Python Set Methods: Union, Intersection, and More

I’ve just published my first blog post on Medium, diving into a Python concept I recently explored: 👉 Mutable vs Immutable Objects Before this, I used to think variables simply “store values.” But in Python, they actually reference objects in memory. Having learned C previously really helped me grasp this concept more quickly, especially when it comes to understanding how memory and references work. In this post, I break down: • The difference between == and is • Mutable vs immutable objects (with clear examples) • Why l1 = l1 + [4] and l1 += [4] behave differently • How Python passes arguments to functions Writing about what I learn pushes me to go deeper and truly understand the concepts. If you’re learning Python or preparing for technical interviews, this is definitely a topic worth mastering. 📖 Read here: https://lnkd.in/guVnYN3Q #Python #SoftwareEngineering #Programming #LearningJourney #Tech

Quick Python Challenge What will be the output of this code? a = [1, 2] b = a b.append(3) print(len(a)) Options: A) 2 B) 3 C) 1 D) Error 💡 At first glance, many people think the answer is 2. But the correct answer is actually 3. Why? Because in Python: b = a does not create a new list. It simply makes b reference the same object in memory as a. So when we run: b.append(3) we are modifying the same list that both a and b point to. The list becomes: [1, 2, 3] So: len(a) = 3 📌 Key Insight: In Python, variables can reference the same mutable object, which means modifying one reference affects the other. 🔥 Lesson of the day: Understanding mutable objects and references is essential when writing reliable Python code—especially in data analysis and AI pipelines. 💬 Curious: Did you get it right on the first try? #Python #AI #DataAnalytics #LearningInPublic #30DayChallenge #PythonTips

💡 Understanding "*args" and "**kwargs" in Python Today I explored how Python functions can accept a flexible number of arguments using "*args" and "**kwargs". "*args" allows a function to receive multiple positional arguments, which are stored as a tuple. "**kwargs" allows a function to receive multiple keyword arguments, which are stored as a dictionary. Example: def func(a, *args, **kwargs): total = a for i in args: total += i for k, v in kwargs.items(): total += v return total print(func(1, 2, 3, x=4, y=5)) 📌 Here’s what happens: - "a = 1" - "args = (2, 3)" - "kwargs = {'x': 4, 'y': 5}" The function sums all values and returns 15. 🔎 A small but powerful concept that makes Python functions much more flexible when dealing with dynamic inputs. #Python #DataAnalysis #Coding #LearningInPublic #30DayChallenge #AI #Instant

Cracking the Code: How Python Calculates Everything Ever wondered how Python decides which math problem to solve first. It all comes down to Expressions and Operators. If you are learning to code, understanding these rules will save you from a lot of bugs later on.. ==> 1. What is an Expression? An expression is just a combination of values, variables, and operators that results in a single value. Example: 1 + 2 evaluates to 3. ==> 2. Meet the Arithmetic Operators Python uses special symbols for math. Some are common, but others are unique: + , - , *: Addition, Subtraction and Multiplication. /(Classic Division): This always returns a float (decimal), like 4 / 2 = 2.0. // (Floor Division): Rounds the result down to the nearest whole number. % (Modulus): Returns the remainder (e.g., 5 % 2 = 1). ** (Exponentiation): Powers (e.g., 2 ** 3 = 8). ==> 3. Unary vs. Binary Operators Unary: Works with only one value (e.g., -5 or +3). Binary: Works with two values (e.g., 10 + 5). ==> 4. Who Goes First? (Operator Priority) Just like in school math, Python has a hierarchy. It follows these rules: 1. Parentheses (): Anything inside brackets is calculated first! 2. Exponentiation **: This has the highest priority. 3. Unary + and - 4. Multiplication, Division, and Modulus (*, /, %) 5. Binary Addition and Subtraction (`+`, `-`): These have the lowest priority. ==> 5. A Tricky Rule: Right-Sided Binding Most math moves from left to right, but the exponentiation operator (**) is different. It uses right-sided binding. Example: 2 ** 2 ** 3 is actually 2 ** (2 ** 3), which equals 256. Mastering these small details makes a huge difference when building complex logic #Python #Coding #MathInCode #ProgrammingBasics #SoftwareDevelopment #TechTips

One of the most underrated features in Python is the dunder (double underscore) method system. These methods are what make Python objects behave like built‑ins. Examples: • __init__ → object initialization • __str__ → human readable representation • __len__ → behavior for len() • __add__ → custom + operator Example: class Vector: def __add__(self, other): return Vector(self.x + other.x, self.y + other.y) Now Python understands how to use + with your objects. Under the hood, Python transforms: a + b into: a.__add__(b) This is one reason Python feels so expressive and flexible. Understanding dunder methods means understanding how Python really works.

Do you know a Python Set is like a group of friends? 👥🐍 In a group of friends, you usually don’t have duplicate people. Each person is unique. 🙋♂️🙋♀️ Python Sets work the same way — they do not allow duplicate values. ❌🔁 Another interesting thing is that a group of friends doesn’t stand in a fixed order. People can stand anywhere. 🚶♂️🚶♀️ Just like that, Sets in Python are unordered, which means items do not have a fixed position or index. 🔢❌ Example: friends = {"Alex", "John", "Maya", "Alex"} print(friends) Even if "Alex" appears twice, the Set will keep only one value, because duplicates are automatically removed. 🧹✨ #Python #PythonLearning #CodingTips #Programming #LearnToCode #PythonBasics #Sets

🚀 Learn Python – Tuple Slicing If you want to learn how to extract portions of a tuple, understanding Slicing is essential. It's a fundamental skill for data manipulation in Python. I’ve created a structured learning section on my website that explains tuple slicing step-by-step with practical examples. 📚 Explore the tutorial: https://lnkd.in/g_DmQ7wa 🔹 What you will learn • Extracting sub-tuples using the start:end:step syntax   • Using positive and negative indexing for flexible slicing   • Reversing tuples easily with slicing tricks   • Understanding slicing behavior and default values   • Practical examples: Slicing from the beginning, end, or with steps This resource is designed to help developers learn Python with practical examples and structured lessons. Happy Learning! 🚀 #Python #Coding #DataStructures #PythonTips #Programming #LearnPython

One small Python concept today… but a very important one. Today’s lesson focused on how Python handles mutable objects like lists. In the example below: def add_item(lst): lst.append(100) a = [1, 2, 3] add_item(a) print(a) The result will be: [1, 2, 3, 100] Why? Because lists in Python are mutable. When we pass a list to a function and modify it using methods like append(), the change happens in-place — meaning the original list itself is modified. 💡 Key takeaway: Understanding the difference between mutable and immutable objects is essential for writing predictable and efficient Python code. Every day in this sprint reminds me that small concepts build strong foundations in data analytics and AI. On to the next challenge. 🚀 #Python #DataAnalytics #AI #MachineLearning #LearningJourney #Coding #TechSkills #AIAnalytics #PythonProgramming #LinkedInLearning

🧠 Python Concept: zip() — Loop Through Multiple Lists Together 💻 Sometimes you have multiple lists and want to loop through them at the same time. 💻 Instead of using indexes, Python gives you a cleaner way. ❌ Old Way names = ["Asha", "Rahul", "Zoya"] scores = [85, 92, 78] for i in range(len(names)): print(names[i], scores[i]) Works… but not very readable. ✅ Pythonic Way names = ["Asha", "Rahul", "Zoya"] scores = [85, 92, 78] for name, score in zip(names, scores): print(name, score) Output Asha 85 Rahul 92 Zoya 78 🧒 Simple Explanation Imagine two lines of students: Names → Asha, Rahul, Zoya Scores → 85, 92, 78 zip() pairs them together. Asha → 85 Rahul → 92 Zoya → 78 💡 Why This Matters ✔ Cleaner loops ✔ Less index mistakes ✔ More readable code ✔ Very Pythonic 🐍 Python often gives you tools that make code simpler and safer 🐍 zip() lets you iterate through multiple lists together without worrying about indexes. #Python #PythonTips #PythonTricks #AdvancedPython #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode

How do "try" and "except" work in Python for handling errors? In Python, errors can occur during program execution (called exceptions). If they are not handled properly, the program may stop unexpectedly. This is where try and except statements come in. 🔹 "try" Used to wrap the code that might raise an error. 🔹 "except" Used to handle the error if it occurs, preventing the program from crashing. Example: try: x = int(input("Enter a number: ")) print(10 / x) except ValueError: print("Invalid input") except ZeroDivisionError: print("Cannot divide by zero") Python also provides additional clauses to make error handling more powerful: ▪ "else" → runs only if no exception occurs ▪ "finally" → always runs (useful for closing files or cleaning resources) ▪ "raise" → allows developers to trigger custom exceptions Understanding exception handling is essential for writing reliable and robust Python applications. #Python #AI #DataScience #Analytics #Programming #MachineLearning #Instant