🚀 Day 34 – LeetCode Journey Today’s problem: Isomorphic Strings ✔️ Solved using a concise Python approach with sets ✔️ Compared unique character patterns efficiently ✔️ Achieved clean and optimized code 💡 Key Insight: By comparing the lengths of set(s), set(t), and set(zip(s, t)), we can quickly verify if both strings follow the same mapping pattern — ensuring a one-to-one relationship. Sometimes, the simplest solutions are the most powerful ✨ Continuing to learn, simplify, and grow every day 🔥💪 #LeetCode #Day34 #Python #Strings #Hashing #ProblemSolving #CodingJourney #100DaysOfCode

As Stack Overflow decreases in popularity, companies move to their own communities on Discourse. For RAG and Agentic workflows to be effective, they need a way to access that data. Our colleague Mauk Muller built Discourse Reader: a simple, yet powerfull Python package to bridge the gap between these communities and your AI pipelines. Check it out on Hacker News and GitHub: 👉 Hacker News: https://lnkd.in/e2WKZaBj 👉 GitHub: https://lnkd.in/eusnecqq #OpenSource #AI #RAG #Discourse #Python #ElNiño

🎬 Building my first Movie Recommendation System. Explored a dataset with 1.4M+ rows and found heavy missing values in key features like genres, keywords, and overview. My approach: dropping missing data to keep recommendations meaningful. What would you do? Drop or fill? Let me know your thoughts in the comments 👇 #MachineLearning #Python #DataScience

Machine Learning Graph Data using pygal #machinelearning #datascience #graphdata #pygal Pygal is a simple yet powerful Python library for generating beautiful SVG charts. It allows users to create a wide variety of static and animated visualizations, including bar charts, pie charts, line charts, and radar charts. https://lnkd.in/gn8-hBUW

🚀 Solved: Find a String (Substring Count) Challenge Just solved another problem on HackerRank under the Python Strings section! ✅ 🧠 Problem Overview: Count how many times a substring appears in a string — including overlapping occurrences. 🔍 Key Learnings: Practiced string traversal techniques Understood why built-in methods like count() may not always work (no overlapping support) Strengthened concepts of slicing and iteration in Python 💡 Example Insight: For string "ABCDCDC" and substring "CDC", the answer is 2 (overlapping counts matter!). ⚡ Approach Used: Iterated through the string Compared substrings using slicing Counted valid matches efficiently 📈 Problems like this help build strong fundamentals in string manipulation, which is crucial for coding interviews and real-world applications. #Python #HackerRank #Coding #Strings #ProblemSolving #DSA #LearningJourney #AI link of #Solution :- https://lnkd.in/gtqcy8fX

Just wrapped an energizing session teaching data loaders for local LLMs in Python! We mapped out Text Loaders pulling PDFs, JSONs, CSVs, and TXTs into strings or key-value trees via #LangChain, Image Loaders converting to binary formats, plus #OCR magic on pure-image #PPTX files using Python’s pptx loader—and Pandas crushing CSV/XLS flows. Total game-changer for building rock-solid AI pipelines! Tell me how do you think about texts on whiteboard? #Python #AI #LocalLLM #whiteboardKnowledge

🐍 Week 16 – Refining My Python Skills 🐍 In Week 16, I focused entirely on implementing the merge sort algorithm. I wanted to better understand it, as it had been difficult for me to fully grasp. Key concepts I worked on: - Focused on the divide (splitting) and conquer (merging and sorting) approach used in merge sort. - Practiced implementing the recursive splitting process. - Practiced coding the merging step that combines sorted sublists. My biggest breakthrough with merge sort was realizing that the recursive part handles the splitting, while the merging step can be approached iteratively within each step. Additionally, seeing merge sort as more of a "tree" than a sequence of steps improved my mental model of it. Breaking the algorithm into two separate parts helped the theory finally connect in practice. #Python #CodingJourney #LearningInPublic

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

📌 Problem: Reverse Vowels of a String 💡 Approach: Used the two-pointer technique to reverse only the vowels in the string. Initialize one pointer at the beginning and one at the end. Move both pointers inward until vowels are found, then swap them. Continue this process until both pointers meet. ⚙️ Key Insight: Use a set for fast vowel lookup (O(1)) Two-pointer approach avoids extra space for storing vowels separately ⏱️ Time Complexity: O(n) 📦 Space Complexity: O(n) (due to string → list conversion) 📚 What I learned: Efficient string manipulation using two pointers Optimizing lookups with hash sets #LeetCode #DSA #Algorithms #Coding #ProblemSolving #Python #TwoPointers #InterviewPreparation #CodingJourney

Many people learn Python and Pandas as tools. But the real transformation happens when you learn Pandas as a way of thinking. Because data isn’t just “numbers in a table”—it’s evidence. And evidence has shape, structure, friction, and sometimes silence (missing values, messy formats, inconsistent categories). When you master core Pandas operations, you stop merely processing datasets… and you start understanding systems. #Python #Pandas #LakkiData #LearningSteps