Grouping Anagrams with Python

📌 Problem: Increasing Triplet Subsequence 💡 Approach: Traverse the array while maintaining two variables: first and second, representing the smallest and second smallest values found so far. If the current number is smaller than first, update first Else if it’s smaller than second, update second If a number is greater than both, we’ve found an increasing triplet This ensures an optimal single-pass solution. ⚙️ Key Insight: Track only two values instead of checking all triplets Greedy + optimization approach reduces complexity significantly ⏱️ Time Complexity: O(n) 📦 Space Complexity: O(1) 📚 What I learned: Greedy strategy for subsequence problems Reducing brute-force O(n³) to optimal linear solution #LeetCode #DSA #Algorithms #Coding #ProblemSolving #Python #Greedy #InterviewPreparation #CodingJourney

🚀 LeetCode Grind: Find First and Last Position of Element in Sorted Array Just solved a classic searching problem! 💻 Problem: Given a sorted array, find the starting and ending position of a given target value. The Challenge: Achieving $O(\log n)$ runtime complexity. Key Takeaway: While a linear scan works, leveraging Binary Search twice (once for the left boundary and once for the right) is the key to meeting the performance constraints. It’s a great reminder of how powerful binary search is for optimizing search operations on sorted data. Checking off another one as I continue to sharpen my problem-solving skills! 🛠️ #LeetCode #CodingChallenge #Python #Algorithms #DataStructures #ProblemSolving #TechJourney #BinarySearch

My thesis was stuck. A matrix had the wrong shape and I had no idea why. I could have printed the entire dataset to find the error. I did not. Instead I used Python's debugger. One breakpoint. One look at the intermediate state. Wrong dimensions. Found in seconds. That moment changed how I work. Not because debugging saved my thesis. But because it taught me something I still use every day: You do not need to see all the data to understand what is wrong. You just need to see the right data at the right moment. Since then, every time a pipeline breaks or a model behaves unexpectedly, I reach for the debugger first. Not print statements. Not guesswork. A breakpoint. An intermediate result. A clear answer. Debugging is not a last resort. It is the fastest way to understand what your code is actually doing. What is your go-to strategy when something breaks unexpectedly? #Python #Debugging #DataScience #MachineLearning #FreelanceDataScientist

I was cleaning a dataset — filtering rows, transforming values, the usual. My 5-line for loop worked fine. But I wanted to be "Pythonic." So I compressed it into a one-liner. Then I added another layer. The next morning I stared at it for two full minutes trying to decode my own logic. If I couldn't read it, my future teammates had no chance. This carousel breaks down: → The mental model that makes list comprehensions click instantly → The reading order most beginners get backwards → The exact rule for when to stop using them and write a real loop What's the longest you've stared at your own code before realizing you had no idea what it does? #Python #DataAnalytics #DataAnalyst #PythonTips #LearnInPublic #AHAMoments #DataAnalystJourney

🚀 Solved a great problem today: “Consecutive 1’s Not Allowed” At first glance, it looked like a simple binary string problem… but it quickly turned into a lesson in pattern recognition and dynamic thinking. 📌 What the problem was about: Count all binary strings of length n such that no two 1’s are consecutive. 💡 What I learned: Instead of brute forcing all combinations (which would be exponential), the key was to observe a pattern: If a string ends with 0 → we can add 0 or 1 If it ends with 1 → we can only add 0 This leads to a recurrence: 👉 dp[n] = dp[n-1] + dp[n-2] Which is basically the Fibonacci pattern in disguise. 🧠 Big takeaway: Many problems are not about coding harder… they’re about seeing the hidden pattern behind the problem. This was a reminder that: Brute force is rarely the answer Thinking in terms of state transitions is powerful Optimization often comes from observation, not syntax 📷 Sharing my solution screenshot below 👇 #DataStructures #DynamicProgramming #ProblemSolving #Python #LearningInPublic #DataAnalyticsJourney

🚀 Day 36 – LeetCode Journey Today’s problem: String to Integer (atoi) ✔️ Handled leading spaces and signs (+/-) ✔️ Processed numeric characters step by step ✔️ Managed overflow conditions within 32-bit integer range 💡 Key Insight: Carefully handling edge cases (like spaces, signs, and overflow) is just as important as the core logic. Small conditions can make a big difference in correctness. This problem strengthened my understanding of string parsing, edge cases, and boundary conditions. Learning to write robust and reliable code every day 💪🔥 #LeetCode #Day36 #Strings #EdgeCases #Python #ProblemSolving #CodingJourney #100DaysOfCode

🚀 Cracked “Minimum Distance to Target” with a clean and efficient approach! My thought process was simple and structured: ➡️ First, I iterated through the array to identify all indices where the target element appears. ➡️ For each match, I calculated the absolute distance from the given start index. ➡️ Stored these distances and finally returned the minimum among them. 💡 This approach keeps things intuitive and avoids overcomplication—focus on correctness first, then optimize if needed. ✅ Time Complexity: O(n) ✅ Space Complexity: O(n) (can be optimized further by tracking min on the go) Sometimes the simplest logic gives the best results 🔥 #LeetCode #ProblemSolving #DataStructures #Algorithms #Python #CodingJourney #TechGrowth #Consistency #LearnInPublic #WomenInTech #100DaysOfCode #CodingLife #SoftwareEngineering #PlacementPrep

Are Matplotlib abstractions helping—or getting in the way? Let’s ask Cameron Riddell! In this week’s Cameron’s Corner, Cameron looks at the layers of abstraction in Matplotlib and how they shape the way we write plotting code. While higher-level interfaces can make things faster to write, they can also obscure what’s actually happening underneath. Learn: ✅ How Matplotlib’s abstraction layers are structured ✅ When higher-level APIs simplify your workflow ✅ Why dropping down a level can sometimes give you more control Read here: https://lnkd.in/gVJKvErq Do you prefer high-level plotting tools or working closer to Matplotlib’s core? Let us know how you approach it 👇 #Python #Matplotlib #DataViz #CameronsCorner

If anyone is interested in developing their skills in NumPy, here are a few quick tips based on my experience that might help: 💬 Start with the basics — understand arrays, shapes, and indexing thoroughly. A strong foundation makes everything easier. 💬 Practice vectorization — replacing loops with NumPy operations can significantly improve performance. 💬 Get comfortable with broadcasting — it may feel tricky at first, but it’s a powerful feature once you grasp it. 💬 Work on real datasets — applying NumPy to actual problems helps solidify concepts much faster than theory alone. 💬 Explore documentation regularly — NumPy’s official docs are incredibly useful and often overlooked. 💬 Combine with other libraries — using NumPy alongside pandas or matplotlib can expand your practical understanding. Consistency is key. Even small daily practice can lead to big improvements over time 🚀 #Python #NumPy #DataScience #MachineLearning #CodingTips

Weekly Challenge 14: Nearest Neighbor For Week 14 of my Python challenge, I decided to practice some real-life optimization by combining my Master's degree homework with my weekly coding goal. I implemented the Nearest Neighbor Heuristic to solve the Traveling Salesperson Problem (TSP). This is a classic "Greedy" algorithm. The logic is simple but powerful: 1.- Start at a location. 2.- Look around and go to the closest unvisited point. 3.- Repeat until all points are visited. 4.- Return home. It might not always find the perfect global minimum, but it finds an incredibly efficient route in $O(n^2)$ time, making it practical for real-world logistics and routing problems. I used NumPy for the vector math and Matplotlib to visualize the exact path the algorithm took. Full source code on my GitHub: https://lnkd.in/g6RqgJDp #Python #OperationsResearch #Optimization #Algorithms #DataScience #TSP #GreedyAlgorithms #CodingChallenge #MastersDegree