🚀 Day 49 of #100DaysOfCode LeetCode #83 – Remove Duplicates from Sorted List Today I solved a classic Linked List problem: 👉 Given the head of a sorted linked list, delete all duplicates such that each element appears only once. 👉 Return the linked list sorted as well. Since the list is already sorted, duplicates will always appear next to each other — which makes the solution efficient and elegant. 💡 Key Insight: Instead of using extra space (like a set), we can simply: Traverse the linked list Compare the current node with the next node Skip the next node if it’s a duplicate 🧠 Python Implementation: class ListNode: def __init__(self, val=0, next=None): self.val = val self.next = next def deleteDuplicates(head): current = head while current and current.next: if current.val == current.next.val: current.next = current.next.next else: current = current.next return head ✅ Why This Works: Time Complexity: O(n) Space Complexity: O(1) (no extra memory used) Efficient because the list is already sorted Problems like this strengthen understanding of: ✔️ Linked List traversal ✔️ Pointer manipulation ✔️ Writing clean and optimal code Consistency in solving small problems builds strong fundamentals. 💪 #LeetCode #Python #DataStructures #LinkedList #ProblemSolving #CodingJourney

𝗜 𝗕𝗎𝗶𝗹𝗍 𝗔 𝗣𝗶𝗽-𝗜𝗻𝘀𝘁𝗮𝗹𝗹𝗮𝗯𝗹𝗲 𝗥𝗔𝗚 𝗖𝗵𝗮𝘁𝗯𝗼𝘁 You can chat with any document in 3 lines of Python. I turned my document Q&A chatbot into a proper Python package with a clean API and CLI. Here's how it works: - You install the package using pip - You import the DocumentQA class - You use the class to index your documents and ask questions You can use it inside a Flask API, run it from the command line, or import it into a Jupyter notebook. You can ask questions like "What are the payment terms?" and get answers from your documents. I pulled the RAG pipeline into a clean DocumentQA class. The key design decisions include: - Auto-detecting file types from extensions - Building conversation memory into the class - Creating a dual mode that works as general chat without any files, and RAG mode with files You can try it out by running pip install docqa-rag and then using the docqa command with your document. Source: https://lnkd.in/gqQErNVG

FastAPI feels like the default choice for new Python APIs more often than Flask now. What stands out to me is the combination of speed, a gentle learning curve, and first-class async support. For backend work, async isn’t just a “nice to have” anymore—handling lots of concurrent requests (and I/O-heavy tasks like database calls) becomes much easier to model when the framework is designed around it. Flask still has a huge ecosystem and is great for simple services, but the industry shift makes sense: FastAPI tends to encourage clearer API structure and modern patterns from the start. When would you still pick Flask over FastAPI today?

I published a Python package this weekend. Not because the world desperately needed it. But because I wanted to see what it feels like. I’ve built a lot of small tools for myself. But publishing something properly feels different. It forces you to think beyond “it works on my machine.” So I looked for a small, real problem I’ve run into before: Comparing .env files. Different environments with slightly drifting keys, or changed values you don’t notice until something breaks. That’s why I built a simple CLI that compares multiple .env files and shows missing keys and changed values instantly. Nothing revolutionary. But shipping it changed something for me. If you work with multiple environments, feel free to try it out. You can find the package on PyPI (https://lnkd.in/ezEAWgkt) and Github (https://lnkd.in/eahHi-_e).

New Studio. New Workflows. And BIG changes at Python Simplified! 🐍💻 First tutorial is going live this weekend - kicking things off with a deep dive into the PostgreSQL + Flask combo. Going from a local "Hello World" static web page to a published full-stack web application in less than 25 minutes! 🚀 Find me on YouTube @ PythonSimplified. See you soon! 😊 #BehindTheScenes #SoftwareDeveloper #ContentProduction #StudioVlog

I replaced 3 Python tools with 1 and my CI pipeline got 10x faster. The tools I dropped: black, isort, flake8. The replacement: Ruff. I've now migrated 7 repositories to Ruff (totaling 800+ Python files) and here's my honest take after 3 months. What's great: - Speed. Ruff lints my entire monorepo in under 2 seconds. The old stack took 20-30 seconds. In CI, that compounds fast across matrix builds. - One config block. Instead of [tool.black], [tool.isort], [tool.flake8] scattered across pyproject.toml, it's just [tool.ruff] and [tool.ruff.lint]. Select the rule codes you want, done. - Auto-fix. Ruff can fix most import sorting and unused import issues on save. I have it wired as a post-tool hook in my editor — every file save auto-formats. What's not perfect: - Rule parity isn't 100%. A few flake8 plugins I relied on (like flake8-bugbear's B950 line length) don't have exact equivalents. You adapt. - The error messages are sometimes less descriptive than flake8's. When a rule fires that you haven't seen before, you're reading docs more often. - If your team has muscle memory around black's opinionated formatting, Ruff's formatter makes slightly different choices in edge cases. Minor, but it can cause noisy diffs during migration. Bottom line: I'd never go back. The speed alone justifies the switch, and consolidating 3 config sections into 1 removes a real source of drift. If you're still running black + isort + flake8 separately, try `ruff check . --fix` on your repo. You'll feel the difference immediately. Have you switched to Ruff yet? What held you back (or convinced you)? #Python #DevTools #Ruff #DeveloperExperience #CodeQuality

✅ Create Virtual Environment Using Ctrl + Shift + P (VS Code) 🔹 Step 1: Open Your Project Folder in VS Code Make sure your project contains: Your Python files (Optional) requirements.txt 🔹 Step 2: Open Command Palette Press: Ctrl + Shift + P 🔹 Step 3: Type Python: Create Environment Click it ✅ 🔹 Step 4: Choose Environment Type You will see options like: Venv Conda Choose Venv. 🔹 Step 5: Choose Python Interpreter Select the Python version you want (for example Python 3.10). VS Code will: ✔ Create .venv folder ✔ Automatically activate it ✔ Select interpreter 📦 Install From requirements.txt Automatically If your project has requirements.txt, VS Code will usually detect it and ask: “Install dependencies from requirements.txt?” Click Yes ✅ It will install all packages for you.

🧩 Day 73 of My Data Science Journey — Jinja Templates & Template Inheritance in Flask Today I explored one of the most powerful features of Flask: Jinja templates. 🔸 Jinja Templates I learned how Jinja allows you to write dynamic HTML using: Variables Loops Conditions Expressions This makes it easy to pass data from Flask to the frontend and render dynamic web pages. 🔸 Template Inheritance I also understood how template inheritance helps structure web pages efficiently: Creating a base template (layout) Extending it in other pages Avoiding repetitive code Keeping the UI consistent across all pages This is super useful for building scalable Flask apps. Flask templates are starting to make full-stack development feel much more organized and powerful. Onwards! 🚀🌐 #Flask #Jinja #WebDevelopment #Python #LearningJourney #BuildInPublic

Build your own plugin in InfluxDB 3! 🔌 We make it easy: write Python code, drop it in a directory, and run it right inside the database. Describe in plain English what you want and let the AI write the code. There are no external services; no containers. Just Python code in your database. We make it easy to go from Python script to production plugin in 15 minutes flat! 👉 https://bit.ly/4bJl4Ei

Built a Python-based web scraper that collects top news headlines from a public website and stores them in a text file. The project uses HTTP requests to fetch HTML content and BeautifulSoup to parse and extract headline data automatically. This helped me practice web scraping, HTML parsing, and basic file handling in Python. GitHub: https://lnkd.in/gi56cKgZ #Python #WebScraping #BeautifulSoup #Automation #Learning