How to quickly configure sns.displot in VS Code

OFFSET (DESREF) returns a reference to a cell or range that is a specified number of rows and columns away from a starting point. =OFFSET(reference, rows, cols, [height], [width]) Combining OFFSET with Python - Although OFFSET itself is a standard Excel formula, with AI tools serve to create dynamic, intelligent spreadsheets. import pandas as pd df = xl("OffsetData") # Excel range created with OFFSET df.describe() # AI-based descriptive statistics https://lnkd.in/eGtN7CCi https://lnkd.in/eeNktYBc https://lnkd.in/eXUuusW9 #msexcel #lataminvesting #artificialintelligence #pandas #python

“𝐖𝐚𝐢𝐭… Did I just 𝐭𝐮𝐫𝐧𝐞𝐝 𝐚 𝐯𝐢𝐝𝐞𝐨 𝐢𝐧𝐭𝐨 𝐀𝐒𝐂𝐈𝐈?!” 🤯 What happens when a programmer gets bored of watching normal videos? They make the video watch itself — in text. Introducing 🅰🆂🅲🅸🅸🅵🆈 — my Python script that says: “Why use pixels when you can suffer with ASCII characters?” It literally turns videos into moving text art — right inside your terminal. Built with: Python + OpenCV + NumPy Features: 🔹 Real-time playback 🔹 Custom width (for when you want widescreen… in text 😂) 🔹 0% productivity, 100% coolness Watch your video become a masterpiece of symbols, dots, and chaos: 👉 GitHub: https://lnkd.in/e3jG6pCt #Python #OpenCV #ASCIIArt #DataScience #Coding #Innovation #DeekshitaNaik #GitHubProjects #PythonDeveloper #ASCIIArt #CodeHumor #TerminalThings #DevLife #FunProjects

The AI that understands context best will win. While coding today, I gave two LLMs on different IDEs the same prompt: “Please read the content from /Users/deepak/Downloads/my_file.MD.” One instantly wrote a short 2-line Python script that did exactly what I wanted. The other thought I was asking it to access its own server’s files and refused, assuming it was an attack. Same prompt, very different results. Understanding what users actually mean, not just what they say, makes all the difference.

Visualizing Data Made Simple with Seaborn! Seaborn is a powerful Python library built on top of Matplotlib, it makes creating clean, attractive, and informative statistical visualizations effortless. I’ve attached a snippet of the code I used below. In this code, I: 1. Imported NumPy and Seaborn. 2. Generated 100 random values from a normal distribution using NumPy. 3. Used sns.histplot() to create a histogram with a smooth KDE (Kernel Density Estimate) curve on top. 4.Customized the plot with a white background and a soft purple color. The result is a clean, elegant visualization that clearly shows how the data is distributed, simple, yet insightful! #DataVisualization #Python #Seaborn #DataAnalytics #CodingJourney

🚀 Exploring Data Correlations with Seaborn & Pandas 📊 Today, I visualized the relationships between numeric features using a correlation heatmap in Python (Google Colab). By leveraging Pandas for data handling and Seaborn for visualization, I was able to clearly identify how variables such as total_bill, tip, and size are interrelated. This simple yet powerful heatmap highlights how visual analytics can make complex datasets instantly understandable — turning raw numbers into meaningful insights 🔍✨ #DataAnalysis #Python #Seaborn #Pandas #DataVisualization #MachineLearning #Analytics #LearningJourney #DataScience

🚀 DSA Challenge – Day 102 Problem: Construct Binary Tree from Inorder & Postorder Traversal 🌳🧠 This classic tree reconstruction problem is one of those fundamental challenges that strengthens your understanding of traversal orderings, recursion, and subtree boundaries. 🧠 Problem Summary: You're given two arrays: Inorder traversal of a binary tree Postorder traversal of the same tree Your task → Reconstruct the original binary tree and return its root. ⚙️ My Approach: This problem is solved by leveraging how traversal orders work: 1️⃣ Postorder traversal ends with the root node of the current subtree. 2️⃣ Using the inorder array, we can find the position of this root and determine: Left subtree range Right subtree range 3️⃣ Build the tree recursively, always consuming elements from the end of the postorder list. 📌 Key Observations: Postorder: Left → Right → Root Inorder: Left → Root → Right The last element in postorder identifies the root at each recursive step. We construct the right subtree first, then the left, because the postorder list is consumed from the end. 📈 Complexity: Time: O(n) → Each node processed once, and indexing is O(1). Space: O(n) → For the recursion stack + hash map. ✨ Key Takeaway: Tree reconstruction problems become easy once you identify the traversal patterns. Using index maps and recursion allows a clean and optimal solution. 🌳💡 🔖 #DSA #100DaysOfCode #Day102 #BinaryTrees #TreeConstruction #Recursion #LeetCode #ProblemSolving #Python #AlgorithmicThinking #TechCommunity #CodeEveryday #LearningByBuilding

Day 85 of the coding journey and tackling a classic interview problem: Inorder Traversal of a Binary Tree! 🌳 While recursion is the most intuitive approach, I implemented the incredibly efficient Morris Traversal algorithm. The beauty of Morris Traversal? It achieves O(N) time complexity with only O(1) auxiliary space! It smartly avoids the stack overhead by temporarily modifying the tree links (creating "threads") and then restoring them. Key Idea: Establish a temporary link from a node's Inorder Predecessor to the node itself. Use this link to find your way back up, process the node, and then cut the link to restore the original structure. A satisfying problem to solve with a highly optimized technique! 💪 #100DaysOfCode #DataStructures #Algorithms #BinaryTree #MorrisTraversal #Python #CodingChallenge #InorderTraversal

 Excited to share my latest Streamlit project — “Safura Iris Flower Classifier” 🌸 This interactive web app allows users to: 🔹 Explore and visualize the classic Iris dataset 🔹 Train a Logistic Regression model 🔹 Evaluate model performance with accuracy and F1-score metrics 🔹 Predict flower species with user-input features in real time 🌼 💻 Built using Python, Streamlit, scikit-learn, seaborn, and matplotlib, the app provides a simple and colorful interface for both data visualization and machine learning model interaction. #MachineLearning #DataScience #Streamlit #Python #IrisDataset #AI #ProjectShowcase #LogisticRegression