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Observable Plot as Jupyter widget

Project description

pyobsplot

pyobsplot allows to use Observable Plot to create charts in Jupyter notebooks. Plots are produced as widgets from Python code with a syntax as close as possible to the JavaScript one.

It allows to do things like :

import polars as pl
from pyobsplot import Obsplot, Plot

penguins = pl.read_csv("data/penguins.csv")

Obsplot({
    "grid": True,
    "color": {"legend": True},
    "marks": [
        Plot.dot(
            penguins, 
            {"x": "flipper_length_mm", "y": "body_mass_g", "fill": "species"}
        ),
        Plot.density(
            penguins, 
            {"x": "flipper_length_mm", "y": "body_mass_g", "stroke": "species"}
        )
    ]
})

Sample plot screenshot

Installation and usage

pyobsplot can be installed with pip:

pip install pyobsplot

For usage instructions, see the documentation website:

Features and limitations

Features:

  • Syntax as close as possible to the JavaScript one
  • Pandas and polars DataFrame and Series objects are serialized using Arrow IPC format for improved speed and data type conversions
  • Works offline, no iframe or dependency to Observable runtime
  • Caching mechanism of data objects if they are used several times in the same plot
  • Custom JavaScript code can be passed as strings with the js method
  • Python date and datetime objects are automatically converted to JavaScript Date objects
  • Plots can be defined with a dictionary, a call to a Plot mark function, or with kwargs. See alternative syntaxes.
  • Works with Jupyter notebooks and Quarto HTML documents

Limitations:

  • When using notebooks inside VSCode, the cells output states are not saved between sessions. So when a notebook is closed and reopened, plots have to be recomputed to be displayed. This is currently a VSCode limitation.
  • Doesn't work in Quarto in formats other than HTML.

Credits

Project details


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