Python Operators: A Quick Overview

💻 100 Days of Python Challenge – Day 2: Operators, Operands & Expressions In Python, operators and operands work together to perform operations and produce results. Let’s understand them step by step 👇 🔹 Operators - Symbols or keywords that represent a computation, instruction, or action. Examples: Arithmetic: +, -, *, /, % Logical/Relational: ==, !=, >, < Assignment: = 🔹 Operands The data, values, or expressions on which the operators act. Examples: Constants: 5, 2 Variables: x, y Expressions: 2 * A, 5 * B Function calls: pow(A + B, 2) 🔹 Expressions A combination of operators and operands that evaluates to a single value. Example: In the expression 2 + 5, + → operator 2 and 5 → operands Result: 7 Every Python program is built upon these core concepts — mastering them sets a strong foundation for logical thinking and coding efficiency! 💡 #Python #100DaysOfPython #LearnPython #CodingJourney #PythonBasics

🐍 Python Trick Question: Can You Guess the Output? 🐍 Here’s a classic loop puzzle that often surprises even experienced Python devs 👇 nums = [1, 2, 3, 4] squares = [lambda: n**2 for n in nums] print([f() for f in squares]) 🤔 What do you think this prints? Most people expect: [1, 4, 9, 16] But the actual output is: [16, 16, 16, 16] 😲 Why? Because the lambda inside the list comprehension captures the variable n, not its value at each iteration. By the time the lambdas run, n equals the last value (4) — so each lambda returns 4**2. ✅ Fix it: Bind the variable in the lambda’s default argument: squares = [lambda n=n: n**2 for n in nums] print([f() for f in squares]) # Output: [1, 4, 9, 16] 💡 Lesson: In Python, closures capture references, not values! #Python #CodingInterview #ProgrammingTips #LearnPython #CodeTricks #Developers

Model: Here's a Python while loop problem and solution, along with an image of the solution on a PC monitor screen, and content for a LinkedIn post! Problem: Write a Python program that asks the user to enter a positive number. If the user enters a non-positive number, the program should keep asking for input until a positive number is provided. Once a positive number is entered, print "You entered a positive number!" Solution: < > Python num = int(input("Enter a positive number: ")) while num <= 0:   print("That's not a positive number. Please try again.")   num = int(input("Enter a positive number: ")) print("You entered a positive number!") #Python #Programming #WhileLoop #CodingChallenge #SoftwareDevelopment #Tech #smallbusiness #b2b #dataanalysis #dataanalytics #data #eurotech #techagency #remotework

Still relying on print statements to debug your Python code? There’s a much better way: clean, structured, multi-destination, non-blocking logging. Python’s built-in logging module has been around since 2002. And while it’s powerful, its age also means this: → outdated patterns → inconsistent conventions (PEP8 didn’t even exist when it was designed) → tutorials that often make things more confusing If logging has ever felt clunky, overly complex, or just not worth the effort—you’re definitely not alone. But here’s the good news: modern Python logging is simple once you understand the updated patterns. And it’s far more powerful than people realize. James Murphy (https://mcoding.io) created an excellent video that cuts through the legacy noise and shows how to use Python logging the right way in 2025. No old baggage—just clean, modern practices that scale. One highlight: he demonstrates how to write a custom JSONFormatter to produce clean JSON Lines (JSONL) logs—perfect for parsing, storing, and analyzing across multiple systems. If you’re ready to level-up your debugging and observability skills, this video is absolutely worth your time: https://lnkd.in/g2sNTx_m #SoftwareDevelopment #Python #Coding #Logging Image credits: frame captured from James Murphy’s video

➡️➡️➡️Logical Operators in Python: Logical operators help you make decisions in your code by combining conditions. They are use with conditioner statement. There are three main logical operators: 1. AND ('and'): Both conditions must be true. Example: 'x >(greater than) 5 and x < (less than)10' (x is between 5 and 10) 2. OR ('or'): At least one condition must be true. Example: 'x > 5 or x < 0' (x is either greater than 5 or less than 0) 3. NOT ('not'): Reverses the condition.Example: 'not x > 5' (x is not greater than 5) These operators help you write more complex and flexible conditions in your code. ➡️➡️➡️A CONDITIONAL EXPRESSION A conditional expression is a short way to make a decision and choose between two options based on a condition. It is also a one line statrment for if and else statement. formular Print('x' if condition else 'y') #Codind #Python #Pythonprogramminglanguage #logicaloperators #conditionalexpression

Python Learning Update — Day [2]: Core Python Concepts Today, I explored some of the most essential Python fundamentals that every developer should master 👇 1. Operations in Python → Arithmetic (+, -, *, /, %, //, **) → Comparison (==, !=, <, >, <=, >=) → Logical (and, or, not) → Assignment (=, +=, -=, etc.) These operators form the backbone of computation and logic flow in Python programs. 2. Strings and String Methods → Creating, indexing, slicing, and formatting strings → Methods like .upper(), .lower(), .strip(), .replace(), .split(), .join() → Learned how Python treats strings as immutable objects Working with strings efficiently is crucial for text manipulation, data cleaning, and file handling. 3. Boolean Logic → True and False values → Conditional statements and logical decisions → Realizing how booleans guide flow control and decision-making Key Takeaway: Mastering these basics helps build a strong foundation for writing logical, structured, and efficient Python programs. Every great project starts with strong fundamentals 💪 #Python #Learning #Coding #Developers #DataScience #Programming #ProblemSolving #LearningEveryday #100DaysOfCode

💻 #Day30: Problem Solving in Python – String Manipulation Challenges Hello LinkedIn family! 👋 Today, I explored Python string problems, focusing on how text data can be processed, reversed, and transformed through logic and loops. Working with strings strengthened my skills in character iteration, conditional logic, and pattern recognition. 🛠️ What I practiced today: ✅ Reversed strings (with and without spaces) ✅ Found frequency of each character in a string ✅ Converted uppercase to lowercase and vice versa ✅ Checked if a string is a palindrome ✅ Reversed the order of words in a sentence 💡 Key Takeaways: 1️⃣ String manipulation enhances logical thinking and loop control 2️⃣ Practicing conditions builds clarity in handling characters and words 3️⃣ Understanding ASCII conversions improves control over case operations 4️⃣ Step-by-step coding sharpens debugging and code structure 🚀

🐍 Understanding Basic Python Syntax — The First Step Toward Writing Clean Code! 💡 Every programming language has its own structure and rules — and in Python, the syntax is designed to be simple, readable, and beginner-friendly. That’s one of the biggest reasons why Python is loved by developers worldwide. Here are some of the key syntax elements I explored: 🔹 Comments – Used with # to make code more understandable 🔹 Print Statement – print("Hello, World!") displays output 🔹 Functions – Defined using def, e.g., def greet(name): 🔹 Loops – Like for i in range(5): to repeat actions 🔹 Indentation – Defines code blocks instead of curly braces {} Learning these basics helped me understand how Python’s clean structure allows developers to focus on logic and creativity rather than complex formatting. A big thanks to Talal Ahmed for explaining these core Python concepts in such a simple and effective way. 🙌 #Python #Programming #Coding #LearningJourney #PythonSyntax #TechEducation #SMIT #AI

Today I learned something that genuinely changed how I look at data processing in Python — Generators. Instead of creating and storing an entire list in memory, generators produce items one at a time, only when needed. And that simple idea makes them incredibly efficient. Here’s a simple example that explains the difference: What surprised me is this: yield doesn’t return all results at once. It pauses the function, remembers its state, and continues from where it left off the next time it’s called. This makes generators perfect for: 🔹 Large datasets 🔹 Streaming data 🔹 Memory-efficient pipelines 🔹 Infinite sequences Instead of thinking in terms of “lists”, generators helped me start thinking in terms of flows — generating data only when the program actually needs it. Learning Python is slowly shifting from “how to write code” to “how to write efficient code.” 👉 This code prints the square of every number from 0 to 4, but it does NOT create any list in memory. It only generates the next value when needed. #Python #LearningInPublic #Generators #DeveloperJourney #ProgrammingConcepts #Efficiency #100DaysOfCode

Do you really know how Python’s default arguments work? One of the most common — and dangerous — misunderstandings in Python is how default arguments behave — especially when they’re mutable objects like lists or dictionaries. At first glance, it seems simple: You define a default value in a function, and it’s used whenever no argument is passed. But here’s the catch: that default value is created only once — when the function is defined, not each time it’s called. The example attached makes this clear: - In the first example, the list persists between calls — because it’s stored in memory from the function’s first definition. - In the second, we create a new list every time, avoiding shared state and unintended side effects. This subtle behavior has caused real issues in production systems — especially when functions are reused across modules or teams. Understanding it helps developers write safer, more predictable code, and helps teams avoid subtle bugs that are hard to trace later. In conclusion: Be careful when using mutable default arguments. If you want a fresh object each time, use "None" as the default and initialize it inside the function. Have you ever encountered a bug caused by default arguments in Python? Share your experience or how your team handled it — many developers learn this one the hard way. #Python #SoftwareEngineering #PythonDeveloper #CodeTips #CleanCode #BestPractices #Programming #TechLeadership #LearningPython #DevCommunity