LeetCode 75 Progress Update: 2 Python Problems Solved

🚀 LeetCode Arrays Progress Update | 07/01/2026 I’ve completed 2 problems from the Arrays section of LeetCode using Python. ✅ Problems Solved: 1️⃣ Remove Element Description: Given an array nums and a value val, remove all instances of val in-place and return the number of remaining elements. The relative order of elements can be changed. Approach: Use two pointers (i for placement, j for traversal) Traverse the array and copy elements not equal to val to the front Return the count of valid elements Achieves O(n) time complexity with O(1) extra space 2️⃣ Search Insert Position Description: Given a sorted array and a target value, return the index if the target is found. If not, return the index where it would be inserted to maintain order. Approach: Apply Binary Search on the sorted array Narrow down the search space using left and right pointers If the target is not found, the left pointer gives the correct insert position Optimized O(log n) solution 📌 Key Learnings: ➡ In-place array manipulation using two-pointer technique ➡ Applying Binary Search to reduce time complexity ➡ Writing clean, optimized solutions with minimal extra space #LeetCode #Python #ProblemSolving #DataStructures #Algorithms #Arrays #BinarySearch #CodingPractice #DataEngineering

🚀 Day 48 of #100DaysOfCode — Getting the Last Element of an Array Hey everyone! 👋 Today’s challenge was simple but fundamental: returning the last element of an array without modifying it. 👨💻 What I practiced today: ✅ Accessing array elements by index ✅ Using negative indexing in Python ✅ Writing clean, one-line return statements ✅ Handling edge cases implicitly 📌 Today's Task: ✔ Create a function get_last(arr) ✔ Return the last element of the given array ✔ Keep the original array unchanged 🧠 Example: Input: [1, 2, 3] Output: 3 Input: ["a", "b", "c"] Output: "c" 💡 Key Takeaway: Even simple operations like accessing the last element are essential building blocks in programming. Python’s negative indexing makes this elegant and readable. #100DaysOfCode #Python #Arrays #Indexing #BeginnerFriendly #CodingJourney #Day48

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

Errors are not bugs — they’re signals. Handling them correctly is a skill ⚠️ In this Python practice, I focused on robust exception handling to make user input safer and predictable. Key concepts applied: try block to execute risky operations ValueError handling for invalid numeric input ZeroDivisionError handling to prevent runtime crashes else block to confirm successful execution finally block to ensure cleanup / final messaging runs every time This pattern mirrors real-world application behavior: Fail gracefully Inform the user clearly Never let the program crash unexpectedly Strong fundamentals like this separate hobby scripts from production-ready code 🚀 #Python #ExceptionHandling #ErrorHandling #CleanCode #LearningJourney

When you start a new project, it feels great. But soon, you are drowning in a sea of independent variables. Or worse, you find yourself copy-pasting the same block of code 10 times just to print a few items. This isn't just annoying; it leads to buggy, unreadable code. In my latest tutorial, we fix this by unlocking two fundamental Python "superpowers": 1️⃣ 𝐃𝐢𝐜𝐭𝐢𝐨𝐧𝐚𝐫𝐢𝐞𝐬: Stop using lists for complex data. Give your data context with key-value pairs. 2️⃣ 𝐋𝐨𝐨𝐩𝐬: Stop repeating yourself. Automate the boring stuff in two lines of code. If you want to move from "messy scripting" to clean programming, this video is for you. Watch it here: https://lnkd.in/dk8EJGXe #Python #LearningToCode #SoftwareDevelopment #CodingTips #CleanCode

Python Fundamentals – Day III Learned conditional statements (if, elif, else), loops (for, while), and module imports (e.g., math, random). These concepts enable automation, iteration over datasets, and application of logic—core requirements for efficient data analysis and reporting. #Python #DataAnalytics #ProgrammingBasics #Automation

🐍 Python Loop Types Explained – For Loop vs While Loop Loops help us execute code repeatedly and efficiently in Python. Understanding when to use each loop makes your code cleaner and more powerful 💡 🔹 For Loop ✔ Iterates over a sequence (list, tuple, range, string) ✔ Best for fixed or known iterations ✔ Simple, readable, and commonly used 🔹 While Loop ✔ Runs as long as a condition remains True ✔ Ideal for unknown or condition-based repetitions ✔ Useful in real-time checks and event-driven logic 🔸 Loop Control Statements 🚫 break – exits the loop immediately ⏭ continue – skips the current iteration 📌 Pro Tip: Use for when you know how many times to loop. Use while when you know when to stop. #Python #PythonProgramming #LearnPython #PythonBasics #LoopsInPython #ForLoop #WhileLoop #CodingForBeginners #ProgrammingConcepts #DataAnalytics #SoftwareDevelopment #TechEducation #DeveloperCommunity #Upskill #anorgtechnologies

Python for automation and Ai Day 96 – Automating Tasks with os & shutil Let Python do the boring work for you 🧹 import os, shutil os.mkdir("demo_folder") shutil.move("file.txt", "demo_folder/") Automate: ✔ File cleanup ✔ Folder organization ✔ Daily repetitive tasks 📌 Automation is where Python shines in real life. 👉 What task would you automate first? #Python #Automation #Productivity #100DaysOfCode

📅 Winter Arc | Task 8/30 🎯 Challenge: Python Today’s focus: String and array mixed problems in DSA, CSS, and recursion in Python Key Takeaways 🔷 Gained a clear understanding of CSS layout systems (Flexbox & Grid) to build responsive, structured, and visually appealing web pages using proper alignment, spacing, and positioning. 🔷 Strengthened DSA fundamentals by solving array and string problems, improving logical thinking, problem decomposition, and efficient use of loops and conditions. 🔷 Learned functions, recursion, and the call stack in Python, understanding code reusability, modularity, and how recursive calls execute and return through the call stack. MATRIX JEC

🚀 Visualizing the Optimal Merge Pattern using Python (Greedy Algorithm) I recently built an interactive Python application that visualizes the Optimal Merge Pattern, a classic greedy algorithm used to minimize the total cost of merging files. 📌 What the project does: Takes a list of file sizes as input Uses a min-heap (priority queue) to repeatedly merge the two smallest files Calculates the minimum total merge cost Animates each merge step using Tkinter, showing how the optimal solution is built ✨ Key features: ✔ Step-by-step animated merging ✔ Tree-based visualization of merges ✔ Real-time running cost calculation ✔ Time & space complexity analysis ✔ Clean GUI with detailed explanations 🧠 This project helped me deeply understand how greedy strategies, heaps, and tree structures work together in real-world optimization problems like file compression and external sorting. 🔧 Tech Stack: Python | Tkinter | Heapq | Greedy Algorithms | Data Structures If you’re learning algorithms and want to see how they work rather than just reading theory, this kind of visualization is incredibly powerful. 💬 I’d love feedback or ideas for extending it further! #Python #DataStructures #Algorithms #GreedyAlgorithm #Tkinter #ComputerScience #LearningByBuilding