https://github.com/thomasballinger/observable-jupyter

Embed visualizations and code from Observable notebooks in Jupyter
https://github.com/thomasballinger/observable-jupyter

jupyter jupyter-notebook notebook observablehq plotting plotting-in-python

Last synced: about 2 months ago
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Embed visualizations and code from Observable notebooks in Jupyter

• Host: GitHub
• URL: https://github.com/thomasballinger/observable-jupyter
• Owner: thomasballinger
• License: isc
• Created: 2021-09-24T15:06:47.000Z (about 3 years ago)
• Default Branch: main
• Last Pushed: 2023-07-23T14:01:08.000Z (over 1 year ago)
• Last Synced: 2024-10-10T19:11:15.571Z (2 months ago)
• Topics: jupyter, jupyter-notebook, notebook, observablehq, plotting, plotting-in-python
• Language: Jupyter Notebook
• Homepage: https://observable-jupyter.readthedocs.io/
• Size: 8.23 MB
• Stars: 55
• Watchers: 8
• Forks: 10
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • Changelog: changelog.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
# observable-jupyter

Embed cells from [Observable](https://observablehq.com/) notebooks into Jupyter notebooks.

[View demo notebook on Colab](https://colab.research.google.com/drive/1t_wcE-NqoPO-dpnrB9VMQ0KUxR5e1rML?usp=sharing)

This library provides a simple way to embed cells and pass custom inputs values to them from Python code. For more complicated data flow in Jupyter notebooks, see the related library [observable-jupyter-widget](https://github.com/thomasballinger/observable-jupyter-widget) which uses the Jupyter Widget system to pass data back and forth between Python and JavaScript.

# Usage

To install the library, import the embed function, and embed the "graphic" cell from [this Observable notebook](https://observablehq.com/@mbostock/epicyclic-gearing):

~~~py

!pip install observable_jupyter

from observable_jupyter import embed

embed('@mbostock/epicyclic-gearing', cells=['graphic'], inputs={'speed': 0.2})

~~~

The simplest way to use `embed()` is to render an entire Observable notebook:

~~~py

embed('@d3/gallery')

~~~

You may want to swap in your own data into a D3 chart:

~~~py

import this

text = ''.join(this.d.get(l, l) for l in this.s)

embed('@d3/word-cloud', cells=['chart'], inputs={'source': text})

~~~

With multiple cells, you can embed interactive charts!

~~~py

embed(

    '@observablehq/visualize-a-data-frame-with-observable-in-jupyter,

    cells=['vegaPetalsWidget', 'viewof sepalLengthLimits', 'viewof sepalWidthLimits'],

)

~~~

Embedding specific cells with the cell keyword parameter of `embed([])` causes only these cells to be shown, but every cell still runs.

This behavior is slightly different than the Observable embed default.

## About this library

This library uses the APIs provided by [Observable](https://observablehq.com) to embed notebooks hosted on Observable in Jupyter.

The library was [developed at Observable](https://github.com/observablehq/observable-jupyter) but is now maintained by Thomas Ballinger.

All code added before Sept 2021 is copyright Observable.

## Development

See [ARCHITECTURE.md](./ARCHITECTURE.md) for an overview.

Because Python library includes JavaScript, you'll need node as well as Python to contribute to it.

The two JavaScript files included in an installed package iframe_bundle.js and wrapper_bundle.js are not saved in this repo.

They are generated by rollup, a JavaScript "bundler" that combines JavaScript source code

from files in the js folder and dependencies listed in [js/package.json](./js/package.json).

Installing the Python package with or `pip install -e .` will automatically run the bundler and produce these files.