pyobsplot

Overview

pyobsplot allows to use Observable Plot to create charts in Jupyter notebooks, VSCode notebooks, Google Colab and Quarto documents. Plots are created from Python code with a syntax as close as possible to the JavaScript one.

import polars as pl
from pyobsplot import Plot

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

Plot.plot(
    {
        "grid": True,
        "marks": [
            Plot.dot(
                penguins,
                {
                    "x": "flipper_length_mm",
                    "y": "body_mass_g",
                    "fill": "sex",
                    "tip": True,
                },
            )
        ],
    }
)

Or, for a bit more complex example:

Plot.plot(
    {
        "marginLeft": 75,
        "marginRight": 70,
        "x": {"insetRight": 10},
        "y": {"grid": True},
        "facet": {"marginRight": 70},
        "marks": [
            Plot.ruleX([0]),
            Plot.barX(
                penguins,
                Plot.groupY(
                    {"x": "count"}, {"fy": "island", "y": "species", "fill": "sex"}
                ),
            ),
            Plot.text(
                ["The Adelie species is the only one on Torgersen Island."],
                {
                    "fy": ["Torgersen"],
                    "frameAnchor": "right",
                    "lineWidth": 16,
                    "dx": -4,
                },
            ),
        ],
    }
)

Installation and usage

Getting started gives installation instructions and a quick usage overview.

Usage gives more detailed usage instructions.

If you just want to try this package without installing it on your computer, you can open an introduction notebook in Google Colab:

Features and limitations

Features:

  • Syntax as close as possible to the JavaScript one
  • Two renderers available: widget, which generates plots as Jupyter widgets, and jsdom, which generates SVG or HTML outputs
  • Pandas and polars DataFrame and Series objects are serialized using Arrow IPC format for improved speed and better 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. Plots without legends are also supported in PDF and docx outputs with the jsdom renderer.
  • Plots can be saved to SVG or HTML files

Limitations:

  • Plot interactions (tooltips, crosshair…) are not available with the jsdom renderer (#16).
  • Plots with legends don’t work in Quarto in formats other than HTML (#9).
  • Very limited integration with IDE (documentation and autocompletion) for Plot methods (unlike in Observable notebooks) (#13).

Credits