marimo Reviews

Plotly Reviews

marimo mentions (15)

• Pluto.jl 1.0 release – reactive notebook for Julia

  Pluto is great. I use it all the time. If you like the reactivity/reproducibility but are wedded to Python, you might want to check out Marimo, which is also great. [https://marimo.io/] It too puts the output of a cell above the code so if you're unable to adapt to things that are different it's also probably not for you. FWIW, Observable's Notebooks (Javascript) work the same way: output above the code... - Source: Hacker News / about 2 months ago

• Show HN: I'm tracking 197 known exposures of health data from UK Biobank

  Marimo notebooks give you the best of both worlds (https://marimo.io). - Source: Hacker News / 3 months ago

• Why DuckDB is my first choice for data processing

  Agree with the author, will add: duckdb is an extremely compelling choice if you’re a developer and want to embed analytics in your app (which can also run in a web browser with wasm!) Think this opens up a lot of interesting possibilities like more powerful analytics notebooks like marimo (https://marimo.io/) … and that’s just one example of many. - Source: Hacker News / 6 months ago

• Building SSR-Friendly Avatars with In-Browser AI: How I Trained Python Models and Ported Them to TensorFlow.js

  The training pipeline uses Marimo notebooks (think Jupyter, but reactive). Models are quantized to uint8 and served via CDN. Total bundle for a predictor: up-to 2MB. - Source: dev.to / 7 months ago

• Installing & Working with Python - in Ubuntu 24.04

  Marimo is a Jupyter notebook with each cell being somewhat logically connected to each other. That's way if you update the value of a variable in a cell and re-run it, related values in other cells will be auto-updated and auto-run. This is called reactive execution. Thus the notebook can act as a single python script or app and has an extension of .py instead of .ipynb. - Source: dev.to / 8 months ago

Plotly mentions (34)

• How to Analyze 47 Million Hacker News Posts: A Data Scientist's Dream Dataset Just Got Better

  Let's dive into some practical examples. First, you'll need to set up your environment with the right tools. I recommend using pandas for data manipulation and plotly for visualization. - Source: dev.to / 4 months ago

• Python for Data Visualization: Best Tools and Practices

  Plotly is perfect for interactive visualizations. You can create interactive charts and graphs that allow users to hover, click, and zoom in. Plotly is also great for web-based visuals, making it easy to share your findings online. - Source: dev.to / over 1 year ago

• Generative AI Powered QnA & Visualization Chatbot

  Front End: A React application that leverages React-Chatbotify library to easily integrate a chatbot GUI. It also uses the Plotly library to display the charts/visualizations. The generative AI implementation and details are entirely abstracted from the front end. The front-end application depends on a single REST endpoint of the backend application. - Source: dev.to / over 1 year ago

• Build a Stock Dashboard in less than 40 lines of Python code!🤓

  In this tutorial, Mariya Sha will guide you through building a stock value dashboard using Taipy, Plotly, and a dataset from Kaggle. - Source: dev.to / over 1 year ago

• Essential Deep Learning Checklist: Best Practices Unveiled

  How to Accomplish: Utilize visualization libraries like Matplotlib, Seaborn, or Plotly in Python to create histograms, scatter plots, and bar charts. For image data, use tools that visualize images alongside their labels to check for labeling accuracy. For structured data, correlation matrices and pair plots can be highly informative. - Source: dev.to / about 2 years ago