Generating String Permutations with Recursion in Python

🚀 Day 5 of My LeetCode Journey – Solved Contains Duplicate! 💻✨ Today I solved LeetCode #217 – Contains Duplicate ✅ A popular Easy-level array problem often asked in coding interviews. 📌 Problem Statement Given an integer array nums, return true if any value appears at least twice in the array, otherwise return false. Example: nums = [1,2,3,1] ✅ Output: true 🔍 My Approach – Using Set I used Python’s built-in set() for an efficient solution. Convert the array into a set Compare the length of the original array with the set If lengths are different → duplicates exist Otherwise → all elements are unique 💡 Why This Works A set stores only unique elements, so any duplicate values are automatically removed. This made the solution short, clean, and optimized 🚀 💡 What I Learned This problem helped me strengthen: ✔ Arrays ✔ Hashing / Set usage ✔ Python built-in functions ✔ Efficient duplicate detection ✔ Interview coding logic ⏱ Complexity Time Complexity: O(n) Space Complexity: O(n) Small problems like this are great for building strong DSA fundamentals 🔥 #LeetCode #Python #DSA #Arrays #Hashing #ProblemSolving #CodingJourney #SoftwareDeveloper #InterviewPreparation #Programming #100DaysOfCode #LearningInPublic

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 of staying consistent 🚀 One thing I’m slowly understanding: Writing code is one thing. Explaining your code is a completely different skill. When I try to explain what I built — how it works, why I chose a method — that’s when I realize what I truly understand and what I don’t. So now I’m focusing on: Thinking before coding Writing cleaner logic Being able to explain my work in simple words Because in the end, it’s not just about building… It’s about understanding. Still learning. Still improving ⚡ #Consistency #LearningJourney #SoftwareDevelopment #DataScience #MachineLearning #Python #GrowthMindset #StudentDeveloper #BuildInPublic #Consistency #LearningJourney #GrowthMindset #BuildInPublic #StudentDeveloper #SoftwareDevelopment #DataScience #MachineLearning #Python #CodingJourney #DeveloperLife #TechJourne #ProblemSolving

Day 50 of my #100DaysOfCode (Python Journey) Being disciplined is crucial to achieve your goals and this journey of 100 days 100 codes of python is about consistency and improving problem solving skills. Halfway through, and this journey has been more about discipline than motivation. Here’s what I’ve worked on so far: 🔹 Strengthened Python fundamentals (loops, functions, problem-solving) 🔹 Practiced Data Structures & Algorithms • Arrays, Lists, Dictionaries • Recursion • Trees (like checking if two trees are identical) 🔹 Learned and implemented OOP concepts • Classes & Objects • Inheritance & Polymorphism 🔹 Solved coding problems consistently to improve logic - Learnings: It’s not about doing something huge every day — it’s about showing up daily, even when you don’t feel like it. Consistency is the key. Still 50 days to go. Next focus: ➡️ More advanced DSA ➡️ Building small real-world projects ➡️ Improving problem-solving speed Let’s finish strong Git-hub :- https://lnkd.in/dDUiHCtj #Python #100DaysOfCode #CodingJourney #DSA #LearningInPublic

🚀 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

🚀 Python in One Image – The Ultimate Mindmap! 🐍 Mastering Python doesn’t have to be complicated. This visual mindmap brings together everything—from basics to advanced concepts—in a single, structured view. 💡 Whether you're a beginner or an experienced developer, this covers: ✔️ Core fundamentals (variables, data types, operators) ✔️ Control flow & functions ✔️ Data structures & OOP ✔️ Libraries, frameworks & real-world use cases ✔️ Advanced concepts like multithreading, async & memory management 📌 This is the kind of resource I wish I had when I started—simple, visual, and powerful. Consistency + clarity = growth 📈 Keep learning. Keep building. 💬 Which part of Python are you currently focusing on? #Python #Programming #Coding #Developer #SoftwareDevelopment #AI #MachineLearning #WebDevelopment #100DaysOfCode #Learning #Tech

Struggling with subarray problems? This one pattern can solve most of them 👇 If you’re preparing for coding interviews or improving your problem-solving skills, the Prefix Sum technique is a must-know pattern. 💡 What is Prefix Sum? It’s a way to store the cumulative sum of elements so that you can quickly calculate the sum of any subarray in constant time. 👉 Example: For array [1, 2, 3, 4] Prefix sum becomes → [1, 3, 6, 10] 🎯 Why use it? . Reduces time complexity from O(n²) → O(n) . Helps solve problems like: - Subarray sum = k - Longest subarray with given sum - Count of subarrays 🧠 Core Idea: Instead of recalculating sums again and again: sum(i to j) = prefix[j] - prefix[i-1] 🔥 Power Boost with HashMap Combine prefix sum with a hashmap to: - Track previously seen sums - Solve complex problems in one pass ⚡ Key Patterns to Remember: - currentSum += nums[i] - Check currentSum - k - Store sum in hashmap - For longest → store first occurrence - For count → store frequency 💬 Learning Tip: Don’t just memorize the code — understand why we store sums and how we use them. 📌 Prefix Sum is not just a trick — it’s a pattern that appears again and again in interviews. Keep practicing. Keep improving. 💪 #DSA #CodingInterview #Programming #Python #SoftwareEngineering #Learning #100DaysOfCode

Why $5 + 5 = 55$ is the "Senior Error" you need to avoid. 🚨 In Python, simplicity is a double-edged sword. On Day 4 of the #100DaysOfPython challenge at Zenith Edureka, we are diving into User Input and Output handling. It sounds basic, but I have seen seasoned developers crash production scripts because they overlooked one fundamental rule: Python treats all user input as a String. If you don't use Type Casting (converting text to numbers), your calculations won’t just be wrong—they’ll be fundamentally broken. Today’s Technical Focus: The Input Trap: Understanding why input() defaults to string data. Type Casting Mastery: Using int() and float() to ensure mathematical integrity. Professional I/O: Using f-strings and advanced print() parameters to create readable, enterprise-grade logs. At Zenith Edureka, our mantra is "Your Job, Our Responsibility." We don't just teach you how to write code; we teach you how to write code that passes a technical interview and survives a real-world environment. 📍 THE DAY 4 CHALLENGE: Build a "Calculator." Take the user's name and current salary. Ask for a percentage increase. Use Type Casting to calculate the new total and output it using an f-string. Comment "Day 4 Complete" below once you've cleared the terminal! 🏆 Don't have the roadmap yet? Check the link in the comments to download the full 100-Day Python Syllabus. 📂 #Python #SoftwareEngineering #100DaysOfCode #ZenithEdureka #TechHiring #CleanCode #DataScience #AIEngineering #ProgrammingTips

Day 7 of #14DayPythonBootcamp 👨💻✨ Yesterday’s session was focused on pattern problems, one of the most important topics for coding interviews 🔥 We started from basic nested loop understanding and gradually moved to solving complex pattern problems using Python 💻🧠. This helped me clearly understand how rows and columns work internally in loops. I practiced multiple patterns like: • Star patterns ⭐ • Number grids 🔢 • Increasing & decreasing triangles 🔺 • Pyramid patterns • Alternating patterns (like 1 0 1 / 0 1 0) I also completed tasks such as: ✔️ Number Pyramid (1 → 1 2 → 1 2 3 …) ✔️ Repeated Number Triangle (1, 2 2, 3 3 3 …) ✔️ Binary pattern (1 0 1 pattern) This session really improved my logical thinking, loop control, and problem-solving skills 💪⚡. Pattern problems may look simple, but they are powerful for mastering nested loops and logic building. Feeling more confident and interview-ready step by step 🚀 #Python #PatternProblems #CodingJourney #ProblemSolving #LearningEveryday #CareerGrowth #JobReady #14DayPythonBootcamp #LearningInPublic #Codegnan

Python collections.Counter` makes counting effortless. Instead of writing loops or manual logic, `Counter` instantly tallies elements in a list, string, or any iterable. It’s a dictionary subclass designed to handle frequency problems with clarity and speed. Why it’s useful: - Count words in text with one line of code. - Track items in lists without extra effort. - Simplify data analysis tasks while keeping code clean. Think of `Counter` as your built‑in frequency calculator — saving time and reducing complexity. At IT Learning AI, we show you how to apply these hidden gems in real projects so you can write smarter, cleaner, and more efficient Python programs. 👉 Explore more tutorials and coding insights at [https://itlearning.ai] #itlearningai #TechEducation #AceYourTechJourney #LearnWithAI #CodingJourney #DeveloperGrowth #ITLearningAI #CodeDaily #ProgrammersLife #CodingIsFun #TechCommunity #BuildWithPython #GrowWithTech