Use enumerate() for cleaner Python loops

“Python Tip of the Day” Python Tip #1: Use List Comprehension for Cleaner Code Instead of writing: squares = [] for i in range(10): squares.append(i**2) If you want a shorter version (Pythonic way), you can write: squares = [i**2 for i in range(10)] The output is: [0, 1, 4, 9, 16, 25, 36, 49, 64, 81] Why it’s better: 1) It’s shorter and easier to read 2) Runs faster than a regular for loop 3) Perfect for data transformations Keep your code clean, simple, and Pythonic! #Python #CodingTips #LearnPython #30DaysOfpythonCode

Problem: LeetCode 98 — Validate Binary Search Tree In this video, we solve the “Validate Binary Search Tree” problem using a Depth-First Search (DFS) approach in Python. We check whether a binary tree satisfies the BST property where every node’s left subtree contains only values less than the node, and every right subtree contains only values greater than the node. Approach: We use recursion with lower and upper bounds (low and high) to ensure each node’s value stays within valid limits as we traverse the tree. Topics Covered: Depth-First Search (DFS) Binary Tree validation logic Recursion in Python Complexity: Time: O(n) - We only visit each node once Space: O(h) - where h is the height of the tree If you’d like to see more walkthroughs like this, subscribe to my channel I’m posting new LeetCode and Python videos pretty often! Original Video: https://lnkd.in/ep82Uf9A #LeetCode #LeetCode98 #ValidateBinarySearchTree #ValidateBST #BinarySearchTree #Python #DFSTraversal #DepthFirstSearch #Recursion #CodingInterview #InterviewPrep #SoftwareEngineering #DataStructures #Algorithms #BinaryTree #PythonCoding #LeetCodePython #ProblemSolving #TechnicalInterview #Programming #DeveloperCommunity #CodeNewbie #100DaysOfCode #CodingChallenge #LearnToCode

🚀 Day 18 of my #100DaysOfCode Journey – Exploring Python Modules 🐍 Today’s focus was on Python Modules, both built-in and custom! Here’s what I practiced: ✅ Standard Library (math module) – Calculated square root, factorial, and rounded pi value. ✅ Random Module – Generated random choices and shuffled lists dynamically. ✅ Custom Module – Created my own calculator.py with add() and sub() functions, then imported it into the main file. 💡 Key Takeaway: “Modules make Python more powerful, organized, and reusable — write once, use everywhere!” #Python #100DaysOfCode #Modules #LearningJourney #SoftwareDevelopment #CodingEveryday #Math #Random #CustomModules #CodeNewbie

🚀 Day 18 of my #100DaysOfCode Journey – Exploring Python Modules 🐍 Today’s focus was on Python Modules, both built-in and custom! Here’s what I practiced: ✅ Standard Library (math module) – Calculated square root, factorial, and rounded pi value. ✅ Random Module – Generated random choices and shuffled lists dynamically. ✅ Custom Module – Created my own calculator.py with add() and sub() functions, then imported it into the main file. 💡 Key Takeaway: “Modules make Python more powerful, organized, and reusable — write once, use everywhere!” #Python #100DaysOfCode #Modules #LearningJourney #SoftwareDevelopment #CodingEveryday #Math #Random #CustomModules #CodeNewbie

🚨 Stop killing your Python performance with string concatenation! I see this mistake everywhere: ❌ The slow way (O(n²)) result = "" for word in words: result += word # Creates a NEW string every time! Here's what's actually happening: Every += creates an entirely new string in memory, copying all previous characters. Process 1000 strings? That's ~500,000 character copies. Ouch. The fix is beautifully simple: # ✅ The fast way (O(n)) result = [] for word in words: result.append(word) # Just adds to list return ''.join(result) # Single concatenation at the end Why it matters: → Strings are immutable in Python → Lists are mutable and efficient for building → One final join operation vs thousands of copies → Can be 100x+ faster on large datasets Real impact: I've seen this single change cut processing time from minutes to seconds in production code. Pro tip: For simple cases, list comprehensions + join are even cleaner: result = ''.join([word for word in words]) What's your favorite Python performance trick? #Python #Programming #SoftwareEngineering #CodingTips #PerformanceOptimization #CleanCode #TechTips #PythonProgramming #SoftwareDevelopment #CodeEfficiency

Day 41 – Scope in Python: Local vs Global Variables Ever got a “NameError” and wondered why Python couldn’t find your variable? 🤔 It’s all about Scope — where a variable lives and breathes. x = 10 # Global variable def show(): x = 5 # Local variable print("Inside function:", x) show() print("Outside function:", x) 🧩 Output: Inside function: 5 Outside function: 10 ➡️ Local variables exist only inside the function. ➡️ Global variables exist everywhere. Use them wisely — globals are powerful but can cause confusion if overused. 👉 Tip: Prefer local scope for clean and bug-free code. #Python #Coding #Learning #100DaysOfCode #PythonTips

🚀 Big news for the Power Platform ecosystem! Microsoft just enabled Python execution directly within Power Platform — a massive leap for automation, analytics, and AI integration. 💡 Why it matters: For business users: This means you can now access advanced data transformation, AI, and visualization capabilities without leaving Power Apps or Power Automate. Think predictive analytics, natural language models, and data science workflows — all in one low-code environment. For developers: It bridges low-code and pro-code like never before. You can now reuse Python scripts, leverage existing libraries (Pandas, NumPy, OpenAI, etc.), and supercharge Power Platform solutions with real computation power. For organizations: Expect faster innovation, less dependency on external pipelines, and more value extracted from your data — directly within Microsoft’s governance and security layer. This is the convergence of low-code + pro-code + AI, and it’s going to reshape how we build and automate business solutions. 🔥 #PowerPlatform #Python #AI #Automation #DataScience #LowCode

Python programm to reverse number using class as below: #Defining python class class Number_Reverse: # Initializing the number in class object     def __init__(self, num):         self.num = num # define class method to perfor reverse the number      def reverse_num(self):         return int(str(self.num)[::-1]) # Creating class object and calling reverse_num method if __name__ == "__main__":     num = 123456789 # Defining the class object     t = Number_Reverse(num)     print(t.reverse_num())

Python Day 5 Tip: Difference Between append() and extend() Both are used to add elements to a list , but they work differently! # Example list1 = [1, 2, 3] list1.append([4, 5]) print(list1) #output is [1, 2, 3, [4, 5]] list1 = [1, 2, 3] list1.extend([4, 5]) print(list1) #output is [1, 2, 3, 4, 5] 1) append() adds the entire object as a single element. 2) extend() adds each element from the iterable individually. Tip: Use append() for single items and extend() for adding multiple items at once. #Python #30DaysOfpythonCode #PythonTips #Coding #FullStackDeveloper #LearnPython #PythonLearning