How to Implement DFS in Python for Graphs

Hello Everyone 👋 , ⚙️ What if you could make your Python code run 5x faster with just two extra lines? That’s exactly what #Joblib does. It lets you run tasks in parallel across multiple CPU cores — no complex setup, no multiprocessing headaches. Here’s all it takes 👇 Parallel(n_jobs=5)(delayed(heavy_square)(n) for n in numbers) In my test, a heavy mathematical loop dropped from 13s (sequential) to just 5s (parallel) — a massive boost! ⚡ 💡 Perfect for: CPU-heavy computations Data and model processing Simple, fast performance tuning If your Python code runs slow — Joblib might be the easiest upgrade you’ll ever make. 🧠 Checkout attached docs for example. #contact: navinkpr2000@gmail.com #Python #Joblib #ParallelProcessing #PythonPerformance #MachineLearning #DataScience #Optimization #DeveloperTips #PythonProgramming #CPUBound #CodeOptimization #SpeedUp #PythonDev #Multiprocessing #Threads #AI #ML #Coding #Productivity #crewxdev

🧠 “Variables forget. Files don’t. And today’s lesson was all about that power.” 💻 Day 64 of #100DaysOfCode — Python File I/O Unlocked 📂🐍 Today I completed the File I/O lecture from CS50’s Python course — and it was one of those topics that feels simple at first, but suddenly makes your programs more real and more powerful. Here’s what clicked today: 📄 Reading & Writing Files Understood how to read text files line-by-line, write new content, and append to existing data — turning Python scripts into tools that interact with real stored information. 🔐 Why with Matters Learned how the with keyword automatically handles opening and closing files, preventing corruption, memory issues, and unexpected behavior. A tiny keyword with massive reliability impact. 📚 Working with CSVs Explored structured data using: csv.reader → raw lists csv.DictReader → clean, readable key–value pairs This makes working with datasets far more intuitive and scalable. 🔎 Real-World Perspective Logs, user data, configs, analytics, exports — File I/O is what separates a toy script from actual software that remembers, stores, and interacts with the world outside RAM. Every concept today felt like adding permanence to my code — a shift from “running something” to “building something.” Step by step, leveling up. 🚀 #100DaysOfCode #Python #CS50 #FileIO #DataProcessing #LearningInPublic #BuildInPublic #SoftwareEngineering #CleanCode #BackendDevelopment #DeveloperLife #CodingJourney #TechSkills #ProblemSolving #ProgrammingFundamentals

Day 53 of Python Problem-Solving – “Missing Number” Challenge! Today’s problem was about finding the missing number from a list of n distinct integers within the range [0, n]. Only one number is missing — and the task is to identify it efficiently. ✅ Approach & Learning I solved this in two ways: 🔹 1. Optimized Approach (O(n) Time | O(1) Space) Used the mathematical formula: Sum of first n natural numbers=n(n+1)2\text{Sum of first n natural numbers} = \frac{n(n+1)}{2}Sum of first n natural numbers=2n(n+1)Simply subtracting the actual sum of the list from the expected sum gives the missing number. 🔹 2. Brute Force Approach Sort the list Compare index with value to find mismatch This helped reinforce the contrast between brute force and optimized logic. 🧠 Key Takeaways ✔ Mathematical formulas can eliminate loops & improve performance ✔ Always think about time and space efficiency ✔ Sometimes the smartest solution is also the simplest one 📍 Code Available On GitHub: 🔗 https://lnkd.in/g2HA9WKb #Day53 #100DaysOfCode #Python #CodingJourney #ProblemSolving #LearningDaily #DataStructures #Algorithms #LeetCode #KeepCoding

🚀 Binary Search Implementation (Data Structures And Algorithms) This Python code demonstrates the binary search algorithm. It leverages the sorted nature of the input list to efficiently locate the target element. The algorithm repeatedly divides the search interval in half, comparing the middle element with the target. The code returns the index of the target if found and -1 if the target is not present in the list. Binary search is a preferred method for searching large, sorted datasets due to its logarithmic time complexity, which significantly reduces the number of comparisons needed compared to linear search. #Algorithms #DataStructures #CodingInterview #ProblemSolving #professional #career #development

🔹 Day 1 of 30 – LeetCode Challenge: Preorder Traversal of Binary Tree Today, I practiced a fundamental Tree Traversal problem using Python. The task was to return the preorder traversal of a binary tree’s nodes (Root → Left → Right). 🧩 Problem Statement: Given the root of a binary tree, return the preorder traversal of its nodes’ values. Example: Input: root = [1, null, 2, 3] Output: [1, 2, 3] 💡 Concept: In preorder traversal, we visit: Root node Left subtree Right subtree This can be solved either recursively or iteratively using a stack. I implemented the recursive approach, which is straightforward and elegant. 🕒 Complexity: Time Complexity: O(n) Space Complexity: O(h), where h = height of the tree 🏆 Result: ✅ All test cases passed 🚀 Runtime: 0 ms (Beats 100%) 💾 Memory: 12.41 MB (Beats 62.16%) 💬 Learning: Understanding tree traversal is a must for mastering recursion and data structures. This problem helped me revise depth-first traversal concepts efficiently. #30DaysOfCode #LeetCode #Python #DataStructures #Algorithms #BinaryTree #Recursion #MTech #CodingChallenge #PreorderTraversal

🚀 Day 35/100 – #100DaysOfCode Topic: Arrays – Two Sum Problem (Google Favorite) 💡 Today I solved one of the most popular interview questions asked in top tech companies like Google, Amazon, and Microsoft — the Two Sum Problem 🧮 🧠 Problem: Given an array of integers and a target value, return the indices of the two numbers that add up to the target. 💻 Example: nums = [2, 7, 11, 15] target = 9 Output → [0, 1] # because nums[0] + nums[1] = 2 + 7 = 9 ⚙️ Approach: ✅ Use a HashMap (dictionary in Python) ✅ Store each element’s value and index ✅ Check if (target - current_value) exists in the map 💡 Time Complexity: O(n) 🧩 Space Complexity: O(n) 🧾 Python Solution: def twoSum(nums, target): hashmap = {} for i, num in enumerate(nums): diff = target - num if diff in hashmap: return [hashmap[diff], i] hashmap[num] = i ✨ Learning: This problem taught me how hashing can reduce nested loops and optimize a brute-force O(n²) problem to O(n). #100DaysOfCode #Day35 #CodingJourney #GoogleInterview #DSA #Python #ProblemSolving #Arrays

🚀 Collections Module: Deque for Efficient Queues (Python) The `collections` module provides specialized container data types, including `deque` (double-ended queue). `deque` is more efficient than using lists for implementing queues because it supports fast appends and pops from both ends. This makes it suitable for scenarios where you need to add and remove elements from both the front and the back of a queue. It avoids the O(n) time complexity associated with inserting or deleting elements at the beginning of a list. 🌟 Smart learning > Hard working 💡 Master tech faster — 10,000+ bite-sized concepts, 4,000+ in-depth articles, and 12,000+ practice questions await! 📱 Download now: https://lnkd.in/gefySfsc 💻 Explore more: https://techielearn.in #Python #PythonDev #DataScience #WebDev #professional #career #development

Did this long back but couldn't post it so, here it is. Demonstrating an AgenticAI Project built using Microsoft's AutoGen. 𝗔𝗻𝗮𝗹𝘆𝘀𝗲𝗿𝗚𝗣𝗧 - 𝘢 𝘤𝘶𝘴𝘵𝘰𝘮 𝘢𝘱𝘱𝘭𝘪𝘤𝘢𝘵𝘪𝘰𝘯 𝘵𝘩𝘢𝘵 𝘢𝘯𝘢𝘭𝘺𝘴𝘦𝘴 𝘵𝘩𝘦 𝘶𝘱𝘭𝘰𝘢𝘥𝘦𝘥 𝘥𝘢𝘵𝘢𝘴𝘦𝘵 𝘢𝘯𝘥 𝘢𝘯𝘴𝘸𝘦𝘳 𝘶𝘴𝘦𝘳'𝘴 𝘲𝘶𝘦𝘳𝘪𝘦𝘴. Project is developed using:  • Round Robin Team - to let the agents communicate with each other to achieve final outcome.  • Assistant Agent - to understand the ask, write python code, give final outcome.  • Code Executor Agent - to execute python code. #python #agenticAI #Autogen #Microsoft

💡 Week 5: Conditional Statements in Python If you’re learning Python, you can’t escape the IF 😉 Conditional statements are how your program makes decisions — like a data-driven “choose your own adventure” story. Here’s a simple example 👇 age = 20 if age >= 18: print("You’re an adult.") elif age > 13: print("You’re a teenager.") else: print("You’re a kid.") 📘 What’s happening here? ✔️ if → Checks the first condition ✔️ elif → Adds another condition if the first is False ✔️ else → Acts as the default (catch-all) Conditional statements let your code react dynamically — essential for logic in AI, data processing, and automation workflows. 🔥 Tip: Always keep indentation (spacing) consistent — Python uses it to define code blocks. #PythonTips #IfElse #LearnPython #CodingForBeginners #CodeNewbie

One of the highlights of my DSA study this week has been learning about Dijkstra’s Algorithm, a classic method for finding the shortest path in a weighted graph—a foundation for so many tech problems, from GPS navigation to network. What fascinates me most is the use of a priority queue (implemented via heapq if you are using python) inside Dijkstra’s algorithm. The heap makes it super efficient to always pick the next node with the smallest tentative distance, keeping the algorithm fast even as the graph grows. Without a heap, finding this node would be much slower, especially for larger graphs. With a min-heap, both adding new nodes and removing the node with the lowest distance take only O(log⁡ n) time—a crucial optimization. Learning how these data structures work in tandem is more than memorizing code—it's about seeing how the right structure unlocks real-world applications, from helping drivers get to their destination quicker, to optimizing data travel across the internet. Every new algorithm or data structure I practice adds another tool to my developer toolkit—one step closer to being interview readyeady! #DSA #DijkstraAlgorithm #Heaps #PriorityQueue #CodingInterview #SoftwareDevelopment #LearnInPublic