Mastering Python String Formatting with format()

Implemented Depth-First Search (DFS) from Scratch in Python! DFS is one of the most fundamental graph traversal algorithms — think of it like navigating a maze: you pick a path and follow it all the way until you hit a dead end, then backtrack and try the next unexplored route. I implemented the iterative version using an explicit stack (preferred in production since it avoids Python's recursion depth limit). Here's how it works: 1. Push the start node onto a stack 2. Pop the top node (LIFO — last in, first out) 3. Visit all its unvisited neighbours and push them onto the stack 4. Repeat until the stack is empty Key takeaways: Time Complexity: O(V + E) — visits every vertex and edge once Space Complexity: O(V) — for the visited set + stack Used reversed() on neighbours so the traversal visits them in natural left-to-right order (since stack is LIFO, reversing ensures the first neighbour gets popped first) The stack is what makes this DFS — swap it with a queue and you get BFS! DFS vs BFS in one line: DFS goes deep before going wide (Stack / LIFO) BFS goes wide before going deep (Queue / FIFO) #Python #DataStructures #Algorithms #DFS #DepthFirstSearch #GraphAlgorithms #CodingJourney #DSA #Programming #SoftwareEngineering #LearningInPublic

LeetCode Problem 1143: "Longest Common Subsequence": Given two strings text1 and text2, return the length of their longest common subsequence. If there is no common subsequence, return 0. A subsequence of a string is a new string generated from the original string with some characters (can be none) deleted without changing the relative order of the remaining characters. For example, "ace" is a subsequence of "abcde". A common subsequence of two strings is a subsequence that is common to both strings. The below implementation in Python resolves this problem in O(m*n) time and space complexity using the dynamic programming approach. A dp array is created whose cells store the value of longest common subsequence upto a specific length of text1 and text2. At the last cell we get the value of "longest common subsequence" for the given two strings. #Python #LeetCode #DynamicProgramming #Algorithms #DataStructures #CompetitiveProgramming #CodingChallenge #ProblemSolving

LeetCode #217 – Contains Duplicate | Python Solution Iterating through nums, each element is checked against a HashSet — if it already exists, return True immediately; otherwise, insert it and continue. No nested loops, no redundant comparisons — just a single pass with instant lookups. 💡 Key Takeaway: Trading space for time is a core algorithmic principle. A set eliminates the need for brute-force O(n²) comparisons by giving constant-time lookups. ⏱ Time: O(n)  💾 Space: O(n) #LeetCode #DataStructures #Python #HashSet #CodingInterview #ProblemSolving #SoftwareEngineering

🌙 Day 28/100 | #100DaysOfCode 🚀 Today was all about File Handling in Python — and it felt really powerful! 🐍📁 Here’s what I learned today: 🔹 File Modes "r" → Read file "w" → Write file (overwrite) "a" → Append data "rb" / "wb" → For binary files like images & PDFs Understanding file modes helped me control how data is read and written in files. 🔹 with Statement I learned how with automatically handles opening and closing files, which makes the code cleaner and safer. No need to manually close files ✅ 🔹 Built a Simple File Copier Using file modes + with statement, I created a program that copies data from one file to another — even works for images and PDFs in binary mode! 😄 Small steps, but learning things that are actually used in real projects 💪 Consistency over perfection — moving forward every day. 👉 Tomorrow: more practice + deeper concepts! #Python #FileHandling #100DaysOfCode #LearningInPublic #PythonBeginner #DeveloperJourney #Consistency #TechSkills #DailyLearning

Weekly Challenge 4: Binary Search (Iterative). Why search harder when you can search smarter? Use Binary Search. Imagine looking for a word in a dictionary. Do you read every page from the beginning? No. You open it in the middle and decide: "Left or Right?". That is the essence of Binary Search, and it's the focus of my coding challenge for Week 4. The Implementation: I wrote a Python script (using NumPy) that generates a random environment to test the algorithm. Key Takeaway: While a simple loop (Linear Search) checks elements one by one ($O(n)$), Binary Search cuts the problem in half with every step ($O(\log n)$). Check out the trace in the console output below to see how few attempts it takes to find the target! 👇 📂 Full code on GitHub: https://lnkd.in/ezPaaiDM #Python #BinarySearch #Algorithms #CodingChallenge #DataStructures #Engineering

Headline: Stop writing loops to clean your data. 🛑 One of the most common tasks in Python is handling duplicate entries. While you could write a for-loop with a conditional check, there’s a much faster, more "Pythonic" way to do it: Sets. Sets are unordered collections of unique elements. By casting your list to a set, Python handles the heavy lifting of deduplication instantly. Why use this? ✅ Cleaner, more readable code. ✅ Better performance for large datasets. ✅ Built-in membership testing (O(1) complexity). How are you using Sets in your current workflow? Let’s discuss below! 👇 #PythonProgramming #Pyspiders #CodingTips #SoftwareDevelopment

Day 3 of my 30 Days Data Analytics Challenge Today I learned how Python repeats tasks automatically and how we can reuse logic using loops and functions. These concepts are used everywhere in data analytics — from cleaning data to running calculations on large datasets. What I learned today: 🔹 Loops: They help run the same block of code again and again. for loop → iterate over a sequence while loop → run until a condition is false 🔹 Functions: Functions are reusable blocks of code that perform a specific task. They make programs: Cleaner Reusable Easier to debug #DataAnalytics #Python #30DaysChallenge #LearningJourney #DataScience #Upskilling

Ever wondered how systems generate unique identifiers that almost never collide? 🤔 𝐔𝐔𝐈𝐃𝐬 (𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐚𝐥𝐥𝐲 𝐔𝐧𝐢𝐪𝐮𝐞 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐢𝐞𝐫𝐬). A simple way to generate identifiers that are unique across systems, time, and space. 🔗 Try this -> https://lnkd.in/eA4cxyWz 📌 Fun fact: A version 4 UUID has 122 random bits, meaning the chance of two UUIDs colliding is so tiny it’s practically zero(about 1 in 2.71×10³⁶). That’s like generating billions of UUIDs every second for years without seeing a duplicate. 👇 Here’s how you generate one in Python: 𝘪𝘮𝘱𝘰𝘳𝘵 𝘶𝘶𝘪𝘥; 𝘱𝘳𝘪𝘯𝘵(𝘶𝘶𝘪𝘥.𝘶𝘶𝘪𝘥4()) Example output: 3b8e5bd7-8fbe-48c9-8fd6-2a606131ab39 Want to dive deeper? I found this explanation really clear: 📺 https://lnkd.in/eK3S6aEg #Python #UUID #Coding #SoftwareEngineering

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

LeetCode Problem 83: Given the head of a sorted linked list, delete all duplicates such that each element appears only once. Return the linked list sorted as well. The below implementation in Python successfully resolves this in time complexity of O(n) where n is length of the list with constant space complexity. The approach is simple, handle the base case first like list having 0 or 1 number of nodes and then write the logic of handling lists of length greater than one. Maintain two pointers, one to keep track of present node and other to keep track of previous node. Check if the value of both nodes match or not, if match update the pointing of previous node, point its next to the present.next. #LeetCode #LinkedList #Python #CompetitiveProgramming #Algorithms #DataStructures #ProblemSolving