LeetCode #76: Minimum Window Substring Python Implementation

LeetCode #141 – Linked List Cycle | Python Implementation I implemented Floyd's Cycle Detection Algorithm using two pointers moving at different speeds. The slow pointer advances one node at a time while the fast pointer advances two nodes. If a cycle exists, the fast pointer will eventually lap the slow pointer and they will meet inside the cycle. If the fast pointer reaches None, the list has no cycle. This eliminates the need for extra space like a HashSet to track visited nodes. This pattern is essential in memory leak detection, distributed system deadlock identification, and graph cycle detection in network topology analysis. Key Takeaway: Floyd's algorithm is a classic space optimization — replacing O(n) HashSet storage with O(1) by leveraging pointer speed differential. The mathematical guarantee is that if a cycle exists, the fast pointer must eventually meet the slow pointer regardless of cycle length or entry point. Time: O(n) | Space: O(1) #LeetCode #DataStructures #Python #LinkedList #TwoPointers #FloydAlgorithm #CodingInterview #ProblemSolving #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

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

Today I implemented Serialize and Deserialize Binary Tree in Python. The goal of the problem is to convert a binary tree into a string so it can be stored or transferred, and later reconstruct the exact same tree from that string. I used a BFS (level order traversal) approach for this. Idea: • Traverse the tree level by level using a queue. • Store node values in a list. • For missing children, store a special marker (#) so the structure of the tree is preserved. • Join the list into a single string. For deserialization, the process is reversed: • Split the string back into values. • Rebuild the tree using a queue. • Attach left and right children in the same order they were stored. What I liked about this problem is that it shows how important it is to preserve structure, not just values. Without storing null nodes, reconstructing the same tree would be impossible. Time Complexity: O(N) Space Complexity: O(N) Problems like this are a good reminder that tree problems often combine traversal, data representation, and careful reconstruction logic. #DSA #BinaryTree #Python #Algorithms #CodingInterview

6 Python Libraries To Boost Your Workflow 1. Tenacity: Offers retry strategies for script resilience. 2. Polars: A fast DataFrame library for large datasets. 3. Shelve: Creates persistent, file-backed dictionaries. 4. Rich: Beautifies terminal output with tables and progress bars. 5. Pendulum: Simplifies datetime management with UTC standards. 6. Inline-Snapshot: Records and updates test values in code. Small tools like these often make a big difference—sometimes it's the unexpected library that solves your biggest headache. New to Python? Learn the basics (nl) 👉 https://lnkd.in/emBgb9gf #GeoICT #Python #GIS #SpatialData #OpenSource

Claude Add-In for Excel Apparently Claude for Excel is powerful because it uses python execution layer behind the scenes. Instead of forcing everything in a formula it translates everything into a python script. This gives it alot of flexibility to handle messier datasets than formulas and is definately more reliable for complex logic. Its like having a python engine for your spreadsheet, since its release about a month ago I was hooked and have not made another excel formula since. Give it a try its extremely powerful #Anthropic #Claude #Excel #AI #Automation

🚀 Generators: Memory-Efficient Iteration (Python) Generators are a special type of function that allows you to create iterators in a memory-efficient way. Instead of returning a list of values, generators yield values one at a time using the `yield` keyword. This is particularly useful when dealing with large datasets, as it avoids loading the entire dataset into memory. Generators can be implemented using either generator functions (using `yield`) or generator expressions (similar to list comprehensions but with parentheses). Generators are essential for optimizing memory usage and improving performance in data processing applications. #Python #PythonDev #DataScience #WebDev #professional #career #development

Before working on the AI employee, spent time learning the core: 𝘀𝘂𝗯𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻. Understanding how Python can execute external commands, capture stdout and stderr, control execution with timeouts, and work with clean string outputs instead of raw bytes. Also looked into managing return codes and controlling how external tools run from inside a Python program. Small piece, but it’s the bridge between Python and the outside world. #Python #Subprocess #Learning

Built Logistic Regression from Scratch using NumPy! Implemented the sigmoid function, trained the model using gradient descent, and visualized the logistic curve for binary classification. This project helped me understand how logistic regression actually works under the hood without using ML libraries. 🔗 GitHub: https://lnkd.in/gnZ-g4aQ #MachineLearning #Python #NumPy #LogisticRegression #DataScience #LearningInPublic

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