Two Sum Problem: Optimizing with Hashmaps in Python

🚀 Day 29 of Python Problem Solving!! Today, I worked on the Top K Frequent Elements problem. 💡 What I Practiced Today: Counting element frequencies using dictionaries and Counter Understanding different approaches to solve the same problem Improving code efficiency and readability Using Python built-in functions for optimized solutions Strengthening problem-solving and data structure concepts 🧠 Problem Statement: Given an integer array nums and an integer k, return the k most frequent elements. 📌 Example: Input: nums = [1,1,1,2,2,3], k = 2 Output: [1, 2] ✨ Approaches I explored: 1️⃣ Sorting Approach Count frequencies using a hashmap Sort based on frequency Extract top k elements 2️⃣ Optimized Approach using Counter Used Python’s Counter and most_common(k) Achieved cleaner and more efficient code 🚀 This problem helped me understand how choosing the right approach and built-in tools can simplify complex logic and improve performance — a key skill for coding interviews. #Day29 #100DaysOfCode #Python #CodingJourney #ProblemSolving #DataStructures #Programming #LearnToCode #TechJourney

Python : 03 🎯String operation: formatting string Here we'll be introduced how we can combine strings from the variable using formatting string. Let's take a look- variable1 = a variable2 = b lets call a variable called 'combined string' combined_string = f"{a} {b}" [💡# Here "f" stands for 'formatting'] print(combined_string) Result: a b [ 💡 Note: In Python (and most programming languages), quotes are the "boundary" that tells the computer: "Do not process this; just treat it as plain text. "When you omit the quotes, Python looks for a variable with that name. It goes to the memory location where combined_string is stored and grabs the value.] So, that is called a formatted string! ✅ This is the modern and most efficient way to format strings in Python. It evaluates the expressions inside the curly braces and converts them to text. Make sure you follow this account for more! #python #CodingCommunity #PythonDeveloper #coding #TechCommunity #Developers #pythonprogramming

Day 21 of #100DaysOfLearning — Python OOP & Operator Overloading Today, I worked on building a Vector class in Python and explored how to make code more intuitive using operator overloading. What I learned: -Creating a class with attributes (x, y) -Implementing __add__() to add two vectors using + -Using __str__() to display vectors in mathematical form (like 5i + 9j) -Taking user input in custom format (5i 9j) and converting it into usable data One interesting part was handling input like: 5i 9j → converting it into numeric values using string methods like .replace() and .split() Result: I can now add two vectors like: (5i + 9j) + (1i + 2j) = (6i + 11j) This small project helped me understand how powerful Python’s magic methods are in making code cleaner and closer to real-world math. Next: Planning to explore vector operations like dot product and magnitude (important for Machine Learning) #Python #MachineLearning #100DaysOfCode #OOP #CodingJourney #LearnInPublic #SkillShikshya

✅ Python: 04 🎯 Nested loops Let's share an interesting concept of python. we've this concept called nested loop, here we can use one loop inside of an another loop. We can get some interesting results. Let's take a look- for x in range(2):  # outer loop   for y in range(3):  # inner loop      print(f"({x} , {y})")   # for co-ordinates 📌Code explanation: The outer loop will be executed 2 times & the inner loop will be executed 3 times, To begin with, the python interpreter will execute the outer loop first then it'll go to the inner loop and execute codes as follows, then it'll print as commanded and then jumps into the outer loop again, this will continue as per the range mentioned in the code. That's how nested loop works. #PythonProgramming #PythonDeveloper #Coding #python #nestedloopinpython #DataScience #pythondeveloper

🚀 Day 6: Mastering the Logic of Python | Flow Control Python isn't just about writing code; it's about making decisions. Today was all about Flow Control Statements—the "logical backbone" that transforms a script into an intelligent program. In my latest session, I dived deep into how Python decides how and when code blocks execute. Here’s a breakdown of the Day 6 deep dive: 🧠 The Decision Engine: Conditional Statements I explored how to guide program execution through branching paths: if, if-else, and if-elif-else: Handling everything from simple checks to complex, multi-layered grading systems. match-case (Python 3.10+): A cleaner, more readable "multi-way" decision-maker that feels like a modern switch-case. 🔄 The Engine of Efficiency: Looping Statements Iteration is where the power lies. I practiced: for & while loops: Repeating operations until conditions are met. Loop-Else: A unique Python feature where the else block executes only if the loop finishes normally (without a break). Nested Loops: Essential for processing complex data like matrices and patterns. 🚦 Fine-Tuning Control: Transfer Statements Knowing when to exit or skip is just as important as knowing when to run: break: Immediate exit from a loop. continue: Skipping the current iteration to move to the next. pass: The ultimate "placeholder" that does nothing but keep the syntax valid. 🛠️ Hands-On Logic Building I applied these concepts to solve real-world logic problems: ✅ Finding the biggest of three numbers using nested if..else. ✅ Building a Digit-to-Word converter. ✅ Mathematical validation: Prime Number and Perfect Number checks. ✅ String Reversal logic using both for and while loops. A huge shoutout to my mentor Nallagoni Omkar Sir for emphasizing that it's not just about syntax—it's about clarity, edge cases, and real-world logic. Next Stop: Functions! 🚀 #Python #CorePython #FlowControl #DataScience #LearningInPublic #CodingJourney #PythonProgramming #LogicBuilding #TechCommunity

🚀 Python Series – Day 7: Loops in Python (for & while) Till now, we learned conditions (if-else) 💻 But what if we want to repeat something multiple times? 🤔 👉 That’s where Loops come in 🔥 🧠 What is a Loop? A loop is used to execute a block of code multiple times 🔁 for Loop Used when we know how many times to run the loop for i in range(5): print(i) 👉 Output: 0 1 2 3 4 🔄 while Loop Used when we don’t know how many times to run i = 0 while i < 5: print(i) i += 1 ⚠️ Important Concept 👉 Infinite Loop (Be careful!) while True: print("Hello") 🛑 Break Statement Stops the loop for i in range(10): if i == 5: break print(i) ⏭️ Continue Statement Skips current iteration for i in range(5): if i == 2: continue print(i) 🎯 Why are Loops Important? ✔ Automate repetitive tasks ✔ Save time & effort ✔ Used in almost every program ❓ Question for you: What will be the output? for i in range(3): print(i * 2) 👉 Comment your answer 👇 📌 Tomorrow: Functions in Python 🔥 #Python #Coding #DataScience #Programming #LearnPython #Beginners #Tech #MustaqeemSiddiqui

🧠 Python Concept: unpacking (Multiple Assignment) Write less, assign more 😎 ❌ Traditional Way a = 1 b = 2 c = 3 ✅ Pythonic Way a, b, c = 1, 2, 3 🧒 Simple Explanation 📦 Think of unpacking like opening a box ➡️ Multiple values ➡️ Assigned in one line ➡️ Clean & simple 💡 Why This Matters ✔ Less code ✔ Cleaner assignments ✔ Very common in Python ✔ Improves readability ⚡ Bonus Examples 👉 Swap values easily: a, b = b, a 👉 Unpack list: nums = [1, 2, 3] a, b, c = nums 👉 Ignore values: a, _, c = [1, 2, 3] 🐍 Assign smarter, not longer 🐍 Python loves clean code #Python #PythonTips #CleanCode #LearnPython #Programming #DeveloperLife #100DaysOfCode

I spent 20 hours exploring the new Python Interpreter Written in Python, and what I found surprised me. The idea of a Python interpreter written in Python itself sounds like a circular dependency, but it's a real project that has the potential to simplify many tasks. As someone who works with Python daily, I was curious to see how this project could impact my workflow. One specific insight I gained was how this interpreter can be used to optimize Python code for better performance. I compared the execution speed of the same code using the traditional CPython interpreter and the new Python interpreter, and the results were interesting. The new interpreter showed a significant improvement in execution speed for certain types of tasks. I also experimented with using this interpreter for automating tasks and found it to be very effective. Are you using this yet? I'd love to hear about your experience with the new Python interpreter and how it's impacted your work. --- I share findings like this every week. Follow — no fluff, just real explorations. #PythonInterpreter #LearningInPublic #DevTips #AITools

Profiling-explorer Launches to Modernize Python Performance Analysis 📌 Adam Chainz’s profiling-explorer redefines Python performance analysis with an interactive, web-based dashboard that turns pstats files into sortable, drillable tables-no more manual parsing or cluttered CLI output. It supports both legacy tracing and future sampling profilers, empowering devs to pinpoint bottlenecks at function-level granularity. A game-changer for teams aiming to slash runtime overhead with precision. 🔗 Read more: https://lnkd.in/dDQbQCWF #Profilingexplorer #Python #Pstats #Webbased #Interactive

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