VIGIL: Personal Task and Habit Tracker Built with Python

💡 Solved “Remove Nodes From Linked List” Using a Stack — But Can It Be Better? Today I worked on the Remove Nodes From Linked List problem, and it was a fun one. 🔍 Problem Statement: Remove every node that has a node with a greater value somewhere to its right. Example: 5 → 2 → 13 → 3 → 8 ✅ Output: 13 → 8 ⚙️ My Approach: Stack I used a stack to maintain values in decreasing order: Traverse the linked list If current value is greater than stack top → pop smaller values Push current value Rebuild the linked list from remaining values ✨ Why it works: The stack keeps only values that are valid survivors after checking future nodes. 💻 🐍 Python code👍 🤩 👍: https://lnkd.in/gcAaycjY 📊 Complexity: Time: O(n) Space: O(n) 🧠 Takeaway: Sometimes using extra space makes the logic cleaner and easier to implement. But now I’m curious... 👉 Can you give the best solution for this problem? (Perhaps O(n) time with O(1) extra space?) Would love to see different approaches from the community 👇 #LinkedList #DataStructures #Algorithms #Python #CodingInterview #ProblemSolving #LeetCode Rajan Arora

This week I spent 2 hours debugging a pipeline that broke because of a subtle mutable default argument. Last week I finished DataCamp's "Intermediate Python for Developers" - and guess what chapter was in there. Funny how that works sometimes. A few takeaways that'll stick with me: • Mutable defaults are a trap, even for people who "know Python" • Decorators aren't magic - they're just functions returning functions (but the mental model matters) • Comprehensions > loops, until they don't fit on one screen anymore Working with Python daily on dbt models, and data transformations, it's easy to get comfortable in a narrow slice of the language. Stepping back to revisit the fundamentals consistently makes my production code cleaner. What's your approach - do you block time for structured learning, or learn purely on the job? #Python #DataEngineering #LearningInPublic

I’ve spent the last few weeks moving beyond just analyzing data to actually building the systems that create it. It’s been a massive learning curve, but I’ve finally started to wrap my head around:   𝗠𝗼𝗱𝗲𝗹𝘀 & 𝗔𝗱𝗺𝗶𝗻: How the database actually structured.   𝗩𝗶𝗲𝘄𝘀 & 𝗧𝗲𝗺𝗽𝗹𝗮𝘁𝗲𝘀: Passing variables between the backend and the UI.   𝗣𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻𝘀: Managing users and groups.   𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽: Keeping things clean and responsive. Huge shoutout to my mentor Rathan Kumar for the guidance. Check out the live site - https://lnkd.in/g-jkXR5x On to the next build! #Django #Python #DataEngineering #Learning #FullStack

**IMPORTANT: The website techtonique dot net [https://lnkd.in/eSRESGyy) is down until further notice.** [https://lnkd.in/eSRESGyy) contained an language-agnostic API for machine learning tasks (classification, regression, survival analysis, forecasting etc.). As a result, do not buy the Gumroad tutorial then. You can still use the R and Python Github packages [https://lnkd.in/eXN_dSKW) locally. PS: It's not an April's fool joke.

Built a Rainfall Prediction model and deployed it live. Here is what actually happened behind the scenes Decision Tree gave me 100% training accuracy. I got excited. Then I checked the test score and realised the model had just memorised the data. It learned nothing real. Naive Bayes gave me 73.9% on both train and test. Consistent That is the one I deployed. 3 models trained. 1 deployed. 1 lesson — a consistent score beats a perfect score every time live app here: https://lnkd.in/d-xaufug Full project and code: https://lnkd.in/d_d2Tx7R Akarsh Vyas Tanishq Vyas #DataScience #MachineLearning #Python #Streamlit #BuildInPublic #MLProject

Day 46/60 of #60DaysOfMiniProjects Built a Smart Study & Mood Tracker using Flask! Excited to share my latest project where I combined productivity tracking with a touch of intelligent suggestions Features: • Track daily study sessions with mood & notes • Smart suggestions based on mood and activity • Productivity score calculation • Daily streak tracking • Search, edit, and manage past sessions • Clean and simple user interface Tech Stack: Python | Flask | JSON | HTML/CSS This project helped me understand how small data insights can improve consistency and focus in daily routines. Would love your feedback and suggestions to improve it further! #Python #Flask #WebDevelopment #StudentProjects #Productivity #CodingJourney #OpenToLearn

Merge In Between Linked Lists — and got it Accepted ✅ This problem really tested my understanding of: 🔹 Linked List traversal 🔹 Pointer manipulation 🔹 Edge case handling One small mistake in pointer connection... and everything breaks. 😅 But that’s where real learning happens. 💡 Key takeaway: In linked lists, it’s not about values—it's about how you connect nodes. Step by step, I’m getting stronger in data structures & algorithms and building the problem-solving mindset needed for top tech roles. 🔥 Consistency is the real game changer. #LeetCode #DSA #ProblemSolving #Python #CodingJourney #SoftwareDeveloper #FullStackDeveloper #KeepLearning

One of the most common sources of subtle bugs in pandas is the index getting into an unexpected state — gaps after filtering, group columns stuck as index levels after groupby, duplicate values after concat. reset_index() is the fix for all of them. But knowing when to use drop=True versus the default behavior and understanding why groupby() always needs reset_index() for clean output is what makes the difference between code that works predictably and code that surprises you. It is a small function with a big impact on the reliability of your data pipelines. Read the full post here: https://lnkd.in/d5eB_mvS #Python #Pandas #DataScience #DataAnalysis #DataEngineering #Analytics

🚀 Day 11/111 — Diving Deeper into NumPy Today I explored array indexing, slicing, and data types in NumPy, and things are starting to feel much more powerful and precise 📊 🔹 What I learned: • How to access specific elements using indexing • How slicing works to extract parts of arrays • Understanding different NumPy data types (int, float, etc.) • How data type affects memory and performance 💡 Key takeaway: Indexing and slicing make it possible to work with exact portions of data instead of the whole dataset, which is super useful for real-world data analysis. Also, learning about data types showed me that even small details like choosing int vs float can impact efficiency and behavior. It’s getting clearer how NumPy is not just about storing data, but about working with it intelligently, appreciating the help, w3schools.com 🙏 Still learning step by step, but it feels like things are connecting more now. On to the next one 🚀 Code for Change #111daysoflearningforchange #day11 #python #codeforchange

Most beginners in pandas get confused between rename() and replace() — they sound similar, but they solve completely different problems. In this video, I’ve explained it with a simple real example 👇 👉 Changing column name from orderid to oid → use rename() (structure change) 👉 Changing value from “Sneha Kapoor” to “Sneha” → use `replace()` (data change) Understanding this small difference can save you from big mistakes during data cleaning and preprocessing. I’ve explained everything step by step in the video 🎥 #Python #Pandas #DataAnalytics #DataScience #Learning #Beginners #DataCleaning Bhavesh Arora Muskaan Khattar Gitanjali Pekamwar