Rebuild Binary Tree from Preorder & Inorder Traversal in Python

LeetCode #230 – Kth Smallest Element in a BST | Python Implementation I implemented an iterative inorder traversal using a stack to find the kth smallest element without building the entire sorted list. Core Insight: Iterative inorder traversal with early termination avoids O(n) space for storing all values. The stack simulates recursion while the counter enables stopping exactly at k. Time: O(h + k) where h = tree height | Space: O(h) for stack #LeetCode #DataStructures #Python #BinarySearchTree #InorderTraversal #Stack #CodingInterview #SoftwareEngineering

When working with different data types, Python requires explicit conversion. Here, numbers and boolean values are converted into strings before combining them. This ensures that all parts are of the same type during concatenation. Answer: A) 52False #Python #TypeCasting #MathModule #PythonBasics #LearningInPublic #ProblemSolving #CodingPractice

Poll Insight: Which data type does not allow duplicate values? The correct answer is Set ✅ A Set stores only unique elements, meaning duplicate values are automatically removed. That’s why sets are useful when you want to keep only distinct values in Python. 👉 Example use cases include removing duplicates from a list or storing unique items. #Python #LearnPython #CodingQuiz #ProgrammingBasics

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

Day 20/100 – #100DaysOfCode 🚀 Solved LeetCode #448 – Find All Numbers Disappeared in an Array (Python). Today I worked on an array problem to find all the numbers in the range [1, n] that are missing from the given array. Approach: 1) Convert the array into a set for quick lookup. 2) Traverse numbers from 1 to n. 3) Check if each number exists in the set. 4) If not present, add it to the result list. 5) Return the final list of missing numbers. Time Complexity: O(n) Space Complexity: O(n) Learning how sets help in fast lookup and simplify problems 💪 #LeetCode #Python #DSA #Arrays #HashSet #ProblemSolving #100DaysOfCode

𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐢𝐧𝐠 𝐭𝐡𝐞 𝐓𝐫𝐮𝐞 𝐒𝐢𝐳𝐞 𝐨𝐟 𝐀𝐟𝐫𝐢𝐜𝐚 - by Jeff Desjardins published by the Visual Capitalist: https://lnkd.in/d5K8qn3f ----- Learn geospatial in Python: https://lnkd.in/dBTUqctW

This compelling story highlights just how vast Africa is compared to other continents worldwide, showcasing the incredible power of GIS visualization.

A quick way to understand the shape of a dataset in Python: df.shape It returns the number of rows and columns in the dataset. This simple check is useful when loading new data, merging datasets, or filtering rows. It helps confirm that your operations are doing what you expect. Small checks like this help avoid many silent mistakes during analysis. #Python #DataAnalytics #MachineLearning #DataScience

I wrote a little post on the basic understanding of data serialization and deserialization using Python examples. Check it out! [Building Again]: https://lnkd.in/gvXpRZNx

Day 75 Back to revisiting tree fundamentals. #Day75 🧩 100. Same Tree Solved it again today. What made it easier this time: • Convert the tree structure into string representation • Compare the left and right serialized strings • Python makes this approach very straightforward This problem is simple, but it reinforces an important idea: Sometimes a representation trick can simplify the comparison logic. Small revisions like this keep the patterns fresh. #LeetCode #DSA #Python #BinaryTree #Recursion #LearningInPublic #Consistency