Solved Group Anagrams Problem with Python LeetCode Solution

🚀 Day 65 of My Python & DSA Journey Today’s problem was Word Pattern (290) — a great exercise in understanding mapping and relationships between data. 🔍 Problem Solved: Given a pattern and a string, determine if the string follows the same pattern with a one-to-one mapping (bijection) between characters and words. 💡 Approach Used: • Split the string into words • Use two hashmaps (dictionaries): • One for character → word • One for word → character • Ensure consistency in both mappings while iterating ⚡ Key Learnings: • Concept of bijection (one-to-one mapping) • Using hashmaps for efficient lookups • Handling edge cases like unequal lengths • Writing clean validation logic 📊 Complexity Analysis: ✅ Time Complexity: O(n) We traverse the pattern and words once ✅ Space Complexity: O(n) For storing mappings 🎯 Why This Works? Using two dictionaries ensures no duplicates or conflicts, maintaining a strict one-to-one relationship. Another step closer to mastering problem-solving patterns! Under the Guidance of: Rudra Sravan kumar and Manoj Kumar Reddy Parlapalli #Day65 #Python #LeetCode #DSA #Algorithms #CodingJourney #100DaysOfCode #10000Coders 🚀

Today's topic: recursion. A function that calls itself. Sounds simple, right? Here are two ways to add up a list of numbers: Without recursion — honest, reliable, easy to follow: python def suma(lista): suma = 0 for i in range(0, len(lista)): suma = suma + lista[i] return suma print(suma([6,3,4,2,10])) # 25 With recursion — elegant, almost poetic... and a little terrifying: python def suma(lista): if len(lista) == 1: return lista[0] else: return lista[0] + suma(lista[1:]) print(suma([6,3,4,2,10])) # 25 Same result. Two completely different roads to get there. The recursive version looks more "pro" — but if you forget to define when it stops, the function calls itself forever. Literally. Forever. 💀 So yes, it's getting challenging. And yes, recursion feels more elegant to write. But I'm not ready to fully trust something that could loop into oblivion if I blink wrong. Lesson of the day: simple is not the same as bad. And documenting the moments that confuse you? That's part of learning too. #Python #LearningToCode #DaysOfCode #PythonProgramming #CodingJourney #Recursion #BeginnerCoder #TechLearning #CodeNewbie #LinkedInLearning

🐍 The most misunderstood line in Python is this: for item in [1, 2, 3]: Most developers think the for loop just "goes through the list". What it actually does: calls iter([1,2,3]) to get an iterator, then calls next() on it repeatedly until StopIteration is raised. That's the entire protocol. Once you understand that, generators click immediately. A generator function with yield IS an iterator — Python implements iter and next automatically. And the magic of yield is that the function pauses at each yield and resumes from there on the next call. Full guide: iterator protocol from scratch, generator functions vs expressions, yield from for delegation, lazy 5-stage file processing pipeline, context managers (enter/exit), @contextmanager, suppress, ExitStack, and send()/throw() for two-way generator communication. A generator expression uses 200 bytes. An equivalent list uses 8MB. For the same data. 📎 Free PDF. Zero pip installs — pure Python standard library. #Python #Generators #Iterators #ContextManagers #PythonProgramming #SoftwareEngineering #CleanCode #BackendDev #Programming

Python tip for modern developers: If you’ve ever stumbled upon xrange() in old tutorials, here’s the truth: it’s Python 2 legacy. In Python 3, range() already behaves like xrange() — it uses lazy evaluation, meaning it doesn’t generate all values at once but creates them on demand. This makes it memory‑efficient and perfect for handling large sequences. 🚫 Forget xrange() — it’s obsolete. ✅ Embrace range() — it’s the modern, optimized way to iterate in Python. At IT Learning AI, we simplify these tricky differences so you can focus on writing clean, future‑proof code without confusion. Whether you’re just starting out or sharpening advanced skills, we’re here to help you ace your tech journey with confidence. 👉 Dive deeper into Python concepts, tutorials, and hands‑on guides at https://itlearning.ai #itlearningai #pythonprogramming #learnpython #pythontip #codesmarter #pythonbasics #pythonforbeginners #phyton3 #pythondatastructures #advancedpython #pythondevelopers #techeducation #aceyourtechjourney #learnwithai #codingjourney #developergrowth

Today I learned about Polymorphism in Python, and it completely changed how I think about writing flexible code. Polymorphism is all about using a single interface to work with different types of objects. In simple terms, the same method can behave differently depending on the object that calls it. For example, a speak() method can return “Woof” for a Dog and “Meow” for a Cat — same method name, different behavior. What I found really interesting is how it works behind the scenes. Python allows this through concepts like method overriding, duck typing, and operator overloading. Instead of writing separate logic for every type, we can write more general and reusable code that adapts automatically. The real-world usefulness is huge. Whether it's handling different types of files, working with multiple payment methods, or building scalable systems, polymorphism helps keep code clean, maintainable, and easy to extend. This is a powerful reminder that writing smart code isn’t about making it complex — it’s about making it adaptable. #Python #Programming #OOP #Learning #SoftwareDevelopment

Day 5/365: Checking Armstrong Numbers in Python 🔢🧠 Today I worked on a classic number theory problem: checking whether a number is an Armstrong number. A number is an Armstrong number if the sum of its own digits each raised to the power of the number of digits is equal to the original number. For example: 153 has 3 digits 1³ + 5³ + 3³ = 1 + 125 + 27 = 153 So 153 is an Armstrong number. What this function does: First, I store a copy of the original number so I can compare at the end. Then I find how many digits the number has using len(str(copy)). Inside the loop, I extract each digit, raise it to the power of the number of digits, and keep adding it to a running sum. Finally, I compare the sum with the original number: If they are equal, it’s an Armstrong number. Otherwise, it’s not. What I learned from this exercise: How to break a number down digit by digit using % 10 and // 10. How to combine math (powers) with loops and conditionals to implement a rule. The importance of keeping a copy of the original value when you are modifying it in a loop. Day 5 done ✅ 360 more to go. If you have any variations of this problem (like finding all Armstrong numbers in a range or handling user input with validation), share them—I’d love to try them out. #100DaysOfCode #365DaysOfCode #Python #LogicBuilding #ArmstrongNumber #NumberTheory #CodingJourney #LearnInPublic #AspiringDeveloper

Day 6 of my Python learning journey: Focusing on writing code that's not just functional, but also dependable. Highlights from today: - Warmed up by practicing iterators and generators. - Solved the "First Non-Repeating Character" problem using a clean two-pass frequency approach with O(n) complexity. - Merged two sorted arrays efficiently through the two-pointer technique (O(n + m))—avoiding any redundant sorting. - Developed a FastAPI endpoint for the above problem, complete with schema validation. Observations: Initially, I attempted a one-pass solution for the non-repeating character problem, but it ended up feeling convoluted. Switching to a two-pass strategy made the solution much more straightforward and maintainable. Current focus areas: - Maintaining input order integrity. - Performing input validation early on. - Keeping logic as simple as possible. - Thoroughly testing edge cases beforehand. Major takeaway: Define inputs/outputs upfront → validate data early → then prioritize optimization. Starting to grasp how even minor design decisions can significantly impact code quality. GitHub link: https://lnkd.in/gGPw8_js #Python #FastAPI #ProblemSolving #SoftwareEngineering #CleanCode #LearningInPublic #OOP #Testing

𝐃𝐚𝐲 𝟏𝟏: 𝐒𝐭𝐨𝐩 𝐒𝐞𝐚𝐫𝐜𝐡𝐢𝐧𝐠, 𝐒𝐭𝐚𝐫𝐭 𝐅𝐢𝐧𝐝𝐢𝐧𝐠  I’ve noticed as I move deeper into Python, is that how we 𝐬𝐭𝐨𝐫𝐞 𝐝𝐚𝐭𝐚 is just as important as the focusing on the code logic. Up until now, I’ve been reaching for a 𝐋𝐢𝐬𝐭 for almost everything. It’s the go-to structure when you start. But as my data grows,I realized why that can be a trap. 👉 If you have 1,000 items, a List forces you to check every single one until you find a match. • With a 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲, I’m not just piling data into a box; I’m giving every piece of data a unique Key. 👉𝐓𝐡𝐞 𝐋𝐢𝐬𝐭 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 (𝐦𝐲𝐥𝐢𝐬𝐭) : Iterating through the stack until you hit "Bob." 👉𝐓𝐡𝐞 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐲 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 (𝐦𝐲𝐝𝐢𝐜𝐭) : Direct access via a unique Key. Instant results✅ A Dictionary is a completely different, and much more efficient, tool for the job. It’s a fundamental shift in how we handle data retrieval. #Python #30DaysOfCode #Day11 #LearningInPublic #SoftwareEngineering

🚀 Mastering Recursion with Gray Code Generation I recently worked on an interesting problem—generating Gray Codes using recursion in Python. This problem is a great example of how powerful and elegant recursive thinking can be. 🔹 What is Gray Code? Gray Code is a binary sequence where two consecutive values differ in only one bit. It has applications in digital systems, error correction, and algorithms. 🔹 Approach Used: Instead of generating all binary numbers and converting them, I used a recursive pattern: Base case: For n = 1 → ["0", "1"] Recursively get Gray codes for n-1 Prefix "0" to the original list Prefix "1" to the reversed list Combine both 🔹 Python Implementation: class Solution: def graycode(self,n): if n ==1: return ["0","1"] prev_gray = self.graycode(n - 1) result = [] for code in prev_gray: result.append("0" + code) for code in reversed(prev_gray): result.append("1" + code) return result 💡 Key Learning: Sometimes the best solutions don’t require complex logic—just recognizing patterns and applying recursion smartly. 📈 This problem strengthened my understanding of: Recursion Pattern building Problem decomposition Would love to hear how others approached this problem or optimized it further! 😊 #Python #Recursion #Algorithms #CodingJourney #DataStructures #ProblemSolving

🚀 Python Secret #1: Even 257 Can Lie 😈 Most developers think: 👉 Every number creates a new object in Python. ❌ Not true. 🧠 Python preloads integers from -5 to 256 in memory. These values are reused instead of recreated. So this happens 👇 a = 256 b = 256 print(a is b) # True 😳 👉 Same memory object (guaranteed) --- But now the twist 👇 a = 257 b = 257 print(a is b) # True OR False 🤯 👉 Wait… what?! This is NOT caching. This is compiler optimization. ⚠️ Meaning: Sometimes Python reuses the object… Sometimes it doesn’t. --- 💀 Want the real truth? a = int("257") b = int("257") print(a is b) # False 🔥 👉 Now Python is forced to create different objects. --- 🧠 Key Difference: ✔️ "==" → checks value (SAFE) ❌ "is" → checks memory (UNRELIABLE for numbers) --- 🔥 Final Insight: “Optimization is not a guarantee. Only -5 to 256 is.” --- 💬 Did this surprise you? Follow for more Python secrets 🐍 Day 1/30 — Let’s master Python together 🚀 #Python #Coding #Programming #Developers #PythonTips #LearnToCode #Tech #AI #100DaysOfCode