Exploring Dijkstra's Algorithm in Python for Graph Theory

🔹 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

🚀 New paper from the BIONETS Lab Very happy to share that our NApy package for fast and memory-efficient statistical analysis in Python is out in GigaScience Press. Great work led by Fabian Woller and Lis Arend! ⚡️ Key features: - Runs on Numba and C++ backends with OpenMP parallelization for high performance - Supports mixed-type data and efficiently handles missing values - Delivers huge gains in runtime and memory use compared to existing Python tools 📎 Links: 📄 Paper: https://lnkd.in/dmnXZNef 💻 Code: https://lnkd.in/dnwanRKs Markus List, Christian Fuchsberger, FAU Erlangen-Nürnberg, Technische Universität München, Eurac Research, Deutsche Forschungsgemeinschaft (DFG) - German Research Foundation

🌟 Understanding Algorithm Analysis — Mind Map for Beginners Data Structures & Algorithms (DSA) सीखते समय सबसे ज़्यादा confusion Time Complexity और Big-O पर होती है। To make it super simple, I created a Mind Map that covers: 🔹 Time Complexity 🔹 Space Complexity 🔹 Big-O Rules 🔹 Common Complexities (O(1), O(n), O(log n), O(n²)) 🔹 How to analyze an algorithm 🔹 Dry-run + operation counting method Mind Maps help in understanding DSA visually and quickly. #MindMap #DSA #Algorithms #BigONotation #TimeComplexity #SpaceComplexity #TechLearning #CodingJourney #Python #ComputerScience

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

Why I Use TypedDict Instead of a Regular Dictionary in LangGraph (and Python) When working with LangGraph, we often pass data between nodes — inputs, outputs, and intermediate states. At first glance, a plain dict seems fine: flexible, simple, and quick to write. But that flexibility comes at a cost — lack of structure. Here’s what happens when you use a normal dict: You might mistype a key name ("usre_id" instead of "user_id") — no warnings. You might forget which fields a node expects — no hints. You only find out about issues at runtime, not during development. That’s where TypedDict shines. 💡 Now your editor (and LangGraph’s type system) can: 1. Warn you about missing or extra fields 2. Autocomplete field names 3. Make refactoring much safer 4. Keep large graphs consistent as they grow In short — TypedDict makes your LangGraph pipelines self-documenting and type-safe, reducing bugs before they even happen. If your LangGraph is getting complex, switching to TypedDicts is one of those small changes that pay off big in the long run. #LangGraph #LangChain #Python #AI #SoftwareEngineering #AIEngineering #GenAI #CleanCode #DeveloperTips #LLM

📘 Python – NumPy Day 2: Going Deeper 🔍 Today I explored: NumPy Array vs Python List | Advanced Indexing | Fancy & Boolean Indexing | Broadcasting | Mathematical Formulas | Handling Missing Values | Plotting Graphs 🌀 NumPy Array vs Python List NumPy arrays are faster, memory-efficient, and support vectorized operations. Python lists are slower for numerical tasks and don’t support direct mathematical operations. 🌀 Advanced, Fancy & Boolean Indexing Powerful indexing helps in easy data selection, filtering, and preprocessing. 🌀 Broadcasting Allows operations on arrays of different shapes without loops. It simplifies and speeds up mathematical computation. 🌀 Mathematical Formulas NumPy applies algebra, trigonometry, exponent and other functions directly on entire arrays. 🌀 Handling Missing Values NumPy identifies, replaces, and processes NaN values efficiently — useful in data cleaning. 🌀 Plotting Graphs With NumPy + Matplotlib, data visualization becomes simple and insightful. ⚡ Key Takeaways ✔ Faster than Python lists ✔ Easy and powerful indexing ✔ No loops needed due to broadcasting ✔ Helpful for Analytics, ML, and scientific computing 📌 Check my full notebook on GitHub: 👉 https://lnkd.in/dQf67y93 #Python #NumPy #DataScience #MachineLearning #MdArifRaza #CodingJourney #CampusX #statistics #Analytics #AI

🌳 Experiment 11 – Decision Tree Analysis In this practical, I implemented the Decision Tree algorithm to classify data and evaluate model performance. Learned how tree-based models make predictions through dataset preparation, training, and testing. 💻 GitHub: [https://lnkd.in/dFff8cPb] 🎓 Guided by: Ashish Sawant #MachineLearning #DecisionTree #DataScience #Python #AI #Coding #CSE #PRMCEAM