D3.js VS marimo
Compare D3.js VS marimo and see what are their differences
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D3.js
Chart.js
Highcharts
Plotly
Google Charts
AnyChart
RAWGraphs
CanvasJS
marimo
Observable
Hyperquery
Zerve AI
PythonSandbox
DataLab
Quadratic
Colab Notebooks
D3 allows you to bind arbitrary data to a Document Object Model (DOM), and then apply data-driven transformations to the document. For example, you can use D3 to generate an HTML table from an array of numbers. Or, use the same data to create an interactive SVG bar chart with smooth transitions and interaction.
D3 is not a monolithic framework that seeks to provide every conceivable feature. Instead, D3 solves the crux of the problem: efficient manipulation of documents based on data. This avoids proprietary representation and affords extraordinary flexibility, exposing the full capabilities of web standards such as HTML, SVG, and CSS. With minimal overhead, D3 is extremely fast, supporting large datasets and dynamic behaviors for interaction and animation. D3โs functional style allows code reuse through a diverse collection of official and community-developed modules.
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Based on our record, D3.js seems to be a lot more popular than marimo. While we know about 175 links to D3.js, we've tracked only 15 mentions of marimo. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
A third option for building stripes is a vector pattern employing D3. - Source: dev.to / 3 months ago
Libraries like D3.js (ISC license) and Chart.js (MIT license) render to SVG because charts need to be sharp at any zoom level and interactive โ tooltips on hover, clickable segments, animated transitions. A chart exported as PNG loses all of that. - Source: dev.to / 4 months ago
This is exactly the goal of the project-graph-generator project: scanning your sources to deduce a dependency graph and produce a simple HTML page using D3.js to display it. - Source: dev.to / 4 months ago
If you wanted to take this one step further, you could instead export the data and build an entire app around it using something like ApexCharts or D3 to create more interactive visualisations. You could even build a dashboard that tracks your performance over time across multiple races. Lots of interesting possibilities here as the data set is pretty rich. I highly recommend checking out the pyrox-client... - Source: dev.to / 4 months ago
That idea stuck with me: build the algorithm in a language where rendering the data structure is easy, then step through the construction visually. JavaScript and D3.js are a natural fit: the algorithm produces a tree, and D3 is very good at drawing trees. - Source: dev.to / 4 months ago
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
Marimo notebooks give you the best of both worlds (https://marimo.io). - Source: Hacker News / 3 months ago
Agree with the author, will add: duckdb is an extremely compelling choice if youโre 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/) โฆ and thatโs just one example of many. - Source: Hacker News / 6 months ago
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
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
Chart.js - Easy, object oriented client side graphs for designers and developers.
Observable - Interactive code examples/posts
Highcharts - A charting library written in pure JavaScript, offering an easy way of adding interactive charts to your web site or web application
Hyperquery - Data notebook built for speed, visibility, and collaboration
Plotly - Low-Code Data Apps
Zerve AI - What if Jupyter + Figma + VSCode had a baby?