Constructing Binary Tree from Preorder and Inorder Traversal

✅ Day 7 of 100 Days LeetCode Challenge Problem: 🔹 #199 – Binary Tree Right Side View 🔗 https://lnkd.in/ghC43a-T Concepts Used: 🔹 Binary Tree 🔹 Breadth-First Search (BFS) 🔹 Level Order Traversal 🔹 Queue (Deque) Approach Summary: 🔹 Used level-order traversal to process the tree level by level. 🔹 For each level, tracked the number of nodes present. 🔹 Captured the value of the last node at each level (rightmost). 🔹 Used a queue to maintain traversal order efficiently. Key Insight: 🔹 The right side view consists of the last node visible at every level. 🔹 BFS is ideal when problems require level-wise processing. 🔹 Identifying patterns in traversal order simplifies tree problems. #LeetCode #DataStructures #Algorithms #SoftwareEngineering #SoftwareDeveloper #ProblemSolving #Programming #ComputerScience #TechCareers #100DaysOfCode #DailyCoding #Consistency #LearningInPublic #Python #TechCommunity

LeetCode Progress | 217. Contains Duplicate (Python) Today I solved “Contains Duplicate” on LeetCode. Problem: Given an integer array nums, return True if any value appears at least twice, otherwise return False. My approach: I used a dictionary to track numbers I’ve already seen. While iterating through the array: -- If the number is already in the dictionary, I return True -- Otherwise, I store it and continue What I learned: -- Using a dictionary makes duplicate detection efficient with average O(1) lookup time -- This gives an overall time complexity of O(n), which is optimal for this problem -- Choosing the right data structure can reduce complexity and make code cleaner #leetcode #python #dsa #datastructures #algorithms #coding #programming #problemSolving #softwareengineering #computerscience #interviewprep #codinginterview #100daysofcode #pythonprogramming

LeetCode Progress | 219. Contains Duplicate II (Python) Today I solved “Contains Duplicate II” on LeetCode. Problem: Given an integer array nums and an integer k, return True if there exist two different indices i and j such that: -- nums[i] == nums[j] -- abs(i - j) <= k My approach: I used a dictionary to store the most recent index of each number. While iterating: -- If the number already exists in the dictionary and the index difference is <= k, return True -- Otherwise, update the number’s index in the dictionary What I learned: -- A dictionary helps track the latest occurrence efficiently (average O(1) lookup) -- Updating the stored index ensures we always compare with the closest previous occurrence -- This gives an optimal solution with O(n) time complexity #leetcode #python #dsa #datastructures #algorithms #coding #programming #problemSolving #softwareengineering #computerscience #interviewprep #codinginterview #100daysofcode #pythonprogramming

One underrated feature of NumPy: vectorization 🚀 One of NumPy’s most powerful features is vectorization—the ability to perform operations on entire arrays without writing explicit loops. This leads to cleaner code and significant performance gains. Example: Suppose you want to compute compound growth for 5 years at 8% . here is the python code---- import numpy as np years = np.arange(1, 6) growth = (1.08) ** years print(growth) [1.08 1.1664 1.259712 1.36048896 1.46932808] With a single expression, NumPy applies the operation across all elements efficiently—no for loop needed. Under the hood, this leverages optimized C code, making NumPy both expressive and fast. If you work with data, simulations, or numerical modeling, mastering vectorization is a game changer. #NumPy #Python #DataScience #ScientificComputing #Programming

Developer Velocity vs. System Performance As engineers, we often debate: Python or Rust? I decided to stop speculating and start testing. I wrote two identical multithreaded prime-finding scripts: One in Python, utilizing multiprocessing to scale across cores. One in Rust, using the Rayon crate for effortless parallelism. My Takeaway: Python is incredible for prototyping and getting an MVP out the door. Its syntax is nearly English-like. However, Rust’s memory safety and "fearless concurrency" make it the winner for high-load, parallel processing tasks. It isn't about which language is "better". It's about which one fits your project's bottlenecks. See the side-by-side performance in my latest video! #RustLang #PythonProgramming #SoftwareEngineering #PerformanceBenchmarking #Coding #Multithreading #ProgrammingTips #TechComparison #RustVsPython

Tuples are one of those Python concepts everyone learns early — but many don’t fully use them. They are: • ordered • immutable • fast and memory-efficient You’ll often see tuples used for: - function returns - coordinates (x, y) - configuration values - data that should not change When you understand what tuples are and when to use them, your code becomes safer and more intentional. This infographic covers the essentials you’ll revisit again and again. Save it for a quick refresh later. #Python #LearnPython #PythonBasics #Programming #Coding #SoftwareEngineering #PythonDevelopers

🚀 Day-32 of #100DaysOfCode 🐍 Python Pattern Programming Challenge Today I worked on generating an Alphabet Triangle Pattern using ASCII values and nested loops. 🔹 Problem: Print a right-angled triangle where each row starts from A and prints characters sequentially. 🔹 Concepts Practiced: ✔ Nested for loops ✔ ASCII value manipulation using chr() ✔ Pattern visualization ✔ Loop resetting logic 🔹 Approach: Use ASCII value 65 to represent 'A' Convert ASCII to characters using chr() Reset the ASCII value at the start of each row Increase characters row-wise Pattern-based challenges help strengthen loop control, logical thinking, and character handling in Python 💡 #Python #PatternProgramming #CorePython #AlphabetPattern #100DaysOfCode #Day32 #LearnPython #CodingPractice #PythonDeveloper

🚀 Day 19: Python Range Deep Dive Today is all about the power of the range() function! It’s not just for loops; it’s a memory-efficient sequence generator. Key takeaways: ✅ Custom Steps: Use range(start, stop, step) to skip numbers ✅ Smart Membership: Use in to instantly check if a number fits the sequence logic ✅ Efficient Length: len() tells you the count without expanding the list x = range(3, 10, 2) print(list(x)) # Output: [3, 5, 7, 9] r = range(0, 10, 2) print(6 in r) # Output: True print(7 in r) # Output: False print(len(r)) # Output: 5 Small steps every day lead to big results in 2026! 💻✨ Stay tuned for more!!! #Python #PythonJourney #Coding #LearnToCode #Programming #PythonCode #DataAnalyst #DataAnalytics #RangeFunction

This solution solves LeetCode 3013: Divide an Array Into Subarrays With Minimum Cost II by applying a sliding window technique with balanced data structures to efficiently track the smallest possible starting elements. Since the first subarray always begins at index 0, its cost is fixed, and the problem reduces to selecting the remaining k−1 starting indices within the given distance constraint such that their values sum to the minimum. By continuously maintaining the smallest k−1 elements as the window moves, the approach achieves optimal performance and avoids time-limit issues common with brute-force methods. #LeetCode #HardProblem #Python #SlidingWindow #DataStructures #Algorithms #CompetitiveProgramming #CodingInterview #Optimization

🎯 Mastering Default Arguments in Python — Write Cleaner, More Flexible Functions! Just explored how default parameters in Python can make functions more intuitive and reduce repetitive code. Check out these practical examples: 🔹 power() – Calculate squares by default, or any exponent when specified. 🔹 greet() – Personalize greetings while keeping a friendly default. 🔹 calculate_bill() – Apply default tax and discount rates, but customize when needed. Default arguments help create functions that are both user-friendly and flexible, promoting cleaner code and better maintainability. Whether you’re building utilities, APIs, or business logic, this feature is a game-changer! 💡 #Python #Programming #Coding #SoftwareDevelopment #PythonTips #LearnToCode #Functions #Developer #Tech #CleanCode #CodingLife #Day31