💡 Cracking the Maximum Product Subarray Problem (Without Overcomplicating It) Today I worked on a classic DSA problem: Maximum Product Subarray — and found a simple yet powerful approach worth sharing. Most solutions focus on tracking max/min dynamically. But there’s a cleaner trick: 👉 Traverse the array from both directions (prefix & suffix) Why this works: Negative numbers can flip the product sign A single left-to-right pass might miss the optimal answer Scanning from both ends ensures we capture every possibility 🔁 Key Idea: Maintain two running products: Prefix (left → right) Suffix (right → left) Reset when product becomes 0 Track the maximum throughout ⚡ Complexity: Time: O(n) Space: O(1) Python code : https://lnkd.in/gZtCw9_i What I liked about this approach is its simplicity and elegance — no extra arrays, no complex state tracking. Sometimes, the best solutions aren’t the most complicated ones — just the most thoughtful. Have you tried solving this problem using a different approach? Would love to hear your thoughts 👇 #DataStructures #Algorithms #CodingInterview #Python #LeetCode #ProblemSolving Rajan Arora

🚀 Stop killing your CPU with Python loops. I recently refactored a data transformation pipeline that was crawling because it processed 5 million rows using a standard row-by-row iteration. Moving from native loops to vectorized operations changed everything. Before optimisation: results = [] for i in range(len(df)): val = df.iloc[i]['price'] * df.iloc[i]['tax_rate'] results.append(val) df['total'] = results After optimisation: df['total'] = df['price'] * df['tax_rate'] Performance gain: 45x faster execution time. Vectorization offloads the heavy lifting to highly optimised C code under the hood. When you use Pandas or NumPy native methods, you stop fighting the interpreter and start leveraging memory alignment. If you are still writing loops for data manipulation, you are leaving massive amounts of compute time on the table. It is the easiest performance win you can claim this week. What is the biggest speed boost you have ever achieved by swapping a loop for a built-in vectorised function? #DataEngineering #Python #Pandas #Performance #Optimization

Day 3: Bringing the Data to Life with FastAPI! 🚀 Progress on my Book Project is moving fast. Today was all about moving beyond simple scripts and building a structured API foundation. What’s new in the build? Object-Oriented Foundation: Created a Book class to ensure every entry has a consistent structure (ID, Title, Author, Rating). The API Layer: Switched on FastAPI to handle web requests. First Endpoints: * GET /books: Successfully retrieving the full library list. POST /new_book: Dynamically adding new titles to the collection using request bodies. Building the logic is one thing, but seeing it respond in real-time through an API endpoint makes it feel official. Next up: sharpening the data validation to make the system more robust! Stack: Python | FastAPI #BuildInPublic #Python #FastAPI #BackendDevelopment #CodingJourney #LearningToCode

Excited to share that I’ve deployed my House Price Predictor app live. This project uses Python, pandas, scikit-learn, and Streamlit to predict house prices based on: number of bedrooms number of bathrooms property size in m² Through this project, I learned more about: data preprocessing feature scaling training a machine learning model saving and loading model files using GitHub for version control deploying a live app with Streamlit Live app: https://lnkd.in/gcHr2ctQ This is part of my Data Science and Machine Learning learning journey, and I’m looking forward to building more projects. #Python #DataScience #MachineLearning #Streamlit #GitHub #LearningJourney

Day 43 of LeetCoding everyday until I get a J-O-B: Check If a String Contains All Binary Codes of Size K. If K = 2, the possible codes are 00, 01, 10, 11. We need to prove every combination exists inside our string. The Noob Trap: Generating all $2^k$ combinations upfront to cross them off. If K = 20, you need over a million strings. You just blew up your RAM. The Senior Fix: Math & Sliding Windows 1. The Math Filter: For a string to even physically fit all combinations, its length must be at least 2^k + K - 1. If it's shorter? Instant return False. Just like my job applications. 2. The Set: We slide a window of size K across the string, dumping every slice into a Python Set (which automatically vaporizes duplicates). 3. The Check: At the end, we check if len(seen) == (1 << k). (We use a Bitwise Left Shift because we are elite). See more: https://lnkd.in/gUki2imQ #LeetCode #Python #DataStructures #Engineering #TechHumor

𝗣𝘆𝘁𝗵𝗼𝗻 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻𝘀 – 𝗟𝗶𝘀𝘁 𝘃𝘀. 𝗦𝗲𝘁 🐍 When you're building a Python app, choosing the right data structure isn't just about syntax, it's about performance. I spent today breaking down the "Why" and "When" of Lists and Sets: 🔹 𝗨𝘀𝗲 𝗮 𝗟𝗜𝗦𝗧 𝘄𝗵𝗲𝗻: 1️⃣ You need to maintain the order of items. 2️⃣ You have duplicate data (e.g., a list of transaction amounts). 3️⃣ You need to access items by their position (Index). 🔸 𝗨𝘀𝗲 𝗮 𝗦𝗘𝗧 𝘄𝗵𝗲𝗻: 1️⃣ You need unique items only (Auto-removes duplicates). 2️⃣ Search speed is critical.Sets use Hashing for O(1) lookups. 𝗗𝗮𝘆 𝟭𝟰/𝟯𝟬 #30DaysOfCode #PythonLearning #DataStructures #Day14

✅ Building a Simple Calibration & Warp Tool in Python (Tkinter Canvas) Today I worked on a small but powerful feature: a 4‑point image calibration and rotation tool built using Python + Tkinter. 🔹 By simply clicking four points on an image, the tool automatically computes the transformation and warps the image on the Tkinter canvas. 🔹 This is especially useful for anyone working with scanned documents, camera feeds, or UI elements where manual alignment is needed. 🔹 The workflow is clean, intuitive, and requires no external heavy dependencies. This short demo video shows the tool in action, selecting points, applying rotation/warp, and instantly stabilising the image. I’m excited about how this can support future computer‑vision‑driven interfaces and interactive annotation tools. If anyone is exploring Tkinter‑based visual tools or lightweight calibration utilities, I’d love to connect and share ideas! #Python #Tkinter #ComputerVision #ImageProcessing #Calibration #ResearchTools #Automation

A quick exercise for anyone working in computer vision: Implement the following in Python from scratch (no external libraries), within 50 minutes: • Brightness adjustment • Contrast enhancement • High-impact contrast & color enhancement • RGB to grayscale conversion • Image normalization The implementations themselves are straightforward. The real point is understanding how pixel intensities behave under transformation, and how small changes affect visual outcomes. A simple task on the surface—but a good check on fundamentals. I’d be happy if you share your code, results, and your thoughts and impressions during and after completing these tasks.

FastAPI just unlocked a massive performance ceiling. 🚀 With the official release of FastAPI 0.136.0 supporting free-threaded Python (No-GIL) , I couldn't just read the changelog—I had to benchmark it. I ran a controlled, head-to-head comparison using identical code and identical hardware: ⚙️ Python 3.12 (GIL) vs. Python 3.13.0t (No-GIL) The result? A ~8x improvement in CPU-bound throughput. Same code. Same API. Zero changes. This is a game-changer for anyone running: 🔹 ML Inference APIs (real-time model serving) 🔹 Data Processing & ETL Workloads 🔹 CPU-Intensive Backend Services Is this the final nail in the coffin for the GIL bottleneck? Curious to hear what the Python backend community thinks. #FastAPI #Python #NoGIL #PerformanceEngineering #BackendDevelopment #Concurrency #MachineLearning

Unlock the power of your data with Python's essential analysis toolkit. 📌 Pandas: Load, clean, and analyze tabular data efficiently. 📌 NumPy: Perform high-performance numerical operations on arrays. 📌 Matplotlib: Create static, interactive, and animated visualizations. ✅ Pandas methods: `pd.read_csv()`, `df.info()`, `df.head()`. ✅ Explore data with `df.groupby()` for deeper insights. ✅ Matplotlib plots: Histograms, scatterplots, and line plots. Mastering these libraries is your first step to becoming a data analysis pro. Save this post for a quick reference! #Python #Pandas #NumPy #Matplotlib #DataAnalysis #DataAnalysisByte