Day 48: LeetCode Challenge - Unique Binary String Generation

🚀 Day 50 of My Python Journey Today I solved Complement of Base 10 Integer on LeetCode. 🔍 Problem Overview: The task is to find the bitwise complement of a given base-10 integer. The complement is obtained by flipping all bits in its binary representation — changing every 0 to 1 and every 1 to 0. 🧠 Approach: 1️⃣ Convert the integer into its binary representation. 2️⃣ Traverse the binary string and flip each bit (1 → 0, 0 → 1). 3️⃣ Convert the resulting binary string back to a decimal integer. ⚡ Key Learnings: • Practiced binary representation and bit manipulation • Improved understanding of number systems (binary ↔ decimal) • Strengthened string manipulation and logical thinking in Python 📊 Complexity: • Time Complexity: O(n) • Space Complexity: O(n) Under the Guidance of : Rudra Sravan kumar and Manoj Kumar Reddy Parlapalli #Day50 #Python #LeetCode #DataStructures #Algorithms #CodingJourney #ProblemSolving #100DaysOfCode 🚀

Learn in Public — Day 15 Today I studied Selection Sort and implemented it in Python. 🔹 Key Idea: Selection Sort repeatedly finds the minimum element from the unsorted part of the array and places it at the correct position in the sorted part. 🔹 How it works: 1️⃣ Start from the first element 2️⃣ Find the smallest element in the remaining array 3️⃣ Swap it with the current position 4️⃣ Repeat for the rest of the array 🔹 Complexity: Time Complexity: O(n²) Space Complexity: O(1) (in-place sorting) Even though Selection Sort is not efficient for large datasets, it’s a great algorithm for understanding how sorting works internally. Every small step builds the foundation for mastering algorithms. #LearnInPublic #100DaysOfCode #Python #Algorithms #DataStructures

Day 64 of the #three90challenge 📊 Today I learned about Functions in Python — a key concept for writing clean and reusable code. Instead of repeating the same logic multiple times, functions allow us to define it once and reuse it whenever needed. What I practiced today: • Creating functions using def • Passing inputs (parameters) • Returning outputs using return • Writing reusable and organized code Example thinking: Instead of writing the same code again and again, functions help turn it into a single reusable block. Example: def calculate_total(a, b): return a + b print(calculate_total(5, 10)) This makes code more efficient, readable, and scalable. From writing code → to structuring it better 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #DataAnalytics #LearningInPublic #Consistency #Upskilling #PythonBasics

🚀 Day 2/30 – Stack & Queue Implementation using Python 🐍📚 Continuing my 30 Days Python Challenge with one of the most important Data Structures fundamentals! Today, I built a Stack & Queue implementation in Python to strengthen my understanding of LIFO and FIFO concepts, along with how data flows in real-world applications 💻 What I focused on today: ✨ Implementing Stack operations: push, pop, peek ✨ Implementing Queue operations: enqueue, dequeue ✨ Strengthening DSA logic and problem-solving skills This challenge is all about consistency, learning in public, and becoming better every single day 🚀 👉 Would love your feedback! Day 3 coming tomorrow… stay tuned 👀 #Python #30DaysChallenge #PythonProjects #DataStructures #Stack #Queue #CodingJourney #LearnPython #BuildInPublic #ProblemSolving

🚀 Day 34 of My Python Full-Stack Journey 🐍 Today, I explored Arrays in Python and understood how they help in storing and managing multiple values efficiently. 🔹 What I learned: • Concept of arrays and how they differ from regular variables • Creating arrays using Python modules like array • Accessing elements using index positions • Performing operations like insertion, deletion, and traversal • Understanding the importance of arrays in handling large datasets 🔹 Key takeaway: Arrays make data handling more structured and efficient, especially when working with collections of similar data types. 💡 Practicing arrays has strengthened my understanding of data structures, which is essential for building efficient applications. Looking forward to diving deeper into more advanced concepts! 🚀 #Python #FullStackDevelopment #LearningJourney #Coding #DataStructures #100DaysOfCode

𝗗𝗮𝘆 𝟵/𝟯𝟬 Instead of forcing my old coding habits into Python, I’m leaning into how the language handles data natively. Why write four lines of code when you can write one? 1. 𝗧𝗵𝗲 𝗦𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗪𝗮𝘆 (Looping & Appending): nums = [1, 2, 3, 4] squared = [] for n in nums: squared.append(n * n) 2. 𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗿𝗻 Approach 🚀: squared = [n * n for n in nums] 👉🏻It’s cleaner, faster, and much more intuitive. These are the small details that make the Python journey so satisfying🤌🏻 At what point do you find List Comprehensions become too complex? Do you stick to them for simple one-liners, or use them for nested logic too? #Python #30daysofcode #CodingJourney #Day9 #SoftwareDevelopment

Python didn’t throw an error… and that’s the problem x = 10 x = "10" Later: print(x + 5) This crashes. Not when the mistake happened… but later, when the variable was actually used. That’s the difference. Python lets you change types freely (dynamic typing), but errors only show up at runtime. In C++, this wouldn’t even compile. You’d catch it immediately. So the real question is: Do you want errors early… or flexibility first and consequences later? Day 3/30 #Python #C++ #LearningInPublic #30DaysOfCode

Started my #30DaysOfPython journey today. 🚀 Here are some of the basics I picked up: 1. Python is a high-level, interpreted, open-source, object-oriented language created by Guido van Rossum. 2. Unlike many languages, Python uses indentation instead of curly brackets to define blocks of code. 3. I also revisited core data types like: (i) Numbers (ii) Strings (iii) Booleans (iv) Lists (v) Dictionaries (vi) Tuples (vii) Sets 4. One simple but useful thing: type() helps check the data type of any value. Day 1 done. One step closer to becoming more confident with Python. 🐍 #Python #30DaysOfPython #SoftwareDevelopment #LearningInPublic #CareerTransition

🚀 Day 61 of My Python & DSA Journey Today I solved LeetCode 2089 — Find Target Indices After Sorting Array, a problem that focuses on counting and understanding sorting behavior efficiently. 🔍 Problem Solved: Given an array and a target value, return the indices of the target after sorting the array in non-decreasing order. 💡 Approach Used: Instead of actually sorting the array, I used an optimized counting approach: • Count numbers less than target • Count numbers equal to target • Generate indices based on these counts This avoids sorting and improves efficiency. ⚡ Key Learnings: • Optimizing without sorting • Counting-based logic • Understanding index positioning after sorting • Writing efficient solutions 📊 Complexity Analysis: ✅ Time Complexity: O(n) Single pass through the array ✅ Space Complexity: O(1) Only storing count variables 🎯 Why This is Efficient? Instead of sorting (O(n log n)), we solved it in linear time O(n). Under the Guidance of: Rudra Sravan kumar and Manoj Kumar Reddy Parlapalli #Day61 #Python #LeetCode #DSA #Algorithms #CodingJourney #100DaysOfCode #10000Coders 🚀