Python DSA Fundamentals: CSS Layout & Recursion

Today I worked on a Python logic exercise focused on list traversal, duplicate handling, and comparing two lists of different lengths using pure loops. 🔹 What this code does: Takes two lists with different lengths, both containing repeated numbers Iterates through them safely using index-based nested loops Collects common elements while preserving order Removes duplicate values manually, without using built-in shortcuts like set() 🔹 Why I approached it this way: Instead of relying on Python conveniences, I deliberately used: Explicit for loops Conditional logic Intermediate lists This forced me to think about: Boundary conditions when list sizes don’t match How duplicates are detected step by step Writing logic that doesn’t assume equal input sizes 🔹 Key takeaway: Understanding fundamentals—especially edge cases like unequal input lengths—builds stronger problem-solving skills than jumping straight to optimized one-liners. Consistent practice, steady improvement. 💻📈 #Python #Programming #LogicBuilding #DataStructures #ProblemSolving #CodingPractice

90% of DSA starts here: Mastering Arrays in Python 📉 They say if you can't handle Arrays, you can't handle Data Structures. Today, I tackled Day 2 of the Chai aur DSA series, and it wasn't just about simple lists. The session with Hitesh Choudhary went deep into how Python handles arrays differently from languages like C++ or Java. My Key Technical Takeaways: Memory Management: Unlike static arrays in C++, Python arrays are dynamic and can handle heterogeneous data. The Power of NumPy: I learned why numpy is industry-standard for creating 1D, 2D, and even 3D arrays efficiently. Slicing Magic: Python makes operations like reversing an array (arr[::-1]) or slicing sub-arrays incredibly intuitive compared to traditional loops. Building a solid foundation here to ensure smooth sailing when we get to Sliding Windows and Two Pointers later! 🚀 Question: Do you prefer using Python lists or NumPy arrays for your daily tasks? #DataStructures #Python #NumPy #CodingJourney #ChaiAurDSA #SoftwareEngineering

🚀 Day 27 of #100DaysOfCode Today’s problem: LeetCode 3010 – Divide an Array Into Subarrays With Minimum Cost I At first glance, it felt like a DP problem. But after breaking it down, the solution turned out to be simple and elegant. 🧠 Key Insight: Split the array into 3 contiguous subarrays Cost of a subarray = its first element First subarray always starts at index 0 To minimize cost → pick the two smallest elements from the remaining array ✅ Python Solution: Copy code Python class Solution: def minimumCost(self, nums: list[int]) -> int: return nums[0] + sum(sorted(nums[1:])[:2]) 💡 Learning: Don’t jump to complex solutions too quickly. Sometimes, the optimal answer comes from understanding the problem constraints deeply. 📈 Staying consistent, one problem at a time. #100DaysOfCode #Day27 #LeetCode #Python #DSA #ProblemSolving #Consistency #LearningInPublic #Learning

Day 1 of my DSA Journey with Python! 🐍 I’ve decided to take the plunge and master Data Structures and Algorithms. Today was all about laying a solid foundation and diving into efficient searching techniques. What I covered today: Array Basics: Understanding memory allocation and how Python handles lists. Binary Search: Moving beyond linear search to achieve $O(\log n)$ time complexity. It’s fascinating how much efficiency you gain by simply "halving" the search space! Implementation: Wrote a clean, iterative version of Binary Search in Python. The Challenge:I capped off the session by solving LeetCode #704 (Binary Search). There’s something incredibly satisfying about seeing that "Accepted" message on the first try! My goal with this series is to document my logic, stay consistent, and connect with fellow learners. Key Takeaway: Always check if the array is sorted before reaching for Binary Search! If it's not, your logic falls apart before you even begin. Follow along as I tackle more complex structures in the coming days! 📈 #DSA #Python #CodingJourney #LeetCode #SoftwareEngineering #DataStructures #BinarySearch #100DaysOfCode #LearningInPublic #babysteps

Day 3 of 365 Code in a year Today I worked on the Median of Two Sorted Arrays problem and faced a common Python error: TypeError: list indices must be integers or slices, not float. The issue happened because I used / for division while calculating the middle index, and in Python / returns a float, which cannot be used for list indexing. This helped me clearly understand the difference between / and //, where // performs integer division and is required for indexing. I also reinforced the concept that Python lists are 0-indexed, so median calculation depends on whether the array length is odd (arr[n//2]) or even ((arr[n//2 - 1] + arr[n//2]) / 2). A small operator mistake caused the error, but fixing it strengthened my understanding of Python indexing and algorithm fundamentals. On to Day 4

Module: Python Fundamentals Class: 09 Topic: Python Fundamentals- Variables, Data Types, Methods, Dataframe, Google Colab ▪️Knowing about Python and Google Colab ▪️Google Colab UI Tour ▪️Variable; Variable Assignment and Naming Convention ▪️Python Data Types: Numeric, String, Boolean, List, Tuple, Set, None ▪️Dictionary and Pandas Dataframe ▪️Knowing different Methods ▪️List vs. Tuples   ▪️Conditionals and Order of Execution; Indentation #python #machinelearning #ML #DataScience

Python became my go-to language for building real-time, day-to-day applications. Its simplicity allowed me to focus more on solving problems than fighting syntax. From automation to real-time processing, Python shines when speed and clarity matter. I’ve used it to build applications that respond quickly, scale smoothly, and remain readable long after deployment. What I appreciate most is how Python encourages clean thinking. When your logic is clear, your application becomes easier to debug, improve, and extend. Simplicity, coupled with clean thinking equals ease in building complex application. This is where Python comes in

Today I learned about Lambda Functions in Python 🐍 A lambda function is a small, anonymous function written in a single line using the lambda keyword. It’s mainly used for: ✅ Short and simple operations ✅ Writing compact code ✅ Using inside functions like map(), filter(), and sorted() Example idea: Instead of writing a full function to square a number, we can do it in one line with a lambda function. Less code. Same logic. Cleaner style. Understanding these small tools really helps in writing more Pythonic and efficient code. Learning one concept every day 🚀 #Python #DataScience #LearningInPublic #Programming #100DaysOfCode #CareerSwitch

Python Lists: When Single Values Aren’t Enough int, float, and string felt powerful, until I realized real programs work with collections. That’s where lists shine 👇 🔹 Store multiple values in one variable 🔹 Access items with indexing (starts at 0) 🔹 Use len() to count elements 🔹 Check existence with in 🔹 Slice lists just like strings (list[1:3]) 💡 Best part? If you understand strings, you already understand lists. Same rules. Same logic. More power. One concept learned → many doors unlocked #Python #LearningInPublic #ProgrammingBasics #VSCode #DeveloperJourney