Feature Request: Implement Bagplot (Bivariate Boxplot) for 2D and 3D Data Visualization

This proposal suggests adding support for Bagplots (also known as bivariate boxplots) as a new visualization type in Plotly.
Bagplots are a powerful extension of the traditional boxplot to two or more dimensions, providing a powerful vizualisation which shows statistical properties of 2-3 different variables.

While Plotly currently provides strong support for univariate distribution plots (box, violin, histogram), there is no direct tool to visualize bivariate distribution summaries in a similar “statistical boxplot” style.

The Bagplot, introduced by Rousseeuw, Ruts, and Tukey (1999), fills this gap by visualizing the data depth and outliers in 2D (and potentially 3D) space.
It provides an intuitive, robust alternative to kernel density estimates or convex hulls when exploring multivariate data.

Reference:
Rousseeuw, P. J., Ruts, I., & Tukey, J. W. (1999). The Bagplot: A Bivariate Boxplot. The American Statistician, 53(4), 382–387.
ResearchGate link

Figure 1: Example of a 2D Bagplot showing data depth and outliers (adapted from Rousseeuw et al., 1999).

Proposed Functionality

Using the plotly api:

Express:

import plotly.express as px

# 2D Bagplot
fig2d = px.bagplot(df, x="feature1", y="feature2", depth_method="tukey", show_outliers=True)
fig2d.show()

# 3D Bagplot
fig3d = px.bagplot_3d(df, x="feature1", y="feature2", z="feature3",
                      depth_method="tukey", show_outliers=True)
fig3d.show()

Graph Objects:

import plotly.graph_objects as go

fig = go.Figure(data=go.Bagplot(
    x=df["feature1"],
    y=df["feature2"],
    show_outliers=True,
    depth_method="tukey"
))
fig.show()

fig = go.Figure(data=go.Bagplot3D(
    x=df["x"], y=df["y"], z=df["z"],
    show_outliers=True,
    depth_method="tukey"
))
fig.show()

Non vengono indicati file di implementazione o test. Inizia esaminando le API Python Express e graph_objects di Plotly insieme all’articolo Bagplot citato, quindi chiarisci se l’ambito include 2D, 3D o entrambe; il lavoro è considerato completato quando sono stati concordati un’API e un comportamento di visualizzazione corrispondente per le funzionalità richieste di data-depth e rilevamento degli outlier.