Vimal Thapliyal’s Post

Consider the following code in Python: def add_item(lst): lst.append(100) a = [1, 2, 3] add_item(a) print(a) What happens here? The correct explanation is: ✅ An in-place modification occurs on the list. Lists in Python are mutable objects, which means they can be modified after they are created. Let’s break it down step by step. 1️⃣ Creating the list When we write: a = [1, 2, 3] Python creates a list object in memory, and the variable a references it: a → [1, 2, 3] 2️⃣ Calling the function When the function is called: add_item(a) The parameter lst inside the function now references the same list object: a → [1, 2, 3] lst → ↑ (same list) ➡️ Both variables point to the same object in memory. 3️⃣ Inside the function Inside the function we execute: lst.append(100) The append() method modifies the list itself. This is called in-place modification, meaning the original list object is updated instead of creating a new one. The list now becomes: [1, 2, 3, 100] 4️⃣ Printing the result Since both a and lst reference the same list, the change is visible through a. Now when we execute: print(a) Output: [1, 2, 3, 100] 📌 Final thought Understanding how variables reference objects in memory is essential when working with mutable data types like lists in Python. #Python #PythonProgramming #Coding #LearnPython #SoftwareDevelopment

🔥 Python Set Methods If you're learning Python, mastering sets is a must! Here's a simple and clean reference 👇 📌 Python Set Methods Table Input → Method → Output {1, 2} → .add(3) → {1, 2, 3} {1, 2, 3} → .remove(2) → {1, 3} {1, 2, 3} → .discard(4) → {1, 2, 3} {1, 2, 3} → .pop() → Random element removed {1, 2} → .update({3, 4}) → {1, 2, 3, 4} {1, 2, 3} → .clear() → set() {1, 2} → .union({3, 4}) → {1, 2, 3, 4} {1, 2, 3} → .intersection({2, 3, 4}) → {2, 3} {1, 2, 3} → .difference({2}) → {1, 3} {1, 2} → .symmetric_difference({2, 3}) → {1, 3} {1, 2} → .issubset({1, 2, 3}) → True {1, 2, 3} → .issuperset({1, 2}) → True {1, 2} → .isdisjoint({3, 4}) → True {1, 2} → .copy() → {1, 2} 💡 Pro Tip: Use .discard() instead of .remove() when you're not sure if the element exists. 📚 Save this for quick revision & share with your friends! #Python #Coding #Programming #Developers #LearnPython #CodingTips #DataStructures

Day 21 – List Methods in Python Lists are one of the most commonly used data structures in Python. Python provides built-in methods to easily modify and manage lists. 1. append() Adds an element to the end of the list. numbers = [10, 20, 30] numbers.append(40) print(numbers) Output : [10, 20, 30, 40] 2. insert() Adds an element at a specific position. numbers = [10, 20, 30] numbers.insert(1, 15) print(numbers) Output : [10, 15, 20, 30] 3. remove() Removes a specific value from the list. numbers = [10, 20, 30, 40] numbers.remove(20) print(numbers) Output : [10, 30, 40] 4. pop() Removes an element by index and returns it. numbers = [10, 20, 30] numbers.pop() print(numbers) Output : [10, 20] 5. sort() Sorts the list in ascending order. numbers = [30, 10, 20] numbers.sort() print(numbers) Output : [10, 20, 30] 6. reverse() Reverses the order of elements. numbers = [1, 2, 3, 4] numbers.reverse() print(numbers) Output: [4, 3, 2, 1] 7. count() Counts how many times an element appears. numbers = [1, 2, 2, 3, 2] print(numbers.count(2)) Output:3 8. index() Finds the position of an element. numbers = [10, 20, 30] print(numbers.index(20)) Output:1 #Python #Programming #LearnPython #CodingJourney

🚀 **DSA with Python – Recursion Practice** Continuing my **Data Structures & Algorithms with Python** journey, today I practiced more **recursion-based problems** to strengthen my understanding of how recursive calls break problems into smaller subproblems. 📚 **Problems I Practiced** 🔹 **Count Number of Digits (Recursion)** Logic: Repeatedly divide the number by 10 until it reaches 0. Each recursive call counts one digit. 🔹 **Sum of Digits of a Number** Example: 5251 → 5 + 2 + 5 + 1 = 13 Using recursion: `sum(n) = (n % 10) + sum(n // 10)` 🔹 **Reverse a String using Recursion** Example: `"abcd"` → `"dcba"` Recursively reduce the string and build the reversed result step by step. 🔹 Palindrome Check using Recursion Compared characters from the start and end of the string recursively until the middle is reached. Works for both odd and even length strings. 🔹 Reverse a String using Recursion Example: "abcd" → "dcba" Recursively append characters from the end to build the reversed string. 🔹 Sum of All Elements in an Array using Recursion Logic: sum(arr, i) = arr[i] + sum(arr, i+1) Continue until the index reaches the length of the array. 💡 **Key Learnings** ✔ Importance of **base conditions** in recursion ✔ How recursive calls create a **call stack** ✔ Breaking complex problems into **smaller subproblems** ✔ Strengthening **algorithmic thinking** Step by step building stronger foundations in **DSA and problem solving**. #DSA #Python #Recursion #Algorithms #DataStructures #CodingPractice #ProblemSolving #CodingInterview #PythonDeveloper #SoftwareEngineering #BackendDevelopment #LearnInPublic #DeveloperJourney #ContinuousLearning #Programming #TechLearning #100DaysOfCode #CodingJourney

🐍 Python Tip: Stop copying lists the wrong way! Every Python developer hits this bug at some point: a = [1, 2, 3, 4] b = a b.append(5) print(a) # [1, 2, 3, 4, 5] 😱 When you write b = a, you're NOT copying the list. You're just creating another variable pointing to the SAME memory (call by reference). ✅ The 3 ways to properly copy a list: • b = a[:]    → Slice copy • b = a.copy()  → Built-in method • b = list(a)   → Constructor copy ⚡ Which one is fastest? a[:] wins — it's a direct C-level memory slice with zero Python overhead. Speed ranking: a[:] > a.copy() > list(a) ⚠️ BUT — all 3 are SHALLOW copies. If your list contains nested lists or objects, changes will still bleed through: a = [[1, 2], [3, 4]] b = a.copy() b[0].append(99) print(a) # [[1, 2, 99], [3, 4]] 😬 For nested structures, use deepcopy: import copy b = copy.deepcopy(a) Quick rule: 📦 Flat list → a[:] or a.copy() 📦 Nested list → copy.deepcopy(a) Small detail. Big bugs if you get it wrong. ♻️ Repost if this helped someone on your team! #Python #Programming #SoftwareDevelopment #CodingTips #PythonTips

💡 Understanding Default Parameters in Python While working with functions in Python, default parameters can make our code more flexible and easier to use. Let’s look at this simple example: def func(a, b=2, c=3): return a + b * c print(func(2, c=4)) 1️⃣ Step 1: Function Definition The function func has three parameters: • a • b with a default value of 2 • c with a default value of 3 ➡️ This means that if we call the function without providing values for b or c, Python will automatically use their default values. 2️⃣ Step 2: Calling the Function func(2, c=4) Here is what happens: • The value 2 is assigned to a. • We did not pass a value for b, so Python uses the default value 2. • We explicitly passed c = 4, which overrides the default value 3. So the values inside the function become: a = 2 b = 2 c = 4 3️⃣ Step 3: Evaluating the Expression The function returns: a + b * c Substituting the values: 2 + 2 * 4 According to Python’s order of operations, multiplication happens before addition: 2 + 8 = 10 ➡️ Final Output: 10 🔹 Important Concept Default parameters allow functions to work with optional arguments. They make functions more flexible, cleaner, and easier to reuse. #Python #Programming #AI #DataAnalytics #Coding #LearnPython

🧠 Python Concept That Explains Attribute Lookup Order: Descriptor Lookup Chain When you access: obj.attr Python follows a precise order 👀 🔍 The Real Lookup Order Python checks in this exact sequence: 1️⃣ Data descriptor (__set__ / __delete__) 2️⃣ Instance __dict__ 3️⃣ Non-data descriptor (__get__) 4️⃣ Class __dict__ 5️⃣ __getattr__ 🧪 Example Insight class D: def __get__(self, obj, owner): return "descriptor" class C: x = D() c = C() c.x = "instance" print(c.x) ✅ Output instance Because instance dict beats non-data descriptor. 🧒 Explain Like I’m 5 Imagine looking for a toy 🧸 1️⃣ Guard holding it 2️⃣ Your bag 3️⃣ Shelf 4️⃣ Store 5️⃣ Ask someone That order = lookup chain. 💡 Why This Matters ✔ Descriptor behavior ✔ Properties ✔ ORMs ✔ Framework internals ✔ Debugging weird attribute bugs ⚡ Key Rule 🐍 Data descriptors override instance attributes. 🐍 Non-data descriptors don’t. 🐍 Attribute access in Python isn’t random. 🐍 It follows a precise chain 🐍 Understanding the descriptor lookup order explains many “magic” behaviors. #Python #PythonTips #PythonTricks #AdvancedPython #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode

One difference that trips people up in Python: / always returns a float. // always returns an integer. So 10 / 2 is 5.0, not 5. If you want a whole number, use //. Same with %: it gives the remainder, not the quotient. So 15 % 4 is 3 (the remainder), and 15 // 4 is 3 (how many times 4 fits). Together: quotient × divisor + remainder = dividend. I put together a complete guide to all seven arithmetic operators: +, -, , , /, //, %. With examples, float vs floor division, modulus, power, and common mistakes. Read it here: https://lnkd.in/gdP_qRbX #Python #Coding #Developer

🧠 Python Concept That Explains Class Namespaces: __prepare__ in Metaclasses Before a class is created… Python prepares its namespace 👀 🤔 What Is __prepare__? When Python executes: class MyClass: x = 1 y = 2 It first asks the metaclass: 👉 “What mapping should I use to store attributes?” That hook = __prepare__. 🧪 Example class OrderedMeta(type): @classmethod def __prepare__(mcls, name, bases): return {} def __new__(mcls, name, bases, namespace): print(list(namespace.keys())) return super().__new__(mcls, name, bases, namespace) class Demo(metaclass=OrderedMeta): a = 1 b = 2 c = 3 ✅ Output ['a', 'b', 'c'] Metaclass saw class body order 🎯 🧒 Simple Explanation 🧸 Before kids put toys in a box, teacher chooses the box. 🧸 That box = namespace. 🧸 Choice = __prepare__. 💡 Why This Matters ✔ Ordered class attributes ✔ DSLs & frameworks ✔ Enum internals ✔ ORM field order ✔ Metaprogramming ⚡ Real Uses 💻 Enum preserves order 💻 Dataclasses fields 💻 ORM column order 💻 Serialization frameworks 🐍 In Python, even class bodies have a setup phase 🐍 __prepare__ decides how attributes are collected, before the class even exists. #Python #PythonTips #PythonTricks #AdvancedPython #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode