingTips #DataScience #PythonLearning #SoftwareDevelopment #TechEducation

🐍 Global vs Local Variables in Python Functions 🌍 Understanding variable scope is very important in Python 👇 ✅ 1️⃣ Local Variable (Inside Function) A local variable is created inside a function It can only be used inside that function def greet(): message = "Hello" # Local variable print(message) greet() ✔️ Works inside the function ❌ Cannot be accessed outside print(message) # ❌ NameError ✅ 2️⃣ Global Variable (Outside Function) A global variable is created outside any function It can be accessed anywhere name = "Danial" # Global variable def greet(): print(name) greet() ✔️ Function can read global variable ⚠️ Modifying Global Variable Inside Function If you want to change a global variable inside a function, use global keyword 👇 count = 0 def increase(): global count count += 1 increase() print(count) # 1 Without global, Python gives an error ❌ 🔑 Simple Difference Local → Lives inside function Global → Lives outside function 💡 Best Practice: Use local variables whenever possible. Avoid too many globals — they make code harder to manage. 🚀 Understanding scope helps you write cleaner and bug-free programs 💻 #Python #Coding #Programming #LearnToCode #Developer

Here’s a clean, professional LinkedIn post you can use: --- 🐍 Identifiers in Python – Explained Simply! In Python, identifiers are the names used to identify variables, functions, classes, modules, and other objects. They are the foundation of writing clean and readable code. ✅ Rules for Naming Identifiers: Must start with a letter (A–Z or a–z) or an underscore (_) Cannot start with a number Can contain letters, numbers, and underscores Are case-sensitive (name and Name are different) Cannot use reserved keywords (like if, for, class, etc.) --- 🌟 Features of Identifiers in Python 1️⃣ Case Sensitive Python treats uppercase and lowercase differently. Example: Age ≠ age 2️⃣ Unlimited Length Identifiers can be as long as needed (though shorter, meaningful names are recommended). 3️⃣ Supports Unicode Python allows Unicode characters in identifiers (Python 3+). 4️⃣ Keyword Restrictions Reserved words cannot be used as identifiers. --- 🚀 Advantages of Proper Identifier Usage ✔️ Improves code readability ✔️ Enhances maintainability ✔️ Makes debugging easier ✔️ Encourages clean coding practices ✔️ Helps in writing self-documenting code --- 💡 Pro Tip: Follow naming conventions like: snake_case for variables and functions PascalCase for classes _single_leading_underscore for internal use Following proper identifier naming makes your Python code professional and industry-ready! #Python #Coding #Programming #SoftwareDevelopment #Learning #TechTips

Excel users get frustrated seeing errors while trying Python in Excel or while practicing Python separately (if they are new to Python) First, remember that every programmer sees errors daily. This is the reason Stack Overflow is one of the most visited developer platforms in the world. If you are coming from Excel with no prior programming experience, understanding why the error occurred makes you faster and more confident. Here are common errors new Python users face 👇 SyntaxError Missing colon, bracket, indentation issue. Python is strict about structure. NameError Using a variable that hasn’t been defined yet. TypeError Trying to combine incompatible types. Example: adding a number to text. IndexError Trying to access a position that doesn’t exist in a list or dataframe. KeyError Trying to access a column or dictionary key that isn’t present. AttributeError Calling a method that doesn’t exist for that object. ValueError Correct type, but inappropriate value (e.g., invalid input). ImportError / ModuleNotFoundError Python can’t find the library you’re trying to use. Shift your mindset from “Why is this breaking?” to “What is Python trying to tell me?” Errors are actually structured hints, if we observe carefully, we learn how that particular language works. What other errors confused you when you started? #Excel_Python #LearnersWorld

Most Python beginners write loops like this 👇 numbers = [1, 2, 3, 4, 5] squares = [] for n in numbers: squares.append(n*n) print(squares) Output: [1, 4, 9, 16, 25] It works… but Python has a cleaner way. 🚀 Using List Comprehension: numbers = [1, 2, 3, 4, 5] squares = [n*n for n in numbers] print(squares) Same result, but shorter and more readable. Example 2 – Filtering numbers numbers = [1,2,3,4,5,6,7,8,9,10] even_numbers = [n for n in numbers if n % 2 == 0] print(even_numbers) Output: [2, 4, 6, 8, 10] 💡 Why developers love List Comprehension: • Cleaner code • Faster execution in many cases • More Pythonic style Small tricks like this make a big difference when writing production code. ❓Question for developers: Do you prefer traditional loops or list comprehension in Python? #Python #Programming #CodingTips #SoftwareDevelopment

The Python Roadmap I Wish I Had When I Started... You want to learn Python for GenAI. But where do you even start? Here's the complete roadmap—17 chapters that take you from zero to building real projects. Why Python matters for GenAI: Every GenAI tool you'll work with uses Python: - ChatGPT API? Python - Building AI features? Python - Automating AI workflows? Python You don't need to be an expert. But you need the fundamentals. What this roadmap covers: Basics (Chapters 1-7): - Variables, loops, functions - File handling - String manipulation Intermediate (Chapters 8-12): - Object-Oriented Programming (OOP) - Exception handling - Advanced data structures Advanced (Chapters 13-17): - Functional programming - Regular expressions - Web development basics - Data analysis (NumPy, Pandas) The best part? This isn't theory. Each chapter = hands-on practice. By Chapter 17, you're working with real data using Pandas and Matplotlib. Where to start: - If you're completely new: Start at Chapter 1. - If you know basics: Jump to Chapter 8 (OOP). - If you want GenAI-specific Python: Focus on Chapters 12, 17 (data structures, data analysis). My advice after 20+ years: - Don't try to learn everything at once. - Pick 2-3 chapters per week. Practice daily for 30 minutes. - In 8-10 weeks, you'll have solid Python skills. Save this roadmap. You'll need it. 📌 Follow Santonu Mukherjee for more #Python #HandwrittenNotes