Interactive Data Visualization with bqplot in Jupyter

>> 🧠 Ever wondered how websites are scraped to collect useful data? → While working on data-related tasks, we often need information that exists on websites → Instead of manually copying data, we can automate the process using web scraping → One of the most beginner-friendly Python libraries for this is Beautiful Soup ✍ What is Beautiful Soup? → It is a Python library used to parse HTML and XML documents → It helps to navigate, search and modify the HTML content fetched from the website → It transforms the complex HTML document into a tree structure where each node corresponds to an element, such as tags, text and attributes and makes it easy to locate and extract specific information 💡 Why use Beautiful Soup? → Easy to learn and use → Flexible parsing → Handles broken HTML → Efficient navigation and search functions → Integration with other libraries → well-documented and active community 🔗 Check out the repository and notes below for hands-on practice and a deeper understanding 👇 https://lnkd.in/gHHqZQ4b #Python #WebScraping #BeautifulSoup #DataScience #MachineLearning #PythonProgramming

No React. No Python. No backend. Just one HTML file. 💡 Summary of full BI Dashboard analyzing 7,166 Coursera courses — with 12 interactive charts, smart cascading filters, and 8 KPIs — all embedded in a single portable file you can open offline and share instantly. Proof that you don't need a complex stack to build something powerful. 🙌 🔗 Hosted free on GitHub Pages. Zero cost. Zero login. Dashboard Link: https://lnkd.in/d65pn8RD #DataVisualization #BI #Dashboard #Coursera #VanillaJS #DataAnalytics #BuildInPublic #SQL #LOOKERSTUDIO #Tableau

Machine Learning Data Visualization using sweetvis #machinelearning #datascience #datavisualization #sweetviz SweetViz Library is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA with just two lines of code. Output is a fully self-contained HTML application. The system is built around quickly visualizing target values and comparing datasets. https://lnkd.in/guHeS_PS

🦀 I got frustrated with every Python Excel library. So I built my own. A few weeks ago I was working on a project that needed to programmatically extract and re-render charts from Excel files. I tried everything: openpyxl → Raw XML soup. No color resolution, no chart placement. xlwings → Needs a live Excel installation. Not happening in a headless pipeline. xlrd → Long deprecated. Barely handles charts at all. None of them could give me what I actually needed — resolved theme colors, exact cell placement, combo chart layer breakdown, and series data — all in one clean API call. So I built sheetforge-charts: a Rust core with Python bindings that extracts everything you need to rebuild any Excel chart programmatically. No Excel. No LibreOffice. No Java. What it gives you: 🎨 Full color resolution — theme accents, sRGB hex, gradients, DrawingML modifiers (lumMod, tint, shade) all resolved to clean #RRGGBB 📍 Exact placement — A1-notation cell range (B2:J17) + EMU dimensions 📊 Combo chart support — multi-layer charts split into ChartLayer objects with their own types, series, and axis refs 🔄 Pivot chart metadata — walks the full chain from chart → pivotTable → pivotCacheDefinition → field names ⚡ Parallel parsing via Rayon — a 50-chart workbook parses in ≈1/N_CORES time 📦 Single universal wheel — abi3-py38, works on Python 3.8 through 3.14 Just pip install sheetforge-charts — the wheel ships a precompiled Rust binary, no Rust toolchain needed. If you've ever stared at raw <c:ser> XML at 11pm wondering why your chart colors are wrong — this one's for you. 📦 PyPI: https://lnkd.in/g-zHysCj 📖 Docs: https://lnkd.in/gVtX6Q8c #RustLang #Python #OpenSource #DataEngineering #Excel #BuildInPublic #PyO3

📘 Setup Using uv-Astral  (Step-by-Step): Anaconda: Conda:= Heavy but powerful uv-Astral:=Fast, modern, and simple 1)Install VS Code. 2)Install UV: 💻 Run in VS Code terminal: PowerShell ➡️ powershell -ExecutionPolicy ByPass -c "irm https://lnkd.in/duPAu-HJ | iex" 💻 For the next steps, run the commands in VS Code terminal:Command Prompt 3)Verify uv Installation: ➡️ uv 4)Install Python version(you can install multiple python versions): ➡️ uv python install 3.12 ➡️ uv python install 3.8 ➡️ uv python install 3.6 5)Set Python Version for Project(If for the current project you want python 3.12) ➡️ uv python pin 3.12 6)Initialize Project ➡️ uv init (This will create pyproject.toml, .python-version, README.md, etc. files in the current project folder.) 7)Create environment & sync dependencies: ➡️ uv sync (This will create .venv by reading the pyproject.toml file.) 8)Add packages ➡️ uv add numpy (This automatically adds numpy to your pyproject.toml file under dependencies.) 9)Remove packages (If needed) ➡️ uv remove numpy 10)Activate environment ➡️ .venv\Scripts\activate 11)Run your project ➡️ python my_project.py or ➡️ uv run python my_project.py #setup #uv #Upskilling

There is nothing quite like the transition from taking notes to writing the actual code. 💻 I spent today getting into the weeds of my current data project, figuring out exactly how the pieces will connect. Here is what I tackled: 🔹 Explored web scraping techniques using Python and BeautifulSoup to extract website data. 🔹 Began building out the storage side by setting up a MySQL database on my Linux machine. It's highly rewarding to see the data pipeline start to take shape. What are you all building or learning this week? See you guys next week! Stay tune for more updates #Python #BeautifulSoup #MySQL #DataProjects #TechJourney

Knowledge bites - Day 46 What is flask in python ? Flask is a lightweight Python web framework used to build web applications and APIs quickly. It follows a minimalistic approach, giving developers full control instead of enforcing strict project structures. Key features : 1. Lightweight and flexible (micro-framework) 2. Built-in development server and debugger 3. Uses Jinja2 templating engine 4. REST API friendly 5. Easy integration with databases and extensions How it works ? 1. Define routes (URLs) using decorators 2. Each route maps to a Python function 3. Function processes request and returns response 4. Server renders output (HTML/JSON) Example use case • Backend for AI apps (e.g., serving a model via API) • Lightweight dashboards • MVPs and quick prototypes Why it’s popular ? • Simple to learn and start • Highly customizable • Large ecosystem of extensions , like Flask SQLAlchemy , Flask Login and more . #Actionpackd #KnowledgeBites

𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗢𝗳 𝗔𝗰𝗍𝗶𝗼𝗻 𝗕𝗨𝗧𝗧𝗢𝗡𝗦 𝗜𝗡 𝗗𝗔𝗧𝗔𝗙𝗥𝗔𝗠𝗘𝗦 You want to add action buttons to your dataframes in Shiny for Python. This is useful for creating interactive tables with View, Edit, and Delete buttons. Here's how you can do it: - Add action buttons to each row of your dataframe - Use Starlette to mount multiple Shiny apps at different routes - Create dedicated pages for record detail and editing - Use URL parameters to drive data loading To add action buttons, you can use the `ui.HTML()` function to inject raw HTML into your dataframe. You can then use JavaScript to capture the click event and trigger server-side logic. Some key things to keep in mind: - Use `render.data_frame` instead of `render.table` to preserve custom HTML - Use parameterized queries to avoid SQL injection - Make sure your input IDs are unique across pages You can find the complete working code for this example on GitHub. Source: https://lnkd.in/gBYjuZKU

One extra line in your Django queryset can turn a 20ms API into a 2s one. ⚡ I ran into this while optimizing an API that was fetching related data inside a loop. Everything worked. But response time kept increasing as data grew. The issue was the classic N+1 query problem. Django gives two powerful tools to solve it. 🔹 select_related Uses SQL JOIN Best for ForeignKey or OneToOne Fetches everything in a single query 🔹 prefetch_related Runs separate queries and combines results in Python Best for ManyToMany or reverse relationships Prevents large JOIN explosions The key difference is not syntax. It is how the data is fetched. Golden rule 👇 Single relationship → select_related Multiple relationship → prefetch_related Getting this wrong does not just slow things down. It can quietly break performance as your data grows. In my experience, fixing N+1 queries is one of the fastest ways to improve API performance in Django. Which one do you end up using more in your projects? #Django #Python #PostgreSQL #BackendDevelopment #DatabaseOptimization #SoftwareEngineering

Since the open-source launch in October, the Fabric CLI has gained one-command deployments, first-class Power BI support, an AI agent execution layer, interactive REPL mode, Python 3.13 support, and contributions from community members whose code ships in the CLI you install today. #MicrosoftFabric #MSFTAdvocate