Floyd's Cycle Detection Algorithm in Python

LeetCode #572 – Subtree of Another Tree | Python Implementation I implemented a recursive DFS approach that checks every node in the main tree as a potential subtree root. Core Insight: Subtree verification is a nested recursion problem — outer recursion finds candidate positions, inner recursion validates exact matches. Reusing the same-tree helper keeps logic clean and modular. Time: O(m × n) worst case where m, n are tree sizes | Space: O(h) recursion depth #LeetCode #DataStructures #Python #BinaryTree #Recursion #DFS #CodingInterview #SoftwareEngineering

LeetCode #33 – Search in Rotated Sorted Array | Python Implementation I implemented a modified binary search to handle rotated sorted arrays. At each step, we determine which half of the array is properly sorted by comparing nums[l] with nums[mid]. Once the sorted half is identified, we check if the target falls within that range — if yes, we search that half; otherwise, we search the other half. This preserves the O(log n) complexity despite the rotation. This pattern is crucial in distributed systems for searching circularly-buffered logs, time-series databases with wraparound indices, and cache eviction policies with rotational priority queues. Key Takeaway: The critical insight is determining which side is sorted at each iteration using nums[l] <= nums[mid]. Once identified, standard binary search range logic applies. This demonstrates how binary search can be adapted to non-standard sorted structures while maintaining logarithmic complexity. Time: O(log n) | Space: O(1) #LeetCode #DataStructures #Python #BinarySearch #Arrays #CodingInterview #ProblemSolving #SoftwareEngineering

Day 24/100 – #100DaysOfCode 🚀 Solved LeetCode #747 – Largest Number At Least Twice of Others (Dominant Index) (Python). Today I practiced array traversal and comparison logic to find the dominant index in the array. Approach: 1) Find the largest element in the array and its index. 2) Traverse through the array. 3) For every other element, check if the largest is at least twice of it. 4) If any element violates this condition, return -1. 5) If all conditions are satisfied, return the index of the largest element. Time Complexity: O(n) Space Complexity: O(1) Learning how simple comparisons can solve array problems efficiently 💪 #LeetCode #Python #DSA #Arrays #ProblemSolving #100DaysOfCode

Conducted variable type verification in Python to ensure correct data classification (int, float, string, categorical). Strong analysis begins with proper data validation and structure. #PythonProgramming #DataScience #DataCleaning #AnalyticsSkills

Numba Optimizes Python Hilbert Curve Encoding to 1.8 ns Per Point 📌 Python’s Hilbert curve encoding just got a massive speed boost - HilbertSFC now processes points in 1.8 ns per point using Numba’s JIT compilation, slashing time by 3–4 orders of magnitude. This Python-only solution rivals Rust’s fastest implementations and scales with multi-threading, making it ideal for spatial indexing, GIS, and ML workflows - all without external dependencies. 🔗 Read more: https://lnkd.in/dpQBRpVb #Hilbertsfc #Numba #Python #Encoding #Performance

LeetCode #105 – Construct Binary Tree from Preorder and Inorder Traversal | Python Implementation I implemented a recursive divide-and-conquer approach that exploits traversal properties to rebuild the tree. Core Insight: Preorder gives root order, inorder gives left/right boundaries. Their intersection uniquely determines tree structure. Each recursive call isolates the correct subsequences for subtree reconstruction. Time: O(n²) due to slicing and index lookup | Space: O(n) recursion depth + slices #LeetCode #DataStructures #Python #BinaryTree #DivideAndConquer #TreeReconstruction #CodingInterview #SoftwareEngineering

Learn in Public — Day 11 Today I explored multiple ways to compute the Greatest Common Divisor (GCD) in Python. Implemented several approaches: • Brute force approach using iteration • Recursive subtraction-based GCD • Optimized recursive version • Euclidean algorithm using modulo • Python's built-in math.gcd() function Key takeaway: The Euclidean Algorithm is significantly more efficient than the naive approach because it reduces the problem size quickly using modulo operations. This exercise helped me understand how the same problem can be solved with different algorithmic strategies — each with different time complexities and performance trade-offs. Consistently learning and improving every day. #LearnInPublic #Python #Algorithms #DataStructures #CodingJourney #SoftwareEngineering #ProblemSolving

#story BREAKING: China has open-sourced a massive Python framework for building AI agents. It’s called AgentScope, a python framework built around Agent-Oriented Programming that lets you build AI agents visually with MCP tools, memory, rag, and reasoning capabilities. 100% Open Source. https://lnkd.in/gUw9d_ea

Day 13/100 – #100DaysOfCode 🚀 Solved LeetCode #219 – Contains Duplicate II (Python). Today I practiced using a HashMap to efficiently check whether two equal elements exist within a given distance k in an array. Approach: 1) Create a hashmap to store numbers and their latest index. 2) Traverse the array using index i. 3) If the current number already exists in the hashmap, check the index difference. 4) If the difference between indices is ≤ k, return True. 5) Update the hashmap with the current index. 6) If no such pair exists, return False. Time Complexity: O(n) Space Complexity: O(n) Learning how hashmaps help optimize search operations in arrays 💪 #LeetCode #Python #DSA #HashMap #Arrays #ProblemSolving #100DaysOfCode

Start strong: XGBoost 3.2.0 delivers major categorical re-coder improvements and ARM CUDA support, scaling predictive modeling efficiently. Release: https://lnkd.in/gWiAbMEc In ML tasks, these expand hardware compatibility. Noticing XGBoost 3.2.0 changes? Views? #XGBoost #MachineLearning #Python #DataScience #AIProgress