Debugging a whitespace in Python: The 3 tools for pros

There’s an opinion that good visualization isn’t really about Python. But here’s a wonderful counterexample to that idea: python-graph-gallery.com by Yan Holtz — it’s truly inspiring. And it’s also an excellent, well-structured guide on how to create great visualizations in Python on your own!

#100DaysOfCode #Day2 Day two was all about understanding data types and manipulating strings in Python. I explored how to work with numbers and f-strings, convert between data types, apply mathematical operations (PEMDAS, popularly known as BODMAS in this part of the world), and even built a BMI Calculator and a Tip Calculator for restaurants. Beyond the code, here are a few life lessons I drew from it: The right type matters. Just as data types determine the kinds of operations that can be performed, in life, not every approach or mindset fits every situation. Wisdom is knowing what type fits the moment. Accuracy over assumption. A single wrong data conversion can throw off your whole calculation, just like one unchecked assumption can distort your perspective. Small steps lead to big functions. Each line of code I wrote contributed to something meaningful. Consistency still beats intensity every single time. In the end, I realized that coding teaches patience and structure, but it also mirrors life itself: when you understand the logic, you can begin to shape better outcomes both on screen and off it. #python #productivity

#Week2 | Mastering Data Structures: A Deep Dive into Linked Lists This week, I moved from Python basics to a fundamental data structure: the Linked List. It's amazing how these simple nodes connect to create powerful and flexible data management systems. What I Did: - Implemented a Singly Linked List from scratch in Python. - Mastered core operations like insertion (at the beginning and end), deletion (by value and from the head), and traversal. - Implemented two interesting algorithms: reversing a linked list in-place and finding the middle node using the fast/slow pointer technique. Tech Stack / Tools Used: `Python`, `Jupyter Notebook` Key Insights / Learnings: The fast and slow pointer approach to finding the middle node in a single pass is a clever and efficient algorithm. It's a great example of how algorithmic thinking can optimize solutions. This Week’s Plan: I'll be moving on to another core data structure: Trees. I'm excited to explore their hierarchical nature and applications. Project / Repo Link: GitHub Repo: https://lnkd.in/gdXyQFZH #DataStructures #Python #LinkedLists #Algorithms #AIJourney #LearningInPublic #12WeeksOfAI #RohitReboot

🧠 “Variables forget. Files don’t. And today’s lesson was all about that power.” 💻 Day 64 of #100DaysOfCode — Python File I/O Unlocked 📂🐍 Today I completed the File I/O lecture from CS50’s Python course — and it was one of those topics that feels simple at first, but suddenly makes your programs more real and more powerful. Here’s what clicked today: 📄 Reading & Writing Files Understood how to read text files line-by-line, write new content, and append to existing data — turning Python scripts into tools that interact with real stored information. 🔐 Why with Matters Learned how the with keyword automatically handles opening and closing files, preventing corruption, memory issues, and unexpected behavior. A tiny keyword with massive reliability impact. 📚 Working with CSVs Explored structured data using: csv.reader → raw lists csv.DictReader → clean, readable key–value pairs This makes working with datasets far more intuitive and scalable. 🔎 Real-World Perspective Logs, user data, configs, analytics, exports — File I/O is what separates a toy script from actual software that remembers, stores, and interacts with the world outside RAM. Every concept today felt like adding permanence to my code — a shift from “running something” to “building something.” Step by step, leveling up. 🚀 #100DaysOfCode #Python #CS50 #FileIO #DataProcessing #LearningInPublic #BuildInPublic #SoftwareEngineering #CleanCode #BackendDevelopment #DeveloperLife #CodingJourney #TechSkills #ProblemSolving #ProgrammingFundamentals

Python in Excel isn’t the future — it’s here. If you’re a finance pro buried in spreadsheets, here’s the cheat sheet: 1️⃣ You already have access (Microsoft 365 Insider builds). 2️⃣ You enable it by typing =PY() in a cell. 3️⃣ You can pull, clean, and forecast data without leaving Excel. No installs. No code bootcamps. Just smarter workflows. Excel finally speaks the same language as your data — Python. 👇 Have you tried it yet? What’s the first thing you’d automate? #PythonInExcel

Wow... just checked the stats and Python PLY Preview has officially passed 20,000 downloads! I'm blown away. It’s been 2 years since I first published this little VS Code extension, and I'm thrilled so many people find it useful for debugging 3D data. To celebrate, I’ve just pushed v0.0.5. This update fixes some annoying bugs and adds features I've wanted for a while, especially for data science work: - PyTorch & NumPy Support: The big one! You can now preview PyTorch Tensors and NumPy arrays with (n, 6) color data right from the debugger. - No More Junk Files: The extension now automatically cleans up temp files after your debug session ends. (They're now stored in .vscode/ply_preview). - Smarter Activation: I've tweaked the logic so it stops trying to activate on variables inside comments or strings. This project is, and always will be, open source. If you find a bug, have an idea, or want to contribute, please head over to GitHub. Pull Requests are always welcome! Thanks for all the support and feedback over the last two years. Get the update: https://lnkd.in/eAHnUdyD Report bugs or contribute: https://lnkd.in/dBkee7Tp #VSCode #Python #PyTorch #NumPy #DataScience #3DVisualization #PointCloud #OpenSource #DeveloperTools

I’m sharing my Python Crash Course repo covering the fundamentals through testing, plus a data-viz section with Matplotlib and Plotly (random walks, cubes/squares plots, dice simulations). It’s set up to clone and run quickly—requirements included. Repo: https://lnkd.in/eWXTJ_BK Included in the repo: Fundamentals: Chapters 1–7 (variables, lists, conditionals, dictionaries, input/loops) Functions: Chapter 8 Classes: Chapter 9 Files & Exceptions: Chapter 10 Testing with pytest: Chapter 11 Visualization: Chapter 15 — Generating Data What’s next (Chapters 16–17) Chapter 16 — Visualizing real-world data: I’ll pull live/online datasets into Python and feed them to Matplotlib/Plotly to build practical visuals—think daily high/low weather trends and a global earthquake map. The focus is turning raw JSON/CSV from the web into clean, labeled charts and geoplots. Chapter 17 — Automating data pipelines: I’ll write a small program that automatically downloads, cleans, and visualizes data on a schedule. The goal is a reproducible script that produces fresh charts without manual steps—ready for reports or a lightweight dashboard. How this maps to network engineering: - Pull telemetry (latency, jitter, drops) or event feeds and visualize time trends - Plot geographic incidents or change windows to highlight risk areas - Automate daily/weekly ops reports that render to HTML/PNG for stakeholders If you’re following along: - I’ve finished Chapter 15 (generating data and building visuals). Next up, I’ll connect to real sources in Chapter 16 and wrap with an automated pipeline in Chapter 17. I'll then double back to Chapters 12-14 where I'll build a 2-d game. Fun times ahead! Feedback is always welcome! Let's learn together!

💡Question: How to combine two DataFrames in pandas? ✅ Answer: The DataFrames in Python can be combined in the following ways: 1️⃣ Concatenating vertically — stacking two DataFrames one over the other. 2️⃣ Concatenating horizontally — combining them side by side. 3️⃣ Joining on a common column — merging based on shared keys (like SQL joins). 📘Function used: 𝙥𝙙.𝙘𝙤𝙣𝙘𝙖𝙩() 📌Syntax: 𝙥𝙙.𝙘𝙤𝙣𝙘𝙖𝙩([𝙙𝙖𝙩𝙖𝙛𝙧𝙖𝙢𝙚1, 𝙙𝙖𝙩𝙖𝙛𝙧𝙖𝙢𝙚2]) ✨When joining DataFrames on a common key, we use joins: Outer Join🌐→ Combines all rows from both DataFrames. Inner Join🔄→ Combines only common rows (intersection). 📌Full Syntax: 𝙥𝙙.𝙘𝙤𝙣𝙘𝙖𝙩([𝙙𝙖𝙩𝙖𝙛𝙧𝙖𝙢𝙚1, 𝙙𝙖𝙩𝙖𝙛𝙧𝙖𝙢𝙚2], 𝙖𝙭𝙞𝙨=’𝙖𝙭𝙞𝙨’, 𝙟𝙤𝙞𝙣=’𝙩𝙮𝙥𝙚_𝙤𝙛_𝙟𝙤𝙞𝙣’) 🔹 Use axis=0 for vertical concatenation 🔹 Use axis=1 for horizontal concatenation

Ever wondered what __init__.py actually does? While setting up my RAG project, my folder looked like this. rag-retriever/ │ ├── src/ │ ├── __init__.py │ ├── data_loader.py │ ├── chunk_splitter.py │ └── semantic_split.py │ └── main.py At first, I kept getting import errors — until I understood the real role of __init__.py. It’s simple yet powerful: 1) It tells Python that “this folder is a package.” 2) It lets you import cleanly from one file to another. So now I can: Inside src/: use from .semantic_split import function_name Outside (in main.py): use from src import chunk_splitter That tiny file makes the entire structure modular and production-ready 🚀 --- In short: __init__.py is your folder’s identity card — it transforms random scripts into a real Python package 📦 #Python #LLM #RAG #MLOps #SoftwareEngineering #DataScience #CodingTips #LearningJourney

💡 Build Like a Pro: How We Solved It Turning messy, manual pipelines into scalable, automated analytics isn’t just about tools — it’s about intentional design. Here’s how we approached it 👇 ✅ Planned for governance from day one ✅ Chose open-source tools for cost + control ✅ Made every step observable and testable By combining Apache Superset, Python, and SQL, the team delivered flexible dashboards without license barriers — improving adoption and scalability. Key takeaway: Don’t stop at “free versions.” Open-source tools scale further — without hidden costs. #BuildLikeAPro #DataEngineering #Analytics #OpenSource #ApacheSuperset #Python #DataPipelines #NexusLeap