Mnemosyne v0.2.0: Persistent Queue and Immutable Data Structures

🚀 LeetCode Daily Progress | Problem 9 | Symmetric Tree (Binary Tree) 🌳 Today I solved “Symmetric Tree” and it was a great reminder that trees are all about recursion + structure matching. ✅ Problem: Check if a binary tree is a mirror of itself (symmetric around its center) 🔍 Key Idea: Two trees are mirrors if: their root values are equal left subtree of one == right subtree of the other right subtree of one == left subtree of the other 🧠 Approach Used: 📌 Recursive DFS (Mirror Check) Time: O(n) Space: O(h) (height of tree due to recursion stack) 💡 What I learned: Recursion becomes easy when you define a strong base case Tree problems are mostly about comparing relationships, not just values Mirror logic is a powerful pattern for future problems too 📌 Next target: More binary tree patterns like Level Order, Diameter, Balanced Tree 💪 #LeetCode #DSA #BinaryTree #SymmetricTree #Python #CodingJourney #ProblemSolving #SoftwareEngineering #Consistency

Class statistics are the backbone of modern geospatial catalogs. Platforms like the Planetary Computer rely on pre-calculated pixel counts to populate STAC metadata, allowing users to filter imagery based on content (e.g., "find scenes with <10% clouds" or ">80% water"). I’ve just released ClassCounter, a Python library designed to make generating these statistics efficient and standardized across your entire pipeline. Instead of writing custom loops for different data types, ClassCounter exposes a single API: count_classes(). It accepts NumPy arrays, PyTorch tensors, or direct GeoTIFF paths, automatically routing data to the fastest backend. The performance gains are significant, using parallel Numba execution and native CUDA operations, it achieves up to an 100x speedup over standard NumPy methods. It can even write the resulting percentages directly back to the GeoTIFF metadata, ready to be indexed into your STAC catalog. Check it out at the link below! https://lnkd.in/gebr_9rP #RemoteSensing #STAC #Python #OpenSource #GeoSpatial

🚀 𝟲𝟬 𝗗𝗮𝘆𝘀 𝗼𝗳 𝗖𝗼𝗱𝗶𝗻𝗴 | 𝗗𝗦𝗔 𝘅 𝗥𝗲𝗮𝗹 𝗪𝗼𝗿𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 #Day57 | 𝗥𝗶𝗱𝗲-𝗦𝗵𝗮𝗿𝗶𝗻𝗴 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗘𝗻𝗴𝗶𝗻𝗲 Built a Ride-Sharing Backend Engine using Greedy algorithms, Heaps, and Graphs to assign the nearest available driver efficiently. Focused on: • Priority queues (min-heaps) • Greedy decision making • Graph-based route modeling • Real-world ride assignment logic 📌 Code and documentation: https://lnkd.in/gxzGJ4nB Open to feedback and improvements. #DSA #Heaps #GreedyAlgorithms #Graphs #Python #60DaysOfCoding #LearningInPublic #SoftwareEngineering

LeetCode Problem 160: "Intersection of two Linked Lists": Given the heads of two singly linked-lists headA and headB, return the node at which the two lists intersect. If the two linked lists have no intersection at all, return null. Approach: Use two pointers technique, maintain one pointer at list A and another at list B, at each iteration check whether pointers point to the same node, if yes then return that node. If pointer A reaches end update it to point to the head of list B and if pointer B reaches end update it to point to the head of list A. In this way each of the pointers traverses both of the lists. Time complexity: O(m+n) Space Complexity: O(1) #LeetCode #LinkedLists #Python #CompetitiveProgramming #ProblemSolving #Algorithms #DataStructures

#Projects 🚀 Excited to share my latest project: Thread Shed 🧵✨ https://lnkd.in/gssBsFwy Working with complex string data in Python has always fascinated me. This project was born out of the challenge of handling messy, layered text structures and turning them into something clean, efficient, and insightful. 🔹 Why it matters: Speeds up data parsing and transformation Handles intricate string manipulations with clarity Demonstrates how concurrency can simplify real-world text-heavy workflows 👉 I’d love to hear how others approach complex string challenges in Python. Do you lean on regex, threading, or something else entirely? #Python #Threading #StringProcessing #DataEngineering #Innovation Would you like me to make this post more technical (with code snippets and performance metrics) or more story-driven (focusing on your personal journey and motivation)?

🌟 New Blog Just Published! 🌟 📌 12 Must-Try Python Libraries of 2026 to Boost AI & Data Speed 🚀 📖 The 2025 wave of Python libraries flooded GitHub with over 12,000 new releases. Developers felt the pressure to sort through tools that promised faster pipelines and smarter AI. What if you could...... 🔗 Read more: https://lnkd.in/d7N5-hFv 🚀✨ #python-ai-librarie #data-pipeline-spee #2026-python-libs

Ever wonder how to minimize costs when equalizing tower heights? Here is a breakdown of the optimal approach for the Equalize the Towers problem. The Goal: Make all towers the same height by adding or removing blocks. Each tower has a unique cost per unit of change, and we need to find the specific height that results in the lowest total expenditure. The Approach: The total cost function here is convex, meaning if you were to graph the cost against all possible target heights, it would form a clear U-shape. This mathematical property allows us to find the minimum without checking every single height. Ternary Search Strategy: Instead of a linear search, we use Ternary Search. We divide our range of possible heights into three segments. By comparing the costs at two internal midpoints, we can determine which third of the range the minimum cost cannot be in and discard it. Efficiency: While a brute-force search would be slow, Ternary Search cuts the search space down exponentially. This results in a time complexity of O(N * log(MaxHeight)), making it highly efficient for large datasets. Check out the clean Python implementation here: https://htmlify.me/r/h0ks #Algorithms #DataStructures #Python #Coding #GeeksforGeeks #Optimization

✅ LeetCode #297 – Serialize and Deserialize Binary Tree (Hard) Solved this classic binary tree problem using DFS (preorder traversal). 🔹 Implemented serialization by converting the tree into a string using recursion 🔹 Used a sentinel ("N") to represent null nodes 🔹 Deserialized the string back into the original tree structure with index-based DFS 🔹 Ensured correctness, readability, and optimal performance 💡 Key learnings: Importance of traversal order in tree reconstruction How recursion and state management work together Handling edge cases like null nodes effectively This problem strengthened my understanding of tree traversal, recursion, and system design concepts. #LeetCode #DataStructures #BinaryTree #Python #ProblemSolving #DSA #CodingJourney

🚀 Day-54 of #100DaysOfCode 📊 NumPy Practice – Filtering Even Numbers Today I practiced generating random arrays and filtering values using NumPy. 🔹 Concepts Practiced: ✔ np.random.randint() ✔ Boolean indexing ✔ Modulo operation ✔ Vectorized filtering 🔹 Key Learning: NumPy allows powerful filtering operations without using loops, making code cleaner and computationally efficient. Step by step moving deeper into NumPy & Data Analysis fundamentals 💡🔥 #Python #NumPy #DataScience #ArrayFiltering #100DaysOfCode #LearnPython #CodingPractice #PythonDeveloper