Python K-th Largest Element in Array Challenge

Day 64 of my #100DaysOfCode challenge 🚀 Today I worked on generating all combinations of a string using Python’s itertools module. This is an important concept in combinatorics and widely used in problem-solving and interviews. What the program does: • Takes a string as input • Generates all possible combinations (subsets) • Uses itertools.combinations() • Prints combinations of all lengths Example (Input: "abc"): All combinations: a b c ab ac bc abc How the logic works: Step-by-step: 1. Loop from length = 1 to n 2. Generate combinations of each length 3. Join characters into strings 4. Store and print results It systematically builds all possible subsets ✔️ Why this is important: – Core concept in combinatorics – Used in problems like: Subset generation Feature selection Probability problems – Frequently asked in interviews – Helps understand power set logic Time Complexity: O(2ⁿ) Space Complexity: O(2ⁿ) Key Takeaways: – Understanding combinations vs permutations – Using Python’s powerful itertools – Generating subsets efficiently – Clean and readable implementation #100DaysOfCode #Day64 #Python #Programming #Combinations #Itertools #Algorithms #DSA #CodingPractice #ProblemSolving #InterviewPrep #LearnByDoing #DeveloperJourney #Consistency #BTech #CSE #AIandML #VITBhopal #TechJourney

Day 5 Consistency is key! 🚀 I’ve been dedicating time to strengthening my Python fundamentals, specifically diving deep into how to work with data sequences. From understanding immutability to mastering indexing and slicing techniques, I’m building a solid foundation to handle data manipulation more effectively. It’s rewarding to see how these concepts translate into cleaner, more efficient. Today I’ve been practicing advanced sequence manipulation in Python. Key takeaways from my study session: Immutability: Understanding why certain data types (like strings) cannot be changed in place. Slicing Syntax: Mastering [start:stop] and how to omit indices for cleaner, faster code. Negative Indexing: Leveraging indexing from the end to make my code more dynamic. There is always something new to learn when it comes to optimizing data extraction! 💡 #PythonProgramming #SoftwareDevelopment #LearningToCode #DataManipulation #CodingTips #Python #CodingJourney #ContinuousLearning #DataHandling #SelfDevelopment #TechSkills

Recursion confused me. So I built a visualizer for it. 🐍 Most beginners (including me) struggle with one thing: "What actually happens when a function calls itself?" So I wrote a Python program that shows you — step by step, with a delay so you can actually follow it. Watch it go DOWN the stack, hit the base case, then come back UP — adding numbers on the way. 👇 What I used: → Recursion — function calling itself → time.sleep() — to slow execution down visually → Print statements — to trace every step This isn't from a tutorial. I built this because I was confused. That's the best reason to build anything. 💡 Important: Dry run your code, It helps alot. #Python #Recursion #LearningInPublic #DataAnalytics #BBA #BuildInPublic

A few weeks ago, I started learning Python, committing to just 3% progress daily. I covered the basics (lists, tuples, sets, loops, conditionals, operators) and at some point, I felt like I was getting the hang of it. Then a friend challenged me with a simple logical problem… and that moment really humbled me. It made me realize something important: knowing syntax isn’t the same as knowing how to think with code. Python (and programming in general) is less about memorizing concepts and more about how well you can apply logic. How you break problems down, manipulate variables, structure conditions, and connect different pieces to get the result you want. I took that advice seriously, paused rushing through topics, and focused on strengthening my problem-solving and logical thinking. The improvement has been very noticeable. Takeaway: Don’t just rush through the basics. Spend time building your logic. Once your thinking improves, everything else becomes easier, and you’ll already be ahead of many who only focus on syntax. #Python #LearnToCode #DataAnalyst #PythonBeginner #TechSkills #ProblemSolving #Logic #DataAnalysis #web3 #100DaysOfCode #TechGrowth #SelfImprovement #ContinuousLearning #Biuldinpublic

🚀 Solved another problem on LeetCode: Concatenation of Array Today I worked on a simple yet important array problem that focuses on understanding how data structures behave in Python. Problem Summary: Given an integer array "nums", create a new array "ans" such that: 👉 "ans = nums + nums" (i.e., the array is repeated twice) 💡 My Approach: I used Python’s built-in list concatenation: "return nums + nums" ⚡ Why this approach is best? - No loops required - Clean and readable code - Uses optimized internal operations 📊 Complexity Analysis: • Time Complexity: O(n) • Space Complexity: O(n) 🔁 Comparison with other approaches: 🔸 Using loops → More lines of code, less readable 🔸 Using append() → Slightly slower due to repeated operations 🔸 Using list comprehension → Still less direct My approach is the most efficient and pythonic way to solve this problem. 🧠Key Learning: Sometimes the simplest solution is the most powerful. Knowing built-in operations can save time and improve code quality. Consistency in DSA is the key 🔥 #LeetCode #DSA #Python #CodingJourney #ProblemSolving #Code

INSTEAD OF WASTING TIME AND TRYING TO GET FIGURES. WHY NOT USING CODE?? Sometimes, lecturers or organizations need to generate different sets of questions for multiple candidates, especially when working with matrices. However, this often requires a lot of manual effort and can be time-consuming. Why not simplify the process using NumPy in Python? With just a few lines of code, you can easily generate multiple variations of matrix-based questions efficiently and save valuable time. #randint is an inbuilt function of the random module of numpy #Syntax: np.random.randint(start, stop (rows, columns)) a=np.random.randint(2,30, (3,3)) b=np.random.randint(2,30, (3,3)) c=np.random.randint(2,30, (3,3)) d=np.random.randint(2,30, (3,3)) e=np.random.randint(2,30, (3,3)) #DataScience #Python #NumPy #Education #Automation

Day 4/120 – I finally understood how Python actually “thinks” 🤯 For the past 3 days, I was learning concepts… But today, things started making sense. Because I learned this 👇 👉 Operators in Python Operators are what make your code “do something” Without them, Python is just… variables sitting idle 😅 Here’s what I explored today 👇 ➕ Arithmetic Operators → +, -, *, / Example: 10 + 5 = 15 📊 Comparison Operators → ==, !=, >, < Example: 10 > 5 → True 🧠 Logical Operators → and, or, not Example: (10 > 5) and (5 > 2) → True This is where logic begins 🔥 Now I can actually: ✔ Make decisions ✔ Compare values ✔ Build logic Feels like I unlocked a new level 🎮 Consistency > Perfection 💪 If you're learning, comment “LEVEL UP” 🚀 #Day4 #Python #DataAnalytics #LearningInPublic #CodingJourney #Consistency #Beginners

Day 18 revision done. Operators. If/Else. Match statements. While loops. For loops. Not just reading through notes this time actually writing the code out, making mistakes, fixing them and doing it again until it felt natural. And honestly? It's working. The things that confused me the first time around are starting to make sense now. I finally get why // and % are different. I understand why indentation is not optional in Python. I know when to use a while loop vs a for loop. Revision isn't glamorous. There's no big aha moment. It's just you sitting down, doing the work and trusting that repetition builds confidence. And slowly it is making sense It is finally sticking and guess what I'm happy. Because Python is one of the most amazing tools used in the data space and I'm out here learning it on my own. Day 19 is next. Let's keep going. #Python #100DaysOfCode #SelfTaught #GrowthMindset #DataAnalysis #W3schools

🚀 Solved: Group Anagrams Problem (LeetCode) Today I worked on an interesting problem — grouping anagrams efficiently using Python. 💡 Approach: I used a hashmap (dictionary) where: The key is the sorted version of each word The value is a list of words (anagrams) matching that key Example: "eat", "tea", and "ate" → all become "aet" after sorting → grouped together 🧠 Key Insight: Sorting each string gives a unique identifier for all its anagrams. ⚙️ Time Complexity: Sorting each word takes O(k log k) For n words → O(n * k log k) 📦 Space Complexity: O(n * k) for storing grouped anagrams ✅ Result: Accepted ✔️ Runtime: 5 ms (faster than ~99% submissions) 📈 Growth & Consistency: Improving step by step by solving problems daily and focusing on writing clean and optimized code. Small consistent efforts are helping me build stronger problem-solving skills and deeper understanding of DSA. 🔁 Staying consistent is the real game changer! #Python #DSA #LeetCode #Coding #ProblemSolving #Consistency #Growth #LearningJourney

🚀 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 🚀