vega-embed VS vega-lite

If you want to embed the visualization part only, you can export a vega-lite/vega specification and then use Vega-Embed: https://github.com/vega/vega-embed or React-Vega to embed in your web app.

At the graphic walker toolbar, active debug mode, there will appear a button on the top-right corner of the chart. Click it, and then you can export the chart spec.

Or Embed the entire Graphic Walker as a react component: https://github.com/Kanaries/graphic-walker

When we add a tooltip to a chart in Altair, as in the example below, it follows a light theme by default. This tooltip implementation comes from Vega Tooltip, a plugin included in Vega-Embed (the package used under the hood to render the charts).

By default, in the upper right corner of each Altair chart, there is a dropdown menu with various options. This menu comes with Vega-Embed, the package used under the hood to render the charts.

I like Vega-Lite: https://vega.github.io/vega-lite/

It’s built by folks from the same lab as D3, but designed as “a higher-level visual specification language on top of D3” [https://vega.github.io/vega/about/vega-and-d3/]

My favorite way to prototype a dashboard is to use Streamlit to lay things out and serve it and then use Altair [https://altair-viz.github.io/] to generate the Vega-Lite plots in Python. Then if you need to move to something besides Python to productionize, you can produce the same Vega-Lite definitions using the framework of your choice.

Box zoom would need to be added to Vega-Lite first, and there has been some discussion around it in https://github.com/vega/vega-lite/issues/4742. Bottom line is that there's nothing blocking its implementation, someone just needs to do the work in Vega-Lite. And once released in Vega-Lite, Altair would pick it up automatically with how we generate the Altair API from the Vega-Lite schema.

I also have difficulties with Gnuplot and Matplotlib. I like Vega that allows me to create visualisations in a declarative way. If I really need something special I go with d3.js, which had a really steep learning curve but with ChatGPT it should have become easier for beginners.

[1] https://vega.github.io/vega-lite/

To ensure you do not miss this: LiveBook comes with a Vega Lite integration (https://livebook.dev/integrations -> https://livebook.dev/integrations/vega-lite/), which means you get access to a lot of visualisations out of the box, should you need that (https://vega.github.io/vega-lite/).

In the same "standing on giant's shoulders" stance, you can use Explorer (see example LiveBook at https://github.com/elixir-explorer/explorer/blob/main/notebo...), which leverages Polars (https://www.pola.rs), a very fast DataFrame library and now a company (https://www.pola.rs/posts/company-announcement/) with 4M$ seed.

Nice, would be nice to have it integrated in GitHub markdown.

Looks similar to Vega or Vega-lite(https://vega.github.io/vega-lite/). Definitely as rich as D3.js but gets the job done for simple visualisations.

Vega-lite can do this https://vega.github.io/vega-lite/

The underlying data is from an online betting site. Data analysis was done in Python and I used Vega/Altair for the visualisation.

I have a bunch of data that I want to plot. I'm using lisp-stat, which is pretty good, for data frames and analysis. However, lisp-stat uses vega-lite for plotting and to put it mildly, vega-lite is fucking awful.