Detroit’s documentation!¶

detroit is wrapper for Python of d3js and Observable Plot.

Installation¶

pip install detroit

To have jupyter, you must add the following dependency :

pip install detroit[jupyter]

Then you will need to install a browser through the Python package playwright. For the moment, only chromium is supported.

playwright install chromium

Features¶

Write as close as possible d3 and Plot code
Render one or multiple plots in your browser or in your jupyter notebook
Save them into .svg, .png or .pdf

Quick Example¶

import polars as pl
from sklearn.datasets import load_digits
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler

from detroit import Plot, js, render, save

mnsit = load_digits()
scaler = StandardScaler()
X_scaled = scaler.fit_transform(mnsit.data)
pca = PCA(n_components=2)
components = pca.fit_transform(X_scaled)

# Prepare your data with Polars, Pandas or manually
df = pl.DataFrame(components, schema=["Component 1", "Component 2"])
df = df.insert_column(2, pl.Series("digit", mnsit.target))

plot = Plot.plot({
    "style": {"backgroundColor": "#131416", "color": "white"},
    "symbol": {"legend": js("true")},
    "color": {"scheme": "rainbow"},
    "marks": [
        Plot.dot(js("data"), {
            "x": "Component 1",
            "y": "Component 2",
            "stroke": "digit",
            "symbol": "digit"
        })
    ]
})

render(df, plot, style={"body": {"background": "#131416", "color": "white"}})